From d4ec947dc7828d55d3f085a7be2b6deef230aa4b Mon Sep 17 00:00:00 2001 From: Paul Date: Fri, 1 Nov 2024 14:20:04 -0600 Subject: [PATCH 01/31] Add initial version --- flasc/data_processing/hoger.py | 270 +++++++++++++++++++++++++++++++++ 1 file changed, 270 insertions(+) create mode 100644 flasc/data_processing/hoger.py diff --git a/flasc/data_processing/hoger.py b/flasc/data_processing/hoger.py new file mode 100644 index 00000000..c504abe5 --- /dev/null +++ b/flasc/data_processing/hoger.py @@ -0,0 +1,270 @@ +"""Module for homogenizing the wind direction data using the HOGER method. + +HOGER was developed by Paul Poncet and Thomas Duc of Engie within the TWAIN project. + +The original code was written in R (link?) amd was translated to Python by Paul Fleming. +TOOO: (1) Fact check (2) Add references (3) Add github ids +""" + +from typing import Union + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import ruptures as rpt +from floris.utilities import wrap_180, wrap_360 + +from flasc import FlascDataFrame +from flasc.utilities.circular_statistics import calc_wd_mean_radial + +# The original code contains 4 functions: modulo, mean_circ, discretize, and homogenize +# modulo: equivalent to wrap_180 (imported from FLORIS) +# mean_circ: equivalent to calc_wd_mean_radial (imported from FLASC.utilities.circular_statistics) +# discretize and homogenize are implemented below + + +def discretize(x: np.ndarray, threshold: float = 100) -> np.ndarray: + """Discretize data points into segments. + + Args: + x (np.ndarray): Data to discretize. + threshold (float, optional): Threshold for discretization. Defaults to 100. + + Returns: + np.ndarray: Discretized data. + """ + # Handle NA values + na = pd.isna(x) + + # Sort indices + o = np.argsort(x) + x_sorted = x[o] + + # Initialize group labels + y = np.ones(len(x_sorted)) + + # Find significant jumps + d = np.diff(x_sorted) + w = np.where(d >= threshold)[0] + + # Assign group labels + for i in range(len(d)): + if i in w: + y[i + 1 :] += 1 + + # Reorder and handle NAs + y = y[np.argsort(o)] + y[na] = np.nan + + return y + + +def homogenize( + df: Union[pd.DataFrame, FlascDataFrame], + threshold: float = 100, + reference: str = "last", + verbose: bool = False, +) -> pd.DataFrame: + """Homegenize wind direction data using the Hoger method. + + Args: + df (Union[pd.DataFrame, FlascDataFrame]): DataFrame containing the SCADA data. + threshold (float, optional): Threshold for discretization. Defaults to 100. + reference (str, optional): Reference point for homogenization. Defaults to 'last'. + verbose (bool, optional): Whether to print verbose output. Defaults to False. + + """ + # Make sure in FlascDataFrame format + df = FlascDataFrame(df) + + # Make sure there are at least 3 turbines + if df.n_turbines < 3: + raise ValueError("At least 3 turbines are required for homogenization.") + + # Determine reference point + if reference == "first": + ref = 0 + elif reference == "last": + ref = len(df) - 1 + else: + try: + ref = np.argmin(np.abs(df["time"].values - pd.to_datetime(reference))) + except ValueError: + raise ValueError( + "Invalid reference point. Please use 'first', 'last', or a valid time string." + ) + + # Initialize results dataframe + df_jump = pd.DataFrame(columns=["Knot", "Jump", "Turbine"]) + + # Loop over combinations of turbines + for t_i in range(df.n_turbines): + t_i_col = "wd_%03d" % t_i + + if verbose: + print(f"Processing turbine {t_i}") + + for t_j in range(df.n_turbines): + if t_i == t_j: + continue + t_j_col = "wd_%03d" % t_j + + if verbose: + print(f"...with turbine {t_j}") + + # Compute the wrapped error + wrapped_error = wrap_180(df[t_i_col].values - df[t_j_col].values) + + # R code uses picor: Piecewise-constant regression, using + # https://github.com/chasmani/piecewise-regression in python + # as a replacement for picor + # I can't find a close python equivalent for picor, so starting with ruptures + # this is convenient as via the dependency on wind-up this is already + # a defacto requirement for FLASC + + # Note these first lines (minus the threshold) + # are verbatim from the example here + # https://github.com/deepcharles/ruptures + # presumably can improve somewhat + algo = rpt.Pelt(model="rbf", min_size=threshold).fit(wrapped_error) + result = algo.predict(pen=10) + + # If results is length 1 or 0, no significant jumps detected, continue + if len(result) <= 1: + if verbose: + print("... No significant jumps detected") + continue + + if verbose: + print(f"... Jumps detected at: {result[:-1]}") + + # Compute the mean values in error in each of the identified segments + # so we can compute the jump size at each jump location + knots = result[:-1] # Exclude the end point returned by ruptures + values = [ + calc_wd_mean_radial(wrapped_error[start:end]) + for start, end in zip([0] + knots, knots + [len(wrapped_error)]) + ] + + # Paul's note: I added wrap_180 here though I don't think it's in original R code + # but it feels correct to me to include it since errors + # should not include values > abs(180) + values = [wrap_180(v) for v in values] + + if verbose: + print(f"... Jump values per area: {values}") + + jumps = np.diff(values) + + if verbose: + print(f"... Jump sizes: {jumps}") + + # Append result to the result dataframe + # TODO: Not a big deal but this is a slow way to do it + df_jump = pd.concat( + [df_jump, pd.DataFrame({"Knot": knots, "Jump": jumps, "Turbine": t_i})] + ) + + # Process change points + if not df_jump.empty: + # Group and summarize change points + df_jump = ( + df_jump.assign(Count=1, Class=discretize(df_jump["Knot"].values, threshold=threshold)) + .groupby(["Class", "Turbine"]) + #TODO Original code uses a "mode" but for now taking a shortcut with median + .agg( + { + "Knot": "median", # Using median instead of shorth + "Jump": "mean", + "Count": "sum", + } + ) + .reset_index() + .query(f"Count > {np.floor(df.n_turbines/2)}") + .sort_values("Count", ascending=False) + .drop_duplicates("Class") + .sort_values("Class") + ) + + # Apply corrections + df_corr = df.copy() + for _, row in df_jump.iterrows(): + m = row["Turbine"] + k = row["Knot"] + j = row["Jump"] + + t_col = "wd_%03d" % m + + # Simple step function approximation + correction = np.where(np.arange(df.shape[0]) >= k, j, 0) + + # Paul note, in original form += used but -= looks better to me here + df_corr[t_col] -= correction - correction[ref] + + # Paul note, I added this because it felt write + df_corr[t_col] = wrap_360(df_corr[t_col]) + + return df_corr + else: + return df + + +if __name__ == "__main__": + # # Test discretize function + # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) + # y = discretize(x) + # print(y) + + # Now make a test dataframe to test the homogenize function + # Imagine there are 3 turbines, the first turbine's wd is + # set by a random walk. Turbine 2 is equal to 1 + white noise + # finally turbine 3 is turbine 1 + white noise, + a jump + # by jump_size deg halfway through + n = 1000 + jump_size = 10.0 + np.random.seed(0) + time = pd.date_range("2020-01-01", periods=n, freq="10min") + wd_000 = wrap_360(np.cumsum(np.random.randn(n))) + wd_001 = wrap_360(wd_000 + np.random.randn(n)) + wd_002 = wd_000 + np.random.randn(n) + wd_002[int(np.floor(n / 2)) :] += jump_size + wd_002 = wrap_360(wd_002) + + # FlascDataFrame requires power signals, just make these up + pow_made_up = np.random.randn(n) + + # Plot the 3 signals + + fig, ax = plt.subplots() + ax.plot(time, wd_000, label="Turbine 0") + ax.plot(time, wd_001, label="Turbine 1") + ax.plot(time, wd_002, label="Turbine 2") + ax.legend() + ax.grid(True) + ax.set_title("Original Wind Directions") + + # Combine into a FlascDataFrame + df = FlascDataFrame( + { + "time": time, + "wd_000": wd_000, + "wd_001": wd_001, + "wd_002": wd_002, + "pow_000": pow_made_up, + "pow_001": pow_made_up, + "pow_002": pow_made_up, + } + ) + + df_corr = homogenize(df, verbose=True) + + # Plot the corrected results + fig, ax = plt.subplots() + ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") + ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") + ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") + ax.legend() + ax.grid(True) + ax.set_title("Corrected Wind Directions") + + plt.show() From 86f134ab95e9772e43033b431bc2aae7643944e7 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 12 Nov 2024 11:43:26 -0700 Subject: [PATCH 02/31] Debugging --- flasc/data_processing/hoger.py | 151 ++++++++++++++++++++++----------- 1 file changed, 101 insertions(+), 50 deletions(-) diff --git a/flasc/data_processing/hoger.py b/flasc/data_processing/hoger.py index c504abe5..d28f2a80 100644 --- a/flasc/data_processing/hoger.py +++ b/flasc/data_processing/hoger.py @@ -61,7 +61,7 @@ def discretize(x: np.ndarray, threshold: float = 100) -> np.ndarray: def homogenize( df: Union[pd.DataFrame, FlascDataFrame], - threshold: float = 100, + threshold: int = 100, reference: str = "last", verbose: bool = False, ) -> pd.DataFrame: @@ -69,7 +69,7 @@ def homogenize( Args: df (Union[pd.DataFrame, FlascDataFrame]): DataFrame containing the SCADA data. - threshold (float, optional): Threshold for discretization. Defaults to 100. + threshold (int, optional): Threshold for discretization. Defaults to 100. reference (str, optional): Reference point for homogenization. Defaults to 'last'. verbose (bool, optional): Whether to print verbose output. Defaults to False. @@ -126,8 +126,16 @@ def homogenize( # are verbatim from the example here # https://github.com/deepcharles/ruptures # presumably can improve somewhat - algo = rpt.Pelt(model="rbf", min_size=threshold).fit(wrapped_error) - result = algo.predict(pen=10) + # algo = rpt.Pelt(model="l1", min_size=threshold).fit(wrapped_error) + # result = algo.predict(pen=40) + algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error) + print(np.log(len(wrapped_error)) ) + print(np.std(wrapped_error)) + print(wrapped_error[:20]) + pen = np.log(len(wrapped_error)) * np.std(wrapped_error)**2 + print(f"Pen: {pen}") + result = algo.predict(pen=pen) + # If results is length 1 or 0, no significant jumps detected, continue if len(result) <= 1: @@ -165,8 +173,13 @@ def homogenize( [df_jump, pd.DataFrame({"Knot": knots, "Jump": jumps, "Turbine": t_i})] ) + # Process change points if not df_jump.empty: + + print(f"Df_jump size: {df_jump.shape}") + df_jump_orig = df_jump.copy() + # Group and summarize change points df_jump = ( df_jump.assign(Count=1, Class=discretize(df_jump["Knot"].values, threshold=threshold)) @@ -186,6 +199,8 @@ def homogenize( .sort_values("Class") ) + print(f"Df_jump size (after summarizing): {df_jump.shape}") + # Apply corrections df_corr = df.copy() for _, row in df_jump.iterrows(): @@ -209,62 +224,98 @@ def homogenize( return df +# Engie test code if __name__ == "__main__": - # # Test discretize function - # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) - # y = discretize(x) - # print(y) - - # Now make a test dataframe to test the homogenize function - # Imagine there are 3 turbines, the first turbine's wd is - # set by a random walk. Turbine 2 is equal to 1 + white noise - # finally turbine 3 is turbine 1 + white noise, + a jump - # by jump_size deg halfway through - n = 1000 - jump_size = 10.0 - np.random.seed(0) - time = pd.date_range("2020-01-01", periods=n, freq="10min") - wd_000 = wrap_360(np.cumsum(np.random.randn(n))) - wd_001 = wrap_360(wd_000 + np.random.randn(n)) - wd_002 = wd_000 + np.random.randn(n) - wd_002[int(np.floor(n / 2)) :] += jump_size - wd_002 = wrap_360(wd_002) - - # FlascDataFrame requires power signals, just make these up - pow_made_up = np.random.randn(n) - - # Plot the 3 signals + df = pd.read_feather('scada_exemple.ftr') + fig, ax = plt.subplots() - ax.plot(time, wd_000, label="Turbine 0") - ax.plot(time, wd_001, label="Turbine 1") - ax.plot(time, wd_002, label="Turbine 2") + ax.scatter(df["time"], wrap_180(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") ax.legend() ax.grid(True) ax.set_title("Original Wind Directions") + + df_corr = homogenize(df, verbose=False) # the erreur occurs at this point. - # Combine into a FlascDataFrame - df = FlascDataFrame( - { - "time": time, - "wd_000": wd_000, - "wd_001": wd_001, - "wd_002": wd_002, - "pow_000": pow_made_up, - "pow_001": pow_made_up, - "pow_002": pow_made_up, - } - ) - - df_corr = homogenize(df, verbose=True) - - # Plot the corrected results fig, ax = plt.subplots() - ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") - ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") - ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") + ax.scatter(df_corr["time"], wrap_180(df_corr["wd_004"] - df_corr["wd_005"]), label="Direction E05 - E06") ax.legend() ax.grid(True) ax.set_title("Corrected Wind Directions") + fig, axarr = plt.subplots(2,1,sharex=True) + ax = axarr[0] + ax.scatter(df["time"], df["wd_004"], label="original") + ax.scatter(df_corr["time"], df_corr["wd_004"], label="corrected") + ax.set_title("Turbine 4") + ax.grid(True) + + ax = axarr[1] + ax.scatter(df["time"], df["wd_005"], label="original") + ax.scatter(df_corr["time"], df_corr["wd_005"], label="corrected") + ax.set_title("Turbine 5") + ax.grid(True) + + plt.show() + +# # Dummy test code +# if __name__ == "__main__": +# # # Test discretize function +# # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) +# # y = discretize(x) +# # print(y) + +# # Now make a test dataframe to test the homogenize function +# # Imagine there are 3 turbines, the first turbine's wd is +# # set by a random walk. Turbine 2 is equal to 1 + white noise +# # finally turbine 3 is turbine 1 + white noise, + a jump +# # by jump_size deg halfway through +# n = 1000 +# jump_size = 10.0 +# np.random.seed(0) +# time = pd.date_range("2020-01-01", periods=n, freq="10min") +# wd_000 = wrap_360(np.cumsum(np.random.randn(n))) +# wd_001 = wrap_360(wd_000 + np.random.randn(n)) +# wd_002 = wd_000 + np.random.randn(n) +# wd_002[int(np.floor(n / 2)) :] += jump_size +# wd_002 = wrap_360(wd_002) + +# # FlascDataFrame requires power signals, just make these up +# pow_made_up = np.random.randn(n) + +# # Plot the 3 signals + +# fig, ax = plt.subplots() +# ax.plot(time, wd_000, label="Turbine 0") +# ax.plot(time, wd_001, label="Turbine 1") +# ax.plot(time, wd_002, label="Turbine 2") +# ax.legend() +# ax.grid(True) +# ax.set_title("Original Wind Directions") + +# # Combine into a FlascDataFrame +# df = FlascDataFrame( +# { +# "time": time, +# "wd_000": wd_000, +# "wd_001": wd_001, +# "wd_002": wd_002, +# "pow_000": pow_made_up, +# "pow_001": pow_made_up, +# "pow_002": pow_made_up, +# } +# ) + +# df_corr = homogenize(df, verbose=True) + +# # Plot the corrected results +# fig, ax = plt.subplots() +# ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") +# ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") +# ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") +# ax.legend() +# ax.grid(True) +# ax.set_title("Corrected Wind Directions") + +# plt.show() From 77d2742a312b5caffeab4972873a10d87541e56f Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 13 Nov 2024 16:16:33 -0700 Subject: [PATCH 03/31] Add notebook --- flasc/data_processing/hoger_nb.ipynb | 334 +++++++++++++++++++++++++++ 1 file changed, 334 insertions(+) create mode 100644 flasc/data_processing/hoger_nb.ipynb diff --git a/flasc/data_processing/hoger_nb.ipynb b/flasc/data_processing/hoger_nb.ipynb new file mode 100644 index 00000000..895b8ae5 --- /dev/null +++ b/flasc/data_processing/hoger_nb.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test out hoger in notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Union\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import ruptures as rpt\n", + "from floris.utilities import wrap_180, wrap_360\n", + "\n", + "from flasc import FlascDataFrame\n", + "from flasc.utilities.circular_statistics import calc_wd_mean_radial" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def discretize(x: np.ndarray, threshold: float = 100) -> np.ndarray:\n", + " \"\"\"Discretize data points into segments.\n", + "\n", + " Args:\n", + " x (np.ndarray): Data to discretize.\n", + " threshold (float, optional): Threshold for discretization. Defaults to 100.\n", + "\n", + " Returns:\n", + " np.ndarray: Discretized data.\n", + " \"\"\"\n", + " # Handle NA values\n", + " na = pd.isna(x)\n", + "\n", + " # Sort indices\n", + " o = np.argsort(x)\n", + " x_sorted = x[o]\n", + "\n", + " # Initialize group labels\n", + " y = np.ones(len(x_sorted))\n", + "\n", + " # Find significant jumps\n", + " d = np.diff(x_sorted)\n", + " w = np.where(d >= threshold)[0]\n", + "\n", + " # Assign group labels\n", + " for i in range(len(d)):\n", + " if i in w:\n", + " y[i + 1 :] += 1\n", + "\n", + " # Reorder and handle NAs\n", + " y = y[np.argsort(o)]\n", + " y[na] = np.nan\n", + "\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_feather('scada_exemple.ftr')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = 100\n", + "reference = 'last'\n", + "verbose = True" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure in FlascDataFrame format\n", + "df = FlascDataFrame(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "if reference == \"first\":\n", + " ref = 0\n", + "elif reference == \"last\":\n", + " ref = len(df) - 1\n", + "else:\n", + " try:\n", + " ref = np.argmin(np.abs(df[\"time\"].values - pd.to_datetime(reference)))\n", + " except ValueError:\n", + " raise ValueError(\n", + " \"Invalid reference point. Please use 'first', 'last', or a valid time string.\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize results dataframe\n", + "df_jump = pd.DataFrame(columns=[\"Knot\", \"Jump\", \"Turbine\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing turbine 0\n", + "...with turbine 1\n", + " Number of jumps: 481\n", + "...with turbine 2\n", + " Number of jumps: 481\n", + "...with turbine 3\n", + " Number of jumps: 481\n", + "...with turbine 4\n", + " Number of jumps: 481\n", + "...with turbine 5\n", + " Number of jumps: 481\n", + "...with turbine 6\n", + " Number of jumps: 481\n", + "...with turbine 7\n", + " Number of jumps: 481\n", + "...with turbine 8\n", + " Number of jumps: 481\n", + "...with turbine 9\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 31\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# R code uses picor: Piecewise-constant regression, using\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# https://github.com/chasmani/piecewise-regression in python\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# as a replacement for picor\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# https://github.com/deepcharles/ruptures\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# presumably can improve somewhat\u001b[39;00m\n\u001b[1;32m 30\u001b[0m algo \u001b[38;5;241m=\u001b[39m rpt\u001b[38;5;241m.\u001b[39mPelt(model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ml1\u001b[39m\u001b[38;5;124m\"\u001b[39m, min_size\u001b[38;5;241m=\u001b[39mthreshold)\u001b[38;5;241m.\u001b[39mfit(wrapped_error)\n\u001b[0;32m---> 31\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malgo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpen\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error)\u001b[39;00m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# pen = 20 # np.log(len(wrapped_error)) * np.nanstd(wrapped_error)**2\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;66;03m# print(f\"Pen: {pen}\")\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 37\u001b[0m \n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# If results is length 1 or 0, no significant jumps detected, continue\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/detection/pelt.py:130\u001b[0m, in \u001b[0;36mPelt.predict\u001b[0;34m(self, pen)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sanity_check(\n\u001b[1;32m 123\u001b[0m n_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcost\u001b[38;5;241m.\u001b[39msignal\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 124\u001b[0m n_bkps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 125\u001b[0m jump\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjump,\n\u001b[1;32m 126\u001b[0m min_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_size,\n\u001b[1;32m 127\u001b[0m ):\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m BadSegmentationParameters\n\u001b[0;32m--> 130\u001b[0m partition \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_seg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 131\u001b[0m bkps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(e \u001b[38;5;28;01mfor\u001b[39;00m s, e \u001b[38;5;129;01min\u001b[39;00m partition\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bkps\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/detection/pelt.py:71\u001b[0m, in \u001b[0;36mPelt._seg\u001b[0;34m(self, pen)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;66;03m# we update with the right partition\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m tmp_partition\u001b[38;5;241m.\u001b[39mupdate({(t, bkp): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcost\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbkp\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m pen})\n\u001b[1;32m 72\u001b[0m subproblems\u001b[38;5;241m.\u001b[39mappend(tmp_partition)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# finding the optimal partition\u001b[39;00m\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/costs/costl1.py:50\u001b[0m, in \u001b[0;36mCostL1.error\u001b[0;34m(self, start, end)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotEnoughPoints\n\u001b[1;32m 49\u001b[0m sub \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignal[start:end]\n\u001b[0;32m---> 50\u001b[0m med \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mabs\u001b[39m(sub \u001b[38;5;241m-\u001b[39m med)\u001b[38;5;241m.\u001b[39msum()\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3927\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_median_dispatcher)\n\u001b[1;32m 3846\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, overwrite_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 3847\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3848\u001b[0m \u001b[38;5;124;03m Compute the median along the specified axis.\u001b[39;00m\n\u001b[1;32m 3849\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3925\u001b[0m \n\u001b[1;32m 3926\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_median\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3928\u001b[0m \u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3823\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m 3820\u001b[0m index_out \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, ) \u001b[38;5;241m*\u001b[39m nd\n\u001b[1;32m 3821\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m out[(\u001b[38;5;28mEllipsis\u001b[39m, ) \u001b[38;5;241m+\u001b[39m index_out]\n\u001b[0;32m-> 3823\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3825\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3960\u001b[0m, in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m 3958\u001b[0m part \u001b[38;5;241m=\u001b[39m a\n\u001b[1;32m 3959\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3960\u001b[0m part \u001b[38;5;241m=\u001b[39m \u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3962\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m part\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m ():\n\u001b[1;32m 3963\u001b[0m \u001b[38;5;66;03m# make 0-D arrays work\u001b[39;00m\n\u001b[1;32m 3964\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m part\u001b[38;5;241m.\u001b[39mitem()\n", + "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/core/fromnumeric.py:771\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 770\u001b[0m a \u001b[38;5;241m=\u001b[39m asanyarray(a)\u001b[38;5;241m.\u001b[39mcopy(order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 771\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Loop over combinations of turbines\n", + "for t_i in range(df.n_turbines):\n", + " t_i_col = \"wd_%03d\" % t_i\n", + "\n", + " if verbose:\n", + " print(f\"Processing turbine {t_i}\")\n", + "\n", + " for t_j in range(df.n_turbines):\n", + " if t_i == t_j:\n", + " continue\n", + " t_j_col = \"wd_%03d\" % t_j\n", + "\n", + " if verbose:\n", + " print(f\"...with turbine {t_j}\")\n", + "\n", + " # Compute the wrapped error\n", + " wrapped_error = wrap_180(df[t_i_col].values - df[t_j_col].values)\n", + "\n", + " # R code uses picor: Piecewise-constant regression, using\n", + " # https://github.com/chasmani/piecewise-regression in python\n", + " # as a replacement for picor\n", + " # I can't find a close python equivalent for picor, so starting with ruptures\n", + " # this is convenient as via the dependency on wind-up this is already\n", + " # a defacto requirement for FLASC\n", + "\n", + " # Note these first lines (minus the threshold)\n", + " # are verbatim from the example here\n", + " # https://github.com/deepcharles/ruptures\n", + " # presumably can improve somewhat\n", + " algo = rpt.Pelt(model=\"l1\", min_size=threshold).fit(wrapped_error)\n", + " result = algo.predict(pen=5000)\n", + " # algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error)\n", + " # pen = 20 # np.log(len(wrapped_error)) * np.nanstd(wrapped_error)**2\n", + " # print(f\"Pen: {pen}\")\n", + " # result = algo.predict(pen=pen)\n", + " break\n", + " break\n", + "\n", + " # # If results is length 1 or 0, no significant jumps detected, continue\n", + " # if len(result) <= 1:\n", + " # if verbose:\n", + " # print(\"... No significant jumps detected\")\n", + " # continue\n", + "\n", + " # if verbose:\n", + " # # print(f\"... Jumps detected at: {result[:-1]}\")\n", + " # print(f\" Number of jumps: {len(result)-1}\")\n", + "\n", + " # # Compute the mean values in error in each of the identified segments\n", + " # # so we can compute the jump size at each jump location\n", + " # knots = result[:-1] # Exclude the end point returned by ruptures\n", + " # values = [\n", + " # calc_wd_mean_radial(wrapped_error[start:end])\n", + " # for start, end in zip([0] + knots, knots + [len(wrapped_error)])\n", + " # ]\n", + "\n", + " # # Paul's note: I added wrap_180 here though I don't think it's in original R code\n", + " # # but it feels correct to me to include it since errors \n", + " # # should not include values > abs(180)\n", + " # values = [wrap_180(v) for v in values]\n", + "\n", + " # # if verbose:\n", + " # # print(f\"... Jump values per area: {values}\")\n", + "\n", + " # jumps = np.diff(values)\n", + "\n", + " # # if verbose:\n", + " # # print(f\"... Jump sizes: {jumps}\")\n", + "\n", + " # # Append result to the result dataframe\n", + " # # TODO: Not a big deal but this is a slow way to do it\n", + " # df_jump = pd.concat(\n", + " # [df_jump, pd.DataFrame({\"Knot\": knots, \"Jump\": jumps, \"Turbine\": t_i})]\n", + " # )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Knot, Jump, Turbine]\n", + "Index: []" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_jump" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 58a4fc5dd457a34ccc5465fdd89224ce30eb797d Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 14 Nov 2024 10:35:22 -0700 Subject: [PATCH 04/31] updates --- flasc/data_processing/hoger_nb.ipynb | 613 +++++++++++++++++++++++---- 1 file changed, 538 insertions(+), 75 deletions(-) diff --git a/flasc/data_processing/hoger_nb.ipynb b/flasc/data_processing/hoger_nb.ipynb index 895b8ae5..4fe48fe7 100644 --- a/flasc/data_processing/hoger_nb.ipynb +++ b/flasc/data_processing/hoger_nb.ipynb @@ -135,61 +135,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Processing turbine 0\n", - "...with turbine 1\n", - " Number of jumps: 481\n", - "...with turbine 2\n", - " Number of jumps: 481\n", - "...with turbine 3\n", - " Number of jumps: 481\n", - "...with turbine 4\n", - " Number of jumps: 481\n", + "Processing turbine 4\n", "...with turbine 5\n", - " Number of jumps: 481\n", - "...with turbine 6\n", - " Number of jumps: 481\n", - "...with turbine 7\n", - " Number of jumps: 481\n", - "...with turbine 8\n", - " Number of jumps: 481\n", - "...with turbine 9\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 31\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# R code uses picor: Piecewise-constant regression, using\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# https://github.com/chasmani/piecewise-regression in python\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# as a replacement for picor\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# https://github.com/deepcharles/ruptures\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# presumably can improve somewhat\u001b[39;00m\n\u001b[1;32m 30\u001b[0m algo \u001b[38;5;241m=\u001b[39m rpt\u001b[38;5;241m.\u001b[39mPelt(model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ml1\u001b[39m\u001b[38;5;124m\"\u001b[39m, min_size\u001b[38;5;241m=\u001b[39mthreshold)\u001b[38;5;241m.\u001b[39mfit(wrapped_error)\n\u001b[0;32m---> 31\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malgo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpen\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error)\u001b[39;00m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# pen = 20 # np.log(len(wrapped_error)) * np.nanstd(wrapped_error)**2\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;66;03m# print(f\"Pen: {pen}\")\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 37\u001b[0m \n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# If results is length 1 or 0, no significant jumps detected, continue\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/detection/pelt.py:130\u001b[0m, in \u001b[0;36mPelt.predict\u001b[0;34m(self, pen)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m sanity_check(\n\u001b[1;32m 123\u001b[0m n_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcost\u001b[38;5;241m.\u001b[39msignal\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 124\u001b[0m n_bkps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 125\u001b[0m jump\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjump,\n\u001b[1;32m 126\u001b[0m min_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_size,\n\u001b[1;32m 127\u001b[0m ):\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m BadSegmentationParameters\n\u001b[0;32m--> 130\u001b[0m partition \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_seg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 131\u001b[0m bkps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(e \u001b[38;5;28;01mfor\u001b[39;00m s, e \u001b[38;5;129;01min\u001b[39;00m partition\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bkps\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/detection/pelt.py:71\u001b[0m, in \u001b[0;36mPelt._seg\u001b[0;34m(self, pen)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;66;03m# we update with the right partition\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m tmp_partition\u001b[38;5;241m.\u001b[39mupdate({(t, bkp): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcost\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbkp\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m pen})\n\u001b[1;32m 72\u001b[0m subproblems\u001b[38;5;241m.\u001b[39mappend(tmp_partition)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# finding the optimal partition\u001b[39;00m\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/ruptures/costs/costl1.py:50\u001b[0m, in \u001b[0;36mCostL1.error\u001b[0;34m(self, start, end)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotEnoughPoints\n\u001b[1;32m 49\u001b[0m sub \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignal[start:end]\n\u001b[0;32m---> 50\u001b[0m med \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mabs\u001b[39m(sub \u001b[38;5;241m-\u001b[39m med)\u001b[38;5;241m.\u001b[39msum()\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3927\u001b[0m, in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_median_dispatcher)\n\u001b[1;32m 3846\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, overwrite_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 3847\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3848\u001b[0m \u001b[38;5;124;03m Compute the median along the specified axis.\u001b[39;00m\n\u001b[1;32m 3849\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3925\u001b[0m \n\u001b[1;32m 3926\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ureduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_median\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3928\u001b[0m \u001b[43m \u001b[49m\u001b[43moverwrite_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_input\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3823\u001b[0m, in \u001b[0;36m_ureduce\u001b[0;34m(a, func, keepdims, **kwargs)\u001b[0m\n\u001b[1;32m 3820\u001b[0m index_out \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, ) \u001b[38;5;241m*\u001b[39m nd\n\u001b[1;32m 3821\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m out[(\u001b[38;5;28mEllipsis\u001b[39m, ) \u001b[38;5;241m+\u001b[39m index_out]\n\u001b[0;32m-> 3823\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3825\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/lib/function_base.py:3960\u001b[0m, in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m 3958\u001b[0m part \u001b[38;5;241m=\u001b[39m a\n\u001b[1;32m 3959\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3960\u001b[0m part \u001b[38;5;241m=\u001b[39m \u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3962\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m part\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m ():\n\u001b[1;32m 3963\u001b[0m \u001b[38;5;66;03m# make 0-D arrays work\u001b[39;00m\n\u001b[1;32m 3964\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m part\u001b[38;5;241m.\u001b[39mitem()\n", - "File \u001b[0;32m~/Projects/FLASC/flasc/.venv/lib/python3.13/site-packages/numpy/core/fromnumeric.py:771\u001b[0m, in \u001b[0;36mpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 770\u001b[0m a \u001b[38;5;241m=\u001b[39m asanyarray(a)\u001b[38;5;241m.\u001b[39mcopy(order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 771\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + " Number of jumps: 494\n" ] } ], "source": [ "# Loop over combinations of turbines\n", - "for t_i in range(df.n_turbines):\n", + "for t_i in [4]:#range(df.n_turbines):\n", " t_i_col = \"wd_%03d\" % t_i\n", "\n", " if verbose:\n", " print(f\"Processing turbine {t_i}\")\n", "\n", - " for t_j in range(df.n_turbines):\n", + " for t_j in [5]:#range(df.n_turbines):\n", " if t_i == t_j:\n", " continue\n", " t_j_col = \"wd_%03d\" % t_j\n", @@ -212,55 +179,214 @@ " # https://github.com/deepcharles/ruptures\n", " # presumably can improve somewhat\n", " algo = rpt.Pelt(model=\"l1\", min_size=threshold).fit(wrapped_error)\n", - " result = algo.predict(pen=5000)\n", + " result = algo.predict(pen=10)\n", " # algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error)\n", " # pen = 20 # np.log(len(wrapped_error)) * np.nanstd(wrapped_error)**2\n", " # print(f\"Pen: {pen}\")\n", " # result = algo.predict(pen=pen)\n", - " break\n", - " break\n", "\n", - " # # If results is length 1 or 0, no significant jumps detected, continue\n", - " # if len(result) <= 1:\n", - " # if verbose:\n", - " # print(\"... No significant jumps detected\")\n", - " # continue\n", "\n", - " # if verbose:\n", - " # # print(f\"... Jumps detected at: {result[:-1]}\")\n", - " # print(f\" Number of jumps: {len(result)-1}\")\n", + " # If results is length 1 or 0, no significant jumps detected, continue\n", + " if len(result) <= 1:\n", + " if verbose:\n", + " print(\"... No significant jumps detected\")\n", + " continue\n", "\n", - " # # Compute the mean values in error in each of the identified segments\n", - " # # so we can compute the jump size at each jump location\n", - " # knots = result[:-1] # Exclude the end point returned by ruptures\n", - " # values = [\n", - " # calc_wd_mean_radial(wrapped_error[start:end])\n", - " # for start, end in zip([0] + knots, knots + [len(wrapped_error)])\n", - " # ]\n", + " if verbose:\n", + " # print(f\"... Jumps detected at: {result[:-1]}\")\n", + " print(f\" Number of jumps: {len(result)-1}\")\n", "\n", - " # # Paul's note: I added wrap_180 here though I don't think it's in original R code\n", - " # # but it feels correct to me to include it since errors \n", - " # # should not include values > abs(180)\n", - " # values = [wrap_180(v) for v in values]\n", + " # Compute the mean values in error in each of the identified segments\n", + " # so we can compute the jump size at each jump location\n", + " knots = result[:-1] # Exclude the end point returned by ruptures\n", + " values = [\n", + " calc_wd_mean_radial(wrapped_error[start:end])\n", + " for start, end in zip([0] + knots, knots + [len(wrapped_error)])\n", + " ]\n", "\n", - " # # if verbose:\n", - " # # print(f\"... Jump values per area: {values}\")\n", + " # Paul's note: I added wrap_180 here though I don't think it's in original R code\n", + " # but it feels correct to me to include it since errors \n", + " # should not include values > abs(180)\n", + " values = [wrap_180(v) for v in values]\n", "\n", - " # jumps = np.diff(values)\n", + " # if verbose:\n", + " # print(f\"... Jump values per area: {values}\")\n", "\n", - " # # if verbose:\n", - " # # print(f\"... Jump sizes: {jumps}\")\n", + " jumps = np.diff(values)\n", "\n", - " # # Append result to the result dataframe\n", - " # # TODO: Not a big deal but this is a slow way to do it\n", - " # df_jump = pd.concat(\n", - " # [df_jump, pd.DataFrame({\"Knot\": knots, \"Jump\": jumps, \"Turbine\": t_i})]\n", - " # )\n" + " # if verbose:\n", + " # print(f\"... Jump sizes: {jumps}\")\n", + "\n", + " # Append result to the result dataframe\n", + " # TODO: Not a big deal but this is a slow way to do it\n", + " df_jump = pd.concat(\n", + " [df_jump, pd.DataFrame({\"Knot\": knots, \"Jump\": jumps, \"Turbine\": t_i})]\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 810.0\n", + "4 2065.0\n", + "8 4460.0\n", + "12 5260.0\n", + "16 5760.0\n", + " ... \n", + "489 52060\n", + "490 52160\n", + "491 52260\n", + "492 52360\n", + "493 52460\n", + "Name: Knot, Length: 1450, dtype: object" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_jump.Knot" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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952 rows × 3 columns

\n", + "" + ], + "text/plain": [ + " Knot Jump Count\n", + "Class Turbine \n", + "1.0 4 265.0 -3.156637 2\n", + "2.0 4 410.0 2.657651 2\n", + "3.0 4 515.0 1.001608 2\n", + "4.0 4 615.0 -3.164829 2\n", + "5.0 0 810.0 -0.070601 1\n", + "... ... ... ...\n", + "492.0 4 52260.0 0.124131 2\n", + "493.0 0 52360.0 0.056769 1\n", + " 4 52360.0 0.577936 2\n", + "494.0 0 52460.0 -0.447198 1\n", + " 4 52460.0 0.406734 2\n", + "\n", + "[952 rows x 3 columns]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_jump = (\n", + " df_jump.assign(Count=1, Class=discretize(df_jump[\"Knot\"].values, threshold=threshold))\n", + " .groupby([\"Class\", \"Turbine\"])\n", + " #TODO Original code uses a \"mode\" but for now taking a shortcut with median\n", + " .agg(\n", + " {\n", + " \"Knot\": \"median\", # Using median instead of shorth\n", + " \"Jump\": \"mean\",\n", + " \"Count\": \"sum\",\n", + " }\n", + " )\n", + " .reset_index()\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Df_jump size: (23452, 3)\n", + "Df_jump size (after summarizing): (462, 5)\n" + ] + } + ], + "source": [ + "\n", + "print(f\"Df_jump size: {df_jump.shape}\")\n", + "df_jump_orig = df_jump.copy()\n", + "\n", + "# Group and summarize change points\n", + "df_jump = (\n", + " df_jump.assign(Count=1, Class=discretize(df_jump[\"Knot\"].values, threshold=threshold))\n", + " .groupby([\"Class\", \"Turbine\"])\n", + " #TODO Original code uses a \"mode\" but for now taking a shortcut with median\n", + " .agg(\n", + " {\n", + " \"Knot\": \"median\", # Using median instead of shorth\n", + " \"Jump\": \"mean\",\n", + " \"Count\": \"sum\",\n", + " }\n", + " )\n", + " .reset_index()\n", + " .query(f\"Count > {np.floor(df.n_turbines/2)}\")\n", + " .sort_values(\"Count\", ascending=False)\n", + " .drop_duplicates(\"Class\")\n", + " .sort_values(\"Class\")\n", + ")\n", + "\n", + "print(f\"Df_jump size (after summarizing): {df_jump.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ClassTurbineKnotJumpCount
01.00810.0-0.07060162
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" ], "text/plain": [ - "Empty DataFrame\n", - "Columns: [Knot, Jump, Turbine]\n", - "Index: []" + " Class Turbine Knot Jump Count\n", + "0 1.0 0 810.0 -0.070601 62\n", + "4 2.0 0 2065.0 -0.033015 71\n", + "8 3.0 0 4460.0 -0.170122 220\n", + "12 4.0 0 5260.0 0.144313 261\n", + "16 5.0 0 5760.0 -0.843092 29\n", + "... ... ... ... ... ...\n", + "1825 458.0 0 52060.0 -0.387025 29\n", + "1829 459.0 0 52160.0 0.377298 29\n", + "1833 460.0 0 52260.0 -0.218838 29\n", + "1837 461.0 0 52360.0 0.056769 29\n", + "1841 462.0 0 52460.0 -0.447198 29\n", + "\n", + "[462 rows x 5 columns]" ] }, - "execution_count": 17, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -308,6 +743,34 @@ "source": [ "df_jump" ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax_arr = rpt.display(wrapped_error, result, figsize=(10, 6))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From ab05283cc12c2cb21fa0e95762ead36c131da78f Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 18 Nov 2024 11:05:26 -0700 Subject: [PATCH 05/31] Update hoger code --- flasc/data_processing/hoger.py | 321 -------------- .../data_processing/northing_offset_hoger.py | 407 ++++++++++++++++++ 2 files changed, 407 insertions(+), 321 deletions(-) delete mode 100644 flasc/data_processing/hoger.py create mode 100644 flasc/data_processing/northing_offset_hoger.py diff --git a/flasc/data_processing/hoger.py b/flasc/data_processing/hoger.py deleted file mode 100644 index d28f2a80..00000000 --- a/flasc/data_processing/hoger.py +++ /dev/null @@ -1,321 +0,0 @@ -"""Module for homogenizing the wind direction data using the HOGER method. - -HOGER was developed by Paul Poncet and Thomas Duc of Engie within the TWAIN project. - -The original code was written in R (link?) amd was translated to Python by Paul Fleming. -TOOO: (1) Fact check (2) Add references (3) Add github ids -""" - -from typing import Union - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import ruptures as rpt -from floris.utilities import wrap_180, wrap_360 - -from flasc import FlascDataFrame -from flasc.utilities.circular_statistics import calc_wd_mean_radial - -# The original code contains 4 functions: modulo, mean_circ, discretize, and homogenize -# modulo: equivalent to wrap_180 (imported from FLORIS) -# mean_circ: equivalent to calc_wd_mean_radial (imported from FLASC.utilities.circular_statistics) -# discretize and homogenize are implemented below - - -def discretize(x: np.ndarray, threshold: float = 100) -> np.ndarray: - """Discretize data points into segments. - - Args: - x (np.ndarray): Data to discretize. - threshold (float, optional): Threshold for discretization. Defaults to 100. - - Returns: - np.ndarray: Discretized data. - """ - # Handle NA values - na = pd.isna(x) - - # Sort indices - o = np.argsort(x) - x_sorted = x[o] - - # Initialize group labels - y = np.ones(len(x_sorted)) - - # Find significant jumps - d = np.diff(x_sorted) - w = np.where(d >= threshold)[0] - - # Assign group labels - for i in range(len(d)): - if i in w: - y[i + 1 :] += 1 - - # Reorder and handle NAs - y = y[np.argsort(o)] - y[na] = np.nan - - return y - - -def homogenize( - df: Union[pd.DataFrame, FlascDataFrame], - threshold: int = 100, - reference: str = "last", - verbose: bool = False, -) -> pd.DataFrame: - """Homegenize wind direction data using the Hoger method. - - Args: - df (Union[pd.DataFrame, FlascDataFrame]): DataFrame containing the SCADA data. - threshold (int, optional): Threshold for discretization. Defaults to 100. - reference (str, optional): Reference point for homogenization. Defaults to 'last'. - verbose (bool, optional): Whether to print verbose output. Defaults to False. - - """ - # Make sure in FlascDataFrame format - df = FlascDataFrame(df) - - # Make sure there are at least 3 turbines - if df.n_turbines < 3: - raise ValueError("At least 3 turbines are required for homogenization.") - - # Determine reference point - if reference == "first": - ref = 0 - elif reference == "last": - ref = len(df) - 1 - else: - try: - ref = np.argmin(np.abs(df["time"].values - pd.to_datetime(reference))) - except ValueError: - raise ValueError( - "Invalid reference point. Please use 'first', 'last', or a valid time string." - ) - - # Initialize results dataframe - df_jump = pd.DataFrame(columns=["Knot", "Jump", "Turbine"]) - - # Loop over combinations of turbines - for t_i in range(df.n_turbines): - t_i_col = "wd_%03d" % t_i - - if verbose: - print(f"Processing turbine {t_i}") - - for t_j in range(df.n_turbines): - if t_i == t_j: - continue - t_j_col = "wd_%03d" % t_j - - if verbose: - print(f"...with turbine {t_j}") - - # Compute the wrapped error - wrapped_error = wrap_180(df[t_i_col].values - df[t_j_col].values) - - # R code uses picor: Piecewise-constant regression, using - # https://github.com/chasmani/piecewise-regression in python - # as a replacement for picor - # I can't find a close python equivalent for picor, so starting with ruptures - # this is convenient as via the dependency on wind-up this is already - # a defacto requirement for FLASC - - # Note these first lines (minus the threshold) - # are verbatim from the example here - # https://github.com/deepcharles/ruptures - # presumably can improve somewhat - # algo = rpt.Pelt(model="l1", min_size=threshold).fit(wrapped_error) - # result = algo.predict(pen=40) - algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error) - print(np.log(len(wrapped_error)) ) - print(np.std(wrapped_error)) - print(wrapped_error[:20]) - pen = np.log(len(wrapped_error)) * np.std(wrapped_error)**2 - print(f"Pen: {pen}") - result = algo.predict(pen=pen) - - - # If results is length 1 or 0, no significant jumps detected, continue - if len(result) <= 1: - if verbose: - print("... No significant jumps detected") - continue - - if verbose: - print(f"... Jumps detected at: {result[:-1]}") - - # Compute the mean values in error in each of the identified segments - # so we can compute the jump size at each jump location - knots = result[:-1] # Exclude the end point returned by ruptures - values = [ - calc_wd_mean_radial(wrapped_error[start:end]) - for start, end in zip([0] + knots, knots + [len(wrapped_error)]) - ] - - # Paul's note: I added wrap_180 here though I don't think it's in original R code - # but it feels correct to me to include it since errors - # should not include values > abs(180) - values = [wrap_180(v) for v in values] - - if verbose: - print(f"... Jump values per area: {values}") - - jumps = np.diff(values) - - if verbose: - print(f"... Jump sizes: {jumps}") - - # Append result to the result dataframe - # TODO: Not a big deal but this is a slow way to do it - df_jump = pd.concat( - [df_jump, pd.DataFrame({"Knot": knots, "Jump": jumps, "Turbine": t_i})] - ) - - - # Process change points - if not df_jump.empty: - - print(f"Df_jump size: {df_jump.shape}") - df_jump_orig = df_jump.copy() - - # Group and summarize change points - df_jump = ( - df_jump.assign(Count=1, Class=discretize(df_jump["Knot"].values, threshold=threshold)) - .groupby(["Class", "Turbine"]) - #TODO Original code uses a "mode" but for now taking a shortcut with median - .agg( - { - "Knot": "median", # Using median instead of shorth - "Jump": "mean", - "Count": "sum", - } - ) - .reset_index() - .query(f"Count > {np.floor(df.n_turbines/2)}") - .sort_values("Count", ascending=False) - .drop_duplicates("Class") - .sort_values("Class") - ) - - print(f"Df_jump size (after summarizing): {df_jump.shape}") - - # Apply corrections - df_corr = df.copy() - for _, row in df_jump.iterrows(): - m = row["Turbine"] - k = row["Knot"] - j = row["Jump"] - - t_col = "wd_%03d" % m - - # Simple step function approximation - correction = np.where(np.arange(df.shape[0]) >= k, j, 0) - - # Paul note, in original form += used but -= looks better to me here - df_corr[t_col] -= correction - correction[ref] - - # Paul note, I added this because it felt write - df_corr[t_col] = wrap_360(df_corr[t_col]) - - return df_corr - else: - return df - - -# Engie test code -if __name__ == "__main__": - - df = pd.read_feather('scada_exemple.ftr') - - fig, ax = plt.subplots() - ax.scatter(df["time"], wrap_180(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") - ax.legend() - ax.grid(True) - ax.set_title("Original Wind Directions") - - df_corr = homogenize(df, verbose=False) # the erreur occurs at this point. - - fig, ax = plt.subplots() - ax.scatter(df_corr["time"], wrap_180(df_corr["wd_004"] - df_corr["wd_005"]), label="Direction E05 - E06") - ax.legend() - ax.grid(True) - ax.set_title("Corrected Wind Directions") - - fig, axarr = plt.subplots(2,1,sharex=True) - ax = axarr[0] - ax.scatter(df["time"], df["wd_004"], label="original") - ax.scatter(df_corr["time"], df_corr["wd_004"], label="corrected") - ax.set_title("Turbine 4") - ax.grid(True) - - ax = axarr[1] - ax.scatter(df["time"], df["wd_005"], label="original") - ax.scatter(df_corr["time"], df_corr["wd_005"], label="corrected") - ax.set_title("Turbine 5") - ax.grid(True) - - - plt.show() - -# # Dummy test code -# if __name__ == "__main__": -# # # Test discretize function -# # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) -# # y = discretize(x) -# # print(y) - -# # Now make a test dataframe to test the homogenize function -# # Imagine there are 3 turbines, the first turbine's wd is -# # set by a random walk. Turbine 2 is equal to 1 + white noise -# # finally turbine 3 is turbine 1 + white noise, + a jump -# # by jump_size deg halfway through -# n = 1000 -# jump_size = 10.0 -# np.random.seed(0) -# time = pd.date_range("2020-01-01", periods=n, freq="10min") -# wd_000 = wrap_360(np.cumsum(np.random.randn(n))) -# wd_001 = wrap_360(wd_000 + np.random.randn(n)) -# wd_002 = wd_000 + np.random.randn(n) -# wd_002[int(np.floor(n / 2)) :] += jump_size -# wd_002 = wrap_360(wd_002) - -# # FlascDataFrame requires power signals, just make these up -# pow_made_up = np.random.randn(n) - -# # Plot the 3 signals - -# fig, ax = plt.subplots() -# ax.plot(time, wd_000, label="Turbine 0") -# ax.plot(time, wd_001, label="Turbine 1") -# ax.plot(time, wd_002, label="Turbine 2") -# ax.legend() -# ax.grid(True) -# ax.set_title("Original Wind Directions") - -# # Combine into a FlascDataFrame -# df = FlascDataFrame( -# { -# "time": time, -# "wd_000": wd_000, -# "wd_001": wd_001, -# "wd_002": wd_002, -# "pow_000": pow_made_up, -# "pow_001": pow_made_up, -# "pow_002": pow_made_up, -# } -# ) - -# df_corr = homogenize(df, verbose=True) - -# # Plot the corrected results -# fig, ax = plt.subplots() -# ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") -# ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") -# ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") -# ax.legend() -# ax.grid(True) -# ax.set_title("Corrected Wind Directions") - -# plt.show() diff --git a/flasc/data_processing/northing_offset_hoger.py b/flasc/data_processing/northing_offset_hoger.py new file mode 100644 index 00000000..390b9a19 --- /dev/null +++ b/flasc/data_processing/northing_offset_hoger.py @@ -0,0 +1,407 @@ +"""Module for homogenizing the wind direction data using the HOGER method. + +HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet) + and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, +and Rubén González-Lope (https://github.com/rglope) and +Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of +CENER within the TWAIN project. +""" + +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib import dates +from scipy.interpolate import interp1d +from sklearn.tree import DecisionTreeRegressor + +_MODE_LIMIT = 0.05 + + +def modulo(x, m: float = 360.0): + """Compute the modulo of an angle with a period of m. + + It normalizes the values of x to the [-m/2, m/2) range. + + Args: + x (np.ndarray): Values to compute the modulo. + m (float, optional): Period. Defaults to 360.0. + + Returns: + np.ndarray: New values of x. + + """ + x = x % m + + w1 = x > m / 2 + x[w1] = x[w1] - m + + return x + + +def _get_leaves_and_knots(tree: DecisionTreeRegressor) -> tuple[np.ndarray, np.ndarray]: + """Function to get the values of the superficial knots and leaves of a Tree Regression. + + Args: + tree (DecisionTreeRegressor): Decision Tree Regression model. + + Returns: + tuple[np.ndarray, np.ndarray]: Values of the leaves and positions of the knots. + """ + # Get the main information from the tree + n_nodes = tree.tree_.node_count + children_left = tree.tree_.children_left + children_right = tree.tree_.children_right + threshold = tree.tree_.threshold + values = tree.tree_.value + + # Explore the results to extract the values of the leaves + is_leaves = np.zeros(shape=n_nodes, dtype=bool) + stack = [(0, 0)] # start with the root node id (0) and its depth (0) + while len(stack) > 0: + # `pop` ensures each node is only visited once + node_id, depth = stack.pop() + + # If the left and right child of a node is not the same we have a split node + is_split_node = children_left[node_id] != children_right[node_id] + # If a split node, append left and right children and depth to `stack` so we can loop t + # hrough them + if is_split_node: + stack.append((children_left[node_id], depth + 1)) + stack.append((children_right[node_id], depth + 1)) + else: + is_leaves[node_id] = True + + # Get the values and the position of the external knots + leave_values = values[is_leaves][:, 0, 0] + knot_positions = np.sort(threshold[~is_leaves]) + + return leave_values, knot_positions + + +def discretize(x: pd.Series, threshold: int) -> np.ndarray: + """Get the class of the knots based on the times they repeat. + + Args: + x (pd.Series): Series of knot positions of the trees for the different wind turbines. + threshold (int): Threshold used to declare a tree branch. + + Returns: + np.ndarray: Classes of the knots. + """ + o = np.argsort(x) # Get the order of the knots + x = x.iloc[o] # Order the knots in ascending value of their positions + y = np.ones(len(x), dtype=np.int32) + d = np.diff(x) + w = np.where(d >= threshold)[0] + + for i in range(len(d)): + y[i + 1] = y[i] + (i in w) + + y = y[np.argsort(o)] + + return y + + +def shorth_mode(x: pd.Series) -> np.float64: + """Estimates the Venter mode through the shorth method for the given data. + + Args: + x (pd.Series): Data for which the mode will be estimated + + Returns: + np.float64: Mode of the data + + """ + ny = len(x) + k = int(np.ceil(ny / 2) - 1) + y = np.sort(x) + diffs = y[k:] - y[: (ny - k)] + i = np.where(diffs == min(diffs))[0] + + if len(i) > 1: + if (np.max(i) - np.min(i)) > (_MODE_LIMIT * ny): + warnings.warn( + "Encountered a tie, and the difference between minimal and maximal value " + f"is > length('x') * {_MODE_LIMIT}.\n The distribution could be multimodal" + ) + i = int(np.mean(i)) + else: + i = i[0] + + mode = np.mean(y[i : (i + k + 1)]) + + return mode + + +def _plot_regression(y_data: pd.Series, y_regr: np.ndarray, date_time: pd.Series, ylabel: str): + """Function to plot the results of the regression tree. + + Args: + y_data (pd.Series): Data used on the regression. + y_regr (np.ndarray): Results obtained from the tree regression. + date_time (pd.Series): Dates of the original data. + ylabel (str): Data that is shown in the plot. + + """ + fig = plt.figure() + sc = plt.scatter(date_time, y_data, c=dates.date2num(date_time), s=5) + cbar = fig.colorbar(sc) + loc = dates.AutoDateLocator() + cbar.ax.yaxis.set_major_locator(loc) + cbar.ax.yaxis.set_major_formatter(dates.ConciseDateFormatter(loc)) + plt.plot(date_time[~y_data.isna()], y_regr, c="tab:red") + plt.ylabel(ylabel) + plt.grid() + plt.tight_layout() + + plt.show() + + +def homogenize( + scada: pd.DataFrame, + var: str = "wd", + threshold: int = 100, + reference: str = "last", + plot_it: bool = False, + max_depth: int = 4, + ccp_alpha: float = 0.0, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Homogenization routine of the Scada directions of the different wind turbines based on "var". + + The Scada data is explored by applying a regression tree procedure to the differences + in direction nof the wind turbines + to get the most common jumps and their positions. + These jumps are then reversed to correct the deviations. + + Args: + scada (Union[pd.DataFrame, FlascDataFrame]): DataFrame containing the SCADA data. + var (str, optional): Variable to homogenize (yaw or wd). Defaults to 'wd'. + threshold (int, optional): Threshold for discretization. Defaults to 100. + reference (str, optional): Reference point for homogenization. Defaults to 'last'. + plot_it (bool, optional): Whether to plot the results. Defaults to False. + max_depth (int, optional): Maximum depth of the regression tree. Defaults to 4. + ccp_alpha (float, optional): Complexity parameter for pruning. Defaults to 0.0. + + Returns: + tuple[pd.DataFrame, pd.DataFrame]: Homogenized SCADA data and the results used to + homogenize it with the jumps and their knots. + """ + # Check the variable to use for the homogenization + if var not in ["yaw", "wd"]: + raise ValueError( + 'Please, select a valid variable to use during homogeneization: "yaw" or "wd".' + ) + + # Select the columns to use in the algorithm + wt_names = scada.columns[scada.columns.str.startswith((var))] + if len(wt_names) < 3: + raise ValueError("There must be at least 3 wind turbines for the algorithm to apply.") + df = scada[wt_names.to_list() + ["time"]].reset_index(drop=True) + + # Reference date + if reference == "first": + ref = 0 + elif reference == "last": + ref = df.shape[0] - 1 + else: + date_ref = pd.to_datetime(reference) + ref = df["time"][df["time"] == date_ref] + if len(ref) == 0: + if date_ref < df["time"].min(): + ref = 0 + elif date_ref > df["time"].max(): + ref = df.shape[0] - 1 + else: + ref = 0 + warnings.warn( + "The reference date seem to be missing in the dataset. " + " The first date is selected as reference." + ) + + # Build the DataFrame to store the results of the Tree + d = pd.DataFrame(columns=["Knot", "Jump", "Turbine"]) + d = d.astype({"Knot": np.float64, "Jump": np.float64, "Turbine": str}) # Assign types + + # Iterate over every wind turbine comparing its direction with every other. + # Get then the points at which + # a jump is produced. These points are called knots. + for m in wt_names: + # Get the wind turbines to compare + ms2 = wt_names[wt_names != m] + df2 = df[ms2] + for m2 in ms2: + # Get the differences in the direction + df2.loc[:, m2] = modulo(df[m2] - df[m]) + y = df2[m2][~df2[m2].isna()] # Do not use the nan values + # Use a decision tree regressor to get the points at which there are knots + # and the values of the + # direction differences + regr = DecisionTreeRegressor( + max_depth=max_depth, + min_samples_split=threshold, + min_samples_leaf=threshold // 2, + ccp_alpha=ccp_alpha, + ) + regr.fit(y.index.to_numpy()[:, np.newaxis], y) + + # Postprocess the results of the decision tree + # regressor to get the information required. + # The leaves are + # the values of the means and the knots the points at which jumps occur + leaves, knots = _get_leaves_and_knots(regr) + + # Plot the results if desired + if plot_it: + _plot_regression( + df2[m2], + regr.predict(y.index.to_numpy()[:, np.newaxis]), + scada.time, + f"{m2} - {m} wind direction [°]", + ) + + # Save the results + if len(knots) > 0 and not np.any(np.isnan(knots)): + if m == "wd_004": + print(knots) + d = pd.concat( + [d, pd.DataFrame({"Knot": knots, "Jump": np.diff(leaves), "Turbine": m})] + ) + + d = d.reset_index(drop=True) + + # Postprocess all the data to get the main jumps for each wind turbine + d2 = d.copy() + d2["Class"] = discretize(d["Knot"], threshold=100) + d2["Count"] = 1 + d2 = d2.groupby(["Class", "Turbine"]).agg({"Count": "sum", "Jump": "mean", "Knot": shorth_mode}) + d2.reset_index(drop=False, inplace=True) + d2["Knot_date"] = df["time"].values[np.floor(d2["Knot"]).astype(int) - 1] + d2 = d2.loc[d2["Count"] > len(wt_names) / 2] + d2.sort_values("Count", ascending=False, inplace=True) + d2.drop_duplicates(subset="Class", inplace=True) + d2.sort_values("Class", inplace=True) + d2.reset_index(drop=True, inplace=True) + + # Predict + if d.shape[0] > 0: + for i in range(d2.shape[0]): + m = d2["Turbine"][i] + k = d2["Knot"][i] + j = d2["Jump"][i] + # Build a piecewise function based on the knot and the jump + f = interp1d( + np.array([0, k, scada.index.max()]), np.array([0.0, j, j]), kind="previous" + ) + + scada[m] = (scada[m] + f(scada.index) - f(ref)) % 360 + + return scada, d2 + + +# Engie test code +if __name__ == "__main__": + df = pd.read_feather("scada_exemple.ftr") + df_orig = pd.read_feather("scada_exemple.ftr") + + fig, ax = plt.subplots() + ax.scatter(df["time"], modulo(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") + ax.legend() + ax.grid(True) + ax.set_title("Original Wind Directions") + + df_corr, _ = homogenize(df, plot_it=False) # the erreur occurs at this point. + + fig, ax = plt.subplots() + ax.scatter( + df_corr["time"], modulo(df_corr["wd_004"] - df_corr["wd_005"]), label="Direction E05 - E06" + ) + ax.legend() + ax.grid(True) + ax.set_title("Corrected Wind Directions") + + wd_cols = sorted([c for c in df.columns if "wd_" in c]) + + for wd_col in ["wd_004"]: + fig, ax = plt.subplots() + ax.scatter(df_orig["time"], modulo(df_orig[wd_col] - df_corr[wd_col])) + ax.set_title("Change in value of wd_004") + ax.grid(True) + + plt.show() + + # fig, axarr = plt.subplots(2,1,sharex=True) + # ax = axarr[0] + # ax.scatter(df["time"], df["wd_004"], label="original") + # ax.scatter(df_corr["time"], df_corr["wd_004"], label="corrected") + # ax.set_title("Turbine 4") + # ax.grid(True) + + # ax = axarr[1] + # ax.scatter(df["time"], df["wd_005"], label="original") + # ax.scatter(df_corr["time"], df_corr["wd_005"], label="corrected") + # ax.set_title("Turbine 5") + # ax.grid(True) + + +# # Dummy test code +# if __name__ == "__main__": +# # # Test discretize function +# # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) +# # y = discretize(x) +# # print(y) + +# # Now make a test dataframe to test the homogenize function +# # Imagine there are 3 turbines, the first turbine's wd is +# # set by a random walk. Turbine 2 is equal to 1 + white noise +# # finally turbine 3 is turbine 1 + white noise, + a jump +# # by jump_size deg halfway through +# n = 1000 +# jump_size = 10.0 +# np.random.seed(0) +# time = pd.date_range("2020-01-01", periods=n, freq="10min") +# wd_000 = wrap_360(np.cumsum(np.random.randn(n))) +# wd_001 = wrap_360(wd_000 + np.random.randn(n)) +# wd_002 = wd_000 + np.random.randn(n) +# wd_002[int(np.floor(n / 2)) :] += jump_size +# wd_002 = wrap_360(wd_002) + +# # FlascDataFrame requires power signals, just make these up +# pow_made_up = np.random.randn(n) + +# # Plot the 3 signals + +# fig, ax = plt.subplots() +# ax.plot(time, wd_000, label="Turbine 0") +# ax.plot(time, wd_001, label="Turbine 1") +# ax.plot(time, wd_002, label="Turbine 2") +# ax.legend() +# ax.grid(True) +# ax.set_title("Original Wind Directions") + +# # Combine into a FlascDataFrame +# df = FlascDataFrame( +# { +# "time": time, +# "wd_000": wd_000, +# "wd_001": wd_001, +# "wd_002": wd_002, +# "pow_000": pow_made_up, +# "pow_001": pow_made_up, +# "pow_002": pow_made_up, +# } +# ) + +# df_corr = homogenize(df, verbose=True) + +# # Plot the corrected results +# fig, ax = plt.subplots() +# ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") +# ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") +# ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") +# ax.legend() +# ax.grid(True) +# ax.set_title("Corrected Wind Directions") + +# plt.show() From 4cbed9ca1d20603515a825a5e5ce2a50df4b83ed Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 18 Nov 2024 11:05:40 -0700 Subject: [PATCH 06/31] Update reqs --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index cf175d1c..cfc6889d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ "ephem", "coloredlogs~=15.0", "res-wind-up~=0.1", + "scikit-learn>=1.5.2", ] [project.optional-dependencies] From 7a602a0947a253b6e8c10b188c4f6de787484dcc Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 18 Nov 2024 11:05:53 -0700 Subject: [PATCH 07/31] fix specifier --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cfc6889d..48e8a2ca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,7 +39,7 @@ dependencies = [ "ephem", "coloredlogs~=15.0", "res-wind-up~=0.1", - "scikit-learn>=1.5.2", + "scikit-learn~=1.5", ] [project.optional-dependencies] From a897da408bf68644d2e7bdc5d6b8ff379734e4a3 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 13:41:07 -0700 Subject: [PATCH 08/31] Update docs --- .github/workflows/deploy-pages.yaml | 1 + docs/_toc.yml | 8 ++++++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/.github/workflows/deploy-pages.yaml b/.github/workflows/deploy-pages.yaml index 606c0e82..436b5ff2 100644 --- a/.github/workflows/deploy-pages.yaml +++ b/.github/workflows/deploy-pages.yaml @@ -29,6 +29,7 @@ jobs: - name: Copy examples to docs working-directory: ${{runner.workspace}}/flasc/ run: | + rsync -av --mkpath examples_artificial_data/03_energy_ratio/ docs/examples/01_raw_data_processing rsync -av --mkpath examples_artificial_data/03_energy_ratio/ docs/examples/03_energy_ratio rsync -av --mkpath examples_artificial_data/floris_input_artificial/ docs/examples/floris_input_artificial ls docs/examples diff --git a/docs/_toc.yml b/docs/_toc.yml index ab8101e2..64ecfd8a 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -24,9 +24,13 @@ parts: # - file: development # - file: testing - - caption: Examples + - caption: Examples Data Processing + chapters: + - file: examples/01_raw_data_processing/00_filter_ws_power_curves + - file: examples/01_raw_data_processing/01_northing_calibration + + - caption: Examples Energy Ratio chapters: - # - file: flascdataframe - file: examples/03_energy_ratio/00_demo_energy_ratio_syntax - file: examples/03_energy_ratio/01_demo_energy_ratio_options From fca8ef677af637f1dff4c182a608ed3be10306d3 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 13:41:23 -0700 Subject: [PATCH 09/31] rename file --- flasc/data_processing/hoger_nb.ipynb | 797 ------------------ ...ger.py => northing_offset_change_hoger.py} | 37 +- 2 files changed, 10 insertions(+), 824 deletions(-) delete mode 100644 flasc/data_processing/hoger_nb.ipynb rename flasc/data_processing/{northing_offset_hoger.py => northing_offset_change_hoger.py} (94%) diff --git a/flasc/data_processing/hoger_nb.ipynb b/flasc/data_processing/hoger_nb.ipynb deleted file mode 100644 index 4fe48fe7..00000000 --- a/flasc/data_processing/hoger_nb.ipynb +++ /dev/null @@ -1,797 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test out hoger in notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Union\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import ruptures as rpt\n", - "from floris.utilities import wrap_180, wrap_360\n", - "\n", - "from flasc import FlascDataFrame\n", - "from flasc.utilities.circular_statistics import calc_wd_mean_radial" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def discretize(x: np.ndarray, threshold: float = 100) -> np.ndarray:\n", - " \"\"\"Discretize data points into segments.\n", - "\n", - " Args:\n", - " x (np.ndarray): Data to discretize.\n", - " threshold (float, optional): Threshold for discretization. Defaults to 100.\n", - "\n", - " Returns:\n", - " np.ndarray: Discretized data.\n", - " \"\"\"\n", - " # Handle NA values\n", - " na = pd.isna(x)\n", - "\n", - " # Sort indices\n", - " o = np.argsort(x)\n", - " x_sorted = x[o]\n", - "\n", - " # Initialize group labels\n", - " y = np.ones(len(x_sorted))\n", - "\n", - " # Find significant jumps\n", - " d = np.diff(x_sorted)\n", - " w = np.where(d >= threshold)[0]\n", - "\n", - " # Assign group labels\n", - " for i in range(len(d)):\n", - " if i in w:\n", - " y[i + 1 :] += 1\n", - "\n", - " # Reorder and handle NAs\n", - " y = y[np.argsort(o)]\n", - " y[na] = np.nan\n", - "\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_feather('scada_exemple.ftr')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "threshold = 100\n", - "reference = 'last'\n", - "verbose = True" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure in FlascDataFrame format\n", - "df = FlascDataFrame(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "if reference == \"first\":\n", - " ref = 0\n", - "elif reference == \"last\":\n", - " ref = len(df) - 1\n", - "else:\n", - " try:\n", - " ref = np.argmin(np.abs(df[\"time\"].values - pd.to_datetime(reference)))\n", - " except ValueError:\n", - " raise ValueError(\n", - " \"Invalid reference point. Please use 'first', 'last', or a valid time string.\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize results dataframe\n", - "df_jump = pd.DataFrame(columns=[\"Knot\", \"Jump\", \"Turbine\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing turbine 4\n", - "...with turbine 5\n", - " Number of jumps: 494\n" - ] - } - ], - "source": [ - "# Loop over combinations of turbines\n", - "for t_i in [4]:#range(df.n_turbines):\n", - " t_i_col = \"wd_%03d\" % t_i\n", - "\n", - " if verbose:\n", - " print(f\"Processing turbine {t_i}\")\n", - "\n", - " for t_j in [5]:#range(df.n_turbines):\n", - " if t_i == t_j:\n", - " continue\n", - " t_j_col = \"wd_%03d\" % t_j\n", - "\n", - " if verbose:\n", - " print(f\"...with turbine {t_j}\")\n", - "\n", - " # Compute the wrapped error\n", - " wrapped_error = wrap_180(df[t_i_col].values - df[t_j_col].values)\n", - "\n", - " # R code uses picor: Piecewise-constant regression, using\n", - " # https://github.com/chasmani/piecewise-regression in python\n", - " # as a replacement for picor\n", - " # I can't find a close python equivalent for picor, so starting with ruptures\n", - " # this is convenient as via the dependency on wind-up this is already\n", - " # a defacto requirement for FLASC\n", - "\n", - " # Note these first lines (minus the threshold)\n", - " # are verbatim from the example here\n", - " # https://github.com/deepcharles/ruptures\n", - " # presumably can improve somewhat\n", - " algo = rpt.Pelt(model=\"l1\", min_size=threshold).fit(wrapped_error)\n", - " result = algo.predict(pen=10)\n", - " # algo = rpt.Window(width=threshold, model='l1').fit(wrapped_error)\n", - " # pen = 20 # np.log(len(wrapped_error)) * np.nanstd(wrapped_error)**2\n", - " # print(f\"Pen: {pen}\")\n", - " # result = algo.predict(pen=pen)\n", - "\n", - "\n", - " # If results is length 1 or 0, no significant jumps detected, continue\n", - " if len(result) <= 1:\n", - " if verbose:\n", - " print(\"... No significant jumps detected\")\n", - " continue\n", - "\n", - " if verbose:\n", - " # print(f\"... Jumps detected at: {result[:-1]}\")\n", - " print(f\" Number of jumps: {len(result)-1}\")\n", - "\n", - " # Compute the mean values in error in each of the identified segments\n", - " # so we can compute the jump size at each jump location\n", - " knots = result[:-1] # Exclude the end point returned by ruptures\n", - " values = [\n", - " calc_wd_mean_radial(wrapped_error[start:end])\n", - " for start, end in zip([0] + knots, knots + [len(wrapped_error)])\n", - " ]\n", - "\n", - " # Paul's note: I added wrap_180 here though I don't think it's in original R code\n", - " # but it feels correct to me to include it since errors \n", - " # should not include values > abs(180)\n", - " values = [wrap_180(v) for v in values]\n", - "\n", - " # if verbose:\n", - " # print(f\"... Jump values per area: {values}\")\n", - "\n", - " jumps = np.diff(values)\n", - "\n", - " # if verbose:\n", - " # print(f\"... Jump sizes: {jumps}\")\n", - "\n", - " # Append result to the result dataframe\n", - " # TODO: Not a big deal but this is a slow way to do it\n", - " df_jump = pd.concat(\n", - " [df_jump, pd.DataFrame({\"Knot\": knots, \"Jump\": jumps, \"Turbine\": t_i})]\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 810.0\n", - "4 2065.0\n", - "8 4460.0\n", - "12 5260.0\n", - "16 5760.0\n", - " ... \n", - "489 52060\n", - "490 52160\n", - "491 52260\n", - "492 52360\n", - "493 52460\n", - "Name: Knot, Length: 1450, dtype: object" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jump.Knot" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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KnotJumpTurbine
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" - ], - "text/plain": [ - " Knot Jump Turbine\n", - "0 1830 2.794897 0\n", - "1 2070 -4.368067 0\n", - "2 2310 2.353011 0\n", - "3 3020 -2.580274 0\n", - "4 3130 4.623524 0\n", - ".. ... ... ...\n", - "489 52060 -1.260391 4\n", - "490 52160 0.635590 4\n", - "491 52260 0.124131 4\n", - "492 52360 0.577936 4\n", - "493 52460 0.406734 4\n", - "\n", - "[23452 rows x 3 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jump" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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KnotJumpCount
ClassTurbine
1.04265.0-3.1566372
2.04410.02.6576512
3.04515.01.0016082
4.04615.0-3.1648292
5.00810.0-0.0706011
...............
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" - ], - "text/plain": [ - " Knot Jump Count\n", - "Class Turbine \n", - "1.0 4 265.0 -3.156637 2\n", - "2.0 4 410.0 2.657651 2\n", - "3.0 4 515.0 1.001608 2\n", - "4.0 4 615.0 -3.164829 2\n", - "5.0 0 810.0 -0.070601 1\n", - "... ... ... ...\n", - "492.0 4 52260.0 0.124131 2\n", - "493.0 0 52360.0 0.056769 1\n", - " 4 52360.0 0.577936 2\n", - "494.0 0 52460.0 -0.447198 1\n", - " 4 52460.0 0.406734 2\n", - "\n", - "[952 rows x 3 columns]" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jump = (\n", - " df_jump.assign(Count=1, Class=discretize(df_jump[\"Knot\"].values, threshold=threshold))\n", - " .groupby([\"Class\", \"Turbine\"])\n", - " #TODO Original code uses a \"mode\" but for now taking a shortcut with median\n", - " .agg(\n", - " {\n", - " \"Knot\": \"median\", # Using median instead of shorth\n", - " \"Jump\": \"mean\",\n", - " \"Count\": \"sum\",\n", - " }\n", - " )\n", - " .reset_index()\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Df_jump size: (23452, 3)\n", - "Df_jump size (after summarizing): (462, 5)\n" - ] - } - ], - "source": [ - "\n", - "print(f\"Df_jump size: {df_jump.shape}\")\n", - "df_jump_orig = df_jump.copy()\n", - "\n", - "# Group and summarize change points\n", - "df_jump = (\n", - " df_jump.assign(Count=1, Class=discretize(df_jump[\"Knot\"].values, threshold=threshold))\n", - " .groupby([\"Class\", \"Turbine\"])\n", - " #TODO Original code uses a \"mode\" but for now taking a shortcut with median\n", - " .agg(\n", - " {\n", - " \"Knot\": \"median\", # Using median instead of shorth\n", - " \"Jump\": \"mean\",\n", - " \"Count\": \"sum\",\n", - " }\n", - " )\n", - " .reset_index()\n", - " .query(f\"Count > {np.floor(df.n_turbines/2)}\")\n", - " .sort_values(\"Count\", ascending=False)\n", - " .drop_duplicates(\"Class\")\n", - " .sort_values(\"Class\")\n", - ")\n", - "\n", - "print(f\"Df_jump size (after summarizing): {df_jump.shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ClassTurbineKnotJumpCount
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83.004460.0-0.170122220
124.005260.00.144313261
165.005760.0-0.84309229
..................
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" - ], - "text/plain": [ - " Class Turbine Knot Jump Count\n", - "0 1.0 0 810.0 -0.070601 62\n", - "4 2.0 0 2065.0 -0.033015 71\n", - "8 3.0 0 4460.0 -0.170122 220\n", - "12 4.0 0 5260.0 0.144313 261\n", - "16 5.0 0 5760.0 -0.843092 29\n", - "... ... ... ... ... ...\n", - "1825 458.0 0 52060.0 -0.387025 29\n", - "1829 459.0 0 52160.0 0.377298 29\n", - "1833 460.0 0 52260.0 -0.218838 29\n", - "1837 461.0 0 52360.0 0.056769 29\n", - "1841 462.0 0 52460.0 -0.447198 29\n", - "\n", - "[462 rows x 5 columns]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jump" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax_arr = rpt.display(wrapped_error, result, figsize=(10, 6))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/flasc/data_processing/northing_offset_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py similarity index 94% rename from flasc/data_processing/northing_offset_hoger.py rename to flasc/data_processing/northing_offset_change_hoger.py index 390b9a19..5153c202 100644 --- a/flasc/data_processing/northing_offset_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -12,6 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from floris.utilities import wrap_180 from matplotlib import dates from scipy.interpolate import interp1d from sklearn.tree import DecisionTreeRegressor @@ -19,27 +20,6 @@ _MODE_LIMIT = 0.05 -def modulo(x, m: float = 360.0): - """Compute the modulo of an angle with a period of m. - - It normalizes the values of x to the [-m/2, m/2) range. - - Args: - x (np.ndarray): Values to compute the modulo. - m (float, optional): Period. Defaults to 360.0. - - Returns: - np.ndarray: New values of x. - - """ - x = x % m - - w1 = x > m / 2 - x[w1] = x[w1] - m - - return x - - def _get_leaves_and_knots(tree: DecisionTreeRegressor) -> tuple[np.ndarray, np.ndarray]: """Function to get the values of the superficial knots and leaves of a Tree Regression. @@ -159,14 +139,15 @@ def _plot_regression(y_data: pd.Series, y_regr: np.ndarray, date_time: pd.Series plt.show() +# TODO: Keep these defaults? def homogenize( scada: pd.DataFrame, var: str = "wd", - threshold: int = 100, + threshold: int = 1000, reference: str = "last", plot_it: bool = False, max_depth: int = 4, - ccp_alpha: float = 0.0, + ccp_alpha: float = 0.09, ) -> tuple[pd.DataFrame, pd.DataFrame]: """Homogenization routine of the Scada directions of the different wind turbines based on "var". @@ -233,7 +214,7 @@ def homogenize( df2 = df[ms2] for m2 in ms2: # Get the differences in the direction - df2.loc[:, m2] = modulo(df[m2] - df[m]) + df2.loc[:, m2] = wrap_180(df[m2] - df[m]) y = df2[m2][~df2[m2].isna()] # Do not use the nan values # Use a decision tree regressor to get the points at which there are knots # and the values of the @@ -306,7 +287,7 @@ def homogenize( df_orig = pd.read_feather("scada_exemple.ftr") fig, ax = plt.subplots() - ax.scatter(df["time"], modulo(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") + ax.scatter(df["time"], wrap_180(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") ax.legend() ax.grid(True) ax.set_title("Original Wind Directions") @@ -315,7 +296,9 @@ def homogenize( fig, ax = plt.subplots() ax.scatter( - df_corr["time"], modulo(df_corr["wd_004"] - df_corr["wd_005"]), label="Direction E05 - E06" + df_corr["time"], + wrap_180(df_corr["wd_004"] - df_corr["wd_005"]), + label="Direction E05 - E06", ) ax.legend() ax.grid(True) @@ -325,7 +308,7 @@ def homogenize( for wd_col in ["wd_004"]: fig, ax = plt.subplots() - ax.scatter(df_orig["time"], modulo(df_orig[wd_col] - df_corr[wd_col])) + ax.scatter(df_orig["time"], wrap_180(df_orig[wd_col] - df_corr[wd_col])) ax.set_title("Change in value of wd_004") ax.grid(True) From 062e3dab63407c3eb58c40d22b68e5633be50a01 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 13:42:17 -0700 Subject: [PATCH 10/31] update northing example --- .../01_northing_calibration.ipynb | 804 +++++++++++++++--- 1 file changed, 682 insertions(+), 122 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb index b8a8679c..89b03723 100644 --- a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb +++ b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb @@ -5,33 +5,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **Import dependencies**" + "# Northing Calibration in FLASC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Northing calibration, that is the detection of bias and changes in measurements of turbine yaw are important for many of the analysis in FLASC. This notebook demonstrates the use of several of these tools in FLASC for the calibration of northing measurements." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" - ] - } - ], + "outputs": [], "source": [ "# from datetime import timedelta as td\n", "import os\n", "import warnings as wn\n", + "from datetime import timedelta as td\n", "\n", "import numpy as np\n", "import pandas as pd\n", + "from floris import TimeSeries\n", + "from floris.layout_visualization import plot_turbine_labels, plot_turbine_points\n", "from floris.utilities import wrap_360\n", "from matplotlib import pyplot as plt\n", "\n", + "from flasc import FlascDataFrame\n", "from flasc.data_processing import (\n", " dataframe_manipulations as dfm,\n", " energy_ratio_wd_bias_estimation as best,\n", @@ -49,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -59,71 +61,571 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 0**: Initial data pulldown\n", - "First, we import the data from the common_windfarm_information folder. This may take a while, so we keep these variables unchanged. These are df_scada_raw and df_metmast_raw. These variables are not manipulated throughout the script." + "## Load FLORIS model and show layout" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Load FLORIS model\n", + "fm, turbine_weights = load_floris()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the layout\n", + "fig, ax = plt.subplots()\n", + "plot_turbine_points(fm, ax)\n", + "plot_turbine_labels(fm, ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data set to illustrate operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity assume a fixed wind speed and turbulence intensity and uniform wind direction. Perturb the wind direction by random noise" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "def load_data():\n", - " root_path = os.getcwd()\n", - " f = os.path.join(root_path, \"postprocessed\", \"df_scada_600s_wspowfiltered.pkl\")\n", - " df_scada = pd.read_pickle(f)\n", + "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", + "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", "\n", - " # # Optionally: downsample to [x] minute averages to speed up things\n", - " # cols_angular = [c for c in df_scada if ((\"wd_\" in c) or (\"yaw_\" in c))]\n", - " # df_scada = fto.df_downsample(\n", - " # df_scada,\n", - " # cols_angular=cols_angular,\n", - " # window_width=td(seconds=600),\n", - " # )\n", + "# Apply noise\n", + "np.random.seed(0)\n", + "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", + "wind_directions = wind_directions + noise\n", "\n", - " return df_scada\n", + "# Set a FLORIS time series object\n", + "time_series = TimeSeries(\n", + " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1753918.68116782, 1753961.25195179, 1753974.02887594,\n", + " 1753984.64239339, 1753954.56987842, 1753926.45363424])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate FLORIS solution\n", + "fm.set(wind_data=time_series)\n", + "fm.run()\n", + "turbine_powers = fm.get_turbine_powers()\n", "\n", + "# Add random noise to the power output\n", + "turbine_powers = turbine_powers + np.random.normal(0, 25.0, turbine_powers.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the results to create a FLASC dataframe representing hypothetical scada data\n", + "df_scada = FlascDataFrame(\n", + " {\n", + " \"time\": pd.date_range(start=\"1/1/2020\", periods=len(wind_directions), freq=\"600s\"),\n", + " \"wind_directions\": wind_directions,\n", + " \"wind_speeds\": 8.0 * np.ones_like(wind_directions),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "FlascDataFrame in FLASC format\n", + "
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timewind_directionswind_speedspow_000pow_001pow_002pow_003pow_004pow_005pow_006wd_000wd_001wd_002wd_003wd_004wd_005wd_006
02020-01-01 00:00:000.8820268.01.300483e+066.782295e+051.062299e+061.753996e+061.753925e+061.753954e+061.753919e+060.4801630.5186760.8672501.3595690.6235290.2622910.860956
12020-01-01 00:10:001.2000798.01.336065e+067.107464e+051.097194e+061.753993e+061.753949e+061.753959e+061.753961e+060.2846841.0691041.8623820.6874901.3843951.0952821.194562
22020-01-01 00:20:002.4893698.01.464618e+068.748210e+051.233091e+061.753934e+061.753949e+061.753993e+061.753974e+062.0296912.9304302.2481663.1681702.6812702.3042222.469416
32020-01-01 00:30:004.1204478.01.588592e+061.075059e+061.396982e+061.753984e+061.753965e+061.753941e+061.753985e+063.1530604.6036433.4632093.6163384.6210693.8104295.051782
42020-01-01 00:40:004.9337798.01.633644e+061.164006e+061.466968e+061.753959e+061.753942e+061.753971e+061.753955e+064.0384525.6512415.7514675.1733014.9751734.2382874.768993
......................................................
17952020-01-13 11:10:00354.7220078.05.540390e+053.631354e+054.072392e+051.753920e+061.753931e+061.753940e+061.753937e+06354.104930355.603609355.974084354.515064354.500130354.891368354.998903
17962020-01-13 11:20:00356.0133698.06.913212e+053.469273e+055.184367e+051.753995e+061.753957e+061.753940e+061.753917e+06355.252039355.760453354.907418355.423705356.723372355.720953355.511321
17972020-01-13 11:30:00357.0917258.08.298914e+053.672902e+056.234497e+051.753920e+061.753938e+061.753954e+061.753921e+06356.338511357.314736357.075436357.273982356.775948357.175943356.677064
17982020-01-13 11:40:00357.7646298.09.225341e+053.940003e+056.904134e+051.753967e+061.753943e+061.753960e+061.753947e+06357.366234358.697730357.640309358.981603357.377406358.350070357.973726
17992020-01-13 11:50:00359.1363988.01.097052e+065.031428e+058.536198e+051.753960e+061.753918e+061.753974e+061.753920e+06358.308969358.423708358.505300359.747302359.438663359.130778359.271976
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1800 rows × 17 columns

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" + ], + "text/plain": [ + " time wind_directions wind_speeds pow_000 \\\n", + "0 2020-01-01 00:00:00 0.882026 8.0 1.300483e+06 \n", + "1 2020-01-01 00:10:00 1.200079 8.0 1.336065e+06 \n", + "2 2020-01-01 00:20:00 2.489369 8.0 1.464618e+06 \n", + "3 2020-01-01 00:30:00 4.120447 8.0 1.588592e+06 \n", + "4 2020-01-01 00:40:00 4.933779 8.0 1.633644e+06 \n", + "... ... ... ... ... \n", + "1795 2020-01-13 11:10:00 354.722007 8.0 5.540390e+05 \n", + "1796 2020-01-13 11:20:00 356.013369 8.0 6.913212e+05 \n", + "1797 2020-01-13 11:30:00 357.091725 8.0 8.298914e+05 \n", + "1798 2020-01-13 11:40:00 357.764629 8.0 9.225341e+05 \n", + "1799 2020-01-13 11:50:00 359.136398 8.0 1.097052e+06 \n", + "\n", + " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", + "0 6.782295e+05 1.062299e+06 1.753996e+06 1.753925e+06 1.753954e+06 \n", + "1 7.107464e+05 1.097194e+06 1.753993e+06 1.753949e+06 1.753959e+06 \n", + "2 8.748210e+05 1.233091e+06 1.753934e+06 1.753949e+06 1.753993e+06 \n", + "3 1.075059e+06 1.396982e+06 1.753984e+06 1.753965e+06 1.753941e+06 \n", + "4 1.164006e+06 1.466968e+06 1.753959e+06 1.753942e+06 1.753971e+06 \n", + "... ... ... ... ... ... \n", + "1795 3.631354e+05 4.072392e+05 1.753920e+06 1.753931e+06 1.753940e+06 \n", + "1796 3.469273e+05 5.184367e+05 1.753995e+06 1.753957e+06 1.753940e+06 \n", + "1797 3.672902e+05 6.234497e+05 1.753920e+06 1.753938e+06 1.753954e+06 \n", + "1798 3.940003e+05 6.904134e+05 1.753967e+06 1.753943e+06 1.753960e+06 \n", + "1799 5.031428e+05 8.536198e+05 1.753960e+06 1.753918e+06 1.753974e+06 \n", + "\n", + " pow_006 wd_000 wd_001 wd_002 wd_003 \\\n", + "0 1.753919e+06 0.480163 0.518676 0.867250 1.359569 \n", + "1 1.753961e+06 0.284684 1.069104 1.862382 0.687490 \n", + "2 1.753974e+06 2.029691 2.930430 2.248166 3.168170 \n", + "3 1.753985e+06 3.153060 4.603643 3.463209 3.616338 \n", + "4 1.753955e+06 4.038452 5.651241 5.751467 5.173301 \n", + "... ... ... ... ... ... \n", + "1795 1.753937e+06 354.104930 355.603609 355.974084 354.515064 \n", + "1796 1.753917e+06 355.252039 355.760453 354.907418 355.423705 \n", + "1797 1.753921e+06 356.338511 357.314736 357.075436 357.273982 \n", + "1798 1.753947e+06 357.366234 358.697730 357.640309 358.981603 \n", + "1799 1.753920e+06 358.308969 358.423708 358.505300 359.747302 \n", + "\n", + " wd_004 wd_005 wd_006 \n", + "0 0.623529 0.262291 0.860956 \n", + "1 1.384395 1.095282 1.194562 \n", + "2 2.681270 2.304222 2.469416 \n", + "3 4.621069 3.810429 5.051782 \n", + "4 4.975173 4.238287 4.768993 \n", + "... ... ... ... \n", + "1795 354.500130 354.891368 354.998903 \n", + "1796 356.723372 355.720953 355.511321 \n", + "1797 356.775948 357.175943 356.677064 \n", + "1798 357.377406 358.350070 357.973726 \n", + "1799 359.438663 359.130778 359.271976 \n", + "\n", + "[1800 rows x 17 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the turbine powers to the dataframe with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"pow_{t_idx:03d}\"] = turbine_powers[:, t_idx]\n", + "\n", + "# Set the turbine wind directions to be the true wind direction with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", + " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + " )\n", "\n", - "df_scada_northing_uncalibrated = load_data()\n", - "df_scada_northing_uncalibrated[\"ti\"] = 0.06 # Assume a certain ambient turbulence intensity" + "df_scada" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 1**: Initialize FLORIS\n", - "and precalculate a large set of solutions using the parallel computing interface in FLORIS" + "#### Northing calibration error\n", + "\n", + "Add to the data two types of northing calibration error:\n", + "1. A constant bias on turbine 001\n", + "2. A change in bias on turbine 002 halfway through the data set" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "# Now we calculate a grid of FLORIS solutions. Since our estimated SCADA\n", - "# data changes as we shift its wind direction, the predicted solutions\n", - "# according to FLORIS will also change. Therefore, we precalculate a grid\n", - "# of FLORIS solutions and insert that into the bias estimation class.\n", - "fm, turbine_weights = load_floris()\n", + "df_scada[\"wd_001\"] = wrap_360(\n", + " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + ")\n", "\n", - "# Grab the precalculated FLORIS model solutions from the 'setup_floris_model' directory\n", - "root_path = os.getcwd()\n", - "fn_approx = os.path.join(root_path, \"..\", \"00_setup_floris_model\", \"df_fm_approx_gch.ftr\")\n", - "if os.path.exists(fn_approx):\n", - " df_fm_approx = pd.read_feather(fn_approx)\n", - "else:\n", - " raise UserWarning(\n", - " \"Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' \"\n", - " \"for the appropriate wake models first.\"\n", - " )" + "mid_point = int(len(wind_directions) / 2)\n", + "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + "wd_change[mid_point:] = wd_change[mid_point:] + 30\n", + "wd_change = wrap_360(wd_change)\n", + "df_scada[\"wd_002\"] = wd_change" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind direction')" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the wd channels for the turbines\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"gray\")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"Wind direction\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 13:33:42\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-11-19 13:33:42\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + ] + } + ], + "source": [ + "# Finally compute df_approx for use in later algorithms\n", + "# Can compute only at 8m/s for this example\n", + "df_fm_approx = ftools.calc_floris_approx_table(\n", + " fm=fm, # fi=fi_pci,\n", + " wd_array=np.arange(0.0, 360.01, 1.0),\n", + " ws_array=np.array([8.0]),\n", + " ti_array=np.array([0.06]),\n", + ")" ] }, { @@ -131,80 +633,107 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 2**: Cross-compare wind direction measurements\n", - "and see if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. The current functionality is limited and cannot account for this yet." + "# Cross-Check Northing calibration " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` is a function to check if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-10-16 11:44:52\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-10-16 11:44:54\u001b[0m T006 T001 T002 T005 T003\n", - "0 18.0 16.0 -6.0 14.0 46.0\n", - "1 18.0 16.0 -6.0 14.0 46.0\n", - "2 18.0 16.0 -6.0 14.0 46.0\n", - "3 18.0 14.0 -6.0 14.0 46.0\n", - "\u001b[32m2024-10-16 11:44:54\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-10-16 11:44:56\u001b[0m T002 T006 T005 T003 T000\n", - "0 -22.0 2.0 -2.0 30.0 -16.0\n", - "1 -20.0 2.0 -2.0 30.0 -16.0\n", - "2 -20.0 2.0 -2.0 30.0 -16.0\n", - "3 -22.0 2.0 -2.0 30.0 -14.0\n", - "\u001b[32m2024-10-16 11:44:56\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-10-16 11:44:57\u001b[0m T001 T003 T005 T000 T006\n", - "0 22.0 52.0 20.0 6.0 24.0\n", - "1 20.0 52.0 20.0 6.0 24.0\n", - "2 20.0 52.0 20.0 6.0 24.0\n", - "3 22.0 52.0 20.0 6.0 24.0\n", - "\u001b[32m2024-10-16 11:44:57\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-10-16 11:44:58\u001b[0m T005 T002 T001 T004 T006\n", - "0 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "1 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "2 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "3 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "\u001b[32m2024-10-16 11:44:58\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T002 T005 T001 T006\n", - "0 30.0 -22.0 -2.0 -2.0 2.0\n", - "1 30.0 -22.0 -2.0 -2.0 2.0\n", - "2 30.0 -22.0 -2.0 -2.0 2.0\n", - "3 30.0 -22.0 -2.0 -2.0 2.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T001 T006 T002 T000\n", - "0 32.0 2.0 4.0 -20.0 -14.0\n", - "1 32.0 2.0 4.0 -20.0 -14.0\n", - "2 32.0 2.0 4.0 -20.0 -14.0\n", - "3 32.0 2.0 4.0 -20.0 -14.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T001 T005 T000 T003 T002\n", - "0 -2.0 -4.0 -18.0 28.0 -24.0\n", - "1 -2.0 -4.0 -18.0 28.0 -24.0\n", - "2 -2.0 -4.0 -18.0 28.0 -24.0\n", - "3 -2.0 -4.0 -18.0 28.0 -24.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -30.0 0.0 0.0 0.0\n", + "1 0.0 -30.0 0.0 0.0 0.0\n", + "2 0.0 -30.0 0.0 0.0 0.0\n", + "3 0.0 -30.0 -26.0 0.0 0.0\n", + "4 0.0 -30.0 -30.0 0.0 0.0\n", + "5 0.0 -30.0 -30.0 0.0 0.0\n", + "6 0.0 -30.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T002 T006 T005 T003 T000\n", + "0 30.0 30.0 30.0 30.0 30.0\n", + "1 30.0 30.0 30.0 30.0 30.0\n", + "2 30.0 30.0 30.0 30.0 30.0\n", + "3 4.0 30.0 30.0 30.0 30.0\n", + "4 0.0 30.0 30.0 30.0 30.0\n", + "5 0.0 30.0 30.0 30.0 30.0\n", + "6 0.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T001 T003 T005 T000 T006\n", + "0 -30.0 0.0 0.0 -0.0 0.0\n", + "1 -30.0 0.0 0.0 -0.0 0.0\n", + "2 -30.0 0.0 0.0 -0.0 0.0\n", + "3 -4.0 26.0 26.0 26.0 26.0\n", + "4 -0.0 30.0 30.0 30.0 30.0\n", + "5 -0.0 30.0 30.0 30.0 30.0\n", + "6 -0.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 -0.0 -30.0 0.0 0.0\n", + "1 0.0 -0.0 -30.0 0.0 0.0\n", + "2 0.0 -0.0 -30.0 0.0 0.0\n", + "3 0.0 -26.0 -30.0 0.0 0.0\n", + "4 0.0 -30.0 -30.0 0.0 0.0\n", + "5 0.0 -30.0 -30.0 0.0 0.0\n", + "6 0.0 -30.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 0.0 0.0 -30.0 0.0\n", + "1 -0.0 0.0 0.0 -30.0 0.0\n", + "2 -0.0 0.0 0.0 -30.0 0.0\n", + "3 -0.0 -26.0 0.0 -30.0 0.0\n", + "4 -0.0 -30.0 0.0 -30.0 0.0\n", + "5 -0.0 -30.0 0.0 -30.0 0.0\n", + "6 -0.0 -30.0 0.0 -30.0 0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -30.0 0.0 -0.0 -0.0\n", + "1 -0.0 -30.0 0.0 -0.0 -0.0\n", + "2 -0.0 -30.0 0.0 -0.0 -0.0\n", + "3 -0.0 -30.0 0.0 -26.0 -0.0\n", + "4 -0.0 -30.0 0.0 -30.0 -0.0\n", + "5 -0.0 -30.0 0.0 -30.0 -0.0\n", + "6 -0.0 -30.0 0.0 -30.0 -0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T001 T005 T000 T003 T002\n", + "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "3 -30.0 -0.0 -0.0 -0.0 -26.0\n", + "4 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "5 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "6 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['clean', 'clean', 'clean', 'clean', 'clean', 'clean', 'clean']\n" + "['clean', 'clean', 'bad', 'clean', 'clean', 'clean', 'clean']\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -215,19 +744,57 @@ ], "source": [ "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", - "df_scada_marked_faulty_northing_drift = df_scada_northing_uncalibrated.copy()\n", + "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", "\n", "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", - " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", ")\n", - "print(turb_wd_consistency)\n", + "print(turb_wd_consistency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` detects that T002 contains a probable jump, one solution is to then remove T002's wind direction data from consideration however this is not done in this notebook as we next take advantage of HOGER recalibration. The code to do this is included below in comments" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# # Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", + "# faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", + "# for ti in np.where(faulty_turbines)[0]:\n", + "# df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homegenization with HOGER" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", + " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", "\n", - "# Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", - "faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", - "for ti in np.where(faulty_turbines)[0]:\n", - " df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" + " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "attachments": {}, "cell_type": "markdown", @@ -3121,11 +3688,9 @@ } ], "metadata": { - "interpreter": { - "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" - }, "kernelspec": { - "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", + "display_name": ".venv", + "language": "python", "name": "python3" }, "language_info": { @@ -3138,14 +3703,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.13.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8f733c0fbb301080c2bcf96db7ac54d1ef0d7be04117d635d35c165c40504989" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 From 6efad2a5ab43e35a418b286e7ce4526dffaf1581 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:06:08 -0700 Subject: [PATCH 11/31] restore 01 --- .../01_northing_calibration.ipynb | 809 +++--------------- 1 file changed, 126 insertions(+), 683 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb index 89b03723..dc3945c1 100644 --- a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb +++ b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb @@ -5,35 +5,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Northing Calibration in FLASC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Northing calibration, that is the detection of bias and changes in measurements of turbine yaw are important for many of the analysis in FLASC. This notebook demonstrates the use of several of these tools in FLASC for the calibration of northing measurements." + "# **Import dependencies**" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# from datetime import timedelta as td\n", "import os\n", "import warnings as wn\n", - "from datetime import timedelta as td\n", "\n", "import numpy as np\n", "import pandas as pd\n", - "from floris import TimeSeries\n", - "from floris.layout_visualization import plot_turbine_labels, plot_turbine_points\n", "from floris.utilities import wrap_360\n", "from matplotlib import pyplot as plt\n", "\n", - "from flasc import FlascDataFrame\n", "from flasc.data_processing import (\n", " dataframe_manipulations as dfm,\n", " energy_ratio_wd_bias_estimation as best,\n", @@ -51,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -61,571 +50,83 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Load FLORIS model and show layout" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Load FLORIS model\n", - "fm, turbine_weights = load_floris()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MzFSbNm1CxsvLy1VVVSWPxyNJ+u///m9t2rRJGzdu1MaNG/WnP/1JkvTOO++osLAwvA0BABDBolu7gEhVUVGhkpISVVZWKi0tTfn5+UpPT1dRUZFGjRqlrKwsZWdna86cOTp48KDGjBmj+Ph4FRQUqKioSImJiXK5XBo/frw8Ho8GDRokSTrvvPNC3mfv3r2SpN69ex91LQ8AADg2Qs4pWLhwoW6//XY5HA4ZY+RwODRz5kx5vV6NHj1ae/bs0bRp0+T3+9W/f3+tXLnSuhh59uzZcjqdGj58uOrq6pSXl6d58+a1ckcAANiPwxhjWruIlhYIBBQfH6/a2lq5XK7T2lZFRYUyMjIUDAaPGnM6nSovL1evXr1O6z0AAMDpH7+5JucklZSUyOFwNDnmcDjk9XrDXBEAAGgKIeckVVZW6lgnv4wxqqysDG9BAACgSYSck5SWlnbcMzlpaWnhLQgAADSJkHOS8vPzj3smp6CgIMwVAQCAphByTlJ6erq8Xq+cTqeioqJCfnq9Xi46BgDgDMHdVado+/bt8nq91vfkFBQUEHAAAGhGp3v8JuQAAIAzEreQAwAANIGQAwAAbImQA+CMM3fuXKWlpSkuLk45OTlav369NXbo0CEVFhaqU6dO6tChg4YPH67q6mpr/KuvvtLVV1+t5ORkxcbGKiUlRePGjVMgEGiNVgC0ohYPOTt37tStt96qTp06qW3bturbt68++OADa9wYo2nTpqlbt25q27atcnNzVVFREbKNffv2aeTIkXK5XEpISFBBQYEOHDjQ0qUDaAVLlixRUVGRpk+frg0bNqhfv37Ky8vT7t27JUkTJ07U8uXLtXTpUq1Zs0a7du3SsGHDrNc7nU5dd911euWVV/Tpp5/qmWee0Ztvvqm77rqrtVoC0FpMC9q3b5/p0aOHGT16tFm3bp357LPPzOuvv262b99uzZkxY4aJj483y5YtM5s2bTLXXnut6dmzp/nmm2+sOVdffbXp16+fWbt2rXnnnXdMr169zIgRI064jtraWiPJ1NbWNmt/AJpfdna2KSwstH5vbGw0ycnJpri42NTU1Jg2bdqYpUuXWuOffPKJkWR8Pt8xt/nYY4+Z7t27t2jdAJrf6R6/WzTkTJ482Vx66aXHHA8Gg8btdptZs2ZZ62pqakxsbKx54YUXjDHGbN261Ugy77//vjXntddeMw6Hw+zcufOE6iDkAJGhrq7OREVFmZdeeilk/a9+9Stz7bXXmtLSUiPJfP311yHjqamp5tFHH21ymzt37jRXXHGFGTlyZAtVDaClnO7xu0U/rnrllVeUlZWlm266SV27dtWAAQP09NNPW+Off/65/H6/cnNzrXXx8fHKycmRz+eTJPl8PiUkJCgrK8uak5ubK6fTqXXr1jX5vnV1dQoEAiELgDPf3r171djYqKSkpJD1SUlJ8vv98vv9iomJUUJCQpPj3zVixAi1a9dO5557rlwul/70pz+1dPkAzjAtGnI+++wzzZ8/X+np6Xr99dc1duxY3XPPPXr22WclydopHWuHdmRO165dQ8ajo6OVmJh41E7tiOLiYsXHx1tLSkpKc7cG4Aw3e/ZsbdiwQS+//LL+93//V0VFRa1dEoAwi27JjQeDQWVlZemRRx6RJA0YMECbN2/WggULNGrUqBZ73ylTpoTs0AKBAEEHiACdO3dWVFRUyN1SklRdXS232y232636+nrV1NSEnM05Mv5dR+ZnZGQoMTFRl112maZOnapu3bqFoxUAZ4AWPZPTrVs39enTJ2Rd7969VVVVJUnWTulYO7Qjc47cVXHE4cOHtW/fvqN2akfExsbK5XKFLADOfDExMcrMzFRpaam1LhgMqrS0VB6PR5mZmWrTpk3IeHl5uaqqquTxeI653WAwKOnbj7IBnD1a9EzOJZdcovLy8pB1n376qXr06CFJ6tmzp9xut0pLS9W/f39J3551WbduncaOHStJ8ng8qqmpUVlZmTIzMyVJq1evVjAYVE5OTkuWD6AFVVRUqKSkxHr+W35+vtLT01VUVKRRo0YpKytL2dnZmjNnjg4ePKgxY8YoPj5eBQUFKioqUmJiolwul8aPHy+Px6NBgwZJkl599VVVV1dr4MCB6tChg7Zs2aJJkybpkksuUVpaWus2DSC8mvlC6BDr16830dHR5uGHHzYVFRXm+eefN+3atTPPPfecNWfGjBkmISHBvPzyy+ajjz4y1113XZO3kA8YMMCsW7fOvPvuuyY9PZ1byIEIVlJSYpxOp4mKigr5uXDhQmOMMU888YRJTU01MTExJjs726xdu9Z67TfffGPuvvtu07FjR9OuXTtzww03mC+//NIaX716tfF4PCY+Pt7ExcWZ9PR0M3ny5KPuyAJw5jvd43eLP6BzxYoVmjJliioqKtSzZ08VFRXpjjvu+G7I0vTp0/XUU0+ppqZGl156qebNm6cf//jH1px9+/Zp3LhxWr58uZxOp4YPH67HH39cHTp0OKEaeEAncOaoqKhQRkaG9RHSdzmdTpWXl6tXr16tUBmAMw1PIT8BhBzgzDFlyhTNmjVLjY2NR41FRUVp0qRJKi4uboXKAJxpeAo5gIhSWVmpY/23lTFGlZWV4S0IgG0RcgCEVVpamhwOR5NjDoeDi4MBNBtCDoCwys/PP+6ZnIKCgjBXBMCuCDkAwio9PV1er1dOp1NRUVEhP71eLxcdA2g2XHgMoFVs375dXq/X+p6cgoICAg6AENxddQIIOQAARB7urgIAAGgCIQcAANgSIQcAgLPc3LlzlZaWpri4OOXk5Gj9+vXW2KFDh1RYWKhOnTqpQ4cOGj58+FEP1pakZ555RhdddJHi4uLUtWtXFRYWhrOFJhFyAAA4iy1ZskRFRUWaPn26NmzYoH79+ikvL0+7d++WJE2cOFHLly/X0qVLtWbNGu3atUvDhg0L2cajjz6qBx54QPfff7+2bNmiN998U3l5ea3RTgguPAYA4CyWk5OjgQMH6sknn5QkBYNBpaSkaPz48Ro7dqy6dOmiRYsW6cYbb5Qkbdu2Tb1795bP59OgQYP09ddf69xzz9Xy5cs1ePDgZq2NC48BAMApqa+vV1lZmXJzc611TqdTubm58vl8KisrU0NDQ8h4RkaGUlNT5fP5JEmrVq1SMBjUzp071bt3b3Xv3l2/+MUvtGPHjrD3832EHAAAzlJ79+5VY2OjkpKSQtYnJSXJ7/fL7/crJiZGCQkJTY5L0meffaZgMKhHHnlEc+bM0V//+lft27dPP/3pT1VfXx+uVppEyAEAAKcsGAyqoaFBjz/+uPLy8jRo0CC98MILqqio0FtvvdWqtRFyAAA4S3Xu3FlRUVFH3S1VXV0tt9stt9ut+vp61dTUNDkuSd26dZMk9enTxxrv0qWLOnfurKqqqpZt4AcQcgAAOEvFxMQoMzNTpaWl1rpgMKjS0lJ5PB5lZmaqTZs2IePl5eWqqqqSx+ORJF1yySXW+iP27dunvXv3qkePHmHqpGnRrfruAAAgLCoqKlRSUmI9Ly4/P1/p6ekqKirSqFGjlJWVpezsbM2ZM0cHDx7UmDFjFB8fr4KCAhUVFSkxMVEul0vjx4+Xx+PRoEGDJEk//vGPdd111+nee+/VU089JZfLpSlTpigjI0NXXnllq/ZMyAEAwOYWLlyo22+/XQ6HQ8YYORwOzZw5U16vV6NHj9aePXs0bdo0+f1+9e/fXytXrrQuRp49e7acTqeGDx+uuro65eXlad68eSHb//Of/6yJEydq6NChcjqduuKKK7Ry5Uq1adOmNdq18D05AADYWEVFhTIyMhQMBo8aczqdKi8vV69evVqhsh/G9+QAAIBjKikpkcPhaHLM4XDI6/WGuaLwIeQAAGBjlZWVOtaHNsYYVVZWhregMCLkAABgY2lpacc9k5OWlhbegsKIkAMAgI3l5+cf90xOQUFBmCsKH0IOAAA2lp6eLq/XK6fTqaioqJCfXq/3jL3ouDlwdxUAAGeB7du3y+v1Wt+TU1BQcMYHnNM9fhNyAADAGYlbyAEAAJpAyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALYUtpAzY8YMORwOTZgwwVp36NAhFRYWqlOnTurQoYOGDx+u6urqkNdVVVVp6NChateunbp27apJkybp8OHD4SobAABEqLCEnPfff19//OMfddFFF4WsnzhxopYvX66lS5dqzZo12rVrl4YNG2aNNzY2aujQoaqvr9d7772nZ599Vs8884ymTZsWjrIBAEAEa/GQc+DAAY0cOVJPP/20OnbsaK2vra2V1+vVo48+qquuukqZmZlauHCh3nvvPa1du1aS9MYbb2jr1q167rnn1L9/f11zzTX63e9+p7lz56q+vr6lSwcAABGsxUNOYWGhhg4dqtzc3JD1ZWVlamhoCFmfkZGh1NRU+Xw+SZLP51Pfvn2VlJRkzcnLy1MgENCWLVtaunQAABDBolty44sXL9aGDRv0/vvvHzXm9/sVExOjhISEkPVJSUny+/3WnO8GnCPjR8aOpa6uTnV1ddbvgUDgVFsAAAARqsXO5OzYsUP33nuvnn/+ecXFxbXU2zSpuLhY8fHx1pKSkhLW9wcAAK2vxUJOWVmZdu/erZ/85CeKjo5WdHS01qxZo8cff1zR0dFKSkpSfX29ampqQl5XXV0tt9stSXK73UfdbXXk9yNzmjJlyhTV1tZay44dO5q3OQAAcMZrsZAzePBgffzxx9q4caO1ZGVlaeTIkdaf27Rpo9LSUus15eXlqqqqksfjkSR5PB59/PHH2r17tzVn1apVcrlc6tOnzzHfOzY2Vi6XK2QBAABnlxa7Juecc87RhRdeGLKuffv26tSpk7W+oKBARUVFSkxMlMvl0vjx4+XxeDRo0CBJ0pAhQ9SnTx/ddtttmjlzpvx+vx588EEVFhYqNja2pUoHAAA20KIXHv+Q2bNny+l0avjw4aqrq1NeXp7mzZtnjUdFRWnFihUaO3asPB6P2rdvr1GjRum3v/1tK1YNAAAigcMYY1q7iJYWCAQUHx+v2tpaProCACBCnO7xm2dXAQAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAABa3Ny5c5WWlqa4uDjl5ORo/fr11tihQ4dUWFioTp06qUOHDho+fLiqq6tDXn/PPfcoMzNTsbGx6t+/f5irR6Qi5AAAWtSSJUtUVFSk6dOna8OGDerXr5/y8vK0e/duSdLEiRO1fPlyLV26VGvWrNGuXbs0bNiwo7aTn5+vm2++OdzlI4LxgE4AQIvKycnRwIED9eSTT0qSgsGgUlJSNH78eI0dO1ZdunTRokWLdOONN0qStm3bpt69e8vn82nQoEEh23rooYe0bNkybdy4MdxtoBXwgE4AwBmrvr5eZWVlys3NtdY5nU7l5ubK5/OprKxMDQ0NIeMZGRlKTU2Vz+drjZJhI4QcAECL2bt3rxobG5WUlBSyPikpSX6/X36/XzExMUpISGhyHDgdhBwAAGBLhBwAQIvp3LmzoqKijrpbqrq6Wm63W263W/X19aqpqWlyHDgdhBwAQIuJiYlRZmamSktLrXXBYFClpaXyeDzKzMxUmzZtQsbLy8tVVVUlj8fTGiXDRqJbuwAAgD1UVFSopKRElZWVSktLU35+vtLT01VUVKRRo0YpKytL2dnZmjNnjg4ePKgxY8YoPj5eBQUFKioqUmJiolwul8aPHy+PxxNyZ9X27dt14MAB+f1+ffPNN9bdVX369FFMTEwrdYwzHSEHAHDaFi5cqNtvv10Oh0PGGDkcDs2cOVNer1ejR4/Wnj17NG3aNPn9fvXv318rV660LkaePXu2nE6nhg8frrq6OuXl5WnevHkh27/99tu1Zs0a6/cBAwZIkj7//HOlpaWFrU9EFr4nBwBwWioqKpSRkaFgMHjUmNPpVHl5uXr16tUKlSHS8T05AIBWVVJSIofD0eSYw+GQ1+sNc0XAtwg5AIDTUllZqWN9KGCMUWVlZXgLAv4PIQcAcFrS0tKOeyaHa2bQWgg5AIDTkp+ff9wzOQUFBWGuCPgWIQcAcFrS09Pl9XrldDoVFRUV8tPr9XLRMVoNd1cBAJrF9u3b5fV6re/JKSgoIODgtJzu8ZuQAwAAzkjcQg4AANAEQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALClFg05xcXFGjhwoM455xx17dpV119/vcrLy0PmHDp0SIWFherUqZM6dOig4cOHq7q6OmROVVWVhg4dqnbt2qlr166aNGmSDh8+3JKlAwCACNeiIWfNmjUqLCzU2rVrtWrVKjU0NGjIkCE6ePCgNWfixIlavny5li5dqjVr1mjXrl0aNmyYNd7Y2KihQ4eqvr5e7733np599lk988wzmjZtWkuWDgAAIpzDGGPC9WZ79uxR165dtWbNGl1++eWqra1Vly5dtGjRIt14442SpG3btql3797y+XwaNGiQXnvtNf3sZz/Trl27lJSUJElasGCBJk+erD179igmJuYH3zcQCCg+Pl61tbVyuVwt2iMAAGgep3v8Dus1ObW1tZKkxMRESVJZWZkaGhqUm5trzcnIyFBqaqp8Pp8kyefzqW/fvlbAkaS8vDwFAgFt2bKlyfepq6tTIBAIWQAAwNklbCEnGAxqwoQJuuSSS3ThhRdKkvx+v2JiYpSQkBAyNykpSX6/35rz3YBzZPzIWFOKi4sVHx9vLSkpKc3cDQAAONOFLeQUFhZq8+bNWrx4cYu/15QpU1RbW2stO3bsaPH3BAAAZ5bocLzJuHHjtGLFCr399tvq3r27td7tdqu+vl41NTUhZ3Oqq6vldrutOevXrw/Z3pG7r47M+b7Y2FjFxsY2cxcAACCStOiZHGOMxo0bp5deekmrV69Wz549Q8YzMzPVpk0blZaWWuvKy8tVVVUlj8cjSfJ4PPr444+1e/dua86qVavkcrnUp0+fliwfAABEsBY9k1NYWKhFixbp5Zdf1jnnnGNdQxMfH6+2bdsqPj5eBQUFKioqUmJiolwul8aPHy+Px6NBgwZJkoYMGaI+ffrotttu08yZM+X3+/Xggw+qsLCQszUAAOCYWvQWcofD0eT6hQsXavTo0ZK+/TLA++67Ty+88ILq6uqUl5enefPmhXwU9cUXX2js2LH6+9//rvbt22vUqFGaMWOGoqNPLKNxCzkAAJHndI/fYf2enNZCyAEAIPJE1PfkAAAAhAshBwAA2BIhB4AtzZ07V2lpaYqLi1NOTk7IV1H80IOBN23apBEjRiglJUVt27ZV79699dhjj7VGGwBOAyEHgO0sWbJERUVFmj59ujZs2KB+/fopLy/P+iqKH3owcFlZmbp27arnnntOW7Zs0QMPPKApU6boySefbK2WAJwCLjwGYDs5OTkaOHCgFUqCwaBSUlI0fvx4jR079gcfDNyUwsJCffLJJ1q9enXY+gDOdlx4DADfUV9fr7KyspAH/zqdTuXm5srn853Qg4GbUltbaz1cGEBkCMtjHQAgXPbu3avGxsYmH+y7bdu2E3ow8Pe99957WrJkif72t7+1VNkAWgBncgDgODZv3qzrrrtO06dP15AhQ1q7HAAngZADwFY6d+6sqKiokLulpP//4N/vPhi4qfHv2rp1qwYPHqw777xTDz74YEuXDqCZEXIA2EpMTIwyMzNDHvwbDAZVWloqj8dzQg8GlqQtW7boyiuv1KhRo/Twww+HtQcAzYNrcgBErIqKCpWUlKiyslJpaWnKz89Xenq6ioqKNGrUKGVlZSk7O1tz5szRwYMHNWbMmBN6MPDmzZt11VVXKS8vT0VFRda1OlFRUerSpUtrtgzgJBByAESkhQsX6vbbb5fD4ZAxRg6HQzNnzpTX69Xo0aO1Z88eTZs2TX6/X/3799fKlSuti5Fnz54tp9Op4cOHhzwY+Ii//vWv2rNnj5577jk999xz1voePXqosrIy3K0COEV8Tw6AiFNRUaGMjAwFg8GjxpxOp8rLy9WrV69WqAxAc+J7cgCcdUpKSuRwOJocczgc8nq9Ya4IwJmIkAMg4lRWVupYJ6GNMXykBEASIQdABEpLSzvumZy0tLTwFgTgjETIARBx8vPzj3smp6CgIMwVATgTEXIARJz09HR5vV45nU5FRUWF/PR6vVx0DEASd1cBiGDbt2+X1+u1vienoKCAgAPYyOkevwk5AADgjMQt5AAAAE0g5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAgGY3d+5cpaWlKS4uTjk5OVq/fr01dujQIRUWFqpTp07q0KGDhg8frurq6pDXV1VV6aabbpIknXfeeZo0aZIOHz58UjUQcgAAQLNasmSJioqKNH36dG3YsEH9+vVTXl6edu/eLUmaOHGili9frqVLl2rNmjXatWuXhg0bZr2+sbFRQ4cOVX19vSRpwYIFeuaZZzRt2rSTqsNhjDHN19aZKRAIKD4+XrW1tXK5XK1dDgAAtpaTk6OBAwfqySeflCQFg0GlpKRo/PjxGjt2rLp06aJFixbpxhtvlCRt27ZNvXv3ls/n06BBg/Taa6/pZz/7mcrLy5Wenq7a2lotWrRIkydP1p49exQTE3NCdXAmBwAANJv6+nqVlZUpNzfXWud0OpWbmyufz6eysjI1NDSEjGdkZCg1NVU+n0+S5PP51LdvX3Xt2tWak5eXp0AgoC1btpxwLYQcAADQbPbu3avGxkYlJSWFrE9KSpLf75ff71dMTIwSEhKaHJckv9/f5OuPjJ0oQg4AALAlQg4AAGg2nTt3VlRU1FF3S1VXV8vtdsvtdqu+vl41NTVNjkuS2+1u8vVHxk4UIQcAADSbmJgYZWZmqrS01FoXDAZVWloqj8ejzMxMtWnTJmS8vLxcVVVV8ng8kiSPx6OPP/5Ye/bsseasWrVKLpdLffr0OeFaIibkHO9+ewAAEH4VFRWaMmWKRowYoSlTpqiiokKSVFRUpKefflrPPvusPvnkE40dO1YHDx7UmDFjFB8fr4KCAhUVFemtt95SWVmZxowZI4/Ho0GDBkmShgwZoj59+ujOO++UJL355pt68MEHVVhYqNjY2BMv0ESAxYsXm5iYGFNSUmK2bNli7rjjDpOQkGCqq6tP6PW1tbVGkqmtrW3hSgEAODuUlJQYp9NpoqKiQn4uXLjQGGPME088YVJTU01MTIzJzs42a9eutV77zTffmLvvvtt07NjRtGvXztxwww3myy+/DNl+ZWWl+elPf2okmU6dOpn77rvPNDQ0nFSNEfE9Oce73/7+++//wdfzPTkAADSfiooKZWRkKBgMHjXmdDpVXl6uXr16nfb7nO7x+4z/uOqH7rdvSl1dnQKBQMgCAACaR0lJiRwOR5NjDodDXq83zBU17YwPOT90v31TiouLFR8fby0pKSnhKBUAgLNCZWWljvVBkDFGlZWV4S3oGM74kHMqpkyZotraWmvZsWNHa5cEAIBtpKWlHfdMTlpaWngLOoYzPuT80P32TYmNjZXL5QpZAABA88jPzz/umZyCgoIwV9S0Mz7k/ND99gAAILzS09Pl9XrldDoVFRUV8tPr9TbLRcfNIbq1CzgRRUVFGjVqlLKyspSdna05c+ZY99sDAIDwGz16tC699FJ5vV5VVlYqLS1NBQUFZ0zAkSIk5Nx8883as2ePpk2bJr/fr/79+2vlypVHXYwMAADCp1evXiouLm7tMo4pIr4n53TxPTkAAEQe239PDgAAwKkg5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFtqkZBTWVmpgoIC9ezZU23bttV5552n6dOnq76+PmTeRx99pMsuu0xxcXFKSUnRzJkzj9rW0qVLlZGRobi4OPXt21evvvpqS5QMAABspkVCzrZt2xQMBvXHP/5RW7Zs0ezZs7VgwQL95je/seYEAgENGTJEPXr0UFlZmWbNmqWHHnpITz31lDXnvffe04gRI1RQUKAPP/xQ119/va6//npt3ry5JcoGAAA24jDGmHC80axZszR//nx99tlnkqT58+frgQcekN/vV0xMjCTp/vvv17Jly7Rt2zZJ0s0336yDBw9qxYoV1nYGDRqk/v37a8GCBSf83oFAQPHx8aqtrZXL5WrGrgAAQEs53eN32K7Jqa2tVWJiovW7z+fT5ZdfbgUcScrLy1N5ebm+/vpra05ubm7IdvLy8uTz+cJTNAAAiFhhCTnbt2/XE088oX/7t3+z1vn9fiUlJYXMO/K73+8/7pwj48dSV1enQCAQsgAAgLPLSYWc+++/Xw6H47jLkY+ajti5c6euvvpq3XTTTbrjjjuatfhjKS4uVnx8vLWkpKSE5X0BAMCZI/pkJt93330aPXr0cef86Ec/sv68a9cuXXnllbr44otDLiiWJLfbrerq6pB1R353u93HnXNk/FimTJmioqIi6/dAIEDQAQDgLHNSIadLly7q0qXLCc3duXOnrrzySmVmZmrhwoVyOkNPGnk8Hj3wwANqaGhQmzZtJEmrVq3S+eefr44dO1pzSktLNWHCBOt1q1atksfjOe57x8bGKjY29iQ6AwAAdtMi1+Ts3LlT//Iv/6LU1FT953/+p/bs2SO/3x9yLc0vf/lLxcTEqKCgQFu2bNGSJUv02GOPhZyBuffee7Vy5Ur913/9l7Zt26aHHnpIH3zwgcaNG9cSZQMAABs5qTM5J2rVqlXavn27tm/fru7du4eMHbljPT4+Xm+88YYKCwuVmZmpzp07a9q0abrzzjutuRdffLEWLVqkBx98UL/5zW+Unp6uZcuW6cILL2yJsgEAgI2E7XtyWhPfkwMAQOSJmO/JAQAACCdCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsKXo1i4gHIwxkqRAINDKlQAAgBN15Lh95Dh+ss6KkLN//35JUkpKSitXAgAATtb+/fsVHx9/0q9zmFONRxEkGAxq165dOuecc+RwOJptu4FAQCkpKdqxY4dcLlezbfdMYOfeJHv3R2+Ry8790Vvkas3+jDHav3+/kpOT5XSe/BU2Z8WZHKfTqe7du7fY9l0uly3/Ykv27k2yd3/0Frns3B+9Ra7W6u9UzuAcwYXHAADAlgg5AADAlgg5pyE2NlbTp09XbGxsa5fS7Ozcm2Tv/ugtctm5P3qLXJHc31lx4TEAADj7cCYHAADYEiEHAADYEiEHAADYEiEHAADYEiHnNMydO1dpaWmKi4tTTk6O1q9f39olHVdxcbEGDhyoc845R127dtX111+v8vLykDmHDh1SYWGhOnXqpA4dOmj48OGqrq4OmVNVVaWhQ4eqXbt26tq1qyZNmqTDhw+Hs5UfNGPGDDkcDk2YMMFaF+m97dy5U7feeqs6deqktm3bqm/fvvrggw+scWOMpk2bpm7duqlt27bKzc1VRUVFyDb27dunkSNHyuVyKSEhQQUFBTpw4EC4WwnR2NioqVOnqmfPnmrbtq3OO+88/e53vwt5Vk0k9fb222/r5z//uZKTk+VwOLRs2bKQ8ebq5aOPPtJll12muLg4paSkaObMmS3d2nF7a2ho0OTJk9W3b1+1b99eycnJ+tWvfqVdu3ZFfG/fd9ddd8nhcGjOnDkh68/U3qQT6++TTz7Rtddeq/j4eLVv314DBw5UVVWVNR6R+1CDU7J48WITExNjSkpKzJYtW8wdd9xhEhISTHV1dWuXdkx5eXlm4cKFZvPmzWbjxo3mX//1X01qaqo5cOCANeeuu+4yKSkpprS01HzwwQdm0KBB5uKLL7bGDx8+bC688EKTm5trPvzwQ/Pqq6+azp07mylTprRGS01av369SUtLMxdddJG59957rfWR3Nu+fftMjx49zOjRo826devMZ599Zl5//XWzfft2a86MGTNMfHy8WbZsmdm0aZO59tprTc+ePc0333xjzbn66qtNv379zNq1a80777xjevXqZUaMGNEaLVkefvhh06lTJ7NixQrz+eefm6VLl5oOHTqYxx57zJoTSb29+uqr5oEHHjAvvviikWReeumlkPHm6KW2ttYkJSWZkSNHms2bN5sXXnjBtG3b1vzxj39std5qampMbm6uWbJkidm2bZvx+XwmOzvbZGZmhmwjEnv7rhdffNH069fPJCcnm9mzZ4eMnam9GfPD/W3fvt0kJiaaSZMmmQ0bNpjt27ebl19+OeSYFon7UELOKcrOzjaFhYXW742NjSY5OdkUFxe3YlUnZ/fu3UaSWbNmjTHm251UmzZtzNKlS605n3zyiZFkfD6fMebbfyhOp9P4/X5rzvz5843L5TJ1dXXhbaAJ+/fvN+np6WbVqlXmiiuusEJOpPc2efJkc+mllx5zPBgMGrfbbWbNmmWtq6mpMbGxseaFF14wxhizdetWI8m8//771pzXXnvNOBwOs3PnzpYr/gcMHTrU5Ofnh6wbNmyYGTlypDEmsnv7/sGkuXqZN2+e6dixY8jfy8mTJ5vzzz+/hTv6/44XBI5Yv369kWS++OILY0zk9/aPf/zDnHvuuWbz5s2mR48eISEnUnozpun+br75ZnPrrbce8zWRug/l46pTUF9fr7KyMuXm5lrrnE6ncnNz5fP5WrGyk1NbWytJSkxMlCSVlZWpoaEhpK+MjAylpqZaffl8PvXt21dJSUnWnLy8PAUCAW3ZsiWM1TetsLBQQ4cODelBivzeXnnlFWVlZemmm25S165dNWDAAD399NPW+Oeffy6/3x/SX3x8vHJyckL6S0hIUFZWljUnNzdXTqdT69atC18z33PxxRertLRUn376qSRp06ZNevfdd3XNNddIiuzevq+5evH5fLr88ssVExNjzcnLy1N5ebm+/vrrMHXzw2pra+VwOJSQkCApsnsLBoO67bbbNGnSJF1wwQVHjUd6b3/729/04x//WHl5eeratatycnJCPtKK1H0oIecU7N27V42NjSH/R0pSUlKS/H5/K1V1coLBoCZMmKBLLrlEF154oSTJ7/crJibG2iEd8d2+/H5/k30fGWtNixcv1oYNG1RcXHzUWKT39tlnn2n+/PlKT0/X66+/rrFjx+qee+7Rs88+G1Lf8f5O+v1+de3aNWQ8OjpaiYmJrdrf/fffr1tuuUUZGRlq06aNBgwYoAkTJmjkyJGSIru372uuXs7kv6tHHDp0SJMnT9aIESOshzpGcm9/+MMfFB0drXvuuafJ8Ujubffu3Tpw4IBmzJihq6++Wm+88YZuuOEGDRs2TGvWrLHqi8R96FnxFHIcrbCwUJs3b9a7777b2qU0ix07dujee+/VqlWrFBcX19rlNLtgMKisrCw98sgjkqQBAwZo8+bNWrBggUaNGtXK1Z2ev/zlL3r++ee1aNEiXXDBBdq4caMmTJig5OTkiO/tbNXQ0KBf/OIXMsZo/vz5rV3OaSsrK9Njjz2mDRs2yOFwtHY5zS4YDEqSrrvuOk2cOFGS1L9/f7333ntasGCBrrjiitYs77RwJucUdO7cWVFRUUddVV5dXS23291KVZ24cePGacWKFXrrrbfUvXt3a73b7VZ9fb1qampC5n+3L7fb3WTfR8ZaS1lZmXbv3q2f/OQnio6OVnR0tNasWaPHH39c0dHRSkpKitjeJKlbt27q06dPyLrevXtbdz4cqe94fyfdbrd2794dMn748GHt27evVfubNGmSdTanb9++uu222zRx4kTrjFwk9/Z9zdXLmfx39UjA+eKLL7Rq1SrrLI4Uub2988472r17t1JTU639yxdffKH77rtPaWlpVm2R2Jv07TEtOjr6B/cxkbgPJeScgpiYGGVmZqq0tNRaFwwGVVpaKo/H04qVHZ8xRuPGjdNLL72k1atXq2fPniHjmZmZatOmTUhf5eXlqqqqsvryeDz6+OOPQ/4xH9mRff8fSDgNHjxYH3/8sTZu3GgtWVlZGjlypPXnSO1Nki655JKjbvf/9NNP1aNHD0lSz5495Xa7Q/oLBAJat25dSH81NTUqKyuz5qxevVrBYFA5OTlh6KJp//znP+V0hu6KoqKirP+6jOTevq+5evF4PHr77bfV0NBgzVm1apXOP/98dezYMUzdHO1IwKmoqNCbb76pTp06hYxHam+33XabPvroo5D9S3JysiZNmqTXX3/dqjsSe5O+PaYNHDjwuPuYiD0+tMrlzjawePFiExsba5555hmzdetWc+edd5qEhISQq8rPNGPHjjXx8fHm73//u/nyyy+t5Z///Kc156677jKpqalm9erV5oMPPjAej8d4PB5r/MgtgkOGDDEbN240K1euNF26dDkjbrP+vu/eXWVMZPe2fv1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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the layout\n", - "fig, ax = plt.subplots()\n", - "plot_turbine_points(fm, ax)\n", - "plot_turbine_labels(fm, ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate data set to illustrate operations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For simplicity assume a fixed wind speed and turbulence intensity and uniform wind direction. Perturb the wind direction by random noise" + "# **Step 0**: Initial data pulldown\n", + "First, we import the data from the common_windfarm_information folder. This may take a while, so we keep these variables unchanged. These are df_scada_raw and df_metmast_raw. These variables are not manipulated throughout the script." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ - "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", - "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", - "\n", - "# Apply noise\n", - "np.random.seed(0)\n", - "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", - "wind_directions = wind_directions + noise\n", + "def load_data():\n", + " root_path = os.getcwd()\n", + " f = os.path.join(root_path, \"postprocessed\", \"df_scada_600s_wspowfiltered.pkl\")\n", + " df_scada = pd.read_pickle(f)\n", "\n", - "# Set a FLORIS time series object\n", - "time_series = TimeSeries(\n", - " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1753918.68116782, 1753961.25195179, 1753974.02887594,\n", - " 1753984.64239339, 1753954.56987842, 1753926.45363424])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Calculate FLORIS solution\n", - "fm.set(wind_data=time_series)\n", - "fm.run()\n", - "turbine_powers = fm.get_turbine_powers()\n", + " # # Optionally: downsample to [x] minute averages to speed up things\n", + " # cols_angular = [c for c in df_scada if ((\"wd_\" in c) or (\"yaw_\" in c))]\n", + " # df_scada = fto.df_downsample(\n", + " # df_scada,\n", + " # cols_angular=cols_angular,\n", + " # window_width=td(seconds=600),\n", + " # )\n", "\n", - "# Add random noise to the power output\n", - "turbine_powers = turbine_powers + np.random.normal(0, 25.0, turbine_powers.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the results to create a FLASC dataframe representing hypothetical scada data\n", - "df_scada = FlascDataFrame(\n", - " {\n", - " \"time\": pd.date_range(start=\"1/1/2020\", periods=len(wind_directions), freq=\"600s\"),\n", - " \"wind_directions\": wind_directions,\n", - " \"wind_speeds\": 8.0 * np.ones_like(wind_directions),\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "FlascDataFrame in FLASC format\n", - "
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timewind_directionswind_speedspow_000pow_001pow_002pow_003pow_004pow_005pow_006wd_000wd_001wd_002wd_003wd_004wd_005wd_006
02020-01-01 00:00:000.8820268.01.300483e+066.782295e+051.062299e+061.753996e+061.753925e+061.753954e+061.753919e+060.4801630.5186760.8672501.3595690.6235290.2622910.860956
12020-01-01 00:10:001.2000798.01.336065e+067.107464e+051.097194e+061.753993e+061.753949e+061.753959e+061.753961e+060.2846841.0691041.8623820.6874901.3843951.0952821.194562
22020-01-01 00:20:002.4893698.01.464618e+068.748210e+051.233091e+061.753934e+061.753949e+061.753993e+061.753974e+062.0296912.9304302.2481663.1681702.6812702.3042222.469416
32020-01-01 00:30:004.1204478.01.588592e+061.075059e+061.396982e+061.753984e+061.753965e+061.753941e+061.753985e+063.1530604.6036433.4632093.6163384.6210693.8104295.051782
42020-01-01 00:40:004.9337798.01.633644e+061.164006e+061.466968e+061.753959e+061.753942e+061.753971e+061.753955e+064.0384525.6512415.7514675.1733014.9751734.2382874.768993
......................................................
17952020-01-13 11:10:00354.7220078.05.540390e+053.631354e+054.072392e+051.753920e+061.753931e+061.753940e+061.753937e+06354.104930355.603609355.974084354.515064354.500130354.891368354.998903
17962020-01-13 11:20:00356.0133698.06.913212e+053.469273e+055.184367e+051.753995e+061.753957e+061.753940e+061.753917e+06355.252039355.760453354.907418355.423705356.723372355.720953355.511321
17972020-01-13 11:30:00357.0917258.08.298914e+053.672902e+056.234497e+051.753920e+061.753938e+061.753954e+061.753921e+06356.338511357.314736357.075436357.273982356.775948357.175943356.677064
17982020-01-13 11:40:00357.7646298.09.225341e+053.940003e+056.904134e+051.753967e+061.753943e+061.753960e+061.753947e+06357.366234358.697730357.640309358.981603357.377406358.350070357.973726
17992020-01-13 11:50:00359.1363988.01.097052e+065.031428e+058.536198e+051.753960e+061.753918e+061.753974e+061.753920e+06358.308969358.423708358.505300359.747302359.438663359.130778359.271976
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1800 rows × 17 columns

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" - ], - "text/plain": [ - " time wind_directions wind_speeds pow_000 \\\n", - "0 2020-01-01 00:00:00 0.882026 8.0 1.300483e+06 \n", - "1 2020-01-01 00:10:00 1.200079 8.0 1.336065e+06 \n", - "2 2020-01-01 00:20:00 2.489369 8.0 1.464618e+06 \n", - "3 2020-01-01 00:30:00 4.120447 8.0 1.588592e+06 \n", - "4 2020-01-01 00:40:00 4.933779 8.0 1.633644e+06 \n", - "... ... ... ... ... \n", - "1795 2020-01-13 11:10:00 354.722007 8.0 5.540390e+05 \n", - "1796 2020-01-13 11:20:00 356.013369 8.0 6.913212e+05 \n", - "1797 2020-01-13 11:30:00 357.091725 8.0 8.298914e+05 \n", - "1798 2020-01-13 11:40:00 357.764629 8.0 9.225341e+05 \n", - "1799 2020-01-13 11:50:00 359.136398 8.0 1.097052e+06 \n", - "\n", - " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", - "0 6.782295e+05 1.062299e+06 1.753996e+06 1.753925e+06 1.753954e+06 \n", - "1 7.107464e+05 1.097194e+06 1.753993e+06 1.753949e+06 1.753959e+06 \n", - "2 8.748210e+05 1.233091e+06 1.753934e+06 1.753949e+06 1.753993e+06 \n", - "3 1.075059e+06 1.396982e+06 1.753984e+06 1.753965e+06 1.753941e+06 \n", - "4 1.164006e+06 1.466968e+06 1.753959e+06 1.753942e+06 1.753971e+06 \n", - "... ... ... ... ... ... \n", - "1795 3.631354e+05 4.072392e+05 1.753920e+06 1.753931e+06 1.753940e+06 \n", - "1796 3.469273e+05 5.184367e+05 1.753995e+06 1.753957e+06 1.753940e+06 \n", - "1797 3.672902e+05 6.234497e+05 1.753920e+06 1.753938e+06 1.753954e+06 \n", - "1798 3.940003e+05 6.904134e+05 1.753967e+06 1.753943e+06 1.753960e+06 \n", - "1799 5.031428e+05 8.536198e+05 1.753960e+06 1.753918e+06 1.753974e+06 \n", - "\n", - " pow_006 wd_000 wd_001 wd_002 wd_003 \\\n", - "0 1.753919e+06 0.480163 0.518676 0.867250 1.359569 \n", - "1 1.753961e+06 0.284684 1.069104 1.862382 0.687490 \n", - "2 1.753974e+06 2.029691 2.930430 2.248166 3.168170 \n", - "3 1.753985e+06 3.153060 4.603643 3.463209 3.616338 \n", - "4 1.753955e+06 4.038452 5.651241 5.751467 5.173301 \n", - "... ... ... ... ... ... \n", - "1795 1.753937e+06 354.104930 355.603609 355.974084 354.515064 \n", - "1796 1.753917e+06 355.252039 355.760453 354.907418 355.423705 \n", - "1797 1.753921e+06 356.338511 357.314736 357.075436 357.273982 \n", - "1798 1.753947e+06 357.366234 358.697730 357.640309 358.981603 \n", - "1799 1.753920e+06 358.308969 358.423708 358.505300 359.747302 \n", - "\n", - " wd_004 wd_005 wd_006 \n", - "0 0.623529 0.262291 0.860956 \n", - "1 1.384395 1.095282 1.194562 \n", - "2 2.681270 2.304222 2.469416 \n", - "3 4.621069 3.810429 5.051782 \n", - "4 4.975173 4.238287 4.768993 \n", - "... ... ... ... \n", - "1795 354.500130 354.891368 354.998903 \n", - "1796 356.723372 355.720953 355.511321 \n", - "1797 356.775948 357.175943 356.677064 \n", - "1798 357.377406 358.350070 357.973726 \n", - "1799 359.438663 359.130778 359.271976 \n", - "\n", - "[1800 rows x 17 columns]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add the turbine powers to the dataframe with some added noise\n", - "for t_idx in range(fm.n_turbines):\n", - " df_scada[f\"pow_{t_idx:03d}\"] = turbine_powers[:, t_idx]\n", + " return df_scada\n", "\n", - "# Set the turbine wind directions to be the true wind direction with some added noise\n", - "for t_idx in range(fm.n_turbines):\n", - " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", - " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - " )\n", "\n", - "df_scada" + "df_scada_northing_uncalibrated = load_data()\n", + "df_scada_northing_uncalibrated[\"ti\"] = 0.06 # Assume a certain ambient turbulence intensity" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "#### Northing calibration error\n", - "\n", - "Add to the data two types of northing calibration error:\n", - "1. A constant bias on turbine 001\n", - "2. A change in bias on turbine 002 halfway through the data set" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "df_scada[\"wd_001\"] = wrap_360(\n", - " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - ")\n", - "\n", - "mid_point = int(len(wind_directions) / 2)\n", - "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - "wd_change[mid_point:] = wd_change[mid_point:] + 30\n", - "wd_change = wrap_360(wd_change)\n", - "df_scada[\"wd_002\"] = wd_change" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Wind direction')" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the wd channels for the turbines\n", - "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"gray\")\n", - "ax.legend()\n", - "ax.set_xlabel(\"Time\")\n", - "ax.set_ylabel(\"Wind direction\")" + "# **Step 1**: Initialize FLORIS\n", + "and precalculate a large set of solutions using the parallel computing interface in FLORIS" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 49, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-11-19 13:33:42\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", - "\u001b[32m2024-11-19 13:33:42\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + "ename": "UserWarning", + "evalue": "Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mUserWarning\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[49], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m df_fm_approx \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_feather(fn_approx)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mUserWarning\u001b[39;00m(\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease run \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m00_setup_floris_model/02_precalculate_floris_solutions.py\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor the appropriate wake models first.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 16\u001b[0m )\n", + "\u001b[0;31mUserWarning\u001b[0m: Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first." ] } ], "source": [ - "# Finally compute df_approx for use in later algorithms\n", - "# Can compute only at 8m/s for this example\n", - "df_fm_approx = ftools.calc_floris_approx_table(\n", - " fm=fm, # fi=fi_pci,\n", - " wd_array=np.arange(0.0, 360.01, 1.0),\n", - " ws_array=np.array([8.0]),\n", - " ti_array=np.array([0.06]),\n", - ")" + "# Now we calculate a grid of FLORIS solutions. Since our estimated SCADA\n", + "# data changes as we shift its wind direction, the predicted solutions\n", + "# according to FLORIS will also change. Therefore, we precalculate a grid\n", + "# of FLORIS solutions and insert that into the bias estimation class.\n", + "fm, turbine_weights = load_floris()\n", + "\n", + "# Grab the precalculated FLORIS model solutions from the 'setup_floris_model' directory\n", + "root_path = os.getcwd()\n", + "fn_approx = os.path.join(root_path, \"..\", \"00_setup_floris_model\", \"df_fm_approx_gch.ftr\")\n", + "if os.path.exists(fn_approx):\n", + " df_fm_approx = pd.read_feather(fn_approx)\n", + "else:\n", + " raise UserWarning(\n", + " \"Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' \"\n", + " \"for the appropriate wake models first.\"\n", + " )" ] }, { @@ -633,14 +134,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Cross-Check Northing calibration " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`crosscheck_northing_offset_consistency` is a function to check if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. " + "# **Step 2**: Cross-compare wind direction measurements\n", + "and see if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. The current functionality is limited and cannot account for this yet." ] }, { @@ -652,88 +147,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T006 T001 T002 T005 T003\n", - "0 0.0 -30.0 0.0 0.0 0.0\n", - "1 0.0 -30.0 0.0 0.0 0.0\n", - "2 0.0 -30.0 0.0 0.0 0.0\n", - "3 0.0 -30.0 -26.0 0.0 0.0\n", - "4 0.0 -30.0 -30.0 0.0 0.0\n", - "5 0.0 -30.0 -30.0 0.0 0.0\n", - "6 0.0 -30.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T002 T006 T005 T003 T000\n", - "0 30.0 30.0 30.0 30.0 30.0\n", - "1 30.0 30.0 30.0 30.0 30.0\n", - "2 30.0 30.0 30.0 30.0 30.0\n", - "3 4.0 30.0 30.0 30.0 30.0\n", - "4 0.0 30.0 30.0 30.0 30.0\n", - "5 0.0 30.0 30.0 30.0 30.0\n", - "6 0.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T001 T003 T005 T000 T006\n", - "0 -30.0 0.0 0.0 -0.0 0.0\n", - "1 -30.0 0.0 0.0 -0.0 0.0\n", - "2 -30.0 0.0 0.0 -0.0 0.0\n", - "3 -4.0 26.0 26.0 26.0 26.0\n", - "4 -0.0 30.0 30.0 30.0 30.0\n", - "5 -0.0 30.0 30.0 30.0 30.0\n", - "6 -0.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T005 T002 T001 T004 T006\n", - "0 0.0 -0.0 -30.0 0.0 0.0\n", - "1 0.0 -0.0 -30.0 0.0 0.0\n", - "2 0.0 -0.0 -30.0 0.0 0.0\n", - "3 0.0 -26.0 -30.0 0.0 0.0\n", - "4 0.0 -30.0 -30.0 0.0 0.0\n", - "5 0.0 -30.0 -30.0 0.0 0.0\n", - "6 0.0 -30.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T002 T005 T001 T006\n", - "0 -0.0 0.0 0.0 -30.0 0.0\n", - "1 -0.0 0.0 0.0 -30.0 0.0\n", - "2 -0.0 0.0 0.0 -30.0 0.0\n", - "3 -0.0 -26.0 0.0 -30.0 0.0\n", - "4 -0.0 -30.0 0.0 -30.0 0.0\n", - "5 -0.0 -30.0 0.0 -30.0 0.0\n", - "6 -0.0 -30.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T001 T006 T002 T000\n", - "0 -0.0 -30.0 0.0 -0.0 -0.0\n", - "1 -0.0 -30.0 0.0 -0.0 -0.0\n", - "2 -0.0 -30.0 0.0 -0.0 -0.0\n", - "3 -0.0 -30.0 0.0 -26.0 -0.0\n", - "4 -0.0 -30.0 0.0 -30.0 -0.0\n", - "5 -0.0 -30.0 0.0 -30.0 -0.0\n", - "6 -0.0 -30.0 0.0 -30.0 -0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T001 T005 T000 T003 T002\n", - "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "3 -30.0 -0.0 -0.0 -0.0 -26.0\n", - "4 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "5 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "6 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-10-16 11:44:52\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-10-16 11:44:54\u001b[0m T006 T001 T002 T005 T003\n", + "0 18.0 16.0 -6.0 14.0 46.0\n", + "1 18.0 16.0 -6.0 14.0 46.0\n", + "2 18.0 16.0 -6.0 14.0 46.0\n", + "3 18.0 14.0 -6.0 14.0 46.0\n", + "\u001b[32m2024-10-16 11:44:54\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-10-16 11:44:56\u001b[0m T002 T006 T005 T003 T000\n", + "0 -22.0 2.0 -2.0 30.0 -16.0\n", + "1 -20.0 2.0 -2.0 30.0 -16.0\n", + "2 -20.0 2.0 -2.0 30.0 -16.0\n", + "3 -22.0 2.0 -2.0 30.0 -14.0\n", + "\u001b[32m2024-10-16 11:44:56\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-10-16 11:44:57\u001b[0m T001 T003 T005 T000 T006\n", + "0 22.0 52.0 20.0 6.0 24.0\n", + "1 20.0 52.0 20.0 6.0 24.0\n", + "2 20.0 52.0 20.0 6.0 24.0\n", + "3 22.0 52.0 20.0 6.0 24.0\n", + "\u001b[32m2024-10-16 11:44:57\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-10-16 11:44:58\u001b[0m T005 T002 T001 T004 T006\n", + "0 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "1 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "2 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "3 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "\u001b[32m2024-10-16 11:44:58\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T002 T005 T001 T006\n", + "0 30.0 -22.0 -2.0 -2.0 2.0\n", + "1 30.0 -22.0 -2.0 -2.0 2.0\n", + "2 30.0 -22.0 -2.0 -2.0 2.0\n", + "3 30.0 -22.0 -2.0 -2.0 2.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T001 T006 T002 T000\n", + "0 32.0 2.0 4.0 -20.0 -14.0\n", + "1 32.0 2.0 4.0 -20.0 -14.0\n", + "2 32.0 2.0 4.0 -20.0 -14.0\n", + "3 32.0 2.0 4.0 -20.0 -14.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T001 T005 T000 T003 T002\n", + "0 -2.0 -4.0 -18.0 28.0 -24.0\n", + "1 -2.0 -4.0 -18.0 28.0 -24.0\n", + "2 -2.0 -4.0 -18.0 28.0 -24.0\n", + "3 -2.0 -4.0 -18.0 28.0 -24.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['clean', 'clean', 'bad', 'clean', 'clean', 'clean', 'clean']\n" + "['clean', 'clean', 'clean', 'clean', 'clean', 'clean', 'clean']\n" ] }, { "data": { - "image/png": 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" ] @@ -744,57 +218,19 @@ ], "source": [ "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", - "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", + "df_scada_marked_faulty_northing_drift = df_scada_northing_uncalibrated.copy()\n", "\n", "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", - " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True\n", ")\n", - "print(turb_wd_consistency)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`crosscheck_northing_offset_consistency` detects that T002 contains a probable jump, one solution is to then remove T002's wind direction data from consideration however this is not done in this notebook as we next take advantage of HOGER recalibration. The code to do this is included below in comments" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# # Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", - "# faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", - "# for ti in np.where(faulty_turbines)[0]:\n", - "# df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Homegenization with HOGER" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", - " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", + "print(turb_wd_consistency)\n", "\n", - " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" + "# Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", + "faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", + "for ti in np.where(faulty_turbines)[0]:\n", + " df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "attachments": {}, "cell_type": "markdown", @@ -806,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1883,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3493,7 +2929,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3546,7 +2982,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3654,7 +3090,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3678,7 +3114,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3688,9 +3124,11 @@ } ], "metadata": { + "interpreter": { + "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" + }, "kernelspec": { - "display_name": ".venv", - "language": "python", + "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", "name": "python3" }, "language_info": { @@ -3703,9 +3141,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.10.4" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8f733c0fbb301080c2bcf96db7ac54d1ef0d7be04117d635d35c165c40504989" + } + } }, "nbformat": 4, "nbformat_minor": 2 From 67625b4b3f75cdd4504ae8ba566fa66ef3fe7174 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:06:23 -0700 Subject: [PATCH 12/31] rename 03 --- .../03_northing_calibration_hoger.ipynb | 3494 +++++++++++++++++ 1 file changed, 3494 insertions(+) create mode 100644 examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb diff --git a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb new file mode 100644 index 00000000..d4f03fe6 --- /dev/null +++ b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb @@ -0,0 +1,3494 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Northing Calibration in FLASC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Northing calibration, that is the detection of bias and changes in measurements of turbine yaw are important for many of the analysis in FLASC. This notebook demonstrates the use of several of these tools in FLASC for the calibration of northing measurements." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# from datetime import timedelta as td\n", + "import warnings as wn\n", + "from datetime import timedelta as td\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from floris import TimeSeries\n", + "from floris.layout_visualization import plot_turbine_labels, plot_turbine_points\n", + "from floris.utilities import wrap_360\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from flasc import FlascDataFrame\n", + "from flasc.data_processing import (\n", + " dataframe_manipulations as dfm,\n", + " energy_ratio_wd_bias_estimation as best,\n", + " filtering as filt,\n", + " northing_offset as nof,\n", + ")\n", + "from flasc.data_processing.northing_offset_change_hoger import homogenize\n", + "\n", + "# from flasc import time_operations as fto\n", + "from flasc.utilities import (\n", + " floris_tools as ftools,\n", + " optimization as flopt,\n", + ")\n", + "from flasc.utilities.utilities_examples import load_floris_artificial as load_floris" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# User settings\n", + "save_figures = True\n", + "plot_figures_in_notebook = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load FLORIS model and show layout" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load FLORIS model\n", + "fm, turbine_weights = load_floris()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the layout\n", + "fig, ax = plt.subplots()\n", + "plot_turbine_points(fm, ax)\n", + "plot_turbine_labels(fm, ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data set to illustrate operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity assume a fixed wind speed and turbulence intensity and uniform wind direction. Perturb the wind direction by random noise" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", + "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", + "\n", + "# Apply noise\n", + "np.random.seed(0)\n", + "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", + "wind_directions = wind_directions + noise\n", + "\n", + "# Set a FLORIS time series object\n", + "time_series = TimeSeries(\n", + " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate FLORIS solution\n", + "fm.set(wind_data=time_series)\n", + "fm.run()\n", + "turbine_powers = fm.get_turbine_powers()\n", + "\n", + "# Add random noise to the power output\n", + "turbine_powers = turbine_powers + np.random.normal(0, 25.0, turbine_powers.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the results to create a FLASC dataframe representing hypothetical scada data\n", + "df_scada = FlascDataFrame(\n", + " {\n", + " \"time\": pd.date_range(start=\"1/1/2020\", periods=len(wind_directions), freq=\"600s\"),\n", + " \"wind_directions\": wind_directions,\n", + " \"wind_speeds\": 8.0 * np.ones_like(wind_directions),\n", + " \"ti\": 0.06 * np.ones_like(wind_directions),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "FlascDataFrame in FLASC format\n", + "
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timewind_directionswind_speedstipow_000pow_001pow_002pow_003pow_004pow_005...wd_004wd_005wd_006ws_000ws_001ws_002ws_003ws_004ws_005ws_006
02020-01-01 00:00:000.8820268.00.061.300516e+066.781713e+051.062367e+061.753991e+061.753944e+061.753939e+06...0.4231101.4482890.8780048.0749008.1460947.8019417.8232627.8340757.9607897.994616
12020-01-01 00:10:001.2000798.00.061.336040e+067.107407e+051.097192e+061.753917e+061.753948e+061.753979e+06...1.3717731.5842672.2773617.9968197.9914317.9638548.0153497.9744998.0379957.912931
22020-01-01 00:20:002.4893698.00.061.464582e+068.748157e+051.233158e+061.753936e+061.753922e+061.753934e+06...2.4503011.9128802.9670877.7488117.8292838.1086498.0554677.9141577.9783147.977682
32020-01-01 00:30:004.1204478.00.061.588553e+061.075012e+061.396981e+061.753931e+061.753918e+061.753955e+06...4.1149144.3366384.2084887.9250977.8813307.9361788.1583237.9373587.8718468.249129
42020-01-01 00:40:004.9337798.00.061.633680e+061.164021e+061.466940e+061.753928e+061.753947e+061.753943e+06...5.4488556.0913714.7207078.1688888.2137617.9828688.0008138.0090527.9444187.949849
..................................................................
17952020-01-13 11:10:00354.7220078.00.065.540650e+053.631716e+054.072597e+051.753983e+061.753948e+061.753977e+06...355.064016354.808745354.4458008.1387997.7469927.9000447.8458098.0722698.1017497.899713
17962020-01-13 11:20:00356.0133698.00.066.913342e+053.469324e+055.184599e+051.753937e+061.753948e+061.753917e+06...356.362617356.601605355.9638348.0933677.9814028.0745787.8717878.0439798.0211977.848560
17972020-01-13 11:30:00357.0917258.00.068.298897e+053.673236e+056.234462e+051.753946e+061.753947e+061.753962e+06...356.966013356.288613357.7294207.9788837.9443767.9383717.7674937.9571028.0159328.007883
17982020-01-13 11:40:00357.7646298.00.069.225898e+053.939484e+056.904172e+051.753973e+061.753951e+061.753965e+06...356.899939358.256734357.8153527.8949707.7898547.9366727.9960958.1297028.0218487.820329
17992020-01-13 11:50:00359.1363988.00.061.097109e+065.031884e+058.536512e+051.753939e+061.753968e+061.753903e+06...359.319785359.078778359.0003777.7917367.8445507.9665318.1949218.0538608.0030637.963603
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1800 rows × 25 columns

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" + ], + "text/plain": [ + " time wind_directions wind_speeds ti pow_000 \\\n", + "0 2020-01-01 00:00:00 0.882026 8.0 0.06 1.300516e+06 \n", + "1 2020-01-01 00:10:00 1.200079 8.0 0.06 1.336040e+06 \n", + "2 2020-01-01 00:20:00 2.489369 8.0 0.06 1.464582e+06 \n", + "3 2020-01-01 00:30:00 4.120447 8.0 0.06 1.588553e+06 \n", + "4 2020-01-01 00:40:00 4.933779 8.0 0.06 1.633680e+06 \n", + "... ... ... ... ... ... \n", + "1795 2020-01-13 11:10:00 354.722007 8.0 0.06 5.540650e+05 \n", + "1796 2020-01-13 11:20:00 356.013369 8.0 0.06 6.913342e+05 \n", + "1797 2020-01-13 11:30:00 357.091725 8.0 0.06 8.298897e+05 \n", + "1798 2020-01-13 11:40:00 357.764629 8.0 0.06 9.225898e+05 \n", + "1799 2020-01-13 11:50:00 359.136398 8.0 0.06 1.097109e+06 \n", + "\n", + " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", + "0 6.781713e+05 1.062367e+06 1.753991e+06 1.753944e+06 1.753939e+06 \n", + "1 7.107407e+05 1.097192e+06 1.753917e+06 1.753948e+06 1.753979e+06 \n", + "2 8.748157e+05 1.233158e+06 1.753936e+06 1.753922e+06 1.753934e+06 \n", + "3 1.075012e+06 1.396981e+06 1.753931e+06 1.753918e+06 1.753955e+06 \n", + "4 1.164021e+06 1.466940e+06 1.753928e+06 1.753947e+06 1.753943e+06 \n", + "... ... ... ... ... ... \n", + "1795 3.631716e+05 4.072597e+05 1.753983e+06 1.753948e+06 1.753977e+06 \n", + "1796 3.469324e+05 5.184599e+05 1.753937e+06 1.753948e+06 1.753917e+06 \n", + "1797 3.673236e+05 6.234462e+05 1.753946e+06 1.753947e+06 1.753962e+06 \n", + "1798 3.939484e+05 6.904172e+05 1.753973e+06 1.753951e+06 1.753965e+06 \n", + "1799 5.031884e+05 8.536512e+05 1.753939e+06 1.753968e+06 1.753903e+06 \n", + "\n", + " ... wd_004 wd_005 wd_006 ws_000 ws_001 ws_002 \\\n", + "0 ... 0.423110 1.448289 0.878004 8.074900 8.146094 7.801941 \n", + "1 ... 1.371773 1.584267 2.277361 7.996819 7.991431 7.963854 \n", + "2 ... 2.450301 1.912880 2.967087 7.748811 7.829283 8.108649 \n", + "3 ... 4.114914 4.336638 4.208488 7.925097 7.881330 7.936178 \n", + "4 ... 5.448855 6.091371 4.720707 8.168888 8.213761 7.982868 \n", + "... ... ... ... ... ... ... ... \n", + "1795 ... 355.064016 354.808745 354.445800 8.138799 7.746992 7.900044 \n", + "1796 ... 356.362617 356.601605 355.963834 8.093367 7.981402 8.074578 \n", + "1797 ... 356.966013 356.288613 357.729420 7.978883 7.944376 7.938371 \n", + "1798 ... 356.899939 358.256734 357.815352 7.894970 7.789854 7.936672 \n", + "1799 ... 359.319785 359.078778 359.000377 7.791736 7.844550 7.966531 \n", + "\n", + " ws_003 ws_004 ws_005 ws_006 \n", + "0 7.823262 7.834075 7.960789 7.994616 \n", + "1 8.015349 7.974499 8.037995 7.912931 \n", + "2 8.055467 7.914157 7.978314 7.977682 \n", + "3 8.158323 7.937358 7.871846 8.249129 \n", + "4 8.000813 8.009052 7.944418 7.949849 \n", + "... ... ... ... ... \n", + "1795 7.845809 8.072269 8.101749 7.899713 \n", + "1796 7.871787 8.043979 8.021197 7.848560 \n", + "1797 7.767493 7.957102 8.015932 8.007883 \n", + "1798 7.996095 8.129702 8.021848 7.820329 \n", + "1799 8.194921 8.053860 8.003063 7.963603 \n", + "\n", + "[1800 rows x 25 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the turbine powers to the dataframe with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"pow_{t_idx:03d}\"] = turbine_powers[:, t_idx]\n", + "\n", + "# Set the turbine wind directions to be the true wind direction with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", + " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + " )\n", + "\n", + "# Set wind speeds to be fixed with small noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"ws_{t_idx:03d}\"] = 8.0 * np.ones_like(wind_directions) + np.random.normal(\n", + " 0, 0.1, wind_directions.shape\n", + " )\n", + "\n", + "df_scada" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Northing calibration error\n", + "\n", + "Add to the data two types of northing calibration error:\n", + "1. A constant bias on turbine 001\n", + "2. A change in bias on turbine 002 halfway through the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_scada[\"wd_001\"] = wrap_360(\n", + " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + ")\n", + "\n", + "mid_point = int(len(wind_directions) / 2)\n", + "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + "wd_change[mid_point:] = wd_change[mid_point:] + 45\n", + "wd_change = wrap_360(wd_change)\n", + "df_scada[\"wd_002\"] = wd_change" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind direction')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the wd channels for the turbines\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\", ls=\"--\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\", ls=\"--\")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"Wind direction\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:04:56\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + ] + } + ], + "source": [ + "# Finally compute df_approx for use in later algorithms\n", + "# Can compute only at 8m/s for this example\n", + "df_fm_approx = ftools.calc_floris_approx_table(\n", + " fm=fm, # fi=fi_pci,\n", + " wd_array=np.arange(0.0, 360.01, 1.0),\n", + " ws_array=np.array([8.0]),\n", + " ti_array=np.array([0.06]),\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cross-Check Northing calibration " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` is a function to check if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -30.0 0.0 0.0 0.0\n", + "1 0.0 -30.0 0.0 0.0 0.0\n", + "2 0.0 -30.0 0.0 0.0 0.0\n", + "3 0.0 -30.0 -40.0 0.0 0.0\n", + "4 0.0 -30.0 -46.0 0.0 0.0\n", + "5 0.0 -30.0 -44.0 0.0 0.0\n", + "6 0.0 -30.0 -44.0 0.0 0.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T002 T006 T005 T003 T000\n", + "0 30.0 30.0 30.0 30.0 30.0\n", + "1 30.0 30.0 30.0 30.0 30.0\n", + "2 30.0 30.0 30.0 30.0 30.0\n", + "3 -10.0 30.0 30.0 30.0 30.0\n", + "4 -14.0 30.0 30.0 30.0 30.0\n", + "5 -16.0 30.0 30.0 30.0 30.0\n", + "6 -16.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T001 T003 T005 T000 T006\n", + "0 -30.0 0.0 0.0 -0.0 0.0\n", + "1 -30.0 0.0 0.0 -0.0 0.0\n", + "2 -30.0 0.0 0.0 -0.0 0.0\n", + "3 10.0 40.0 40.0 40.0 40.0\n", + "4 14.0 44.0 46.0 46.0 46.0\n", + "5 16.0 46.0 46.0 44.0 46.0\n", + "6 16.0 44.0 46.0 44.0 44.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 -0.0 -30.0 0.0 0.0\n", + "1 0.0 -0.0 -30.0 0.0 0.0\n", + "2 0.0 -0.0 -30.0 0.0 0.0\n", + "3 0.0 -40.0 -30.0 0.0 0.0\n", + "4 0.0 -44.0 -30.0 0.0 0.0\n", + "5 0.0 -46.0 -30.0 0.0 0.0\n", + "6 0.0 -44.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 0.0 0.0 -30.0 0.0\n", + "1 -0.0 0.0 0.0 -30.0 0.0\n", + "2 -0.0 0.0 0.0 -30.0 0.0\n", + "3 -0.0 -40.0 0.0 -30.0 0.0\n", + "4 -0.0 -44.0 0.0 -30.0 0.0\n", + "5 -0.0 -46.0 0.0 -30.0 0.0\n", + "6 -0.0 -44.0 0.0 -30.0 0.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -30.0 0.0 -0.0 -0.0\n", + "1 -0.0 -30.0 0.0 -0.0 -0.0\n", + "2 -0.0 -30.0 0.0 -0.0 -0.0\n", + "3 -0.0 -30.0 0.0 -40.0 -0.0\n", + "4 -0.0 -30.0 0.0 -46.0 -0.0\n", + "5 -0.0 -30.0 0.0 -46.0 -0.0\n", + "6 -0.0 -30.0 0.0 -46.0 -0.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m T001 T005 T000 T003 T002\n", + "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "3 -30.0 -0.0 -0.0 -0.0 -40.0\n", + "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['clean', 'clean', 'bad', 'clean', 'clean', 'clean', 'clean']\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", + "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", + "\n", + "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", + ")\n", + "print(turb_wd_consistency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` detects that T002 contains a probable jump, one solution is to then remove T002's wind direction data from consideration however this is not done in this notebook as we next take advantage of HOGER recalibration. The code to do this is included below in comments" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# # Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", + "# faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", + "# for ti in np.where(faulty_turbines)[0]:\n", + "# df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homegenization with HOGER" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", + " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", + "\n", + " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[899.5]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/Projects/FLASC/flasc/flasc/data_processing/northing_offset_change_hoger.py:105: UserWarning: Encountered a tie, and the difference between minimal and maximal value is > length('x') * 0.05.\n", + " The distribution could be multimodal\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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ClassTurbineCountJumpKnotKnot_date
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\n", + "
" + ], + "text/plain": [ + " Class Turbine Count Jump Knot Knot_date\n", + "0 1 wd_002 6 -45.005012 899.5 2020-01-07 05:40:00" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scada_non_homogenized = df_scada.copy()\n", + "df_scada_homogenized, d2 = homogenize(df_scada_marked_faulty_northing_drift, threshold=10)\n", + "\n", + "# Show the search results\n", + "d2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The HOGER homeginization removes changes in northing calibration of turbines with respect to others, however the final selected value may contain a steady offset, as happens in this case" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind direction')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the effects of homogenization\n", + "# Show the wd channels for the turbines\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_001\"],\n", + " label=\"wd_001\",\n", + " color=\"blue\",\n", + " ls=\"--\",\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_002\"],\n", + " label=\"wd_002\",\n", + " color=\"red\",\n", + " ls=\"--\",\n", + ")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"Wind direction\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recalculate which turbines are clean with respect to offset changes" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -30.0 -46.0 0.0 0.0\n", + "1 0.0 -30.0 -46.0 0.0 0.0\n", + "2 0.0 -30.0 -44.0 0.0 0.0\n", + "3 0.0 -30.0 -46.0 0.0 0.0\n", + "4 0.0 -30.0 -46.0 0.0 0.0\n", + "5 0.0 -30.0 -44.0 0.0 0.0\n", + "6 0.0 -30.0 -44.0 0.0 0.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T002 T006 T005 T003 T000\n", + "0 -16.0 30.0 30.0 30.0 30.0\n", + "1 -16.0 30.0 30.0 30.0 30.0\n", + "2 -14.0 30.0 30.0 30.0 30.0\n", + "3 -16.0 30.0 30.0 30.0 30.0\n", + "4 -14.0 30.0 30.0 30.0 30.0\n", + "5 -16.0 30.0 30.0 30.0 30.0\n", + "6 -16.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T001 T003 T005 T000 T006\n", + "0 16.0 44.0 46.0 46.0 44.0\n", + "1 16.0 46.0 46.0 46.0 46.0\n", + "2 14.0 44.0 46.0 44.0 44.0\n", + "3 16.0 46.0 44.0 46.0 46.0\n", + "4 14.0 44.0 46.0 46.0 46.0\n", + "5 16.0 46.0 46.0 44.0 46.0\n", + "6 16.0 44.0 46.0 44.0 44.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 -44.0 -30.0 0.0 0.0\n", + "1 0.0 -46.0 -30.0 0.0 0.0\n", + "2 0.0 -44.0 -30.0 0.0 0.0\n", + "3 0.0 -46.0 -30.0 0.0 0.0\n", + "4 0.0 -44.0 -30.0 0.0 0.0\n", + "5 0.0 -46.0 -30.0 0.0 0.0\n", + "6 0.0 -44.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 -44.0 0.0 -30.0 0.0\n", + "1 -0.0 -46.0 0.0 -30.0 0.0\n", + "2 -0.0 -44.0 0.0 -30.0 0.0\n", + "3 -0.0 -46.0 0.0 -30.0 0.0\n", + "4 -0.0 -44.0 0.0 -30.0 0.0\n", + "5 -0.0 -46.0 0.0 -30.0 0.0\n", + "6 -0.0 -44.0 0.0 -30.0 0.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -30.0 0.0 -46.0 -0.0\n", + "1 -0.0 -30.0 0.0 -46.0 -0.0\n", + "2 -0.0 -30.0 0.0 -46.0 -0.0\n", + "3 -0.0 -30.0 0.0 -44.0 -0.0\n", + "4 -0.0 -30.0 0.0 -46.0 -0.0\n", + "5 -0.0 -30.0 0.0 -46.0 -0.0\n", + "6 -0.0 -30.0 0.0 -46.0 -0.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m T001 T005 T000 T003 T002\n", + "0 -30.0 -0.0 -0.0 -0.0 -44.0\n", + "1 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "2 -30.0 -0.0 -0.0 -0.0 -44.0\n", + "3 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", + "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['clean', 'clean', 'clean', 'clean', 'clean', 'clean', 'clean']\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", + "df_scada_marked_faulty_northing_drift = df_scada_homogenized.copy()\n", + "\n", + "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", + ")\n", + "print(turb_wd_consistency)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove steady offset from a single turbine\n", + "By looking at the energy ratios and looking at the average offset between turbines' wind direction measurements, we can align every turbine that was flagged 'green' in the above plot with true north. Wind directions of turbines flagged red should not be used." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing wd bias estimator object for turbine 000...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999863\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 0. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WD bias for first clean turbine: 0.000 deg\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_bias_for_single_turbine(\n", + " df, fm, ti, opt_search_range=[-180.0, 180.0], plot=True, figure_save_path=None\n", + "):\n", + " print(\"Initializing wd bias estimator object for turbine %03d...\" % ti)\n", + "\n", + " # Copy variables and unlink them\n", + " df = df.copy() # Unlink from input\n", + "\n", + " # Calculate which turbines are upstream for every wind direction\n", + " df_upstream = ftools.get_upstream_turbs_floris(fm, wd_step=2.0)\n", + "\n", + " # We assign the total datasets \"true\" wind direction as equal to the wind\n", + " # direction of the turbine which we want to perform northing calibration\n", + " # on. In this case, turbine 'ti'.\n", + " df = dfm.set_wd_by_turbines(df, [ti])\n", + "\n", + " # We define a function that calculates the freestream wind speed based\n", + " # on a dataframe that is inserted. It does this based on knowing which\n", + " # turbines are upstream for what wind directions, and then knowledge\n", + " # of what the wind direction is for every row in the dataframe. However,\n", + " # since the shift the \"true\" wind direction many times to estimate the\n", + " # northing bias, we cannot precalculate this. It changes with every\n", + " # northing bias guess. Hence, we must insert a function.\n", + " def _set_ws_fun(df):\n", + " return dfm.set_ws_by_upstream_turbines_in_radius(\n", + " df=df,\n", + " df_upstream=df_upstream,\n", + " turb_no=ti,\n", + " x_turbs=fm.layout_x,\n", + " y_turbs=fm.layout_y,\n", + " max_radius=5000.0,\n", + " include_itself=True,\n", + " )\n", + "\n", + " # We similarly define a function that calculates the reference power. This\n", + " # is typically the power production of one or multiple upstream turbines.\n", + " # Here, we assume it is the average power production of all upstream\n", + " # turbines. Which turbines are upstream depends on the wind direction.\n", + " def _set_pow_ref_fun(df):\n", + " return dfm.set_pow_ref_by_upstream_turbines_in_radius(\n", + " df=df,\n", + " df_upstream=df_upstream,\n", + " turb_no=ti,\n", + " x_turbs=fm.layout_x,\n", + " y_turbs=fm.layout_y,\n", + " max_radius=5000.0,\n", + " include_itself=True,\n", + " )\n", + "\n", + " # We now have the reference power productions specified, being equal to\n", + " # the mean power production of all turbines upstream. We also need to\n", + " # define a test power production, which should be waked at least part of\n", + " # the time so that we can match it with our FLORIS predictions. Here, we\n", + " # calculate the energy ratios for the 3 turbines closest to the turbine\n", + " # from which we take the wind direction measurement ('ti').\n", + " turbines_sorted_by_distance = ftools.get_turbs_in_radius(\n", + " x_turbs=fm.layout_x,\n", + " y_turbs=fm.layout_y,\n", + " turb_no=ti,\n", + " max_radius=1.0e9,\n", + " include_itself=False,\n", + " sort_by_distance=True,\n", + " )\n", + " test_turbines = turbines_sorted_by_distance[0:3]\n", + "\n", + " # Now, we have all information set up and we can initialize the northing\n", + " # bias estimation class.\n", + " fsc = best.bias_estimation(\n", + " df=df,\n", + " df_fm_approx=df_fm_approx,\n", + " test_turbines_subset=test_turbines,\n", + " df_ws_mapping_func=_set_ws_fun,\n", + " df_pow_ref_mapping_func=_set_pow_ref_fun,\n", + " )\n", + "\n", + " # We can save the energy ratio curves for every iteration in the\n", + " # optimization process. This is useful for debugging. However, it also\n", + " # significantly slows down the estimation process. We disable it by\n", + " # default by assigning it 'None'.\n", + " plot_iter_path = None # Disabled, useful for debugging but slow\n", + " # plot_iter_path = os.path.join(out_path, \"opt_iters_ti%03d\" % ti)\n", + "\n", + " # Now estimate the wind direction bias while catching warning messages\n", + " # that do not really inform but do pollute the console.\n", + " with wn.catch_warnings():\n", + " wn.filterwarnings(action=\"ignore\", message=\"All-NaN slice encountered\")\n", + "\n", + " # Estimate bias for the entire time range, from start to end of\n", + " # dataframe, for wind speeds in region II of turbine operation, with\n", + " # in steps of 3.0 deg (wd) and 5.0 m/s (ws). We search over the entire\n", + " # range from -180.0 deg to +180.0 deg, in steps of 5.0 deg. This has\n", + " # appeared to be a good stepsize empirically.\n", + " wd_bias, _ = fsc.estimate_wd_bias(\n", + " time_mask=None, # For entire dataset\n", + " ws_mask=(6.0, 10.0),\n", + " er_wd_step=3.0,\n", + " er_ws_step=5.0,\n", + " er_wd_bin_width=3.0,\n", + " er_N_btstrp=1,\n", + " opt_search_brute_dx=5.0,\n", + " opt_search_range=opt_search_range,\n", + " plot_iter_path=plot_iter_path,\n", + " )\n", + " wd_bias = float(wd_bias[0]) # Convert to float\n", + "\n", + " # Print progress to console\n", + " print(\"Turbine {}. estimated bias = {} deg.\".format(ti, wd_bias))\n", + "\n", + " if plot:\n", + " # Produce and save calibrated/corrected energy ratio figures\n", + " fsc.plot_energy_ratios(show_uncorrected_data=True, save_path=figure_save_path)\n", + " if figure_save_path is not None:\n", + " print(\"Calibrated energy ratio figures saved to {:s}.\".format(figure_save_path))\n", + "\n", + " # Finally, return the estimated wind direction bias\n", + " return wd_bias\n", + "\n", + "\n", + "# We will calibrate the turbine nacelle heading for the first 'clean' turbine\n", + "first_clean_turbid = np.where([c == \"clean\" for c in turb_wd_consistency])[0][0]\n", + "\n", + "# Calculate optimal bias for the first clean turbine, covering all possibilities\n", + "# (from -180 deg to +180 deg offset)\n", + "wd_bias = get_bias_for_single_turbine(\n", + " df=df_scada_homogenized,\n", + " fm=fm,\n", + " ti=first_clean_turbid,\n", + " opt_search_range=(-180.0, 180.0),\n", + " plot=True,\n", + ")\n", + "print(\"WD bias for first clean turbine: {:.3f} deg\".format(wd_bias))\n", + "\n", + "# Now calculate the northing-bias-corrected wind direction for this\n", + "# turbine and call it our reference\n", + "wd_ref = wrap_360(df_scada_homogenized[\"wd_{:03d}\".format(first_clean_turbid)] - wd_bias)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Step 4**: Estimate the biases of the remaining turbines\n", + "Now that we know the wind direction bias of a single turbine, we roughly know where true north lies. What we can now do is simply calculate the average offset between the wind direction of any turbine and this northing-calibrated wind direction. The wind direction bias for that turbine is likely very close to this number. We use this as a first guess for the energy-ratio-based bias estimation, and optimize within +- 5 degrees within this initial value." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing wd bias estimator object for turbine 000...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999863\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 0. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 001...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turbine 1. estimated bias = 30.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 002...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turbine 2. estimated bias = 44.962500000000006 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 003...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999854\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 3. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 004...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999876\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 4. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 005...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999888\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 5. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Initializing wd bias estimator object for turbine 006...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -0.999892\n", + " Iterations: 1\n", + " Function evaluations: 2\n", + "Turbine 6. estimated bias = 0.0 deg.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Wind direction biases: [ 0. 30. 44.9625 0. 0. 0. 0. ]\n" + ] + }, + { + "data": { + "image/png": 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7e3c45vHHH+eJJ57osH3hwoV4eXkpYrsSVFdX8/LLL3f5/dRENT/NGuRAiwQCgcD16O7HWNA1MX4S+ff6olZ1n6K+v2GUZSa83sj2EiPQ8WFYIBAow/Evs4yyTEGdTKSvhJdmYN1z+srUlY38nN9xiO4999xDcHCwEyzqmpaWFpYuXSqCTXfp2TQYDBQWFnbY3trayuuvv84lM89m0q/Xopb+cpdBlvg28BrOq30flbXbI2cDcF7Zq9Yf4wCtNce0zu1E60ufy7io6RO3qcs34bORVDZqdVN+l/V3VR8rWP7ngTfipfXivPKOWmuCruHcmk7Opa62d3OOuWq7uOy534tj1gRcw7l17bcbZYlvgmchyXrOrfnvCd9BjhxNmqqEE9nkNYWgcxdTW1NFfVku/pHJBEcldtjvRAwGA7m5uSQnJyt631da0x66BRve56zCFztsrzH6ECg1cfwyaUZZ4rvAqzBKHpxb824nvryac+s+sO+5730pFzV/2vF8CboeZP2xY463GQ4Rx2Cp4+8owE5jCtWqYE6Xt7cbApc67cYe26473OWcMj9L3HrrrYoNz+zKzurSPJuuS2t17WGrq2naw09g//rXVRQy4acr212v3bHOYxo6rxDOqe/4+/al9yVc1PyZ6/3uOeA3VC+rWDVkOUNHndzBT3Fxcb0eRuvsnk2NIqU6iMjISMrLy9ttKy8vx9/fv9NAE0Cr1Xa64KpWq8XDwwOtVqto4/dFc/DgwR226XQ6goODOWXymfxatoBJh55FIxnRyyo2pD3IBdctYv17cTZtB1j/rj+Tsp9TRuu9gPbfpT7ABdc/ZJPW+d1opY25kF/+SO7U3k6P6aku78Yqp3XC9g+9ruLyW59Aq9XarNWb+tuk1UP9e6V14jHHfK9EW25IfYAhYy4kIyODXz7oqHV+N+dSd+fYT+/4MTnneaec+7a2pSuf+7bWvyu//KUV2+67X1PuZ1uhjvtaX273kg3g1JZfaPr8PAbLOlQSGPZL/Jq2gKnXLaI7DAYDRqNR8R9dpTWV1i3Z8hkTC1/tWIasovHGn9i16fMOfjnX4pfoLnwZ38FfF9zwsMLnfko352t8B63UU/+G4e1T2p0vsgwyMFqVA+TAsQBVLcnMKHuVMo9ZxCZ3PYWlJ9zlnDI/S6Snpyu2CH1ndq5/72kmH3rGFNBbeV1ao2sPW11R0x5+AvvWPzk5hQ1fPt5poPlTwj1Myftnu2tSL6sYfPXTxCYPYf17KV1c+6ku+bt34nbzfa83Wp0ek/oAQ0acpLif7BnvWINb9WwuWLCAb775hj179li2XXPNNVRXV7t9gqCu0Ol0LF26lIULF6LVainKPUBZ7l4ik4e3+4G0dbvBYGDzL2vx1NcTnTKyT1rm70pydtOq8WfilLMs9e+tlvm7qIQ0S5uW5h+y6pieyumu7r2x2bw9OC6ddz/83OIrR9XfWi1r6m+rlnl7Z77vS1ueWHe1Wq3Yua/T6Xjp+ac4/aShxKSNdvi535v6u+q535v6W9suYTFJLF26lClJmnYvB372PpPU5p0kSBXtjtXLKspmbe42cLBnogQlNRXTlWUKv3qKmB3Po5Jk8uUIYqlEfdxDjzkQ6O35WnxoJz9v38fc+x/u8b7Xk5YS5/76957u8GB38oxr2fvhI5xc9UWHJto+6Q1OOvOy3rUv7nNOnfgsoQQn2lmUe4CoE4J9a67LnnTtYauratrDT2C/+m/d8QdHf3ias1t/6PC92ffZnbzMOv4FhLv97nV13+uNVnf1d+Xz1NYEQchO5OjRo/Iff/wh//HHHzIgL1u2TP7jjz/k/Px8WZZleeHChfL1119v2T83N1f28fGRH3jgAXn//v1yZmamrFar5TVr1lhdZl1dnQzI1dXV8p49e2S9Xq9YffR6veKaLS0t8uOPPy63tLQopinL9rF1IGvKsn185S71dxdNWRZ+skeb2vveV5izX9629n9yYc5+WZZl+ecv35Xlxf4d/m1b+z+H2+mKbVqYs1/evuZdOf+fF1ja5ssnLpZzi0rlX3/8Vt7y/UeWtuwrrng9nXi+yLIs5x3aK+sfC2h3vugfC+xzO7jLOWVvPxXm5cibn5zeq+uyO1172OrKmu7yzFeYs19e82Gm/Mfik45dSwHy1mfOl9seC5Tlxf5y22OB8k/vLmm3/4nXpCPstJemu/jJ3prV1dUyINfV1XW7v1OH0W7fvp0zzjjD8nn+/PkAzJo1i5UrV1JaWtpuOZCkpCRWr17NvHnzeOmll4iNjeX1119nxowZDrddIBAIBI4hNnlIu7fBycNOwrBdateDYpQlIpOHO8M8l8I8jDH2WNsYZIl/e93MVXP+QcAgLY01sYr3wroaJ54vALFJQ/g+5u+cXfwv1JIpWdBBOQ5f3yhnmOj2FOUeoCR7Fy2yFxs2vMG40g+IlZo77KeXVeK67GeceI/RyRoKzshk/NSr2vXSTT3uGuzsmhQMHJwabE6dOhW5m1G8K1eu7PSYP/74w45WCQQCgcCViU0ewvq0v+awAxzFh7CIgR04FOUesMyXO54Zl9xMsL8PBkPHDIcDibhTr6TI6ypqdn7N0L3PMFSVz6uvP8as+5/H29PD2ea5DccHG7KMKbmUBFmqZMoDx3JalSmxkyzDz4lzmS6CjH5DZ/cYDQa840cDIqgUdI5yq/AKBAKBQOAgpl63iLJZm9lw0j8pkYMJkBrZ8d7DzjbLqZTm7O4QaKolmZrCA06yyPWITRrC6MsWUH3qowDcrHuXl99828lWuQ8nBhuSZErC9EPs30l5aBuT73mDgmt+pgp/JAnadC1OtligJF3dY8py9zrJIoE7IIJNgUAgELglsclDmHTBDfyZYZqCMbbkQ0py9znZKucRlTKSEwcLiWGMnRN51r2URJ2Jp2TgmpKlvPDJer7fmUd+RZ2zTXNpOgs2JAkCE0ej1pgGyyUOHsWhIXMAOKn0v1RXH3G4nUpRlHuAbWs/pihXvLABGBQcJe4xApsRwaZAIBAI3Jppl93FTtUwvKQ2iv73oLPNcRoRwQHo+WsupjnzoxjW1gmSRPSsN6nxjCJOVcnYXY/x1scfcdWyL/j32j09Hz9AiUoZidGKYGP8JfMoIZxQqZ5tHz3jQAuVY/17TxP19imM33gbUW+fwvr3nna2SU6n/I/Vlt5swLJch7jHCLpDBJsCgUAgcGvUGjWc9SQGWeLk5l/ZuX6Vs01yCvs+exoPycCfciIbJ7xK2azNNq9xOKDwCqBp5r/RyxJnqHfxgecSNmjv4fD6t0UPZxeEhATTdly6j66CDbWnlsJhpoXrx5d9QGVlpUPt7CtFuQeYctxwYbUkM+nQswO6h9OobyO96FMAfoq6la2nvcZvUz9g8jULnGyZwNVxaoIgZ2JOlKBkwgR7aWo0GgwGg1vYOlA1zXpK+8pd6u8ummY94Sfl21RpXVv9NOLkqWzYMIPTG9fg8/Pj6E45B41H+6Qv/blNDU11JBV8AsC+xBu55OwrOhzvCn6yVvP4v/bUzDrqyfFppdSSzD80b7Ah+1JiQ052KVv7qqmEn/789BlOkvRkyzFUnPIoRq8gTj1tWqeaY2beRdG+/xBLGd99vIQzZz9vta3H/1UCWzVLsndasq2a0UhGSnJ2E5WQZlc7XfWZb8/adxlNBbXyIEZdthA//wCysrJczk5Habqqn1xRU5K7Swfbj8jMzCQzMxODwUBWVhabN2/G19fX2WYJBAKBQCEaaioZ+sM1+EtNfBM0C3XkUAaFJxIYFuts0+xOzaaVTC55jcNyFOUz38fXy9PZJrkFrXm/MXb7fR227xi/DG3CBCdY5LrIbc3EfnEJQdTzUcR9DJt8SY/HlG77nLPyn6dW9mX/jI/w8w+0v6EKUPz7N8zIfardNoOsYvPUDwbE/aRTVt3BcP1evvG5mPjzHnC2NQIXoKGhgYkTJ1JXV4e/v3+X+w2YYNNMfX09AQEBVFZWUlZWRnp6umLrjZkDWSU1dTody5cvZ968eWi1WkU0wT62DmRNsI+v3KX+7qIJwk/2aFNXuvdtWPkIpxe+YlmSwSBL/Jr6IJOvWdBv29TQqqP2meGEU8WqqHnMvPVRh9nq9tdTfTHSS6NQYbRsMqJCnrsL/GNcy9Y+oISf9n3+LCP2LqVIDsVz7g4CfX16tNPQ1kbZM2OIl0v4PvQGpt/5Yo/luEKb7nz2XMbptmCUQSWZtm3zPo2x939lVztd9ZmvOns7YR+cjUGW2HnhWkaPHusSfnKWpqv6ydGakZGRhIWF9RhsDthhtOYGV6vVii9uraSmWq1Gr9fbxU6zvivX35007ekroSn85OqaSuv21k/xk65Efv8V09p/HJtrlf0cpfmXWIa/9bc23fHNvzmZKirkQE677J4e93cFP1mrbXfNoHi48CXkr+YiyUZkGXal382YoHjXs7WPWn3yk9FA2L63ANgadhmXBAdahtB1p6lWqykZOYf4XQ9zSuXHbPpqJEkjJ1uVUMZZbVqavYsxLVtBgk2n/Jumfd9x9tHPGNq8g+bqYnzD2p8bLuUnK/R7o1vy/UuEARvVJzNl3HhFNLvD1TVd1U/O0LQGkSBIIBAIBP2GIwVZlkDTjEYy9tt14IwGA2F73wDg9/BLCAsJdrJFbsjYG5Du3UuZZyKSBHuLRXKgE8ld/zaRxnKqZV9Gzvy7TceePPMOKgjCX2ph8s77XT6za/7q51FJMlvVY5l07lVMnvMqe0lhEC3kvHevs81zOMaGIwyp+h6AqqHXO9kagTsigk2BQCAQ9BuiUkZikNtHm/15Hbhta94jSS6kQfZmzKX3O9sc9yUghrZxtwBwcsOPlNc0ONkgF0KWUW3OBOCnQeeTmmDbnMWSghxC5VrLZ1fO7NpcW8Go6u8AaBhlOh+8vbQUj38Uoywxqm4d5bvWOtNEh5P9zUtoaeNPOZEzzrnM2eYI3BARbAoEAoGg3xCbPIRf0xZY1oEzyFK/XWuyIGcfIdtMGT63B51HRGTPcwwFXRM35Xpa8WCwqpCfflzjbHNchopda0hsy6ZZ9iT6bNt6NQFKc3aj6iSzqyuONti76gW8JR0HSWDyuVdZtp917kV85zENAN3XD4JR2ezTLotBT8iB/wLwe+jFBPp6O9kggTsigk2BQCAQ9CumXreIjT6mB8PNvmf2y7Um17/3NLHvnEoqBcgyyD5hzjbJ/fEOIjfoVAA8D3zuZGNch7q1zwHwvcc0Jo4eYfPx7jLawNjWQlLeRwBkxV+Nh8dfaU1UKhUhFzxBnexDfFsuh7990UlWOpaaHf8jxFjFEdmfIWfd5GxzBG6KCDYFAoFA0O/QJ54OQFhzrpMtUZ6i3ANMPvSMpbdIkmBy8X9ccliiuxEycRYAk1t/JauowsnWOJ+8TZ+R1rgDvSyhmji7Vxrm0QbGYwGnUcYlRxsc+O41QqmhXA5kwsUd63ry6BF8FXgdAKHbnmPnt29SW1nkaDMdR10x+h+XAvCtx1mMH5LoXHsEbosINgUCgUDQ74gZcQYAyYY8WhrrnWyNspTm7EbtJsMS3Y2wcRdSJ/kTJtWx/Yf/Odscp7L+vadJ+M7Um6VGxrdkc6+1pl63iJ9TFgCQL0W73mgDWWbQztcB2Bx4IeHBQZ3udtpVCygxBuNHM+O238/E9Vfx6/vPONJSx/D7O8jLhxHWkocsQ2pMpLMtErgxItgUCAQCQb8jZfBwyuUgPCQD2Tt+crY5ihKVMtLSS2TGFYcluiVqD4qiZgAQlv81RqOxhwP6J+bec3NmZ0mCydnP9an3POGUCwGIk8vQNbtWAqaSHatJ0OfRJGtJOffuLvfzaD5CpFRj+WxeWqlfjSqoK8b45Vwk/ho5MSF/BdQVO9kwgbsyYNfZNK8PZf7rypoajQaDweAWtg5UTbOe0r5yl/q7i6ZZT/hJ+TZVWlcJP+V4DiGibTO1Wb9iOO2CftOmUQlpbPKZxqTmdYAp0NyQ+gCTE9K6tcNV/dSZ5vF/Ha0Zc/rN8MH/mGzcxtY9Bxg/fLAiutbiCn4qyd5FbCe95yU5uy3r1dpqZ2xCGtWyH8HSUbJ2b2LwSdO7tNUWXWvoVrO+mLYf/g+An7RncE5aapdlW9MufbXT2c985dk7iab9SxYVRkqydxEx+q8eTnc69+2h6Ww/uZOmJMuy3PNu7k9mZiaZmZkYDAaysrLYvHkzvr6+zjZLIBAIBHbi8I9vMLP6TXZ4noT2wpecbY6iVKx5mmkNX7PZ4xQMp84jMMy25SgE3SDLBK66ilhDEe8EzmHsmdc42yKHU1tZxKnrr0R1XAe6Xlbx29QP+nSutXx2JycZd/N9zN1ET7xaAUv7RtDhr4je8QwSMrIMW2JuxPfU27rcv7ayiInrr2o3jF2JdnElDuQc5m+/X9+hjl+Me4fByUlOtEzgajQ0NDBx4kTq6urw9/fvcr8B07M5Z84c5syZQ319PQEBAaSmplJWVkZ6ejpqtVqRMsyBrJKaOp2O5cuXM2/ePLRarSKaYB9bB7Im2MdX7lJ/d9EE4Sd7tKmr3vtqS6fAxjdJaT2I35AhGIzGftOmxlV5ADQnnMHpU85ymq399Xo6sOdiYrP+RUbNj6SmPY6HpvNjXMFWa7DZTxkZlP0cTjSmJEmW3vPjzrXe2PmTXzrU7cb3aC4ZGRmd7uOwNq0vRvrk2XbDRU8ueQc55j7w72IZoYwMfi16kCnZz6KSZIwy/JpyP1OsvAZ7whWe+XxCo1mx9ULu9vgCMPn+Ef0t3H7yaSSEBfRK0x52OlPTFfzkCpqpqalW7T9ggs0TMTe4Wq1W9AFBaU21Wo1er7eLnWZ9V66/O2na01dCU/jJ1TWV1lXCT0PHT0O3wYNA6SiVBX8SnDBccTvNOLRNZZmEtsMgQWDyOJvLdTU/daftLM3UGbdjPJjJeGk/P2zZwplTJiuiawvO9JOx5ShhchVIsDrpUUZNvoCpXWSPtcnOyBFQ9wkhjYd6PMbebVqW9yeRnQwXLcvbR+SY+C41pl7/EF/+p5ULS5aTr4plyrUL3eJ6Muv3pJscGUxUdBxUwi5DEne2zWfW9DEkRwb3WtMedjpT0xX85Cqa1iASBAkEAoGgXxIcGMABVQoABTvXOdka5ThSfIgAqZE2WU3ysJOdbU6/RBuSQLb3SADkX5f1rwQwVlC052c8JAOlcjBTr/i7YsuUhKZPACBRn4exTaeIZm852BLc6fqfWbrOM9EeT8gIU09mjFwGhja72OdMko15AGR5j+KD+Rdxx1m2r68qEJgRwaZAIBAI+i2lg4YBYCzY6mRLlKPoz98A0xISgQEBPewt6C31WlMylLPafiTq7VNY/97TTrbIcVTu+RGAg5oMBnkrN0xw8LCxHJW90UptlBz6QzHd3pCYMoRX9BdZPpuHiyZYEVinZ4ziqOyNJ3qOFv5pTzOdgn/dQQA8okeQEC7uMYK+IYJNgUAgEPRbjDHjAAiv7z9rUDYV7gKgxFMk67AXRbkHGFP7veWzWpKZdOjZAdPD6V2+A4DakNHK6nppyVElAlB2oPfrdipBQngAwZEJAOwxJDJF9xJJU2dZFVyFBfqSLZmG2pYc2GJXOx2OQU+MPg+AkNSTnGuLoF8ggk2BQCAQ9FuiR0wFIMFYiKGxpvud3QSvalPA0xiQ7mRL+i+lObvbZeME0xIXZbn956VFlxj0JOhM55h/evdzVXtDhY9piRB98W7FtW0lSS4AIN9nmM3DRcu0ppc9LUW77GKbs6jK240XbTTIXgwbOd7Z5gj6ASLYFAgEAkG/ZdiQIeTJEQAU7vrRydYoQ3hzLgCa6OFOtqT/EpUystP5fJHJ/b/NK7K2MIgW6mUfRo9XPtjUhw4FILDe+b3EfkdzANBGD7N5uGjTsZc93jUHFbfLmRTu3QhAjpRAsP8gJ1sj6A+IYFMgEAgE/RYPjZpsD9McrNqDG5xsTd8xthwl2lgKQHia6HWwF7HJQ/g1bYEl4JRlWJ98n2KJclyZop0/APCnejDBAcqvR+6fbBqaGdd2GIzGHva2L9Ft+QAEJIy2+VhtjKkXNFKXq6RJTkdXtBOACh/rlrUQCHpCBJsCgUAg6NfUh4wCYFDlTucaogDlh3agkmQq5EAGp/f/wMeZTL1uEaXX/kybrEKSQEo8zdkmOQR1kWkO4pGAkXbRTxtxMi2yB4Nopq7Yeb2bVaX5hFCHUZZIG3WKzcdHDz4JgywRJNdhrC+1g4XOwbfW1FNr7oEWCPrKgF1n02AwtPvrypoajQaDweAWtg5UTbOe0r5yl/q7i6ZZT/hJ+TZVWldJP/kkTYByiNcdJNeod+s2rcjaShRwWJXASWrJpnJd3U/Hax7/15maUSnDOaxNJ7X1AFUHNmCYNEUR3e5wqp9kmbhG07xUj8SJ3e7bWztDA3zZJ8UzghwK9vzK0OjBiuh2R2eaubs3EgIUSRHE+AfaXF5qbCR5ciQpUiml+38j8qQLFbHTqc98skxcm6mnNih5nF383x3upCmeza3XlGRZlnvezf3JzMwkMzMTg8FAVlYWmzdvxtdX+eEhAoFAIHAtKo+2MG7NBfhLzew5/Q2kMPftEWxc+w8m1H3Ll14XkXzBg842Z0DQ9NPznFz1OV9rziLx4sedbY5d0VflMfqna9HJGn47ezUhdhhGC1D05eOc07qWjcGXEjBtvl3K6NGGn9/knMo3+M1jAr4XLeuVRvmn9zFd/o0tMTczaOItClvoeBqPFDBh/dW0ymr+uOA7Bnl7O9skgQvT0NDAxIkTqaurw9/fv8v9BkzP5pw5c5gzZw719fUEBASQmppKWVkZ6enpqNVqRcowB7JKaup0OpYvX868efPQapVb68oetg5kTbCPr9yl/u6iCcJP9mhTV7/3ZQBbv0tjIrtpyv+DMadd6LZtmvvlYQDaQoeSkZHhdFsHwvVUUn0m/Pg5Ka0HiUtOxVvr4bK2doW1ftr9hWm5l/1SCpNO6X5OcF/szN4wFMrWEtSYw+ATzmNHtWndN3kANAekMd7Ga8ms+ad3EjT9xqDGPJuvx85w9jPf9tWbAMiT4jhp7FhFNO1hp7M1ne0nV9FMTbVuXu+ACTZPxNzgarVa0QcEpTXVajV6vd4udpr1Xbn+7qRpT18JTeEnV9dUWldpP5UMGgaNu/E8ssd929RoJKY1DwC/hFG9Ls+V/XSititoxoyaBj/CYKmQX/bnMHXcMEV0e8IZfjLm/wZAie8IRltZdm/s9IkfA2UQrctBrVKBJHXYx95tGtpsenGjjRnR63J0AanQBIEN2YrY6uxnPt2xZVzKvVNIt6P/3V3T2X5yJU1rEAmCBAKBQNDvMUSb3tJHN+13siW9R191GB9a0MkexA/pvtdBoByqgGgq1BGoJJnivb862xy7Ell/bO3LuAl2LSdx2ATaZDWBHEVXXWDXsjpDp9ORYCwETIl+eosm3LRmaKS+CNpaFLHNmQyqMSVsagvt+EJFIOgtItgUCAQCQb8nethkjLJEhLECY12Js83pFaUHTL1Oh+QYUqPDnGzNwKI60JSZVVX6u5MtsR/N1UXEyKUYZYnkcdPtWlZKbCQ5xABQvNfxSxJl7/8dL6mNZtmT+PRRvdaJiIihRvZFg5GW0r0KWugcYo8lBwpMGedkSwT9CRFsCgQCgaDfM2ZICgflOAAq9/3iZGt6R91hU6BT6JGERi1+vh2Jd8pEAOKa9tGmVzbzsKuQs/U7ALKJJT05ya5lqdUqCjxSAKjP3WHXsjqjIstUZqE6DpXGo4e9uybM15Ms4gEo3febIrY5i+LCw0RQjVGWSB012dnmCPoR4tdKIBAIBP2eQV6eHPIwLbGg2/UJ1BU72SLb0VTuA6DOVyy27mhiRp0JwEgpmx3Z7tkz3hNN2aYexjyf4ahU9n88rPdPB8DziON7BPVlpmup2ie5TzoqlYoST1Ng3lSws69mOZW83Sb/F0uR+AeFONkaQX/C6cFmZmYmiYmJeHl5MWHCBLZu3drt/i+++CKDBw/G29ubuLg45s2bR0uL+4+TFwgEAoF9CfM2JSFJqvwR+cXh8Ps7TrbINoIbswGQw8Vi645GEzmMZrzwl5o5uLv75xR3JaTGlBymLbL3cxhtwSPGNDQ5vOmQQ8o7Ht9607VkDOt7BtmmANO8Ta9j8x3dlaaCPwAo805xsiWC/oZTg82PPvqI+fPns3jxYn7//XdGjRrFjBkzqKio6HT/999/n4ULF7J48WL279/PG2+8wUcffcRDDz3kYMsFAoFA4FbUFTOhYa3loyQbMX451316OJtrCTeafhuDk0VyIIej1lDmawry2wq2OdkY5dE31ZGoN83Xixo1zSFlRg2egFGWCJWrMR4td0iZAEajkZi2PAACE0f3WU8TPQKAiJZccOOl632OBcu6EPEyS6AsTg02ly1bxm233cZNN93E0KFDefXVV/Hx8eHNN9/sdP9NmzZx2mmncc0115CYmMjZZ5/N1Vdf3WNvqEAgEAgGNmW5u1HR/kFQhZGy3D1Ossg2zEsSFMmhZKT2beifoHeo4k4GIOzoXoxGo5OtUZbcHT+glmSK5VBGDO99whxbGJaawGE5EoAjWVscUiZAcVkFsZhe3CQMP6XPelEpo2mT1fjKjVBX1Gc9Z2A0GonRmV42BCSJ5EACZXHaOputra3s2LGDRYsWWbapVCrOPPNMNm/e3Okxp556Ku+99x5bt27l5JNPJjc3l2+++Ybrr7++y3J0Oh06nc7yub6+3rK9ra0NnU6n6CKnSmuabT++DkpgD1sHsibYx1fuUn930QThJ3u0qTvc+/Y1+BMmS6ilvwJOvaxif6MfQX0ow1FtWrJvM0nAIeI5xdejV+3iDn4C1z33QzMmwf5XGSlnsTO3jGFxIS5r64n05KfqfT8BcMhzKKEGPTqD3u52aiQ4rEkixVhK+YHfCBg+QxHdnmzN3rWJOEmmigB8fUN7fe6aNQfHx5ItR5MhFVJ5cDP+o8N7baeznvmKSstIogyAmKETrCrfXc59d7nvgfvU/3hNa5Bk2Tl9/iUlJcTExLBp0yYmTpxo2f7ggw/y888/s2VL52+5Xn75Ze6//35kWUav1zN79mxWrFjRZTmPP/44TzzxRIftCxcuxMvLq+8VEQgEAoHL0+YxiLbGMp7WvIZKAqMs8ZD+VjSDIvFoa3S2eT1ylnozp+o3867qYnJl0bPpDLzkFhbwCgAP+ixlUHPnU37ckb9J3zLSuJ+3tdeR19r7YMlWEtXFzNJ/xFbNOL41nO6QMuM9K7lJ9y47VcP4Qp6hiOZ4eSvnsYFvPWawVe9+a1QGaPXc2/IylQTxinSTs80RuAktLS0sXbqUuro6/P39u9zPaT2bvWH9+vUsWbKEV155hQkTJpCdnc3cuXN58sknefTRRzs9ZtGiRcyfP9/yub6+nri4OObOnUtpaSmDBw9WNNI/ePCgopo6nY7ly5czb948tFqtIppgH1sHsibYx1fuUn930QThJ3u0qbvc+974aR8v/3qEez0+5yfjKBKmXM/NU4f0SdNRbVqx7DTQgy5kCAtvnd+zgINsHWjXU/kLXxPRWkCstp475i50aVuPpzs/Gdta4Ll/ggTRQ07mqvOucpidn328EnI+Il4uZuHChYrpdmfrr/+8DQBd8BAW3rZQEc3P/rUQmjaQPKiZaXf2XtNZz3zfv/kEtECpNoWF862z313OfXe574H71N+sGRUVxdKlS3vc32nBZmhoKGq1mvLy9pPCy8vLiYyM7PSYRx99lOuvv55bb70VgBEjRtDY2Mjtt9/Oww8/3Gmqbq1W2+mJoNVq8fDwQKvVKtr4Smua6aoevcUetg5kzeNR0lfuUn930Twe4SdlAyN3uPfdcdZI3szJgMrPSfGsYfqMvs9Nc0ibIhOuywdAGz2i1+3hLn5y5XO/NWoc5BfgX7MXrVbr0rZ2Rmd+2vjGvZwmtQEwbedcfm0sYep1izo7XHE7w9InQA5EGsqg7jCED7F7m4a2HAZAGzuyT+fs8ZqGsAzIh8CGbEWuA0c/83nXmpIDtYQMtbpcdzn33eW+B+5T/+M1rcFpCYI8PT0ZN24c69ats2wzGo2sW7eu3bDa42lqauoQUJobzkmjgQUCgUDgRvhGmNL6RxlKwF2SvFTnoKWVRllLbOpwZ1szoAkdOhWADGMWOWU1zjVGAYpyDzCx/H3LZ7UkM+nQsxTlOmYZj/Gt2ywJXOVXTrH7ckRHm3QkGU0vbqIHj1dM1/9YVtswfSnoGhTTdQRGo5HoFlNyIP8kkelaoDxOzUY7f/58XnvtNd5++23279/PnXfeSWNjIzfdZBovfsMNN7RLIDRz5kxWrFjBhx9+yOHDh1m7di2PPvooM2fOtEvPh0AgEAj6F4FhcehkDVraMNQUONscq2jI/x2Ag3IcIxIcN59O0BHvlNMAGCXlsHGfe5w/3VGasxuV1H6bRjJSlrvX/oXXFeO37kGkY+VLyKbliOrttxzRgawDBEsNGGSJ8FTlAqvBqWlUyIGokNGXOaDtFCSvtIoUCgFIHDHJydYI+iNOnbN55ZVXUllZyWOPPUZZWRmjR49mzZo1REREAFBQUNCuJ/ORRx5BkiQeeeQRiouLCQsLY+bMmTz11FPOqoJAIBAI3IjIQG/y5QjSpWIqcncRFZLobJN6pDp7G75AriqBsf4+zjZnYBOSSqPKj0HGo5RmbYPT3bunOSplJPIGLAEfmLI0Rybbv15lubuJpP3oAhVGSnL3gjbeLmVWZu8AoFQdTayHt2K66dHBbJLjCZdqqTy4laiEvi+p4iiy924hWdLTgA++EanONkfQD3F6gqC7776bu+++u9Pv1q9f3+6zRqNh8eLFLF682AGWCQQCgaC/4aFWUaqOJl0upiZ/D1HjL3K2ST0iH+spOeIjHgSdjiRRFzKKQZUb8KrY5Wxr+kxs0mCa8MSHVsAUaG5Ie5CpyX1LnGUNB1uCO12OKEsXRKhy0+DaoS/bD0C1TzKxCupq1CpKPJNAv5uGgj8UVLY/lYe2A1DokUSGJPWwt0BgO04dRisQCAQCgaOp9jL1mrRVZDnZEusIqj8IgDYgwsmWCAD80ycDkNJ2kE82H6L8qLJr7TmS2opCfKRWjDJsOOllymZttio5kBIkpgzhIf2tGGRTgCPL8Ij+FuKTB9utTN+j2QAYQ5Uvo8Hf9DJIW+WY+a5K8O+1e9CUmYbp72wO499r9zjZIkF/pE/BpizLIjGPQCAQCNyKtoBEALzq85xqhzVIv63A31gLwKzSf9g9gYqgZ3zTTPPaTlJlseDrbG79oozX1v3pZKt6R+EBU69WsRTOpAtmEeuAHk0zCeEBJE+dxcW6/wPAgETKlGtJCAuwS3kGo5HoNlNyoAA7JMLRRI0AILL5ENQWKq6vNPkVdeSuf5sr1D8DcKX6Zw6vf5v8ijonWybob/Qq2HznnXcYMWIE3t7eeHt7M3LkSN59912lbRMIBAKBQHE8w9MACNW59gOhpqkCae0jls8qjPDVvVBnvwQqgp4p8Eg1zWuUarhAtZkIqnj2x0K3fEivLTAN0S7TxDml/DvOGsH/3XYZLbIHGknmymHKzaM8kbLaFlIxXTuxGRMU1x+vyUGWwZM2eGmky78Yyss5wBLN65b5uipJ5h+aN8h3UCZiwcDB5jmby5Yt49FHH+Xuu+/mtNNMWdk2bNjA7NmzOXLkCPPmzVPcSHtgMBja/XVlTY1Gg8FgcAtbB6qmWU9pX7lL/d1F06wn/KR8myqta08/hcQPg90QIldjaKoFrV+fNe3RptqGQiROGD0kGzAcyQbfztej7knz+L9KMBCvp30VTUhyMHHSEf7l+S8MssQi/a0cKM4gNsS3z/qO9JN0xDSU/OigRJvLU8rOkYkRHCaCVIooOrSb1PBkRXSPx2AwUFmai1ZqoxktnsEJfdZvV//6YobufuqvREuyEfmrezEmTQX/GJs0HfXMl+Z5pN18WTBlIk71qLKqbFe/Tu2tKZ7NrdeUZBvHwSYlJfHEE09www03tNv+9ttv8/jjj3P48GFb5BxGZmYmmZmZGAwGsrKy2Lx5M76+ff9REAgEAoF7UdfcRvrXFxIm1bN3ymsQPtTZJnVKbWURp62/skOm0N+mfkBgmJLpTQS2UFtZxKnrr2y3ZIi7+sXw+WxGGfbwddQ9JJ52pdPsOPr5XCYatrMu+k4iTr3OLmXsWP85s448T44mjeaLVyqqPahiB0m/3NNh++Ep/6Qx3DXXrtQ0VZD+zSWojnuhZURF1nmfovcRSywJeqahoYGJEydSV1eHv79/l/vZ3LNZWlrKqaee2mH7qaeeSmlpqa1yDmPOnDnMmTOH+vp6AgICSE1NpaysjPT0dMXW6DQHskpq6nQ6li9fzrx589BqlUvPZg9bB7Im2MdX7lJ/d9EE4Sd7tKm73PvMdo4fNYw9X0cTRj3atmqSMzL6rGmPNv2wqIE4OZwEqQIwBTQP6W9hemA6EzMSXMLWAXk9eR/pdG3KifHekNj7c8mMI/1U9UkRABFpJ5Fh43WgpJ3rv4uBo9vRtpSTnp5uF//v/SIXgDq/VEb14Zo/XtNsZ6mXhKGTzLqq+HFkJFk/D9axz3wZ7Px5MmMbfwFAltRw/jLSxpzeB0172Ol6muLZ3KSZmmpdhnSbg83U1FQ+/vhjHnrooXbbP/roI9LS0myVcxrmBler1Yo+ICitqVar0ev1drHTrO/K9XcnTXv6SmgKP7m6ptK69vZThUcs6A/QXHpQEX172BnjryFYOgrAfa13sNE4nApCmBMT0qey3MlPLqkZmgaSCuS/1oiUJTXq0FRQ+F5lTz/V11QSTg0AicPG97osJeyUAxPgKHg1Ftvl+ey1dX+S0XII1LC+wodtP+7jjrNGKKKtVqs52DCITP2tPK15HbUkI8vwkP4Wzmr0JcGGOjj6ma9Sb5ojezD0bAZf/yJSgPVDfrvSVAJX1xTP5n9pWoPNweYTTzzBlVdeyS+//GKZs7lx40bWrVvHxx9/bKucQCAQCAQOp3FQPNSBqjrH2aZ0SaymFj+pmVZZzRfG0zCiYcH0eBLC7ZOtU2AlATFw3gvIq+chYRp6yPnLevWg7kwK9m1nOFAhBxIeEe1UW3yi0qAQgltLFNfOr6jj8M/vcIfGtKzHvZrPeGh9MPmjHlXsWhocHcQdhjPIMUbzqfYJWtHwmeF05kQFKqJvL8xJ0loTpprOa4HADticjfbSSy9ly5YthIaGsmrVKlatWkVoaChbt27lb3/7mz1sFAgEAoFAUaRQ0/Af/6Z8J1vSNR61pkA4V45m0dmp/DR/kmK9MYI+Mv5mjniYArS3Au5GHnO9kw2ynep8U/BV6qRMtMcTlTTc9NdYjl6vbLItR2RdTQgPYOH0eHbKaehkD7SSnidP9XDpF0MtrXriZFNwH5Y80snWCPozNvdsAowbN4733ntPaVsEAoFAIHAIvjEZkAMR+mIwGkHVp2Wn7UJrhWkB+mw5hhumDMFDo/xwLUHvORqYQVhlCXJTtbNN6RVypSkTbZ2P7fN/lSYuZRh6WYW31Ep23iFFtQd7VXeadTVdW6NoOXecNYLTBkeS+3oUGVIB58Q0K6qvNLn5+QyVagEIF8GmwI5Y9etaX1/f7v/d/RMIBAKBwNWJSxlKq6xGSxuGmgJnm9Mp0rEhvuXaBBFouiAekcMACG913d7x7hjUYFo9wBhsXZIPe6Lx1FIuhQFQevhPRbUjk0dipH1GJyMqIpOVHyUwPD6MIpWpx7syd5fi+kpSlmOyr5JgVN6u2wMrcH+sCjaDgoKoqDBlwwsMDCQoKKjDP/N2gUAgEAhcnZToUPJk01qVFS76UDioIQ+AZv9k5xoi6JTQVNOSFslyIRW1jU62xnYi2kzz9XzjhjvZEhPVHqbrsbFU2Z5NAmLY6T/N8lGW1KgufMlucxRrvOMBaKs4aBd9pWguMQ0jrvB0r+V6BO6HVcNof/zxR4KDgwH46aef7GqQQCAQCAT2RuuhoVQdTbpcTHX+HqLGX+Rsk9pjNBDWagoGPCP6vkyDQHm8Y01DD9OkYn4prCQqpOt15lyNhqP1RMsVIEHc4HHONgeAZt84qNmFXKN8T3GRHMpY4KD/KQy+5U27JsPRB6VAM3jX59qtDCVQ15hGTjT4JjrXEEG/x6pg8/TT/1pzJykpibi4OCSp/ZAEWZYpLCxU1jqBQCAQCOxEjVccNG9DX5HlbFM6UpuPllZaZA+ikkWw6ZIEJaHDE63USlnunzA6xdkWWc3h/b8zQpKpkwcRFu38OZsAqqBEqAHvpiLFtX1aKgFoCB5p96yrvtFDoQTCdIUgy3DC87Kr4N9smj4ghbrPsoUC98TmjAhJSUlUVlZ22F5dXU1SUpIiRgkEAoFAYG9aA0zDU73q85xrSCe0lprmrWXLMYxMiHCyNYJOUamo0JoCtbay/U42xjaq83YDUKyOQWWHdQJ7g1/MYABC28qU19YfAcAjyP7Le8SkjcIgS/jRiPGo8nVRiqg2U1DvGzvUyZYI+js2Z6OVZblDryZAQ0MDXl5eihjlCAwGQ7u/rqyp0WgwGAxuYetA1TTrKe0rd6m/u2ia9YSflG9TpXUd4SfP8FQogxBdYa/LsFebVmbvIB7IJZZzg3wU0XdXP7mqJkBTQBpUHEJbl+3y9T/eT/py03zCWu8Elzn3IxKHwq8QSxn7WvSK6RqNRoKNVSCBb1jv63siXdV/cFwEhXI4iVI5Fdm/EzbqHJs0HfHMV11bTyzlAESljOpVWe5ynbrLfc+se/zf/qIpybIs97wbzJ8/H4CXXnqJ2267DR8fn3aFbtmyBbVazcaNG2212SFkZmaSmZmJwWAgKyuLzZs34+vr62yzBAKBQOAkDhSWcdmWSwHYd9H3GD0GOdmiv9D88BBDan/mDY9rmHDRHGebI+gC/Y53GH3436yVTybq8uXONsdq6r+4n1PbNrM27CaiTr/V2eYAIOmbGbbqTAA+nfApg+MiFdE92qJn2Ffn4C81s2fae0jB9h+FV/vJ35nE72xKmIP/+GvsXp6tFOTu57zfb6VJ1pJ72TqXHeorcG0aGhqYOHEidXV1+Pt3PWfd6p7NP/74AzD1bO7ZswdPT0/Ld56enowaNYr777+/Dybblzlz5jBnzhzq6+sJCAggNTWVsrIy0tPTUSs0hMQcyCqpqdPpWL58OfPmzUOr1SqiCfaxdSBrgn185S71dxdNEH6yR5u6y73vRDtDoxOo/M2fMKmeWD8jg5Jsnxtprzat+NKUJKUtMJWMDGXmbLqrn1xVE6DBeDoc/jfJciE+0QmEB/j0fFAPOMJPeZ+YhlAGJY/p9fllDzurvggiRK6h8UgB6dOnKKK7JysXf8m05uXgsaehVmiZj+7q/413PDT/jldTiU3t66hnvvL9vwCmYdQZQ3s3jNZdrlN3ue+B+9TfrJmaat2ySVYHm+YstDfddBMvvfRStxGsO2BucLVaregPj9KaarUavV5vFzvN+q5cf3fStKevhKbwk6trKq3rCD9FBvuxg2jCqKcidw+pqRP7rKkIhjZCW03BgFfMUJf2/0C/nnzjRwOQKJXxa0EJUaMHK6ZtLz+1trYSK5eCBNGDx/W5DCXtrPWMJkRXg7G2SDHd2nLTi5ujDMLHO8Ah55TBkpH2sE3lOeqZz1BpWl6mxjvepfzvLpri2fwvTWuwOUHQW2+95faBpkAgEAgEABUepoQhTSUulOClOhcP9DTKWmITlQteBHbAN4J6yRe1JFORu8fZ1lhFzoFdaCU9zbInkQlDnG1OO3R+cQB4NhYrptlQacq6WqUKVkyzJ7yjTb2Zoa0FDivTFryPJUXTBYg1fAX2x+YEQQDbt2/n448/pqCggNbW1nbfffbZZ4oYJhAIBAKBvWkclAB1oKrOcbYpFhoKduELHJJjGZkY7mxzBN0hSZR7JODf+ietpfucbY1VHMndBUCxOppUda8eA+2GOjQZjoC/TrksroZa0yiBo5pQxTR7IjptDGyHMLkaY3MtKu9Ah5VtDSE68xq+4mWWwP7Y3LP54Ycfcuqpp7J//34+//xz2tra+PPPP/nxxx8JCFBmHLxAIBAIBI5ACjXNOfFrcp0eiCM5vwOQp4ojxM/bydYIeuKobyIAPnXZzjXESlqPZaKt9nKN9TWPJyjW1NMaZihD16ZXRFNqMAWuTZ6OCzbTkxOpkAMBKMn6w2HlWoNBbyDWaOo5Dk0c4WRrBAMBm4PNJUuWsHz5cr766is8PT156aWXOHDgAFdccQXx8fH2sFEgEAgEArvgG2N6uI3QF4PR6GRrTOiPrdlYpRW/qe6AMSgFgHBdvpMtsQ6v+lwA2gJTnGxJR0LiTclqEqRysstqFdH0aqkAoM07TBE9a9B6aChSmYbol+fsdFi51lBcmIO/1IRBlohNH+VscwQDAJuDzZycHM4//3zAlIW2sbERSZKYN28e//nPfxQ3UCAQCAQCexGXMoxWWY0XrRhqXKN30/eoqYesxd/+SzQI+o53pGkoYjIFVNY1OdmangnVmc5zryhlshwriTrUFABHSjVkFykzlNav7QgAsq9jh6RXe5teFrWWHXBouT1Rlr0TgFIpHK23WAJQYH9sDjaDgoI4evQoADExMezduxeA2tpamppc/yYrEAgEAoGZlOhQ8mXTen4Vx+ayORW9jrC2EgA0oSJ5hztgHoodLVVzMPewk63pnpbWNuKMpvMrKm2Mk63pBO8gGiXTerdVhYcUkQw2VAGg9otQRM9a9Md6js09ya5CY7Ep+C33iHOyJYKBgs3B5pQpU1i7di0Al19+OXPnzuW2227j6quvZvr06YobKBAIBAKBvdB6aChRRwNQne/8bKLGyoOoMVIn+xAWFuVscwRWYPTwpVIyzQesyHGt+XknkpdzED+pGb2sIjLFBefrSRJ1WtP1qDvS9yCtsaWVcKoB8A5y7PXkcywjrTkZj6ugrjGNnGjwdb05u4L+ic1pyP71r3/R0tICwMMPP4yHhwebNm3i0ksv5ZFHHlHcQHthMBja/XVlTY1Gg8FgcAtbB6qmWU9pX7lL/d1F06wn/KR8myqt60g/1XjFQfM22ioO2VyW0nWvzv6dMEyZaBOCvV3e/wP5ejpe74h3ImFNRzCU7etzGfb0k7n3vkQVSYzKo09l2KtNW/3ioeUQHkcL+6xdUFJGhtQAgIdfuEPPqciUUbAdoo3l6Job0Xh6WaVp72c+v0bT3GJjUKrL+d+dNMWzufWakizLslKFNzc34+3tmpnzMjMzyczMxGAwkJWVxebNm/H1FWPVBQKBYKCz68cPubb6n+z3HIHhwledaot+4z8ZXfohn0tnknbpE061RWA9DT8t45SqT/naYwaJFz3mbHO6JPvHt7i4+nV2eJ6M9sLlzjanUzy2ZjK44H0+MJ7FsMseQ6WyeRCehezsg1y882aa0JJ76TqQJAUt7R693kDq5zPwlZr59ZQ3LJl2nU3wJxcRzRG+G7aMmIwJzjZH4MY0NDQwceJE6urq8Pf373I/RRZY0ul0ZGZm8uyzz1JWptzaSEoyZ84c5syZQ319PQEBAaSmplJWVkZ6ejpqtVqRMsyBrJKaOp2O5cuXM2/ePLRarSKaYB9bB7Im2MdX7lJ/d9EE4Sd7tKm73Pu6sjM7axhUQ1hbMUEZtiVNUbruuWtNyVvqByUCuLz/B/L1dLxuQNIYqPqUqLZ8Mmw8h7rStIefTlGbzq/WoFRGu6CdAC3146DgfaLlckJjEokIHNRrrcKsHQBUq0JAkhx+TmV9EUOGnI26qYKMjL/1qGnvZ774mEj8MSVMGnbKNKKie5/x2l2uU3e574H71N+smZqaatX+VgebOp2Oxx9/nLVr1+Lp6cmDDz7IxRdfzFtvvcXDDz+MWq1m3rx5vTbc0ZgbXK1WK3qTVFpTrVaj1+vtYqdZ35Xr706a9vSV0BR+cnVNpXUd6afwpBGwG0LlatA3gdavz5q9JaDBlGBGDh2sqO7xuKufXFUTIGrwybAdkuUi6hp1BPv79FnTHn4K0ZvmD3pGZiiqrWSbekWmA6blTw6U1BAd0nWPSU+01ZqSIdV7mJY9cfQ5Ve0dD03ZtJYftKpcez/zlebswR+olv2IjknoU6+xGXe5Tt3lvmfWd+X6H69pDVafZY899hgrVqwgMTGRvLw8Lr/8cm6//XaWL1/OsmXLyMvLY8GCBb02WCAQCAQCZ5CWnEy1bJpW0XToF+cZ0tpEqME0OigwYbjz7BDYjF/8cAxIBEkNHMza72xzOkVGIs5YDEB4ymjnGtMdQYkAxEhHOFRa3Scp6WgpAM1axy57Yqb1WEZabZ1rZKStztsNQLE6RpFAUyCwBqvPtP/973+88847fPLJJ3z//fcYDAb0ej27du3iqquusktkLxAIBAKBvQnL+YQgTElEvD+5Fn5/xyl26CsOoELmiOxPWop1w5MELoLGi1LVsazGuTuda0sXqD00hEj1AES74rInZvyiaMUTD8lAdUnflpLxbK4AwOAbqYRlNvNXRlrXWMPXUGlaTqZa2/vhswKBrVgdbBYVFTFu3DgAhg8fjlarZd68eUgOnGwtEAgEAoGi1BVj/HKuJW+IhIzxy7lQV+xwU8qztgGQLceSGhnk8PIFfaPKOwkAffk+J1vSOf6qZgDKpVDU3rYPFXcYkoo6rWlNzLaqvgWbvq2m+YmawOg+m9UbolLHAhBnLEHX2uoUG45HW29qT11AonMNEQworA42DQYDnp6els8ajUZkcxUIBAKBW1OWuxsVxnbbVBgpy3X8mpv1+aYhbqUe8WjUYoibu9EWbJpr6Hs0x8mWdE6YwfQCpUoT4WRLeqbFJwYAbUNRn3QCDaZg0yfUOWtKxqYOo1XW4C21knNwr1NsOB7zmp8e4YOdbIlgIGH1r5ksy9x4441ccsklXHLJJbS0tDB79mzLZ/M/W8nMzCQxMREvLy8mTJjA1q1bu92/traWOXPmEBUVhVarJT09nW+++cbmcgUCgUAgONgSjEFuP0JHL6vI0jm+Z1FTdRCABr9kh5ct6DvesSMAiGzNd7IlHdn00fNc1fopAENa/2T9e0872aLukQLiAAhuK6dZ19YrDV2bnnBMcz4DI5MUs80WVBpPSlRRAJRn73SKDRZkA9EGU8Kk4MQRzrVFMKCwOticNWsW4eHhBAQEEBAQwHXXXUd0dLTls/mfLXz00UfMnz+fxYsX8/vvvzNq1ChmzJhBRUVFp/u3trZy1llnkZeXxyeffMLBgwd57bXXiImJsalcgUAgEAgAElOG8JD+VkvAKcvwiP4WEpIdvyZeUFMeAOoI11iPT2AbsRknA6aMtDX1DU625i+Kcg8wJec5VMfeqagkmHToWYpyDzjXsG6QAmIBiJfKOVjSuyRBxRXVhEl1AARHO+8FTpWXaX5kS5lzE0fpakrwktrQyRqS0kWwKXAcVi998tZbbyle+LJly7jtttu46aabAHj11VdZvXo1b775JgsXLuyw/5tvvkl1dTWbNm3Cw8MDgMTERMXtEggEAsHAICE8gOSps7h0XSyrvBbThork068jIdy2l6d9pqWeUGMlACGJoxxbtkARAmIzaMEDb6mVPft3cfKE05xtEgClObuJleR22zSSkbLcvcQ64aWKNbT5/RVs7i6pZXSS7UN/K4tzSQZ0eKDxDYVj60s6mrbAFGjeiLaub/NP+0pDWTYABVIUab59X5pHILAWq4NNpWltbWXHjh0sWrTIsk2lUnHmmWeyefPmTo/58ssvmThxInPmzOGLL74gLCyMa665hgULFnSZDVen06HT6Syf6+vrLdvb2trQ6XSKLnKqtKbZ9uProAT2sHUga4J9fOUu9XcXTRB+skebusu9rys7b5ySztBIH/SfPIGnZOTaUf5Wl6tU3Vv3rcUPqJADSEuIHdC/Ue587her40gx5FKd8zu60Se5hK0h8UMwbJBQHxdw6mUVwXHpffKbPdu06ViCoASpguV/ljImIZiEMNvW26wtNS03UiUFE9ja6rRzyjMiHUohpKWgx/a25zOf4ViypXJNDPEK6LvLdeou9z1wn/ofr2kNkizLcs+7KU9JSQkxMTFs2rSJiRMnWrY/+OCD/Pzzz2zZsqXDMUOGDCEvL49rr72Wu+66i+zsbO666y7uueceFi9e3Gk5jz/+OE888USH7QsXLsTLy0u5CgkEAoHAbZFluE1+hxjpCK9qbqbcEOiwssfIe5jJWqRjdnwpncVOSQxzc0fOUP/GFP0m/udxMfv0rjP3dpg6h8v0XwCmQPNDr6vIaXXOciDWoJINPMQ/UWNkfEsmRwji3NBqIhqtX68yyrOO23VvsEc1hM/k8+xobfeEqOu5W/86NbIvL6pmd0hI5igmq7czTf8Ln2pmsteQ5hQbBP2LlpYWli5dSl1dHf7+Xb8MclrPZm8wGo2Eh4fzn//8B7Vazbhx4yguLua5557rMthctGgR8+fPt3yur68nLi6OuXPnUlpayuDBgxWN9A8ePKiopk6nY/ny5cybNw+tVquIJtjH1oGsCfbxlbvU3100QfjJHm3qLve+nuzc//Q3xHCECSPTGDrjFkU0e6S+BI/MsZhTFEkSzGQdZ89eyoGSoy7v/4F8PXWmu+P9xZC/iVRDFuOuuo+YRNuzftrD1jUf/AvyviBfisFw9Ydc3gu7TsSebbphx16K175PvKqSBKmcSjmINUeCWXP3OVb3cH7/n0Wgg1afKB646wGnnVNGXSPGF94gSGrgggsvZOTQrocu2/OZL2/ZNAC0McNZeM08RTTd4Tp1l/seuE/9zZpRUVEsXbq0x/2dFmyGhoaiVqspLy9vt728vJzIyM7ftkVFReHh4dGusTIyMigrK6O1tbXd0ixmtFptpyeCVqvFw8MDrVaraOMrrWmmq3r0FnvYOpA1j0dJX7lL/d1F83iEn5R9OHSHe19PdtZ5hEPbAVprCq0us691Lys6QGQnS6/UFGXhoY13C//DwLyeOtM11pcCMMa4D8P7U/g1bQFTr1vUg4r9bVXVmoZQHvEbwrjBIxXRtGebljUa8ZYjiKeSRFU52w1DMAL5VU2kx4ZZpePZbEo2afCNdO4zn1ZLmSqMSLmCP/7YSnhMYo9zwu3xzBepN2Wi9Y0bpoi2u1yn7nLfA/ep//Ga1mDzQl6NjY02G9UZnp6ejBs3jnXr1lm2GY1G1q1b125Y7fGcdtppZGdnYzT+9cOclZVFVFRUp4GmQCAQCATW0ux9LAlJXd/W9rMFV1p6RdA3inIPMLH6C8tntSS7TNbXQY2m5VgMga4ztLc7YgI8yJdN1+Opqr1EUoUKSI8KtFrDR2dKuKUKcP6KBYVqU8IjVe46rlr2Bf9e69h1fA0NVQRjyswbF+X666wK+hc2B5sRERHcfPPNbNiwoc+Fz58/n9dee423336b/fv3c+edd9LY2GjJTnvDDTe0SyB05513Ul1dzdy5c8nKymL16tUsWbKEOXPm9NkWgUAgEAxsjH7RAGibyhxWpnnpFXP2BIMs8Yj+FuKTxaLr7kZpzu52SXjgr6yvziaszdSr5ROV7mRLrCPCT8vEcAMAl6g3slF7D28O321TluhAgyn7rHdInF1stJb8ijrLWqG3ar5lg/YeDq9/m/yKOofZ0PDTMsv/kz+ZAb+/47CyBQKbg8333nuP6upqpk2bRnp6OkuXLqWkpKRXhV955ZU8//zzPPbYY4wePZqdO3eyZs0aIiJMb10KCgooLS217B8XF8d3333Htm3bGDlyJPfccw9z587tdJkUgUAgEAhswTMkAQD/1s7XerYHCeEBDDn1IqRjnZtn6F4gaeosEsIcvPSKoM9EpYzstJc6Mnm4kywyUdvQTLxsek4LT1JmCK290TRVkFz9s+WzWpKZmvMc1BVbdbzBYCRUNq3PGRjl3N7cvJwDTFL99cJBLcn8Q/MG+Y7q8a4rJmjnq5aPkmzE+OVcq9tSIOgrNgebF198MatWraK4uJjZs2fz/vvvk5CQwAUXXMBnn32GXq+3Se/uu+8mPz8fnU7Hli1bmDBhguW79evXs3Llynb7T5w4kd9++42WlhZycnJ46KGH7DKfSyAQCAQDi4BI00Opeb1LR3FRmil9QqUcwGt/v5g7zhKZaN2R2OQh/Jq2AOOxzk2jLLEh7UGnr2WZnZtNgNSEUZYIiHXNdTVPRNtQiMQJiyXIBqi2LhtteU0d4dQAEBrj3GBzsFc1qk56vNO1NQ4pvyx3d4e2VGGkLNexQ3kFAxebg00zYWFhzJ8/n927d7Ns2TJ++OEHLrvsMqKjo3nsscdoampS0k6BQCAQCOxKZIJp6Ko/jbQ1OuZBEOBI4SEAyghlcEyIw8oVKM/U6xaxNegCAH72Ot3m5ED24MhhU1BRLoWCh7eTrbEOnW8csnTCI6qkhmDrAseyosOoJZk2WY02MMoOFlpPZPJIjLTv8TaiIjLZMS+VDrYEc+Iih2JeuMCR9DrYLC8v59lnn2Xo0KEsXLiQyy67jHXr1vHCCy/w2WefcfHFFytopkAgEAgE9iU6MpJaeRAApflZDiu3sdKUKbRGY12WTYFr4xk7GgDvNsfNyesOXbnpXC5XuU9iGL1POPL5yy39cQZZwnjBcrAy2U9dWR4A1aogUDl59FtADKqZL1nqYkSF6sKXrK5LX0lMGUKBHG75rJdVPKK/hQQn97gLBg42B5ufffYZM2fOJC4ujvfff5+77rqL4uJi3nvvPc444wyuv/56vvjiC9avX28HcwUCgUAgsA9qtYoKKRSAqqJDDivXUGPKfntU2/myXwL3IiguA4AIQ2m77PnOQlObB0CV2r1eZshjrsc45CIA3tSfw+GYC60+trmqEIBadahdbLOZcbMolkwJyNanPwRjb3BY0QnhAQxS6QBY2HoLU3QvmeaF25BsSSDoCzavs3nTTTdx1VVXsXHjRsaPH9/pPtHR0Tz88MN9Ns6eGAyGdn9dWVOj0WAwGNzC1oGqadZT2lfuUn930TTrCT8p36ZK6zrLT7WaMNDn01hx2Kpylai7R4MpUUfboMgOeq7u/4F8PXWlG5k0FIBYKskvqyI+IrjPmn0hoOVY4KUKdnk/nairiRsPB74gVnWEPflHSAzzt06jzpQQqVEb1q7OzjynqrziiW0uoaGupstj7HE9GXUNhB5b9iRjyqXcPjqDhLCAPuu7Qps6U1M8m1uvKcnyiSO5u6epqQkfHx/bLXMymZmZZGZmYjAYyMrKYvPmzfj6+jrbLIFAIBC4EGWr/8GZzd+yLuBSIs6a75AytZ/fQJohhw+jH2L4qec7pEyBHZGNpH06DS1tfDp6JYNT05xmisFoxO/TK0iWStk25jm8U051mi29YVDFDpJ+uYfDxgjeHPwaV46ybk5zwepnOK/5SzYEXkTgmQ/a2UrrqPvhWU6r/YJvvC4g/gLHzeXVlR9i3K83Ui/7cOhv36HV9HoGnUDQjoaGBiZOnEhdXR3+/l2/CLK5Z1Ov11NfX99huyRJaLVaPD09bZV0CHPmzGHOnDnU19cTEBBAamoqZWVlpKenK5bN1hzIKqmp0+lYvnw58+bNQ6vVKqIJ9rF1IGuCfXzlLvV3F00QfrJHm7rLvc8aO8s2xEIz+LYeISMjQxHNnqj7xLQeYGjCUEuZ7uL/gXw9dadbsiqSOEMh6uYjZGRYP/xTaVtzSyqJw7SUz6bdOdx21nUu7acOuvFh8AskSBXUNDRbdU0CVK2qAkzLGWVkZLjEOfXHnnSohcC2si7rYY/r6UDZHwCUSGGMGDrEpa8pd9EUz+YmzdTUVKv2tznYDAwMRJKkLr+PjY3lxhtvZPHixahUrvv2xNzgarVa8aVTlNRUq9Xo9Xq72GnWd+X6u5OmPX0lNIWfXF1TaV1n+UkTHA9l4NtaYVO5vbazrZngY0PcQuPSOmi4uv/F9dS5br1XDDQWoq863OvylLC1KHc/aZKBZrTUGrzdr039I2j2DMW79QhSdTZq9TlWHRugNwWb2pD4drY5s/7+MUPgIITry7rc3x7XU1O5abmYI+pw0tzE/66uKZ7N/9K0BpujwZUrVxIdHc1DDz3EqlWrWLVqFQ899BAxMTGsWLGC22+/nZdffpmlS5fabLRAIBAIBM7E/9hamyGGCoeU11BuykTbKGtJiIt3SJkC+6MPSADAs6HAqXbUFe0DoFwdBd10FLgybWGmObCBjblWJVwyGo2EyKZgMyAy0Z6m2URE8jAAYqigvrHZYeUaa/IBqPd07hIwgoGLzT2bb7/9Ni+88AJXXHGFZdvMmTMZMWIE//73v1m3bh3x8fE89dRTPPTQQ4oaKxAIBAKBPYmIN82vC5OraWvV4eGp3BCpzijPP4gvUEooqX7ulw9B0Dme4alQAoEtJU61Q67KAaDOOx4anWpKr/GJHwXFv5BGAYcr6kiJ7H59yNqGZiKoBiA81rphfo4gICoVvazCW2ol+3AWI4aPcki52kZTAjKdj8h2LXAONvdsbtq0iTFjxnTYPmbMGDZv3gzApEmTKChw7ts8gUAgEAhsJTI2hVZZjVqSKcnPtnt59WWmYKBK5V7LUgi6JyzB1BsXaSyjubXNaXb4HDX1ahkCk5xmQ1/RRI0EYIiqgL2FVT3uX1KUh6dkwCBL+ITE2ts861F7UHHsOq8q2O+wYv11ZQDIftEOK1MgOB6bg824uDjeeOONDtvfeOMN4uLiAKiqqiIoqPs3TwKBQCAQuBpqjcay1uaRwoN2L6+t2rQsxVFteA97CtyJ4HhTsJkgVZBV1HOAZC9C20xruHpFpjvNhj4TYRp+OkQq4GBJXY+7V5eY5ihWS4Gg9rCnZTZT5WEaytpSkeuYAmWZcGM5AJ6BMY4pUyA4AZuH0T7//PNcfvnlfPvtt5Z1Nrdv386BAwf45JNPANi2bRtXXnmlspYKBAKBQOAAatShxBrKOVqeZ/eyVEdNQ9xavMV8qv6EKjAePWq0UhuFeVmMSnb8EMbaxhbi5DKQICxpJOz4yeE2KEJoOgZJgz/NVJfmAuO63b3pyLF1RdWhuNp4gSafGGjdiVSb55DydPWV+GKaH+ofLuaEC5yDzT2bF154IQcPHuS8886jurqa6upqzj33XA4cOMAFF1wAwJ133smyZcsUN1YgEAgEAnvToDUFBobaQruX5dNiGuIm+Yteh36FWsMRdQQAdcX27yHvjEN5BYRLtQD4xw11ig2KoPagfpBpGLBHdVaPuxvqTC9wGrSuFmqCMdCUOGpQU7FDyivLO5YgSg4k0FfMCRc4B5t6Ntva2jjnnHN49dVXefrpp+1lk0AgEAgETqPNNwqaQNNg/+QugW2mrLdeYQl2L0vgWI56xxLZUIK+Os8p5Vfk7gZMw0kHaf2cYoNiRAyDhkOENB/GaDR2u7SeutH0AqfV2/WGpnuFp0IeBLeVOqS86sKDJADlqnCXXo5Q0L+x6czz8PBg9+7d9rJFIBAIBAKnow405R8YpCu3b0FGI6FG03y+wKhk+5YlcDiGwEQAvBrs30PeGc2lph7VI54ulCSnl/gljgUgjXwOV3Q/b9O7pRJwzYQ4wXFDAIgylmMw9LyMS18xzw2t9RCZaAXOw+Y5m9dddx1vvPGG26+jaTAY2v11ZU2NRoPBYHALWweqpllPaV+5S/3dRdOsJ/ykfJsqretMP/mEJUAWBOkre9y3L3VvqS5mkKTHIEtExKW003AX/w/k66knXa+IFCiCoNZSm8pVylZ1XR4ATb6JbuOnrnSlSHOSoEL2FBwhMcy/y+P92kzBpmdQTActZ59TEYkZAARJDeQXFRAbG9dBU0k/SXWmlSGafaJttrUnXKVNnaUpns2t15RkWZZtKeDvf/8777zzDmlpaYwbN45Bgwa1+95V52pmZmaSmZmJwWAgKyuLzZs34+vr62yzBAKBQOBiHC07xMQNN9Ige5H9t+/RaNT2KSf/DyZuu5sSOYQjl64Sw9z6GZ6FG0jfsoB9xgSqZ75NoLdjM6NWfHYf04y/sTn2NvxOudGhZSuNuqWajK9nYpQl/pH6MZeM6brX0u9/l5AglbNhzHICU052oJXWEfnJ+YRSy7cj/0VceselBJXEsOouRul38UXE30mZfJVdyxIMPBoaGpg4cSJ1dXX4+3f9Asjmns29e/cydqxpOENWVvuJ2pIk2SrnMObMmcOcOXOor68nICCA1NRUysrKSE9PR61W5kHCHMgqqanT6Vi+fDnz5s1Dq1VucXF72DqQNcE+vnKX+ruLJgg/2aNN3eXeZ62d+qR42AC+UgsBgf7Ex3WdxbEvdd99eAMAlaowhg8bppiuPWztioF8PfWoG6aBLZAglVGuDSIjI65zETvYajAYUetLQQUxQ8YTmpzsFn7qTrfh20B8DbWo6/PJyJje6bENTTq0VAMwdPQpBMQOtputvdU8qI4k1FCLZ2sVGRkZ7b5T+noq/dQ0J9w/1lSOK9S/P2iKZ3OTZmpqqlX72xxs/vSTm6bOPgFzg6vVakVvkkprqtVq9Hq9Xew067ty/d1J056+EprCT66uqbSuM/2kHuRPNf4EU09l4SGSEpP6rNkZuqp8AGo9wro81tX9L66nbnRDkjCgYpCko6Q4H/XIxL5rWkleRS2JkikJTVT6GIxu5qfOdBsCBuNbvQXvuuwuyystL2GI1AZAYFQiqhP2c4X613tFQ+MB5Oq8Dscpej0ZDYQbK0GCkLjBvbLVGgaipng2/0vTGno9Zic7O5vvvvuO5mbT+j02jsYVCAQCgcBlqVKblk2oLztstzKketPyB81eInlHv0SjpVZjOo+OlmY7tOjD2QfwllppQ41HSP9IPqWOGg5A2LGMtJ1RXWxKiFOPDyVFBQ6zzRZa/UwjJbR2ThxVW3oYT0lPm6wmLmmwXcsSCLrD5mCzqqqK6dOnk56eznnnnUdpqenN2S233MJ9992nuIECgUAgEDiao56mZRPaauz3wKptMv1+Gl0wa6ZAGRp8TJlg5Zo8h5ZbV2haX7FCHQVqmwexuSSByaYpXOlS1xlpm7e9A4A/TUS9fQrr33O9Zfo0x4L/gBb7Lq1Ueth0DpQRgr9YY1PgRGwONufNm4eHhwcFBQX4+Px18l555ZWsWbNGUeMEAoFAIHAGrT5RAKiP2u+B0L/VNJ/KM1issdlfkYNMQ7C9G4scWq6hKgeAOm/r5om6Ax4xowAYIhXwZ8GRDt8X5R7gjKNfWz6rJZlJh56lKPeAw2y0Bv+YNADCDWV2Lae+9BAAleoIu5YjEPSEzcHm999/zzPPPENsbPt1m9LS0sjPz1fMMIFAIBAInIUUaPqN826x3wNhqNG0RINfZM9zQgXuyaBIU2AR2laK3gHrKprxPmp6HtMH9o8htACEpqNHTYDURHFBToevs/7cjkpqP6VLIxkpy93rKAutIirZNBw4nBoajtbbrRz9sTnh9doou5UhEFiDzcFmY2Njux5NM9XV1YpmZBIIBAKBwFn4hCUCEHhszT6l0TfV4k8jABHx6XYpQ+B8guNNWYbjpHJyy2sdVm5oq6kn1TuqH83V02ip0pp6avWlHQPI3TnFHbbpZRWRx4I7VyEkIo4G2RuVJFOUY79AWHPUdA60+sbYrQyBwBpsDjYnT57MO++8Y/ksSRJGo5Fnn32WM844Q1HjBAKBQCBwBsHmoW5ypV16pMryTUuH1cqDiI0Wczb7K+rQFACSpDIOFFU5pMzaxhbiZNPw74jkUQ4p01E0Bw0BYFB9+4RLO3PLOLX6MwCMxzo39bKKDWkPEps8xKE29ogkUaoyDW2tKbTfEF8/nekcUIth+gInY/Os8WeffZbp06ezfft2WltbefDBB/nzzz+prq5m48aN9rBRIBAIBAKHEpFg6hGKkGrJrzhCQlS4ovpVRVnEAhVSKIHqXieGF7g6QYkA+EtNFJUWA2l2L/JgfgnjMQW2/vGu1avXV7QxI6BsDRE6U0Zalcp07Xz92ds8ojpAK55UXfYJxeWVRCYPZ6qrBZrHqPGMAl0euoqOw4GVIlRfDoBvpHVrIQoE9sLmYHP48OFkZWXxr3/9Cz8/PxoaGrjkkkuYM2cOUVHuMy7cYDC0++vKmhqNBoPB4Ba2DlRNs57SvnKX+ruLpllP+En5NlVa19l+knxCaMEDL9ooyssiNjykz5rH01SRB0C1JqzTY93F/872kzM1rdJVa2nQhBKgP0JjWTYGw+S+a/ZAac5eVJLMUXzx0QbCMd+4g5960g1KGQc7YDAF5JTWkBwZyDc7srms/l1QQf2IGwkfOonwoZ1ruco51egTB7rNqOoL2h2vlJ/0umZC5RqQICx+sMvVvz9oimdz6zUleYAskJmZmUlmZiYGg4GsrCw2b96Mr6+vs80SCAQCgYsS+OllxMql/C95CRljT1dUu/KHZZxR+ylrvc4l6oJHFNUWuBYB39xBXNNentXew3kzr7R7eTt+/oJZlc+S45FG80Ur7V6eI9E0VzJk9cUYZIl3TvqcMfEhrP7yvzxseJVGyZf8mf/D6OnvbDN7pGDjB5xX+i92aMagvfhfiuvXl2Zz6sZZNMpaDv1tLRqNWvEyBIKGhgYmTpxIXV0d/v5dX3e9WnyptraWrVu3UlFR0WFh3RtuuKE3knZnzpw5zJkzh/r6egICAkhNTaWsrIz09HTUamUuQnMgq6SmTqdj+fLlzJs3T9EETPawdSBrgn185S71dxdNEH6yR5u6y73PVjsPeEYQqyvFs7WWjIwMRTTNNH1jWrrB6B/bqba7+N8V/OQsTWt1K7anQd5e/HSlXZ5HStq6/9tXAGj2S7KU5y5+6lFXHkL9N/74U4/UUMbWYl9u0n8MEhhOu5fBoyY4zNa+aB4tGg6lEKYvI+a4c0IpP+0q3gVAqRTOiBHDXa7+7q4pns1Nmqmp1g3RtjnY/Oqrr7j22mtpaGjA398fSZIs30mS5LLB5omYG1ytVnfa+AaDgba2Nps0DQYDRqORtra2DkF4b9Hr9Wi1WvR6vWVughLYw9aBrAn28ZW71N9dNEE5P3l6enY4vqv7SV9wF02lddVqNXq93qn11/lEgg6ko8U97m+rnX4603wqdVBst8e5uv9dwU/O1uxJ1y9mCORBuKGUplY9ft7WPZz21lbfpkIAWvyT2j3ruJOfutM94pOMf+NOWkv2cvTAr0RL1dR7huN/+t9B4eu0L3Z2R1jiMPgNIuUKJNmISuNh0VLCT80VuQBUayJIPU7HVerv7pr2vJ7M+q5c/+M1rcHmYPO+++7j5ptvZsmSJZ0ugeLuyLJMWVkZtbW1vTpWlmXy8/PbBeF9tee0006jqKhIMU2zrj1sHaiaZl2lfeUu9XcXTbOuEn5SqVQkJSXh6empmG0CF8M/FmrAu0n5tTaDDKYlVQaF96N1EAWd4httSjaVKJWzv6iak9Psl9/i1e93M8lQACr4NEvPjrV7uOOsEXYrzxm0hQyBxp2E1O7iCtXvAHid/Rh4eDnZMuuJTRxMq6zGUzJQVphNZFLPPd62YKw1rbHZ6C0yXQucj83BZnFxMffcc0+/DDQBS6AZHh6Oj4+PTQ+jsiyj0+nQarWKPRwbjUaOHDlCaGiooj2b9rB1IGuCfXzlLvV3F01Qxk9Go5GSkhJKS0uJj49XzDaBa6ENTYB88Fd4rU1jm44wuRokCI23f3ZSgZMJNr1QSJDKWVNaa7dgM7+ijsM/v8MdmjwA/uHxJg+vV5E/6lEiA9wnEOuJA8Y4BgOXqn8FoMQjgeix1zjXKBvx8tKSL4WTQCnleX8qHmx6NZrWHDX4xymqKxD0BpuDzRkzZrB9+3aSk/vf21iDwWAJNENCOs882B3mXEteXl6KBpsajQYvLy/Fg01Q1taBrAn28ZW71N9dNEE5P4WFhVFSUqL4EHeB6xAUnQo7INRYicFgRK3QEiVHSg4TLsnoZA2x8f3vt1RwAkFJAIRK9RSWlgHKBhZm8nIOsETzOubbpVqS+YfmDTblXk7kmNF2KdPR5FfUkXc4Gzz+2vZd8xCmHWkgITzAeYb1gkpNJAn6UhpKDymuHdBqGo3hGZqkuLZAYCs2B5vnn38+DzzwAPv27WPEiBF4eHi0+/7CCy9UzDhHY56j2V97bQUCgXKYh88aDAYRbPZTwuLSAYiWqiiuqic+PFAR3cqCg4QD5YQQr2ByCYGL4uVPoyaIQfoamisPA2fYpZjBXtWopfYLDGgkI+naGruU5wzycg5wt2ZVu23Xq9eyKfcACeGdJwdyVY56xUDDH8jVhxXXDjdUgASBMWLkhMD52Bxs3nbbbQD83//9X4fvJEnq1ToumZmZPPfcc5SVlTFq1Cj++c9/cvLJJ/d43IcffsjVV1/NRRddxKpVq2wutyuU7EURCAT9E3Gf6P94BMdhRMJbaqWoKF+xYLO+3PRwWaUOQwzCHhi0+MYyqLYGdX2B3cqITB6JEQkVfwWcRlREJo9AZ7dSHUt/Cqjb/OKgAbwaihTVPVpbSaDUAEBsynBFtQWC3mDz63ij0djlv94Emh999BHz589n8eLF/P7774waNYoZM2ZQUVHR7XF5eXncf//9TJ7c8wLJAoFAIBDYjEZLjRQIQHVJjmKy+ipTwHFUG66YpsC1kUJSAPBrLlE0u3Y7AmL4w/Mky0dZUqO68CUIiLFPeU7AFFC3f3Q1B9TuhkeYaQh9YGuJorolOXsBqJb9CA4JVVRbIOgNTh/7tWzZMm677TZuuukmhg4dyquvvoqPjw9vvvlml8cYDAauvfZannjiiX45d1RgO1OnTuXee++16ZjHH3+c0aNH28Uea5kyZQrvv/++U23oL6xfvx5JkiyZpNesWcPo0aPt92AnGBDUeZgCwubKPMU01Q2mh8tWH/tlJRW4Fn7HMtLGyOVsPVRqt3JqDKZh2blxf0O6dw+MdY/l6KwmIAbVhS8hS6YlF9w5oA6IGQJAhLEMZLmHva2nusg0B7RcFaGYpkDQF6weRnveeefxwQcfEBBgmoC9dOlSZs+eTWBgIABVVVVMnjyZffv2WV14a2srO3bsYNGiRZZtKpWKM888k82bN3d53P/93/8RHh7OLbfcwq+//tptGTqdDp3urwEk9fX1lu1tbW3odDrLOjGtra3IsmzpqbUV81INRqNR8SQpZt2uuOmmm3jnnXe4/fbbWbFiRbvv7r77blasWMENN9zAW2+9ZVdbnanZUxudqGluW1t8rVar+fTTT7n44os71bXFji+//JLy8nKuuOKKLvd3dpvaW3P9+vVMnz6dqqoqy72kt5rmNjRfv2effTaPPvoo7777Ltdff307XfPfvgSiRqMRWZZpbW21rAd6/P2kr5jX+nV1TXvpmu/bx9+/+0pv7GzyioTWgzRUFpBVVElCmH+fNX1aTMGG7BfdZf3cxf+u4idnaNqi+2upJ2cCiaoyrnprJw+ecYSbpw5R3NZQfTlIYEw4HZ1XKJzgH1f3k1W6w66EuMlINYeRg5LAP9pST0fa2lfNsLg0jLKEn9RMXUUBXoGRivip5dgam7Wef+m5Yv3dWdMe1xO4T/2P17QGq4PN7777rp3okiVLuOKKKywPiHq9noMHD9pk7JEjRzAYDEREtH/7EhERwYEDBzo9ZsOGDbzxxhvs3LnTqjKefvppnnjiiQ7bX3rpJby82qcCHzRoEKeddhpHjhxBo7F5OmsH2traaGtrw8PDo0MiJVspLy/v9vvm5maio6P58MMPefDBB/H29gagpaWF999/n5iYGJqbmykrU369OKVobW3t9ZqFra2tNDY22lS/xsZG2trabG6T2trabo/pyVdmXnjhBS699NIeh4w7G4PBgCRJHZLg9MVfZqqrqwFTm7W0tCiu9be//Y3ly5dz1llnddjfWj91hV6vp66ujm+++YbGxsY+aQm6Zvny5U4tf7Tam+FA+NE/mfWvbxkTKhHRmNsnzRtk01Iq2WX1bFq6VAErnY+z/eTKtHkMYu9RD87UQrxUgQw891MRxTvW4tGm3L3DoNZyD6b72tqNv1OxseP80P7np+47HFwZWYY7CCKKav77+stU6v96kdUXP52izgKg0ujP0n5yf3FV+t/1ZBvWPrdZHVHJJ3Txn/jZERw9epTrr7+e1157jdBQ68ahL1q0iPnz51s+19fXExcXx9y5cyktLWXw4MGWSF+n01FUVERoaKglELWl9+P4dQErKirIy8uzfJeSktIhqO4J88N9eXk5ERER3fbueHt7c9JJJ5Gbm8umTZu49tprAXj//fdJSEggMTERb29vIiMjAVMAsWTJElauXElZWRnp6ek8/PDDXHbZZZbv77jjDn766SfKysqIj4/nzjvv5J577rGUuX79ehYuXMiff/6Jh4cHQ4cO5c033yQtLY2bb76Z2tpaPv/8c8v+8+bNY9euXfz4448ATJs2jWHDhqHRaPjvf//LiBEjWLduHXv37uXBBx9kw4YNDBo0iGnTpvHiiy8SFhYGmILEu+66i88//xw/Pz/uu+8+PD09GTRokKV+nfHMM8/w4osv0tTUxCWXXEJERAQeHh6WY7Zt28bDDz/Mzp07aWtrY/To0bzwwguMHTsWwDJk+5ZbbgEgISGB3NxccnJyuO+++9iyZQsNDQ0MHTqUp556ijPPPLNLWyorK9m4cSOvvPKKpfy8vDxSUlLYsWOHZXhvTU0NoaGh/PDDD5xxxhmWnsDvv/+eRYsWsW/fPkaPHs0bb7zB4MGDLfpfffUV//jHP9izZw++vr5MmjSJzz77DDAFZffccw/ffvstOp2OKVOm8NJLL5GWZspat3LlSubPn8/KlSt56KGHyMrKIisri2nTpnHzzTdz6NAhvvjiC/72t7/x1ltvsWHDBh566CF27NhBaGgoF198MUuWLGHQoEGA6bpavHgxH3zwARUVFcTFxbFgwQKmT5/O5ZdfDsDQoUMBLL3vRqORZ555htdee43y8vIO5yfAN998w/z58yksLOSUU06x9F5GRERYXoJdc801PPzwwzQ2NpKSYpozJcuyVddUT7S0tNDQ0MDtt9+ORqPh4MGD7e4nfcVgMLiFpr10dTody5cvZ968eWgVythqq535lfXsf/UG0MD56q2co9rGwzW3cvWdCyw9nDbXXZYxPJ0JEkw68yLSR5zU6W7u4n9X8JOzNK3VXbenkK8+2QFAlFRNAqXkE8VpMy5m2vCO6yD21tbf9+cQuuppAG6e9zhofS3fuYuf7KXrqpr7lq4hSq5m/JB4Rl4wWxE//fn8OQD4xmaw8KoFitl6IgNZ0x7XE7hP/c2aUVFRVr3Q6Hv3XR8IDQ1FrVZ36GEoLy/vNGjIyckhLy+PmTNnWraZA0Hzw575gdKMVqvt9ETQarV4eHig1WotjS/LsqUHxxzoGQwGNm3a1LeKHrM9J8e2BBOTJk2yPAx31rN0PJIkIUkSN998M2+//bblwXvlypXcdNNNlvlsZo0lS5bwwQcfsGLFCtLT0/nll1+44YYbiIiI4PTTT8dgMBAXF8f//vc/QkJC2LRpE7fffjvR0dFcccUV6PV6LrnkEm677TY++OADWltb2bJli6XtzPYcb7O5Lsdve+edd7jzzjvZuHEjYHoZcOaZZ3LrrbdaAsMHH3yQq6++2hKkLliwgF9++YUvvviC8PBwHnroIX7//XdGjx7dZRt9/PHHPPHEE2RmZnLaaafx1ltvsWLFCpKTky3HNDY2cuONN3LSSSchyzIvvPACF1xwAYcOHcLPz49t27YRHh7OW2+9xTnnnINarUalUtHU1MT555/PP/7xD+rr61mzZg0XXXQRBw8eJD6+81yTmzZtwsfHh2HDhlnKP/5vV9vMnx999FFeeOEFwsLCmD17NrfeequlDVevXs2ll17Kww8/zDvvvENrayvffPON5dhbbrmFrKwsvvjiCwICAliwYAEXXHAB+/btw8PDw1Kn5557jtdff52QkBDL9fjCCy/w2GOP8fjjjwNw+PBhzjvvPJ588klWrFhBfX09f//737nnnnssQ7ZvvPFGNm/ezMsvv8yoUaM4fPgwR44cISEhgU8//ZRLL72UgwcP4u/vj7e3NyqViqeffpr33nuPl19+mWHDhvHrr7+2Oz8LCwu57LLLmDNnDrfffjvbt2/nvvvu69B+iYmJREREsHHjRkswbb5n9HRN9YT5PPf09LSMXjj+ftJXDAaDW2jaUxe6vof3BlvtLC3MZab6rykdlnULiy4nPXZCrzRrK0sIlEyjhBLSh3dZN3fyPzjXT87StFZ3WHwY56i3IcsgSfCj9n4e1t/K0LhJnbZZb22tLTU9Y9RK/gT6d75WuKv7yV66rqpZq42Cln0Yqg+380tf/BTUZnqm9o1ItWi4av3dVdOMktcTuE/9j9e0BquDTXPwcOK2vuDp6cm4ceNYt26dZQ6c0Whk3bp13H333R32HzJkCHv27Gm37ZFHHuHo0aO89NJLxMV1fEM40LjuuutYtGgR+fn5AGzcuJEPP/yQ9evXW/bR6XQ8/fTTrF69mtNPPx1JkkhOTmbDhg38+9//5vTTT8fDw6Pd8OOkpCQ2b97Mxx9/zBVXXEF9fT11dXVccMEFlgB/yJAhNg+FTEtL49lnn7V8/sc//sGYMWNYsmQJYHoBYA6Is7KyiI6O5o033uC9995j+vTpALz99tvExsZ2W86LL77ILbfcwi233IIsyzz++OP8/PPP7eydNm1au2P+85//EBgYyM8//8wFF1xg6VkNDAxs9zJk1KhRjBo1CqPRSFlZGf/3f//HqlWr+PLLLzs9jwHy8/OJiIjodbDz1FNPcfrppwOwcOFCzj//fFpaWvDy8uKpp57iqquuaue/UaNGAXDo0CG+/PJLfvzxRyZPnowkSfz3v/8lLi6OVatWWXoa29raeOWVVyzHHd9G5qAO4NZbb+Xaa6/l3nvvtZT/8ssvc/rpp7NixQoKCgr4+OOPWbt2raWn9/ikXsHBwQCEh4dbeiN1Oh1Llixh7dq1jBkzBi8vL1JSUtqdnytWrCAlJYUXXngBgMGDB7Nnzx6eeeaZDm0VHR1tuR4EAlsY7FWNSuFlFkrzDhAIHJEDCPV3r0XoBb0jQdvA0x5vYH5iUksySzzeQKWdByh3DjSXZwNQpYkiUDFVgT1pGRQHLaBRaEkco8FApFwJEoQmdD4nWCBwNDYNo73xxhstUWxLSwuzZ89uN1SuN8yfP59Zs2Zx0kkncfLJJ/Piiy/S2NjITTfdBJiG1cXExPD000/j5eXF8OHt1wwyP6CeuF0pVCoVkyZNsmpfWZZpaWlBkiS2b9/e4fvx48fb9AZEpVLZPFw5LCyM888/n5UrVyLLMueff36HIcfZ2dk0NTVxwQUXtNve2trKmDFjLJ8zMzN58803KSgooLm5mdbWVsvwzuDgYG688UZmzJjBWWedxZlnnsnll19OUFCQTfaOGzeu3eddu3bx008/4evr22HfnJwcix0TJvy1eHNwcHC7IaSdsX//fmbPnt1u2ymnnNIuCC8vL+eRRx5h/fr1VFRUYDAYaGpqoqCg+x+BhoYGHn/8cVavXk1JSQkGg4Hm5uZuj2tubu4wZ9gWRo4cafl/VJQpo2VFRQXx8fHs3LnTsh7uiezfvx+NRsP48eMt20JCQhg8eDD79++3bPP09GxXhpmTTmo/5G/Xrl3s3r2b//73v5Zt5qHnhw8fZs+ePajVaktgbA3m8/Pss89ut/3483P//v3tzgGAiRMndqrn7e1NU1OT1eULBGbMyyyo+GsqRV+XWag71vtUqQpFLEowQKjOaXcOAabP1bmKZlGVavMAaPDp/uWrwHWQghKgCnybixXRKy/OI0pqxSBLxCRlKKIpEPQVq4PNWbNmtft83XXXddjnhhtsT7F95ZVXUllZyWOPPUZZWRmjR49mzZo1lvmNBQUFfRrq1lckSbK621mWZdRqNV5eXpaeODPp6en4+PjYXH5v5sbefPPNlh61zMzMDt83NJgW+/3ss89ISkpq10NtDoY//PBD7r//fl544QUmTpyIn58fzz33HFu2bLHs+9Zbb3HPPfewZs0aPvroIx555BG+/vprpkyZ0mmg3NbW1sEW88uK422bOXOmpYfq+Hmw0dHRZGdn29we1jJr1iyqqqp46aWXSEhIQKvVMnHiRFpbW7s97v7772ft2rU8++yzBAYGEhcXxxVXXNHtcaGhodTUtO8dMZ/nx7dbZ20GtEs4ZfafeXioOTlUX/D29u505EJn/rrjjjv4+9//bvGT+bj4+Phe+ct8fn799deEhoa20+zNcJXq6mpLr7RAYBPmZRa+/DsSYETq8zILuipTL7t5SRXBACA4BSQVyO1fWqiClV26zbvJFLAYAxIU1RXYD5/INMiGKH0hRbkHCItJ6pNeWd4+ooAKKYQor74/CwgESmB1sGmef2UP7r777i6HGx7f89QZK1euVN4gBYiKiiI4OJjm5ma8vb0VHdPdE+eccw6tra1IksSMGTM6fD906FC0Wi2FhYWcddZZnQYVGzdu5NRTT+Wuu+6ybOtszumYMWMYM2YMixYtYuLEiXz00UdMmTKFsLAw9u7d227fnTt39piVd+zYsXz66ackJiai0WgsvcVeXl5IkkRKSgoeHh5s2bLFMh+ypqaGrKysbnvPMjIy2LJlS7sXIscHzuY6v/LKK5x33nkAFBYWcuTIkXb7eHh4YDAYOhx344038re//Y2ysjJ8fX3bJYfqjDFjxlBWVkZNTY2lN9gcEJWWllp68KzNunw8I0eOZN26dZbRAceTkZGBXq9n27ZtTJ06FTAtW3Tw4EFLkh5bGDt2LPv27SM1NbWdn8yMGDECo9HIzz//3GnCJHM22+Pb1Hx+FhQUMGHChA6a5np8+eWX7bb99ttvHfRbWlrIyclp12MvENjE2Bso2vUjcfmfs5rJzOzjuoVSvSkgaPEWa2wOGAJiYOZL8NVckI3IMnwWOZfLFF4bMrjVtKSONiKlhz0FrkJTjmlOeACN+L59Cr+kPNAnvaOlphe8R9QRiDuMwFVwXpfhAECr1RIYGOjQQBNMa0Hu37+fffv2ddora87gumDBAt5++21ycnL4/fff+ec//8nbb78NmOZSbt++ne+++46srCweffRRtm3bZtE4fPgwixYtYvPmzeTn5/P9999z6NAhy3DWadOmsX37dt555x0OHTrE4sWLOwSfnTFnzhyqq6u5+uqr2bZtGzk5Oaxdu5abb74Zg8GAr68vt9xyCw888AA//vgje/fu5cYbb+yx93vu3Lm8+eabvPXWW2RlZfHkk0/y559/ttsnLS2Nd999l/3797NlyxauvfbaDr2EiYmJrFu3zhIomo/77LPP2LlzJ3/++SfXXnttjxmMx4wZQ2hoqCWpD5h6E0855RSWLl3K/v37+fnnn3n00Ud7bLMTMWd+Xbx4Mfv37283lzEtLY2LLrqIOXPmsGHDBnbt2sV1111HTEwMF110kc1lLViwgE2bNnH33Xeza9cuS6Za88ujxMREZs2axc0338yqVas4fPgw69ev5+OPPwZMGX0lSeLrr7+msrKShoYG/Pz8uP/++5k/fz7vvfdep+fn7NmzOXToEA888AAHDx7k/fff7/TF02+//WbpoRYIekvQCNNLu3hjEWU1DX3S0jaZlk0y+rvfIvSCPjD2BphtSjYoSfBl82hF5XVteqJkU2KYkHgxfNIdKMo9wFklr1g+qyWZyTnP460xdHNU9xhKdwPQ6BHYV/MEAsUQwWY/xd/fH39//y6/f/LJJ1m4cCFLly4lIyODc845h9WrV5OUZBrCcccdd3DJJZdw5ZVXMmHCBKqqqtr1cvr4+HDgwAEuvfRS0tPTuf3227nrrru49dZbAZgxYwaPPvooDz74IOPHj+fo0aNWDbOOjo5m48aNGAwGzj77bEaOHMmDDz5IQECAJaB87rnnmDx5MjNnzuTMM89k0qRJHeZ+nsiVV15pseekk06isLCwwxzON954g5qaGsaOHcv111/PPffcQ3h4+6FuL7zwAmvXriUuLs7SW7Zs2TKCgoKYNGmSZR6rebmUrlCr1dx0003t5joCvPnmm+j1esaNG8e9997Lk08+2WObncjUqVP53//+x5dffsno0aOZNm0aW7dubVfGmDFjmDlzJhMnTkSWZb755pterQU7cuRIfv75Zw4dOsRZZ53F2LFjeeyxx4iOjrbss2LFCi677DLuuusuhgwZwm233WZZlzImJoYnnniChQsXEhERYQlSn3zySR555BGef/55hg4d2uH8jI+P59NPP2XVqlWMGjWKV1991ZJU6ng++OADrr322l4NYRcIzPimnApAhpTPtoN9SzYVrDP1bEoeYojbgCMig2ZfUyJDbe1Bq5dVs4b8siNEYxqJE5Zg+ygVgeMpzdmNupMEZH6q3uVAWf/e00ytMS1xdnLzRta/93SfbRQIFEEeYNTV1cmAXF1dLe/Zs0fW6/WW75qbm+V9+/bJzc3NvdI2Go1yU1OTbDQalTJXNhgMcnFxsWwwGBTTlGX72DqQNWXZdl+VlpbKwcHBcl5eXpf7uEv9XVGzsrJSDg4OlnNzc9ttV+qaOv5+odfrO9xP+oq7aNpLt6WlRX788cfllpYWxTR7bafRKNc9ESfLi/3l/6x8q9eaP727RDY+5i/Li/1lw2MB8k/vLlHe1m7o935ysGZvdFs/uF6WF/vLTz90u1xQWaeIpizL8k8//yjLi/3lxsVhstzJPdNd/GQvXVfULMzZL+sfC5Dlxf6Wf22PBcrP/ONRm/3UlVZhzn5FbO2Mgaxpj+tJlt2n/mbN6upqGZDr6jq/l5lx6jqbzsQ8R+z4uWIGgwFZli3/bMV8TG+O7U5TkqRe29Sd7vF/haYyurb4KiIigtdff538/Pwu1+N0l/q7oubhw4fJzMwkMTGxnYZS15T5eIPB0G5dXqXo7B7lipr20jUYDGg0GgwGg2K6fbGz0n8Y/rWb0JTv7PC7YY1m0eEDTD70DObpxypJZtKhZ8nPvpDYpI5LFLiL/13NT47U7I2uOno0HPiC4ao8duSUEx00qMM+vbG1vuQQAJXqSGI76TF1Fz/ZS9cVNaMS0vg19UGmZD+LSpKRZfg15T7aCiWb/VSSvYvYTnpJS3J2E5WQ5pL1d3dNpa8ns+7xf/uLpiQr/cTtomRmZpKZmYnBYCArK4vNmzd3WF7DaDQiy7IlE6lAIBB0hU6nIz8/H0mSnJoxW+AYjNteY2T+Sr6RJxJ76bM2+7z4zw3M2L+gw/bvhz5L9NDTlDJT4OIMKt9G0q/3kmeM4NXUf3PdWGUyZe//YSWX177GTq+T0VywXBFNgWPYvf0XrslbRAseZF/yA6hs7weqrSxi4vqr2g3L1csqfpv6AYFhYikcgX1oaGhg4sSJ1NXVdTt1b8D0bM6ZM4c5c+ZQX19PQEAAqamplJWVkZ6ebkmi09LSQn5+PlqttldrIMrHLdPRWYbX3mA0GqmoqCA8PFzRB1p72DqQNcE+vnKX+ruLJijrJw8PDxISEvDw8CArK6vd/aSvmF+MubqmvXR1Oh3Lly9n3rx5ir3864udrerzIX8lI+RsmkOiSYkMsknTz0vCuA9Ux53KelnF4LGTuuzZdAf/u5qfHKnZK92ECPj1XhJV5VQfbSAjY4oithasNiUHMgQkMiKjY4Igd/GTvXRdWXNQSBQNrz6Br9RCjE8rr3y02nY/ZWSwYfvZnN74HWC6t2xIfYDJU85S1NbjGcia9riewH3qb9ZMTU21av8BE2yeiLnB1Wp1u/9LkmT511v6evyJWuZhf0o+cB+vr7TuQNW0p6+Epuv5yXz8ifcQJR+63ElTaV21Wo1er3eZ+nsnTcCIRJyqkk8OZJEec6pNmgmpwzikSiBNNiUY0ssqNqQ9yNTUYYrb2hP92U/O0LRJ1y+MJp9ofJpK8Kg6iFo9s++aQICuxHRMaFKnx7ibn+yl64qaCRFB/EEi4zhA+cEtvfaTXm1KhLfd82Qir/onU5M7vsRyxfq7o6Y9ryezvivX/3hNaxBjvwQCgUAg6AmvAMo9TMPR6rO39LBzJ8gyoXI1AD/EzaVs1mamXrdISQsFboIqajQA0bocahtb+qxnNBoJN5iW1PGPHtxnPYFjUalUlHiZ1kZtKfij1zphDQcAaE05m9hOAk2BwFmIYFMgEAgEAiuoDx4JgE/VHpuPLc76gyCO0iJ7MOHKB8TD4ADGK8G0VNdwVR478yr7rFdV30wsFQBEJHXfUy5wTZqDTcvV+Nbu793xLS2kGHIBiB42WTG7BAIlEMGmQCAQCARW4JM8AYC4loPoDbatkVi0ax0AB1Wp+Pn6KW6bwI2IHg3AcOkwuwuq+ixXcPgQ3lIrelnFoIiUPusJHI9vomlt7hhdDvQib+fBnb8xSNLRKHsRP6T7dccFAkcjgk2BQCAQCKwgavhUAEZIOezNL7fpWKloKwDl/sOVNkvgbhwbRpuiKiW3yLbzqDOqCk29YZWqUFB79FlP4HhSh42nWfbEGx0h1Nh8/JGszQAc9khBpRHngMC1EMGmQCAQCARWoIkcRgta/KVmsvZut+nY6KOmobdS3Mn2ME3gTgwKpdErwvT/yn19ltNV5gBQ4xnVZy2Bc0iNDuEACQCEeDTafLy6fBcAtQEdMxELBM5GBJuCfsHUqVO59957bTrm8ccfZ/To0Xaxx1qmTJnC+++/b/ksSRKrVq3qcv+8vDwkSWLnzp32N26AkZiYyIsvvghAa2sriYmJbN9uW0Ah6OeoNZR4pwGgy7f+3GipKSNWLgUgbvQ0u5gmcC+MEab5v2FN2eja9H3SUtUVANDkI9ZTdFdUKhVFnqZlJMKxfWh1ZNNBADzjxRBageshgs1+wo033ogkScyePbvDd3PmzEGSJG688UbHG9bP6CkYtIUvv/yS8vJyrrrqKquPiYuLo7S0lOHDxVA8aB8gKomnpyf3338/CxYsUFxb4N7owkcDEFC71+pjDu/4HoBsOYb05GR7mCVwMwYljgcgQ8pjf1Hf5m36NhcDIAcl9tUsgRNpCjIlDYs0lNh0XG1dHSlG05JKCSNPV9wugaCviGDTjpTWNbMp5wildc0OKS8uLo4PP/yQ5ua/ymtpaeH9998nPj7eITb0hdbWVmeb4FBefvllbrrpJlQq6y9DtVpNZGQkGo37LJHb1tbWYZs7+Praa69lw4YN/Pnnn842ReBCBKWb1tdMaTtEs67jud0ZDdkbATjsNdSm613Qf1HFjAFghHSYP/L6FmyGtJl6zb0jrFtgXeCaeMebkgQlGPNBtj4B2cHff8VTMlCLHxGJQ+1lnkDQawbsr57BYLD8Pf6fLMvt/hmNRhp1bVb/a2o10Khr451Nhzlt6Y9c89oWTlv6I+9sOmyTTqOuDaPRaFl8/kS7TvwHMHbsWOLi4vj0008t2z/99FPi4+MZM8b0w2bebjAYeO655/6fvfuOr+n+/wD+OtmJLJHIICRC7Ei0NaoSJQQ1qzVqhSiKrxHUKBKj9vZDlxFaVUpVUVuoNEZKzBAJIUbETET2vef3R3pPc3Oz7824yev5eHhw1vvz+Zz3ucf93HPO56BOnTowNjZGs2bNsHv3bml5ZmYmhg8fDmdnZxgbG6N+/fpYvXq1UpmnTp1CixYtUKVKFVhaWuKDDz7AgwcPIIoifH190atXL6X1J0yYgHbt2knT7dq1w9ixYzFhwgRYW1vDx8cHoiji2rVr6NKlC0xNTWFnZwc/Pz88e/ZM2i4pKQlDhgyBqakp7O3tsXz5cqW25fVn0aJFsLW1hbm5OUaPHo3U1FSl7S5cuICOHTvC2toaFhYW8PLywj///CMtd3JyAgD07t0bgiDAyckJoigiKioKPXv2hL29PerVq4eWLVvi2LFj+dYlPj4eJ0+eRLdu3VTy+PjxY3Tp0gXGxsZwcXHBb7/9Ji2/d+8eBEHA5cuXNZKn3P7ExsZiwIABsLKyQpUqVfDuu+/i3Llz0vINGzbAxcUFBgYGqF+/PrZt2ybVXXG8btiwAT169ECVKlWwYMECBAQEwN3dHd9//z2cnZ1hZGQEURTx6tUr+Pn5wcbGBubm5mjfvj3Cw8OV9sf+/fvx3nvvwcjICNbW1ujdu7d0/Ny/fx+TJk2CIAhKn5O//voLbdu2hbGxMRwdHfG///0PSUlJ0vIXL16gR48eMDY2hrOzM3788UeVY8jS0hJt2rTBzz//nG8uFeeO3M4n6v7RlpglFVdPT6/c1bNagw8AAPWFWIRF3C1UTIsX4QCAZBuPMt+nlSVPpRWz2HFts+5OcREeIzI2rtgxk1PT4CBmDTJUrVaDCpEnbcq/JmPWatAcqaI+TJGCjGd3Cr3d66hzAID7BvUgk8u1tv3aFrMkPk/a1P7sfamCaM/lETWtX78e69evl3ZMVFQUTE1NERkZKa2j6NylpaVJ85LTZXhn0Wm1ypaLwJz9NzFnf9EGAvhnhhdMDHRhaWlZ4JUgRfIHDx6MzZs3o0+fPgCATZs2YdCgQThz5gxkMpnUwVqyZAl27tyJNWvWoG7dujh79iwGDx4MCwsLtG3bFhkZGbCzs8P27dtRrVo1nDt3DuPGjYO1tTX69OmDzMxM9O7dG8OGDcOWLVuQnp6OsLAwCIKAtLQ0qT6K8hR1lMvl0jy5XI5t27bh888/x4kTWa8FiIuLQ4cOHTB06FAsWrQIKSkpmD17Nvr27Ys///wTAODv74/g4GDs2rULNjY2CAgIwKVLl9CkSROl8rLbs2cP5s6di1WrVuH999/Hjh07sHHjRjg5OUnbvHjxAgMGDMCyZcsgiiLWrFmDrl274tq1azAzM8OZM2dQu3ZtfPvtt+jYsSN0dXWRmpqKFy9ewNvbG7Nnz4ahoSF++ukn9OjRA1euXIGjo2Ou9Tl58iRMTEzg7OysUuc5c+Zg3rx5WLJkCXbs2IEhQ4agYcOGaNCggXRspqWlITU1Ve085ZSUlAQvLy84ODhg9+7dsLW1RXh4OFJTU5Gamorff/8dEydOxNKlS9G+fXv8+eefGD58OKpXrw4vLy8p5ty5czFv3jwsXrwYenp6CAoKQlRUFHbv3o0dO3ZI++6TTz6BkZERfvvtN1hYWGDTpk3w9vbGlStXYGVlhT///BN9+/bFl19+ie+++w7p6ek4cuQIUlNT8dNPP6Fly5YYPnw4hg0bBiDrSv7du3fRpUsXBAQEYMOGDXj+/DkmTZqEL774At999x0AYMqUKXjy5AkOHz4MPT09TJkyBfHx8cjMzFTKR/PmzXHmzJlcj6u0tDRkZGQgOjpaulqV/XyiKdoSsyTi9uzZE3fv3tVoTED9etoLVVENrxD1zwlU1fPMN6YgS0O9jCgAgMy6ASIiivYePW3If3nNU2nFLG7c2npWMMt8iYwn1xARUb1YMeOev4C3kAQAeJGsgxf5HF/alKeSiluuY8rkuC3WQjMhGpHnj8CgQeGuBxk+uwoAeFmlboHnl3Ldfi2LWVKfJ0A72g9k9aUKo9J0NseOHYuxY8ciMTERFhYWqFu3LuLi4uDq6gpdXV0AWV9U79+/D0NDQxgZGQEA5DrqPbivDiMjQxjo6SA+Ph7Vq1fP9/YrXV1d6OrqwtfXF3PmzMHTp1m/dIaGhuKXX35BSEgIdHV1YWRkhLS0NCxbtgwHDx6Ep6cnBEFAw4YNcf78eWzduhUdO3aEkZERvv76ayl+gwYNEBYWhn379mHgwIF4+fIlEhIS0LNnTzRqlHXbRrNmzZCWlgZDQ0OpPor9qKijjo6ONE9HRwf16tXDihUrpHUWLFgADw8PLF26FEDWlaaNGzfC1dUVDx48gIODA4KCgrB9+3Z06dIFALB9+3Y4OjqqlJfdhg0bMHz4cIwePRqiKCIwMBCnT59GamqqtE3nzp2Vttm0aROqVq2K8+fPo1u3blLH0cbGRrrKCQAtWrRAixYtIJfLER8fj6+//hoHDhzAkSNHMG7cuFzr8+TJE9ja2sLExERl2SeffIIvvvgCALBw4UKcPHkS3333HTZs2ABDQ0MAkI5RdfMkCIJS2du2bcPz589x8eJFWFlZAQAaN/7vJeHr1q3D0KFDMWHCBABA06ZN8c8//2Dt2rXw8vKS6jdgwACMHDlS2k5PTw/p6en48ccfYWNjAwA4e/YswsLC8PTpU2m7VatW4cCBAzhw4AA+//xzLF26FP369VNqY4sWWaN5Ojg4QE9PD1WrVlXKx8qVK/HZZ59hypQpSvVu164dvvvuO8TExODo0aMIDQ1Fy5ZZ703cvHkzGjVqBD09PaVjSHGnQF7Hlb6+PmrXrg19fX1ERkYqnU/UJZPJtCJmScVNS0vDqlWrMGnSJOn4UJem6hlp1gjVEkNgknAbrq4j8o357PpJGCATz0VzeH3oA0tT41Kta0nHLM95KumY6sZN/NsNeBwMq7fRqF9/jPR/fFFivgw+lPU3LNDQ/b0819OWPJVUXG2JeegPFzTLjIb+62g0bFi4kWVjfs0ajdjCtU2e22hL+7UlZkl8ngDtab8iZt26hbt1v9J0NnNS7HBFp0jxb8XteIov4SYGerg5z6dQMUVRRGpqGl6niei46gzk2d7LqyMAx/29YGeR+5fW3Bjr60q36mWvU36qV6+Ojz76CEFBQRBFER999JH05R7IGuAmOjoaycnJ6Natm9K26enp8PDwkMpZv349Nm/ejAcPHiAlJQXp6elwd3eHIAioVq0afH190blzZ3Ts2BHe3t749NNPUbVqVaV65vbv7PPeeecdpemrV6/i1KlTMDNTfen53bt3kZqaivT0dLRq1Urarlq1aqhfv36++ygiIgKjR49WWt6qVSsEBwdL854+fYpZs2YhODgY8fHxkMlkSE5ORmxsrEo7sk8nJSUhMDAQBw8exOPHjyGTyZCSkqKyXXaKTm5uy99//32l+S1btsT169eVys3+b3XylLP8K1euwMPDA9WqVctzP44cOVJpuzZt2mDNmjVSvQDgvffeU9lntWvXRvXq//16f/XqVSQlJcHa2lqpjJSUFNy9exeCIODq1asq5eWUsx1Xr17F1atXlUb5VdwSHxMTg1u3bkFPTw/vvvuutF3Dhg1haWmpEsvExATJycm5lq9YN+c5RJNfurQppqbj6urqIjMzs1y2X3RoDiSGwOZNRIG5j78eDDsAN3QbwMvCtNTrWtIxy3OeSitmceOa1nkPeBwMV/EeYl+8RR07yyLHTH6adWXlmZ4drPJZV9vyVFJxy3vMNxb1gRdHYfgiolAxn8Q9gbP4CBAAl3faF7hNeW+/tsQsyc+TIn55bn/2mIVRaTubhSUIAkwMCrebRFGEjjwTVuZGWPRxU8zcex0yUYSuIGDhx01Qx6boXzQUz60VxfDhw6UrauvXr1dZnpSUdcvN3r174ezsrPRFWvELzc6dOzFlyhSsWLECrVu3hpmZGZYtW4bz589L627ZsgXjx4/H4cOH8csvv2DWrFk4cOAAPD09oaOjo1L33AaKqVKlikrdunfvjiVLlkjtV1yFc3BwKPQl++IYOnQoXrx4gTVr1qB27dowNDRE69atC7yFecqUKTh27BiWLl0KS0tLODo6om/fvvluZ21tjVeviv7i5pzUzVNOxsaFu+pSkJx5zW1eUlIS7O3tERwcrLKupaVlseuTlJSEUaNGYfz48SrLatWqhVu3bhU61suXL5V+rCECALvGXsCtdagvj8LLN/kPAKf3+CIA4JlF09KoGmkRfces11Q0Ee7hcswzlc5mYchf3gMAvDGqocmqURnRr9EMeAHYp0YBoggUcJHhzuUzsBdEPEU12FYv/wNBUuXEzmYJ6fdeLXi62iDmeTKcrE1gb6GZL/GF0blzZ6Snp0MQBPj4qF6VbdSoEQwNDREbG4uOHTvmetUmJCQE77//PsaMGSPNi46OVlnPw8MDHh4emDFjBlq3bo1ffvkFnp6esLGxwfXryq8GCA8Ph76+fr51b968Ofbs2QMnJyfo6en9e7X4v6uALi4u0NfXx/nz56URdl+9eoXIyEh4eeU95LfiNuEhQ4ZI87J3yBRt3rBhA7p27QoAiI2NxfPnz5XW0dfXV3kgOiQkBL6+vujduzfi4uJgamqKmJiYfNvp4eGBuLg4vHr1ClWrVlVadu7cOaV6XrhwAc2bN881jrp5ysnNzQ0//PADXr58Kd1Gm13Dhg0REhKCoUOHKtVBcYtuUTRv3hxxcXHQ09NTug1WQRRFNGnSBCdPnsTw4cNzjWFgYKCSj+bNm+PmzZt53t7RoEEDZGZm4p9//pFuo719+zZev36tsu7169elwbWIFKxcW0MGATWEFzhx4wZsLVR/XAEAyOWokZz1rL5+rZalWEPSCvbNAAD1hIfY8+AJ0KpekUMYJz0EAGSas6NREdR0fQdpV/RghiTIX8ZAp5pzvusn38v6MeuhsStsS6OCRMVQaUejLQ32FsZo7VKtVDuaQNZl7YiICNy8eTPXS9xmZmaYPHkypk2bhqCgIERHR+PSpUtYt24dgoKCAAD16tVDWFgYjhw5gsjISMyePRsXL16UYty7dw8zZsxAaGgo7t+/j6NHj+LOnTuoX78+AKB9+/YICwvDtm3bcOfOHQQEBKh0PnMzduxYvHz5EgMGDMDFixcRHR2NY8eOYfjw4ZDJZDA1NYWfnx+mTp2KkydP4vr16/D19S3wdQITJkzA5s2bsWXLFkRGRmL+/Pkqr7SoV68etm/fjoiICJw/fx4DBw5UubLm5OSEEydOSB1FxXZ79+5FeHg4bty4gYEDB0Iuz3/Ycg8PD1hbWyMkJERl2e7du7F582ZERkYiICAAYWFheT77qW6echowYADs7OzQq1cvhISE4O7du9izZw9CQ0MBAFOnTsXWrVuxceNG3LlzBytXrsTevXsxefLkfNubG29vb7Ru3Rq9evXC0aNHERMTg7///htfffUVwsLCAAAzZ87Ezz//jICAAERERODatWvSVW8gKx9nzpzBo0ePpB8Gpk2bhr///hvjxo1DeHg47ty5g99//13ah/Xr18eHH36IL774AufPn8c///yDESNG5HoV9a+//kKnTp2K3Daq4AxN8Ugv68v9i8jQPFdLfxoBC/ENUkV91HX/oLRqR9rCzB5v9apCVxCR8rh4r1iySM96J6O+Dd/fWhE0crJFpJg1PsTTW38XuL7Zq6zjJs2ad05Q+cXOZgVlbm4Oc3PzPJfPnz8f06dPx+LFi9GwYUN07twZBw8ehLNz1q9oo0aNwscff4x+/fqhZcuWePHihdLVMxMTE9y6dQt9+vSBq6srRo4ciTFjxmDEiBEAAB8fH8yePRtffvkl3nvvPbx580bpal1eHBwcEBISAplMhk6dOsHNzQ1ffvklLCwspA7lsmXL0LZtW3Tv3h3e3t744IMP8M477+Qbt1+/flJ93n33XcTGxmL06NFK62zatAmvXr1C8+bNMXjwYIwfP17pGUMAWLFiBY4dOwZHR0fpitfKlStRtWpVfPDBB/D19YWPj0+eVyIVdHV1MWzYMPz0008qy+bOnYudO3fCzc0N27dvR1BQUJ5XDtXNU04GBgY4evQoqlevjq5du6Jp06ZYvHix9KNFr169sGbNGixfvhyNGzfGt99+iy1btqBdu3b5tjc3giDg0KFD8PT0xLBhw+Dq6or+/fvj/v37sLXN+o3W09MTu3btwv79++Hu7o727dvjwoULUox58+YhJiYGLi4u0u2ubm5uOH36NCIjI9G2bVt4eHhgzpw5cHBwkLZbuXIl7O3t4eXlhY8//hgjR45UyXVoaCgSEhLwySefFLltVPG9ssx6dYX4+BKevlEd2RkAYi8fBwBcgwsa1LIvtbqRlhAEpFTLGoDNPOF2kTeXy+Wwk2UNBmhRI/cfEEm7GOnr4Z6uEwDgVdSFfNeVy+WonX4HAGBZt1VJV42o+MRKJiEhQQQgvnz5Urx27ZqYmZkpLUtJSRFv3rwppqSkFCu2XC4Xk5OTRblcrqnqijKZTHz06JEok8k0FlMUS6aulTmmKBY9V0+ePBGtrKzEmJiYPNfRlvZrS0xRLHye+vbtK3799dd5Ls9+vsjMzFQ5n6hLW2KWVNzU1FQxMDBQTE1N1VhMTdZz94Y5ohhgLl6f1VRsNS1I3Hjkiso6N9b1FcUAc3H3omFlWteSjFne81SSMTUR9+2hrONo51c9xBeJyUWK+fT5C1E2x0IUA8zFpOex+a6rLXkqqbjaEjM1NVX8ZsH/RDHAXIxc+mG+6965c1sUA8xFMcBcTEl4Xup1rcwxS+LzJIra035FzJcvX4oAxISEhHzX55VNojJiZ2eHTZs24cGDB2VdFcohPT0dTZs2xaRJk8q6KlQO3Y9PwIOHsQCAxrr3cdZwPGJOb8P9+ASl9axeZb3/Ls2Wz/1S7kycsl5X0lTnHjYH31I5hvLzMPomdAQRyTBEFSsOEFRRvNDLurOnevKdrEGC8vDg2l9ZfwsOMDLPfQR5ovKAnU2iMtSrVy+0bdu2rKtBORgYGGDWrFkaG52XKpaY6FuYoLdXmtYVRCzQ24T7d7ONdJz0DHayrOfprBvwM055yDZI0PchMWi38iy+P1G45zcTHmXdehunY1fgqKWkPZJEM2SIurAQE4GEh3mulx57CQDw1IS3UFP5VmlHo1WMYJl9JEuZTCa911IsxitHFNsUZ9v8YgqCUOw65Rc3+9+MqZm4ms6VtrRfW2Iq4mkiT4rtZTKZ0svYNSW3c1R5jFlScWUyGfT09CCTyTQWV1P1rGfwHLqC8rGjJ8hRV/+FFPvVjZOwBnBbXhPvNKpX5DK1Jf/lOU8lHVMTce+nmMBCNEM14Q3qC7G4Krpg6clY1OtpB9cCYqY9yxp5/LWBfYHla0ueSiquNsU0kifjjlgTjYT7iL/1N6q9l/u4AZYJEQCATFu3QuW/JOpamWNq+vOkiJv974oSUxA1/U2unFq/fj3Wr18PmUyGyMhIhIaGwtRU+b2XcrkcoihK71gkIspLWloa7t+/D0EQChwNmSoWveR4uB76GDpQ/u/zgmU3mHSYBgg6yPxrBdyf7sU+oQPq9plXRjWl8u7SwyQ4hUyBp+41/JDZBT9kdkUcqmH+h9XgUSP/d3M/PrQQnZIP4rRFL1TrOLWUakyl4d6er9BdDMYl+wEwaKM6Gn1mpgw1fusOGyEBIe+uhYVT/oMkEpWEpKQktG7dGgkJCfkOSlpprmyOHTsWY8eORWJiIiwsLFC3bl3ExcXB1dVVGmkzNTUV9+/fh6GhIYyMjIpchiiKSEtLg6GhYa7vriwOuVyO+Ph4VK9eXaNfaEuirpU5JlAyudKW9mtLTECzedLX10ft2rWhr6+PyMhIpfOJuhQ/jJX3mCUVNy0tDatWrcKkSZM09uOf5urZENBZDfGgPwRRBhECBIho8foAYkPS4OC7FY8OZL1f81VVNzRs2LAM61qyMct3nko2pibimlgnICYk6zw0Qu9PDNM9jK8yR8DBfECBMZP3xQEA9KvXLfAY05Y8lVRcbYmpyFMtM1cgMRhmSdGok0tur129BBshARmiLt77sDd0jfJ4128J1rUyxyyJzxOgPe1XxMzrfeY5VZrOZk6KHa6rq6v0b0EQpD/Fpe72OWMpbvvT5Bfu7PE1HbeyxizJXDFm+cuTYvuc5xBNfunSppiajqurq4vMzMzy2/53fYF6HSF7HoXIZxk4d+EvDHyxDo5xx/Dm+66wT/33lQQ16qlVVrltf7ZY5TpPpRBTnbh1jFPgrHv1vziCiIX6mxCp27XAmNaZWZ1NU/uCv0RqW55KKm55j6nIk2DXFEgEbJIioaujo/JMbvytcwCAWN1aqFMl7ytKJVnXyhyzJD9Pivjluf3ZYxYG7/0iIiIqDosagNMHkJnaod/ImZhnGoDXYhWYvbgCfWQ9y9Lr2jjg0rYyriiVWy+jIeS4HVsHchgm5T0wDACkpqWjhhgPALB1blxi1aOyYePiAZkowFJ8DdzYByQ8Ulqe8TBrcKAnJq6lXzmiImJnk4iISE2G+nqY/MUozNKfqvS2AgFyyPdPUPmySAQAsHIBBOWvYqKgizTTmvlu9uDebRgKGcgQdWHjyA5HReNW1xHxomXWxK++EFc3kX60+vbYNVRPvA4AOPPKCt8eu1ZGtSQqHHY2iYiINKCqqRFGt62t8hYKHcgRd5dfCCkXFjWA7muk2yRFEfjLZSoyTarnu9mjq8EAgOdCVejoGZR0LamUWclfwU54JU0Lohzi/v8hcumHaHRmNN7ViQQATNPbiXvBQUV6PytRaWNnk+hfgiBg3759AICYmBgIgoDw8PBix9NEDCLSLi8MakAmKvc2M0UdRKZVLaMaUbnXfAgw6i/IIUAQgD0va+e7evCPi+B1YzYAwE58juAfF5VGLakUxd+7pvKjlQDANfkS2upel5bp5PaOX6Jyplx0NtevXw8nJycYGRmhZcuWuHDhQp7rfv/992jbti2qVq2KqlWrwtvbO9/1KwtfX1+lwY0Uf6KioqTlvXr1ynP7lJQUBAQEwNXVFYaGhrC2tsann36KGzeUXy4dGBioNDCKo6MjRo4ciZcvXyqt5+TkhNWrV0vTV65cQY8ePVC9enUYGRnByckJ/fr1Q3x8vMb2gSY5OjriyZMnaNKkSaHW9/X1Re/evdWKQUTaz8mlAWZmjkCmmPXfa6aog1mZfqhdp0EZ14zKNbumeGPbAgBg/+wvpGXKc13t4d1baHtnCXT+fc+rIAAf3FmKh+xsVCh30qqp/GglEwX8qddeZV09QQ5Xw1cq84nKizLvbP7yyy/w9/dHQEAALl26hGbNmsHHxyfPTkhwcDAGDBiAU6dOITQ0FI6OjujUqRMePSqHz8MkPALunSm1Z3U6d+6MJ0+eKP1xdnYucLu0tDR4e3tj8+bNWLBgASIjI3Ho0CFkZmaiZcuWOHfunNL6jRs3xpMnT/DgwQNs2bIFhw8fxhdffJFn/GfPnqFDhw6wsrLCkSNHEBERgS1btsDBwQFv375Vu93ZZWRkaCSOrq4u7OzsoKdX/AGbNRGDiLRL7eoWqNNuKDzT1qB/+ix4pq2Bc7uhqF3doqyrRuWcWfM+AIBOwgVcepSU6zpPoq9CV1AeUEhPkCPu7vUSrx+VnprOrio/Wn2VOQJNP1sEeY6v7nLowK5O07KoJlGhlHlnc+XKlfj8888xbNgwNGrUCN988w1MTEywefPmXNf/6aefMGbMGLi7u6NBgwb44YcfIJfLceLEiZKpoCgC6W+L/ufC98DqJkBQ96y/L3xf9BiiWHD9sjE0NISdnZ3Sn8IMS7x69WqEhobiwIED6Nu3L2rXro0WLVpgz549aNiwIfz8/CBmq4uenh7s7OxQo0YNeHt749NPP8WxY8fyjB8SEoKEhAT88MMP8PDwgLOzMz788EOsWrUq386wk5MT5s+fj6FDh8LU1BQ1atTA+vXrldYRBAEbN25Ejx49UKVKFXz99dcAgN9//x3NmzeHkZER6tSpg7lz5yIzM1PaLioqCl5eXjAyMkKjRo1U6p/bLbA3btxAt27dYG5uDjMzM7Rt2xbR0dEIDAxEUFAQ9u/fjxo1akBXVxfBwcG5xjh9+jRatGgBQ0ND2NvbY/r06Ur1ateuHcaPH48vv/wSVlZWsLe3x4IFC/LcR0RU/ozq2BQ/+/eEX7+++Nm/J0Z15BdBKphOwx4AgOY6UbgdE5vrOvYubpDncpu2XR3eQVOR1LYxz/VHq5p1GkCnxxqIQtZ3O1HQhU6PNVnP/hKVU2V6ySU9PR3//PMPZsyYIc3T0dGBt7c3QkNDCxUjOTkZGRkZsLKyynV5Wloa0tLSpOnExERpfkZGBtLS0qQOWXp6OkRRhFwuh1z+7y0s6W+hszj/UeEUBADGuS0Q5cChKVl/ikA+/SFEfZOsEP/WKy+iKOa7Ts7l2ad37NgBb29vNG3aVGX7CRMmYPDgwbh8+TLc3d2lTqdivZiYGBw5cgQGBgaQy+UqZcjlclSvXh2ZmZnYs2cPPvnkkyK923D58uWYMmUK5s6di2PHjmHChAmoW7cuOnbsKK0TGBiIhQsXYuXKldDT08Pp06cxZMgQrF69WuoQjh49GqIoYs6cOZDJZOjfvz/s7OwQGhqKhIQE+Pv7S+3Knn/Fvx89egRPT094eXnh+PHjMDc3R0hICNLT0+Hv74+bN28iMTERixcvho2NDapVq4bHjx+rxOjatSuGDh2KrVu34tatWxg1ahQMDQ0REBAgtScoKAiTJk1CaGgoQkNDMXz4cLRt2xadOnUq9H7LT/bca+r9ldoSUxFX8Xd+n6mCKI739PR0yOVylfOJumQymVbELKm4ivN29vO3ukpzn9pZGMHOwg5A8dqgLfnX9jyVq7iGVkiydEO111dhHf83klM+goG+vvIqlvZ4CkvYI+u2yUxRB3+5TMH7NZzzzYG25Kmk4mpLzOx58vV0xYcN7RD9NAEuthaobWOetbxxP8CxLYRX9yBWdQbMHYAC8qot7deWmCXxeQK0p/3ZYxZGmXY2nz9/DplMBltbW6X5tra2uHWrcM8fTJs2DQ4ODvD29s51+aJFizB37lyV+WvWrIGRkZHSvCpVqqBNmzZ4/vy5dOujkJEM+0LVRPOePn0qdTafPn2a77opKSk4ePAgzMzMpHkffvghvvvuO2l5Wloa4uLiVLaNjIzEe++9l+uy6tWzRsS7cOEC7OzskJSUhGvXrsHMzAxyuRypqakAgICAAKU6ymQyJCYmIi4uDk5OTvjf//6HQYMGYfTo0fDw8ECbNm3wySefwMbGJs82yWQyvPvuuxgxYgQA4JNPPsGJEyewdOlSNG3635WCHj16oEuXLtL0rFmzMGbMGPj4+AAAmjZtCn9/f3z99dcYOXIkTp8+jcjISGzfvh22trawtbXF5MmTMWjQILx+/RpxcXF49uwZgKxjNC4uDkuXLoWpqSlWrVoF/X//81eUmZT03+1Oiv318uVLlRjLli2Dvb09vvrqKwiCgFatWmHSpElYuHAhPv/8c+jo6CA9PR0NGjTAyJEjAQCdOnVCs2bNcODAAbi5ueW5r6joCvpMFSQzMxMJCQk4dOiQxm8Hp/+sWrWqrKtAhcA8aUZL0QqdAXjJL2DB+iCYpD5XWm5mKIO/8AopMMAO/c/wAhZIuZeJM4sXFyo+86QdcuYp75FJ/irpqlA+KvvnSdEHKIhWP0y2ePFi7Ny5E8HBwSodR4UZM2ZIV62ArCubjo6OmDBhAp48eYL69etLPf20tDQ8fPgQ1tbW/8UTRcin5/9yZQVRFJGWlg7DtBfQ+aYVBPG/qyaioAvxi9CsX6AKyVbfBCKyvhTb2trme3XH2NgY7dq1w4YNG6R5VapUgZ2dnbQ8LS1Nms6qaxoMDQ0BACYmJtKy7J48eQIAsLS0hJ2dHUxNTVG/fn3s27cPqamp+OmnnxAeHo6ZM2dCV1dXiqmrqwtzc3Mp5urVqzF79mycPHkSFy5cwI4dO/B///d/CA4OVuo4ZqerqwtPT09YWlrC0NAQgiDgww8/xJo1a5Tq6unpqTR969YthIWFYd26ddI8mUyG1NRUmJubIy4uDjVr1kSzZs2kfdq1a1eldio+QNbW1rCzs5Nuu3V0dMxz/yt+4VHkKmeM2NhYfPDBB7C3/+/niy5duuCrr75CZmYmatWqBQMDAzRq1EgpTzVq1MDbt29zzU9xZM+9Jq9CakNMRdzCfKYKkpqaiqSkJIwcORJ6enq4ffu20vlEXTKZTCtillTctLQ0rFq1CpMmTZLOU+qq7PuUedKCPL2+D2x8Dy10InCyRh1M+XSEtCg1IxPXlmf9X3XHrjsGDVta6LDakqeSiqstMUsiT4D2tF9bYjJPWTHt7e2xuBA/dJVpZ9Pa2hq6uroqVxiePn1a4Bfr5cuXY/HixTh+/Hi+V3wMDQ1zPRAMDQ2hr68vdYyArC+hgiBAR0cHOjrZHmfVNVPZPjeiKEJAKnQsqkHovgb4YyIgygBBF0L31RCq1y9UnOwUt/kp6pUXQRBgamoKV9fcX+6sGEFWESN7W11dXXHr1q1c49++fRsA0KBBA+jo6EAQBBgYGEjluLm54aOPPsL8+fMxb948pTJy1tnGxgb9+vVDv379sGjRInh4eGDlypUICgrKt12KOIp/A1CKa2ZmpjSdlJSEuXPn4uOPP1aJZ2JiohQjZzxF7nNOK7bLKwfZOy05jyHFv3PmILdyAcDAwEAlT6Io5pv/osiee012DLUhJlD4z1RBsn8e9PX1Vc4n6pLJZFoRsyTjAnmfw4ujsu9T5kkL8mTriuemrrBOioRBzEkYGv53184fR47iE4RDDgGuvacXa3+X9zyVVFxtiamgyTwB2tN+bYmpwDwVru1lOkCQgYEB3nnnHaXBfRSD/bRu3TrP7ZYuXYr58+fj8OHDePfdd0ujqkXXfAgw8Row9EDW382HlHWN8tS/f38cP34cV65cUZovl8uxatUqNGrUCM2aNctz+1mzZmH58uXSM4qFYWBgABcXlwJvPzx//rzS9Llz59CwYcN8t2nevDlu376NunXrqvzR0dFBw4YN8fDhQ+mqrSJuftzc3PDXX3/lOdqtgYEBZDJZvjEaNmyI0NBQpcGWQkJCYGZmhpo1C/dcMBERVWwGTbNeo9Ui7Ryi47KezZTJ5DC4nDVw4h3LD2Bkm/sPy0RE5U2Zj0br7++P77//HkFBQYiIiMAXX3yBt2/fYtiwYQCAIUOGKA0gtGTJEsyePRubN2+Gk5MT4uLiEBcXp/TcXLlhUQNwbltuRglLSEhAeHi49OfKlSuIjY3FpEmT0KJFC3Tv3h27d+/GgwcPcPHiRfTp0wcRERHYtGlTvleWWrduDTc3NyxcuDDX5QcOHMCgQYNw4MABREZG4vbt21i+fDkOHTqEnj175lvnkJAQrFy5EpGRkVi/fj12796NCRMm5LvNnDlzsG3bNsydOxc3btxAREQEdu7ciVmzZgEAvL29Ua9ePfj6+uLKlSv466+/8NVXX+Ubc9y4cUhMTET//v0RFhaGO3fuYPv27dKVXycnJ1y7dg1RUVF4/vx5rp3SMWPGIDY2Fv/73/9w69Yt/P777wgICIC/v7/GrloSEZF2q+KedVfO+zo3cOxC1itNDv51AT7yrOfz7Lt8WWZ1IyIqqjL/htuvXz8sX74cc+bMgbu7O8LDw3H48GFp0KAHDx4oXYHauHEj0tPT8cknn8De3l76s3z58rJqgtYIDg6Gh4cHPDw80Lx5c7Ru3Rpz586FkZERTp48iSFDhmDmzJmoW7cuOnfuDF1dXZw7dw6tWrUqMPakSZOwadMmPHyo+nxro0aNYGJigsmTJ8Pd3R2tWrXCrl278MMPP2Dw4MH5xvX398elS5fQvHlzLFiwACtXrpQG/smLj48PDhw4gKNHj+K9995Dq1atsGrVKtSuXRtA1u2PO3fuREpKClq0aIERI0ZIr0zJS7Vq1XDy5EkkJSXBy8sL77zzDr7//ntpsKDPP/8crq6u6Nq1K2xtbRESEqISo0aNGjh06BAuXLiAZs2aYfTo0fDz85M6wURERLCuh6cGtaAvyJB66wjkcjlen/0BhkIGYo3qw9y1bVnXkIio0MrFAEHjxo3DuHHjcl0WHBysNB0TE1PyFdJCW7duLXB59nVEUURqaqo0EJKJiQkWLFhQ4DsdAwMDERgYqDK/f//+6NevnzQoTvY81alTRxoVt6jMzc3x448/wsjIKNerq9lvSc3Ox8cn305pvXr1cObMGaWY2WM5OTmpxHZzc8ORI0dyjWdjY4MjR44gLi4OdnZ2Ss9cZufl5YULF/IeVy7n8Q4Au3btynMALCIiqngSa3jB9t52NH7zN/aFXkfXjCOAAJi2mwBo8Bl2IqKSVuZXNomIiIgoG5es17l56lzF7cPfwlpIxEtdG1R9r18ZV4yIqGjY2SQiIiIqR9IsXPBYxw6GQgYm6+wAAFyy7QPolosb0oiICo1nLSq3YmJipNt9iYiIKounSel4lmELB904GAhyiCJw9n4K6sUnoHZ1i7KuHhFRoVXazqbiNRXZX1chk8kgiqL0p6gU2xRn2/xiKt6zqOm42f9mTM3E1XSutKX92hJTEU8TeVJsL5PJpGd0C3r9TVHkdo4qjzFLKq5MJoOenh5kMpnG4nKfMk/akqdX8Y/xsc5VaZ4gALP0tuNs1ADUrNaiWDG1IU8lFVebYmo6T4q42f9mTPVjMk+FjymImv4mV06tX78e69evh0wmQ2RkJEJDQ2Fqaqq0jlwuhyiKqFWrFgdkIaJ8paam4sGDBxAEga+uISKNSo85h+Zhk1Xm//PeShjWblkGNSIiUpaUlITWrVsjISEB5ubmea5XaTqbComJibCwsMCzZ88QFxcHV1dX6OrqAsjqoUdFRaF69eqoVq1akWOLooi0tDQYGhrm+17KopDL5YiPj0f16tU1+oW2JOpamWMCJZMrbWm/tsQENJenxMREPHr0CC4uLhAEAZGRkUrnE3Upfhgr7zFLKm5aWhpWrVqFSZMmwdDQUCMxK/s+ZZ60J093w8/C9VAf6EAuzZdDB+KEK4B50d/drS15Kqm42hKzJPIEaE/7tSUm85QV087ODjY2NgV2NivtbbSKHa6rq6v076pVq+LZs2cQBAEmJiZF+pKr+HIMQKOdzYyMDKSlpZVIZxPQXF0rc0ygZHKlLe3XlpiAZvIkl8vx7NkzVKlSBQYGBpDLs74QZj+faIq2xNR0XF1dXWRmZmpN+7Vhn5ZETOapZOJmmlQHuq2CeNAfgiiDKOhCp/tqoGqtYtdPm/JUUnHLe8ySzJMifnluv7bEZJ7+i1kYlbazmRc7OzsAQHx8fJG3FUURGRkZ0NfX1+gX7oSEBCQlJWn0C3dJ1bWyxlTE1XSutKX92hJTEVcTedLR0UGtWrU0WjciIgXRYzB06nUEXt6FYFUHsCj6FU0iorLGzmYOgiDA3t4e1atXR0ZGRpG2lclkiI6ORu3atTX260F6ejoOHTqEkSNHwsDAQCMxgZKpa2WOCZRMrrSl/doSE9BcngwMDPisJhGVLIsa7GQSkVZjZzMPxbncrBiV0sjISGNfjgVBwNu3b2FoaKjx+8I1XdfKHBMomVxpS/u1JSZQcp8pIiIiIlLGn+WJiIiIiIhI49jZJCIiIiIiIo1jZ5OIiIiIiIg0rtI9s6l4rWhiYiKSkpKQmJio0WfMNB0zLS0NqampSExM1Pgzm9rQfm2JCZRMrrSl/doSE2CeSmKfasu5r7LvU+aJeSrveSqpuNoSk9/5tCMm8/RfTOC/vlVeBLGgNSqYhw8fwtHRsayrQUREREREpNViY2NRs2bNPJdXus6mXC7H48ePYWZmhhYtWuDixYsajf/ee+9pNGZiYiIcHR0RGxsLc3NzjcUFNF/Xyh6zpHKlLe3XlpjMk+ZjlkTcyp6nkorLPDFP5T1PJRVXG2LyO592xGSesmJeuHABb968gYODQ76vgqt0t9Hq6OhIvW9dXV2NHyQlERMAzM3NtaKulTmmgqZzpS3t15aYCsyTZmnLua+y71PmiXkq73kqqbjaEhPgdz5tiAkwTxYWFrCwsChw3Uo9QNDYsWO1ImZJ0Zb2a0vMkqIt7deWmCVFW9pfUvtUW3JV2fcp86R52lRXTavs+1Rb8gRoT/u1JWZJ0Zb2FyVmpbuNVtskJibCwsICCQkJJXaFhzSDudIOzJN2YJ60A/OkHZgn7cA8aQfmqWgq9ZVNbWBoaIiAgACNjnZFJYO50g7Mk3ZgnrQD86QdmCftwDxpB+apaHhlk4iIiIiIiDSOVzaJiIiIiIhI49jZJCIiIiIiIo1jZ5OIiIiIiIg0jp1NIiIiIiIi0jh2NomIiIiIiEjj2NkkIiIiIiIijWNnk4iIiIiIiDSOnU0iIiIiIiLSOHY2iYiIiIiISOPY2SQiIiIiIiKNY2eTiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI49jZJCKicik4OBiCIODXX38t66oUytOnT/HJJ5+gWrVqEAQBq1evLpVyt27dCkEQEBMTUyrlVTSBgYEQBKGsq0FEVCGxs0lEVIkpOipGRkZ49OiRyvJ27dqhSZMmZVAz7TNp0iQcOXIEM2bMwPbt29G5c+c81xUEQfqjo6MDBwcHdOrUCcHBwaVXYQA3b95EYGBgheuoOjk5Ke1jIyMj1KtXD1OnTsXLly/LunpERJUGO5tERIS0tDQsXry4rKuh1U6ePImePXtiypQpGDRoEBo0aJDv+h07dsT27dsRFBSE0aNH4+rVq2jfvj3+/PPPIpU7ePBgpKSkoHbt2kWu882bNzF37twK19kEAHd3d2zfvh3bt2/H//3f/8Hb2xurV69W+RFg1qxZSElJKaNaEhFVbHplXQEiIip77u7u+P777zFjxgw4ODiUdXVK1du3b1GlShW148THx8PS0rLQ67u6umLQoEHSdO/eveHm5obVq1ejS5cuhY6jq6sLXV3dolRV62VmZkIul8PAwCDPdWrUqKG0f0eMGAFTU1MsX74cd+7cQb169QAAenp60NPj1yEiopLAK5tERISZM2dCJpMVeHUzJiYGgiBg69atKssEQUBgYKA0rXgWLjIyEoMGDYKFhQVsbGwwe/ZsiKKI2NhY9OzZE+bm5rCzs8OKFStyLVMmk2HmzJmws7NDlSpV0KNHD8TGxqqsd/78eXTu3BkWFhYwMTGBl5cXQkJClNZR1OnmzZv47LPPULVqVXzwwQf5tvnu3bv49NNPYWVlBRMTE7Rq1QoHDx6UlituRRZFEevXr5du3Syqpk2bwtraGvfu3ZPmnTx5Em3btkWVKlVgaWmJnj17IiIiQmm73J7ZdHJyQrdu3XD27Fm0aNECRkZGqFOnDrZt26a03aeffgoA+PDDD6V6K27lDQsLg4+PD6ytrWFsbAxnZ2cMHz68wHYoyj569Cjc3d1hZGSERo0aYe/evSrrvn79GhMnToSjoyMMDQ1Rt25dLFmyBHK5XFpHccwtX74cq1evhouLCwwNDXHz5s1C7dfs7OzsAECpc5nbM5uCIGDcuHHYt28fmjRpAkNDQzRu3BiHDx8ucplERJUZO5tERARnZ2cMGTIE33//PR4/fqzR2P369YNcLsfixYvRsmVLLFiwAKtXr0bHjh1Ro0YNLFmyBHXr1sWUKVNw5swZle2//vprHDx4ENOmTcP48eNx7NgxeHt7K936ePLkSXh6eiIxMREBAQFYuHAhXr9+jfbt2+PChQsqMT/99FMkJydj4cKF+Pzzz/Os+9OnT/H+++/jyJEjGDNmDL7++mukpqaiR48e+O233wAAnp6e2L59O4D/bo1VTBfFq1ev8OrVK1SrVg0AcPz4cfj4+CA+Ph6BgYHw9/fH33//jTZt2hTqtteoqCh88skn6NixI1asWIGqVavC19cXN27ckOo9fvx4AFk/Nijq3bBhQ8THx6NTp06IiYnB9OnTsW7dOgwcOBDnzp0rVFvu3LmDfv36oUuXLli0aBH09PTw6aef4tixY9I6ycnJ8PLywo8//oghQ4Zg7dq1aNOmDWbMmAF/f3+VmFu2bMG6deswcuRIrFixAlZWVvnWISMjA8+fP8fz58/x8OFD/PHHH1i5ciU8PT3h7OxcYBvOnj2LMWPGoH///li6dClSU1PRp08fvHjxolD7gIiIAIhERFRpbdmyRQQgXrx4UYyOjhb19PTE8ePHS8u9vLzExo0bS9P37t0TAYhbtmxRiQVADAgIkKYDAgJEAOLIkSOleZmZmWLNmjVFQRDExYsXS/NfvXolGhsbi0OHDpXmnTp1SgQg1qhRQ0xMTJTm79q1SwQgrlmzRhRFUZTL5WK9evVEHx8fUS6XS+slJyeLzs7OYseOHVXqNGDAgELtn4kTJ4oAxL/++kua9+bNG9HZ2Vl0cnISZTKZUvvHjh1bqLgARD8/P/HZs2difHy8eP78ebFDhw4iAHHFihWiKIqiu7u7WL16dfHFixfSdleuXBF1dHTEIUOGSPMUObx37540r3bt2iIA8cyZM9K8+Ph40dDQUJw8ebI0b/fu3SIA8dSpU0r1++2336TjoqgUZe/Zs0eal5CQINrb24seHh7SvPnz54tVqlQRIyMjlbafPn26qKurKz548EAUxf+OOXNzczE+Pr5Idcj5p02bNuLz58+V1lUcE9kBEA0MDMSoqChp3pUrV0QA4rp16wq3I4iISOSVTSIiAgDUqVMHgwcPxnfffYcnT55oLO6IESOkf+vq6uLdd9+FKIrw8/OT5ltaWqJ+/fq4e/euyvZDhgyBmZmZNP3JJ5/A3t4ehw4dAgCEh4fjzp07+Oyzz/DixQvpatbbt2/RoUMHnDlzRum2TAAYPXp0oep+6NAhtGjRQulWW1NTU4wcORIxMTHFupVTYdOmTbCxsUH16tXRsmVLhISEwN/fHxMnTsSTJ08QHh4OX19fpSt4bm5u6Nixo9T2/DRq1Aht27aVpm1sbPLcxzkpnj09cOAAMjIyitw2BwcH9O7dW5o2NzfHkCFDcPnyZcTFxQEAdu/ejbZt26Jq1apSzp4/fw5vb2/IZDKVq9x9+vSBjY1NoevQsmVLHDt2DMeOHcOBAwfw9ddf48aNG+jRo0ehBgTy9vaGi4uLNO3m5gZzc/NC7T8iIsrCJ+KJiEgya9YsbN++HYsXL8aaNWs0ErNWrVpK0xYWFjAyMoK1tbXK/NxuUVQM5KIgCALq1q0r3Up6584dAMDQoUPzrENCQgKqVq0qTRfmNkoAuH//Plq2bKkyv2HDhtLy4r4apmfPnhg3bhwEQYCZmRkaN24sDVR0//59AED9+vVzLfvIkSMFDmyUc78DQNWqVfHq1asC6+bl5YU+ffpg7ty5WLVqFdq1a4devXrhs88+g6GhYYHb161bV+U5SFdXVwBZz2Da2dnhzp07uHr1ap4dyPj4eKXpwuZMwdraGt7e3tL0Rx99hPr16+OTTz7BDz/8gP/973/5bq/O/iMioizsbBIRkaROnToYNGgQvvvuO0yfPl1leV4D38hksjxj5jZSal6jp4qiWMia/kdx1XLZsmVwd3fPdR1TU1OlaWNj4yKXo2k1a9ZU6gxpmjr7WBAE/Prrrzh37hz++OMPHDlyBMOHD8eKFStw7tw5lf1ZHHK5HB07dsSXX36Z63JF51RBEznr0KEDAODMmTMFdjY1eYwSEVVW7GwSEZGSWbNm4ccff8SSJUtUlimuDr5+/VppvuJKXElQXLlUEEURUVFRcHNzAwDpVkdzc3ONd95q166N27dvq8y/deuWtLwkKOLmVba1tbVGXtdS0Ki5rVq1QqtWrfD1119jx44dGDhwIHbu3Kl0a3RuoqKiIIqiUvzIyEgAWaPVAll5S0pKKtEOd06ZmZkAgKSkpFIrk4ioMuMzm0REpMTFxQWDBg3Ct99+Kz1fp2Bubg5ra2uV5+k2bNhQYvXZtm0b3rx5I03/+uuvePLkifQuynfeeQcuLi5Yvnx5rp2IZ8+eFbvsrl274sKFCwgNDZXmvX37Ft999x2cnJzQqFGjYsfOj729Pdzd3REUFKTUsb9+/TqOHj2Krl27aqQcRYc1548Hr169UrmCp7hqnJaWVmDcx48fS6P1AkBiYiK2bdsGd3d36fUjffv2RWhoKI4cOaKy/evXr6WOoSb98ccfAIBmzZppPDYREanilU0iIlLx1VdfYfv27bh9+zYaN26stGzEiBFYvHgxRowYgXfffRdnzpyRrlqVBCsrK3zwwQcYNmwYnj59itWrV6Nu3brSK0t0dHTwww8/oEuXLmjcuDGGDRuGGjVq4NGjRzh16hTMzc2lTkZRTZ8+HT///DO6dOmC8ePHw8rKCkFBQbh37x727NkDHZ2S+8122bJl6NKlC1q3bg0/Pz+kpKRg3bp1sLCwUHqfqTrc3d2hq6uLJUuWICEhAYaGhmjfvj127NiBDRs2oHfv3nBxccGbN2/w/fffw9zcvFAdXVdXV/j5+eHixYuwtbXF5s2b8fTpU2zZskVaZ+rUqdi/fz+6desGX19fvPPOO3j79i2uXbuGX3/9FTExMSrP9RbFo0eP8OOPPwIA0tPTceXKFXz77bewtrYu8BZaIiLSDHY2iYhIRd26dTFo0CAEBQWpLJszZw6ePXuGX3/9Fbt27UKXLl3w559/onr16iVSl5kzZ+Lq1atYtGgR3rx5gw4dOmDDhg0wMTGR1mnXrh1CQ0Mxf/58/N///R+SkpJgZ2eHli1bYtSoUcUu29bWFn///TemTZuGdevWITU1FW5ubvjjjz/w0UcfaaJ5efL29sbhw4cREBCAOXPmQF9fH15eXliyZEmRB8vJi52dHb755hssWrQIfn5+kMlkOHXqFLy8vHDhwgXs3LkTT58+hYWFBVq0aIGffvqpUGXXq1cP69atw9SpU3H79m04Ozvjl19+gY+Pj7SOiYkJTp8+jYULF2L37t3Ytm0bzM3N4erqirlz58LCwkKttoWHh2Pw4MEAsn6QsLa2xscff4z58+ejRo0aasUmIqLCEUQ+6U5EREQa4uTkhCZNmuDAgQNlXRUiIipjfGaTiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI4/jMJhEREREREWkcr2wSERERERGRxrGzSURERERERBpX6d6zKZfL8fjxY5iZmUEQhLKuDhERERERkVYRRRFv3ryBg4MDdHTyvn5Z6Tqbjx8/hqOjY1lXg4iIiIiISKvFxsaiZs2aeS6vdJ1NMzMzAFk7xtzcvIxrU7CMjAwcPXoUnTp1gr6+fllXh/LBXGkH5kk7ME/agXnSDsyTdmCetAPzlCUxMRGOjo5S3yovla6zqbh11tzcXGs6myYmJjA3N6/UB7Q2YK60A/OkHZgn7cA8aQfmSTswT9qBeVJW0GOJHCCIiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI48rVM5uLFi3C3r17cevWLRgbG+P999/HkiVLUL9+fWmd1NRUTJ48GTt37kRaWhp8fHywYcMG2NraaqweoigiMzMTMplMYzGLKyMjA3p6ekhNTS0X9aG8MVfaQVN50tfXh66urgZrRkRERFSxlKvO5unTpzF27Fi89957yMzMxMyZM9GpUyfcvHkTVapUAQBMmjQJBw8exO7du2FhYYFx48bh448/RkhIiEbqkJ6ejidPniA5OVkj8dQliiLs7OwQGxvL94KWc8yVdtBUngRBQM2aNWFqaqrB2hERERFVHOWqs3n48GGl6a1bt6J69er4559/4OnpiYSEBGzatAk7duxA+/btAQBbtmxBw4YNce7cObRq1Uqt8uVyOe7duwddXV04ODjAwMCgzDsNcrkcSUlJMDU1zfeFqVT2mCvtoIk8iaKIZ8+e4eHDh6hXrx6vcBIRERHlolx1NnNKSEgAAFhZWQEA/vnnH2RkZMDb21tap0GDBqhVqxZCQ0Nz7WympaUhLS1Nmk5MTASQdStdRkaGyroymQw1atSAiYmJxttTHKIoIj09HYaGhmXe8aX8MVfaQVN5qlatGpKSkpCSkgJDQ0MN1pAASOfnnOdpKl+YJ+3APGkH5kk7ME9ZCtv+ctvZlMvlmDhxItq0aYMmTZoAAOLi4mBgYABLS0uldW1tbREXF5drnEWLFmHu3Lkq848eParSodTT04OdnR2Sk5ORmZmpmYZoyJs3b8q6ClRIzJV2UDdP6enpSElJwenTp8vd+aIiOXbsWFlXgQqBedIOzJN2YJ60Q2XPU2EfOSy3nc2xY8fi+vXrOHv2rFpxZsyYAX9/f2k6MTERjo6O6NSpE8zNzZXWTU1NRWxsLExNTWFkZKRWuZoiiiLevHkDMzMzXi0r5yp1ruKuqs6zcyu78vMpW1N5Sk1NhbGxMTw9PcvN+aIiycjIwLFjx9CxY0e+NLscY560A/OkHZgn7cA8ZVHcLVqQctnZHDduHA4cOIAzZ86gZs2a0nw7Ozukp6fj9evXSlc3nz59Cjs7u1xjGRoa5nqLm76+vsoBIpPJIAgCdHR0ys0zd3K5HACkelH5VblzJarOKtV9kKP8fMrWVJ50dHQgCEKu5xLSHO5f7cA8aQfmSTswT9qhsuepsG0vV9+IRVHEuHHj8Ntvv+HkyZNwdnZWWv7OO+9AX18fJ06ckObdvn0bDx48QOvWrUu7ulSOtGvXDhMnTizSNoGBgXB3dy+R+hSWp6cnduzYUaZ1qCiC/w6DUKM5Xidk3R57+PBhuLu7S51LIiIiIipd5erK5tixY7Fjxw78/vvvMDMzk57DtLCwgLGxMSwsLODn5wd/f39YWVnB3Nwc//vf/9C6dWu1R6ItyOnTp0s0fk5eXl5FWt/X1xdBQUEYNWoUvvnmG6VlY8eOxYYNGzB06FBs3bpVg7WsfARBwG+//YZevXqpHWv//v14+vQp+vfvr37FtFRwcDA+/PBDvHr1SuVZbHV17twZs2fPxk8//YTBgwdrNDYRERERFaxcXdncuHEjEhIS0K5dO9jb20t/fvnlF2mdVatWoVu3bujTpw88PT1hZ2eHvXv3lmGtyw9HR0fs3LkTKSkp0rzU1FTs2LEDtWrVKsOaFU56enpZV6FUrV27FsOGDSv3t9zKZLJcrw5qQ758fX2xdu3asq4GERERUaVUrr7liqKY6x9fX19pHSMjI6xfvx4vX77E27dvsXfv3jyf16xsmjdvDkdHR6XO9969e1GrVi14eHgorSuXy7Fo0SI4OzvD2NgYzZo1w6+//iotl8lk8PPzk5bXr18fa9asUYoRHByMFi1aoEqVKrC0tESbNm1w//59AFlf8nNe/Zs4cSLatWsnTbdr1w7jxo3DxIkTYW1tDR8fHwDA9evX0aVLF5iamsLW1haDBw/G8+fPpe3evn2LIUOGwNTUFPb29lixYkWh9s/ixYtha2sLMzMz+Pn5ITU1VWn5xYsX0bFjR1hbW8PCwgJeXl64dOmStNzJyQkA0Lt3bwiCIE1HR0ejZ8+esLe3R82aNdGyZUscP34837o8e/YMJ0+eRPfu3aV5MTExEAQB4eHh0rzXr19DEAQEBwcDyNrngiDgxIkTePfdd2FiYoL3338ft2/fVor/xx9/4L333oORkRGsra3Ru3dvadmrV68wZMgQVK1aFSYmJujSpQvu3LkjLd+6dSssLS2xf/9+NGrUCIaGhnjw4AGcnJwwf/58DBkyBObm5hg5ciQA4OzZs2jbeziMXVrD8d0uGD97Kd4m//eDR1paGqZNmwZHR0cYGhqibt262LRpE2JiYvDhhx8CAKpWrQpBEKTPekHHJwAcOnQIrq6uMDY2xoefjERM7GOV/dy9e3eEhYUhOjo633wQERERkeaVq84mqW/48OHYsmWLNL1582YMGzZMZb1FixZh27Zt+Oabb3Djxg1MmjQJgwYNkm4XlsvlqFmzJnbv3o2bN29izpw5mDlzJnbt2gUAyMzMRK9eveDl5YWrV68iNDQUI0eOLPLonkFBQTAwMEBISAi++eYbvH79Gu3bt4eHhwfCwsJw+PBhPH36FH379pW2mTp1Kk6fPo3ff/8dR48eRXBwsFKnMDe7du1CYGAgFi5ciLCwMNjb22PDhg1K67x58wZDhw7F2bNnce7cOdSrVw9du3aVXpFx8eJFAMCWLVvw5MkTaTopKQldu3bFsWPHcPr0afj4+KB79+548OBBnvU5e/YsTExM0LBhwyLtL4WvvvoKK1asQFhYGPT09DB8+HBp2cGDB9G7d2907doVly9fxokTJ9CiRQtpua+vL8LCwrB//36EhoZCFEV07dpV6X1JycnJWLJkCX744QfcuHED1atXBwAsX74czZo1w+XLlzF79mxER0ejc+fO6NO1A64e+wW/bFyMsxfCMe6rxVKsIUOG4Oeff8batWsRERGBb7/9FqampnB0dMSePXsAZD17/eTJE+kHjYKOz9jYWHz88cfo3r07wsPDMeKzXpi+aJ3KfqpVqxZsbW3x119/FWs/ExEREVHxlatnNkl9gwYNwowZM6QrjCEhIdi5c6d0ZQzIutK0cOFCHD9+XBpYqU6dOjh79iy+/fZbeHl5QV9fX+n9pM7OzggNDcWuXbvQt29fJCYmIiEhAd26dYOLiwsAFKvjVK9ePSxdulSaXrBgATw8PLBw4UJp3ubNm+Ho6IjIyEg4ODhg06ZN+PHHH9GhQwcAWR3W7KMW52b16tXw8/ODn5+fVM7x48eVrm62b99eaZvvvvsOlpaWOH36NLp16wYbGxsAgKWlpdLV9GbNmqFZs2aQy+VITEzEvHnzsG/fPuzfvx/jxo3LtT7379+Hra1tsW+h/frrr6XneqdPn46PPvoIqampMDIywtdff43+/fsr5a9Zs2YAgDt37mD//v0ICQnB+++/DwD46aef4OjoiH379uHTTz8FkDWs94YNG6Ttsu+jyZMnS9MjRozAwIEDMfHzgQCAenVqYe38qfDq8zk2pqbiwYMH2LVrF44dOwZvb28AWceagpWVFQCgevXq0jObhTk+N27cCBcXF+mqdn2zrrh2KwpL1m9V2VcODg7S54GIiIiISg87mxWMjY0NPvroI2zduhWiKOKjjz6CtbW10jpRUVFITk5Gx44dleanp6cr3W67fv16bN68GQ8ePEBKSgrS09Ol0VutrKzg6+sLHx8fdOzYEd7e3ujbty/s7e2LVN933nlHafrKlSs4deoUTE1NVdaNjo6W6tGyZUtpvpWVFerXr59vORERERg9erTSvNatW+PUqVPS9NOnTzFr1iwEBwcjPj4eMpkMycnJ+V6hBLKubAYGBuLgwYN4/PgxZDIZUlJS8t0uJSVFrXczurn99x5JxT6Pj49HrVq1EB4ejs8//zzX7SIiIqCnp6e0/6pVq4b69esjIiJCmmdgYKBUhsK7776rNH3lyhVcvXoVP/24XZoniiLkcjnu3buHa9euQVdXt0gDXhXm+IyIiFBqAwC0fif3d2saGxsX+sXDRERERKQ57GxWQMOHD5euqK1fv15leVJSEoCs2y1r1KihtEzxTtKdO3diypQpWLFiBVq3bg0zMzMsW7YM58+fl9bdsmULxo8fj8OHD+OXX37BrFmzcOzYMbRq1Qo6OjoQReV3H2a/TVOhSpUqKnXr3r07lixZorKuvb09oqKiCrMLimXo0KF48eIF1qxZg9q1a8PQ0BCtW7cucCCcKVOm4NixY1i6dCns7OxgY2ODvn375rudtbU1Xr16pTRPcZUz+37LbZ8Byu82Uty6rBjEx9jYON/6FoaxsXGut0Tnlq9Ro0ZhfL8OKuvWcnEpVr4Kc3wWxcuXL6Wr0kT5CrTIZV5C6deDqDB4vFJFkfNY5nFcobCzWQF17twZ6enpEARBGnQnu+yDvuR1xUlxm+WYMWOkebkNsuLh4QEPDw/MmDEDrVu3xo4dO9CqVSvY2Njg+vXrSuuGh4cX+ALY5s2bY8+ePXBycoKenurh6eLiAn19fZw/f14aYffVq1eIjIzM9+pZw4YNcf78eQwZMkSad+7cOZU2b9iwAV27dgWQ9Vxg9oGJgKxOnkwmU9nO19cXvXv3RmJiInR0dBATE5NvOz08PBAXF4dXr16hatWqACB1iJ48eSJdwcs+WFBhubm54cSJE7k+q9uwYUNkZmbi/Pnz0m20L168wO3bt9GoUaMil9W8eXPcvHkTdZ1Vy4KBAZo2bQq5XI7Tp09Lt9Eqr2IAAEr7tDDHZ8OGDbF//36leecuXVNZLzU1FdHR0SoDZBERERFRyeMAQRWQrq4uIiIicPPmTejq6qosNzMzw5QpUzBp0iQEBQUhOjoaly5dwrp16xAUFAQg61nKsLAwHDlyBJGRkZg9e7Y0IA4A3Lt3DzNmzEBoaCju37+Po0eP4s6dO9Jzm+3bt0dYWBi2bduGO3fuICAgQKXzmZuxY8fi5cuXGDBgAC5evIjo6GgcOXIEw4YNg0wmg6mpKfz8/DB16lScPHkS169fh6+vb4HPPk6YMAGbN2/Gli1bEBkZiYCAANy4cUNpnXr16mH79u2IiIjA+fPnMXDgQJWrhE5OTjhx4oTUUVRst3fvXoSHh+PatWsYOHBgrq8Kyc7DwwPW1tYICQmR5hkbG6NVq1ZYvHgxIiIicPr0acyaNavAfZZTQEAAfv75ZwQEBCAiIgLXrl2TrhTXq1cPPXv2xOeff46zZ8/iypUrGDRoEGrUqIGePXsWuaxp06bh77//xrivFiP8+m3cufsAvx8JlgYIcnJywtChQzF8+HDs27cP9+7dQ3BwsDTQVO3atSEIAg4cOIBnz54hKSmpUMfn6NGjcefOHUydOhW3b9/Gjt/+xNZdf6jU79y5c9IVaiIiIiIqXbyyWUhFeeasPDA3N893+fz582FjY4NFixbh7t27sLS0RPPmzTFz5kwAwKhRo3D58mX069cPgiBgwIABGDNmDP78808AgImJCW7duoWgoCC8ePEC9vb2GDt2LEaNGgUA8PHxwezZs/Hll18iNTUVw4cPx5AhQ3DtmurVp+wcHBwQEhKCadOmoVOnTkhLS0Pt2rXRuXNnqUO5bNky6XZbMzMzTJ48GQkJ+d9y0a9fP0RHR0v16dOnD7744gscOXJEWmfTpk0YOXKk9AqZhQsXYsqUKUpxVqxYAX9/f3z//feoUaMGYmJisHLlSgwfPhwffPABrKysMH36dGkE27zo6upi2LBh+Omnn9CtWzdp/ubNm+Hn54d33nkH9evXx9KlS9GpU6d8Y+XUrl077N69G/Pnz8fixYthbm4OT09PafmWLVswYcIEdOvWDenp6fD09MShQ4cKvOqcGzc3N5w+fRpfTRmPth/7QRRFuNSuiX49/qvzxo0bMXPmTIwZMwYvXrxArVq1pOOsRo0amDt3LqZPn45hw4ZhyJAh2Lp1a4HHZ61atbBnzx5MmjQJ69atQwv3Rlg4fSyG+89Vqt/PP/+MgQMHwsTEpMhtIyIiIiL1CGLOB+squMTERFhYWCAhIUGlQ5aamop79+7B2dlZrcFbNEkxwqm5uXmxRy6l0lHUXMXFxaFx48a4dOkSateuXQo1LEGPL6vOcyjFW1dzlu/ggefPn6N+/foICwuDs7OztEhTn6nyeL6oSDIyMnDo0CF07dq1WD+EFAufgSuyMskTZSnC8co8aYdKmycte2az0uYph/z6VNmx90JURuzs7LBp06YCR7ul4omJicGGDRuUOppEREREVHp4Gy1RGerVq1dZV6HCevfdd1Ve1UJEREREpYdXNomIiIiIiEjj2NkkIiIiIiIijWNnk4iIiIiIiDSOz2ySdinrUU+p5DHHJU8bR13VstEKy7XKvi8re/uLqjj7i/uYiP7FK5tERERERESkcexsEhERERERkcaxs0lEREREREQax2c2C8lp+sFSLS9m8UelWp62a9euHdzd3bF69epCbxMYGIh9+/YhPDy8xOpVEE9PT4wePRqfffYZAEAQBPz22295vn8zJiYGzs7OuHz5Mtzd3UuvopWAk5MTJk6ciIkTJyI9PR2urq749ddf+a5OIiIiomLilc0KwtfXF4IgYPTo0SrLxo4dC0EQ4OvrW/oVq2AEQcC+ffs0Emv//v14+vQp+vfvX+htHB0d8eTJEzRp0kQjddB2Tk5ORfqBobAMDAwwZcoUTJs2TeOxiYiIiCoLdjYrEEdHR+zcuRMpKSnSvNTUVOzYsQO1atUqw5oVTnp6ellXoVStXbsWw4YNg45O4T+Gurq6sLOzg56e9tyUkJGRoTJPG3I9cOBAnD17Fjdu3CjrqhARERFpJXY2K5DmzZvD0dERe/fulebt3bsXtWrVgoeH8qsj5HI5Fi1aBGdnZxgbG6NZs2b49ddfpeUymQx+fn7S8vr162PNmjVKMYKDg9GiRQtUqVIFlpaWaNOmDe7fvw8g60przltBJ06ciHbt2knT7dq1w7hx4zBx4kRYW1vDx8cHAHD9+nV06dIFpqamsLW1xeDBg/H8+XNpu7fJKRgyfjZM67WBvUcnrFixolD7Z/HixbC1tYWZmRn8/PyQmpqqtPzixYvo2LEjrK2tYWFhAS8vL1y6dEla7uTkBADo3bs3BEGQpqOjo9GzZ0/Y29ujZs2aaNmyJY4fP55vXZ49e4aTJ0+ie/fuKsuePHmCLl26wNjYGHXq1FHKS0xMDARBkG79VTdPuXn48CEGDBgAKysrVKlSBe+++y7Onz8vLd+4cSNcXFxgYGCA+vXrY/v27UrbCzWaY2PQbvTo0QNVqlTB119/jcDAQLi7u+OHH36As7MzjIyMAACvX7/GiBEjYGNjA3Nzc7Rv3x5XbkQqxfvj6Gm89957MDIygrW1NXr37g0g6/i5f/8+Jk2aBKFGcwg1mkvbnD17Fm3btoWxsTEcHR0xfvx4vH37Vmn/9+jRA8bGxnB2dsZPP/2ksh+qVq2KNm3aYOfOnXnuKyIiIiLKGzubFczw4cOxZcsWaXrz5s0YNmyYynqLFi3Ctm3b8M033+DGjRuYNGkSBg0ahNOnTwPI6ozWrFkTu3fvxs2bNzFnzhzMnDkTu3btAgBkZmaiV69e8PLywtWrVxEaGoqRI0dCEIQi1TcoKAgGBgYICQnBN998g9evX6N9+/bw8PBAWFgYDh8+jKdPn6Jv377SNlPnr8bpc//g980rcXTHegQHByt1CnOza9cuBAYGYuHChQgLC4O9vT02bNigtM6bN28wdOhQnD17FufOnUO9evXQtWtXvHnzBkBWZxQAtmzZgidPnkjTSUlJ6Nq1K44dO4bTp0/Dx8cH3bt3x4MHD/Ksz9mzZ2FiYoKGDRuqLJs9ezb69OmDK1euYODAgejfvz8iIiJyjaPpPCUlJcHLywuPHj3C/v37ceXKFXz55ZeQy+UAgN9++w0TJkzA5MmTcf36dYwaNQrDhg3DqZCLSnECV36L3r1749q1axg+fDgAICoqCnv27MHevXulzvKnn36K+Ph4/Pnnn/jnn3/QvHlzdOg3Gi9fZb2T7eDxv9B7xBR07doVly9fxokTJ9CiRQsAWT+k1KxZE/PmzcOTy0fx5PJRAEB0TCw6d+6MPn364OrVq/jll19w9uxZjBs3TqrfmDFj8PDhQ5w6dQq//vorNmzYgPj4eJX90aJFC/z111+5J5GIiIiI8qU99+JRoQwaNAgzZsyQrlyFhIRg586dCA4OltZJS0vDwoULcfz4cbRu3RoAUKdOHZw9exbffvstvLy8oK+vj7lz50rbODs7IzQ0FLt27ULfvn2RmJiIhIQEdOvWDS4uLgCQa8epIPXq1cPSpUul6QULFsDDwwMLFy6U5m3evBmOjo6IjIyEg5CMTTv34ce1C9ChbUsAWR3WmjVr5lvO6tWr4efnBz8/P6mc48ePK13dbN++vdI23333HSwtLXH69Gl069YNNjY2AABLS0vY2dlJ6zVr1gzNmjWDXC5HYmIi5s2bh3379mH//v1KHZzs7t+/D1tb21xvof30008xYsQIAMD8+fNx7NgxrFu3TqVzDEDjedqxYweePXuGixcvwsrKCgBQt25dafny5cvh6+uLMWPGAAD8/f1x7tw5LP9mOz5s85603me9Oqv8yJGeno5t27ZJ+/Hs2bO4cOEC4uPjYWhoKMXft+cX/HrwOEYO6oOv125C/56dlNrYrFkzAICVlRV0dXVhZmYGu+rW0vJF/7cFAwcOxMSJEwFkHWNr166Fl5cXNm7ciJiYGBw/fhznzp1Dy5ZZx9CmTZty3S8ODg75XgUmIiIioryxs6mNHl9Wnnb47xZZGxsbfPTRR9i6dStEUcRHH30Ea2trpdWjoqKQnJyMjh07Ks1PT09Xut12/fr12Lx5Mx48eICUlBSkp6fDvbEr8PgyrAD49u0OH59O6NixE7y9vdG3b1/Y29sXqSnvvPOO0vSVK1dw6tQpmJqaqqwbHR2NFL1EpKdnoGXz/wbIsbKyQv369fMu5PFlRNy8jtH9umTtu3/3V+vWrXHq1ClptadPn2LWrFkIDg5GfHw8ZDIZkpOT871CCWRdDQwMDMTBgwfx+PFjyGQypKSk5LtdSkqKdCtpToofALJP5zdibq55+nekWisrK/j6+sLHxwcdO3YsME/h4eHw8PCQOpo5RUREYOTIkUrz2rRpgzUrlynNe7dZI5Vta9euLXU0gaxcJyUloVq1akrrpaSkIPr+w6z63IjE5wN759n23Fy5GYmrEYeUbo0VRRFyuRz37t3DrVu3oKenp3TsNWjQAJaWliqxjI2NkZycXKTyqRwJtMgxnVC5yieqyHJ+voD/PmP87OWuMuwXTbYxv2OsLGNpGXY2K6Dhw4dLV9TWr1+vsjwpKQkAcPDgQdSoUUNpmeIK086dOzFlyhSsWLECrVu3hpmZGZYtW4bzZ4OldbesmovxfgNw+J8Y/PLLL5g1axaOHTuGVq1aQUdHB6IoKsXObaCYKlWqqNSte/fuWLJkicq69vb2iPr7QCH2QPEMHToUL168wJo1a1C7dm0YGhqidevWBQ5mM2XKFBw7dgxLly6FnZ0dbGxs0Ldv33y3s7a2xqtXr9Suc555yvaM5ZYtWzB+/HgcPnxYJU85GRsbq10nAKhiohont1zb29srXXUHADy9AUsLs6z6GBkWueykt8kYNWoUxo8fr7KsVq1auHXrVqFjvXz5UqmDTERERESFx2c2K6DOnTsjPT0dGRkZ0qA72TVq1AiGhoZ48OAB6tatq/TH0dERQNbtt++//z7GjBkDDw8P1K1bF9HR0SqxPJo0wIwZM/D333+jSZMm2LFjB4CsK6xPnjxRWrcw77Ns3rw5bty4AScnJ5W6ValSBS5ONaGvr4fzl65L27x69QqRkZH5RAUa1nXG+cvXlOadO3dOaTokJATjx49H165d0bhxYxgaGioNTARk3bYqk8lUtvP19UXv3r3RuHFj2NnZISYmJt/6eHh4IC4uLtcOZ856nTt3Ls9bXwudJw+PXPOUk5ubG8LDw/Hy5ctclzds2BAhISEqdWhUzznX9fPTvHlzxMXFQU9PTznXzrVgbVU1qz4N6+HE2Qt5xjAwMFDJR/OmDXHz5k2V46du3bowMDBAgwYNkJmZiX/++Ufa5vbt23j9+rVK/OvXr6sMrkVEREREhcPOZgWkq6uLiIgI3Lx5E7q6uirLzczMMGXKFEyaNAlBQUGIjo7GpUuXsG7dOgQFBQHIes4tLCwMR44cQWRkJGbPni0NiAMA9x48woxF6xAadgX379/H0aNHcefOHalT1L59e4SFhWHbtm24c+cOAgICcP36dZW65DR27Fi8fPkSAwYMwMWLFxEdHY0jR45g2LBhkMlkMK1iAr/+vTB1wWqcPHsB129FwdfXt8DXh0zwG4DNv+zHll9+R2RkJAICAlReaVGvXj1s374dEREROH/+PAYOHKhypc/JyQknTpxQ6ijWq1dPGvTm2rVrGDhwoDSgTl48PDxgbW2t0nEDgN27d2Pz5s1SPS9cuJDns58F5unePcyYMQOhoaG55imnAQMGwM7ODr169UJISAju3r2LPXv2IDQ0FAAwdepUbN26FRs3bsSdO3ewcuVK7N27F1NGD8m3vbnx9vZG69at0atXLxw9ehQxMTH4+++/8dXi/0PYlZsAgAD/kfh53xEEBAQgIiIC165dU7rq7eTkhDNnzuDRk3g8f5mVj2ljhuLvv//GuHHjEB4ejjt37uD333+X9mH9+vXRoUMHfPHFFzh//jz++ecfjBgxIterun/99Rc6depU5LYREREREW+jLbSYxR+VdRWKxNzcPN/l8+fPh42NDRYtWoS7d+/C0tISzZs3x8yZMwEAo0aNwuXLl9GvXz8IgoABAwZgzJgx+HN/1mtVTIyNcCsqBkG7/8CLV4mwt7fH2LFjMWrUKACAj48PZs+ejS+//BKpqakYPnw4hgwZgmvXruVZJyBrQJaQkBBMmzYNnTp1QlpaGmrXro3OnTtLHcplsyci6W0yuvtOhJlpFUyeOg0JCfnf996vpw+i7z/ElwvWIHXOCvTp0wdffPEFjhw5Iq2zadMmjBw5UnqFzMKFCzFlyhSlOCtWrIC/vz++//571KhRAzExMVi5ciWGDx+ODz74AFZWVpg+fbo0gm1edHV1MWzYMPz000/o1q2b0rK5c+di586dGDNmDOzt7fHzzz+jUSPVZyCBfPL0558AABMTE9y6dQtBQUF48eKFSp5yMjAwwNGjRzF58mR07doVmZmZaNSokXQ7dq9evbBmzRosX74cEyZMgLOzM7Zs2YJ27zfJNV5+BEHAoUOH8NVXX2HYsGF49uwZ7Ozs4PleE9haZz0z2u79d7H72yWYv/4nLF68GObm5vD09JRizJs3D6NGjYJLmx5IS0uH+OgS3Bq54vTp0/jqq6/Qtm1biKIIFxcX9OvXT9pu/fr18Pf3h5eXF2xtbbFgwQLMnj1bqX6hoaFISEjAJ598UuS2EREREREgiDkfrKvgEhMTYWFhgYSEBJUOWWpqKu7du6f0HsCyphjh1Nzc/L+rd/kMEFTicpatDeWX0v7KNVf5iIuLQ+PGjXHp0iXUrl27ROpUajR5XJRwjgubp379+qFZs2bSDzA5lcfzRaFpwUAFGRkZOHToELp27Qp9ff3iDfpQ1G00vV/K62AcGqyXSp60QXnNS1EV4XhVK0+l8dkrjgo4QFCJf57K634pyUF9SmCAoGLlSQv+3y2q/PpU2fE2WqIyYmdnh02bNhU42i2VvvT0dDRt2hSTJk0q66oQERERaS21O5tBQUE4ePCgNP3ll1/C0tIS77//Pt9PR1SAXr16oW3btmVdDcrBwMAAs2bN0tjovERERESVkdqdzYULF0pfyEJDQ7F+/XosXboU1tbWvCpARERERERUSak9QFBsbCzq1q0LANi3bx/69OmDkSNHok2bNmjXrp264YmIiIiIiEgLqd3ZNDU1xYsXL1CrVi0cPXoU/v7+AAAjIyOkpKSoXUEqgrIcOKg45VeEwYaKs406bcwrVlnXSxvFXQWQbXy0kmi/JgcEKE6ssi6/Mqjs+3hRTaDZd1l/y1M5EIxCeR1Up7wqrWO/rAeiyWsbxXwdo/8+T3OeFr9umqKN56TSkPO8B3C/5EPtzmbHjh0xYsQIeHh4IDIyEl27dgUA3LhxA05OTuqGJyIiIiIiIi2k9jOb69evR+vWrfHs2TPs2bMH1apVAwD8888/GDBggNoVJCIiIiIiIu2j9pXNxMRErF27VuV9dYGBgYiNjVU3PBEREREREWkhta9sOjs74/nz5yrzX758CWdnZ3XDExERERERkRZS+8qmKIq5zk9KSoKRkZG64cuP3B6SLtHy+KBxaRNqNMdvv/2GXr16ISYmBs7Ozrh8+TLc3d2LFU+KceRnuDepr9nKEhERERGVc8W+sunv7w9/f38IgoA5c+ZI0/7+/pgwYQL69etX5C/pZ86cQffu3eHg4ABBELBv3z6l5b6+vhAEQelP586di9uECsXX1xdCjebKfwQBUVFR0vJevXrluX1KSgoCAgLg6uoKQ0NDWFtb49NPP8WNGzeU1gtc8Y0UX9fxXTi+2wUjR47Ey5cvldZzavkRVq9eLU1fuXIFPXr0QPXq1WFkZAQnJyf0Gz0N8c+VtysvHB0d8eTJEzRp0qRQ6/v6+qJ37965x2jgUhJVJCIiIiIq14p9ZfPy5azXJoiiiGvXrsHAwEBaZmBggGbNmmHKlClFivn27Vs0a9YMw4cPx8cff5zrOp07d8aWLVukaUNDw2LUvmLq/OH72LIy8L8Zdk1hY2NT4HZpaWnw9vbGgwcPsGLFCrRs2RJPnz7FokWL0LJlSxw/fhytWrWS1m9c3wXHd26ETCZHxJ17GP7lQiQkJOCXVdNzjf/s2TN06NAB3bp1w5EjR2BpaYmYmBjs3/ED3iZr9vU4GRkZ0NfXVzuOrq4u7OzsNBPj8RO160NEREREpG2K3dk8deoUAGDYsGFYs2YNzM3N1a5Mly5d0KVLl3zXMTQ0VLsTUFEZGhjArrr1fzMKuZ9Wr16N0NBQXL58Gc2aNQMA1K5dG3v27EHLli3h5+eH69evQxAEAICerq5UTg376vj000///QEg985mSEgIEhIS8MMPP0BPL+uQc3Z2xof1LfOtl5OTE/z8/HDz5k3s378flpaWmDl2MMb69pPWEWo0x4YNG/Dnn3/ixIkTmDp1KgIDA/H7779j7ty5uHnzJhxsrTH00274aryfVP6dO3fgN9gPF8JvoE6tGlgzb6pS2bndRnvjxg1MmzAeZ85fhiiKcG/siq2r5mL7d78jKCgIAFC1alUAWZ8PJycnldtoT4f+g6lLRuHKlSuwsrLC0KFDsWDBAqle7dq1g5ubG4yMjPDDDz/AwMAAo0ePRmBgYEFpJCIiIiIqV9R+ZjP7VcbSEBwcjOrVq6Nq1apo3749FixYIL1uJTdpaWlIS0uTphMTEwFkXQHLyMhQWjcjIwOiKEIul0MulystU3skpSJSlK94JlZRryxCzpUhiiJEAPLsy7K1QRTFHDH+s2PHDnh7e6Np06YqyydMmIDBgwdLnS7x3/iKcmJiH+PIkSMwMDBQLjtbnatXr47MzEzs2bMHn3zyidRpVWlHjjoDwLJlyzBjxgwEBATg6NGjmODvj7p1nNDR878rrYGBgVi4cCFWrlwJPT09nD59GkOGDMHq1avRtm1bRIcdx+gv50OEgDn+oyDPzMTHH38MW0sThP6xDQlvkuAfsFza79nzr/j3o0eP4OnpCa9W7ji+61uYm5oiJCwc6Zly+Pv74+bNm0hMTMSaNWtgamqKatWq4fHjx9K+kkPAoyfx6Dr4fxjqOwxbt27FrVu3MGrUKBgaGiIgIEBqT1BQECZNmoTQ0FCEhoZi+PDhaN26NTp27Jhr7gu7L1XlFas4ilO+JmMVvi3SZwo6kCPbekWsr/zfz11GRgZ0dXVzX0knl+fWc5x3Cq04sfLaRpOxSoji/Cydp3OWX5iyi7qNpvdLaZRfHMXZl3nI+DeW4u9SqW/2ckrjuCiOcna8qnyeiqKctaVQ2+RVviZzr8n9kuNzlKFjVD6OSy08J5VorH/jqZz3ClNOKf8fWhoKez4RxLxG+Cmkt2/fYvHixThx4gTi4+NVOit3794tVlxBEKTBWhR27twJExMTODs7Izo6GjNnzoSpqSlCQ0Pz/LIXGBiIuXPnqszfsWMHTExMlObp6enBzs4Ojo6OSrcFA4Dl6trFakdxvZ54v0jrjxkzBrt27VIalMnb2xtbt26VlickJOCnn35S2dbe3h6+vr5YtGiRyrKrV6/Cy8sLmzdvRu/evbF48WIsW7YMxsbGkMlkSE1NBQB8/fXXGDNmjLSdm5sbvvjiC3zxxRcAgPnz52Pt2rUwMzND8+bN4enpif79+6N69ep5tsnNzQ2urq749ddfpXnDhw/HmzdvsHv3bgBZVxK/+OILLFy4UFqnV69e8PT0hL+/vzTvl19+QWBgICIiInDy5En069cPV69ehb29PQDg+PHj+PTTT/Hjjz/io48+woMHD9CsWTOcOXMGTZs2xbx587B3715cvHgx19t0c9u/OWPMnz8ff/zxB86fPy91uH/44QfMnTsX9+/fh46ODrp16waZTIY///xTitOhQwe0bduWVzfLmfT0dMTGxiIuLg6ZmZllXR0iIiKiUpOcnIzPPvsMCQkJ+d7hqvaVzREjRuD06dMYPHgw7O3ts1210rz+/ftL/27atCnc3Nzg4uKC4OBgdOjQIddtZsyYodTpSExMhKOjIzp16qSyY1JTUxEbGwtTU9MyH0lXUTdRFPHmzRuYmZn9t2/jriqvbOcGfX19tHv/XWxYNFOaXcXpHSmOvr4+9PT0VA+GuKsAROhnJMA8OQawc1NaXKVKFQCAsbExzM3NYZjxGvVdamPfltVITUvHT3sPIvzOY0yZMgV6z29K2+mImTAyMpLKW7ZsGaZPn46TJ0/iwoULCAoKwqqVyxG8ZxOaNqyn1BYpho4O2rZtq1RnT/e6WPPDT1l1/Vfr1q2V1rlx4wbOnz+PlStXZs0Q5ZDJ5UhNTYPeiwg8ePAAjo6OqG+RBvwbp0MTW6V2mpqaSu03NzdHREQEPD09US3jEZDzh5x/97/iVlhFrqQYKY9hnmyGuxHheN+jISwsLKR936GZI6YmJSEx+jxq1bCHnp4e3NzclNpXw9rsvw9yLrn/L4+q9cpXXrGKozjlazJWEdoifaaSH0DIfmWziGWkZoowNjaGp6dn3ueLRTVV5814mPsyxfy8FCdWXtvkF6skyy+oXtlkZGTg2LFj6NixY9YPPEXdX/mVX9j11d2mNMovDg3uy4wldXGs6Vp0vDYe+vLUwuVYk8erJo+L8hqrsOvns43K56ko5ZezthRqG022pbDla+B4ydAx+u/zNC2qeOWU1/93ilJOaeelsLH+jady3itMOaV1fi9FirtFC6J2Z/PPP//EwYMH0aZNG3VDFVmdOnVgbW2NqKioPDubhoaGuQ4ipK+vr3LClclkEAQBOjo60NEp7RtnlSnKV1wpVtQri5hz5azOjYkxXJ0d/5vvUEP6p2L0XtV2iXB1roVbd+5BByKQY/nt27cBAA0aNICOjg4EiDDQ15fKcZs5Hh99Pgvz58/H/C+UB3XKWZ6NjQ369euHfv36YdGiRfBo2hArv9mGoDXzlNqSXwzh37brZNsHZmZmSuskJSVh7ty5/w0y9fS/EXVNDA2kTnv2GIp/K3KviKf4t4mJSVZdcu77f+uc/UeWnMeQDkToQITw75/seVT8S7EOkDXAllLdBAGiqNhONfeKWLnVK395xSqO4pSvyViFb4v0mYJcOZ9FLEMHIgRByPVc8l9hqarzFOvmXFbQwFbFiZXXNvnFKsnyC6pXLqT9W9T9lV/5hV1f3W1Ko/zi0OS+/He+vjw160tXYXKsyeO1BNpS7mIVdv1CbFOk81U5b4tGz0nFUYLHi7489b88lcb/FZqMpYlclnZeChtLES/nea8w5ZTW+b0UFXZATrV7VFWrVoWVlZW6YYrl4cOHePHihXQrJBVP/54+OP7XeVy5Eak0Xy6XY9WqVWjUqJE0cFBuZs2aheXLl+Nx3LNCl2lgYACX2jULHI323LlzytOXrqFhPed8t2nevDlu376NunXrZv1xriX90dHRQcOGDREbG4snT/+r77lL1/KN6ebmhr/++ivP+9MNDAwgk8nyjdGwrjNC/7mm9G7akIvhMDOtgpr2tvluS0RERESkbdTubM6fPx9z5sxBcnKy2pVJSkpCeHg4wsPDAQD37t1DeHg4Hjx4gKSkJEydOhXnzp1DTEwMTpw4gZ49e6Ju3brw8fFRu+zKICEhQdq/ij+xj+Iw6fOBaOHeGN19J2L37t148OABLl68iD59+iAiIgKbNm3K9/bo1q1bw83NDQvXbcp1+YEDBzBo0CAcOHAAkZGRuH37NpYvX45DJ0PQ08cr3zqHhIRg6dKliIyMxPr167H7wHFM8BuQ7zZz5szBtm3bMHfuXNy4cQMRd+5i5+9HMGvJegBZz7K6urpi6MQAXLkRib/OX8JX/y7Ly7hx45CYmIj+Y2Yg7MpN3Ln7ANt/PYDbUTEAskbOvXbtGu7cuYPnz5/n2ikdM7QvYh/H4X//+x9u3bqF348EI2DFN/AfObDMr6QTEREREWma2rfRrlixAtHR0bC1tYWTk5PKJdVLly4VOlZYWBg+/PBDaVrxrOXQoUOxceNGXL16FUFBQXj9+jUcHBzQqVMnzJ8/v3TetRmYUPJllLDg4GB4eHgozfMb0As/LJ+Dk7u/xcK1mzFz5kzcv38fZmZm+PDDD3Hu3Dk0adKkwNiTJk2Cr+9QTBvjC8cayq9cadSoEUxMTDB58mTExsbC0NAQ9erVww/LZmPwJ93yjTt58mSEhYVh7ty5MDc3x8oAf/i0ez/fbXx8fHDgwAHMmzcPS5Ysgb6eLhrUdcKIAb0AZN0a+9tvv8FvcD+06DYYTjUdsHb+VHQeOC7PmNWqVcPJkycxdfwoePUZAV1dXbg3dkWb99wBAJ9//jlOnTqF9u3bIykpSXr1SXY17Kvj0PZ1mLrkOzRr1gxWlubwG9ALsyaMyLc9RERERETaSO3OZvbRYtXVrl075Dc47pEjRzRWVkWzdetW4PHlfJcrRqZV8u82JsbGWDBtLBas+SHfcgInj0bg5NEq8/v374/+nvWl6ZjzBwGHrI5tnTp18N133+VZdn7Mzc2xa9euPLcRH12SysnOx8fnvyveuZTj6uqKv37bnGcsJycnlWPRzc0NR3ZsyLWeNjY2OHLkCBITE2Fubi5dqRRFUal8r9bv4MKFC3nWKzg4WGXZvs0rc20jEREREVF5pnZnM/v7AYmIiIiIiIgADTyzSURERERERJRTsa5sWllZITIyEtbW1qhatWq+g8e8fPmy2JWjyi0mJqasq0BERERERMVUrM7mqlWrYGZmBgBYvXq1JutDFVFuz2ZWtGcQH18GIAAmTkDcVQBixWsjoJrLytBGoOB2BlrkmNb+AcWonMnrGMs5X7Esr/lUdGW9L4tTfs5tpfLGFQAAK9pJREFUvnquufpURPnt46Ke38v6eKlINLkvNZnjopTD3Bevszl06NBc/11R5DdIERERAPA0QURERJQ/tQcIAgCZTIZ9+/YhIiICANC4cWP06NEDurq6mghfahSvbUlOToaxsXEZ14aIyrN0edbf2naeIyIiIiotanc2o6Ki0LVrVzx69Aj162e9+mLRokVwdHTEwYMH4eLionYlS4uuri4sLS0RHx8PADAxMcn3edTSIJfLkZ6ejtTUVOl1GsjMcUklNTX/+fkp6jY51y9M+Xltk1+s4pRf2G0Kqldxys8UIQeycpUpQgdi8fdLUbbRZFvUOV6KU35hyyhMrCK0RfpMKfKUc5tCHK9yEXiWkAqTqtWgp6eR3+yIiIiIKhy1vyWNHz8eLi4uOHfuHKysrAAAL168wKBBgzB+/HgcPHhQ7UqWJjs7OwCQOpxlTRRFpKSkwNjY+L+O7+tnyiu9vZf//PwUdZuc6xem/Ly2yS9Wccov7DYF1as45b9+BhECUgzkME5/AQFi8fdLUbbRZFvUOV6KU35hyyhMrCK0RfpMKfKUc5tCHa8idFJeolajd8v8BykiIiKi8krtzubp06eVOpoAUK1aNSxevBht2rRRN3ypEwQB9vb2qF69OjIyMsq6OsjIyMCZM2fg6ekp3eaL//tUeaVxYfnPz09Rt8m5fmHKz2ub/GIVp/zCblNQvYpT/v99igzBEGcazIPnrTnQF9OKv1+Kso0m26LO8VKc8gtbRmFiFaEt0mdKkaec2xTmeJXLYJASD532n+VfLyIiIqJKTO3OpqGhId68eaMyPykpCQYGBuqGLzO6urrl4lksXV1dZGZmwsjI6L/OZlKs8kpGRvnPz09Rt8m5fmHKz2ub/GIVp/zCblNQvYpTflIsdHWMsnL19iH05anF3y9F2UaTbVHneClO+YUtozCxitAW6TOlyFPObYpyvBIRERFRnnTUDdCtWzeMHDkS58+fhyiKEEUR586dw+jRo9GjRw9N1JGIiIiIiIi0jNqdzbVr18LFxQWtW7eGkZERjIyM0KZNG9StWxdr1qzRRB2JiIiIiIhIy6h9G62lpSV+//13REVFSa8+adiwIerWrat25YiIiIiIiEg7FbuzKZfLsWzZMuzfvx/p6eno0KEDAgIC+H7KiiLQIsd0AsvXVmXdlrzKL+t6lVeVfb8o2q9jBDT7rvDrK80r5/usvOZYG/eltinrfbyoJiDP8Wqo0iy/rNtfkZTX80heNJ17bWt/JVbs22i//vprzJw5E6ampqhRowbWrFmDsWPHarJuREREREREpKWK3dnctm0bNmzYgCNHjmDfvn34448/8NNPP0Eul2uyfkRERERERKSFit3ZfPDgAbp27SpNe3t7QxAEPH78WCMVIyIiIiIiIu1V7M6m4t2P2enr6yMjI0PtShEREREREZF2K/YAQaIowtfXF4aGhtK81NRUjB49GlWqVJHm7d27V70aEhERERERkdYpdmdz6NChKvMGDRqkVmWIiIiIiIioYih2Z3PLli2arAcRERERERFVIMV+ZpOIiIiIiIgoL+xsEhERERERkcYV+zZa0iKBFrnMSyj9epS0nO2siG0sDeX5eGGOS15l2Mfl+RgnIiptleG8X55V8P3PK5tERERERESkccXqbDZv3hyvXr0CAMybNw/JyckarRQRERERERFpt2J1NiMiIvD27VsAwNy5c5GUlKTRShEREREREZF2K9Yzm+7u7hg2bBg++OADiKKI5cuXw9TUNNd158yZo1YFiYiIiIiISPsUq7O5detWBAQE4MCBAxAEAX/++Sf09FRDCYLAziYREREREVElVKzOZv369bFz504AgI6ODk6cOIHq1atrtGJERERERESkvdR+9YlcLtdEPYiIiIiIiKgC0ch7NqOjo7F69WpEREQAABo1aoQJEybAxcVFE+GJiIiIiIhIy6j9ns0jR46gUaNGuHDhAtzc3ODm5obz58+jcePGOHbsmCbqSERERERERFpG7Sub06dPx6RJk7B48WKV+dOmTUPHjh3VLYKIqHgCLVTnffW89OtB2i/nsRSYUDb1oMLJ7bPPnGlWae1jfvaItJraVzYjIiLg5+enMn/48OG4efOmuuGJiIiIiIhIC6nd2bSxsUF4eLjK/PDwcI5QS0REREREVEmpfRvt559/jpEjR+Lu3bt4//33AQAhISFYsmQJ/P391a4gERERERERaR+1O5uzZ8+GmZkZVqxYgRkzZgAAHBwcEBgYiPHjx6tdQSIiIiIiItI+anc2BUHApEmTMGnSJLx58wYAYGZmpnbFiIiIiIiISHup/cxmdmZmZmp1NM+cOYPu3bvDwcEBgiBg3759SstFUcScOXNgb28PY2NjeHt7486dO2rWmoiIiIiIiDRNo51Ndb19+xbNmjXD+vXrc12+dOlSrF27Ft988w3Onz+PKlWqwMfHB6mpqaVcUyIiIiIiIsqP2rfRalKXLl3QpUuXXJeJoojVq1dj1qxZ6NmzJwBg27ZtsLW1xb59+9C/f//SrCoRERERERHlo1x1NvNz7949xMXFwdvbW5pnYWGBli1bIjQ0NM/OZlpaGtLS0qTpxMREAEBGRgYyMjJKttIaoKijUl11jHKuVLT5xdlGk7EUyzQZqyjblFBbMv5dpvhb69pSEvulrMrPJ5b0markx6vGYhVlmyLEkj5P3C/F26aU2lLo85465VeEvJTxfsnzvFec8pnjEoul9HnS8raoLKsIOf53mcp5r6TKL+cK248SRFEU1Smkc+fO+Oabb1CvXr3ihsmVIAj47bff0KtXLwDA33//jTZt2uDx48ewt7eX1uvbty8EQcAvv/ySa5zAwEDMnTtXZf6OHTtgYmKi0ToTERERERFVdMnJyfjss8+QkJAAc3PzPNdT68qmvr4+rl69qk6IEjdjxgyl930mJibC0dERnTp1ynfHlBcZGRk4duwYOnbsCH19/ayZi2oqrzTjYdHmF2cbTcZSLNNkrKJsU0JtydAxwrGma9Hx2njoy1O1ry0lsV/Kqvx8YmVMuZf1mVLkqbDlV4T9olimBW2RPk+Kcx/3S9G2KaW2ZCypW7jznha0RWtiKZYVIVae573ilM8cl1gspe8R06K0ui0qyypCjv9dpnLeK6nyyznF3aIFUfs22kGDBmHTpk1YvHixuqHyZWdnBwB4+vSp0pXNp0+fwt3dPc/tDA0NYWhoqDJfX1//v86bFlCqb87/KIo6vzjbaDKWYpkmYxVlmxJui748Nevko21tKYn9UlblFyKWlKfCblMR9otimRa1RTr3cb8UbZtSbkuB5z11yq8IeSkn+0XlvFec8stJW8pdXjQYS1+eyu98miq/BNui9HkqifLLucL2o9TubGZmZmLz5s04fvw43nnnHVSpUkVp+cqVK9UtAgDg7OwMOzs7nDhxQupcJiYm4vz58/jiiy80UgYRERERERFphtqdzevXr6N58+YAgMjISKVlgiAUKVZSUhKioqKk6Xv37iE8PBxWVlaoVasWJk6ciAULFqBevXpwdnbG7Nmz4eDgID3XSUREREREROWD2p3NU6dOaaIeAICwsDB8+OGH0rTiWcuhQ4di69at+PLLL/H27VuMHDkSr1+/xgcffIDDhw/DyMhIY3UgIiIiIiIi9Wns1SdRUVGIjo6Gp6cnjI2NIYpika9stmvXDvkNjisIAubNm4d58+apW10iIiIiIiIqQTrqBnjx4gU6dOgAV1dXdO3aFU+ePAEA+Pn5YfLkyWpXkIiIiIiIiLSP2p3NSZMmQV9fHw8ePFB6b2W/fv1w+PBhdcMTERERERGRFlL7NtqjR4/iyJEjqFlT+R0x9erVw/3799UNT0RERERERFpI7Subb9++VbqiqfDy5ctc329JREREREREFZ/anc22bdti27Zt0rQgCJDL5Vi6dKnSyLJERERERERUeah9G+3SpUvRoUMHhIWFIT09HV9++SVu3LiBly9fIiQkRBN1JCIiIiIiIi2j9pXNJk2aIDIyEh988AF69uyJt2/f4uOPP8bly5fh4uKiiToSERERERGRltHIezYtLCzw1VdfaSIUERERERERVQAa6Wy+evUKmzZtQkREBACgUaNGGDZsGKysrDQRnoiIiIiIiLSM2rfRnjlzBk5OTli7di1evXqFV69eYe3atXB2dsaZM2c0UUciIiIiIiLSMmpf2Rw7diz69euHjRs3QldXFwAgk8kwZswYjB07FteuXVO7kkRERERERKRd1L6yGRUVhcmTJ0sdTQDQ1dWFv78/oqKi1A1PREREREREWkjtzmbz5s2lZzWzi4iIQLNmzdQNT0RERERERFqoWLfRXr16Vfr3+PHjMWHCBERFRaFVq1YAgHPnzmH9+vVYvHixZmpJREREREREWqVYnU13d3cIggBRFKV5X375pcp6n332Gfr161f82hEREREREZFWKlZn8969e5quBxEREREREVUgxeps1q5dW9P1ICIiIiIiogpE7VefAMDjx49x9uxZxMfHQy6XKy0bP368JoogIiIiIiIiLaJ2Z3Pr1q0YNWoUDAwMUK1aNQiCIC0TBIGdTSIiIiIiokpI7c7m7NmzMWfOHMyYMQM6Omq/SYWIiIiIiIgqALV7h8nJyejfvz87mkRERERERCRRu4fo5+eH3bt3a6IuREREREREVEGofRvtokWL0K1bNxw+fBhNmzaFvr6+0vKVK1eqWwQRERERERFpGY10No8cOYL69esDgMoAQURERERERFT5qN3ZXLFiBTZv3gxfX18NVIeIiIiIiIgqArWf2TQ0NESbNm00URciIiIiIiKqINTubE6YMAHr1q3TRF2IiIiIiIioglD7NtoLFy7g5MmTOHDgABo3bqwyQNDevXvVLYKIiIiIiIi0jNqdTUtLS3z88ceaqAsRERERERFVEGp3Nrds2aKJehAREREREVEFovYzm0REREREREQ5qX1l09nZOd/3ad69e1fdIoiIiIiIiEjLqN3ZnDhxotJ0RkYGLl++jMOHD2Pq1KnqhiciIiIiIiItpHZnc8KECbnOX79+PcLCwtQNT0RERERERFqoxJ7Z7NKlC/bs2VNS4YmIiIiIiKgcK7HO5q+//gorK6uSCk9ERERERETlmNq30Xp4eCgNECSKIuLi4vDs2TNs2LBB3fBERERERESkhdTubPbq1UtpWkdHBzY2NmjXrh0aNGigbngiIiIiIiLSQmp3NgMCAjRRDyIiIiIiIqpASuyZzZISGBgIQRCU/vAKKhERERERUflS7CubOjo6Ss9q5kYQBGRmZha3iDw1btwYx48fl6b19NS+QEtEREREREQaVOxe2m+//ZbnstDQUKxduxZyuby44fOlp6cHOzu7EolNRERERERE6it2Z7Nnz54q827fvo3p06fjjz/+wMCBAzFv3jy1KpeXO3fuwMHBAUZGRmjdujUWLVqEWrVq5bpuWloa0tLSpOnExEQAQEZGBjIyMkqkfpqkqKNSXXWMcq5UtPnF2UaTsRTLNBmrKNuUUFsy/l2m+Fvr2lIS+6Wsys8nlvSZquTHq8ZiFWWbIsSSPk/cL8XbppTaUujznjrlV4S8lPF+yfO8V5zymeMSi6X0edLytqgsqwg5/neZynmvpMov5wrbjxJEURTVLezx48cICAhAUFAQfHx8sGjRIjRp0kTdsLn6888/kZSUhPr16+PJkyeYO3cuHj16hOvXr8PMzExl/cDAQMydO1dl/o4dO2BiYlIidSQiIiIiIqqokpOT8dlnnyEhIQHm5uZ5rqdWZzMhIQELFy7EunXr4O7ujiVLlqBt27bFDVcsr1+/Ru3atbFy5Ur4+fmpLM/tyqajoyOeP3+e744pLzIyMnDs2DF07NgR+vr6WTMX1VReacbDos0vzjaajKVYpslYRdmmhNqSoWOEY03XouO18dCXp2pfW0piv5RV+fnEyphyL+szpchTYcuvCPtFsUwL2iJ9nhTnPu6Xom1TSm3JWFK3cOc9LWiL1sRSLCtCrDzPe8UpnzkusVhK3yOmRWl1W1SWVYQc/7tM5bxXUuWXc4mJibC2ti6ws1ns22iXLl2KJUuWwM7ODj///HOut9WWBktLS7i6uiIqKirX5YaGhjA0NFSZr6+v/1/nTQso1TfnfxRFnV+cbTQZS7FMk7GKsk0Jt0Vfnpp18tG2tpTEfimr8gsRS8pTYbepCPtFsUyL2iKd+7hfirZNKbelwPOeOuVXhLyUk/2ict4rTvnlpC3lLi8ajKUvT+V3Pk2VX4JtUfo8lUT55Vxh+1HF7mxOnz4dxsbGqFu3LoKCghAUFJTrenv37i1uEYWSlJSE6OhoDB48uETLISIiIiIiosIrdmdzyJAhBb76pCRMmTIF3bt3R+3ataVnRXV1dTFgwIBSrwsRERERERHlrtidza1bt2qwGoX38OFDDBgwAC9evICNjQ0++OADnDt3DjY2NmVSHyIiIiIiIlJV7M5mWdm5c2dZV4GIiIiIiIgKoFPWFSAiIiIiIqKKh51NIiIiIiIi0jh2NomIiIiIiEjj2NkkIiIiIiIijWNnk4iIiIiIiDSOnU0iIiIiIiLSOHY2iYiIiIiISOPY2SQiIiIiIiKNY2eTiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI49jZJCIiIiIiIo1jZ5OIiIiIiIg0jp1NIiIiIiIi0jh2NomIiIiIiEjj2NkkIiIiIiIijWNnk4iIiIiIiDSOnU0iIiIiIiLSOHY2iYiIiIiISOPY2SQiIiIiIiKNY2eTiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI49jZJCIiIiIiIo1jZ5OIiIiIiIg0jp1NIiIiIiIi0jh2NomIiIiIiEjj2NkkIiIiIiIijWNnk4iIiIiIiDSOnU0iIiIiIiLSOHY2iYiIiIiISOPY2SQiIiIiIiKNY2eTiIiIiIiINI6dTSIiIiIiItI4djaJiIiIiIhI49jZJCIiIiIiIo1jZ5OIiIiIiIg0jp1NIiIiIiIi0jh2NomIiIiIiEjjtLazuX79ejg5OcHIyAgtW7bEhQsXyrpKRERERERE9C+t7Gz+8ssv8Pf3R0BAAC5duoRmzZrBx8cH8fHxZV01IiIiIiIigpZ2NleuXInPP/8cw4YNQ6NGjfDNN9/AxMQEmzdvLuuqEREREREREQC9sq5AUaWnp+Off/7BjBkzpHk6Ojrw9vZGaGioyvppaWlIS0uTphMSEgAAL1++REZGRslXWE0ZGRlITk7GixcvoK+vnzUz3UB5pRcvija/ONtoMpZimSZjFWWbEmpLho5BVq7SDaAvl2tfW0piv5RV+fnEynjxQjlPhS2/IuwXxTItaIv0eVKc+7hfirZNKbUlI72Q5z0taIvWxFIsK0KsPM97xSmfOS6xWErfI7S8LSrLKkKO/12mct4rqfLLuTdv3gAARFHMdz1BLGiNcubx48eoUaMG/v77b7Ru3Vqa/+WXX+L06dM4f/680vqBgYGYO3duaVeTiIiIiIioQouNjUXNmjXzXK51VzaLasaMGfD395em5XI5Xr58iWrVqqFFixa4ePGiRst77733NBozMTERjo6OiI2Nhbm5ucbiApqva2WPWVK50pb2a0tM5knzMUsibmXPU0nFZZ6Yp/Kep5KKqw0x+Z1PO2IyT1kxL1y4gDdv3sDBwSHfdbWus2ltbQ1dXV08ffpUaf7Tp09hZ2ensr6hoSEMDQ2V5llaWgIAdHV1NX6QlERMADA3N9eKulbmmAqazpW2tF9bYiowT5qlLee+yr5PmSfmqbznqaTiaktMgN/5tCEmwDxZWFjAwsKiwHW1boAgAwMDvPPOOzhx4oQ0Ty6X48SJE0q31RbG2LFjNV29EolZUrSl/doSs6RoS/u1JWZJ0Zb2l9Q+1ZZcVfZ9yjxpnjbVVdMq+z7VljwB2tN+bYlZUrSl/UWJqXXPbAJZrz4ZOnQovv32W7Ro0QKrV6/Grl27cOvWLdja2pZ19TQqMTERFhYWSEhIKLErPKQZzJV2YJ60A/OkHZgn7cA8aQfmSTswT0WjdbfRAkC/fv3w7NkzzJkzB3FxcXB3d8fhw4crXEcTyLoNOCAgQOVWYCp/mCvtwDxpB+ZJOzBP/9/evQdFdZ5hAH+WcAdhVS67xIAgiqUoRYkMseEiFHAyDia9JNY6eBkTCU4Tr4Q4appMBqJNZzRjpE3TQDJGk9iYtEnVKrIYEBBQ4oVIZcWQVpBRLoLc4e0fDmdyEAR1YVGe38zO7H7fd855z3n91nk5l30wME8PBubpwcA83Z0H8swmERERERERjW4P3D2bRERERERENPqx2CQiIiIiIiKTY7FJREREREREJsdik4iIiIiIiEyOxeYot2vXLkyePBm2trYICQnByZMnzR3SmPbaa69Bo9GoXtOnT1f629rakJSUhIkTJ8LR0RG//OUvcfXqVTNGPDYcP34cCxYsgIeHBzQaDb744gtVv4hgy5Yt0Ov1sLOzQ3R0NC5evKgaU1dXh8WLF8PJyQlarRYrVqxAc3PzCO7Fw2+wPC1duvS2+RUXF6cawzwNv9TUVDz++OMYN24c3NzcsHDhQpSXl6vGDOW7rqqqCk899RTs7e3h5uaGDRs2oKurayR35aE2lDxFRETcNqdWrVqlGsM8Da/du3dj5syZcHJygpOTE0JDQ3Hw4EGln3NpdBgsT5xL947F5ij2ySefYO3atdi6dStOnTqFwMBAxMbGora21tyhjWk//elPUV1drbxyc3OVvjVr1uCf//wnPvvsM+Tk5ODKlSt45plnzBjt2HDz5k0EBgZi165d/fZv27YNO3fuRHp6OgoLC+Hg4IDY2Fi0tbUpYxYvXozz58/jyJEj+Oqrr3D8+HE8//zzI7ULY8JgeQKAuLg41fzau3evqp95Gn45OTlISkpCQUEBjhw5gs7OTsTExODmzZvKmMG+67q7u/HUU0+ho6MDJ06cQGZmJjIyMrBlyxZz7NJDaSh5AoCVK1eq5tS2bduUPuZp+E2aNAlpaWkoKSlBcXEx5s2bh/j4eJw/fx4A59JoMVieAM6leyY0as2ZM0eSkpKUz93d3eLh4SGpqalmjGps27p1qwQGBvbb19DQIFZWVvLZZ58pbd99950AkPz8/BGKkADIgQMHlM89PT2i0+lk+/btSltDQ4PY2NjI3r17RUSkrKxMAEhRUZEy5uDBg6LRaOR///vfiMU+lvTNk4hIQkKCxMfHD7gM82QetbW1AkBycnJEZGjfdf/617/EwsJCampqlDG7d+8WJycnaW9vH9kdGCP65klEJDw8XF566aUBl2GezGP8+PHy17/+lXNplOvNkwjn0v3gmc1RqqOjAyUlJYiOjlbaLCwsEB0djfz8fDNGRhcvXoSHhwd8fHywePFiVFVVAQBKSkrQ2dmpytn06dPh6enJnJlRZWUlampqVHlxdnZGSEiIkpf8/HxotVoEBwcrY6Kjo2FhYYHCwsIRj3ksMxgMcHNzg5+fHxITE3H9+nWlj3kyj8bGRgDAhAkTAAztuy4/Px8zZsyAu7u7MiY2NhY3btxQnSkg0+mbp1579uyBi4sLAgICkJKSgpaWFqWPeRpZ3d3d2LdvH27evInQ0FDOpVGqb556cS7dG0tzB0D9u3btGrq7u1X/aAHA3d0dFy5cMFNUFBISgoyMDPj5+aG6uhp/+MMf8OSTT+LcuXOoqamBtbU1tFqtahl3d3fU1NSYJ2BSjn1/c6m3r6amBm5ubqp+S0tLTJgwgbkbQXFxcXjmmWfg7e0No9GIV199FfPnz0d+fj4eeeQR5skMenp68PLLL2Pu3LkICAgAgCF919XU1PQ753r7yLT6yxMA/Pa3v4WXlxc8PDxw5swZJCcno7y8HJ9//jkA5mmknD17FqGhoWhra4OjoyMOHDgAf39/lJaWci6NIgPlCeBcuh8sNonuwvz585X3M2fOREhICLy8vPDpp5/Czs7OjJERPfiee+455f2MGTMwc+ZMTJkyBQaDAVFRUWaMbOxKSkrCuXPnVPem0+gzUJ5+fD/zjBkzoNfrERUVBaPRiClTpox0mGOWn58fSktL0djYiP379yMhIQE5OTnmDov6GChP/v7+nEv3gZfRjlIuLi545JFHbnsi2dWrV6HT6cwUFfWl1Woxbdo0VFRUQKfToaOjAw0NDaoxzJl59R77O80lnU5324O3urq6UFdXx9yZkY+PD1xcXFBRUQGAeRppq1evxldffYXs7GxMmjRJaR/Kd51Op+t3zvX2kekMlKf+hISEAIBqTjFPw8/a2hq+vr6YPXs2UlNTERgYiB07dnAujTID5ak/nEtDx2JzlLK2tsbs2bORlZWltPX09CArK0t1/TiZV3NzM4xGI/R6PWbPng0rKytVzsrLy1FVVcWcmZG3tzd0Op0qLzdu3EBhYaGSl9DQUDQ0NKCkpEQZc+zYMfT09Cj/odDI++9//4vr169Dr9cDYJ5Giohg9erVOHDgAI4dOwZvb29V/1C+60JDQ3H27FnVHweOHDkCJycn5bI0uj+D5ak/paWlAKCaU8zTyOvp6UF7ezvn0ijXm6f+cC7dBXM/oYgGtm/fPrGxsZGMjAwpKyuT559/XrRarepJVzSy1q1bJwaDQSorKyUvL0+io6PFxcVFamtrRURk1apV4unpKceOHZPi4mIJDQ2V0NBQM0f98GtqapLTp0/L6dOnBYD86U9/ktOnT8v3338vIiJpaWmi1Wrlyy+/lDNnzkh8fLx4e3tLa2urso64uDgJCgqSwsJCyc3NlalTp8qiRYvMtUsPpTvlqampSdavXy/5+flSWVkpR48elVmzZsnUqVOlra1NWQfzNPwSExPF2dlZDAaDVFdXK6+WlhZlzGDfdV1dXRIQECAxMTFSWloqhw4dEldXV0lJSTHHLj2UBstTRUWFvP7661JcXCyVlZXy5Zdfio+Pj4SFhSnrYJ6G3yuvvCI5OTlSWVkpZ86ckVdeeUU0Go38+9//FhHOpdHiTnniXLo/LDZHuXfeeUc8PT3F2tpa5syZIwUFBeYOaUx79tlnRa/Xi7W1tTz66KPy7LPPSkVFhdLf2toqL774oowfP17s7e3l6aeflurqajNGPDZkZ2cLgNteCQkJInLr5082b94s7u7uYmNjI1FRUVJeXq5ax/Xr12XRokXi6OgoTk5OsmzZMmlqajLD3jy87pSnlpYWiYmJEVdXV7GyshIvLy9ZuXLlbX9cY56GX385AiAffPCBMmYo33WXL1+W+fPni52dnbi4uMi6deuks7NzhPfm4TVYnqqqqiQsLEwmTJggNjY24uvrKxs2bJDGxkbVepin4bV8+XLx8vISa2trcXV1laioKKXQFOFcGi3ulCfOpfujEREZufOoRERERERENBbwnk0iIiIiIiIyORabREREREREZHIsNomIiIiIiMjkWGwSERERERGRybHYJCIiIiIiIpNjsUlEREREREQmx2KTiIiIiIiITI7FJhEREREREZkci00iInqgGAwGaDQaNDQ03Nd6li5dioULF5okJlOuazRv+/3330dMTMyIx3Po0CH87Gc/Q09Pj0nXS0REw4vFJhERmUV6ejrGjRuHrq4upa25uRlWVlaIiIhQje0tMI1GI5544glUV1fD2dl5WOPr3aZGo4GFhQWcnZ0RFBSEjRs3orq6WjV2x44dyMjIGNZ4Ll++DI1Gg9LS0hHfNgC0tbVh8+bN2Lp167Bvq6+4uDhYWVlhz549I75tIiK6dyw2iYjILCIjI9Hc3Izi4mKl7ZtvvoFOp0NhYSHa2tqU9uzsbHh6emLKlCmwtraGTqeDRqMZkTjLy8tx5coVFBUVITk5GUePHkVAQADOnj2rjHF2doZWqx1wHR0dHcMW32DbNpX9+/fDyckJc+fOHfZt9Wfp0qXYuXOnWbZNRET3hsUmERGZhZ+fH/R6PQwGg9JmMBgQHx8Pb29vFBQUqNojIyOV9z++jDYjIwNarRaHDx/GT37yEzg6OiIuLk519rG7uxtr166FVqvFxIkTsXHjRojIkOJ0c3ODTqfDtGnT8NxzzyEvLw+urq5ITExUxvS9dDQiIgKrV6/Gyy+/DBcXF8TGxgIAzp07h/nz58PR0RHu7u5YsmQJrl27pizX09ODbdu2wdfXFzY2NvD09MSbb74JAPD29gYABAUFQaPRKGd/+267vb0dv//97+Hm5gZbW1v8/Oc/R1FRkepYajQaZGVlITg4GPb29njiiSdQXl5+x+Owb98+LFiwQNU2lOPa09OD1NRUeHt7w87ODoGBgdi/f79qzD/+8Q9MnToVtra2iIyMRGZm5m2XSi9YsADFxcUwGo13jJOIiEYPFptERGQ2kZGRyM7OVj5nZ2cjIiIC4eHhSntraysKCwuVYrM/LS0t+OMf/4iPPvoIx48fR1VVFdavX6/0v/3228jIyMDf/vY35Obmoq6uDgcOHLinmO3s7LBq1Srk5eWhtrZ2wHGZmZmwtrZGXl4e0tPT0dDQgHnz5iEoKAjFxcU4dOgQrl69it/85jfKMikpKUhLS8PmzZtRVlaGjz/+GO7u7gCAkydPAgCOHj2K6upqfP755/1ud+PGjfj73/+OzMxMnDp1Cr6+voiNjUVdXZ1q3KZNm/D222+juLgYlpaWWL58+R33Ozc3F8HBwaq2oRzX1NRUfPjhh0hPT8f58+exZs0a/O53v0NOTg4AoLKyEr/61a+wcOFCfPvtt3jhhRewadOm27bv6ekJd3d3fPPNN3eMk4iIRhEhIiIyk/fee08cHByks7NTbty4IZaWllJbWysff/yxhIWFiYhIVlaWAJDvv/9eRESys7MFgNTX14uIyAcffCAApKKiQlnvrl27xN3dXfms1+tl27ZtyufOzk6ZNGmSxMfHDxhb3+382MGDBwWAFBYWiohIQkKCal3h4eESFBSkWuaNN96QmJgYVdsPP/wgAKS8vFxu3LghNjY28t577/UbT2VlpQCQ06dPq9p/vO3m5maxsrKSPXv2KP0dHR3i4eGh7H/vfh09elQZ8/XXXwsAaW1t7Xfb9fX1AkCOHz+uah/suLa1tYm9vb2cOHFCtdyKFStk0aJFIiKSnJwsAQEBqv5Nmzb1e+yDgoLktdde6zdGIiIafSzNVOMSEREhIiICN2/eRFFREerr6zFt2jS4uroiPDwcy5YtQ1tbGwwGA3x8fODp6Tngeuzt7TFlyhTls16vV846NjY2orq6GiEhIUq/paUlgoODh3wpbV+9y93pvtHZs2erPn/77bfIzs6Go6PjbWONRiMaGhrQ3t6OqKioe4qpdz2dnZ2q+yqtrKwwZ84cfPfdd6qxM2fOVN7r9XoAQG1tbb/HubW1FQBga2urtA3luFZUVKClpQW/+MUvVOvr6OhAUFAQgFv3xD7++OOq/jlz5vS7f3Z2dmhpaRlg74mIaLRhsUlERGbj6+uLSZMmITs7G/X19QgPDwcAeHh44LHHHsOJEyeQnZ2NefPm3XE9VlZWqs8ajeaeC8mh6C3cJk+ePOAYBwcH1efm5mYsWLAAb7311m1j9Xo9Ll26ZNIYB/PjY9ZbNA/00yITJ06ERqNBfX39XW2jubkZAPD111/j0UcfVfXZ2Njc1boAoK6uDq6urne9HBERmQfv2SQiIrOKjIyEwWCAwWBQ/eRJWFgYDh48iJMnT97xfs3BODs7Q6/Xo7CwUGnr6upCSUnJPa2vtbUVf/nLXxAWFnZXhc+sWbNw/vx5TJ48Gb6+vqqXg4MDpk6dCjs7O2RlZfW7vLW1NYBbD+UZSO/TevPy8pS2zs5OFBUVwd/ff8ix9rdtf39/lJWVKW1DOa7+/v6wsbFBVVXVbfv82GOPAbj1oKgfP5EYgOqBRr3a2tpgNBqVM6JERDT6sdgkIiKzioyMRG5uLkpLS5UzmwAQHh6OP//5z+jo6LivYhMAXnrpJaSlpeGLL77AhQsX8OKLL6qedHontbW1qKmpwcWLF7Fv3z7MnTsX165dw+7du+8qhqSkJNTV1WHRokUoKiqC0WjE4cOHsWzZMnR3d8PW1hbJycnYuHEjPvzwQxiNRhQUFOD9998HcOupuHZ2dsqDhRobG2/bhoODAxITE7FhwwYcOnQIZWVlWLlyJVpaWrBixYq7irev2NhY5ObmqtoGO67jxo3D+vXrsWbNGmRmZsJoNOLUqVN45513kJmZCQB44YUXcOHCBSQnJ+M///kPPv30U+V3Q398mXJBQQFsbGwQGhp6X/tBREQjh5fREhGRWUVGRqK1tRXTp09XnrwK3Co2m5qalJ9IuR/r1q1DdXU1EhISYGFhgeXLl+Ppp5/ut2Dry8/PDxqNBo6OjvDx8UFMTAzWrl0LnU53VzF4eHggLy8PycnJiImJQXt7O7y8vBAXFwcLi1t/+928eTMsLS2xZcsWXLlyBXq9HqtWrQJw637InTt34vXXX8eWLVvw5JNPqn42pldaWhp6enqwZMkSNDU1ITg4GIcPH8b48ePvKt6+VqxYgeDgYDQ2NsLZ2RnA0I7rG2+8AVdXV6SmpuLSpUvQarWYNWsWXn31VQC3ftJl//79WLduHXbs2IHQ0FBs2rQJiYmJqktt9+7di8WLF8Pe3v6+9oOIiEaORobzphYiIiJ6aPz617/GrFmzkJKSMqzbefPNN5Geno4ffvgBAHDt2jXlctve3xslIqLRj5fREhER0ZBs376936fp3q93330XRUVFuHTpEj766CNs374dCQkJSv/ly5fx7rvvstAkInrA8MwmERERmdWaNWvwySefoK6uDp6enliyZAlSUlJgacm7fYiIHmQsNomIiIiIiMjkeBktERERERERmRyLTSIiIiIiIjI5FptERERERERkciw2iYiIiIiIyORYbBIREREREZHJsdgkIiIiIiIik2OxSURERERERCbHYpOIiIiIiIhM7v+R87HDwvlfWQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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Wj7p8Z87LUFqfxl6LpHPsvAxX/oeBv+OMjnc1OZerIi7k5MZ/2b3dqTZ24pihzvH74Zfib/LnlGoHtMJ+z8nNb/XbblMkPg8+gwBLPbO713AgCSE6EvbLSazXSbywIIgrn3yLyIT0AfsfiCzLFBUVkZmZqen9xBc0XaXbe++78sorNZv2N9bP6Vhvp+2f/ZOzml5Rp9Mp6pTF7OMu9Upbx2o7jaRbvOlr9DvvHHBMoZJItT6BI+WN7O+HDvVMcFd/wBmtVziTuNh4Tq3xTD9tyDKGrYtjfQtn+jzDHaNtXdzQFx7iGKui44aHX+TZKYcPuPZTUlKc+p25emTzkUceGXF/n3I24+Pjqa6u7returqa0NDQQUc1AUwm06AJV00mE0ajEZPJpOnJH41mbm7ugG1ms5nIyEiOPOZ41lTdwayCxzBINqyKjrXjbue0i5aw+o0Uu7efepE6Krz69VBm7X58wHdDHTNUGQCr3wjr/132bZx28V0O2TWcVs600/luU8YAe/cdE+rYeXk9edC6D1r+CPU/cPtb/r/n3Cv/jMlkGlRrxHYZpP7jpp3Od5syB62/Q+eyty6vJw3R9skOaQ1afk/bO3TMUOc4+zbGTzudvLw8vvu3o1ppA7afdNESyop2Ir965AEvbXS8F/x7zm1/s99vTy/Z8GstJnfOyOvBZVnGZrNpfjP3BU1X6fbe+3JycjRNQj+Wz+lYbqeS3b9xdtMrfR07vaRwUtVyyvUXkZo9kbKinVQWbiUh6xCSM4de0uIOW8dyO42kG0gryg7YP4e8VdFhuuR9jsocz+o3Hh7iuZdq9zPU4f7AMH0eR7XWZP2R8j1WLr7yTr77j/39kRH7aYP0+Rx6Ho/QTzn14rsG7UMM1bdwps8z3Lkcti4a9fmGaq/TFt7tsF3D9XnHTz5U8/uJK/0de/CpabR33HEHn376Kb/++mvftgsuuICGhgZWrVplVzktLS2EhYXR0NBAeXk5eXl5mp78HTt2aKppNpt55JFHuPPOOzGZTJQV7aSqaBvxmZP6PRAd3S7LMuu/+wI/awuJBzxcHdXq/a6icCsWQygzjz2hr/7OavV+l5A2ru+cVpYU2HXMSOUMV3dnbO7dHpmSw+tvvd/XVu6qv71a9tTfUa3e7YO1/WjO5YF11+v1ml3737z2IMcU/q3fjT/7qLNIOMAJBWgkhNYFL5I64ySGwxW/fV/RdJXugfc+LRjr53Qst9O3Ly1h9t5nB2yvV0KpMiSRZ92JrmfEc824O5hz0ZJBVNxj61hup5F0t794FRPK3kFRVIez9x6+f3t58rnnrFbv9pikDNHn84E+X3nBZr7dsJ2b/nh3v9+TlvX35vtJr2ZSUhKRkZEjTqNF8SCtra3Kpk2blE2bNimA8uSTTyqbNm1SSkpKFEVRlDvvvFO5+OKL+/YvKipSAgMDldtuu03ZsWOHsmzZMkWv1yurVq2yu8zm5mYFUBoaGpRff/1VsVqtmtXHarVqrtnV1aXcf//9SldXl2aaiuIaW8eypqK4pq18pf6+oqkoajs9+tc/KT+s+reyt3BH3/ZvXn9I6b43XFHuC1Ws94YpFX9KU5T7QhXLvRHK2hcWKz999na//V1tq69oukp3LP+eXKU7rGZTmaIUfav+dQBfaKeGok1Ky31xinJfaL9/tntDB2xT7gtVuu8NH/K37mpbXaXpC+00km5bwRpFvi9MUe4LVf61/AHl5y/+I9rJTnyl/r6iKdpJ1WxoaFAApbm5edj9PTqNdsOGDcydO7fvc+/ayksuuYQVK1ZQWVnZLx1IRkYGn3zyCYsXL+bpp58mOTmZF1980efTnggEAvfSadUzdc5Z/d5IzrloCWVFZ/W9Yew2hvLdq1dwrPV7ji5/CcpfQl4nsdqBUQ+BwCf45TWUj25CUmwokg5pwdMwfaGnrdIEW0sV3a+fSwSdlChxJFOLvncKXPpNdHe2cGLNS/2OMUg2qoq2OTSdVuBirGba/3M9QSh8zLGcddkdBJiMnrZKIBDYgUedzTlz5qAMM4t3xYoVgx6zadMmF1olEAjGKsmZ4/t1MKXz/4btjaP6Ak7oJYVZBY9RVnSW6IgKDg6ay7F9eBM6bABIik39nDUPwpI8bNwosbRT+/wZxNlqKLbFseWoZZCdSG3xDuIzJzE3c3zPOu6XB6zjjs+c5EHDBQfS8L8HiTWXUKuE0n3cn4SjKRD4ENpl4RUIBIKDjNri3zgwwn7vqIfAuykr2snPX7xDWdFOT5vi1VQVbe1zNHvRYaOq6NchjvABmsuhcDVNL/+OuPadNCjBfDn5b2QlJ5CcMZ5Dj/9d38ui5MzxrBl3B7Kyrzv0g9+R4mWSN1G9ndCNywB4OfBKzjjmUA8bJBAIHEE4mwKBQDAECVmHICv9vU2bIolRDy9n9RsPk/DqkRy27ioSXj2S1W887GmTvJb8jlAOnGCkKFDQEewZg0bLL6/BU5Pg9TMIr/qebkXPk2FLuOTMk4c8ZM5FS6i8ZD3fR58HQJ5lKwV7it1ksGBYmkppe/0CDFj5XJ7B8edcjU4nuq4CgS8hfrECgUAwBL2jHtb9Rj268CMhIdGDVgmGo6xoJ8cUPNo3LXLf1GcxwjkY4xq/RZLoczh7o3yO3/Y4yN2eNc5Rmsvho5tA2TdSq5ds3HTu/BEdlOTM8Rx57TLKdElESm1s+c+DrrZWMALSptdRnjqE4LY9KAq0R09hRnaCp80SCAQOIpxNgUAgGIY5Fy2h6pL1rD1iOXuVaAIlMzs+fMLTZgmGoLJw64AUNmLq8xDUFxK9cSkAD1ov4PeWe7ih+/8wK0ZiKlfT9u/LwCZ72EgHaCjs52gC6FCIsVbZdbjO4If12DsAOKn9Q776YaPmJgrsw9BRAx/fjIT6W5YkOKNxhfpCQSAQ+BQeDRDkSWRZ7vfXmzUNBgOyLPuErWNVs1dP67bylfr7imavnqPtlJA2joS0cby7Zxfn1zxF4s4VyObbweDvMlt9RdNVus7+nmIzJqKs7Z/0XVFg6658Jh7d7RI7tdZ0lW4/TcVG5zvXEqxYWCNPIm7OdRwWHUx4oIklbwfxiPw4wbs/ov2da/A/8U/QuAciMyE0aYCmt9z3yuQokhWp38sGq6KjXI4kwU7NlFkXUvr9M6RadlP3+RN0TVuB0TB0bjpf+Z16UzvZo9tRU4SO/i+NdNio2L2FuKnxTmnu/1cLfKWdenX3/ys0R68p2sl+TUkZLhzsQcSyZctYtmwZsiyTn5/P+vXrCQ720TUpAoHAI1Q2tjHhywtIkurZmnMjukPO97RJggNo7uhi+icnEyBZAPolf/9X0MWkTj0JS2MpQbHphMcke9hazxG++z2SNz9Bh2JicegT/N/8aX3fVbWY+ezzD3lYeQaDZEMBJEBBR8WM22nMWOAxu4fjl7I2lLV/4xLDF4Da5ndZr2Dysb9jWpL9z3up9Hsm/nQbZsXIc9nPM29arqtMFgzBzsI9nP3LRf0CtFkVHR/MeI3czAzPGSYQCPpoa2tj5syZNDc3ExoaOuR+Y8bZ7KWlpYWwsDBqa2upqqoiJycHvX7ot5aO0OvIaqlpNptZunQpixcv7pcTcLS4wtaxrAmuaStfqb+vaMLo2+n1pbdzaduL1OtjCb/jV9Abfab+rjqn3nTv+2rVB5z482W04c/2o54iPi0X81ePMK7mM2Cf8ykrEmuyb+eYC+4YlZ2+dE77NOODkJYfhVHu4AHrQs699j7GJUT023dHWT0fvvRXlkiv9hsltqFDuWlL3winN933Smqb+fAft3Kz4T2+lQ/hju6rqCGKL2+eSXJksP2aikLl0/NIbt3Mf2xzUU56hEMzY0mLCdPM1uHwpt/TcLjy2l+z8Vdi/ncFh+j2AKqjeY/1Cq7+v7sGbQdP2Oor7QS+U39f0RTtpGrGx8cTExMzorM5ZqfR9p5wvV6v6U1Sa029Xo/VanWJnb363lx/X9J0ZVsJTe9pp9R5V1O78h1i5BpaNrxF6FGXusxWX9LUWtfZdurc/S0AewKncviJf1A35rzNr6/fzqTCF/ocJ72kMGv341SWnK1JmgtfOKcAhvZqdO8+gEHuYKNtHPIhFzA+OXrAfpPSYmk96iikH17tt12Hjari7cRPS+2zz1vue5nxkRwdWAYWWG2bQg1R3DEvlcz4yL7pXvZqxp/9MLx6MmdJq7nt4xUstU3g0nnTuOaEyZrYag/e8HuyV1trzbgQEyF6CyjwYPcf+EQ+ikvmTSMzPnJUut5ef9Hn8w1N0U77NO1BBAgSCAQCB5gzZRzvGU8DoPvbv/lWAJUxQFzTLwDIqUfv2yhJdCUc1m+EDsZe4CBp0+vk/u8cDFWbUBT4STeN206fMeT+lrAMbAek/rEqOvLNEUMc4XnSrepIWGjKRL65ZdaQzuFIlAdNZLstFYOksNRvOWtNN7Jn9auU1DRraa5gCCTZTJJSCUDIlDP49y1nON2WAoHAswhnUyAQCBxAp9Phf9ilNCuBRJn30r1tpadNEvRQUlnDFJua4iTjiNP6fTdYzlSrohs7OVOby5E+Wdwvuuc1vEuQuXbIQ9KzxvNn68K+z3LPVMY0DUaCXUJHAzE2tT5TDz2GtFjHp1v2Uly4k1xpb99nvaTwV8NLlIgUOm5B11SCARvNSiBnzJ45qrYUCASeRTibAoFA4CDnzZnOvzkJgLZVf4E936mh+gUeZdv6zwmQLDQSRlj6tH7fJWeOZ0327X0jdYoCa7Nv02QKrU/QUIg0IC2IDRqKhjwkLTaMuGOvoFJRRzJvsPwfGXMu8dqOf1PhBgBKbLFMyc0alVauf8OgKXRyTI2j0hXYR2tlPgC7SSElZui1YAKBwPsRzqZAIBA4SIDJSNP4CzErBiI6itG/cSa5n56DtOl1T5s2ppH3rAGgNGQaA+bMAsdccAc/Hv4PAGxA8LRz3GmeRylTYrEdEA7QqugoU2KGPe76+VMo1KnRPy/M03n1VMaqnesBKNSlExkSMCqt+MxDsB3QRbKhIz7Te+t/MKHUFwJQ7ZeKTie6qgKBLyN+wQKBQOAElxyVghFr32cJG3y8WCQd9xA2m43k1k0A6LOOHXK/kLSpVOoS0EtQuOEzd5nncba3+FOl7Ftr2ZsWZEfryE5Zc1AmAAGN+S6zTwvkiq0A1Adlj14sLAnd6U+jSPu6Sb9lXwNhScMcJNCKgFZ17W176OhGqAUCgecRzqZAIBA4gdRQ1C8HHPRE6iz61TMGeYiyop1s/OpdmmrLPGrH9sISJiu7Acg8cvg8kPXRhwJgLPvB5XZ5C3mREok6dQro1ZbFzDI/zbvyXHISwkc81haTB0BI625Xmjhqwlt3ASDHTNBGcPpCpJu3Ueo3DoDddZ3a6ApGJMZcCoAxXqO2FAgEHkM4mwKBQOAEu7oiBw04482ROp2lrGgnP3/xDmUHBEf5fMVfSXj1SA7//mpmrv49a9581EMWwu6fP8coyVRLsQTG5wy7b9Sk4wEYb9lGbXOHO8zzOKF1apTeQlsCn9sO60sLYs/6y4j0KQAkWUvBZhthbw/R3Um8VX3hEZl9mHa6YUk05vwOgOymtdi8tf4HE5Z2EpRqAOKypnrWFoFAMGqEsykQCAROkJ41nrusV6L0rIOzKZJ3R+p0ktVvPEzCq0dy2LqrSHj1SD595v94d/n9fPXn+Zyw5/G+ICq9eSsPdEjdhW7v9wBURkwfcd/YKfMBmKArYfXGrS61y1so3/wFAL/q8nj+/DyH0oKMm3QYFkVPEJ00VxS40kynaSvdjB6FWiWUSXnajoaNm3MBNkViMrvZsWPspMrxFI3F6m+yVgklLzfXw9YIBILRYvC0AZ6iN8Fz719v1jQYDMiy7BO2jlXNXj2t28pX6u8rmr16WrRTclQwGbMX8tJ3e7nSsIpVtsNIO/ZikqOCNbHZG85p2Z6dHFPwaD+H8pSG1/btMEjeyorCrSSkjdPEVnvbySrbyOrYDBL4j5s75P599Q+IotYvlXhLKbXbvkGe7fxImCuvUy11A6p+BqApYjKnTExGr9fbrR0dHsJuKZlcSij5bT0TE/atifSW+175tnXkAgVSOkeEBw04djTn0xSRRL4hh/HyLip+eJfx4ydqojsUvtKXcNW1X7nrZ6KBYimF6f7Gg+Z+aq+m6PP5hqZoJ/s1JUVRlJF3832WLVvGsmXLkGWZ/Px81q9fT3BwsKfNEggEPs6XX37CzU0PUa5PpvGstz1tjqaU/7aW+TvuGLB9jy6VmtBJHNb4ab91q1ZFxw9z/k14TLIbrYTdZVWcvv536CSFbSe/D0GxIx5jWPMI46s/4t/KiUw450/oD+aIl5Y2cj88BSMyb095lYnjHA+gU/fBnczpXsM3MRcRM/s6Fxg5Orq+eIBDm1fxnukMchbcrrl+5eoXOKHuVTbpJ2M86znN9QX7aP3qcWY2ruQTv5NJO/0eT5sjEAiGoK2tjZkzZ9Lc3Exo6NApisbMyOaiRYtYtGgRLS0thIWFkZ2dTVVVFTk5Oej1ek3K6HVktdQ0m80sXbqUxYsXYzKZNNEE19g6ljXBNW3lK/X3FU3Qvp12VjXBWkiQy4lJS0QfqE0OQm84pyH+EvJ2qV++QVnRobvgLQ7NGM+al+5kdsULfdvXZP2RY489QRNbHWmnnT9/jk5SKNMnk3fo7CH327/+NutZsPIjZii/Ue8XxeHj4p2y01XXqZa6RetXYkSmXIkmKz3DKc3Pv82B2jWEtpeQl5fXt91b7nslH6j5Qq3RE/vZNxrN/THYLoT3XmWSdTsd8VEER8RqojsYvtKXcNW1v+NjNTiQJXLcoG3pDGO5ncB36u8rmqKdVM3sbPteXI4ZZ/NAek+4Xq/X9CaptaZer8dqtbrEzl59b66/L2m6sq2Epve205QJEyhfE0WSVI9csRm/3OM0sHIfnjynadkTWR16KnNaPwbUkcu1425nTrY6jXD2lY/S9eeX8ZesfJr3KKece4VH7n0BlT8CUBd1GMl2lK/X6/HLmYsNiRxdOU9u2cbM8aNLaeHN9+ja375hHFDgN4Foo94pTVPSJKiF6M6ifsd6xX3PJpPYXQxAaMa0YY9x1s6cSYdR+F4yWVIZu9e8w4yzbtJEdzh8pS+htWacuQSAgMSJXm+r1pqiz+cbmqKd9mnaw0E8b0ggEAhcT1Z8ONtRc8FV/rbGw9Zoj+ynjtRuMkyl6pL1zLloyb4vdTrK9eqUWZu5zRPm0WnuJtesppsJn3i8/QcGRvblY+wq+t4VpnkNIbVq/lFLwqFOayTmqutak2yVWM3eFcG3q3IH/lhoV0zkTHS+jsOh0+nID5sFgKFglUvKEIDc3kiMUg9AUu4MD1sjEAi0QDibAoFAMAp0Oh3VgWqqDeveXzxsjfaENe8AoC11LsmDRNpt8E8FQNdY7E6z+vjxl01kS+XYFInUQ09x6Fi/rGMBSG/fetCmQGlva2Vct5p/MmWaA874AWSPm0CTEoRBslGy/WetzNOEvdvUlwX5pJEeF+6ycvwmnAZATsdG6BY5N11B+a4NAFQqkYxLT/OwNQKBQAuEsykQCASjRI5VU0hEtOzwsCXaYpNl0rsLAYjKPnzQfbrDMwEI6ih1m137U/PrlwCU+mWhC4p06NiwiScCMFP3G59tKdHcNm9g8/ov8Je6aSCU7InOR901GPSU6NXOf3XBRq3M04T2EvUlT4UpE50LAz3NOPp4ypRoAjBT9tMHLitnLFNbqF5bpboUjAbtpycKBAL3I5xNgUAgGCXROaojFiNXQ3u9h63Rjj2FO4mWmpEVicwpswbdxxSv5sGLsZS507Q+Qqt/AqA57gjHD06biQ0dGbpqNv/2m8aWeQdNO78DoDhgErpRrtdpCFRfLMhV3nWu/Bu2A9AV4doct+HBAWw0qddZy6b3XVrWWMVapb6wq/cXo5oCwcGCcDYFAoFglEwfn02RTY1m2lr4g4et0Y6929YCUKpLxj9o8LDmsVlTAUhRKujutrrLNAC27qlmklVdrxk3Zb7jAv5htIar0S5NlRuQZZuW5nkFkQ3qek0l5chRa9liVGcupGX3qLU0Q1FI6FIj0QamTnV5ce2p6lTklPq1ILv3eh8LBLeqbdkVmuFhSwQCgVYIZ1MgEAhGSWx4EPm6niBB29d62Brt6C7bAkBNUM6Q+yRmTcGmSIRL7RTuKXSXaTz/xa/85YV/kSzVYVF0fFKf4JROUN48AKbafuPn3VVamuhx9lTWM9GmrtfMPMwJZ/wAwtOnApBkLR61llZ0N5QSRivdip70CYNP9daS8UeeSIMSTIjShnn3ty4vb6yRYFGns+ujsjxsiUAg0ArhbAoEAoEG1ASOU/9TvsmzhmhIWM8a1N41qYOh9w+iSqfmHKwp3OIWu0pqmila/Srv+P0FACM2Cta8Q0lNs8Nahiw1L+dM/XY+27pXUzs9zcYfVhMqddJOABFZzq/X7CVz8kwAYmiitsoza3QPZO+2dQAUkUhOqnMvHBxhamYi30lqlNTKH952eXljic7GSiJRf8PhieM8bI1AINAK4WwKBAKBBlgi1SmG0e07PWyJNsiyjfRudUpbVO7wUzBr/dT0J52V7ql7ceFOHjK8iE5SAJAk+KvhJUqKnCg/dSaypCdZqmPbrh1OOazeSmdPSpfy4EmgG32wlYjIaMpQXywU/+od08WbitTopXuNmej1ru/S6HQ6yqPVKMYRpZ/Dnu8wdNS4vNyxwJ5tar7cvUosEaHBHrZGIBBohcHTBngKWZb7/fVmTYPBgCzLPmHrWNXs1dO6rXyl/r6i2avninYKTspD3isRaWtEbtwLoYmj1tz/rxY4orlz529MlJqQFYm0iTOHPaYjOB3MGzE0FWp6Todqp3F+deh7HM1eDJKNbGP9sOUPWn+9PxWBeaS0byOjcxtznozhjuNSuGreRLvtHKCpAaPVtco24lq2ggSG9KP6ncfR2Frpl06ypYaW0i3I8jkev+8Z69TgQK1huY63vZNETToe8zcPEyY3whtnkosOmSeQZ1wyam3wnb6E1nY2FW8GoMKYSpBO5/XPE19pp17d/f8KzdFrinayX1NSFEUZeTffZ9myZSxbtgxZlsnPz2f9+vUEB4s3ZwKBQBs6u2VC3r+Q8bq9bJ36ALrs4zxt0qjYveEzziz+C3ukFNrPeWvYfWt+fJvj9j7DT9IUAs951uW2GTpqyPn0bHTse3zZ0JF/yn+xBsY6pFXdamb7J8/wf4aVrJSP4ubuG9AB/zwjnrgQk8aWu49tFW0cv+5coqUWCo5dhjl2qia6VV8+xfFN/2G1/zyiT/uLJpqjIea/ZxKn1PJWxsNMmnGsW8rsaqxgxpfnIkn7timSjl0nO379CfbR+L+/cEz7Z/wv+GxSTrrV0+YIBIIRaGtrY+bMmTQ3NxMaOngQQRhDI5uLFi1i0aJFtLS0EBYWRnZ2NlVVVeTk5KAfZTj4XnodWS01zWYzS5cuZfHixZhM2nV8XGHrWNYE17SVr9TfVzTBte1UaMhmvG0vhuZCcvMWaaLpqXNa9tUyAGqDc5mRlzfsvsaWw2EvJNnKic3N1STX4fDtlIdFfgLTqluQJLWjz6lLGTdt9rCag9W/bGsJ39sm8n+s5FjdFuKpo4porP4R5OWNnH7BVdfpaHVXb36faKkFC0Yyjz4bDCZNbG3JnwZN/yHWXEJuXp5H73tyWz1+Si0AuYfNIy83e9SadlFc18/RBJAUG+Oi9JA+/G/FHnylL6G1nbveV9cBG+InAHj988RX2gl8p/6+oinaSdXMzh76nrs/Y8bZPJDeE67X6zXtIGitqdfrsVqtLrGzV9+b6+9Lmq5sK6HpG+3UHJYHjd9grN6q6T3AE+c0vEWNYmqLO2TEfVPGz4DPIUmqo7i6jvTk0QdqGamdiuPmkNXT4VcWbUAXbX/0yv0185KiyJQqUBSIlNpZZ7qJu61XMj5plkPn3dvu0dZSNf9oXehEEk2BmmgCJI4/HLZCum0v3VbZo/e98vyfSAX2KjFMysm2q3wt7CyT4klUpL41wwBWRUeVFEeyxvcqX+hLaKKpKCRbS0GCqMyp2ukegLdrij6fb2iKdtqnaQ8iQJBAIBBohDF5OgDxnfngwysUuq0ymVY1jUnMCMGBAExhcTQSAsDefPdE422o3ANAIyEOOZoHkmZq4wHjq30jVXpJ4SHjS6SZ2rQw0yNsKqoitfM3AALGaTu1NDVnCmbFSKBkZvd2z0Zers1XHepiQyZGg/YdvqHY3uLPfdZ96zNlRcdd1ivY0RrgNhsONmrLiwiV2rEqOjLyZnjaHIFAoCHC2RQIBAKNSJ9wKGbFQIjShlxX5GlznGbnzm3E9gQHSp98tF3HVBmSAGgp2+5K0/poq1Gn3DXookYn1FCIDlu/TTps0OCb7ff8F79y9gsbOVynRuZd15Whqb7O4EepPgWA6t0bNdV2FKl6GwBNwfZN5dKK3MQI3pBPpEoJB+Bay028K88lJyHcrXYcTJRu74lEK8UTGhrmYWsEAoGWCGdTIBAINGJyZhI7FXWdX8Vv33nYGuep3L4egL36ZPT+9gVSa/JXHRCpfrfL7Nqf7qYKAFqNo3Q2I7NA6v8oVCQdRGaOTtcDlNQ088hXpUyhgGSpDqsicefGMM3TuTQEqufGWumeFwtDEdPaU/4oIz87SlpsGHfOSyXfpl7zUbo2bj8uhbRY4SQ5S+veXwGo8Rt5nbRAIPAthLMpEAgEGmE06Ck1qaMszQXekYfQGawVWwCoC8q1+xhziNpJDGkvdoVJA2mrAqDTFDM6nbAkWPB0n8OpKPDz+CXqdh9jV0Uj5+q/4T3T/QDoUThFv578yiZNy7FFq9dFcEuBprqO8N2rfyHZVgnAKaV/Y/UbD7u1/GtOmExTQKpaflSV3alyBINjbFCvpfZQ56fECwQC70Q4mwKBQKAh7ZFqp9O/7lcPW+I8ET3BgZT4yXYfY4hKByCuu8wVJg3Ar7MGADkofvRi0xfCDRuwAZIE30vTRq/pASaEdvGw4cW+wDWSBA8ZXiIvpFPTckLT1fOT2F2CzWYbYW/tKSvayayiJ/uts51V8BhlRTvdaocSpb5YCu9yzzV/MBPZqa7B9ksQTrtAcLDhcWdz2bJlpKen4+/vzxFHHMFPP/007P5PPfUUubm5BAQEkJKSwuLFi+nq6nKTtQKBQDA8wemHApBk3g027ZIou4sui5VMWQ0OFDd+pt3HhcarHe80qqhpbHGJbfsTaFFTXujDRh/5FoCoLJr9VMfVXOO5EbvRkCzVoJf6B6YySDaSpVpNy8mYpAaNSqOKyto6TbXtobJwa79IsKDWs6pom1vtCE1RHaNYy163lutOyop28vMX77jUkZetVlJl9RzGjZvusnIEAoFn8Kiz+fbbb3PLLbdw33338csvvzBlyhTmz59PTU3NoPu/+eab3Hnnndx3333s2LGDl156ibfffpu77rrLzZYLBALB4ORMPpx2xUQAZroqfvO0OQ6zY+dvxPUEB0qeeJTdx+nCEunCiEnqpnCX60d1w60NAPhHJmum2RGiBtPxb9mjmaZbicxCOfCxLuk1X38aGJVMI6HoJIXi7Rs01baHhKxDsB0Q7Nmq6IjPnORWO1LHq1FT46nD3N7k1rLdweo3Hibh1SM5bN1VJLx6pMumKm9e+ylBkhmLoidjvG/OKhAIBEPjUWfzySef5KqrruKyyy5jwoQJPPfccwQGBvLyyy8Puv/333/P0UcfzQUXXEB6ejonnngif/jDH0YcDRUIBAJ3kZ0YxXZUp2XvVt8LElSx/XsAyvVJ6PxD7D9Q0lOpUwO1NJa4foQpSlGdzdBY7QKK6GPHAxDZVaqZplsJS2JD7i37Pkt6WPCU9utPJYkKv3QA9u76hW5jkLb6I5CcOZ4txql9n62KjrXjbic5c7xb7chIz6ReCQWgeMcvbi3b1ZQV7eSYgkf7RspdNVV59RsPM+2bhQAYkVn7zpOa6gsEAs9j8FTBFouFjRs3smTJkr5tOp2O448/nvXr1w96zFFHHcUbb7zBTz/9xOGHH05RURGffvopF1988ZDlmM1mzGZz3+eWlpa+7d3d3ZjNZs2SnMqyrLlmr+3710ELXGHrWNYE17SVr9TfVzTBPe1U4Z8D5p207/nJ6XI8dU5tFVsBNThQnJ229+o2BaRCewndNbtGfX6Ha6em5mbiJPVeHh6XandZI9U/KCkPdkCqUkFZTSMxYYGj1nQWZ3U3W9M5DGiTgjBev06N1NpzfrS0tVSXykS2ElP3A891X0z8179x1XHarLezx06zMQyssDrwJFLPuJuZ6bnDXgeuaqcKQzJR8naqdm8ma6o2OU29oS9Rlr+J5EGmKpcXbCYmKUMTO8uLd3FMwaP91hjPKniM4vxT6O62ef3zxBvayV58pf6+oinaaZ+mPUiK4pnM4xUVFSQlJfH9998zc+a+dUG333473377LT/++OOgxz3zzDP88Y9/RFEUrFYr1157LcuXLx+ynPvvv58///nPA7bfeeed+Pv7j74iAoFAcACxfm1cZ36BMuJ5hwW0Sg6MEHqYecpqZvEL//U7i23djuVonGHYyWndn7JKN5sfFdclZg8wKtxuWYpZMfKIdAN9kWJGSYpSzuW8TZkSzTOBfySoa/AlHd5MmLGdmy3Ps0s3jreUBS4po9sYxJSObznXoI7cy4rE3dYrMQTFY+xud0mZB3IG/2OqsoNXTJdQahll+ptRcLR+E8dbv+ED46lsttofvdnbCTDI/NHyTL+1sVZFx5N+/0enVZsOa6xfO9eZnx+wfbnpWmosI7/oEQgEnqWrq4tHHnmE5uZmQkNDh9zPp5zN1atX8/vf/56//vWvHHHEEezevZubbrqJq666ij/96U+DljPYyGZKSgqVlZVUVlaSm5urqae/a9cuTTXNZjNLly5l8eLFmEwmTTTBNbaOZU1wTVv5Sv19RRPc004F797PpIJnATVno/XkJ7BNuXBUmq6w80A6Ld10/W0S8VIjJaf+m/hD5jmkK+39nqm/3MVmcslbsmZUtg7XThu//Yijvr+CcmKJXmL/lN0Rz2lHA6an1amYrxz5GRfMHXn9mKuuU2d1//P4dVxk/S8FCaeTeumLLrF13U8bmPPlqQMckTUnfMxRhx3qtK4jdlY/PIVUKvlh5gtMm3OmJprOsOGtv3L0nmdYbzic6bd9rImmt/QlSh45ghxFXb+sKLA66zaOOv82zewsL95F2pvH9gtqZVV0lPx+NS1dNq9/nnhLO9mDr9TfVzRFO6maCQkJJCQkjOhsemwabXR0NHq9nurq6n7bq6uriY8fPJT9n/70Jy6++GKuvPJKACZPnkx7eztXX301d999NzrdwCWoJpNp0AvBZDJhNBoxmUyannytNXsZqh7O4gpbx7Lm/mjZVr5Sf1/R3B+XtVNbFRMKnuv7TlJs6D+9FWPufIfWznninG7ZvoPDpUZsikTK5Fno7Dw/vbpxOdPhF0hVyrHYICRg9Od3sHayNFUA0KSPIsmBNhzxnJoSaNWFEWJrpr2qAJPpyNFrOomzutGWctCBf2LegPOmla0TglsGjQabF9SqyW9qJDttsqyu2ZUgOinTrjJd1U6R6YfAHojv3ovRaBy0H+Io3tCXsMky0bY6kMCmgE6CrAkz+h07Wjszcw9h9bg7mF3wCJKkjpCvHXc7x+RMYseOHV7/PPGGdrIXX6m/r2j2ItrJvrp7LECQn58fM2bM4KuvvurbZrPZ+Oqrr/qNdO5PR0fHgBt574nz0ACtQCAQ9KOqaCs6+uce1GGjqsj7825W71DXy5fpk9H5D/2Wcigi0ydhUyQipTbyC1yXPsTa42y2+0Vrrt0cqAYckhoKNdd2NTXN7aShnpvo9ENcVk585iHYDug+2NARn2l/XtbR0FBfS5CkzlhKSBvnljKHIi3vMABSqGZPpbYpZjxJ/pYfiJRa6VT8WG08BoD69a9rXs4Rv7sVS8+4R8FJbzLnoiUjHCEQCHwNj0ajveWWW/jnP//Jq6++yo4dO7juuutob2/nsssuA2DhwoX9AggtWLCA5cuX89Zbb7Fnzx6++OIL/vSnP7FgwQKXjHwIBAKBo+zqikRW+q8htCo68s0RHrLIfpQqNThQQ4iTa8+MgdToYgCoKdqqlVkD0LerM2LM/jGaa1sj1XyhIe0lmmu7mp1760iXqgAISMxzXUFhSehOf5reV7w2JORTntA+6u0QVJepLwJalCCCQsLdUuZQmCKTaScAg2SjcPvBE5G2ausXAOwy5NKaew4A2fWrwaptQJSSkiJMkhWbIpFz6HGaagsEAu9gVM6moiijGlE8//zz+dvf/sa9997L1KlT2bx5M6tWrSIuLg6A0tJSKisr+/a/5557uPXWW7nnnnuYMGECV1xxBfPnz+f55wcuMBcIBAJPkJ41nrusV2LrcThtCtxjvYI0N6dlcIa4FnX01Rqa4rRGvUk91lzluiTwpi51BMkWPPiSi9EQmDgBgNjuMmTZNsLe3kVlaQEBkoVuDBCuXUqYQZm+kCKdGkDqTdPvHV6TPBqaqooBaNB5wQscSaLSoOZ6bSp1fcofdxFQqaaUa4yewRHHnU2lEkkI7ZT/8F9Ny6kt2w1AgxSOziiCNgoEByNOOZuvvfYakydPJiAggICAAA455BBef9256RU33HADJSUlmM1mfvzxR4444oi+71avXs2KFSv6PhsMBu677z52795NZ2cnpaWlLFu2jPDwcKfKFggEAq1Jiw0jc84l/M16LgBrbJPJmHMJabFhHrZseL569UEOV1Rnc3rpCqcTuHeFZQLg17xHM9sOJKS7DgBjhPYjaZEZ6vTTTMoprm3WXN+VdFTuAqDeGA9614dkaDGpzr4NbaIB20tnQzkAzQbPRaHdn9bAVPU/9bs9a4hG2GSZ7C71XhA2fg7xUWGsN6lTaVt/flPTstpqigFoNGg/JV4gEHgHDjubTz75JNdddx2nnHIK77zzDu+88w4nnXQS1157LUuXLnWFjQKBQOBTXHPCZHImqJFMEw1tXHOCe9ayOUtZ0U7mFD3el0FEN4oE7sY4dQpuRFeplib2I8JWD0BQtPMjsENhiFOnn6ZLVeSX+dYaPF1jEQBtQeluKa/bX3UQgmytbimvF1uzui61y+QdDooSng5AeIfvTb0ejIJffyJKaqFLMZJ3xAkAdOedCUBm83robNSsLGtjGQDtpjjNNAUCgXfhsLP597//neXLl/Poo49y+umnc/rpp/PYY4/x7LPP8swzz7jCRoFAIPA5kjNUpytG8X6HpbJwa7/0A6BGF60qcnxaYG9gmmRbOeZuqyb27Y9NlolR1M5uRLxjeUDtIjSZLkz4STI1Jbu013chIR17AVCistxSnhIcC0CwrcUt5fWi71Dzn1qDvMNBCYhT1/mm2Mqpa+nwsDWjp2KLul4z35BDQGAwALPnnMhOWwp+WKn6XrvRTX2bulTKGqT9lHiBQOAdOOxsVlZWctRRRw3YftRRR/VbXykQCARjmbg0dY1mOG2Y25s8a8wIJGQd0rfGtBeroiM+c5LDWvHj1BHdJOrYvbdCE/v2p7a6HJPUDUBcigucKp2OepO6Bq+7xnecTVm2EW9VR4mCkya4pUxDaAIAITb3TjcOMKvTqPU95XsafZQ6dTxTquSXQt/vB5kqfwagMWp637b4iGB+DJwNgOWXtzQrK9CsBvvSRyRrpikQCLwLh53N7Oxs3nnnnQHb3377bcaN82wIcoFAIPAWEuMTaFECAagszvewNcOTnDmejYYpfZ+tio61424n2YmgRrrgGFoIRicplBdoH5G2rkKdKtqghGAKCNJcH6AjRHUeTC2uW3eqNaV1LaRLqqMTk+G6tCf7ExSVCEC4rckt5fUSalWnUQdEaT+N2hksQQlY8MMkdbO3cLunzRkVNlkmq1NdrxmSO7vfd7pJZwGQ2r4VmrSZJh9uVV8cBMW4YJaCQCDwChyOIPDnP/+Z888/n++++46jjz4agHXr1vHVV18N6oR6K7Is9/vrzZoGgwFZln3C1rGq2aundVv5Sv19RbNXz13tVCNFE0opdXt3kTJ+hiaao2E4zS5JjQS5PnwBiact4ZiM8XaXfaBulV8KoZYdtJVvR5ZPdtrWwdqpuS8SaSRhDp4be8+pLiYH6r4gorN0xH1deZ06oltQUs6JUgMAUvS4QY/T2taQGNXZi6QZs6Ubg0GboETD2Wmz2YhSGkCCsNhUp69RrZBlGSQ9Df7JxHcV0V6xE1k+ZfSaaP/bt+e+l79tA3lSIxbFQM5hJ/Tb9/iZh7P+pwnM1G2nes2rRMy/Y1R2tndZiEN9cRCdnDWg3t7+PBF9vrGtKdrJfk1JcSJ3ycaNG1m6dCk7duwAIC8vj1tvvZVp06Y5KuU2li1bxrJly5Blmfz8fNavX09wcLCnzRIIBAcxHe//H4fLv/BZ4g0kHfUHT5szLPp3LyKPPXw3/s9ETjp+VFqdn93HYa1f8qX+WPxnLSI8Rrspcnu/f4uTK/7OBsM0/M/8h2a6+yPt/oKJm+9nsy0L+ewVmAweTUltFz9s/Jkr99xMixRC6Tmr3FKm1FbNxFVnY1V0rDv5C6KCXZ+6oqndzJGfHo9BsvHr/PeQQrxj3ab/13eR3fAty3UXcMzZizxtjtOUrP03p1b9g226PDj7xQHff/3R69xofo5qYwq1p/+bvqhiTlBa3878r+ejlxS2n7ISW6D2eXMFAoHraGtrY+bMmTQ3NxMaGjrkfk69hpwxYwZvvPGG08Z5gkWLFrFo0SJaWloICwsjOzubqqoqcnJy0Ov1mpTR68hqqWk2m1m6dCmLFy/GZDJpogmusXUsa4Jr2spX6u8rmuDedlq7KgHawN9ST15eniaarrCzy2KlW6kBCcZNnUnsuNHZuuXTLgCOl79DXr2GNdm3c8wFdzikOVQ71X6nrg/sNMUyzUXn1BapwOb7yZIqKAiOJi9jaIfGVdepo7q/fPchoOY5Hepa09xWORvb/yQMkg1/g+TwNT6k7DB2btn2KwbJhqxITDh0Fujs68a4up0iMqZBw7fEdpeRnplNgMk4ak1P9CWaP1an0NZHTWfWIO35Y8npmH96ibjuvUSGW9jVbHLazsp136OXFLrRkzt9FkjqSx1feZ6IPt/Y1RTtpGpmZ2fbtb9dd+mWlpY+j7WlZfioc8N5tt5E7wnX6/WaPni01tTr9VitVpfY2avvzfX3JU1XtpXQ9M12kkNUZ9OvvdLpstxhZ3llCTlSOwBx6RPQjcLWypICpravozf1ol5SmLX7cSpLznZoDehQ7WTsUAOKWAPjXHZO9bE5yOgIkTrZW1rE9OzEUWs6i726ptZiALpCM0bcXzNb9XoapRAiaKG1vgK93rGp4iPLD7SzqVqNuNsohRNtdLyT56p2Cks7BDZCllTOb2UNHJEz8jUzEu7uS9hkmczObSCp6zUH2++0o6fy1Q/TOEX/Ex1fPY5hwtXo9XlO2dnSMyW+XhdNvGGgc+4LzxOtNUWfzzc0RTvt07QHu+YGRUREUFOjhhoPDw8nIiJiwL/e7QKBQCBQMUaoyd6DLTUetmR4qveoSyIaCEPnHzIqrcrCreg0SqMyGIE9kUgJdWGqBIOJWr2q317uGwFfIrpUJ0wf495Afc169bnf2ah95OHBaK9T69lsiHRLefYixaipjrKkCraU1HnYGufYvXMrCVI93YqevCPmD7pPXHgQSoB67sOK/0fup+cgbXrdqfIsPTk22/xinTNYIBD4BHaNbH799ddERqo3l2+++calBgkEAsHBQkhcBmyHSNm7c222V+8GoFYfx2i78AlZhyCvlfrl7XQ2jcpghPZErzS5OFVCU2Aa8a0VSPW7XVqOFnRZrCTKFaCDiFRtzrO9tBujQC7B2uKeFypysxpxt8PPy9b3RWX1jYaX7d0DuCcisJbs/eUzcoDdhizyQsIH36m5nJMtn/V9lLChfLwYxp0AYUkOlSe1qi8oLIEix6ZAcDBjl7M5e/a+8NcZGRmkpKQgHbAoXFEU9u7dq611AoFA4MPEpqmjHbFKA2azWdO1HVpia1TTGLQGjH7qX3LmeFaPu4PZBY8gSSArEmvH3c4cJ9KoDEakTY1EGhKbponeUMiR2dC6nqC2YpeWowWFlY1k9KQ9icqY7NayzaYo6AJdu3ucTV27Oo2629uCyej9aAtIJqyzFGtNgaetcQpTxY8A1EZMZ6jVt1VFW4mn/8wFHTaqin4lfppjzmZAl9qWUrjIsSkQHMw4HGIvIyOD2tqBb+kbGhrIyBB5kgQCgaCXuKRMuhU9RkmmosR7R8hMbep0tu5gbTp9cy5aQrkuAYAP05Yw56Ilmuh2d3UQKbUCEJ2UqYnmUAQkqN3t2O4yl5ajBaV7dhEkmbGiRxeV5dayeyOIGs31binP1DONWgpJcEt5jqD0TKUNai9Glm0etsYxbDYbGR1qcKDgnNlD7rerKxJZ6T/YYFV05JsdX0YV1q32JQOjXfviSCAQeBaHnU1FUQaMaoIa/tbf3/VhzwUCgcBX0BuN1ErqxNSavd472hFqVkfFjFHavTBsNalT4+Tubs00a8oLATArRmLjHBtFcZS4bDWVVwbl1DZ3uLSs0dJSpq65rdXHgd75KKjOoOtJPRLU7R5nM6RbdTb9I71vNCwkRR1VTlcqKKhq9LA1jrF71zaSpFqsio68I08acr/0rPHcZb0SW4/DqShwj/UK0hycudDaaSZGUa+ZyCT7IloKBALfxO7UJ7fccgsAkiTxpz/9icDAwL7vZFnmxx9/ZOrUqZobKBAIBL5Moz6GRLmW9ppiT5syJDFytTo1NVG74DLdATHQBVLPtEctqK/YQxJQK0WQbNA+AuD+BCVPBCBOauL74hJipmiT1sMVKA3qqHlzQCruHu/zj1CnXofITS4vy2azqdOodRAW532jYfpY1eHK1pWzubiO8UlRHrbIfko3riIHKNRnkhs69MrttNgwMudcwpVfh/Gy6W/UE0r67IWkxYY5VF5RdTMpkupshsS7dpaCQCDwLHY7m5s2bQLUkc1ff/0VPz+/vu/8/PyYMmUKf/zjH7W3UCAQCHyYdv84aN+OtWddpLfR1NpOIup0triMidoJh8RDI/h1aReZs6NWPYcNuihcPq7lH0a9FEGU0kj9nl/Bi53NwLYSAKwR7u+0h0SrI8yRtkZk2YZe7/CEKbupbuogVlJHDKMS3Ttd2C56ptFmS+U8t62SmePiHXbCPIWx/CdAXa+ZO8K+15wwmUh9J3z3N6KlFq6aleJweXurqpkitakfQl07S0EgEHgWu53N3ii0l112GU8//bTP5NMUCAQCT9IdnAjtYGiv9LQpg1K6ZxeHSDLdip7weO2m0fpFJEEpBFm0m17Z3aRGr2wzRmumORx1phSiuhqxVO9yS3nOEm0pB8A/XpsgTI4QEa+m94mRmqhubicxcnSpc4ajtLqWI3ocFFOkFzoo0erMgCiplS17KpnzZDt3zkvlmhPcG7TJUWw2G9kdm0ACXUyOXcccech4ar8NJUZqwVxdQGDG4Q6V2VhRDECHFECgv2845AKBwDnsdjZ7eeWVV1xhh9uRZbnfX2/WNBgMyLLsE7aOVc1ePa3bylfq7yuavXrubCd9eDJUQ1BXtUPlueuc1peqjlS1LoYEBXCivMF0g6PV0Y5wWwPd3d3odI6NeA3aTq2qw95linbqvDh6TttDMqBrK8bmoiGPceV1ao9uS4eZVKUCJIhKnzTs/q6wVReirs0Nksxsr6wkLixwhCNGZig768r3AGDGiMEv1KFr1R3tVNJgxmiLIUVXS7ZUzk9KKI9+VcoJk5NIi7HfoXJ3X+Kbf97J8ZI6A+GIHQ+y+vVujrngjmH14sMC2EICMbRQVbiFtNQZDtnTVaeOxrcYYjDZ+gdT8pXniejzjW1N0U72a0qKoigj79afDRs28M4771BaWorFYun33XvvveeonFtYtmwZy5YtQ5Zl8vPzWb9+PcHBwZ42SyAQHOTUbV/NnO13U0AK5t+95WlzBlCw5m3Oqn6GLYYp6M98VjNdQ82vjP/uWsqUaIpPfZfwwNEHrmn76HaONK/jk6jLSZt7hQZWDk/zT29wdOly1kozCD/nGZeX5wy7qxo5fc0CdJLCjgUfI5scjwo6WjLfPY5AzLw9+UUm5rpuuvHPP67hsr13UqWLo+5s7+tr/FLWRsq625mr38Jd3VfwpjwPgAfmRjEtyTv7G021ZRy1+vfoDsiL+8OcfxMeM/xk9dL37uEU2zf8lHARgUdf51C5a/73Bte1L6cgcDrmU/7ulO0CgcCztLW1MXPmTJqbm4ed8erwyOZbb73FwoULmT9/Pp9//jknnngi+fn5VFdXc9ZZZ43KaFeyaNEiFi1aREtLC2FhYWRnZ1NVVUVOTg56vTaBJnodWS01zWYzS5cuZfHixZrm6HOFrWNZE1zTVr5Sf1/RBPe3U7WxE7ZDnFKPPmsc/n723XbddU7Lv1TzI3YGJXFYnnOOwqC2JobAdxBLI2Wh0eSNcyyH52DttOv9BgD8Y9LIc8JWR8/p3rYjoXQ5SbZyknJyB12P6Krr1F7dPSWfoJMUWgkmZ8pMGCRavCttNZvNNErhBCrVmJQup9rlQIayc8u6TwBo94t2uBx3tFNgdBtfr01iLlsYJ6kpc3TA0VNyHB7ZdFdfYmPFb/0cTQCDZMPP2jLiOd4emAJtENBZ6XB7bFyprhPXR6QOONZXnieizzd2NUU7qZrZ2fZFknbY2XzooYdYunQpixYtIiQkhKeffpqMjAyuueYaEhK8L+/VUPSecL1er+mDR2tNvV6P1Wp1iZ29+t5cf1/SdGVbCU3fbaf4NDXcRqjUwe7qSrLT00etOVr21wzoUNdB2sJSR11OP1vDVOfST5KpralEP96xICKDtVO4rK7/DIwana32ntOEnBmwClKoobS2gcykuFFrOspIuuYqdRp0jV8SWQb7HulaP6NapDCSlGqszVWa/1b315Pa1MjGloBYp8txZTtlxkdSOv4Q2P0p06V8Eqjn0nnTyIwfOrrrSJqu7kskZk9BWdf/HYVV0ZGYdciIZSsR6dAGwe2lDtsZ0l0HEpii04Y81heeJ1prij6fb2iKdtqnaQ8Oh40rLCzk1FNPBdQotO3t7UiSxOLFi3nhhRcclRMIBIKDGp1/KM2oU+iqS7wv12ZEdxUAphiNI5nqjTRJ6mhOa40GkXgVhShFjUQaFp8+ej07MEUk004ABslG+e6tbinTUfRNRQC0B3kuFUiL1DN9qr3WpeX4dfXo96wT9UbmxJsBmKLfwzr/G7kmapOHLRqe5MzxVLHPGbYqOtaOu51kO/JmBsarwYRiusvVhJt20tJhJtqmvjgKTxBpTwSCgx2Hnc2IiAhaW1sBSEpKYtu2bQA0NTXR0eHdia8FAoHAE9TrYwBoqS7ysCX9sdlsxNvU0aKI5JESHjhOm1HNM9jVOPpIvO2NVZikbgDiU9yU9kKSqDCo69Za9253T5kOEtKxV/1PlH3TmVxBu059mWLodK2zGdytBrExhnthJFqA5nJY93TfRx0KfHSzut1LMVu6CaUdgO8n3E/VJeuZc9ESu46Ny5iATZEIpgPa7U9xVFTdRFJPQKKgWO0iYAsEAu/EYWfz2GOP5YsvvgDg3HPP5aabbuKqq67iD3/4A/PmzdPcQIFAIPB1Wv3U6ZfdDXs9bEl/qmtriZGaAUjI1DDHZg9mf9XJpq1q1Fq15YUA1CuhhIe6Lr3GgTQHqCOGAXu/paxop9vKtQebzUacVXVkQpI9lwe0gyAAAjVMc3Mgsmwj3KaObIfEem4Ud1gaCkHpH1kVRYYG73rJtD8lewoIkszIisSRZ11n14hmLzmpiVSgvlBqKN1m93F7alpIkNT114S5PGOuQCDwMA47m//4xz/4/e9/D8Ddd9/NLbfcQnV1Neeccw4vvfSS5gYKBAKBr2MJUtez69sqPGxJfyr27ACghSACwmI017cFqU62UYMRr+bKYgDqpEiH06iMim511Geu+SsSXj2S1W887L6yR6CmqZ101FHjuOxpHrOjnQAAguUml5VR1dROHKqDEpXgpaNhkVkgHXBtSnqI9N6polV7VCexWopBZ/R36NiQAD/KJPXeVlP0q93H1VRVECip040J9dJRaoFAoBkOBwiKjNw3t1+n03HnnXf2fe7s7NTGKoFAIDiI0IUnQ52aa9ObaKnIB6BGF8fQQcudxxiRCJXajHh19owKN+udC7biDGVFO5ne+QP0BE/RSwqzCh6jrOgsh0aAXEVRUQFHSZ3ISH3r5zxBl+IHQIStEVm2DRq1d7SU1DQzVWoCwBDhpQ5KWBIseBrlwxuRULApEp3zHycozEvtBdor1XtAnV8ijsWLVqkzJkL3r3T1BKqyh4569bfcqg8nxEEHVyAQ+B6aPBHMZjNPPvkkGRle+rZRIBAIPEhgz7qkcKtr17Q5iqVuDwBNfq4JuBIUrUagDZVVJ2Q02JrVEbx2P+1HYIeisnDroGkhqorsnzLoShpL1NGkGl0cGLQLv+8oHVY1ImG01ERNs2tiN9TUVu8bDQvx4sj30xciHftHAL62TWVb1MkeNmh4dI3FAHQEORYtupe2ANVFNTTtsfsYW88a1g7T0NGdBQLBwYPdzqbZbGbJkiUceuihHHXUUaxcuRKAV155hYyMjL58MwKBQCDoT3SyGrwlRqmjy2L1sDX7MLSouQDNwa5ZNxUelw5AnNRAVVP7qLT07fvSXriLhKxDkJX+eSutio74zElus2E4WsvV0aRao2fXvbUTCEAUrZTXN7mkjOaeiMZtUjAYA1xShmYkHar+kerZXd3iYWOGJ7hDvQcQ4dxUX1tIKgChnfavRze0q2u4ZW9+aSAQCDTDbmfz3nvvZfny5aSnp1NcXMy5557L1VdfzdKlS3nyyScpLi7mjjvucKWtAoFA4JNEJY0DIJ4GSqrtj9roaoK7etaQhrsm4IoxQnWC4qRGSmpH1+n2N6ujwkqI+0ZDkjPHs2bcHX1ZHWRFsjsthKt5/otfUWp+A2BbezDPf2H/mjmt6SAAKzp0kkJ9lWuCYFka1Wu1tSfCsVcTrf7e06Uq9tQ0e9iY4YmyqjMGQpKcm4ZtiFLvHXFyJdjsm70QZFHvgYYI50ZTBQKBb2H3ms3//Oc/vPbaa5x++uls27aNQw45BKvVypYtW5AkaWQBL0OW5X5/vVnTYDAgy7JP2DpWNXv1tG4rX6m/r2j26rm9nYJisGDAT7JSUbKb7MSRp4K645xGW9URhsC4zFGVM6StQbHogWiaWV3TiDzO/um6B7ZTSG/ai7BEp2115pwe84fb2fbI/5jcvYXPoy7mxD/c3u94V16nQ+mW1DZTtPpVHjGsBuD3+m+5a/WrFE2+i7SYMKc0R2On3uhHizWMSKWRtrqyUesPZqfSpo5sd5linNJ3azuFJoNkIAALrbWlyPKho9fUwM4D73utHWZSlEqQIDZ9ksPlybJMREwKFkWPSeqmq64YY9TwL66a2rqIUXrTnqQPWqavPE9En29sa4p2sl9TUhT7MvH6+fmxZ88ekpLUhe4BAQH89NNPTJ482UlT3cuyZctYtmwZsiyTn5/P+vXrCQ4O9rRZAoFgjBD137NJUKr5V/ojTDn0GE+bg9Uqk/P+CQRKZtbPep2QeBdEzFRk8v47Bz02nsh8hfnTnQ9ik/zuSYTTyseHPEd6jnufO41fPs4xTStZ5X8Kyafd7dayB2Nn4R7O+uVi9PutJ7UqOj6Y8Rq5mZ6JnRC88mLSrUW8Enc3hx1ziub6P638O5db32J75AnYjrtfc32tSfjo90SZ97LEdBcXLjjV0+YMSml5OaesPw9Zkdh59tcoej+HNWSbjeD/nk+WVMHP0x4nIOuoYfffXddJ4teLOFy3i9Ij/kJLikiZJxD4Km1tbcycOZPm5mZCQ4cOM2j3yKYsy/j57bsRGQwGn3LWFi1axKJFi2hpaSEsLIzs7GyqqqrIyclBr9drUkavI6ulptls7lsPazJpFwDCFbaOZU1wTVv5Sv19RRM81067/eJIMFfjZ2kgL2/knIiuPqelxUUESmZsisSMo+eh93M+KuRwtraujCBcrsfQ3WJXvXvZv52MkoyRVgCyJx/KuEznHGNnz+mm7ROgaSWRlvIBdXDVdTqcbqS5BP2mgYGLZibpiRvmHLvyGXWOKQqsRZisrQ61s712bn9XTXsSFJtOshP67m6npvXjoHwv4V0DrxlP2DrYfa+yJ11JrS6a8ZOmOKzZa2elLpEspQKjuW7Euu76qZBESY1OnTThcJIGyQ/rK88T0ecbu5qinVTN7Oxsu/a329lUFIVLL72076R2dXVx7bXXEhQU1G+/9957zwFzPUfvCdfr9Zo+eLTW1Ov1WK1Wl9jZq+/N9fclTVe2ldD0/XYyByWAeSu61gqHynWVnXVlu8gGqqUoEgKCRjzGXt0Dbe00xRDeUY+ttdrheve2U0dtKWFAl2IkOSl11OfD0XMalT4ZtkOCXAFIg6b2cOc9OjF7KjYkdOxzOG3oSMyeAnbY4IpnlDUsBtrB0FmrqbZer8cq2wiXG0APwbHpo9J3VzsFJE6A8q9JkCvosFgJCXC8Q+rqvkRXzW4A6v2SiB9FOc3+SdC5AWvd7hHt3Vvfwhk9+VL1EWnDXq++8DzRWlP0+XxDU7TTPk17sDtA0CWXXEJsbCxhYWGEhYVx0UUXkZiY2Pe5959AIBAIBiFMDZbj3+kduTbbK3s6mgbXBtyRg9TosfoO59O+1JYXAlBDJMGB7k/xkThuKgBJ1FFaWeP28gcQlsTuzEv6PiqSHt3pT6t5Hj1FsHod+VsaNJeuaGgjVlJ1w+PTNdd3BYGJEwDIlCrIr2j0sDWDo2sqAaAzeHSBerrD0gEIaCkecd/mugoMkg0Zfd81IxAIDm7sHtl85ZVXXGmHQCAQHNQERKfBHgi3eoGzAig9Hc1Wf2dSuduPLjQBaiHAUu+0RltP2osGXSSpWhnmAKbwBFoJJETqoLRgKxnJrslL6gjF+nRygBJdKmk3rfKsown4havnJNiqvbNZWtdCltQEgD7MtderZvREpM3UVbKmupkZWZ6/Zg4kpFNNe6KLzBqVjl9sNlRBhLl8xH3lJnWfdr9oQnXajwgJBALvw+6RTYFAIBA4T2RST65Nm3fk2vRrUzua3SGuzdEYGK2OmoTKDVhl+1IjHIilUbW1xeChtBeSRLVBdeZayrZ7xoYDsDWrnfaagEyPO5oAQVGqDeG2JmQn23koKupbiKFJ/eAruRmj1N97otRAWZXzo/quJLov7UnuqHSi0iYCEGurAatl2H31PTk2LUE+0o4CgWDUCGdTIBAI3EBET67NRKmekpomzxoDhJnVjqYxyrXRS0Nie/Lw0Uh5fatTGkqramuHaeSUMa6iJbBnTLW+0GM27I+xQz0nlkDvmIoYFqu+VIihidqWDk21G2vUqZc2dBAcq6m2ywiMpF2vLi0y1xR42JiB1DS1kYrq+CVkHTIqrczMHNoVEwbJRnNF/rD7Bvbky9WHu/Yll0Ag8B6EsykQCARuQBeudsaDpS72lo883czVxMjqdN7QBPuiyTlL77THOKmRktoWpzSMHaqt1kDPORpKhBoBN7C91GM27E+QWT0nUph3dNqN4Wo7x0pNTr9UGIrOnpHtVn0E+NDUy7Yg9UWLsbnYs4YMQtGe3QRLXdiQCE4YNyqtyNBASlGnCVfu3jLkfo1tnUTb1BybgbGeSdEjEAjcj8edzWXLlpGeno6/vz9HHHEEP/3007D7NzU1sWjRIhISEjCZTOTk5PDpp5+6yVqBQCBwEmMAzZKah6qpwrOjY21tbcSjrqGMz5zk2sJ6pj3GSo3sbWh3SiK0ZxQ2MDBQM7McJShJTdEQYynzmA37E25VO+2myNEFd9GMnkBQJqmbmlptg2DZWlS9Tn/PjWw7gxKlOnGhnXs9bMlA6ovV6eC1UjQYRh90q9aoTqNuLd8x5D6F1c19aU9MUWmjLlMgEPgGDjub7e3OdRYG4+233+aWW27hvvvu45dffmHKlCnMnz+fmprBA2hYLBZOOOEEiouLeffdd9m1axf//Oc/SUry/HoVgUAgGIkmozrlsaves6Nj5Xt2oJMUOhQTUfEuDrnT42xGSa1U1zc5fLhuy7/IsqqRc+cWPwW/vKahcfbTG5E2jQoaW7WdJuoMvSNEYQleMkJkDKBdUl8GtNdp65AbOz0/su0MvS8oEuQK2ruGX8vobsy16guvBpM2/afWwJ6XHsNMMy+ubSGhx9nES0bkBQKB63HY2YyLi+Pyyy9n7dq1oy78ySef5KqrruKyyy5jwoQJPPfccwQGBvLyyy8Puv/LL79MQ0MDK1eu5OijjyY9PZ3Zs2czZYrjyYgFAoHA3XQF9ESkbPXsNNqGMnVdVbUuFiTJtYUFRGCVjAC0N1Y6dGiI0or+01vptVBCwfbhTdDs/vMXmjwBmyIRJnWQX+jZkemm5maiJXVKckzy6KZAakmLPhKATgfbeST8eyIZ670gEJIj9DqbWVIFu6uaPGvMAeib1WjU5mBtXjb1TjMPah96FLe0vn2fsxnqW20pEAicx+7UJ7288cYbrFixguOOO4709HQuv/xyFi5cSGKiY+HILRYLGzduZMmSJX3bdDodxx9/POvXrx/0mA8//JCZM2eyaNEiPvjgA2JiYrjgggu44447hk6kbjZjNpv7Pre0tPRt7+7uxmw2a5bkVJZlzTV7bd+/DlrgClvHsia4pq18pf6+ogmebSdbSCI0g39H5Yjlu/Kcdtb0jGoY40nU4DyMZGuXMYowSxXW5pHr3YvZbCbM0IXO2j+yqQ4be3f9QuyUaM3tHB4dTbpo4pRaqgu3YM7L0UDTOVvL9+wgHOhSjPiHRNl9Tl39jOowRoK1DLmlalS/r/3ttCkQZm0Ag5qCxlldT7STFJaBH5AhVfFxWS3jE+zLRe6OvkRv2hMiMzQ5pwFx2VAC0d3lQ+pV1jYS0/OSxOwfA0Ps5yvPE9HnG7uaop32adqDpCiK4kxBtbW1vP7666xYsYIdO3Ywf/58Lr/8ck4//XQMhpF92IqKCpKSkvj++++ZOXNm3/bbb7+db7/9lh9//HHAMePHj6e4uJgLL7yQ66+/nt27d3P99ddz4403ct999w1azv3338+f//znAdvvvPNO/P39HaixQCAQjI5phl2c3v0JnylH8oPuKI/ZMd2wnQXdq/if4Xh+kkcXidIeLpbeJ9O2hz/pbsKg2P+w05kCuLvzIXTSvseUVdHxcMASbOZOV5g6LGfqPmOK/Bsv+l1KeXek28vvJcbUwfVdz1FKPK9IF3jMjgM5Wf8dh1s38KLhAsplbfJKdhuDOKHzA2brt/I+89kqTdRE1x3oFJkl/AMDMrcHPkRQZ52nTQJAUeB3tveYpCvmNcMf2COPPg2JzmjkT5ZHAXiIG+iW/AbsU2xM5BXLHzHjxyMscv2sCoFA4FK6urp45JFHaG5uJjQ0dMj9HB7Z7CUmJoZbbrmFW265hb///e/cdtttfPrpp0RHR3Pttddy5513ah7MwWazERsbywsvvIBer2fGjBmUl5fz+OOPD+lsLlmyhFtuuaXvc0tLCykpKdx0001UVlaSm5urqae/a9cuTTXNZjNLly5l8eLFmEyjX8TfiytsHcua4Jq28pX6+4omeLadmn/5L3z2CTFKHTff8kf8/Ya+BbvynJr/p84eCUgcz50X3qmZ7lC2trz+K5TtIUhuYvFdj9ulaTabeewfL/CePIvfGdYAqqN5j/UKLr/oMtJihn6wOWvnSGx/sQhqfyNW38TFtz6miaYztq77z9OwG5oMMdx5m/3t5+pnVMV/KmDvBsJo5eI7n3Jac387f9hdTdy7KwA49fzLOSVz7qg13dVOAI1P/JcYSxkpwTauvsm+tnJ1O9W0mIn753IAzrroGvwSJjilub+dVptCw+PLiJTauPCsE4kff2S/fV9evZOd334GflBmiyL26LO4fM74EXW9+Xki+nxjV1O0k6qZkJDAI488MuL+Tjub1dXVvPrqq6xYsYKSkhJ+97vfccUVV1BWVsajjz7KDz/8wOeffz7k8dHR0ej1eqqr+0etq66uJj5+8DeiCQkJGI3GficrLy+PqqoqLBYLfn4D36SZTKZBLwSTyYTRaMRkMml68rXW7GWoejiLK2wdy5r7o2Vb+Ur9fUVzfzzRTtEpavL0JKmOypYuxidFjVrTGTsDu9X8ev6xWZqcg5FsDYhKgTIIlRtRJP2wTvb+GLvbCQiLgXZYJR/KX7ov4ZJ508hJdi4q6WjPqV98LtRCeFdZ33lz1XU6nK7Upq6JbPOLcaj9XP2M8otMgr0QZG0c1XW1v52/VTQzTWoEwC8qDZzU9UQ7AXQEp0NDGabWPXafE1e3U1l5AdlSJzYkQpLywDj6cxqo1/OrlEgk+TTs3UHalNl9+5XUNPPYN2WcrVPXa5Yr0Tz+TRmnTEsnLXbg1GJfeZ6IPt/Y1exFtJN9dXc4QNB7773HggULSElJ4c033+T666+nvLycN954g7lz53LxxRfzwQcfsHr16mF1/Pz8mDFjBl999VXfNpvNxldffdVvWu3+HH300ezevRubbd8anvz8fBISEgZ1NAUCgcCb0EWo4f7jpCY++Tmfkppmt9tgs9mIt6kv+aJ6nF9XExilRqqMkxopczAHY0pP4vmasKn8+5YzuOaEyZrbZy+RaWrZiXIF5m6rx+wwtKvnxBKozVRVrQiKUoO+hNka+z2nneWfX/3GP1bvIUJqA+CVjY2j1nQ3UrQawCm80ztS5gA07FXTk9TrosCo3XKi+p7Itl1V+f2276poRAESJXUacaUShQ3Ir2zSrGyBQOC9OOxsXnbZZSQmJrJu3To2b97MDTfcQHh4eL99EhMTufvuu0fUuuWWW/jnP//Jq6++yo4dO7juuutob2/nsssuA2DhwoX9Aghdd911NDQ0cNNNN5Gfn88nn3zCQw89xKJFixythkAgELifwCjMqJFZV/64gzlPruX5L351qwntbU2ESWrqjuQsF+fY7EEXpgaQi6OB0roWh44N61Ijz0Ym5w46CuJOYjPV9a2pUjW7yz23/i6wq2dGkJdF9AyPU1+mxNBEbcvo1tRWt5p59Ou9xPaManYqfjzwbYNHXtCMhpAUdY1pglxBl8VzLyj2x1JbBECjSdv0I53Bavvrm4r7bS+uU18w9UairSQSHZCTEK5p+QKBwDtxeBptZWXliGsxAwIChlxDuT/nn38+tbW13HvvvVRVVTF16lRWrVpFXJyai660tBSdbp8/nJKSwmeffcbixYs55JBDSEpK4qabbuKOO+5wtBoCgUDgdkpqW7DaosnSVZIk1bNXiePRr0o5aUqq2xyp9lo1NUGdEkZ0sJuct55cm3FSE+sbHMjVrCjEyeqU0bBk94zCDoc+PIUu/PCXLOwt3MbEdM+MLIZbVUfX1DNi7C2YIlTnN0ZqZk9DK3HhQU5rlTd3owDxqM5mlRKBDYn8yiaPv3RwhLAeZzNDV0lRdRMTUhyPoqw1xhY1z685RNscu/roLKiHsM596U9qmttZtkb9nCGpI/Idij93zHPfPU8gEHgWh51Nq9Xalz5kfyRJUtdsODid9YYbbuCGG24Y9LvBpuLOnDmTH374waEyBAKBwBvYVdFIgBJFFpUcK22hmDiqiHJrB9pSoY6k1kuRuK3b2+dsNlLRZP+IVwjt+GPBquhIzfKCKKQ6HTWGJFKte2gt2wkc7xEzom11IEFIfKZHyh+SYPVFcYTUxvq6Jsh03hlPCjMiAbmS6qg0EeyTo2G6GPUlSbJUx8dllV7hbIZ1qVN6DTHZ2uqm5MEuiJMr+rbd9u+faTYrXBP0LUfK6vTdJcZ/I0XNBDw3JV4gELgPh6fRhoeHExERMeBfeHg4AQEBpKWlcd9992myXkMgEAgOJnITIzDRDcD1xo9YZ7qR3+u/cVsHes2bj3JSlRqFMkfZw+o3HnZLuYSoTkeo1EF9Y5Pdh4UaLQCUEUNyrOdSjexPa5A6mqg0FHqk/M62JsIkdXQ4LmWcR2wYkoAIunveYTfXjm6NYlyIiVcmbuEvxlcAmCoV8vKkrb43GhYURasUAkBT2Q4PGwPdVpk4WR1hjEjO01Q7ddxUAMJpo72xin+v3cW3xe0kSnXcKb9Ab6ITCQU+uhmayzUtXyAQeCcOO5srVqwgMTGRu+66i5UrV7Jy5UruuusukpKSWL58OVdffTXPPPOMXaFwBQKBYCyRZmrjMP2+4Bl6SeEh40ukmdpcXnZZ0U6O2f0Yup4enyTBrILHKCva6fKyMYVg0QUAYGmusvuwkB6nqkqfiF7v8OPKJSiR6mhQYHupR8qvLi0AoFUJICo61iM2DIkk0aIPB6CzsWL4fUfA0FHD7KLH+12vcwof90kHpd6kvqCQa3d72BLYU91MWs901ph0bWcLxMVEUamoL4W2bFzPg58Vkkgd/wn7u+pg7o8iQ0ORpuULBALvxOFptK+++ipPPPEE5513Xt+2BQsWMHnyZJ5//nm++uorUlNTefDBB7nrrrs0NVYgEAh8mobCAZ0uHTa10xXm2mAvlYVbSZb6l22QbFQVbSM5c/B8d5ohSZj9Y/DrKEVqr7H7sHClCYAmk/cEwglOGg97ILa73CMzeBorCkkHaqQosnTe4YDvT4cxiii5Dltr9cg7D4OpbS+ScsD57XVQXPxb0Zqu0HTo2o6ptcTTplCyt4TJPWlP9NFZmmrrdDqq9Ikk2BrY/t27XGAL4Sb/lQR1DTJ1XtJDpJdNAxcIBC7B4SfV999/z7Rp0wZsnzZtGuvXq4nCZ82aRWmpZ976CgQCgdcSmQXSAbddN3W6ErIOQVakftusio74TPdEpCVYnUrr11Vv9yERNnVfS6i2gUxGQ3zWVADSqaSy0YFgRxrRWac+Wxv1nl/7NxgWf9UuqWN00XrNwSkoB3ZRfNRB0cfkABDZtXeEPV1PS/kuABo0TnvSiw71BcGVuo+5y/hvguiE5MPhuHvU9gP174KnfO6lgUAgcA6HRzZTUlJ46aWXBkyTfemll0hJUaeK1NfXExERoY2FLkKW5X5/vVnTYDAgy7JP2DpWNXv1tG4rX6m/r2j26nmsnYLjkY5/AN0XamooGzo49UmU4Hg44Fit65+QNo5vMxYzd8+TSBLIio612bdxTNq4UZdhj62G8ASogRC5gbaOLgJMxhE1Y23qKKgxOlOT86DFOTXGqY5DrNTEl4V7iJk6ftSagzGUrbZG1WFp84t1uEx3PKOUoFhoAlNXndPlyLKMNTCWz1IXM7/kCSQJFEmHMsRvxV7N/f9qhT26Icl58BskyuV0mS0YDcMnVndlO1nr9wDQ5J9MhMa/+7I9O5ksb4f93mnZFIm9xz5BctZEmHw+NOyByAw1bc8Q5fvK80T0+ca2pmgn+zUlRVGUkXfbx4cffsi5557L+PHjOeywwwDYsGEDO3fu5N133+W0005j+fLlFBQU8OSTTzpovutYtmwZy5YtQ5Zl8vPzWb9+PcHBwZ42SyAQjDUUhaz/Hk8AXTwX/xdmzZrntqLrynczZ/0ldClGfj72dSLi3Jc6I27L34kpeIsXrKeSetKtJIebhj9AsZH93+Pwp5uPpq8gI9N7guEk/vdUIpUmXk5/gsMPPdKtZTd/ei9Hd3zFJ6F/IO3EwSO5exLlx+eYvPd13mMeOb/7y6i0Xl2bz+NVl2EDCk56m+5gbfNCugtDUxHjv7yYNsWfdSd8QlK49iOK9vLrB0/yh+7/sjniJAzz/qSpdvlva5m/Y2Aqus8nPEbihKM1LUsgEHietrY2Zs6cSXNzM6GhoUPu5/DI5umnn86uXbt4/vnn2bVLnY5x8skns3LlStLT0wG47rrrnLPahSxatIhFixbR0tJCWFgY2dnZVFVVkZOTg14//FtGe+l1ZLXUNJvNLF26lMWLF2MyjdA5cwBX2DqWNcE1beUr9fcVTfCOdqr9JJmArt2YLRby8gaPCOmK+v+w52cAynUJHDXnRE00wT5bpeaJUADxUgNSUAR5eWnDajaX7cKfbroVPTNmziYucvRRSLU6p8X+yUR2NuHXUUlOTo5LrtOhbP3tA3V6qiEqdchrx1HN0XDg76muKhf2Qpitkdzc3H65sh21069DDWTT7hdL9mEnjMpOV91P7NKVs7B+qSNY6kKvWMjLG7gUydW29rbTod1q3trgpDwyHLx+DuRAO0P8JeTtEvr91oZbFR2502eRnGH/unBfeZ6IPt/Y1RTtpGpmZ9uXPskhZ7O7u5uTTjqJ5557jocfdlPIfBfRe8L1er2mDx6tNfV6PVar1SV29up7c/19SdOVbSU0D652soSmQdduTG17R9xfSzvNtWr0x3pjIlnuvp/0rM+KkxrZ3tQ1Yp1qSrYTCZQRS1pUuFNOi1N22oElNAM6t+HXUuLSZ8lguuFW1dn0j0p1ujxXPqPC49WXCNE08WtpPdOznM+16d+uRp41h6QSoqG9bn+W6gOo1MWRYKuktXwnev1Ro9d0wj6LrBBvqwIdRKVP0lRbr9eTlj2R1ePuYFbBYxgkG1ZFx9pxtzMn27mot77wPNFaU/T5fENTtNM+TXtw6OltNBrZunWrUwYJBAKBQMUQrQY5CTNXurVcfZMaDbMzyAOBOUISAIilkXI7Auu0lqspWar1CZo6mlpgjMsFINzdAV8UhWhFdTaDYzPcW7adfJKvRh6NkZo4558bef6LX53SsdlsRHSr6VP0Ub4XFOhAGgPUIFe2es+lP7EYQ8noSXsSluyaCNRzLlpC1SXr2TDrJaouWc+ci5a4pByBQOA7OPwEv+iii3jppZdcYYtAIBCMCcJS1OlribYqGtsGSQvgIoI6e3IURnjAUQlRR7jipCaqmrtG3F3uCWTSHOB9ESuj0icDkGyrpK3T4rZyu9saCMQMQGyyfdOX3ElJTTN/+0m1L4ZmwMajX5VSUtPssFaLWSZJUdOnBCe6ODWPGzCHqr+5gNZij9kg6SVCpQ4AdFHapj3Zn+TM8Rx6/O9cn1JJIBD4BA6v2bRarbz88st8+eWXzJgxg6CgoH7fe1NQIIFAIPBGguLViKapUjX5FY0ckRPglnKje0aKAuNc19Eckp7UJ4GSmZbmxhF3929TU3x0hw6/ttMTRKSpzmaGVMmWsjrcFWqurqyABKBBCSYhLsZNpdrPropGalHX1holmRz2sos08iubSIt1bM1tVWs3mZLqbBpjvc+xdhRDXA5UQqS5zGM2+NnUGQX1uiiijO655wgEAoHDI5vbtm1j+vTphISEkJ+fz6ZNm/r+bd682QUmCgQCwUFGT67AZKmOwsoGtxRp7e4mQVFTiUSnO7eGalT4BWIxhKj/b68dcfdIszoK6xfthVMow1OxoidAslBeXOC2Yhsr1TW3NVI0Br13TS0GyE2M4Gz9Gnpj3H9quovf678hJyHcYa2q1m7SepxNX8yteSARqWo+2yRbBVbZ5vbyX169k+aWJgB2dcc5Pb1ZIBAIHMXhkc1vvvnGFXYIBALB2CEkgW6MGKVuGioKgUkuL7Jszy7SJSsWRU9SWq7LyxsMOSgOmlsxdNUPv6NNJt5WDRJEpHjhVDy9kTpDAvHWMtoqdkJyrFuK7agtBqBBH+2W8hwlzdTGw8aX+tIs6iWFh4wvoTMtBhwb2WxubiJKalU/RKRraaZHiM+eCkAStWz8+XsOO3KW28ouqWnmsW/KuMegTk2vVsJ49KtSTpqS6vCIs0AgEDiK069Gd+/ezWeffUZnp7reyMF0nQKBQDB20eloNCUCYG3Y45Yiq4u3A1ApxaI3Gt1S5oEYwtU6h8kN/FZaN+R+zZW78ZOsmBUDqZmjS8/gKlqD1IAvNLqn/QDkJnW0t83PPc6twzQUoqP/qJ0OGzQUOSyltKjTTVv1EWAK0cQ8T7L2g5dQFNBJMON/p7H6DfdF9N9V0ci5+m+4XL8KgDP06/md/hvyK5vcZoNAIBi7OOxs1tfXM2/ePHJycjjllFOorFSjKV5xxRXceuutmhsoEAgEByPmENVZMbS4Zw1XR1U+AHUG51NRjJaijkBATX9y2rM/DjmVr6JAjXpeTizhIUGD7uNxotR1hMEdpW4r0tiuPm8tAXFuK9MhIrNA6t+tUCS9U9NgTe3q+uK2wGRNTPMkZUU7OWb3Y0g9Q746SWFWwWOUFe10S/kTQrt42PDifuXDQ4aXyAtxX3AygUAwdnHY2Vy8eDFGo5HS0lICAwP7tp9//vmsWrVKU+MEAoHgYKU3nUNoV7lbylMa1bQnLaYEt5R3ICU1zXxV6QeozqYCQ0YqbalQO+FVOs85xiMRkqSOuMZ0l1PRbHZLmQFd6ppbJTTRLeU5TFgSLHgapWciraJAw5yH+3KsOkJIT1ogOTxdSws9QmXhVvRS/9lfBslGVdE2t5SfLNUMWn6yNPLaaYFAIBgtDq/Z/Pzzz/nss89ITu7/tnHcuHGUlJRoZpirkWW5319v1jQYDMiy7BO2jlXNXj2t28pX6u8rmr163tBOIYk5sAvibdU0tXUSEuA3as3hCGpXc0JagpJcck73/zsYO8rrqVYiANXZBLABO8vrSY7qH8/V1pP2pF4f6/F2GorvG0I4G8iTSjj9o98obDNw9fHarb0dzNawbtU58ItIdqoObnlGTbkQ9Cb0719NsRJHfugJHO9ged3d3UR3V4Ee/GKyNLHXlfeTkXRjMyYir5X6OXxWRUdMet6gx2lua3g6OiQk9pWvSHps4WkwijI8eU4PZk3R5/MNTdFO9mtKioOLLUNCQvjll18YN24cISEhbNmyhczMTDZs2MD8+fOprx8h8IOHWLZsGcuWLUOWZfLz81m/fj3Bwe4KWC8QCAT9Ca76kfS1t7DLlsyO41aQHe3aVAR+715IDsV8nvNnEg853qVlDUZ1q5n/fPQhz/k9xUbbOM6x/Bkd8M8z4okLMfXb1/LBjUzv3sgHMdeSNftit9s6EtWtZr7/+GX+ZHwDAFmRuNt6JSec9ocBddEMRWHcf+diopuV018lO9N704EY26vI/d85WBQ9Syf+l1MnOJampb69G/9PruUI3U72HHof7eknushS97H3+7eZX/53dJKCosCqpBtJOep8t5Vf+/njzG1ZCYAi6aiYfjuNGQvcVr5AIDj4aGtrY+bMmTQ3NxMaGjrkfg6PbB5zzDG89tprPPDAAwBIkoTNZuOxxx5j7ty5zlvsYhYtWsSiRYtoaWkhLCyM7OxsqqqqyMnJQa/Xa1JGryOrpabZbGbp0qUsXrwYk0m7TowrbB3LmuCatvKV+vuKJnhRO8X5w1pIlWrYpA8iL69/hFgt62+TZcxKFUgQEJWm+Tm1x9Y8oLkiH37dN7J56aExzDl86oB9y/+rTqGsaNNzQmam1/2ean/8ibsM/+r7rJcU/mp4ibXKOeTlTdXA0oG22lqrMdKNTZHIm3oE2UmOBwly2zNKycWyyoQfZqSOevLyjnVIc832vX1pT1KnHAOJow8S5ar7ib26eXn3s+WLLKb9cBN1hHL8Zfei0w2+kskVtv78mTolvTxgPPFX/4f40CRGO0nd0+f0YNQUfT7f0BTtpGpmZ9v30tNhZ/Oxxx5j3rx5bNiwAYvFwu23385vv/1GQ0MD69atc9hgT9F7wvV6vaY3Sa019Xo9VqvVJXb26ntz/X1J05VtJTQPwnaKTENGR4Bkoa6qFL1+wug1h6CyrIRkqQubIhESk+Kx+8nZ82bt52wqGA0D97dZLST0pD1plv09306DkBfQNOgauPEBzS6ztam6hCigljBS46NHVY7rn1F6GvyTiekshIYih8uqrK1nTs8LCX1UFmj8+/fUszRv5mnww03ESC2U1dWTnDC8u6elrSE9gazaYqahj0jVRLMX0T8Rfb6xpinaaZ+mPTgcIGjSpEnk5+cza9YszjjjDNrb2zn77LPZtGkTWVlZDhsqEAgEYxK9kSY/tbNpqXU8NYQjVBWpUV+rpSgMfv4uLWtYgtUoqkZkcinlm4KByy7qywowSjJmxUhXtzTge28gPvMQbAc8Pm3oiM+c7LIyGysKAaghigA/z6SucQRLeAYAgT1rhR2htWo3AG1SMARGamqXJ/EPi6YZdflOeeHgkZhdgc1mI7ZbDUTmn+CFeWsFAsFBjcMjmwBhYWHcfffdWtsiEAgEY4quoBSwVKBvcbxD7gitlWrak1pDgvPJlbVg61t9//3UdBdLmq5kb92RpETvW+tRUfgrMUCZFId0QM5GryEsCd3pT6N8eCMSimrlaUudirpqLx21atCkBn20y8rQEr+4XKj8kmhLGbJsQ6+3/8pTGooBaDIlcbBFVqjWJxAmF9Bavgs4wS1llje0kY6aSiYq3XUvRAQCgWAwnHI2m5qa+Omnn6ipqcFm698ZWLhwoSaGCQQCwcGOFJUBjT8S4uL0J73RXVv9EwlzaUnD0FwOH93U91EvKTxkeIk3vj+JS0/fF7CovSftSa0hAbQNcqkt0xdSXlZK8i+P87OSx6HTXBvISG5U87G2+Tm+VtMThKdOgs2QLlVSVt9KWqz9V55fm1rXrpAUF1nnOZr9k6C9AGvPb9IdFOytZI5UB4AxLsdt5QoEAgE44Wx+9NFHXHjhhbS1tREaGook7ZvmJEnSQeNsyrJMd3e3w8fYbDa6uro0mxdtsVgICgrCbDbjYODgYXGFrWNZE1zTVr5Sf1/RBO3ayc/Pb8gAH/YSkpgLuyFWrqa9y0KQv9/IBzmBf7vaebeGpblE3y4aCkHp/3LSINnYnf8bsM/ZVBrUTnhbYAq0utNAx4kYdyT8AtFKE41tXUSHBbmsLEO7GjTJHBDnsjK0xBirBrzKlCrZUtnkkLMZblZH4aSIdFeY5lG6Q1KhHUytpW4rs65kBzpJoZVA/AIdiwwsEAgEo8VhZ/PWW2/l8ssv56GHHiIwMNAVNnkURVGoqqqiqanJqWMVRaGkpKSfEz5ae44++mjKyso00+zVdYWtY1WzV1frtvKV+vuKZq+uFu2k0+nIyMjAz895BzEoQR1lSJOq2F3ZxJQM14xaRfR03v2iPbiuPjILJF0/h1NWJL6pj6CxrZOIYDX1S+8aP1tYmtc7m0HxaiS+ZKmWLVUNLnU2A7pqAFBCElxWhqZEq+cmVmqitKISptj3okOWbcTKlaDb9/s4mDDEZEEVhJtdO5thf8zVuwCo0sWTquG9VCAQCOzBYWezvLycG2+88aB0NIE+RzM2NpbAwECHOqOKomA2mzGZTJp1jm02G3V1dURHR496FGV/XGHrWNYE17SVr9TfVzRBm3ay2WxUVFRQWVlJaqrzkR11UarzlybV8HlVo0ucTZvNRrxNTXsSkTIeq+Yl2ElYEix4Gj66GRR1fmyBlE65EsUnvxRz0bFqeovobtUxDojLhrJCT1lrH6HJyOgwSVaqy4sh13XTPsO6awEwRmobSdRl+IfRoo8gVG6kvXIncKRdh5U3tPWlPYlIHTxCsy8TkZIHv0K8XIXNZtP0uT4Ufs3FANTqYvGRq0cgEBxEOOxszp8/nw0bNpCZmekKezyKLMt9jmZUVJTDx/dOyfP399fU2TQYDPj7+2vubIK2to5lTXBNW/lK/X1FE7Rrp5iYGCoqKrBarc7r9EwTDJU6qKqsAHKH3d0ZamuqiJPUIcLkrEkUl1dpXobdTF8IWfOg6Bv4YBE5FJMllfP59iAuOjYPudtCvK0GJIhJnwgbvdzZ1Buo18cQK1fTVlUIHOOacmwyEUoDAMGxHpwK7SAtgamEtjaiayy2+5g9lbUcgxql2BBtXw43XyIx+xAAEqR6ahobiI1yfcCn8E51ym6jFOHysgQCgeBAHHY2Tz31VG677Ta2b9/O5MmTMRr7h2A//fTTNTPO3fSu0TxYR20FAoF29E6flWXZeWfTGECzIZowax2dtYXAXO0M7KG8cBtxQB1hRIRGgCedTVBHOKddBDs/QbfrU67Rf8y9ZdfQaemmrng7KZJMl2IkOX2cZ+20k2ZTArEd1ciNJa4rpLUKAza6FT2xib7jbFrDM6F1C8Ht9q9PbCjLRycpdGLCL+jgW18YFBFPK4GE0EFZwTZio+a4tDyrbCNBrgAdNCkhLi1LIBAIBsNhZ/Oqq64C4C9/+cuA7yRJQpa9OXzgPnrt3N9eWZb7RlOcCRwymmOH05QkqW/9mpa6+/8Vmtroat1WvlJ/X9Hs1dOqnRRF6edsOnP/aw9MJqylDn1T6YD7kbOa+9NctgOAGn08oRppHohTth51E/pdn3KWYS1Lu37HZ5uLSarfSgpQLsWToNNhMBiQZVkze7U6p/tjCUqCjs34tZZpqru/rW2VuwkDqokgITLE6XJcUX9Zlodsp4D4XNgLMd3lWCzddqU/6apWc2zWGhKIt9nAi9veWd0qXTwhtiIa9+5APmzgaLiWtu6uaCBDUoNLtSrBXv97cpWuL2lqfd/r1d3/r9AcvaZoJ/s1HXY2D0x14issW7aMZcuW9Z2Y3bt3ExwcTH5+ft8+Nputb53YaBjt8QcSHh6OxWLRVLMXrW0d65quaitfqb+vaGrRTmazme7ubgoLC/uczf3vJ/bi7x8PLRDYWc6OHTsGfO+M5v509uTYbDQm9GmNVnMoHNMNJj1mGsG1m7jK8Akf/ZLIecpWQHU0OouKOOOMMygqKvKwncPTZVJH30LMVYO232jJz8/HsvNnpgNVROFfuodKDTS1ZKh2CtSHA5AhVbJm41biQkwjaikN6tTpJr8EWlxwnXrDtd9uTABzEZ2VO4e9ZrSwdWvBHsZLnVjRcczpF3v978nVur6g6ar7HvhG/X1FU7ST6kvZg1N5Nn2RRYsWsWjRIlpaWggLCyM7O5uqqipycnL60ip0dXVRUlKCyWTC39/f4TJcFSCopqaG2NhYESBoGM25c+cyZcoUnnrqKbs1H3nkET744AM2bdqkia3OtNXs2bO55ppruOCCC4a19WBpJ1dqrl69muOOO46GhgbCw8NZtWoVS5YsYePGjf3aQ8vflNFoJC0tDaPRSH5+fr/7ib20lh0CNauIlatIzxpHgJ96W5Zl2WnN/Wn4RJ02K4enkZOTo4nmgThtq9/d8Obv+L3+G16tPIvAMNXWjuBUDsnMZOnSpSxevBiTaWQnxaV2DkNZzRQogxi5mtzcXM3u0/vbWrj9XQAaddEcl5eniaZW9TebzUO3U6wRflCdzVJTGHl5GSPq7flAvQa6ghKZqqGdrqi7s7rrv0uDmnUEdVWRN0h7amnrjq0/AVCrj2Plx//z+t+Tq3R9RXPY39Mo8JX6+4qmaCdVMzvbvnX1djubp5xyCv/+978JC1NzZT3yyCNce+21hIeHA1BfX88xxxzD9u3bHbfaA/SecL1e3+//kiT1/XOW0R5/oFbvtL/hNC+99FJeffVVrrnmGp577rl+3y1atIhnn32WSy65hBUrVrjMVk9rOluuI8dIksT777/PmWeeOeh39rRVLx9++CHV1dX84Q9/cFndvF1z9erVzJ07l8bGxr57ibOavfv07n/yySdz77338uabb3LxxRf328+RdhquPEmSBtxDHL2Zhyarnc1UqYaS2lYmpvYPGOKM5v6E9aQ9MUZnjcpOe3BYd9zx2OKnEFi1hbOtq5CaS0ECKTIDvV6P1Wp1ia1aasakqe2XJNVS12omITJYE91e9Ho9cpOaJ7XVL0YTu7Ws/7DtFJWJFT0BkoWa8j3op4/cMYnqVsdtbaHJXt/2zuoaorOhBsK6yoc9RgtblfoCAJr8U7B2eP/vydW63q7pyvter743199XNEU77dO0B7tfwX722Wf9prM99NBDNDQ09H22Wq3s2rXLARMPfsxmM42NjS6ZBjgYKSkpvPXWW3R2dvZt6+rq4s033xxVegZ34aqpwt7KM888w2WXXeaW0PejQZblQafP+0J7XXrppTzzzDOeNmNYdJFqZO80qYqCqibN9eNldaQoPHm85tqjRpLQHXsrAJfqPyMLNcdmUIL2UXldhSlWTV+TIDVQVFHtkjL0baoDZg6Ic4m+y9AbqTeqeUG7qkaewmWVbSTY1OvVL8L7n1nOEpKo5g+Nk10frCugVQ1cZQ4beVRZIBAIXIHdvdwDA2loHbTDW+kNAOLov/Lycn744Qe2bt3KDz/8QHl5ucMajp7j6dOnk5KSwnvvvde37b333iM1NZVp06b129dms/H444+TmZlJQEAAU6ZM4d133+37XpZlrrjiCjIyMggICCA3N5enn366n8bq1as5/PDDCQoKIjw8nFmzZlFaqkYdvPTSSweM/t18883MmTOn7/OcOXO44YYbuPnmm4mOjmb+/PkAbNu2jZNPPpng4GDi4+O54oorqKur6zuuvb2dhQsXEhwcTEJCAk888YRd5+eRRx4hLi6O0NBQrr32Wrq6uvp9//PPP3PCCScQHR1NWFgYs2fP5pdffun7Pj09HYCzzjoLSZL6PhcWFnLGGWeQkJDAuHHjOOKII/jyyy+HtaW2tpavv/6aBQsW9G0rLi5GkiQ2b97ct62pqYnAwEBWr14NqOdckiS++uorDj30UAIDAznqqKMGvOj56KOPOOyww/D39yc6Opqzzjqr77vGxkauvPJKIiMjCQwM5OSTT6agoKDv+xUrVhAeHs6HH37IhAkTMJlMlJaWkp6ezgMPPMDChQsJDQ3l6quvBmDt2rUce+yxREZGkpqayo033kh7e3ufntls5o477iAlJQWTyUR2djYvvfQSxcXFzJ2rRl+NiIhAkiQuvfRSQL0+H374YfLy8ggMDBxwfQJ8+umn5OTkEBAQwNy5cykuLh5wnhcsWMCGDRsoLPTiFBqRaicwRmphb5W2zkpjYwNxkvpSMDF7sqbamjF+Ac2BaYRJHST02GoM8KGomQGRdKAuu6jf65q1awFdNQDYQhJcou9K2oLU6LmG5j0j7lta3UCypOYT9Y/2nai7jpI0Tk1/Ek8djU3NLi0ryqKOivvF5ri0HIFAIBiKMbNm01lsNhtr164dtc7u3bvtXkjby6xZsxye5nf55ZfzyiuvcOGFFwLw8ssvc9lll/U5K708/PDDvPnmmyxfvpycnBy+++47LrroImJiYpg9ezY2m43k5GT+85//EBUVxffff8/VV19NQkIC5513HlarlTPPPJOrrrqKf//731gsFn788UeH7X311Ve57rrrWLduHaA6V8cddxxXXnklS5cupaOjg9tvv53zzz+fr7/+GoDbbruNb7/9lg8++IDY2FjuuusufvnlF6ZOnTpkOe+88w73338/y5Yt4+ijj+aVV15h+fLl/fLFtra2cskll/D3v/8dRVF44oknOOWUUygoKCAkJISff/6Z2NhYXnnlFU466aS+6QNtbW2ccsopPPDAA7S0tLBq1SoWLFjArl27hhxRXrt2LYGBgYOu17GHu+++myeeeIKYmBiuvfZaLr/88r5z+Mknn3DWWWdx991389prr2GxWPj000/7jr3sssvIz8/ngw8+ICwsjDvuuINTTjmF7du396Uy6ujo4NFHH+XFF18kKiqK2NhYAP72t79x7733ct999wGqo33SSSfxwAMP8Oyzz9LS0sL//d//ccMNN/DKK68AsHDhQtavX88zzzzDlClT2LNnD3V1daSkpPDf//6Xc845h127dhEaGkpAQACgXp9vvPEGzzzzDBMnTmTNmjX9rs+9e/dy9tlns2jRIq6++mo2bNjArbfeOuA8paamEhcXx5o1a8jKynLqXLsc/zDa9GEEy810VhcCR2smvbfgVyKAFiWQsKgEZG8M8KbTYTx2May6uW/TIV/8nu+KbvOcTY4gSdTp40iVS+iocU2wiLBu1dk0RiS7RN+VKJGZ0LSO0I69I+5bWbKLTEnGjBFbkI+N4jpAeGwqHYqJQMnM3sJtRMzQ7je/P53mblJsatqTyLRJsOVHl5QjEAgEw2G3sznY+iat13sJRs9FF13EkiVLKClRp86sW7eOt956q5+zaTabefjhh/nkk0+YPXs2kiSRmZnJ2rVref7555k9ezZGo5E///nPfcdkZGSwfv163nnnHc477zxaWlpobm7mtNNO6+vEjx8/fsBo4UiMGzeOxx57rO/zX//6V6ZNm8ZDDz0EqCPLvQ5xfn4+iYmJvPTSS7zxxhvMmzcPUB3W5OThO2FPPfUUV1xxBVdccQWKonD//ffz7bff9rP3uOOO63fMCy+8QHh4ON9++y2nnXYaMTFq1Mnw8HDi4+P79psyZQpTpkzBZrNRVVXFX/7yF1auXMmHH37IDTfcMKg9JSUlxMXFOT2F9sEHH2T27NkA3HnnnZx66ql0dXXh7+/Pgw8+yO9///t+7TdlyhQACgoK+PDDD/n666855phjkCSJf/3rX6SkpLBy5UrOPfdcQM05++yzz/Ydt/852t+pu/LKK7nwwgu5+eab+8p/5plnmD17NsuXL6e0tJR33nmHL774guOPPx6gn4MfGRkJQGxsbN+aTbPZzEMPPcQXX3zBtGnT8Pf3Jysrq9/1uXz5crKysvpGtXNzc/n111959NFHB5yrxMTEvt+Dt9IemExwazNSk7Z2NpbtBKBKH0+oF9+vG6JmEKBAr4l6SeGYwr/xs9//edYwO2nxi4POEmiyP5+k3cgWwpQmAKJDgrTXdzFBSROgCOKs5ciybdj0Jy0V6shwjS4OJO9eXjAqJIlKfTxZthIa9+4EFzmbBaWVTJLUWUFhqRMB4WwKBAL3Y7ezqSgKl156aV/Upa6uLq699lqCgtSHn7vWJbobnU7HrFmz7NpXURS6urqQJIkNGzYM+P6www5zKGqVTqdzeCptTEwMp556KitWrEBRFE499VSio/sHHNm9ezcdHR2cdtpp/bZbLJZ+022XLVvGyy+/TGlpKZ2dnVgslr7Rw8jISC699FLmz5/PCSecwPHHH8+5555LRESEQ/bOmDGj3+ctW7bwzTffEBw8MMhGYWFhnx1HHHFE3/bIyEhyc4df47Vjxw6uvfbaftuOPPLIfk54dXU199xzD6tXr6ampgZZluno6OibGjwUbW1t3H///XzyySdUVFQgyzKdnZ3DHtfZ2elUxONeDjnkkL7/JySoU+tqampITU1l8+bNfflwD2THjh0YDAYOO+ywvm1RUVHk5ub2C8Hv5+fXr4xeDj300H6ft2zZwtatW/nXv/7Vt01RFGw2G3v27OHXX39Fr9f3Ocb20Ht9nnjiif2273997tixo981ADBz5sxB9QICAujo6LC7fE9gC0+H1t8I7KjQVNdSq460NZsSNdXVmsrinSQf4AsbJBshOt94rnQFJkAnmNq1bT8A6Ydn+9a7zF13IUQ9DdMXal6Oq4jJOATW9ESkrWshIy58yH3lOvV6bTQl2r/Gx0f5f/buPC6q6n3g+GfYQUBEVFYB9y0UK81MUXPLNLVyy33JSv2ammuZYJpbWpo/tc0FLXMpM9PKfUnELcMVBVHcFVcQ2Wfu74+JG8O+DMLo8369eMHce+5zn3vPzDBnzr3n3LfygKRLpNwu2BVPBXHz4kn8NApxlMG6TIVi248QQuQm343N/v37Gzzu06dPljL9+hXuH+CiRYv47LPPuHnzJvXr12fhwoU0atQoz+3WrFlDr1696Ny5Mxs3bizUvvOSPtpkfiiKgrm5OTY2NmpPXLoaNWpgZ2dX4P0X5t7YQYMGqT1qixYtyrI+Pj4e0N/P6evra9BDnd4YXrNmDWPHjmXevHk0adIEBwcHPvvsMw4d+u+b0eXLlzNy5Ej+/PNP1q5dy+TJk9m8eTPNmzfPtqGcmpqaJZf0Lysy5tapUye1hyrj9Bfu7u4FvhS5IPr378/du3dZsGAB3t7eWFtb06RJkzwHwhk7dizbt29nzpw5ODk54eXlRffu3XPdzsXFhfv37xssS+/lzHjesjtngHq5K/x3hUH6ID7pl6IWha2tbbZXLmRXX++88w7/+9//skxTUrly5ULVV/rzc/Pmzbi4uBjELMwQ4/fu3VN7pUsrO9fqcAVc0m6QnJqGtaVx7nCwjNP3lCbZl+7BVtyq+qHdr8Fc899zP00x46HOeEPKFyedgwfcBaeUos6AacgiIQbNrmnqYw06+G0UVH0ZynoYdV/FxaKi/otAd+6y8+rNXBubVg/1X9AllvHC9PpwCybJ3guSwCK2GHrD//Xomv7KhhhLL7xK8ZUNQognW74/0aTff2Vsa9euZcyYMXz11Vc0btyY+fPn065dO86dO6feJ5ad6Ohoxo4dS7NmzYolr6Jyc3PD2dmZxMREbG1tjToPT17at29PSkoKGo1GHXQno/RBX65cuUKbNm2ybVSEhITw4osvMmzYMHVZdoOs+Pv74+/vz6RJk2jSpAlr166lefPmVKhQgVOnThmUDQsLM2gkZadhw4b8/PPP+Pj4YGFhofYW29jYoNFoqFq1KpaWlhw6dEi9H/L+/ftERETk2ntWu3ZtDh06ZPCFSMaGc/oxL168mA4dOgBw5coVg4GJQN/I02q1WbYbMGAAXbt25ebNm9jb22c7WE1G/v7+3Lx5k/v376u9wekNohs3bqg9eBkHC8ovPz8/du7cycCBA7Osq127NmlpaRw5ckQdrOnu3bucO3eOOnXqFHhfDRs25MyZM1SrVs2gntI988wz6HQ69u7dq15Gm5GVlRWAwTnNOChR48aNs8RMP45NmzYZLDt48GCW+ElJSURFRWUZIKu0cXDXfyD34hbRMbHU9ChvnLhJ+p428/KleyRKzyq12FN9Ai9FzsFCoyNNMeOvqmNJvJhW0qnli2U5T4iGiroY0rQ6LHK5VLQgrOOvoCHTF46KFu5dMJnGJmVciNeUwZ5H3L0cDs/mPCqyY5J+MBvK+Tye3EqQmXMVuAMOiVeLbx/39f+zH5Yp3V82CSGebCV+pcrnn3/O22+/zcCBA6lTpw5fffUVdnZ2LFu2LMdttFotvXv3ZurUqQb3f5U21tbWODk5PdaGJujnvQkPD+fMmTPZ9so6ODjwwQcfMGHCBIKDg4mKiuLYsWMsXLiQ4OBgQH8v5dGjR9m6dSsRERF8/PHHHDlyRI1x8eJFJk2aRGhoKJcuXWLbtm1ERkaql7O2atWKo0ePsnLlSiIjIwkMDMzS+MzO8OHDuXfvHr169eLIkSNERUWxfft2Bg0ahFarxd7ensGDBzNu3Dh27drFqVOnGDBgQJ73Pr7//vssW7aM5cuXExERwbRp0zh9+rRBmerVq7Nq1SrCw8M5dOgQvXv3ztJL6OPjw86dO9WGYvp2GzZsICwsjNOnT9O7d+9spwrJyN/fHxcXF3VQH9D3Jr7wwgvMmjWL8PBw9u7dy8cff5znOcssMDCQH3/8kcDAQMLDww3uZaxevTqdO3dm+PDh7N+/n+PHj9OnTx88PDzo3Llzgfc1YcIEDhw4wIgRIzh+/DiRkZH8+uuvas+6j48P/fv3Z9CgQWzcuJGLFy+yZ88e1q1bB4C3tzcajYbNmzdz+/Zt4uPjcXBwYOzYsYwZM4bvv/8+2+fnu+++S2RkJOPGjePcuXOsXr06yxyyoG+ApvdQl2ZmLvr5B73NbhF503ijU1bU6nva0huzpVmLPpO42T+Uoy8t5Wb/UF7sMbakU8o3G2f9PeNemhguxxiv/q5pKqHL1NZMU8y4qpTunnoDGg13rfQN49SYyFyLVkjTTwViW6l6sadV0uw99KPDVizG6U8cE/RXNijl8zfxuhBCFIcSHY02JSWFv//+m0mTJqnLzMzMaN26NaGhoTlu98knn1CxYkUGDx7MX3/9les+kpOTDe4njYuLU5enpqaSnJysNshSUlLU+83yaixkR1EUdXtjDZ6Ufklletz87BtQ73lMf5x5/dSpU3FycmLWrFkMHToUJycntYdSp9Px9ttvc+zYMXr06IFGo6Fnz5689957/Pnnn+h0OmxsbAgPDyc4OJi7d+/i5ubGe++9x+DBg9HpdLRp04bJkyczfvx4kpKSGDhwIH379uXUqVMGx5H5uFxdXfnrr7+YOHEibdu2JTk5mcqVK6s9tDqdjtmzZ/Pw4UM6deqEg4MDY8aMITY2Ntdz1K1bN86fP6/m07lzZ9555x22b9+ubvPtt9/y7rvvqlPITJ8+nfHjxxvE/eyzzxg7dizffvstHh4eXLhwgblz5zJkyBBeeuklypUrx8SJE3n48GGu+aRP8/H999+rPakA3333HW+//TbPPvssNWvWZObMmbzyyivqczI9Xua/My5r3rw5a9eu5dNPP2XWrFk4OjrSrFkztdx3333HyJEj6dSpEykpKTRr1ozNmzdjbm6ebdzMz7OMy+vVq8fu3buZPHkybdq0QVEUqlatSvfu3dVyixYt4qOPPmLYsGHcvXuXypUrM3HiRHQ6HW5ubgQFBTFx4kT1ObJ8+XKmTp1K+fLlmTt3LsOHD8/y/EwfKfmDDz5QL7ufPn06Q4YMMTiG1atX89Zbb2FjY2PwWsjuWApKp9OhKAopKSnodLos7ycFUsYda8CNe2y4HkNyPQ+0Wm2RYj6Mf4Sbcgc04FK5JsnJyUWOmRNjxa3g4UsFD30vbPr7tjHHAyiO49dqtSTb6K/CcdQkcuTSRTycC37LRHZxIxMccFcqUUWjnxInTTHjw7TBtLxvQYVCnJfiOP781NMje29IjsAy9mKO5ZJTUvBQboEGynpU516S8eupND33Xbz0Pbxuym3u379vcJuNsXKtmHoNNGDrWsNkXk/FFddUYhZHPYHpHL+pxJR6+i9mfmiUEpww8/r163h4eHDgwAGDnofx48ezd+/eLJc5gn7KiJ49exIWFoaLiwsDBgzgwYMHOd6zGRQUZDAqZ7qJEydmGaClTJkyNG3aFA8PDywsZFYYUbxiYmJo2bIlW7duzXM0XVFw9+7do1mzZvzxxx85TkFTFGlpaVy7do2QkBCDeUULRVEYzxJsSWKs7XQcku4VOT87S4VxKV+QqFgxRzP8v6FeRbEYoSyjPA+YbTuOpCRt3hvkQ5qFHRNS5mKvSeKDlHcI0dUjhvK85RiOZWoRn3OP0fMW4XRI/YM/eInDmuzHY7CxUJiQ+gWpijkzNf9DeZJHowVQFCYo/4eNJpU5VqNJTDXu61NrZsX4tPnYa5L40mwI9xVHo8YXQoikpCRmzZpFbGwsjo45v8eYVIvq4cOH9O3bl2+//TbLCKs5mTRpEmPGjFEfx8XF4eXlxfvvv8+NGzeoWbOm2tJPTk7m6tWruLi4FGqk0IyD2RizZ/PWrVtUqlTJqFPNFFeuT2vM9LgFqStXV1eWLl1KYmKiwVQqxZ3r0xLz6tWrLF68OMtgY8Z6TSUlJREfH8/QoUOxsLDg3LlzBu8nBRW7YDO2CZHY6h7Sa/AwPJ3LFClm6J+r4R+4aVaJiRP1V49otdoi55md4oibnJzMF198wejRo412K0Jx5Jke88Eff1A+5QHezlZ07fe+UeJG/70T+51JJCmWbNS9hII541p6MqhF1vufC5Lr466nW6E/wp4/cNPdZNzE8dlOf3Js9y9wEG5pXBg7fkKx1VNpeu5fn/UTvsoVGtf15oX2bxk1139OHMd+yyzSFDPeGRtEcppiEq+n4oprKjGL430PTOf4TSWm1JM+ppubG7NmzcqzfIk2Nl1cXDA3N+fWrVsGy2/dupXth++oqCiio6Pp1KmTuiz9Mrj0D3uZJ263trbO9olgbW2NpaUl1tbW6slXFAWNRoOZmVmh5j/MuL2xPnCnH196XGMpjlyf5phQuLp6/fXXc11vKsdfGmM2atQo21GtjfWaSs/LysoKS0vLLO8nBXXDrBIVicQy/jrt/u8wE1p58ZJ74WOm/juNxD0rd3z/fQ/UarVFzjM7xRUXcn4PL4ziyDM9ZoKdB6SEY/HwqlHy1Wq1WMXqR3M+q1Rm2qvVaVrTHe+KZYuc6+OuJ7caz8Ie8NVc52ZsElXcsk6RlXznIgC3Ld1wy+b/c1GVxuf+PSt3fJOvkHrngsG5M0ausVf1U1ndNHPF084B/r3crbS/noorrqnETGfMegLTOX5TiZlO6il/x16i16lYWVnx7LPPsnPnTnWZTqdj586d2Q7oUatWLU6ePElYWJj689prr9GyZUvCwsLw8vJ6nOkLIYRRXIqJJeSBMwCVNbdQgDm7rnDrYeHvB9HdiwYgzqZ0z7H5pFDK6v//GHOu1OQb+gbDeY0PPZvWLFJDsyRZVqiGDg1lNQlcunwh2zKa+/rG5kObp+eWgoQy+ueMeewlo8dOH4zp3lN0PoUQpVOJX0Y7ZswY+vfvz3PPPUejRo2YP38+jx49Uqdt6NevHx4eHsycORMbGxvq1atnsL2TkxNAluVCCGEqzl2/zyWlEgANNFG4cpeblOd6XPbzrObl6+0nqR13AczhyB1LIraf5J02zxgzZZGJdcWqcAnKpRlvdFGr+/oGwwOHaka9suWxs7TljpkLFXW3eXD5DDR+NksRhzj9saZamWaDulDK+cA9sE8w/vQnVrH6Rn2iQ+me9kgI8eQr8f9ePXr0YO7cuUyZMoUGDRoQFhbGn3/+SaVK+g9ely9f5sYN406ULYQQpUlN93JU0eh7xBqYRxFiPZKe5rtxd8x9XtrsXIqJ5cKeYJqZ6acaGmuxnot7grlkxCk5RFblPPVTWbjpYkhMKdyXBJlVSNQ3GCxcTf+LgnvW+l68tNtZpz/Z8/1M/FOOAdDyzmr+Wj37seZWUsq46Z8zLmnG/4xT7t85S80rPvnTyAghSrcS79kEGDFihDovX2Z79uzJddvs5tYTQghT4m0dz9sWv6uPzTUKMyyXEmHeIZetshcddZYZFt+pg8+aaRSmWyzlwIVueJZ/zlgpi0zKeernMvXQ3CHyxj3qeFcqWsCHN3FSHqBVNHjUMv16S3L0gcRjWD80vGT06oWzNIucbfB8fen8Zxz0bAi1az/+RB+jSr71YD+4KzEkJSVjY2Oce790Oh3uWv20J05ectWXEKJklXjPphBCPPXuRWGG4SxUZuiwji/45XU1be5hrjGMZaHRUcP6fpFSFLkzc3QnFXMsNVpuXs7ae1dQ9yIPA3BBcee5WqZ/KaR5BX0vXnqPW7qbEUezfb4m3Db+fYyljZt3DVIUc6w1aVy5cNZocW/G3MFDcwcAj5r+RosrhBCFIY1NIYQoac5VIdO8gorGnGT7gg/u4VrFDx2Go/fqMMO1iulfilmqmZlz26wiAHE3oooc7laEfp7pixY+lLMv+FRcpU25ynUBcNddQ6vVjwit06ZhGRacpWyaYoZdBe/Hml9JMLOw5IZG3wN++9Jpo8W9EhEGwAMcsHXKflotIYR4XKSxKYQQJa2sB3RagPJvI1GnwI2m00izq1ioWOFl/pvyRdGYY/baAv0+RLGKs3YDIO3fkYCLQrmpv+c21qFGkWOVBpWq1gegMjFcunkXnU7Hnv97l/pJh0lTNGgV/XM/TTFjf7VxOFV4OkZRvWulf84k3jpvtJgPr54B4Ka5jEQthCh50tgUT4QWLVowatSoAm0TFBREgwYNiiWf/GrevDmrV69WH2s0GjZu3Jhj+ejoaDQaDWFhYcWf3FPGx8eH+fPnA5CSkoKPjw9Hjx59fAk07Iemw1wAwpXK7LFuVehQD1P0v09W6oJm1Elo2M8YGYo8JP3bE23xsOiji7o80l+Ka+XhV+RYpYFlOS+SsMJSo+XqhdP8vGQKre6vB2BP9Q+50f8gR19ays3+oTR7a0IJZ/v4PLLTD5xkdj/aaDF1d/UN11i7ykaLKYQQhVUqBggqCVqt1uB3+t+Koqg/BZW+TWG2zS2mRqPJM6eBAwcSHBzM0KFD+eqrrwzWDR8+nCVLltC/f3+WL19erLmWZMz81lvmMgXJ18zMjA0bNtClS5ds4+anrtJt2rSJW7du0aNHD4PyGbfP/NvT05Pr16/j4uJS6PNc0vVkzJi+vr68//77BfqiIbd6Sl9maWnJBx98wIQJE9ixY0eOcRRFQavVqtNSZHw/KRTfFpgD1TQ3WH3lDn7lyhUqpnuq/n43bfUOaO1dIdP7XZHzzKQ44mq1WiwsLNBqtUaLW1x5pv/WOHnDbXBIul6kfWgfPcBNp59CxaNWo1J//Pmtpxvm7vhqo9Hu/ITXlb9BA4c8B9Gy1wcAuHlXL9Y8jR3TGHF1Tt7wAOwSrmSJVdiYdvHRAKQ6VTGIZQqvp+KKa0oxjV1P6XEz/paYRY8p9ZT/mE9NY3PRokUsWrRIPTHnz5/H3t6eiIgItYxOp0NRFJKTCz+ROqBufzMuiUt3E/Eub4urY+HvuXFyciIlJSXXMlqtFk9PT9auXcvMmTOxtbUFICkpiR9//BEvLy+0Wi1JSUnZ5mpMhY2ZkpKClZVVoWLqdLpsjy83aWlp6HS6Am2TnmdO2+SnrtItWLCAPn36ZCmfXfyMx+/k5ERaWhppaWkFyjuzx1X3qampWFoaTuGRW13nJ2Y6RVFIS0srcB1mV0+ZY73xxhuMHTuWY8eOUadOnWzzSk1NJSoqSm1sZnw/KRRFoZpZGWx0j3h07Qz4NS1wzPj4hzRSboEGUm0rEh4enqVMkfPMgbHjdu7cmQsXLhg1JhTP8UdERJBkWQ4Al7Rb2Z73/IqNOkRT4LpSHgtFV6RY2SmperJS9K+TlugbmhEWNSnTeFCOx1dc9VQcChs32boCAC6pN7Kch8LGdE2OBuCRztogpim9noorrinELK56AtM4flOJKfWkb0vli/KUiY2NVQDl9u3bysmTJ5Xk5GQlLS1NSUtLU+Lj45XTp08rCQkJik6nU3Q6naLVapX4pJR8/TxMTFbuPHioPExMVoJDLii+Ezcr3hM2K74TNyvBIRfyHSf9R6vVKmlpacr169eVtLQ0Nafsfvr376907txZqVevnrJq1Sp1+ffff6/4+fkpnTt3Vvr3768uT01NVaZOnar4+PgoNjY2ip+fn7Ju3TqD9QMHDlTX16hRQ/niiy8M9rlr1y7l+eefV+zs7JSyZcsqL774onL27FlFq9Wq+WQsP3LkSCUgIEB9HBAQoAwbNkwZOXKkUr58eaVFixaKTqdTTpw4obRv314pU6aMUrFiRaVXr17KrVu31O0ePnyo9O3bVylTpozi6uqqfPbZZ0pAQIAycuTIXM/RjBkzlIoVKyr29vZKv379lPHjxyv169dX1x86dEhp3bq1Ur58ecXR0VFp3ry5cvToUXW9t7e3Aqg/3t7eik6nUyIjI5XXXntNqVixomJnZ6c899xzyrZt23LN5datW4pGo1FOnjxpsBxQFi1apLRv316xsbFRfH19lR9++EHRarWKTqdTLly4oADKsWPHjFJP2eV2+fJlpWfPnkq5cuUUOzs75dlnn1VCQ0PV9YsWLVKqVKmiWFpaKjVq1FCCg4MVrVarJCQkKFqtVj2GTp06KXZ2dsqUKVOUKVOmKPXr11e++eYbxcfHR9FoNIpOp1Pu3bunDBo0SHFxcVEcHByUli1bKv/884/62ktISFA2btyoPPfcc4q1tbVSvnx5pUuXLurzJ2N9AGqO+/btU1566SXFxsZG8fT0VEaMGKE8fPhQ0el0SlpamnLixAnl1VdfVWxsbBQfHx9l1apVire3t/L5558bnIuWLVsqH330UbbnKSEhQTl9+rQSHx+vJCcnZ3k/KezP/UWtFSXQUZkyZWyhYh7evUlRAh2VO4FeWdYZM8/ijvvo0SNl+vTpyqNHj0p1nhlj3j93QFECHZWYKV7KvbjC5x0S/LGiBDoqBz5pVWy5Pu56io48pWinOCpK4H8/aVOclOjIU4+9nowV0xhxL5w+oiiBjkrCFBclKSmpyDF3BU9XdFPSz29ZZffKTwtUT0/COTXlmMVRT6Z0/KYSU+pJH/P27dsKoMTGxuba9npqejYzMzc3V39n/Fuj0ag/AImpWuoGbivSvnQKTNl0himbzhRouzOftMPGwky97C89p9wMGjSIFStW0KdPHwCWL1/OwIED1flK02PMmjWL1atXs2TJEmrUqMG+ffvo27cvFStWJCAgAEVR8PLyYv369ZQvX54DBw4wdOhQ3N3d6d69O2lpaXTt2pW3336bH3/8kZSUFA4dOpQlz+z+zrhs5cqVvPfee4SEhAAQGxvLyy+/zJAhQ/jiiy9ISEhg/Pjx9OzZk127dgEwfvx49u7dy6+//krFihX58MMPOXbsGA0aNMjxHK1bt46pU6eyaNEimjZtyvLly1myZAlVqlRRt4mPj6d///4sXLgQRVGYN28er776KpGRkTg4OHDkyBEqVqzI8uXLad++vfp8efToER06dGDatGnExcXx559/8tprr3Hu3DkqV87+npmQkBDs7OyoU6dOlpynTJnCrFmzWLBgAStXrqRfv340aNDAoGz6eS5qPWXed3x8PC1atMDDw4NNmzbh6urKsWPH1OfgL7/8wqhRo5g/fz6tW7dm8+bNDBo0CE9PT5o0aaLGmzp1KrNmzWL+/PlYWFiwbNkyzp8/z4YNG9iwYYN67rp3746trS1//PEHZcuW5euvv6Z169ZERERQrlw5/vjjD7p3785HH33EypUrSUlJ4ffff0ej0bBhwwbq16/P0KFDefvtt9XzEhUVxSuvvML06dNZtmwZt2/fZsSIEfzvf/9j+fLlaDQaRo0axd27d9m9ezeWlpaMHDmSmJiYLOekUaNG7N+/P9vnVXrZzO8h6X8Xlp3P8xBzmKrai9x5lErtAsZ8ePkkANcsvCmfw3bGyLO445qbm5OWllYsuRZXTCevWgBU0MQSdv0mDWpWKVQssxj9yKQP7KqU+uPPbz3FXDyNd6aXkblGx+3ocLyr1S32PIszZlHielV7hjTFDFtNCtFXLuBT7b+5RQsa8+qFszSP+kyds9T83zlLb1x6nQoevib1eiquuKU9ZnG+76XHL83HbyoxpZ7+i5kfT21j80nVp08fJk2axKVL+nu2QkJCWLNmjdrYBP3lfzNnzmTLli0EBASg0WioUqUK+/fv5+uvvyYgIABLS0umTp2qbuPr60toaCjr1q2je/fuxMXFERsbS8eOHalatSoAtWrVKvDljNWrV2fOnDnq4+nTp+Pv78+MGTMA/aWN6Q3iiIgI3N3dWbp0Kd9//z0vv/wyAMHBwXh65j5y4fz58xk8eDCDBw9GURSCgoLYu3evQb6tWhkOyPLNN9/g5OTE3r176dixIxUq6C93cnJywtX1v+Hk69evT/369dHpdNy8eZNPPvmEjRs3smnTJkaMGJFtPpcuXaJSpUrq5ZcZdevWjSFDhgAwbdo0tm3bxsKFC1myZEmWssaup9WrV3P79m2OHDmCs7MzANWqVVPXz507lwEDBjBs2DAAxowZw8GDB5k3bx4//fSTWu6tt95i4MCBBrFTUlJYuXKleh7379/P4cOHiYmJwdraWo2/ceNGfvrpJ95++23mzJlDz549DY6xfn39qJbOzs6Ym5vj4OBgUB8zZ86kd+/e6n2c1atX58svvyQgIIAlS5YQHR3Nrl27OHjwII0bNwZg6dKl1M5mAnl3d3f1tfS4WHk1hMNQz+wif99JpFkBt9fc0c/XF+tQLY+Swuhsy/GQMjjwiJjLEVDIxmb64EA6lydjJFoAt6p+aPdrDObUTFPMcK1SrwSzKnkWVtZc1VTAk1vERJ82aGwW1I2oE3hmM2fpzQunqOBh+nO1CiFMkzQ282Brac6ZT9rlq6yiKCQlJfMgWaHNF/vQZXjPN9PAjjEBuJbN/72btpbmBR5wpUKFCrz66qusWLECRVF49dVXcXFxMShz/vx5EhIS6Nixo8HylJQU/P3/mwB60aJFLFu2jMuXL5OYmEhKSoo6equzszMDBgygXbt2tGnThtatW9OtWzfKlStXoHyfffZZg8fHjx9n9+7d2NvbZykbFRWl5pHeSEjPpWbNmrnuJzw8nHfffddg2QsvvGDQCL916xaTJ09mz549xMTEoNVqSUhI4PLly7nGjo+PJygoiC1btnD9un5gkMTExFy3S0xMxMYm++dCkyZNDB43btyYU6dO5RjLmPUUFhaGv7+/2tDMLDw8nKFDhxosa9q0KQsWLDBY9txzz2XZ1tvbW21ogr6u4+PjKV++vEG5xMREoqL08xSeOHEiy/7ycvz4cU6cOMEPP/ygLlMUBZ1Ox8WLFzl79iwWFhYGz71atWrh5OSUJZatrS0JCQkF2n+RuTUAoLbmMuvvFHzfzo/+neOxYuE/tIrCu2NRCYe0CyTEFG6uzfiHD/HWXQUNlPXMeq+wqfKsUos91SfwUuQcLDQ6/RQn1cfTokqtkk6txN22dMMz9RaPTv3O1Sr11IGSCsqtqh+6/frPG+mkQS+EKGnS2MyDRqPBzip/p0lRFMx0aTg72jDz9Wf4cMMptIqCuUbDjNfrUaVC1gZUfmIW1KBBg9QetUWLFmVZHx8fD8CGDRvw9fU1uEQwvYdpzZo1jB07lnnz5tGkSRMcHBz47LPPOHTokFp2+fLljBw5kj///JO1a9cyefJkNm/eTPPmzTEzM8uSe2pqapZcypQpkyW3Tp06MXv2bPX4k5OTsba2xt3dPf83IxdC//79uXv3LgsWLMDb2xtra2uaNGmS54A/Y8eOZfv27cyZMwcnJye8vLzo3r17rtu5uLhw//79Iudc1HrKLH1gqaLKXK/ZLYuPj8fNzc2gwZ8uveFXmHzi4+N55513GDlyZJZ1lStX5uzZs/mOde/ePYMG8mPhXIVkMztsdQmk3I0u0KY6rRavtEuggfJV/PPeQBjdQxt3iL+Aci/3L6lyEh4WwvMaLbFKGRxdnqy5Jlv0mcTVC125eeEUrlXqSUPzX2Y6/WBvLR/8jDZ4A39VG4+L/2sFjuNZpRYRGm9qoL8aI2ODvjgGhBNCiPyQxmYx6fF8ZZrXqED0nQR8XOxwK2ucD/H50b59e1JSUtBoNLRrl7VXtk6dOlhbW3PlyhXatGmT7f1oISEhvPjii+rlkoDa25SRv78//v7+TJo0iSZNmrB27VqaN29OhQoVsvTGhYWFZRmVNLOGDRvy888/4+Pjg4WFxb+9xUnY2Nig0WioWrUqlpaWHDp0SL0f8v79+0RERBAQEJBj3Nq1a3Po0CH69ftvvsGMDbL0Y168eDEdOnQA4MqVK9y5c8egjKWlZZahnkNCQhgwYABdu3bl5s2b2NvbEx0dnetx+vv7c/PmTe7fv5+ll/HgwYMGeR4+fJiGDRtmG6eo9ZSZn58f3333Hffu3cu2d7N27dqEhITQv39/gxyyG601Lw0bNuTmzZtYWFjg4+OTZb2iKNSrV49du3YxaNCgbGNYWVllqY+GDRty5swZg8t/M6pVqxZpaWn8/fffag/5uXPnePDgQZayp06dMujtfyzMzHhUrjbWd//GIS6yQJtev3IRT81DtIoG37qNiilBkZs0B0+IB+tHhZtr806kfm7XK1a+mBXDvUAlzbNKLTylkam6euEsz6Sdgkz3WR70bAjZXNqfG51OR1klDjSw1+d9qjZ/Sxr0QogSl/WGMWE0bmVtaVK1/GNtaIL+ht3w8HDOnDmT7c27Dg4O6hyCwcHBREVFcezYMRYuXEhwcDCgv8/t6NGjbN26lYiICD7++GOOHDmixrh48SKTJk0iNDSUS5cusW3bNiIjI9XLWVu1asXRo0dZuXIlkZGRBAYG5nopaLrhw4dz7949evXqxZEjR4iKimL79u0MGjQIrVaLvb09gwcPZty4cezatYtTp04xYMCAbO99zOj9999n2bJlLF++nIiICKZNm8bp06cNylSvXp1Vq1YRHh7OoUOH6N27d5aeNR8fH3bu3Kk2FNO327BhA2FhYZw+fZrevXuj0+lyzcff3x8XFxd1YKSM1q9fz7Jly4iIiCAwMJCjR4/meO9nUesps169euHq6kqXLl0ICQnhwoUL/Pzzz4SGhgIwbtw4VqxYwZIlS4iMjOTzzz9nw4YNfPDBB7keb3Zat25NkyZN6NKlC9u2bSM6OpoDBw7w0UcfcfSo/gP3hx9+yI8//khgYCDh4eGcPHlS7fUGfX3s27ePa9euqV8MTJgwgQMHDjBixAjCwsKIjIzk119/Vc9hzZo1admyJe+99x6HDh3i77//ZsiQIdn2ov7111+0bdu2wMdWVDaV9V8ueKdd5N7DxHxvdy1c/wXKVY0rtvZliyU3kTuL8j4AOKbcLNT25rf1A8nFl5VGwtPgRtQJg8teQX+fZcLtgt8rfulCJJU099EqGhq9OUYa9UKIUkEam08oR0dHHB0dc1w/bdo0Jk6cyKxZs6hduzbt27dny5Yt+PrqBxF45513eP311+nRoweNGzfm7t27Br1ndnZ2nD17ljfeeIMaNWowdOhQhg0bpg5s065dOz7++GPGjx/P888/z8OHDw1663Li7u5OSEgIWq2Wtm3b4ufnx/jx4ylbtqzaoPzss89o1qwZnTp1onXr1rz00ktZ7v3MrEePHmo+zz33HFeuXMlyD+fSpUu5f/8+DRs2pG/fvowcOZKKFSsalJk3bx7bt2/Hy8tL7fH6/PPPKVeuHC+99JJ6f2ROPZHpzM3NGThwoMF9hemmTp3KmjVr8PPzY9WqVQQHB+fYc1jUesrMysqKbdu2UbFiRTp06MAzzzzDrFmz1C8tunTpwoIFC5g7dy5169bl66+/Zvny5bRo0SLX482ORqPh999/p3nz5gwcOJAaNWrQs2dPdfAkgObNm7Nu3To2bdpEgwYNaNWqFYcPH1ZjfPLJJ0RHR1O1alX1clc/Pz/27t1LREQEzZo1w9/fnylTpuDu7q5u9/nnn+Pm5kZAQACvv/46Q4cOzVLXoaGhxMbG8uabbxb42IrKzud5QD9I0LGLMfneLuGqfiTaW9Y+xZGWyAdHd/39dhW1t/L80ik7FRP1twrYejUwZlqilHKr6odWMWxtpilm2FXwLnCsK6f+AuCymSe29k7GSE8IIYqueGazLL3S59m8d++ecvLkSSUtLU1dl5iYqJw5c0ZJTEwsVOz0ufd0Op2x0lW0Wq1y7do1RavVGi2mohRPrk9zTEUpeF3duHFDcXZ2VqKjo3MsYyrHbyoxFSX/9dS9e3fl008/zXF9xveLtLS0LO8nRXIrXFECHZVHUyooc389nO/NQmZ3UZRAR2XXohHZrjd6nsUYNykpSQkKClKSkpKMFrM48swcM/HaGUUJdFTip1RQbt57WKBY1+48UB5OqagogY7K/fN/F3uuxmCq9VSa4u5eNUOdG1P779yYhYm5feFwRQl0VELndMmyzlTqqbjimkrM4qgnRTGd4zeVmFJP+pj37t3L1zyb0rMpRAlxdXVl6dKleY52Kx6/lJQUnnnmGUaPHl0yCbhUJ0Vjg50mmXtXzuV/s8SLAFi6ZT9voSh+NhWroFM0lNEkc/nShQJtezLsKPaaJJKxxKGy1OHTokWfSfxVRj++wkHb5jR7a0Kh4jg+0F+CnVbxGaPlJoQQRfXUDhCUPqhIxsFFtFotiqKoPwWVvk1hts0tpkajKXROucXN+FtiGiduQeuqc+fOueZiKsdvKjHT4+VVT5aWlnz00Ue57j99e61Wq17inXmwoqKIK1sDlwcnsLkXnq+4aakp6pQZFas9m+022b3vGUNxxNVqtVhYWKDVao0Wt7jyNIipseCemTMVlLscCztGeVdPvCvk7/7Ze1H6e5WvW/ng8e+llcWaq5FimmQ9lbK4ZjXaQNhWPJIiCxVTp9PhkxoFGnCq2ijLtqZST8UV15RiGrue0uNm/C0xix5T6in/MTWKsT/JlVKLFi1i0aJFaLVaIiIiCA0NzTKXo06nQ1EUddoLIYTISXJyMpcuXUKj0eQ5QFVhlD08F6/Lv7A07RXqvzEJG8vcRya9fy2SZqEDSFCsiei6DQuLp/a7xBKn/XkI9ZVwFqZ2YbX2Zbo868urtfOeg/jMr5/RPXUj/zi1w7L1lMeQqSgtHsbeo/G21zDTKBxs9TP2zq4F2v7OrWu0+Ks7WkXDiU7bsLSxK6ZMhRBCLz4+niZNmhAbG5vrODFPzaeR4cOHM3z4cOLi4ihbtizVqlXj5s2b1KhRQx38JCkpiUuXLmFtbY2NjU2B96FkmBMyu+lECkOn0xETE0PFihWN+oG2OHJ9mmNC8dSVqRy/qcQE49aTpaUl3t7eWFpaEhERYfB+UlTaxJZw+RfqmV0k0doZ/5ruuZY/GLEXgEtmXjzzTPaX0aV/2WbMPIsrbnJyMl988QWjR4822pd/xZFn5piXbsdyT5sGZvA/y40Ms/iVj8KGYNfsw1x7OLVaHfeTL4AZOFVvjEeNGsWeqzGYaj0ZizHjnt/hTQ0lGu2t0+DsWqCY+yL0I5tfNvPEzz/rgHmmUk/FFddUYhZHPYHpHL+pxJR60sfMaYq5zJ6axmZm6Sfc3Nzc4G+NRqP+FFZRt88cK/2yP2N+4M4Y39hxn9aYxVlXErP01VP69pnfQ4z2octDP6JxHc0lVl2+S7M6XrkW197QT+Vzx9aX2nnkYNQ8iymuubk5aWlpxZJrcca8cjGSZpr/5kc11yhMt1jKgehuVHFtnOP2O05coqEmGgCvuk3RFMdzKlOuxoplivVkbMaIe9WhATXiouFyKNR+uUAx064eByDGrga+2WxjavVUXHFLe8zirKf0+KX5+E0lptTTfzHzQwYIEkKI0qhCDVI0VjhoErl9+WyexcvE6afMSC6X/Ryq4vGoaXMv23kTa1jfz3Gbr7ef5OO1B6igiUOraAg+a/wPL6L0M/NpCoBX/PECb+sUp3+PSK3kZ9SchBCiqKSxKYQQpZGZBfds9fPeWt45k2dx1+RoAOy8ZCTKkuRaxQ9dpn+tOsxwrZJ9vVyKiWXWzss0NdPPkXpZqcSne2K4dDu22HMVpUuNxq+gUzRUVq6T+OBWvrfTpmn1gwMB5Wvk3HsuhBAlQRqbQghRSqX3UlZMPE9qWs6jviU+vIcH+g+nXrXlw2aJKuuB2WsLSB95T4cGs9cWQFmPbIufu36fbua7mWf5FQA+mpu8ab6byBsPHk++otRw9/AiUuMNQOyFQ/neLup8OBU0D0hTzKji17S40hNCiEKRxqYQQpRSZpVqA1CHi5y5cjfHcpdOHwYgRnHCw8v7seQmctGwH7dfmgbAVZ0Ld6q+kWPROo5JzLT4Tr30VqOBGRZLqeWQ+DgyFaXMFYf6AFjfCsv3NtdO/zc4kLVdziNCCiFESZDGphD/0mg0bNy4EYDo6Gg0Gg1hYWGFjmeMGOLpluxcC4B6ZtH8E307x3L3LvwDwBUL72KZhkUUXMWXBpCCBZXNbhMSsifHcp6aGMw1hjOQWWh0eGruFHOGojTSeL8IQOWEk/neJu1aGAAxZWoUR0pCCFEk8qnkCTFgwACDkXTTf86fP6+u79KlS47bJyYmEhgYSI0aNbC2tsbFxYVu3bpx+vRpg3JBQUEGo3B6eXkxdOhQ7t27Z1DOx8eH+fPnq4+PHz/Oa6+9RsWKFbGxscHHx4cePXoQExNjtHNgTF5eXty4cYN69erlq/yAAQPo2rVrkWIIkVmyoy9pWOCoSeBGdHjOBWP093Q+KFPlMWUm8mTjyIUy+hGFU878nmOxRAdvdJlnu9aYg7NvMSYnSquqz7VHp2jwVq4TG3M5X9s4xeoHB9LK4EBCiFJIGpvFKfYaXNyn//0YtG/fnhs3bhj8+Prm/YElOTmZ1q1bs2zZMqZPn05ERAS///47aWlpNG7cmIMHDxqUr1u3Ljdu3ODy5cssX76cP//8k/feey/H+Ldv3+bll1/G2dmZrVu3Eh4ezvLly3F3d+fRo0dFPu6MUlNTjRLH3NwcV1dXLCwKPzuQMWKIp5tiZsk9e/08VpqY0zmWKxuvHxxE51L7seQl8kdTqwMAtR4eIDk1LdsyWyKTuKNkmH9TYw6d5oNj9vd4iiebj4+Pet/mxcN/5Flem6bFN03/pXKFmi8Ua25CCFEY0tjMi6JAyqOC/xz+FubXg+BO+t+Hvy14DCXz1925s7a2xtXV1eAnP3PgzJ8/n9DQUDZv3kz37t3x9vamUaNG/Pzzz9SuXZvBgwejZMjFwsICV1dXPDw8aN26Nd26dWP79u05xg8JCSE2NpbvvvsOf39/fH19admyJV988UWujWEfHx+mTZtG//79sbe3x8PDg0WLFhmU0Wg0LFmyhNdee40yZcrw6aefAvDrr7/SsGFDbGxsqFKlClOnTiUt7b8Pe+fPnycgIAAbGxvq1KmTJf/sLoE9ffo0HTt2xNHREQcHB5o1a0ZUVBRBQUEEBwezadMmPDw8MDc3Z8+ePdnG2Lt3L40aNcLa2ho3NzcmTpxokFeLFi0YOXIk48ePx9nZGTc3N6ZPn57jORJPPsVV31vhHB/JxVvZTJ+hKHikXQLA0af+40xN5KFq817oFA3PaC5w8MjRbMuEH95JRbNYkrGGt9bBqJPQsN9jzlSUJpfK6F/zaRf351k2MvI0Lpo40hQzfJ+RwYGEEKXPU9vlotVqDX6n/60oivoDQMojNDPz9w2zBrDNboWig9/H6n8KQJl0DcXCVp2EXslH4zOvMunrM/5evXo1bdq0wc/Pz2B7jUbDqFGj6NOnD2FhYTRo0CDL9tHR0WzduhUrK6tsYyuKQqVKlUhLS2PDhg28+eabaDSZJqHLxdy5cxk3bhyffPIJ27Zt4/3336d69eq0adNGLRMUFMTMmTP54osvsLCwYN++ffTr148FCxaoDcJ33nkHRVEIDAxEq9XSq1cvXF1dOXjwILGxsYwePdog58zHcO3aNZo3b06LFi3YuXMnjo6OhISEkJqaygcffEB4eDixsbHMnj0bFxcXXFxcuH79epYYHTp0oH///gQHB3P27FmGDh2KtbU1QUFB6vEEBwczevRoDh48yIEDBxg0aBDNmzenbdu2+T5vuclcT09TzPR4BXlN5RZHURS0Wq16n2TG95OiSo91WutFJaA2F2n1xQEmtPLi7ZfrquXibl6kHPGkKWZ413o21xyye98zZq7GPn4LCwu0Wq3R4hZXnjnF1NhX4Lx1bWqknOHusY1oGz9vsP5BfBJ17/wB5hBb5VXKV22dHvSx51qUmKZeT6Utrs6rMZzdhFtsWJ5xr5/aTy3gsrkX3la2OZY3lXoqrrimFNPY9ZQeN+NviVn0mFJP+Y/51DQ2Fy1axKJFi9QTc/78eezt7YmIiFDL6HQ6FEUhOTn5vw1TkrJvQD4GSUlJYGWOk5MTKSkpuZbVarVs3rwZBwcHdVnbtm354Ycf1PVarVYfM4Pk5GQiIiJo1qxZlnUAVaro7wE7ffo0tWrVIi0tjZMnT+Lg4GAQb/bs2ep5S05ORlEU0tLSSEpKokGDBowbN47evXvz3nvv8eyzz9KiRQveeustKlWqlOMxKYrCCy+8wNix+kb622+/zV9//cW8efNo1qyZWq579+706tVLfTxw4EA++OADevToAYC7uzsff/wxH330ERMmTGDHjh2cO3eOX3/9FXd3dwACAwPp0qULKSkpJCUlGRxLUlISCxYswNHRkeXLl2NpaQlA5cqV1X1aWVlhaWlJjRr6ARp0Ol2WGF9++SWenp7MnTsXjUaDj48PH330ER9//DHjx4/HzMwMnU5HvXr1mDBhAgA9evTg//7v/9i+fTvNmzfP8VwVhsHz/CmLmZ/XVF6Sk5NJTU0lKipKbWxmfD8xhlsPk5l/thytrKGB2XkqcYc5u6C6QyqVHKwBuHNmDy2Ay7iREHOLOzF5z89n7DyLK27nzp25cOGCUWNC8Rx/TjEfuLxIjetn8LrzF+Hhhvfd7j5zjSFm+tsU4rzaEBOe9b7cx5lrYT0J9VSa4lpWqosuXENlrhF2eC8WDhVzLJt0+W8AbthUIyGb509GplRPxRXXFGIWVz2BaRy/qcSUekIdFyYvT01jc/jw4QwfPpy4uDjKli1LtWrVuHnzJjVq1FAvNU1KSuLSpUtYW1tjY2Oj39DaGmVS/u65TG+oWiffRbPkBTSK7r91GnMYdhAc3fOds42lHTpFISYmhooVK+Y6yqS5uTktW7Zk8eLF6rIyZcqox2Fubo65ubn6WM3VWv+B1czM7L9jziB9vaWlJTY2NlhYWFCzZk1+/fVXkpKS+P777zl+/DijR4/G3NxcjanRaLCwsFBjzp49m/Hjx7Nr1y4OHTrE0qVL+eyzz9i7dy/PPJP9ZOcajYamTZuqeaQ/XrBggUGujRs3Nnh88uRJQkNDmTNnjrosvWGs0+mIiorC09MTX19ftZc1ICAA0DcabWxs1ONOfy6cPn2a5s2bGzTmM59/MzMzHjx4oNZV5hjnz5/nxRdfxNb2v68vWrRoQXx8PHfu3KFy5cqYmZlRr149g3pydXXl7t272dZPYWSs+4L0Mj8JMUH/RUB+XlP5YWlpibe3N5aWlkRERBi8nxSVVqvl2K5jPGN2AUUBB00SIdbvMyltCGk2w6hdW39f1+HD3wNw3cqbJrVzv2dTq9UaPc/iipucnMwXX3zB6NGj1ddSURVHnnnFjHMcBMu+w185wzlLS2pVq6au+2tLMLaaFGKsvfFt1l0/70kJ5loYT0o9laa41apVI2JPZWpxCe6eo3ajgBzLJv6i/7CncatP7Vxe/6ZST8UV11RiFkc9gekcv6nElHrSx6yW4f9Zbp6axmZm6Sc8vRGW/nfGkVwB/T9/a/v8BVUUUCzQOJZH02kB/DYKFC1ozNF0mg8VCj4suebf3laDnHJQpkwZqlevnnu8TDE0Gg01atTg7Nmz2cY/e1Y/yl3NmjXVHKysrNT9zJ49m1dffZVPPvmETz75xGAfmXN2cXGhe/fudO/enZkzZ+Lv78+8efMIDg7O4yyQpV4yxrW3tzd4HB8fz9SpU3n99dezxMnY0MsuXnb70Wg06nZ51UHGusrpPGT3d8YyVlZWWcqkxzWm/DynnsSYGc9nUeJmHJU5u/cTY6hi/YDeFsvVNoi5RmGmxXdMD2tGO3/9VQcW9/Sv0XjHavnet7HzLI645ubmpKWlFUuujzNmucp1iTbzxkd3iYsHfqFuzYkAXL/3kKaPtoMZKP59MM9hILHSfvxPSj2VtrgXbOtRK+kSqRdCMDfPfgC+tDQtVdIugAZcar2Y6/5NrZ6KK25pj1mc9ZQevzQfv6nElHr6L2Z+yABBxaVhP/1AD/03l/oBH3r27MmOHTs4fvy4wXKdTscXX3xBnTp1qF8/54FHJk+ezNy5c9V7FPPDysqKqlWr5jka7aFDhwweHzx4MNdvbwEaNmzIuXPnqFatWpYfMzMzateuzdWrV7lx44ZB3Nz4+fnx119/5TjarZWVVZ7XrteuXZvQ0FCD+wRDQkJwcHDA09Mz123F08lDuZVlDkZzjULtqO/4ZK1+8JDyCRcBMKtU57HnJ/LnlmsLAJyv7VSX7d75B35mF0nBgkrNBpdQZqK0euSi/59b6cE/OZaJOHeK8po4UhVzqvjJ4EBCiNJJGpvFqawH+DbT/y4FYmNjCQsLU3+OHz/OlStXGD16NI0aNaJTp06sX7+ey5cvc+TIEd544w3Cw8NZunRprj1ATZo0wc/PjxkzZmS7fvPmzfTp04fNmzcTERHBuXPnmDt3Lr///judO3fONeeQkBA+//xzIiIiWLRoEevXr+f999/PdZspU6awcuVKpk6dyunTpwkPD2fNmjVMnjwZgNatW1O9enUGDBjA8ePH+euvv/joo49yjTlixAji4uLo2bMnR48eJTIyklWrVnHu3DlAP3LuyZMnOX/+PHfu3Mm2UTps2DCuXLnC//73P86ePcuvv/5KYGAgY8aMKfLlnOLJlGzvhaLJ+tzobrGPPqcHs2rZIjy1VwCwccz5vi5RstyadAPAPzWM23fuAGB/7icALjg3gzLlSyw3UTo5+DyHTtFQWXeVpHvZf5F743QIoB8cyMLa7nGmJ4QQ+SafcJ8ie/bswd/fH39/fxo2bEiTJk2YOnUqNjY27Nq1i379+vHhhx9SrVo12rdvj7m5OQcPHuSFF/Keu2v06NEsXbqUq1evZllXp04d7Ozs+OCDD2jQoAEvvPAC69at47vvvqNv3765xh0zZgzHjh2jYcOGTJ8+nc8//5x27drluk27du3YvHkz27Zt4/nnn+eFF17giy++wNtbf4+bmZkZa9asITExkUaNGjFkyBB1ypSclC9fnl27dhEfH09AQADPPvss3377rTpY0Ntvv02NGjXo0KEDlSpVIiQkJEsMDw8Pfv/9dw4fPkz9+vV59913GTx4sNoIFiKzNLuKKK9+oZ97EfS/Gw4g3sKZKmY36Xv5Q6w0+h71pqGD2fP9zBLMVuSkcr2XuEEFbDUpnNzzExGXrtEy9S8AXJoNLeHsRGnkXrECEegHoYs6vCXbMtrrYQDctq/5uNISQogCe2rv2XzSrFixIs/1GcsoikJSUpI66IydnR3Tp0/Pc07HoKAgg2k60vXs2ZMePXqoo9NGR0er66pUqcI333yTr+PIzNHRke+//x4bG5tse1dzmrqiXbt2uTZKq1evzr59+wxiZozl4+OTJbafnx9bt27NNl6FChXYunUrN2/exNXVVe2pzBwjICCAw4cP55jXnj17sixbt26d0QYHEqZH8e8L1dvAvQvgXAXKemDf9hP+XjKEhg+2GdzP+VLkHK5e6IpnlVolm7QwpNFwoVxT3O5vxDJqK2di71NDk8Ats4pUqt++pLMTpZCZmRkX7Z6hVuIl0sLWcbVGsyyva+eH+vu1qeRXAhkKIUT+SM+mEEKUdpkvybcpi65eNzJ//2Kh0XHzwqnHn5/Ik319/S0DzyQcxvfKLwBc8eoMcgm9yIEl+hHt6ycdxi34BYMrF1JT0/SDAwEVar1YIvkJIUR+lIr/cosWLcLHxwcbGxsaN26ca8/Pt99+S7NmzShXrhzlypWjdevWuZYXQognkVtVP7SKYWszTTHDtUq9EspI5KZu047cV+xx0sRTn3NoFajWPvtRRoV4cPsqLRP+UB+baxSaR87mwoFfQFE4vPc3nDUPSVXM8KmX960uQghRUkq8sbl27VrGjBlDYGAgx44do379+rRr146YmJhsy+/Zs4devXqxe/duQkND8fLyom3btly7lr+5MIXpiI6OZtSoUSWdhhClkmeVWvxVfQJpiv5tPE0xY3/18XIJbSllYWnFNfP/BoszA8J2riu5hESp9igmOstI1GYahSrbBnAvyJMX/xoAgAU6/lq/oAQyFEKI/Cnxxubnn3/O22+/zcCBA6lTpw5fffUVdnZ2LFu2LNvyP/zwA8OGDaNBgwbUqlWL7777Dp1Ox86dO7MtL4QQT6oWfSZxs38oR19ays3+obToM6mkUxI5uHrhLHW0EepjjYZ/77E9W4JZidKqTEWfLFcuKAokKeY4a+LVS+jleSSEKO1KdICglJQU/v77byZN+u8DkpmZGa1btyY0NDRfMRISEkhNTcXZ2Tnb9cnJySQnJ6uP4+Li1OWpqakkJyerk5KmpKSgKAparRadTlfg41EUBUVR0Ol0RpuEPn2AmfS4xlJcuT6tMdPjpv82Vl2ZyvGbSsz0uOm/i1JPWq0WRVFISUlBp9NleT8pKq1Wm6+YFTx8qeDhC2DwXleUmMWVa0GkH0tex1QQxZFnfmNejfgHz0w9VRYaHdciw9T6Ky25FsSTVk+lIa5Wq6WMUyX2VR1Ls6h5WGh0pClm/FV1LGaOrgSEjTEon9vzKJ2p1FNxxTWVmMVRT2A6x28qMaWe/ouZHxolp+E8H4Pr16/j4eHBgQMHaNKkibp8/Pjx7N27l0OHDuUZY9iwYWzdupXTp09nO2JnUFAQU6dOzbJ84sSJWcpbWFgQEBCAq6srdnYyZ5UQImfJyclcv36dffv2kZKSUtLpiFLO1kLLBylfGlwamaaY8bnV/0hMM94HdfFksbXQ4mCWzEOdNYlp5vI8EkKUGklJScyaNYvY2FgcHR1zLGfSU5/MmjWLNWvWsGfPnhynhpg0aRJjxvz3LWBcXBxeXl68//773Lhxg5o1axq09GNiYnj48CFly5bFzs6uQD0q6b0cVlZWRu3duXv3LuXLlzd6705x5Pq0xkyPa+y6MpXjN5WY6XGLWk86nY4bN27g5ubG6NGj0el0nDt3Lsv7SVFotVqTiFlccZOTk/niiy8YPXo01tbWRolZ0ud031prmkXNNeiper/H2FKZa349ifVU0nHzilmQ51E6U6mn4oprKjGLo57AdI7fVGJKPeljurm5MWvWrDzLl2hj08XFBXNzc27dumWw/NatW7i6uua67dy5c5k1axY7duzAzy/nOaasra2zfSJYW1tjaWmJtbW1wcn39PTk5s2b3Llzp4BHo/8Qm5qaiqWlpVE/cMfGxhIfH2/0D9zFkevTGjM9rrHrylSO31Ripsc1Rj2ZmZnh7e2NlZUVWq022/eTojCVmMUZF3J+Dy+Mkj6nLft9xNULb3Dzwilcq9SjZS6DOZV0rgX1JNVTScfNK2ZBnkeZlfZ6Kq64phIznTHrCUzn+E0lZjqpp/wde4k2Nq2srHj22WfZuXMnXbp0AVAH+xkxYkSO282ZM4dPP/2UrVu38txzzxk1J41Gg5ubGxUrViQ1NbVA22q1WqKiovD29jZahaakpPD7778zdOhQrKysjBITiifXpzkmFE9dmcrxm0pMMF49WVlZYSZzJIoC8qxSS0YMFkUmzyMhhKko8ctox4wZQ//+/Xnuuedo1KgR8+fP59GjRwwcOBCAfv364eHhwcyZ+smMZ8+ezZQpU1i9ejU+Pj7cvHkTAHt7e+zt7Y2Wl7m5eYE/4Gq1WszMzLCxsTHah2ONRsOjR4+K5dsTY+f6NMeE4qkrUzl+U4kJxfeaEkIIIYQQhkq8sdmjRw9u377NlClTuHnzJg0aNODPP/+kUqVKAFy+fNmg92DJkiWkpKTw5ptvGsQJDAwkKCjocaYuhBBCCCGEECIHJd7YBBgxYkSOl83u2bPH4HF0dHTxJySEEEIIIYQQokjkhiMhhBBCCCGEEEZXKno2H6f0aUXj4uKIj48nLi7OqPeYGTtmcnIySUlJxMXFGf2eTVM4flOJCcVTV6Zy/KYSE6SeiuOcmsp739N+TqWepJ5Kez0VV1xTiSmf+UwjptTTfzHhv7ZVTjRKXiWeMFevXsXLy6uk0xBCCCGEEEIIk3blyhU8PT1zXP/UNTZ1Oh3Xr1/HwcGBRo0aceTIEaPGf/75540aMy4uDi8vL65cuYKjo6PR4oLxc33aYxZXXZnK8ZtKTKkn48csjrhPez0VV1ypJ6mn0l5PxRXXFGLKZz7TiCn1pI95+PBhHj58iLu7e65TwT11l9GamZmprW9zc3OjP0mKIyaAo6OjSeT6NMdMZ+y6MpXjN5WY6aSejMtU3vue9nMq9ST1VNrrqbjimkpMkM98phATpJ7Kli1L2bJl8yz7VA8QNHz4cJOIWVxM5fhNJWZxMZXjN5WYxcVUjr+4zqmp1NXTfk6lnozPlHI1tqf9nJpKPYHpHL+pxCwupnL8BYn51F1Ga2ri4uIoW7YssbGxxdbDI4xD6so0SD2ZBqkn0yD1ZBqknkyD1JNpkHoqmKe6Z9MUWFtbExgYaNTRrkTxkLoyDVJPpkHqyTRIPZkGqSfTIPVkGqSeCkZ6NoUQQgghhBBCGJ30bAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMDppbAohhCiV9uzZg0aj4aeffirpVPLl1q1bvPnmm5QvXx6NRsP8+fMfy35XrFiBRqMhOjr6sezvSRMUFIRGoynpNIQQ4okkjU0hhHiKpTdUbGxsuHbtWpb1LVq0oF69eiWQmekZPXo0W7duZdKkSaxatYr27dvnWFaj0ag/ZmZmuLu707ZtW/bs2fP4EgbOnDlDUFDQE9dQ9fHxMTjHNjY2VK9enXHjxnHv3r2STk8IIZ4a0tgUQghBcnIys2bNKuk0TNquXbvo3LkzY8eOpU+fPtSqVSvX8m3atGHVqlUEBwfz7rvvcuLECVq1asUff/xRoP327duXxMREvL29C5zzmTNnmDp16hPX2ARo0KABq1atYtWqVfzf//0frVu3Zv78+Vm+BJg8eTKJiYkllKUQQjzZLEo6ASGEECWvQYMGfPvtt0yaNAl3d/eSTuexevToEWXKlClynJiYGJycnPJdvkaNGvTp00d93LVrV/z8/Jg/fz6vvPJKvuOYm5tjbm5ekFRNXlpaGjqdDisrqxzLeHh4GJzfIUOGYG9vz9y5c4mMjKR69eoAWFhYYGEhH4eEEKI4SM+mEEIIPvzwQ7RabZ69m9HR0Wg0GlasWJFlnUajISgoSH2cfi9cREQEffr0oWzZslSoUIGPP/4YRVG4cuUKnTt3xtHREVdXV+bNm5ftPrVaLR9++CGurq6UKVOG1157jStXrmQpd+jQIdq3b0/ZsmWxs7MjICCAkJAQgzLpOZ05c4a33nqLcuXK8dJLL+V6zBcuXKBbt244OztjZ2fHCy+8wJYtW9T16ZciK4rCokWL1Es3C+qZZ57BxcWFixcvqst27dpFs2bNKFOmDE5OTnTu3Jnw8HCD7bK7Z9PHx4eOHTuyf/9+GjVqhI2NDVWqVGHlypUG23Xr1g2Ali1bqnmnX8p79OhR2rVrh4uLC7a2tvj6+jJo0KA8jyN939u2baNBgwbY2NhQp04dNmzYkKXsgwcPGDVqFF5eXlhbW1OtWjVmz56NTqdTy6Q/5+bOncv8+fOpWrUq1tbWnDlzJl/nNSNXV1cAg8ZldvdsajQaRowYwcaNG6lXrx7W1tbUrVuXP//8s8D7FEKIp5k0NoUQQuDr60u/fv349ttvuX79ulFj9+jRA51Ox6xZs2jcuDHTp09n/vz5tGnTBg8PD2bPnk21atUYO3Ys+/bty7L9p59+ypYtW5gwYQIjR45k+/bttG7d2uDSx127dtG8eXPi4uIIDAxkxowZPHjwgFatWnH48OEsMbt160ZCQgIzZszg7bffzjH3W7du8eKLL7J161aGDRvGp59+SlJSEq+99hq//PILAM2bN2fVqlXAf5fGpj8uiPv373P//n3Kly8PwI4dO2jXrh0xMTEEBQUxZswYDhw4QNOmTfN12ev58+d58803adOmDfPmzaNcuXIMGDCA06dPq3mPHDkS0H/ZkJ537dq1iYmJoW3btkRHRzNx4kQWLlxI7969OXjwYL6OJTIykh49evDKK68wc+ZMLCws6NatG9u3b1fLJCQkEBAQwPfff0+/fv348ssvadq0KZMmTWLMmDFZYi5fvpyFCxcydOhQ5s2bh7Ozc645pKamcufOHe7cucPVq1f57bff+Pzzz2nevDm+vr55HsP+/fsZNmwYPXv2ZM6cOSQlJfHGG29w9+7dfJ0DIYQQgCKEEOKptXz5cgVQjhw5okRFRSkWFhbKyJEj1fUBAQFK3bp11ccXL15UAGX58uVZYgFKYGCg+jgwMFABlKFDh6rL0tLSFE9PT0Wj0SizZs1Sl9+/f1+xtbVV+vfvry7bvXu3AigeHh5KXFycunzdunUKoCxYsEBRFEXR6XRK9erVlXbt2ik6nU4tl5CQoPj6+ipt2rTJklOvXr3ydX5GjRqlAMpff/2lLnv48KHi6+ur+Pj4KFqt1uD4hw8fnq+4gDJ48GDl9u3bSkxMjHLo0CHl5ZdfVgBl3rx5iqIoSoMGDZSKFSsqd+/eVbc7fvy4YmZmpvTr109dll6HFy9eVJd5e3srgLJv3z51WUxMjGJtba188MEH6rL169crgLJ7926D/H755Rf1eVFQ6fv++eef1WWxsbGKm5ub4u/vry6bNm2aUqZMGSUiIsJg+4kTJyrm5ubK5cuXFUX57znn6OioxMTEFCiHzD9NmzZV7ty5Y1A2/TmREaBYWVkp58+fV5cdP35cAZSFCxfm70QIIYRQpGdTCCEEAFWqVKFv375888033Lhxw2hxhwwZov5tbm7Oc889h6IoDB48WF3u5OREzZo1uXDhQpbt+/Xrh4ODg/r4zTffxM3Njd9//x2AsLAwIiMjeeutt7h7967am/Xo0SNefvll9u3bZ3BZJsC7776br9x///13GjVqZHCprb29PUOHDiU6OrpQl3KmW7p0KRUqVKBixYo0btyYkJAQxowZw6hRo7hx4wZhYWEMGDDAoAfPz8+PNm3aqMeemzp16tCsWTP1cYUKFXI8x5ml33u6efNmUlNTC3xs7u7udO3aVX3s6OhIv379+Oeff7h58yYA69evp1mzZpQrV06tszt37tC6dWu0Wm2WXu433niDChUq5DuHxo0bs337drZv387mzZv59NNPOX36NK+99lq+BgRq3bo1VatWVR/7+fnh6OiYr/MnhBBCT+6IF0IIoZo8eTKrVq1i1qxZLFiwwCgxK1eubPC4bNmy2NjY4OLikmV5dpcopg/kkk6j0VCtWjX1UtLIyEgA+vfvn2MOsbGxlCtXTn2cn8soAS5dukTjxo2zLK9du7a6vrBTw3Tu3JkRI0ag0WhwcHCgbt266kBFly5dAqBmzZrZ7nvr1q15DmyU+bwDlCtXjvv37+eZW0BAAG+88QZTp07liy++oEWLFnTp0oW33noLa2vrPLevVq1alvsga9SoAejvwXR1dSUyMpITJ07k2ICMiYkxeJzfOkvn4uJC69at1cevvvoqNWvW5M033+S7777jf//7X67bF+X8CSGE0JPGphBCCFWVKlXo06cP33zzDRMnTsyyPqeBb7RabY4xsxspNafRUxVFyWem/0nvtfzss89o0KBBtmXs7e0NHtva2hZ4P8bm6elp0BgytqKcY41Gw08//cTBgwf57bff2Lp1K4MGDWLevHkcPHgwy/ksDJ1OR5s2bRg/fny269Mbp+mMUWcvv/wyAPv27cuzsWnM56gQQjytpLEphBDCwOTJk/n++++ZPXt2lnXpvYMPHjwwWJ7eE1cc0nsu0ymKwvnz5/Hz8wNQL3V0dHQ0euPN29ubc+fOZVl+9uxZdX1xSI+b075dXFyMMl1LXqPmvvDCC7zwwgt8+umnrF69mt69e7NmzRqDS6Ozc/78eRRFMYgfEREB6EerBX29xcfHF2uDO7O0tDQA4uPjH9s+hRDiaSb3bAohhDBQtWpV+vTpw9dff63eX5fO0dERFxeXLPfTLV68uNjyWblyJQ8fPlQf//TTT9y4cUOdi/LZZ5+latWqzJ07N9tGxO3btwu97w4dOnD48GFCQ0PVZY8ePeKbb77Bx8eHOnXqFDp2btzc3GjQoAHBwcEGDftTp06xbds2OnToYJT9pDdYM395cP/+/Sw9eOm9xsnJyXnGvX79ujpaL0BcXBwrV66kQYMG6vQj3bt3JzQ0lK1bt2bZ/sGDB2rD0Jh+++03AOrXr2/02EIIIbKSnk0hhBBZfPTRR6xatYpz585Rt25dg3VDhgxh1qxZDBkyhOeee459+/apvVbFwdnZmZdeeomBAwdy69Yt5s+fT7Vq1dQpS8zMzPjuu+945ZVXqFu3LgMHDsTDw4Nr166xe/duHB0d1UZGQU2cOJEff/yRV155hZEjR+Ls7ExwcDAXL17k559/xsys+L6z/eyzz3jllVdo0qQJgwcPJjExkYULF1K2bFmD+UyLokGDBpibmzN79mxiY2OxtramVatWrF69msWLF9O1a1eqVq3Kw4cP+fbbb3F0dMxXQ7dGjRoMHjyYI0eOUKlSJZYtW8atW7dYvny5WmbcuHFs2rSJjh07MmDAAJ599lkePXrEyZMn+emnn4iOjs5yX29BXLt2je+//x6AlJQUjh8/ztdff42Li0uel9AKIYQwDmlsCiGEyKJatWr06dOH4ODgLOumTJnC7du3+emnn1i3bh2vvPIKf/zxBxUrViyWXD788ENOnDjBzJkzefjwIS+//DKLFy/Gzs5OLdOiRQtCQ0OZNm0a//d//0d8fDyurq40btyYd955p9D7rlSpEgcOHGDChAksXLiQpKQk/Pz8+O2333j11VeNcXg5at26NX/++SeBgYFMmTIFS0tLAgICmD17doEHy8mJq6srX331FTNnzmTw4MFotVp2795NQEAAhw8fZs2aNdy6dYuyZcvSqFEjfvjhh3ztu3r16ixcuJBx48Zx7tw5fH19Wbt2Le3atVPL2NnZsXfvXmbMmMH69etZuXIljo6O1KhRg6lTp1K2bNkiHVtYWBh9+/YF9F9IuLi48PrrrzNt2jQ8PDyKFFsIIUT+aBS5010IIYQQRuLj40O9evXYvHlzSacihBCihMk9m0IIIYQQQgghjE4am0IIIYQQQgghjE4am0IIIYQQQgghjE7u2RRCCCGEEEIIYXTSsymEEEIIIYQQwuiksSmEEEIIIYQQwuieunk2dTod169fx8HBAY1GU9LpCCGEEEIIIYRJURSFhw8f4u7ujplZzv2XT11j8/r163h5eZV0GkIIIYQQQghh0q5cuYKnp2eO65+6xqaDgwOgPzGOjo4lnE3eUlNT2bZtG23btsXS0rKk0xG5kLoyDVJPpkHqyTRIPZkGqSfTIPVkGqSe9OLi4vDy8lLbVjl56hqb6ZfOOjo6mkxj087ODkdHx6f6CW0KpK5Mg9STaZB6Mg1ST6ZB6sk0SD2ZBqknQ3ndligDBAkhhBBCCCGEMDppbAohhBBCCCGEMDppbAohhBBCCCGEMLpSdc/mzJkz2bBhA2fPnsXW1pYXX3yR2bNnU7NmTbVMUlISH3zwAWvWrCE5OZl27dqxePFiKlWqZLQ8FEUhLS0NrVZrtJiFlZqaioWFBUlJSaUiH5EzqSvTYKx6srS0xNzc3IiZCSGEEEI8WUpVY3Pv3r0MHz6c559/nrS0ND788EPatm3LmTNnKFOmDACjR49my5YtrF+/nrJlyzJixAhef/11QkJCjJJDSkoKN27cICEhwSjxikpRFFxdXbly5YrMC1rKSV2ZBmPVk0ajwdPTE3t7eyNmJ4QQQgjx5ChVjc0///zT4PGKFSuoWLEif//9N82bNyc2NpalS5eyevVqWrVqBcDy5cupXbs2Bw8e5IUXXijS/nU6HRcvXsTc3Bx3d3esrKxKvNGg0+mIj4/H3t4+1wlTRcmTujINxqgnRVG4ffs2V69epXr16tLDKYQQQgiRjVLV2MwsNjYWAGdnZwD+/vtvUlNTad26tVqmVq1aVK5cmdDQ0Gwbm8nJySQnJ6uP4+LiAP2ldKmpqVnKarVaPDw8sLOzM/rxFIaiKKSkpGBtbV3iDV+RO6kr02Cseipfvjzx8fEkJiZibW1txAwFoL4/Z36fFqWL1JNpkHoyDVJPpkHqSS+/x19qG5s6nY5Ro0bRtGlT6tWrB8DNmzexsrLCycnJoGylSpW4efNmtnFmzpzJ1KlTsyzftm1blgalhYUFrq6uJCQkkJaWZpwDMZKHDx+WdAoin6SuTENR6yklJYXExET27t1b6t4vniTbt28v6RREPkg9mQapJ9Mg9WQanvZ6yu8th6W2sTl8+HBOnTrF/v37ixRn0qRJjBkzRn0cFxeHl5cXbdu2xdHR0aBsUlISV65cwd7eHhsbmyLt11gUReHhw4c4ODhIb1kpJ3VlGoxVT0lJSdja2tK8efNS837xJElNTWX79u20adPGdCfNnulp+HjS1ZLJoxg9EfX0FJB6Mg1ST6ZB6kkv/WrRvJTKxuaIESPYvHkz+/btw9Pzv3/Wrq6upKSk8ODBA4PezVu3buHq6pptLGtr62wvcbO0tMzyBNFqtWg0GszMzErNPXc6nQ5AzUuUXlJXpsFY9WRmZoZGo8n2vUQYj0mfX12S4WNTPY58MOl6eopIPZkGqSfT8LTXU36PvVR9IlYUhREjRvDLL7+wa9cufH19DdY/++yzWFpasnPnTnXZuXPnuHz5Mk2aNHnc6YpSpEWLFowaNapA2wQFBdGgQYNiySe/mjdvzurVq0s0hyfFnj170Gg0PHjwANAPONagQQO1cSmEEEIIIR6vUtWzOXz4cFavXs2vv/6Kg4ODeh9m2bJlsbW1pWzZsgwePJgxY8bg7OyMo6Mj//vf/2jSpEmRR6LNy969e4s1fmYBAQEFKj9gwACCg4N55513+OqrrwzWDR8+nMWLF9O/f39WrFhhxCyfPhqNhl9++YUuXboUOdamTZu4desWPXv2LHpiJmrPnj20bNmS+/fvZ7kXu6jat2/Pxx9/zA8//EDfvn2NGlsIIYQQQuStVPVsLlmyhNjYWFq0aIGbm5v6s3btWrXMF198QceOHXnjjTdo3rw5rq6ubNiwoQSzLj28vLxYs2YNiYmJ6rKkpCRWr15N5cqVSzCz/ElJSSnpFB6rL7/8koEDB5b6S261Wm22vYOmUF8DBgzgyy+/LOk0hBBCCCGeSqXqU66iKNn+DBgwQC1jY2PDokWLuHfvHo8ePWLDhg053q/5tGnYsCFeXl4Gje8NGzZQuXJl/P39DcrqdDpmzpyJr68vtra21K9fn59++kldr9VqGTx4sLq+Zs2aLFiwwCDGnj17aNSoEWXKlMHJyYmmTZty6dIlQP8hP3Pv36hRo2jRooX6uEWLFowYMYJRo0bh4uJCu3btADh16hSvvPIK9vb2VKpUib59+3Lnzh11u0ePHtGvXz/s7e1xc3Nj3rx5+To/s2bNolKlSjg4ODB48GCSkgzvpzpy5Aht2rTBxcWFsmXLEhAQwLFjx9T1Pj4+AHTt2hWNRqM+joqKonPnzri5ueHp6Unjxo3ZsWNHrrncvn2bXbt20alTJ3VZdHQ0Go2GsLAwddmDBw/QaDTs2bMH+O9S0Z07d/Lcc89hZ2fHiy++yLlz5wzi//bbbzz//PPY2Njg4uJC165d1XX379+nX79+lCtXDjs7O1555RUiIyPV9StWrMDJyYlNmzZRp04drK2tuXz5Mj4+PkybNo1+/frh6OjI0KFDAdi/fz/NmjXD1tYWLy8vRo4cyaNHj9R4ycnJTJgwAS8vL6ytralWrRpLly4lOjqali1bAlCuXDk0Go36Ws/r+Qnw+++/U6NGDWxtbWnZsiXR0dFZznOnTp04evQoUVFRudaHEEIIIYQwvlLV2BRFN2jQIJYvX64+XrZsGQMHDsxSbubMmaxcuZKvvvqK06dPM3r0aPr06aNeLqzT6fD09GT9+vWcOXOGKVOm8OGHH7Ju3ToA0tLS6NKlCwEBAZw4cYLQ0FCGDh1a4NE9g4ODsbKyIiQkhK+++ooHDx7QqlUr/P39OXr0KH/++Se3bt2ie/fu6jbjxo1j7969/Prrr2zbto09e/YYNAqzs27dOoKCgpgxYwZHjx7Fzc2NxYsXG5R5+PAh/fv3Z//+/Rw8eJDq1avToUMHdYqMI0eOALB8+XJu3LihPo6Pj6dDhw5s376dvXv30q5dOzp16sTly5dzzGf//v3Y2dlRu3btAp2vdB999BHz5s3j6NGjWFhYMGjQIHXdli1b6Nq1Kx06dOCff/5h586dNGrUSF0/YMAAjh49yqZNmwgNDUVRFDp06GAwX1JCQgKzZ8/mu+++4/Tp01SsWBGAuXPnUr9+ff755x8+/vhjoqKiaN++PW+88QYnTpxg7dq17N+/nxEjRqix+vXrx48//siXX35JeHg4X3/9Nfb29nh5efHzzz8D+nuvb9y4oX6hkdfz88qVK7z++ut06tSJsLAwhgwZwsSJE7Ocp8qVK1OpUiX++uuvQp1nIYQQQghReKXqnk1RdH369GHSpElqD2NISAhr1qxRe8ZA39M0Y8YMduzYoQ6sVKVKFfbv38/XX39NQEAAlpaWBvOT+vr6Ehoayrp16+jevTtxcXHExsbSsWNHqlatClCohlP16tWZM2eO+nj69On4+/szY8YMddmyZcvw8vIiIiICd3d3li5dyvfff8/LL78M6BusGUctzs78+fMZPHgwgwcPVvezY8cOg97NVq1aGWzzzTff4OTkxN69e+nYsSMVKlQAwMnJyaA3vX79+tSvXx+dTkdcXByffPIJGzduZNOmTQaNrowuXbpEpUqVCn0J7aeffqre1ztx4kReffVVkpKSsLGx4dNPP6Vnz54G9Ve/fn0AIiMj2bRpEyEhIbz44osA/PDDD3h5ebFx40a6desG6If1Xrx4sbpdxnP0wQcfqI+HDBlC79691cGZqlevzpdffklAQABLlizh8uXLrFu3ju3bt9O6dWtA/1xL5+zsDEDFihXVezbz8/xcsmQJVatWVXu1a9asycmTJ5k9e3aWc+Xu7q6+HoQQQgghxOMjjc0nTIUKFXj11VdZsWIFiqLw6quv4uLiYlDm/PnzJCQk0KZNG4PlKSkpBpfbLlq0iGXLlnH58mUSExNJSUlRR291dnZmwIABtGvXjjZt2tC6dWu6d++Om5tbgfJ99tlnDR4fP36c3bt3Y29vn6VsVFSUmkfjxo3V5c7OztSsWTPX/YSHh/Puu+8aLGvSpAm7d+9WH9+6dYvJkyezZ88eYmJi0Gq1JCQk5NpDCfqezaCgILZs2cL169fRarUkJibmul1iYmKR5mb08/NT/04/5zExMVSuXJmwsDDefvvtbLcLDw/HwsLC4PyVL1+emjVrEh4eri6zsrIy2Ee65557zuDx8ePHOXHiBD/88IO6TFEUdDodFy9e5OTJk5ibmxdowKv8PD/Dw8MNjgHIcURqW1vbfE88LIQQQgghjEcam0+gQYMGqT1qixYtyrI+Pj4e0F9u6eHhYbAufU7SNWvWMHbsWObNm0eTJk1wcHDgs88+49ChQ2rZ5cuXM3LkSP7880/Wrl3L5MmT2b59Oy+88AJmZmYoimIQO+NlmunKlCmTJbdOnTpl20Pl5ubG+fPn83MKDF3/BxQt3L+k/9vdP9ti/fv35+7duyxYsABvb2+sra1p0qRJngPhjB07lu3btzNnzhxcXV2pUKEC3bt3z3U7FxcX7t+/b7AsvZcz43nL7pyB4dxG6Zcupw/iY2trm2u++WFra5vtJdHZ1dc777zDyJEjs5StXLlyoeorP8/Pgrh3757aK/3ECiqb6XFsyeQhHr/MdQ+lo/5La15ClCR5ry458p5UYqSx+QRq3749KSkpaDQaddCdjDIO+pJTj1P6ZZbDhg1Tl2U3yIq/vz/+/v5MmjSJJk2asHr1al544QUqVKjAqVOnDMqGhYXlOQFsw4YN+fnnn/Hx8cHCIuvTs2rVqlhaWnLo0CF1hN379+8TERGRa+9Z7Wq+HPrnJP26dVSXHTx4MMsxL168mA4dOgD6+wIzDkwE+kaeVqvNst2AAQPo2rUrcXFxmJmZZTtYTUb+/v7cvHmT+/fvU65cOQC1QXTjxg21By/jYEH55efnx86dO7O9V7d27dqkpaVx6NAh9TLau3fvcu7cOerUqVPgfTVs2JAzZ85QrVq1bNc/88wz6HQ69u7dq15Gm5GVlRWAwTnNz/Ozdu3abNq0yWBZ5voE/WjMUVFRWQbIEkIIIYQQxU8GCHoCmZubEx4ezpkzZzA3N8+y3sHBgbFjxzJ69GiCg4OJiori2LFjLFy4kODgYEB/793Ro0fZunUrERERfPzxx+qAOAAXL15k0qRJhIaGcunSJbZt20ZkZKR632arVq04evQoK1euJDIyksDAwCyNz+wMHz6ce/fu0atXL44cOUJUVBRbt25l4MCBaLVa7O3tGTx4MOPGjWPXrl2cOnWKAQMG5Hnv4/uDe7Fs7SaWr/2ViIgIAgMDOX36tEGZ6tWrs2rVKsLDwzl06BC9e/fO0kvo4+PDzp071YZi+nYbNmwgLCyMkydP0rt372ynCsnI398fFxcXQkJC1GW2tra88MILzJo1i/DwcPbu3cvkyZPzPGeZBQYG8uOPPxIYGEh4eLjBvYzVq1enc+fOvP322+zfv5/jx4/Tp08fPDw86Ny5c4H3NWHCBA4cOMCIESMICwsjMjKSX3/9Ve1Z9/HxoX///gwaNIiNGzdy8eJF9uzZow405e3tjUajYfPmzdy+fZv4+Ph8PT/fffddIiMjGTduHOfOnWP16tXZziF78OBBtYdaCCGEEEI8XtKzmU8FueesNHB0dMx1/bRp06hQoQIzZ87kwoULODk50bBhQz788EMA3nnnHf755x969OiBRqOhV69eDBs2jD/++AMAOzs7zp49S3BwMHfv3sXNzY3hw4fzzjvvANCuXTs+/vhjxo8fT1JSEoMGDaJfv36cPHky17zc3d0JCQlhwoQJtG3bluTkZLy9vWnfvr3aoPzss8/Uy20dHBz44IMPiI3N/VKIHp3bEXXpKuOnLyBpyjzeeOMN3nvvPbZu3aqWWbp0KUOHDlWnkJkxYwZjx441iDNv3jzGjBnDt99+i4eHB9HR0Xz++ecMGjSIl156CWdnZyZOnKiOYJsTc3NzBg4cyA8//EDHjv/1ti5btozBgwfz7LPPUrNmTebMmUPbtm1zjZVZixYtWL9+PdOmTWPWrFk4OjrSvHlzdf3y5ct5//336dixIykpKTRv3pzff/89z17n7Pj5+bF3714++ugjmjVrhqIoVK1alR49eqhllixZwocffsiwYcO4e/culStXVp9nHh4eTJ06lYkTJzJw4ED69evHihUr8nx+Vq5cmZ9//pnRo0ezcOFCGjVqxIwZMwxG5QX48ccf6d27N3Z2dgU+NiGEEEIIUTQaJfONdU+4uLg4ypYtS2xsbJYGWVJSEhcvXsTX17dIg7cYU/oIp46OjoUeufSpd/0fw8c53LNZVAWtq5s3b1K3bl2OHTuGt7d3seT0NLtz5w41a9bk6NGj+Pr6qsuN9ZoqVe8XT+B9QKmpqfz+++906NChUF+ElAqPo15K+D6kHOtJ7o8qVZ6I19OTII/3BKmnYmTE9ySpJ73c2lQZSetFiBLi6urK0qVL8xztVhROdHQ0ixcvNmhoCiGEEEKIx0cuoxWiBHXp0qWkU3hiPffcc1mmahFCCCGEEI+P9GwKIYQQQgghhDA6aWwKIYQQQgghhDA6aWwKIYQQQgghhDA6uWdTiCfdYxqNV4hi9QSOuFvqyAiyQjzZSuv7aGnNSxiF9GwKIYQQQgghhDA6aWwKIYQQQgghhDA6aWwKIYQQQgghhDA6uWczn3wmbnms+4ue9epj3Z+pa9GiBQ0aNGD+/Pn53iYoKIiNGzcSFhZWbHnlpXnz5rz77ru89dZbAGg0Gn755Zcc59+Mjo7G19eXf/75hwYNGjy+RJ8CPj4+jBo1ilGjRpGSkkKNGjX46aefZK5OIYQQQohCkp7NJ8SAAQPQaDS8++67WdYNHz4cjUbDgAEDHn9iTxiNRsPGjRuNEmvTpk3cunWLnj175nsbLy8vbty4Qb169YySg6nz8fEp0BcM+WVlZcXYsWOZMGGC0WMLIYQQQjwtpLH5BPHy8mLNmjUkJiaqy5KSkli9ejWVK1cuwczyJyUlpaRTeKy+/PJLBg4ciJlZ/l+G5ubmuLq6YmFhOhclpKamZllmCnXdu3dv9u/fz+nTp0s6FSGEEEIIkySNzSdIw4YN8fLyYsOGDeqyDRs2ULlyZfz9Dae70Ol0zJw5E19fX2xtbalfvz4//fSTul6r1TJ48GB1fc2aNVmwYIFBjD179tCoUSPKlCmDk5MTTZs25dKlS4C+pzXzpaCjRo2iRYsW6uMWLVowYsQIRo0ahYuLC+3atQPg1KlTvPLKK9jb21OpUiX69u3LnTt31O0ePXpEv379sLe3x83NjXnz5uXr/Mz6v+VUqt8aBwcHBg8eTFJSksH6I0eO0KZNG1xcXChbtiwBAQEcO3ZMXe/j4wNA165d0Wg06uOoqCg6d+6Mm5sbnp6eNG7cmB07duSay+3bt9m1axedOnXKsu7GjRu88sor2NraUqVKFYN6iY6ORqPRqJf+5queDhyl0at9KVPtRZxqNzeop+xcvXqVXr164ezsTJkyZXjuuec4dOiQun7JkiVUrVoVKysratasyapVqwy212g0LFmyhNdee40yZcrw6aefEhQURIMGDfjuu+/w9fXFxsYGgAcPHjBkyBAqVKiAo6MjrVq14vjx4wbxfvvtN55//nlsbGxwcXGha9eugP75c+nSJUaPHo1Go0Gj0ajb7N+/n2bNmmFra4uXlxcjR47k0aNHBuf/tddew9bWFl9fX3744Ycs56FcuXI0bdqUNWvW5HiuhBBCCCFEzqSx+YQZNGgQy5cvVx8vW7aMgQMHZik3c+ZMVq5cyVdffcXp06cZPXo0ffr0Ye/evYC+Merp6cn69es5c+YMU6ZM4cMPP2TdunUApKWl0aVLFwICAjhx4gShoaEMHTrU4AN/fgQHB2NlZUVISAhfffUVDx48oFWrVvj7+3P06FH+/PNPbt26Rffu3dVtxo0bx969e/n111/Ztm0be/bsMWgUZmfdpm0Eff41MyaO4OjRo7i5ubF48WKDMg8fPqR///7s37+fgwcPUr16dTp06MDDhw8BfWMUYPny5dy4cUN9HB8fT4cOHdi+fTt79+6lXbt2dOrUicuXL+eYz/79+7Gzs6N27dpZ1n388ce88cYbHD9+nN69e9OzZ0/Cw8OzjZOveho8hoAXGnJix1pCN63ItZ7i4+MJCAjg2rVrbNq0iePHjzN+/Hh0Oh0Av/zyC++//z4ffPABp06d4p133mHgwIHs3r3bIE5QUBBdu3bl5MmTDBo0CIDz58/z888/s2HDBrWx3K1bN2JiYvjjjz/4+++/adiwIS+//DL37t0DYMuWLXTt2pUOHTrwzz//sHPnTho1agTov0jx9PTkk08+4caNG9y4cQPQN/7bt2/PG2+8wYkTJ1i7di379+9nxIgRan7Dhg3j6tWr7N69m59++onFixcTExOT5Xw0atSIv/76K/tKFEIIIYQQuTKda/FEvvTp04dJkyapPVchISGsWbOGPXv2qGWSk5OZMWMGO3bsoEmTJgBUqVKF/fv38/XXXxMQEIClpSVTp05Vt/H19SU0NJR169bRvXt34uLiiI2NpWPHjlStWhUg24ZTXqpXr86cOXPUx9OnT8ff358ZM2aoy5YtW4aXlxcRERG4u7uzdOlSvv/+e15++WVA32D19PTMdT/zv1vN4J6dGdyrC7jXZPr06ezYscOgd7NVq1YG23zzzTc4OTmxd+9eOnbsSIUKFQBwcnLC1dVVLVe/fn3q16+PTqcjLi6OTz75hI0bN7Jp0yaDBk5Gly5dolKlStleQtutWzeGDBkCwLRp09i+fTsLFy7M0jgG8ldPcfF0bN2cqj5eANQO8M8SJ93q1au5ffs2R44cwdnZGYBq1aqp6+fOncuAAQMYNmwYAGPGjOHgwYPMnTuXli1bquXeeuutLF9ypKSksHLlSvU87t+/n8OHDxMTE4O1tbUaf+PGjfz0008MHTqUTz/9lJ49exocY/369QFwdnbG3NwcBwcHg/qYOXMmvXv3ZtSoUYD+Ofbll18SEBDAkiVLiI6OZseOHRw8eJDGjRsDsHTp0myfv+7u7rn2AgshhBBCiJxJY/MJU6FCBV599VVWrFiBoii8+uqruKRcgaRYSNDB9X84fy6KhIQE2rRpY7BtSkqKweW2ixYtYtmyZVy+fJnExERSUlLUEVCdnZ0ZMGAA7dq1o02bNrRu3Zru3bvj5uZWoHyfffZZg8fHjx9n9+7d2NvbZykbFRVF4qV/SElJobFPGbj+jz4Xd39q1qyZ637Cz1/k3b5vGixr0qSJQY/crVu3mDx5Mnv27CEmJgatVktCQkKuPZSg7w0MCgpiy5YtXL9+Ha1WS2Ji4n/b/Zunyt2fxMRE9VLSzNK/AMj4OLcRc/Osp+6daNd7OG2aNaZ1s8Z0f9s1x3oKCwvDv251nJMuwfUMjSx3/fMiPDycoUOHGmzTtGnTLJfuZjeCq7e3t9rQBDi+bzPx8fGUdy6nX6DRN7wTExOJiopS83n77bdzPPbsHD9+nBMnThhcGqsoCjqdjosXL3L27FksLCx41sNSrZtajvovETKztbUlISGhQPt/YgWVzWZZbMG2yau8ENkpzHOvtHpcx5J5Px/dyb6cyJu8jxXMk/R6FUYhjc0n0KBBg9QetUWLFmVZH/9I/+F5y5YteHh4GKxL72Fas2YNY8eOZd68eTRp0gQHBwc+++wzg3v3li9fzsiRI/nzzz9Zu3YtkydPZvv27bzwwguYmZmhKIpB7OwGiilTpoxhbvHxdOrUidmzZ2cp6+bmxvkDm/NzCgqlf//+3L17lwULFuDt7Y21tTVNmjTJczCbsWPHsn37dubMmYOrqysVKlSge/fuuW7n4uLC/fv3i5xzvurpi6mMHNyLP3cfYO2mbUz+7Cu1njKztbUtqaTk5QAAQpVJREFUck6QtV6zWxb/KBG3ii7s+ekb/YJKddV16Q2/wuQTHx/PO++8w8iRI7Osq1y5MmfPns13rHv37hk0kIUQQgghRP7JPZtPoPbt25OSkkJqaqo66E5GdWpUwdramsuXL1OtWjWDHy8v/aWWISEhvPjiiwwbNgx/f3+qVaum9jZl5O/vz6RJkzhw4AD16tVj9erVgL6HNf0eunT5mc+yYcOGnD59Gh8fnyy5lSlThqo+nlhaWnDo2Cl1m/v37xMREZFr3NrVfDn0z0mDZQcPHjR4HBISwsiRI+nQoQN169bF2traYGAi0F+2qtVqs2w3YMAAunbtSt26dXF1dSU6OjrXfPz9/bl582a2Dc7MeR08eDDHS5TzXU/1ajHpf4M4sGmFQT1l5ufnR9jpCO7dz/5byNq1axMSEpIlhzp16mRbPjcNn6nFzdt3sbCwoJpvZYO6dnFxUfPZuXNnjjGsrKyy1EfDhg05c+ZMludPtWrVsLKyolatWqSlpfH3if/ugz13PpoHDx5kiX/q1Kksg2sJIYQQQoj8kcbmE8jc3Jzw8HDOnDmDubl5lvUO9mUYO3Yso0ePJjg4mKioKI4dO8bChQsJDg4G9Pe5HT16lK1btxIREcHHH3+sDogDcPHiRSZNmkRoaCiXLl1i27ZtREZGqo2iVq1acfToUVauXElkZCSBgYGcOnUqSy6ZDR8+nHv37tGrVy+OHDlCVFQUW7duZeDAgWi1WuzL2DG4ZxfGTZ/Prv2HOXX2PAMGDMhz+pD3B/di2dpNLF/7KxEREQQGBmaZ0qJ69eqsWrWK8PBwDh06RO/evbP0rPn4+LBz506DhmL16tXVQW9OnjxJ79691QF1cuLv74+Li0uWhhvA+vXrWbZsmZrn4cOHc7z3M1/1NHMhoUePc+nqdbbtDTWop8x69eqFa4XydBk8hpAjYVy4dJWft+wkNDQU0A/OtGLFCpYsWUJkZCSff/45GzZsYOzYsbkeb3ZaN2tMk2efocugMWzbG0p0dDQHDhzgo48+4ujRowAEBgby448/EhgYSHh4OCdPnjTo9fbx8WHfvn1cu3ZN/WJgwoQJHDhwgBEjRhAWFkZkZCS//vqreg5r1qzJyy+/zHsTpnPo2En+PnGGIeM+ybYX9a+//qJt27YFPjYhhBBCCCGX0eZb9KxXSzqFAnF0dMx1/bRp06hQoQIzZ87kwoULODk50bBhQz788EMA3nnnHf755x969OiBRqOhV69eDBs2jD/++AMAOzs7zp49S3BwMHfv3sXNzY3hw4fzzjvvANCuXTs+/vhjxo8fT1JSEoMGDaJfv36cPHkyx5xAPyBLSEgIEyZMoG3btiQnJ+Pt7U379u3VBuVnH48i/lECnQaMwsG+DB+Mm0BsbO73A/To3I6oS1cZP30BSVPm8cYbb/Dee++xdetWtczSpUsZOnSoOoXMjBkzsjSi5s2bx5gxY/j222/x8PAgOjqazz//nEGDBvHSSy/h7OzMxIkT1RFsc2Jubs7AgQP54Ycf6Nixo8G6qVOnsmbNGoYNG4abmxs//vhjjj2H+aqn89EEr/+Nu/djcavoYlBPmVlZWbHtx0V8MPULOvQdSVpaGnVqVGHRN80B6NKlCwsWLGDu3Lm8//77+Pr6snz5coMpbfJLo9Hw+6qFfDR7EQPHBHH77ihcXV1p3rw5lSpVAvTTm6xfv55p06Yxa9YsHB0dad68uRrjk08+4Z133qFq1aokJyejKAp+fn7s3buXjz76iGbNmqEoClWrVqVHjx7qdosWLWLM8LcJePNtKrk4M338MD7+fKlBfqGhocTGxvLmm4b3+gohhBBCiPzRKJlvrHvCxcXFUbZsWWJjY7M0yJKSkrh48aLBPIAlLX2EU0dHxzx773KUeYAaUAd8MTmFOZZsBugpDjnWVQ77v3nzJnXr1uXYsWN4e3sXS0657T/f5fOzTWE8pnrJTK2nhGjMyPD2l2n/PXr0oH79+uoXMJmVqveLxzGAxWMeICg1NZXff/+dDh06YGlpaZqDdJTWejFirCz1VEryKrVKaICg1I/uZF9PIm/GfB3nESvH15OxlNb3pNKaVw6KvZ5MRG5tqozkMlohSoirqytLly7Nc7Rb8filpKTwzDPPMHr06JJORQghhBDCZBW5sRkcHMyWLVvUx+PHj8fJyYkXX3xR5qcTIg9dunShWbNmJZ2GyMTKyorJkycbbXReIYQQQoinUZEbmzNmzFA/kIWGhrJo0SLmzJmDi4uL9AoIIYQQQgghxFOqyAMEXblyhWrVqgGwceNG3njjDYYOHUrTpk0LNWiIEEIIIYQQQgjTV+TGpr29PXfv3qVy5cps27aNMWPGAGBjY0NiYmKRExQlxJiDtzxJAxQZUyke7KhEPUnnpaQHSniSBlZ5Gkh9Gc/TMEBRCQ02lK99lPSAL6Y4oFhOcjqWJ+kYTc1MT6j/jf63Lkm/rBgHjjJ1RW5stmnThiFDhuDv709ERAQdOnQA4PTp0/j4+BQ1vBBCCCGEEEIIE1TkezYXLVpEkyZNuH37Nj///DPly5cH4O+//6ZXr15FTlAIIYQQQgghhOkpcs9mXFwcX375ZZY5IIOCgrhy5UpRwwshhBBCCCGEMEFF7tn09fXlzp07WZbfu3cPX1/fooYXQgghhBBCCGGCityzqShKtsvj4+OxsbEpavjSI7sb0Yt1f0/WzcGmQKPR8Msvv9ClSxeio6Px9fXln3/+oUGDBoWKp8bY+iMN6tU0brJCCCGEEEKUcoXu2RwzZgxjxoxBo9EwZcoU9fGYMWN4//336dGjR4E/pO/bt49OnTrh7u6ORqNh48aNBusHDBiARqMx+Gnfvn1hD+GJkt250Wg0nD9/Xr9+VCBdBo3JcfvExEQCAwOpUaMG1tbWuNRrRbeh4zl9LsqgXFBQkBrb3NwcLy8vhg4dyr179wzK+fj4MH/+fPXx8dMRvDZgFBX9Xsamygv4+PjQo0cPYmJijHcSjMjLy4sbN25Qr169fJUfMGAAXbt2zT5GrarFkaIQQgghhBClWqF7Nv/5Rz/VgKIonDx5EisrK3WdlZUV9evXZ+zYsQWK+ejRI+rXr8+gQYN4/fXXsy3Tvn17li9frj62trYuRPZPpsznBqBChQpw60Su2yUnJ9O6dWsuX77MvHnzaNy4MbdO7WPm/y2nccd+7FizhBcyTCVRt25dduzYgVarJTw8nEGDBhEbG8vatWuzjX/77n1e7vEuHVs3Y+vqRTg5OhCdWIZNmzbx6NGjoh94BqmpqVhaWhY5jrm5Oa6ursaJcf1GkfMRQgghhBDC1BS6sbl7924ABg4cyIIFC3B0dCxyMq+88gqvvPJKrmWsra2L3Ah4UhX23MyfP5/Q0FD++ecf6tevD4C3ZT1+/vYzGnfsx+Cxn3CqYz80Gg0AFhYW6n48PDzo1q1blkZuRiFHwoh9GM93cz/GwkL/lPN196dly5a55uXj48PgwYM5c+YMmzZtwsnJiQ+H92X4gB5qGY1HQxYvXswff/zBzp07GTduHEFBQfz6669MnTqVM2fO4F7Jhf7dOvLRyMHq/iMjIxk8eDCHDx+mSpUqLFiwwGDf2V1Ge/r0aSZMmMC+fftQFIUGDRqwYsUKVq1aRXBwMADlypUD9K8PHx+fLJfR7g39m3Gz3+H48eM4OznSv1tHpo8fpubVokUL/Pz8sLGx4bvvvsPKyop3332XoKCg3CtRCCGEEEKIUqbI92zm1sgoDnv27KFixYqUK1eOVq1aMX36dHW6lewkJyeTnJysPo6LiwP0PWCpqakGZVNTU1EUBZ1Oh06nM1hX5JGUCih9/+n3xKbnlRNFUXIpo0EBFECHJn0H6trVq1fTunVrnnnmmQzba8DMnPff7kPfER+qja70fNLLRUdHs3XrVqysrLLsOz2fihVcSEtL4+c/9vBmx9b6Rmsux5LRZ599xqRJkwgMDGTbtm28P2YM1ar40Kb5C2qZoKAgZsyYweeff46FhQV79+6lX79+zJ8/n2bNmhF1dAfvjp+GgoYpY95Bl5bG66+/TqVKlQgNDSU2NpYxY8aox5Wx/tP/vnbtGs2bNycgIIAdO3bg6OhISEgIKSkpjBkzhjNnzhAXF8eCBQuwt7enfPnyXL9+XR8DDTo0XLsRQ4e+/6P/gIGsWLGCswe38s64T7C2tibwg3fV4wkODmb06NGEhoYSGhrKoEGDaNKkCW3atNHXS0b5Oo8F3UaTdVE+66tgcsqrMPvP/zGqrynM0JGhXAGPUafToSgKqampmJubZ1/ILJv71jO97+S5TV7lc9umMPvP7z7yE6swx6IWTTX4XZRYJaagORvjHOdnGyPGylJPpSSvEt9/ScfKtC7HeirMforynmRMBTj+Iu3/McbKtp7y+/5uCvWS321KS145SP03VmrGmKXlOfYY5ff9RKPkNMJPPj169IhZs2axc+dOYmJisjQ4Lly4UKi4GQdrSbdmzRrs7Ozw9fUlKiqKDz/8EHt7e0JDQ3P8sBcUFMTUqVOzLF+9ejV2dnYGy9J77Ly8vAwuCwZwmu9dqOMorAejLhWo/LBhw1i3bp3BoEytW7dmxYoV6vrY2Fh++OGHLNu6ubkxYMAAZs6cmWXdiRMnCAgIYNmyZXTt2pVZs2bx2WefYWtri1arJSkpCYBPP/2UYcOGqdv5+fnx3nvv8d577wEwbdo0vvzySxwcHGjYsCHNmzenZ8+eVKxYMcdj8vPzo0aNGvz000/qskGDBvHw4UPWr18P6HsS33vvPWbMmKGW6dKlC82bN1cbkABr164lKCiI8PBwdu3aRY8ePThx4gRubm4A7Nixg27duvH999/z6quvcvnyZerXr8++fft45pln+OSTT9iwYQNHjhzJ9jLd7M5v5hjTpk3jt99+49ChQ2ov8XfffcfUqVO5dOkSZmZmdOzYEa1Wyx9//KHGefnll2nWrJn0bpYyKSkpXLlyhZs3b5KWllbS6QghhBBCPDYJCQm89dZbxMbG5nqFa5F7NocMGcLevXvp27cvbm5u6ofo4tCzZ0/172eeeQY/Pz+qVq3Knj17ePnll7PdZtKkSQaNjri4OLy8vGjbtm2WE5OUlMSVK1ewt7cv8ZF003NTFIWHDx/i4ODw37m9mekeTFc/LC0tadGiBYsXL1YXlylTRh/n5gks0+Kx0CbgmBCtbpORpaWl4fn4dx9lkvS9c7a2tjg6OmJtbU3NmjXZuHEjSUlJ/PDDD4SFhTF27Fgs7pxRNzdT0rCxsVH3/9kHfZk4sBO7Qo5w+J+TBAcH88Xnc9nz81KeqV3d4FjUGGZmNGvWzCCv5g2qseC7H/47DqBJkyYGZU6fPs2hQ4f4/PPP9QsUHVqdjqSkZCzuhnP58mW8vLyoWTYZ/o3zcr1KBsdpn3hVf/yJ13FMcCA8PJzmzZvn2IuuP7+JADgkXEaDDnt7R4MYF8LDeNG/NmXLllXP8cv1vRgXH09c1CEqe7hhYWGBn5+fwfF4eHj890LOpu7zVNBtMpfP734KKqe8CrP/AsRSKj2jf039W0/53kcmSUlJ2Nra0rx585zfL2Z6Zl026Wr26/Janpv8xkpfl1te+d1HYbbJz7H8KzU1le3bt9OmTRv9FzwFPcbc9l+Y82LMeslv+eLaxoixstRTUfJ6XM/Xgr72Htc5NubzJdO61LEXs6+n3OIV5bwU4bWfbyV0Lot0LHnEyvb19Dj+VxSGMV8vphLr321SZ1dj+zNf0ubkSCx1Sbnv53HXy2OUfrVoXorc2Pzjjz/YsmULTZs2LWqoAqtSpQouLi6cP38+x8amtbV1toMIWVpaZnnD1Wq1aDQazMzMMDN73BfOGkrff3pPcXpeekrmwmg0Guzt7alRo0Y20RQ06C80NEvfNsPx1ahRg7Nnz2Y6Zn25c5H6nulatWph9u9+rKys1P34+fnx6quvMm3aNKa9Zzio038562NVcC5Lj06t6dGpNTO//A7/Z2rz+VcrCV7wicGxZB/j38f/xjLLcA4cHBwMysTHxzN16tT/Bpm6dVpdZ2dtpTbaM8ZI/zu97s0y7McMBTs7uyy5GOSZ4Zxp0Om3+7dsegzNvz8Z6zH9r/QyoB9gK+N+zMzMUBTF4FzmdL6yV9BtsrnYoVheDznlVZj95z+W7t/6T6+n/O8j8y70r4fs3kv+21lS1mXpZTOvy2t5bvIbK31dbnnldx+F2aYQg3ep57egx5jb/gtzXoxZL/ktX1zbFEOsLK8DYx6Lsc9LQV97j+scG/P5kkOsAr1fFeW8GOG1n6cSPpeFks9YBvX0OP5XFIYxXy+mEit9m3+XW+qS/mtslpZ6eYzyOyBnkT9BlitXDmdn56KGKZSrV69y9+5d9VJIUTg9e/Zkx44dHD9+3GC5Tqfji29/oE6NKurAQdmZPHkyc+fO5frN2/nep5WVFVW9PXmUkJhruYMHDxo+PnaS2tV9c92mYcOGnDt3jmrVqul/fCurP2ZmZtSuXZsrV65w49Z/+R48djLXmH5+fvz11185Xp9uZWWBVpv7PX+1q/kS+vdJg7lpQ46E4WBfBk+3SrluK4QQQgghhKkpcmNz2rRpTJkyhYSEhCInEx8fT1hYGGFhYQBcvHiRsLAwLl++THx8POPGjePgwYNER0ezc+dOOnfuTLVq1WjXrl2R9/00iI2LJ+zUOf3Pv+f5ypUrjB49mkaNGtGpUyfWr1/P5cuXORJ2mjfeHkd45EWWzp2S6+XRTZo0wc/PjxkLl2a7fvP2ffT530ds3r6PiKhLnDsfzdy5c/l9Vwid2wXkmnNISAhz5swhIiKCRYsWsX7zDt4f3CvXbaZMmcLKlSuZOnUqp0+fJjzyAmt+3crk2YsA/b2sNWrUoP+oQI6fjuCvQ8f46N91ORkxYgRxcXH07NmTo0ePEhkZyapVqzh37hwAPp7unAyPIDIykjv37mfbKB3WvztXrt/kf//7H2fPnuXXrXsInPcVY4b2LvGedCGEEEIIIYytyJfRzps3j6ioKCpVqoSPj0+WLtVjx47lO9bRo0cNpsNIv9eyf//+LFmyhBMnThAcHMyDBw9wd3enbdu2TJs27fHMtRkUW/z7KGZ7Qo/i386woTZ48GC+++47du3axYwZM/jwww+5dOkSDmXsaPnicxz8LZh6tarlGXv06NEMGNCfCcMG4OVhOP1KnRpVsLO14YNPvuDK9VtYW1tSvUYtvvvsY/q+2THXuB988AFHjx5l6tSpODo68nngGNq1eDHXbdq1a8fmzZv55JNPmD17NpYW5tSq5sOQXl0A/eWPv/zyC4P79qBRx774eLrz5bRxtO89IseY5cuXZ9euXYwbN46AgADMzc1p0KCBevn4271fZ3fo37Rq1Yr4+Hh2r/8Gn+faGMTwcKvI76sWMm72N9SvXx9nJ0cG9+rC5PeH5Ho8QgghhBBCmKIiNzYzjhZbVC1atCC3wXG3bt1qtH09adJHnc1x/fyprJifYVRed3+D9XZ2dkyfPp3p06frF1z/J9s4QUFB2Y6K2rNnT3o2r6k+jj60Rd1HFW9PvpnzseEG7v457iMjR0dH1q1b99+CTNso145lORbQNzjVHu9s9lOjRg3++mVZjrF8vNz1jzPw8/PL8TlYoXw5tv64hDg7HxwTovX3Arr76J/PGfYf0ORZDh8+nGNee/bsybJs48aN2e5TCCGEEEKI0qzIjc3AwEBj5CGEEEIIIYQQ4gkiN4oJIYQQQgghhDC6QvVsOjs7ExERgYuLC+XKlct18Jh79+4VOjnxdIuOji7pFIQQQgghhBCFVKjG5hdffIGDgwMA8+fPN2Y+oigy3wOYzb2MRt9Hce1HmJbcnhfGfF4+jud4fvedpgC2j2//j0tQ2UyPH/PgaOn7N7OB+t883n2LnJX08yInJZ1XSe/f1GQ+X6A/Zzktz26bopxjqa/syXkpfrk9x59whWps9u/fP9u/nxS5DVIkhBAA8jYhhBBCCJG7Ig8QBKDVatm4cSPh4eEA1K1bl9deew1zc3NjhH9s0qdtSUhIwNb2CeyxEEIYTYpO/9vU3ueEEEIIIR6XIjc2z58/T4cOHbh27Ro1a+qnvpg5cyZeXl5s2bKFqlWrFjnJx8Xc3BwnJydiYmIA/XQgud2P+jjodDpSUlJISkrCzOzf8ZzSMnWpJCUVbHnGdTkpTKyCbmOMvAqzTVGOJZd96EBfV2mKfuqTx1kvxtzG2PVizOerEWLpkpIM6ynzNjnJEEunwO3YJOzKlcfCwijf2QkhhBBCPHGK/Clp5MiRVK1alYMHD+Ls7AzA3bt36dOnDyNHjmTLli1FTvJxcnV1BVAbnCVNURQSExOxtbX9r+H74LZhoUcXC7Y847qcFCZWQbcxRl6F2aYox5LLPhQ0JFrpsE25iwbl8daLMbcxdr0Y8/lqhFhKvI3+NZVeT5m3yYlBLAWzxHtUrvNciX8hJYQQQghRWhW5sbl3716DhibA/7d379FRlff+xz8TyI1LEoGQiwiEq0XkLpwIchHk0pYD9ZwWET2ALBDEJQIKRA7X1hPEyhL8obQ9KtClolLAag9QQALlFghCkYtIQhQtAQqBhBCSDMzz+wMzdciFZGZPZiZ5v9ZiyTzP3s/zffY3e5Ove89Mw4YNtWjRIvXs2dPT4auczWZTXFycGjduLLvd7utwZLfbtXPnTvXu3dv5mK/+3y9dN3o2rXLtP+4riztjVXYfK+JyZx9P1lLOHHZbqHbeu1C9v5qrYFNYtXmxch+r82Llz6sFY9mf3nvrnCrO0+37lOXHYzluKuT6BQU9/Hj5+wAAANRgHheboaGhunr1aon2vLw8hYSEeDq8z9SqVcsv3otVq1Yt3bhxQ2FhYf8qNvO+c90oLKxy7T/uK4s7Y1V2HyvicmcfT9ZSzhy1gsJu5era9wp2FFRtXqzcx+q8WPnzasFYtcJuy9Pt+5SltLgAAABQpiBPB/j5z3+uCRMmKDU1VcYYGWO0b98+TZw4Uf/+7/9uRYwAAAAAgADjcbG5bNkytWzZUomJiQoLC1NYWJh69uypVq1aaenSpVbECAAAAAAIMB4/RhsVFaVPPvlE6enpzq8++clPfqJWrVp5HBwAAAAAIDC5XWw6HA69+uqr+vOf/6yioiL1799f8+bN4/spA838yNte5/gmjtsFSlySf8TmTlzuHGN/zYuVqupY1mT+eh5VFV//vFg5f3ITqePvb/3XUVCz8lgeK4+xledLcZ6sGKumKyvHnvwbEhT2r/Np7nnPY/SUP1+rvXmO+csaqwm3H6N9+eWX9dJLL6levXq6++67tXTpUk2ePNnK2AAAAAAAAcrtYnP16tV68803tXnzZm3YsEGffvqp3nvvPTkcDivjAwAAAAAEILeLzTNnzuinP/2p8/WAAQNks9l09uxZSwIDAAAAAAQut4vN4u9+/LHg4GDZ7XaPgwIAAAAABDa3PyDIGKMxY8YoNDTU2VZQUKCJEyeqbt26zrZ169Z5FiEAAAAAIOC4XWyOHj26RNsTTzzhUTAAAAAAgOrB7WLz3XfftTIOAAAAAEA14vZ7NgEAAAAAKAvFJgAAAADAcm4/Rgv4nfmRt73O8U0cQE1Tk8+929cueWf9lT3GVRWXO2ryzwvgbf567vtrXPA67mwCAAAAACznVrHZpUsXXb58WZK0cOFC5efnWxoUAAAAACCwuVVsnjhxQteuXZMkLViwQHl5eZYGBQAAAAAIbG69Z7NTp04aO3asevXqJWOMfvvb36pevXqlbjt37lyPAgQAAAAABB63is2VK1dq3rx5+uyzz2Sz2bRx40bVrl1yKJvNRrEJAAAAADWQW8Vm27ZttWbNGklSUFCQtm3bpsaNG1saGAAAAAAgcHn81ScOh8OKOAAAAAAA1Ygl37OZkZGh119/XSdOnJAktWvXTlOmTFHLli2tGB4AAAAAEGA8/p7NzZs3q127dtq/f786dOigDh06KDU1Vffdd5+2bNliRYwAAAAAgADj8Z3NWbNmaerUqVq0aFGJ9pkzZ+qRRx7xdAqg5pkfedvrHN/EAf9x+8+ExM8FAADwax7f2Txx4oTGjRtXov2pp57S8ePHPR0eAAAAABCAPC42o6Ojdfjw4RLthw8f5hNqAQAAAKCG8vgx2vHjx2vChAk6ffq0HnzwQUnS7t279corr2jatGkeBwgAAAAACDweF5tz5sxR/fr19dprrykpKUmSFB8fr/nz5+u5557zOEAAAAAAQODxuNi02WyaOnWqpk6dqqtXr0qS6tev73FgAAAAAIDA5fF7Nn+sfv36HhWaO3fu1NChQxUfHy+bzaYNGza49BtjNHfuXMXFxSk8PFwDBgzQqVOnPIwaAAAAAGA1S4tNT127dk0dO3bU8uXLS+1fvHixli1bphUrVig1NVV169bVoEGDVFBQUMWRAgAAAADK4/FjtFYaMmSIhgwZUmqfMUavv/66/vu//1vDhg2TJK1evVoxMTHasGGDHnvssaoMFQAAAABQDr8qNsuTmZmpc+fOacCAAc62yMhI9ejRQ3v37i2z2CwsLFRhYaHzdW5uriTJbrfLbrd7N2gLFMfoEmtQ2O0bVa7dnX2sHKu4z8qxKrOPl9Zi/6Gv+L8BtxZvHBdfzV/OWM5zqob/vFo2VmX2qcRYzvOJ4+LePlW0lgpf9zyZvzrkxcfHpczrnjvzk2OvjeVyPgX4Wkr0VYcc/9BX4rrnrfn9XEXrKJsxxngyyeDBg7VixQq1bt3a3WFKZbPZtH79eg0fPlyStGfPHvXs2VNnz55VXFycc7tf/epXstls+vDDD0sdZ/78+VqwYEGJ9vfff1916tSxNGYAAAAAqO7y8/P1+OOPKycnRxEREWVu59GdzeDgYB05csSTIbwuKSnJ5fs+c3Nzdc8992jgwIHlHhh/YbfbtWXLFj3yyCMKDg6+1ZjcxHWjpO8r1+7OPlaOVdxn5ViV2cdLa7EHhWnL/cv0yJfPKdhREHhr8cZx8dX85YxlfyHz1jlVnKeKzl8djktxXwCsxXk+FV/7OC6V26eK1mJ/pVXFrnsBsJaAGau4rxJjlXndc2d+cuy1sVx+j5iZHtBrKdFXHXL8Q1+J65635vdzxU+L3onHj9E+8cQTevvtt7Vo0SJPhypXbGysJOn8+fMudzbPnz+vTp06lblfaGioQkNDS7QHBwf/q3gLAC7x3v4PRWXb3dnHyrGK+6wcqzL7eHktwY6CWxefQFuLN46Lr+avwFjOPFV0n+pwXIr7Amgtzmsfx6Vy+1TxWu543fNk/uqQFz85LiWue+7M7ydr8bu8WDhWsKOA3/msmt+La3E5n7wxv5+raB3lcbF548YNvfPOO9q6dau6du2qunXruvQvWbLE0ykkSQkJCYqNjdW2bducxWVubq5SU1M1adIkS+YAAAAAAFjD42Lz6NGj6tKliyTp66+/dumz2WyVGisvL0/p6enO15mZmTp8+LAaNGigpk2b6vnnn9dvfvMbtW7dWgkJCZozZ47i4+Od7+sEAAAAAPgHj4vN7du3WxGHJCktLU39+vVzvi5+r+Xo0aO1cuVKzZgxQ9euXdOECRN05coV9erVS5s2bVJYWJhlMQAAAAAAPGfZV5+kp6crIyNDvXv3Vnh4uIwxlb6z2bdvX5X34bg2m00LFy7UwoULPQ0XAAAAAOBFQZ4OcOnSJfXv319t2rTRT3/6U2VlZUmSxo0bp+nTp3scIAAAAAAg8HhcbE6dOlXBwcE6c+aMy/dWjhgxQps2bfJ0eAAAAABAAPL4Mdq//vWv2rx5s5o0cf2OmNatW+vbb7/1dHgAAAAAQADy+M7mtWvXXO5oFsvOzi71+y0BAAAAANWfx8XmQw89pNWrVztf22w2ORwOLV682OWTZQEAAAAANYfHj9EuXrxY/fv3V1pamoqKijRjxgwdO3ZM2dnZ2r17txUxAgAAAAACjMd3Ntu3b6+vv/5avXr10rBhw3Tt2jU9+uijOnTokFq2bGlFjAAAAACAAGPJ92xGRkZq9uzZVgwFAAAAAKgGLCk2L1++rLffflsnTpyQJLVr105jx45VgwYNrBgeAAAAABBgPH6MdufOnWrevLmWLVumy5cv6/Lly1q2bJkSEhK0c+dOK2IEAAAAAAQYj+9sTp48WSNGjNBbb72lWrVqSZJu3rypZ555RpMnT9aXX37pcZAAAAAAgMDi8Z3N9PR0TZ8+3VloSlKtWrU0bdo0paenezo8AAAAACAAeVxsdunSxflezR87ceKEOnbs6OnwAAAAAIAA5NZjtEeOHHH+/bnnntOUKVOUnp6uf/u3f5Mk7du3T8uXL9eiRYusiRIAAAAAEFDcKjY7deokm80mY4yzbcaMGSW2e/zxxzVixAj3owMAAAAABCS3is3MzEyr4wAAAAAAVCNuFZvNmjWzOg4AAAAAQDXi8VefSNLZs2e1a9cuXbhwQQ6Hw6Xvueees2IKAAAAAEAA8bjYXLlypZ5++mmFhISoYcOGstlszj6bzUaxCQAAAAA1kMfF5pw5czR37lwlJSUpKMjjb1IBAAAAAFQDHleH+fn5euyxxyg0AQAAAABOHleI48aN08cff2xFLAAAAACAasLjx2iTk5P185//XJs2bdL999+v4OBgl/4lS5Z4OgUAAAAAIMBYUmxu3rxZbdu2laQSHxAEAAAAAKh5PC42X3vtNb3zzjsaM2aMBeEAAAAAAKoDj9+zGRoaqp49e1oRCwAAAACgmvC42JwyZYreeOMNK2IBAAAAAFQTHj9Gu3//fn3++ef67LPPdN9995X4gKB169Z5OgUAAAAAIMB4XGxGRUXp0UcftSIWAAAAAEA14XGx+e6771oRBwAAAACgGvH4PZsAAAAAANzO4zubCQkJ5X6f5unTpz2dAgAAAAAQYDwuNp9//nmX13a7XYcOHdKmTZv04osvejo8AAAAACAAeVxsTpkypdT25cuXKy0tzdPhAQAAAAAByGvv2RwyZIj+9Kc/eWt4AAAAAIAf81qxuXbtWjVo0MBbwwMAAAAA/JjHj9F27tzZ5QOCjDE6d+6c/vnPf+rNN9/0dHgAAAAAQADyuNgcPny4y+ugoCBFR0erb9++uvfeez0dHgAAAAAQgDwuNufNm2dFHAAAAACAasRr79n0lvnz58tms7n84Q4qAAAAAPgXt+9sBgUFubxXszQ2m003btxwd4oy3Xfffdq6davzde3aHt+gBQAAAABYyO0qbf369WX27d27V8uWLZPD4XB3+HLVrl1bsbGxXhkbAAAAAOA5t4vNYcOGlWg7efKkZs2apU8//VSjRo3SwoULPQquLKdOnVJ8fLzCwsKUmJio5ORkNW3atNRtCwsLVVhY6Hydm5srSbLb7bLb7V6Jz0rFMbrEGhR2+0aVa3dnHyvHKu6zcqzK7OOltdh/6Cv+b8CtxRvHxVfzlzOW85yq4T+vlo1VmX0qMZbzfOK4uLdPFa2lwtc9T+avDnnx8XEp87rnzvzk2GtjuZxPAb6WEn3VIcc/9JW47nlrfj9X0TrKZowxnk529uxZzZs3T6tWrdKgQYOUnJys9u3bezpsqTZu3Ki8vDy1bdtWWVlZWrBggf7xj3/o6NGjql+/font58+frwULFpRof//991WnTh2vxAgAAAAA1VV+fr4ef/xx5eTkKCIiosztPCo2c3Jy9D//8z9644031KlTJ73yyit66KGH3B3OLVeuXFGzZs20ZMkSjRs3rkR/aXc277nnHl28eLHcA+Mv7Ha7tmzZokceeUTBwcG3GpObuG6U9H3l2t3Zx8qxivusHKsy+3hpLfagMG25f5ke+fI5BTsKAm8t3jguvpq/nLHsL2TeOqeK81TR+avDcSnuC4C1OM+n4msfx6Vy+1TRWuyvtKrYdS8A1hIwYxX3VWKsMq977sxPjr02lsvvETPTA3otJfqqQ45/6Ctx3fPW/H4uNzdXjRo1umOx6fZjtIsXL9Yrr7yi2NhYffDBB6U+VlsVoqKi1KZNG6Wnp5faHxoaqtDQ0BLtwcHB/yreAoBLvLf/Q1HZdnf2sXKs4j4rx6rMPl5eS7Cj4NbFJ9DW4o3j4qv5KzCWM08V3ac6HJfivgBai/Pax3Gp3D5VvJY7Xvc8mb865MVPjkuJ65478/vJWvwuLxaOFewo4Hc+q+b34lpczidvzO/nKlpHuV1szpo1S+Hh4WrVqpVWrVqlVatWlbrdunXr3J2iQvLy8pSRkaEnn3zSq/MAAAAAACrO7WLzv/7rv+741Sfe8MILL2jo0KFq1qyZ872itWrV0siRI6s8FgAAAABA6dwuNleuXGlhGBX3/fffa+TIkbp06ZKio6PVq1cv7du3T9HR0T6JBwAAAABQktvFpq+sWbPG1yEAAAAAAO4gyNcBAAAAAACqH4pNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJaj2AQAAAAAWI5iEwAAAABgOYpNAAAAAIDlKDYBAAAAAJYL2GJz+fLlat68ucLCwtSjRw/t37/f1yEBAAAAAH4QkMXmhx9+qGnTpmnevHn64osv1LFjRw0aNEgXLlzwdWgAAAAAAAVosblkyRKNHz9eY8eOVbt27bRixQrVqVNH77zzjq9DAwAAAABIqu3rACqrqKhIBw8eVFJSkrMtKChIAwYM0N69e0tsX1hYqMLCQufrnJwcSVJ2drbsdrv3A/aQ3W5Xfn6+Ll26pODg4FuNRSGuG126VLl2d/axcqziPivHqsw+XlqLPSjkVq6KQhTscATeWrxxXHw1fzlj2S9dcs1TReevDseluC8A1uI8n4qvfRyXyu1TRWuxF1XwuhcAawmYsYr7KjFWmdc9d+Ynx14by+X3iABfS4m+6pDjH/pKXPe8Nb+fu3r1qiTJGFPudjZzpy38zNmzZ3X33Xdrz549SkxMdLbPmDFDO3bsUGpqqsv28+fP14IFC6o6TAAAAACo1r777js1adKkzP6Au7NZWUlJSZo2bZrztcPhUHZ2tho2bKju3bvrwIEDls73wAMPWDpmbm6u7rnnHn333XeKiIiwbFzJ+lhr+pjeylWgrD9QxiRP1o/pjXFrep68NS55Ik/+nidvjRsIY/I7X2CMSZ5ujbl//35dvXpV8fHx5W4bcMVmo0aNVKtWLZ0/f96l/fz584qNjS2xfWhoqEJDQ13aoqKiJEm1atWy/IfEG2NKUkREREDEWpPHLGZ1rgJl/YEyZjHyZK1AufbV9GNKnsiTv+fJW+MGypgSv/MFwpgSeYqMjFRkZOQdtw24DwgKCQlR165dtW3bNmebw+HQtm3bXB6rrYjJkydbHZ5XxvSWQFl/oIzpLYGy/kAZ01sCZf3eOqaBkquafkzJk/UCKVar1fRjGih5kgJn/YEyprcEyvorM2bAvWdTuvXVJ6NHj9bvfvc7de/eXa+//ro++ugjffXVV4qJifF1eJbKzc1VZGSkcnJyvHaHB9YgV4GBPAUG8hQYyFNgIE+BgTwFBvJUOQH3GK0kjRgxQv/85z81d+5cnTt3Tp06ddKmTZuqXaEp3XoMeN68eSUeBYb/IVeBgTwFBvIUGMhTYCBPgYE8BQbyVDkBeWcTAAAAAODfAu49mwAAAAAA/0exCQAAAACwHMUmAAAAAMByFJsAAAAAAMtRbPq55cuXq3nz5goLC1OPHj20f/9+X4dUo82fP182m83lz7333uvsLygo0OTJk9WwYUPVq1dP//Ef/6Hz58/7MOKaYefOnRo6dKji4+Nls9m0YcMGl35jjObOnau4uDiFh4drwIABOnXqlMs22dnZGjVqlCIiIhQVFaVx48YpLy+vCldR/d0pT2PGjClxfg0ePNhlG/LkfcnJyXrggQdUv359NW7cWMOHD9fJkyddtqnIte7MmTP62c9+pjp16qhx48Z68cUXdePGjapcSrVWkTz17du3xDk1ceJEl23Ik3e99dZb6tChgyIiIhQREaHExERt3LjR2c+55B/ulCfOJfdRbPqxDz/8UNOmTdO8efP0xRdfqGPHjho0aJAuXLjg69BqtPvuu09ZWVnOP7t27XL2TZ06VZ9++qk+/vhj7dixQ2fPntWjjz7qw2hrhmvXrqljx45avnx5qf2LFy/WsmXLtGLFCqWmpqpu3boaNGiQCgoKnNuMGjVKx44d05YtW/TZZ59p586dmjBhQlUtoUa4U54kafDgwS7n1wcffODST568b8eOHZo8ebL27dunLVu2yG63a+DAgbp27Zpzmztd627evKmf/exnKioq0p49e7Rq1SqtXLlSc+fO9cWSqqWK5EmSxo8f73JOLV682NlHnryvSZMmWrRokQ4ePKi0tDQ9/PDDGjZsmI4dOyaJc8lf3ClPEueS2wz8Vvfu3c3kyZOdr2/evGni4+NNcnKyD6Oq2ebNm2c6duxYat+VK1dMcHCw+fjjj51tJ06cMJLM3r17qyhCSDLr1693vnY4HCY2Nta8+uqrzrYrV66Y0NBQ88EHHxhjjDl+/LiRZA4cOODcZuPGjcZms5l//OMfVRZ7TXJ7nowxZvTo0WbYsGFl7kOefOPChQtGktmxY4cxpmLXuv/7v/8zQUFB5ty5c85t3nrrLRMREWEKCwurdgE1xO15MsaYPn36mClTppS5D3nyjbvuusv87//+L+eSnyvOkzGcS57gzqafKioq0sGDBzVgwABnW1BQkAYMGKC9e/f6MDKcOnVK8fHxatGihUaNGqUzZ85Ikg4ePCi73e6Ss3vvvVdNmzYlZz6UmZmpc+fOueQlMjJSPXr0cOZl7969ioqKUrdu3ZzbDBgwQEFBQUpNTa3ymGuylJQUNW7cWG3bttWkSZN06dIlZx958o2cnBxJUoMGDSRV7Fq3d+9e3X///YqJiXFuM2jQIOXm5rrcKYB1bs9Tsffee0+NGjVS+/btlZSUpPz8fGcfeapaN2/e1Jo1a3Tt2jUlJiZyLvmp2/NUjHPJPbV9HQBKd/HiRd28edPlh1aSYmJi9NVXX/koKvTo0UMrV65U27ZtlZWVpQULFuihhx7S0aNHde7cOYWEhCgqKspln5iYGJ07d843AcN57Es7l4r7zp07p8aNG7v0165dWw0aNCB3VWjw4MF69NFHlZCQoIyMDL300ksaMmSI9u7dq1q1apEnH3A4HHr++efVs2dPtW/fXpIqdK07d+5cqedccR+sVVqeJOnxxx9Xs2bNFB8fryNHjmjmzJk6efKk1q1bJ4k8VZUvv/xSiYmJKigoUL169bR+/Xq1a9dOhw8f5lzyI2XlSeJc8gTFJlAJQ4YMcf69Q4cO6tGjh5o1a6aPPvpI4eHhPowMCHyPPfaY8+/333+/OnTooJYtWyolJUX9+/f3YWQ11+TJk3X06FGX96bD/5SVpx+/n/n+++9XXFyc+vfvr4yMDLVs2bKqw6yx2rZtq8OHDysnJ0dr167V6NGjtWPHDl+HhduUlad27dpxLnmAx2j9VKNGjVSrVq0Sn0h2/vx5xcbG+igq3C4qKkpt2rRRenq6YmNjVVRUpCtXrrhsQ858q/jYl3cuxcbGlvjgrRs3big7O5vc+VCLFi3UqFEjpaenSyJPVe3ZZ5/VZ599pu3bt6tJkybO9opc62JjY0s954r7YJ2y8lSaHj16SJLLOUWevC8kJEStWrVS165dlZycrI4dO2rp0qWcS36mrDyVhnOp4ig2/VRISIi6du2qbdu2OdscDoe2bdvm8vw4fCsvL08ZGRmKi4tT165dFRwc7JKzkydP6syZM+TMhxISEhQbG+uSl9zcXKWmpjrzkpiYqCtXrujgwYPObT7//HM5HA7nPyioet9//70uXbqkuLg4SeSpqhhj9Oyzz2r9+vX6/PPPlZCQ4NJfkWtdYmKivvzyS5f/ObBlyxZFREQ4H0uDZ+6Up9IcPnxYklzOKfJU9RwOhwoLCzmX/FxxnkrDuVQJvv6EIpRtzZo1JjQ01KxcudIcP37cTJgwwURFRbl80hWq1vTp001KSorJzMw0u3fvNgMGDDCNGjUyFy5cMMYYM3HiRNO0aVPz+eefm7S0NJOYmGgSExN9HHX1d/XqVXPo0CFz6NAhI8ksWbLEHDp0yHz77bfGGGMWLVpkoqKizCeffGKOHDlihg0bZhISEsz169edYwwePNh07tzZpKamml27dpnWrVubkSNH+mpJ1VJ5ebp69ap54YUXzN69e01mZqbZunWr6dKli2ndurUpKChwjkGevG/SpEkmMjLSpKSkmKysLOef/Px85zZ3utbduHHDtG/f3gwcONAcPnzYbNq0yURHR5ukpCRfLKlaulOe0tPTzcKFC01aWprJzMw0n3zyiWnRooXp3bu3cwzy5H2zZs0yO3bsMJmZmebIkSNm1qxZxmazmb/+9a/GGM4lf1FenjiXPEOx6efeeOMN07RpUxMSEmK6d+9u9u3b5+uQarQRI0aYuLg4ExISYu6++24zYsQIk56e7uy/fv26eeaZZ8xdd91l6tSpY37xi1+YrKwsH0ZcM2zfvt1IKvFn9OjRxphbX38yZ84cExMTY0JDQ03//v3NyZMnXca4dOmSGTlypKlXr56JiIgwY8eONVevXvXBaqqv8vKUn59vBg4caKKjo01wcLBp1qyZGT9+fIn/uUaevK+0HEky7777rnObilzrvvnmGzNkyBATHh5uGjVqZKZPn27sdnsVr6b6ulOezpw5Y3r37m0aNGhgQkNDTatWrcyLL75ocnJyXMYhT9711FNPmWbNmpmQkBATHR1t+vfv7yw0jeFc8hfl5YlzyTM2Y4ypuvuoAAAAAICagPdsAgAAAAAsR7EJAAAAALAcxSYAAAAAwHIUmwAAAAAAy1FsAgAAAAAsR7EJAAAAALAcxSYAAAAAwHIUmwAAAAAAy1FsAgACSkpKimw2m65cueLROGPGjNHw4cMticnKsfx57rffflsDBw6s8ng2bdqkTp06yeFwWDouAMC7KDYBAD6xYsUK1a9fXzdu3HC25eXlKTg4WH379nXZtrjAzMjI0IMPPqisrCxFRkZ6Nb7iOW02m4KCghQZGanOnTtrxowZysrKctl26dKlWrlypVfj+eabb2Sz2XT48OEqn1uSCgoKNGfOHM2bN8/rc91u8ODBCg4O1nvvvVflcwMA3EexCQDwiX79+ikvL09paWnOtr/97W+KjY1VamqqCgoKnO3bt29X06ZN1bJlS4WEhCg2NlY2m61K4jx58qTOnj2rAwcOaObMmdq6davat2+vL7/80rlNZGSkoqKiyhyjqKjIa/HdaW6rrF27VhEREerZs6fX5yrNmDFjtGzZMp/MDQBwD8UmAMAn2rZtq7i4OKWkpDjbUlJSNGzYMCUkJGjfvn0u7f369XP+/ceP0a5cuVJRUVHavHmzfvKTn6hevXoaPHiwy93Hmzdvatq0aYqKilLDhg01Y8YMGWMqFGfjxo0VGxurNm3a6LHHHtPu3bsVHR2tSZMmObe5/dHRvn376tlnn9Xzzz+vRo0aadCgQZKko0ePasiQIapXr55iYmL05JNP6uLFi879HA6HFi9erFatWik0NFRNmzbVyy+/LElKSEiQJHXu3Fk2m8159/f2uQsLC/Xcc8+pcePGCgsLU69evXTgwAGXY2mz2bRt2zZ169ZNderU0YMPPqiTJ0+WexzWrFmjoUOHurRV5Lg6HA4lJycrISFB4eHh6tixo9auXeuyzZ///Ge1bt1aYWFh6tevn1atWlXiUemhQ4cqLS1NGRkZ5cYJAPAfFJsAAJ/p16+ftm/f7ny9fft29e3bV3369HG2X79+Xampqc5iszT5+fn67W9/qz/+8Y/auXOnzpw5oxdeeMHZ/9prr2nlypV65513tGvXLmVnZ2v9+vVuxRweHq6JEydq9+7dunDhQpnbrVq1SiEhIdq9e7dWrFihK1eu6OGHH1bnzp2VlpamTZs26fz58/rVr37l3CcpKUmLFi3SnDlzdPz4cb3//vuKiYmRJO3fv1+StHXrVmVlZWndunWlzjtjxgz96U9/0qpVq/TFF1+oVatWGjRokLKzs122mz17tl577TWlpaWpdu3aeuqpp8pd965du9StWzeXtooc1+TkZK1evVorVqzQsWPHNHXqVD3xxBPasWOHJCkzM1P/+Z//qeHDh+vvf/+7nn76ac2ePbvE/E2bNlVMTIz+9re/lRsnAMCPGAAAfOQPf/iDqVu3rrHb7SY3N9fUrl3bXLhwwbz//vumd+/exhhjtm3bZiSZb7/91hhjzPbt240kc/nyZWOMMe+++66RZNLT053jLl++3MTExDhfx8XFmcWLFztf2+1206RJEzNs2LAyY7t9nh/buHGjkWRSU1ONMcaMHj3aZaw+ffqYzp07u+zz61//2gwcONCl7bvvvjOSzMmTJ01ubq4JDQ01f/jDH0qNJzMz00gyhw4dcmn/8dx5eXkmODjYvPfee87+oqIiEx8f71x/8bq2bt3q3OYvf/mLkWSuX79e6tyXL182kszOnTtd2u90XAsKCkydOnXMnj17XPYbN26cGTlypDHGmJkzZ5r27du79M+ePbvUY9+5c2czf/78UmMEAPif2j6qcQEAUN++fXXt2jUdOHBAly9fVps2bRQdHa0+ffpo7NixKigoUEpKilq0aKGmTZuWOU6dOnXUsmVL5+u4uDjnXcecnBxlZWWpR48ezv7atWurW7duFX6U9nbF+5X3vtGuXbu6vP773/+u7du3q169eiW2zcjI0JUrV1RYWKj+/fu7FVPxOHa73eV9lcHBwerevbtOnDjhsm2HDh2cf4+Li5MkXbhwodTjfP36dUlSWFiYs60ixzU9PV35+fl65JFHXMYrKipS586dJd16T+wDDzzg0t+9e/dS1xceHq78/PwyVg8A8DcUmwAAn2nVqpWaNGmi7du36/Lly+rTp48kKT4+Xvfcc4/27Nmj7du36+GHHy53nODgYJfXNpvN7UKyIooLt+bNm5e5Td26dV1e5+XlaejQoXrllVdKbBsXF6fTp09bGuOd/PiYFRfNZX21SMOGDWWz2XT58uVKzZGXlydJ+stf/qK7777bpS80NLRSY0lSdna2oqOjK70fAMA3eM8mAMCn+vXrp5SUFKWkpLh85Unv3r21ceNG7d+/v9z3a95JZGSk4uLilJqa6my7ceOGDh486NZ4169f1+9//3v17t27UoVPly5ddOzYMTVv3lytWrVy+VO3bl21bt1a4eHh2rZtW6n7h4SESLr1oTxlKf603t27dzvb7Ha7Dhw4oHbt2lU41tLmbteunY4fP+5sq8hxbdeunUJDQ3XmzJkSa77nnnsk3fqgqB9/IrEklw80KlZQUKCMjAznHVEAgP+j2AQA+FS/fv20a9cuHT582HlnU5L69Omj3/3udyoqKvKo2JSkKVOmaNGiRdqwYYO++uorPfPMMy6fdFqeCxcu6Ny5czp16pTWrFmjnj176uLFi3rrrbcqFcPkyZOVnZ2tkSNH6sCBA8rIyNDmzZs1duxY3bx5U2FhYZo5c6ZmzJih1atXKyMjQ/v27dPbb78t6dan4oaHhzs/WCgnJ6fEHHXr1tWkSZP04osvatOmTTp+/LjGjx+v/Px8jRs3rlLx3m7QoEHatWuXS9udjmv9+vX1wgsvaOrUqVq1apUyMjL0xRdf6I033tCqVaskSU8//bS++uorzZw5U19//bU++ugj5/eG/vgx5X379ik0NFSJiYkerQMAUHV4jBYA4FP9+vXT9evXde+99zo/eVW6VWxevXrV+RUpnpg+fbqysrI0evRoBQUF6amnntIvfvGLUgu227Vt21Y2m0316tVTixYtNHDgQE2bNk2xsbGViiE+Pl67d+/WzJkzNXDgQBUWFqpZs2YaPHiwgoJu/b/fOXPmqHbt2po7d67Onj2ruLg4TZw4UdKt90MuW7ZMCxcu1Ny5c/XQQw+5fG1MsUWLFsnhcOjJJ5/U1atX1a1bN23evFl33XVXpeK93bhx49StWzfl5OQoMjJSUsWO669//WtFR0crOTlZp0+fVlRUlLp06aKXXnpJ0q2vdFm7dq2mT5+upUuXKjExUbNnz9akSZNcHrX94IMPNGrUKNWpU8ejdQA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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def estimate_biases_with_reference_wd(df_scada, fm, wd_ref):\n", + " # Now use this knowledge to estimate bias for every turbine\n", + " num_turbines = len(fm.layout_x)\n", + " wd_bias_list = np.zeros(num_turbines)\n", + "\n", + " for ti in range(num_turbines):\n", + " # Calculate the offset between this turbine's wind direction and that\n", + " # of the calibrated (reference) wind direction. Note that 'wd_ref' may\n", + " # also be a met mast' wind direction signal, if available. The offset\n", + " # between a turbine's wind direction and wd_ref is very likely to be\n", + " # the bias or close to the bias in this turbine's northing.\n", + " wd_test = df_scada[\"wd_{:03d}\".format(ti)]\n", + " x0, _ = flopt.match_y_curves_by_offset(\n", + " wd_ref, wd_test, dy_eval=np.arange(-180.0, 180.0, 2.0), angle_wrapping=True\n", + " )\n", + "\n", + " # Then, we refine this first guess by evaluating the cost function\n", + " # at [-5.0, 0.0, 5.0] deg around x0, and let the optimizer\n", + " # converge.\n", + " x_search_bounds = np.round(x0) + np.array([-5.0, 5.0])\n", + "\n", + " # Calculate and save the results to a list\n", + " wd_bias_list[ti] = get_bias_for_single_turbine(\n", + " df=df_scada,\n", + " fm=fm,\n", + " ti=ti,\n", + " opt_search_range=x_search_bounds,\n", + " plot=True,\n", + " figure_save_path=None,\n", + " )\n", + " print(\" \")\n", + "\n", + " return wd_bias_list\n", + "\n", + "\n", + "wd_bias_list = estimate_biases_with_reference_wd(df_scada_homogenized, fm, wd_ref)\n", + "print(\"Wind direction biases: {}\".format(wd_bias_list))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Step 5**: Correct turbine wind directions for bias\n", + "The next step is to apply the northing corrections directly on the data." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wd_bias_list: [ 0. 30. 44.9625 0. 0. 0. 0. ]\n", + "Removing 0.00 deg bias for ti = 000.\n", + "Removing 30.00 deg bias for ti = 001.\n", + "Removing 44.96 deg bias for ti = 002.\n", + "Removing 0.00 deg bias for ti = 003.\n", + "Removing 0.00 deg bias for ti = 004.\n", + "Removing 0.00 deg bias for ti = 005.\n", + "Removing 0.00 deg bias for ti = 006.\n" + ] + } + ], + "source": [ + "def apply_bias_corrections(df_scada, wd_bias_list):\n", + " # Copy dataframe\n", + " df_out = df_scada.copy()\n", + "\n", + " # Load the SCADA data\n", + " num_turbines = dfm.get_num_turbines(df_scada)\n", + "\n", + " # Set turbine-individual bias corrections\n", + " for ti in range(num_turbines):\n", + " bias = wd_bias_list[ti]\n", + " print(\"Removing {:.2f} deg bias for ti = {:03d}.\".format(bias, ti))\n", + " df_out[\"wd_{:03d}\".format(ti)] = wrap_360(df_out[\"wd_{:03d}\".format(ti)] - bias)\n", + "\n", + " return df_out\n", + "\n", + "\n", + "# Get bias corrections\n", + "print(\"wd_bias_list: {}\".format(wd_bias_list))\n", + "df_scada_homogenized = apply_bias_corrections(\n", + " df_scada=df_scada_homogenized.copy(), wd_bias_list=wd_bias_list\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Step 6**: Deal with inter-turbine faults\n", + "Deal with faults at one turbine causing issues at another turbine. For example, if a turbine is shedding a wake on a second turbine, then for a fair comparison both of these two turbines should be operating normally. If the upstream turbine is curtailed or offline, the power production of the downstream turbine also changes. Hence, if we are unsure about the operating mode of one machine, we cannot make accurate FLORIS predictions on the second turbine either. In this scenario, we would classify the second turbine's measurement as faulty too, because of this." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating the 'df_impacting_turbines' matrix...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6\n", + "wd \n", + "0.0 [6] [5] [3] [] [] [] []\n", + "3.0 [6] [5] [3] [] [] [] []\n", + "6.0 [6] [5] [3] [] [] [] []\n", + "9.0 [6] [5] [3] [] [] [] []\n", + "12.0 [] [5] [3] [] [] [] []\n", + "... ... ... ... .. .. .. ..\n", + "345.0 [6, 5] [5] [3] [] [] [] []\n", + "348.0 [6, 5] [5] [3, 5] [] [] [] []\n", + "351.0 [6] [5] [3] [] [] [] []\n", + "354.0 [6] [5] [3] [] [] [] []\n", + "357.0 [6] [5] [3, 5] [] [] [] []\n", + "\n", + "[120 rows x 7 columns]\n" + ] + } + ], + "source": [ + "def filter_for_faults_in_impacting_turbines(df):\n", + " # Determine which turbines impact which other turbines through their wakes\n", + " print(\"Calculating the 'df_impacting_turbines' matrix...\")\n", + " df_impacting_turbines = ftools.get_all_impacting_turbines(\n", + " fm_in=fm,\n", + " wd_array=np.arange(0.0, 360.0, 3.0),\n", + " change_threshold=0.005,\n", + " ws_test=9.0,\n", + " )\n", + " print(df_impacting_turbines)\n", + "\n", + " # Filter the measurements for each turbine: make sure all\n", + " # other turbines affecting this turbine's\n", + " # power production are marked as good measurements. If they are not,\n", + " # then classify this turbine's\n", + " # measurement as faulty too.\n", + " num_turbines = dfm.get_num_turbines(df)\n", + " for ti in range(num_turbines):\n", + " # Assign a reference wind direction for this turbine. In this case,\n", + " # we have such a small farm so we assume that the farm average wind\n", + " # direction of representative of every turbine.\n", + " df = dfm.set_wd_by_all_turbines(df)\n", + "\n", + " df_scada = filt.filter_df_by_faulty_impacting_turbines(\n", + " df=df,\n", + " ti=ti,\n", + " df_impacting_turbines=df_impacting_turbines,\n", + " verbose=True,\n", + " )\n", + "\n", + " return df_scada\n", + "\n", + "\n", + "df_scada_northing_calibrated_interturbine_filtered = filter_for_faults_in_impacting_turbines(\n", + " df=df_scada_homogenized.copy()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show the final yaw angles" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind direction')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the wd channels for the turbines\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", + "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_001\"],\n", + " label=\"wd_001\",\n", + " color=\"blue\",\n", + " ls=\"--\",\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_002\"],\n", + " label=\"wd_002\",\n", + " color=\"red\",\n", + " ls=\"--\",\n", + ")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"Wind direction\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c615348f6c5c8a27d214fac558c85cdd372d2a39 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:06:41 -0700 Subject: [PATCH 13/31] Fix defaults --- flasc/data_processing/northing_offset_change_hoger.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 5153c202..f73119f0 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -159,11 +159,11 @@ def homogenize( Args: scada (Union[pd.DataFrame, FlascDataFrame]): DataFrame containing the SCADA data. var (str, optional): Variable to homogenize (yaw or wd). Defaults to 'wd'. - threshold (int, optional): Threshold for discretization. Defaults to 100. + threshold (int, optional): Threshold for discretization. Defaults to 1000. reference (str, optional): Reference point for homogenization. Defaults to 'last'. plot_it (bool, optional): Whether to plot the results. Defaults to False. max_depth (int, optional): Maximum depth of the regression tree. Defaults to 4. - ccp_alpha (float, optional): Complexity parameter for pruning. Defaults to 0.0. + ccp_alpha (float, optional): Complexity parameter for pruning. Defaults to 0.09 Returns: tuple[pd.DataFrame, pd.DataFrame]: Homogenized SCADA data and the results used to From 98b03832805f1c8bdecf5b61a15e493766ad43d5 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:08:26 -0700 Subject: [PATCH 14/31] Update 03 --- .../03_northing_calibration_hoger.ipynb | 3058 +++++++++-------- 1 file changed, 1535 insertions(+), 1523 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb index d4f03fe6..f308c12a 100644 --- a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb +++ b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -88,7 +88,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -552,7 +552,7 @@ "[1800 rows x 25 columns]" ] }, - "execution_count": 8, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -590,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -616,7 +616,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 10, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -648,15 +648,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:04:56\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + "\u001b[32m2024-11-19 15:07:20\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" ] } ], @@ -688,15 +688,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T006 T001 T002 T005 T003\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T006 T001 T002 T005 T003\n", "0 0.0 -30.0 0.0 0.0 0.0\n", "1 0.0 -30.0 0.0 0.0 0.0\n", "2 0.0 -30.0 0.0 0.0 0.0\n", @@ -704,8 +704,8 @@ "4 0.0 -30.0 -46.0 0.0 0.0\n", "5 0.0 -30.0 -44.0 0.0 0.0\n", "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T002 T006 T005 T003 T000\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T002 T006 T005 T003 T000\n", "0 30.0 30.0 30.0 30.0 30.0\n", "1 30.0 30.0 30.0 30.0 30.0\n", "2 30.0 30.0 30.0 30.0 30.0\n", @@ -713,8 +713,8 @@ "4 -14.0 30.0 30.0 30.0 30.0\n", "5 -16.0 30.0 30.0 30.0 30.0\n", "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T001 T003 T005 T000 T006\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T001 T003 T005 T000 T006\n", "0 -30.0 0.0 0.0 -0.0 0.0\n", "1 -30.0 0.0 0.0 -0.0 0.0\n", "2 -30.0 0.0 0.0 -0.0 0.0\n", @@ -722,8 +722,8 @@ "4 14.0 44.0 46.0 46.0 46.0\n", "5 16.0 46.0 46.0 44.0 46.0\n", "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T005 T002 T001 T004 T006\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T005 T002 T001 T004 T006\n", "0 0.0 -0.0 -30.0 0.0 0.0\n", "1 0.0 -0.0 -30.0 0.0 0.0\n", "2 0.0 -0.0 -30.0 0.0 0.0\n", @@ -731,8 +731,8 @@ "4 0.0 -44.0 -30.0 0.0 0.0\n", "5 0.0 -46.0 -30.0 0.0 0.0\n", "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T003 T002 T005 T001 T006\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T003 T002 T005 T001 T006\n", "0 -0.0 0.0 0.0 -30.0 0.0\n", "1 -0.0 0.0 0.0 -30.0 0.0\n", "2 -0.0 0.0 0.0 -30.0 0.0\n", @@ -740,8 +740,8 @@ "4 -0.0 -44.0 0.0 -30.0 0.0\n", "5 -0.0 -46.0 0.0 -30.0 0.0\n", "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T003 T001 T006 T002 T000\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T003 T001 T006 T002 T000\n", "0 -0.0 -30.0 0.0 -0.0 -0.0\n", "1 -0.0 -30.0 0.0 -0.0 -0.0\n", "2 -0.0 -30.0 0.0 -0.0 -0.0\n", @@ -749,8 +749,8 @@ "4 -0.0 -30.0 0.0 -46.0 -0.0\n", "5 -0.0 -30.0 0.0 -46.0 -0.0\n", "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m T001 T005 T000 T003 T002\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m T001 T005 T000 T003 T002\n", "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", @@ -758,13 +758,13 @@ "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:56\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -804,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -833,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ "0 1 wd_002 6 -45.005012 899.5 2020-01-07 05:40:00" ] }, - "execution_count": 14, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -922,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -931,7 +931,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 15, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -983,15 +983,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T006 T001 T002 T005 T003\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T006 T001 T002 T005 T003\n", "0 0.0 -30.0 -46.0 0.0 0.0\n", "1 0.0 -30.0 -46.0 0.0 0.0\n", "2 0.0 -30.0 -44.0 0.0 0.0\n", @@ -999,8 +999,8 @@ "4 0.0 -30.0 -46.0 0.0 0.0\n", "5 0.0 -30.0 -44.0 0.0 0.0\n", "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T002 T006 T005 T003 T000\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T002 T006 T005 T003 T000\n", "0 -16.0 30.0 30.0 30.0 30.0\n", "1 -16.0 30.0 30.0 30.0 30.0\n", "2 -14.0 30.0 30.0 30.0 30.0\n", @@ -1008,8 +1008,8 @@ "4 -14.0 30.0 30.0 30.0 30.0\n", "5 -16.0 30.0 30.0 30.0 30.0\n", "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T001 T003 T005 T000 T006\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T001 T003 T005 T000 T006\n", "0 16.0 44.0 46.0 46.0 44.0\n", "1 16.0 46.0 46.0 46.0 46.0\n", "2 14.0 44.0 46.0 44.0 44.0\n", @@ -1017,8 +1017,8 @@ "4 14.0 44.0 46.0 46.0 46.0\n", "5 16.0 46.0 46.0 44.0 46.0\n", "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T005 T002 T001 T004 T006\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T005 T002 T001 T004 T006\n", "0 0.0 -44.0 -30.0 0.0 0.0\n", "1 0.0 -46.0 -30.0 0.0 0.0\n", "2 0.0 -44.0 -30.0 0.0 0.0\n", @@ -1026,8 +1026,8 @@ "4 0.0 -44.0 -30.0 0.0 0.0\n", "5 0.0 -46.0 -30.0 0.0 0.0\n", "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T003 T002 T005 T001 T006\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T003 T002 T005 T001 T006\n", "0 -0.0 -44.0 0.0 -30.0 0.0\n", "1 -0.0 -46.0 0.0 -30.0 0.0\n", "2 -0.0 -44.0 0.0 -30.0 0.0\n", @@ -1035,8 +1035,8 @@ "4 -0.0 -44.0 0.0 -30.0 0.0\n", "5 -0.0 -46.0 0.0 -30.0 0.0\n", "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T003 T001 T006 T002 T000\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T003 T001 T006 T002 T000\n", "0 -0.0 -30.0 0.0 -46.0 -0.0\n", "1 -0.0 -30.0 0.0 -46.0 -0.0\n", "2 -0.0 -30.0 0.0 -46.0 -0.0\n", @@ -1044,8 +1044,8 @@ "4 -0.0 -30.0 0.0 -46.0 -0.0\n", "5 -0.0 -30.0 0.0 -46.0 -0.0\n", "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m T001 T005 T000 T003 T002\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m T001 T005 T000 T003 T002\n", "0 -30.0 -0.0 -0.0 -0.0 -44.0\n", "1 -30.0 -0.0 -0.0 -0.0 -46.0\n", "2 -30.0 -0.0 -0.0 -0.0 -44.0\n", @@ -1053,13 +1053,13 @@ "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -1101,22 +1101,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:21\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -1130,767 +1130,762 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:04:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-19 15:04:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-19 15:05:01\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-19 15:05:02\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:03\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:04\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-19 15:05:05\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:06\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:07\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:08\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:09\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:10\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" ] }, { @@ -1908,9 +1903,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-19 15:07:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -2100,27 +2100,25 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n" ] }, { @@ -2134,62 +2132,64 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -2207,21 +2207,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:05:13\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n" + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n" ] }, { @@ -2236,147 +2236,147 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:05:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2390,15 +2390,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n" ] }, { @@ -2413,150 +2414,149 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:05:17\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:19\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2570,18 +2570,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" ] }, { @@ -2596,59 +2596,57 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" ] }, { @@ -2658,7 +2656,29 @@ "Optimization terminated successfully.\n", " Current function value: -0.999854\n", " Iterations: 1\n", - " Function evaluations: 2\n", + " Function evaluations: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Turbine 3. estimated bias = 0.0 deg.\n" ] }, @@ -2666,25 +2686,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n" ] }, { @@ -2699,65 +2712,64 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:21\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -2775,21 +2787,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:22\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" ] }, { @@ -2804,64 +2816,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:23\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" ] }, { @@ -2879,21 +2886,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n" ] }, { @@ -2908,59 +2920,64 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -2978,14 +2995,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:05:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:05:25\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" ] }, { @@ -3258,7 +3270,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -3311,7 +3323,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -3338,13 +3350,13 @@ "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:05:33\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" ] }, { @@ -3417,7 +3429,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3426,7 +3438,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 21, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, From 7a0ed963613bbbb601cbd25e34fd36a663817619 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:08:59 -0700 Subject: [PATCH 15/31] Formatting --- .../03_northing_calibration_hoger.ipynb | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb index f308c12a..e5264a1b 100644 --- a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb +++ b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb @@ -17,11 +17,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# from datetime import timedelta as td\n", "import warnings as wn\n", "from datetime import timedelta as td\n", "\n", @@ -40,8 +39,6 @@ " northing_offset as nof,\n", ")\n", "from flasc.data_processing.northing_offset_change_hoger import homogenize\n", - "\n", - "# from flasc import time_operations as fto\n", "from flasc.utilities import (\n", " floris_tools as ftools,\n", " optimization as flopt,\n", From f77aa04172055c7656216dfeda68c1516e17faa0 Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:24:24 -0700 Subject: [PATCH 16/31] fix toc reference --- docs/_toc.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/_toc.yml b/docs/_toc.yml index 64ecfd8a..4af30813 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -23,11 +23,9 @@ parts: # - file: contributing # - file: development # - file: testing - - caption: Examples Data Processing chapters: - - file: examples/01_raw_data_processing/00_filter_ws_power_curves - - file: examples/01_raw_data_processing/01_northing_calibration + - file: examples/01_raw_data_processing/03_northing_calibration_hoger - caption: Examples Energy Ratio chapters: From a07a41e3424dc96ff97629cb8e9db5a18ef04eea Mon Sep 17 00:00:00 2001 From: Paul Date: Tue, 19 Nov 2024 15:25:32 -0700 Subject: [PATCH 17/31] Remove test code --- .../northing_offset_change_hoger.py | 109 ------------------ 1 file changed, 109 deletions(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index f73119f0..8a12c53a 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -279,112 +279,3 @@ def homogenize( scada[m] = (scada[m] + f(scada.index) - f(ref)) % 360 return scada, d2 - - -# Engie test code -if __name__ == "__main__": - df = pd.read_feather("scada_exemple.ftr") - df_orig = pd.read_feather("scada_exemple.ftr") - - fig, ax = plt.subplots() - ax.scatter(df["time"], wrap_180(df["wd_004"] - df["wd_005"]), label="Direction E05 - E06") - ax.legend() - ax.grid(True) - ax.set_title("Original Wind Directions") - - df_corr, _ = homogenize(df, plot_it=False) # the erreur occurs at this point. - - fig, ax = plt.subplots() - ax.scatter( - df_corr["time"], - wrap_180(df_corr["wd_004"] - df_corr["wd_005"]), - label="Direction E05 - E06", - ) - ax.legend() - ax.grid(True) - ax.set_title("Corrected Wind Directions") - - wd_cols = sorted([c for c in df.columns if "wd_" in c]) - - for wd_col in ["wd_004"]: - fig, ax = plt.subplots() - ax.scatter(df_orig["time"], wrap_180(df_orig[wd_col] - df_corr[wd_col])) - ax.set_title("Change in value of wd_004") - ax.grid(True) - - plt.show() - - # fig, axarr = plt.subplots(2,1,sharex=True) - # ax = axarr[0] - # ax.scatter(df["time"], df["wd_004"], label="original") - # ax.scatter(df_corr["time"], df_corr["wd_004"], label="corrected") - # ax.set_title("Turbine 4") - # ax.grid(True) - - # ax = axarr[1] - # ax.scatter(df["time"], df["wd_005"], label="original") - # ax.scatter(df_corr["time"], df_corr["wd_005"], label="corrected") - # ax.set_title("Turbine 5") - # ax.grid(True) - - -# # Dummy test code -# if __name__ == "__main__": -# # # Test discretize function -# # x = np.array([0, 1, 2, 3,np.nan,2, 105, 1, np.nan]) -# # y = discretize(x) -# # print(y) - -# # Now make a test dataframe to test the homogenize function -# # Imagine there are 3 turbines, the first turbine's wd is -# # set by a random walk. Turbine 2 is equal to 1 + white noise -# # finally turbine 3 is turbine 1 + white noise, + a jump -# # by jump_size deg halfway through -# n = 1000 -# jump_size = 10.0 -# np.random.seed(0) -# time = pd.date_range("2020-01-01", periods=n, freq="10min") -# wd_000 = wrap_360(np.cumsum(np.random.randn(n))) -# wd_001 = wrap_360(wd_000 + np.random.randn(n)) -# wd_002 = wd_000 + np.random.randn(n) -# wd_002[int(np.floor(n / 2)) :] += jump_size -# wd_002 = wrap_360(wd_002) - -# # FlascDataFrame requires power signals, just make these up -# pow_made_up = np.random.randn(n) - -# # Plot the 3 signals - -# fig, ax = plt.subplots() -# ax.plot(time, wd_000, label="Turbine 0") -# ax.plot(time, wd_001, label="Turbine 1") -# ax.plot(time, wd_002, label="Turbine 2") -# ax.legend() -# ax.grid(True) -# ax.set_title("Original Wind Directions") - -# # Combine into a FlascDataFrame -# df = FlascDataFrame( -# { -# "time": time, -# "wd_000": wd_000, -# "wd_001": wd_001, -# "wd_002": wd_002, -# "pow_000": pow_made_up, -# "pow_001": pow_made_up, -# "pow_002": pow_made_up, -# } -# ) - -# df_corr = homogenize(df, verbose=True) - -# # Plot the corrected results -# fig, ax = plt.subplots() -# ax.plot(df_corr["time"], df_corr["wd_000"], label="Turbine 0") -# ax.plot(df_corr["time"], df_corr["wd_001"], label="Turbine 1") -# ax.plot(df_corr["time"], df_corr["wd_002"], label="Turbine 2") -# ax.legend() -# ax.grid(True) -# ax.set_title("Corrected Wind Directions") - -# plt.show() From acb1aa762225a646f9993a8ce3eba6c68975228e Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 06:20:43 -0700 Subject: [PATCH 18/31] pass threshold --- flasc/data_processing/northing_offset_change_hoger.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 8a12c53a..7621c6ae 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -254,7 +254,7 @@ def homogenize( # Postprocess all the data to get the main jumps for each wind turbine d2 = d.copy() - d2["Class"] = discretize(d["Knot"], threshold=100) + d2["Class"] = discretize(d["Knot"], threshold=threshold) d2["Count"] = 1 d2 = d2.groupby(["Class", "Turbine"]).agg({"Count": "sum", "Jump": "mean", "Knot": shorth_mode}) d2.reset_index(drop=False, inplace=True) From c5bf57d34aabc799420faa295d43be02104fd859 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 06:27:28 -0700 Subject: [PATCH 19/31] use hidden functions --- flasc/data_processing/northing_offset_change_hoger.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 7621c6ae..71e20884 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -60,7 +60,7 @@ def _get_leaves_and_knots(tree: DecisionTreeRegressor) -> tuple[np.ndarray, np.n return leave_values, knot_positions -def discretize(x: pd.Series, threshold: int) -> np.ndarray: +def _discretize(x: pd.Series, threshold: int) -> np.ndarray: """Get the class of the knots based on the times they repeat. Args: @@ -84,7 +84,7 @@ def discretize(x: pd.Series, threshold: int) -> np.ndarray: return y -def shorth_mode(x: pd.Series) -> np.float64: +def _shorth_mode(x: pd.Series) -> np.float64: """Estimates the Venter mode through the shorth method for the given data. Args: @@ -254,9 +254,11 @@ def homogenize( # Postprocess all the data to get the main jumps for each wind turbine d2 = d.copy() - d2["Class"] = discretize(d["Knot"], threshold=threshold) + d2["Class"] = _discretize(d["Knot"], threshold=threshold) d2["Count"] = 1 - d2 = d2.groupby(["Class", "Turbine"]).agg({"Count": "sum", "Jump": "mean", "Knot": shorth_mode}) + d2 = d2.groupby(["Class", "Turbine"]).agg( + {"Count": "sum", "Jump": "mean", "Knot": _shorth_mode} + ) d2.reset_index(drop=False, inplace=True) d2["Knot_date"] = df["time"].values[np.floor(d2["Knot"]).astype(int) - 1] d2 = d2.loc[d2["Count"] > len(wt_names) / 2] From b49345c0c69fd0f2242d8e8f6f9a5c3a34784fbb Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 06:56:49 -0700 Subject: [PATCH 20/31] clean up --- flasc/data_processing/northing_offset_change_hoger.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 71e20884..59616595 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -8,6 +8,7 @@ """ import warnings +from typing import Union import matplotlib.pyplot as plt import numpy as np @@ -17,6 +18,8 @@ from scipy.interpolate import interp1d from sklearn.tree import DecisionTreeRegressor +from flasc import FlascDataFrame + _MODE_LIMIT = 0.05 @@ -46,7 +49,7 @@ def _get_leaves_and_knots(tree: DecisionTreeRegressor) -> tuple[np.ndarray, np.n # If the left and right child of a node is not the same we have a split node is_split_node = children_left[node_id] != children_right[node_id] # If a split node, append left and right children and depth to `stack` so we can loop t - # hrough them + # through them if is_split_node: stack.append((children_left[node_id], depth + 1)) stack.append((children_right[node_id], depth + 1)) @@ -141,7 +144,7 @@ def _plot_regression(y_data: pd.Series, y_regr: np.ndarray, date_time: pd.Series # TODO: Keep these defaults? def homogenize( - scada: pd.DataFrame, + scada: Union[pd.DataFrame | FlascDataFrame], var: str = "wd", threshold: int = 1000, reference: str = "last", @@ -176,7 +179,7 @@ def homogenize( ) # Select the columns to use in the algorithm - wt_names = scada.columns[scada.columns.str.startswith((var))] + wt_names = scada.columns[scada.columns.str.startswith((var + "_"))] if len(wt_names) < 3: raise ValueError("There must be at least 3 wind turbines for the algorithm to apply.") df = scada[wt_names.to_list() + ["time"]].reset_index(drop=True) From b09d7af30f0099dd1954459b81d773a402fcc127 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 06:56:58 -0700 Subject: [PATCH 21/31] Add initial tests --- tests/northing_offset_change_hoger_test.py | 60 ++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 tests/northing_offset_change_hoger_test.py diff --git a/tests/northing_offset_change_hoger_test.py b/tests/northing_offset_change_hoger_test.py new file mode 100644 index 00000000..bc5bfbbc --- /dev/null +++ b/tests/northing_offset_change_hoger_test.py @@ -0,0 +1,60 @@ +import numpy as np +import pandas as pd + +from flasc import FlascDataFrame +from flasc.data_processing.northing_offset_change_hoger import ( + _discretize, + _shorth_mode, + homogenize, +) + + +def test_discretize(): + """Test discretize function.""" + + x = pd.Series([0, 5, 1000, 2, 75]) + expected_result = pd.Series([1, 1, 2, 1, 1]) + threshold = 100 + result = _discretize(x, threshold) + assert isinstance(result, np.ndarray) + assert len(result) == len(x) + np.testing.assert_array_equal(result, expected_result) + + +def test_shorth_mode(): + """Test shorth function.""" + x = pd.Series([1.0, 1.0, 1.0, 1.5, 2.0]) + expected_result = 1.0 + result = _shorth_mode(x) + assert isinstance(result, np.float64) + assert result == expected_result + + +def test_homogenize(): + """Test homogenize function.""" + N = 10 + + df = FlascDataFrame( + { + "time": pd.date_range("2024-01-01", periods=N, freq="600s"), + "wd": np.random.randint(0, 360, N), + "ws": np.random.randint(0, 20, N), + "pow_000": np.random.randint(0, 100, N), + "pow_001": np.random.randint(0, 100, N), + "pow_002": np.random.randint(0, 100, N), + "pow_003": np.random.randint(0, 100, N), + "pow_004": np.random.randint(0, 100, N), + "wd_000": np.zeros(N), + "wd_001": np.zeros(N), + "wd_002": np.zeros(N), + "wd_003": np.zeros(N), + "wd_004": np.zeros(N), + } + ) + + # Add a step change at N/2 in wd_004 + df.loc[N // 2 :, "wd_004"] = 20 + + df_hom, d2 = homogenize(df, threshold=10) + print(df_hom) + print(d2) From 643d915c340b1ede5ec674b1b879b272de21884f Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 07:07:02 -0700 Subject: [PATCH 22/31] Update tests --- tests/northing_offset_change_hoger_test.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/tests/northing_offset_change_hoger_test.py b/tests/northing_offset_change_hoger_test.py index bc5bfbbc..cd687ee4 100644 --- a/tests/northing_offset_change_hoger_test.py +++ b/tests/northing_offset_change_hoger_test.py @@ -32,7 +32,7 @@ def test_shorth_mode(): def test_homogenize(): """Test homogenize function.""" - N = 10 + N = 100 df = FlascDataFrame( { @@ -55,6 +55,16 @@ def test_homogenize(): # Add a step change at N/2 in wd_004 df.loc[N // 2 :, "wd_004"] = 20 - df_hom, d2 = homogenize(df, threshold=10) - print(df_hom) - print(d2) + # If threshold is larger than number of points, df_hom should match df + df_hom, d2 = homogenize(df.copy(), threshold=N * 2) + assert df.equals(df_hom) + + # If threshold is smaller than number of points, df_hom should homogenize wd_004 + df_hom, d2 = homogenize(df.copy(), threshold=10) + assert not df.equals(df_hom) + assert df_hom["wd_004"].nunique() == 1 # Test homogenized column + + # If threshold == N should homogenize all columns + df_hom, d2 = homogenize(df.copy(), threshold=N) + assert not df.equals(df_hom) + assert df_hom["wd_004"].nunique() == 1 # Test homogenized column From 9cd058d29afccd6c6aa900baa9e458abdfa6b95b Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 21 Nov 2024 07:07:54 -0700 Subject: [PATCH 23/31] Add future --- flasc/data_processing/northing_offset_change_hoger.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 59616595..8c567dd3 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -7,6 +7,8 @@ CENER within the TWAIN project. """ +from __future__ import annotations + import warnings from typing import Union From 214f4ffed0f177d77f9d071f418ed73cbedab6f4 Mon Sep 17 00:00:00 2001 From: Paul Date: Fri, 22 Nov 2024 10:11:49 -0700 Subject: [PATCH 24/31] Change threshold input to discretize to /2 --- flasc/data_processing/northing_offset_change_hoger.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 8c567dd3..5b4046c2 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -259,7 +259,7 @@ def homogenize( # Postprocess all the data to get the main jumps for each wind turbine d2 = d.copy() - d2["Class"] = _discretize(d["Knot"], threshold=threshold) + d2["Class"] = _discretize(d["Knot"], threshold=threshold // 2) d2["Count"] = 1 d2 = d2.groupby(["Class", "Turbine"]).agg( {"Count": "sum", "Jump": "mean", "Knot": _shorth_mode} From 98a665636caeac8aaaee503d473a24fc75e0dbef Mon Sep 17 00:00:00 2001 From: Paul Date: Fri, 22 Nov 2024 10:19:32 -0700 Subject: [PATCH 25/31] Update tests --- tests/northing_offset_change_hoger_test.py | 48 ++++++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/tests/northing_offset_change_hoger_test.py b/tests/northing_offset_change_hoger_test.py index cd687ee4..67400eff 100644 --- a/tests/northing_offset_change_hoger_test.py +++ b/tests/northing_offset_change_hoger_test.py @@ -64,7 +64,55 @@ def test_homogenize(): assert not df.equals(df_hom) assert df_hom["wd_004"].nunique() == 1 # Test homogenized column + # All columns besides wd_004 are unchanged + assert df["wd_000"].equals(df_hom["wd_000"]) + assert df["wd_001"].equals(df_hom["wd_001"]) + assert df["wd_002"].equals(df_hom["wd_002"]) + assert df["wd_003"].equals(df_hom["wd_003"]) + # If threshold == N should homogenize all columns df_hom, d2 = homogenize(df.copy(), threshold=N) assert not df.equals(df_hom) assert df_hom["wd_004"].nunique() == 1 # Test homogenized column + + +def test_homogenize_double_change(): + """Test homogenize function with two changes.""" + N = 250 + + df = FlascDataFrame( + { + "time": pd.date_range("2024-01-01", periods=N, freq="600s"), + "wd": np.random.randint(0, 360, N), + "ws": np.random.randint(0, 20, N), + "pow_000": np.random.randint(0, 100, N), + "pow_001": np.random.randint(0, 100, N), + "pow_002": np.random.randint(0, 100, N), + "pow_003": np.random.randint(0, 100, N), + "pow_004": np.random.randint(0, 100, N), + "wd_000": np.zeros(N), + "wd_001": np.zeros(N), + "wd_002": np.zeros(N), + "wd_003": np.zeros(N), + "wd_004": np.zeros(N), + } + ) + + # Add a step change at N/2 in wd_004 + df.loc[N // 3 :, "wd_004"] = 20 + df.loc[2 * N // 3 :, "wd_004"] = 40 + + # If threshold is smaller than number of points, df_hom should homogenize wd_004 + df_hom, d2 = homogenize(df.copy(), threshold=N // 5) + assert not df.equals(df_hom) + assert df_hom["wd_004"].nunique() == 1 # Test homogenized column + + # All columns besides wd_004 are unchanged + assert df["wd_000"].equals(df_hom["wd_000"]) + assert df["wd_001"].equals(df_hom["wd_001"]) + assert df["wd_002"].equals(df_hom["wd_002"]) + assert df["wd_003"].equals(df_hom["wd_003"]) + + # If threshold is larger than number of points, df_hom should match df + df_hom, d2 = homogenize(df.copy(), threshold=N * 2) + assert df.equals(df_hom) From 4a539a3fb35a4cf3b6001233096b1c48a316f1b7 Mon Sep 17 00:00:00 2001 From: Paul Date: Fri, 22 Nov 2024 10:25:57 -0700 Subject: [PATCH 26/31] fix rsync --- .github/workflows/deploy-pages.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy-pages.yaml b/.github/workflows/deploy-pages.yaml index 436b5ff2..b6a32951 100644 --- a/.github/workflows/deploy-pages.yaml +++ b/.github/workflows/deploy-pages.yaml @@ -29,7 +29,7 @@ jobs: - name: Copy examples to docs working-directory: ${{runner.workspace}}/flasc/ run: | - rsync -av --mkpath examples_artificial_data/03_energy_ratio/ docs/examples/01_raw_data_processing + rsync -av --mkpath examples_artificial_data/01_raw_data_processing/ docs/examples/01_raw_data_processing rsync -av --mkpath examples_artificial_data/03_energy_ratio/ docs/examples/03_energy_ratio rsync -av --mkpath examples_artificial_data/floris_input_artificial/ docs/examples/floris_input_artificial ls docs/examples From a47122e75b6a502f20c18bc9329d0783925d5a46 Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 25 Nov 2024 21:32:08 -0700 Subject: [PATCH 27/31] Revert "restore 01" This reverts commit 6efad2a5ab43e35a418b286e7ce4526dffaf1581. --- .../01_northing_calibration.ipynb | 809 +++++++++++++++--- 1 file changed, 683 insertions(+), 126 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb index dc3945c1..89b03723 100644 --- a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb +++ b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb @@ -5,24 +5,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **Import dependencies**" + "# Northing Calibration in FLASC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Northing calibration, that is the detection of bias and changes in measurements of turbine yaw are important for many of the analysis in FLASC. This notebook demonstrates the use of several of these tools in FLASC for the calibration of northing measurements." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# from datetime import timedelta as td\n", "import os\n", "import warnings as wn\n", + "from datetime import timedelta as td\n", "\n", "import numpy as np\n", "import pandas as pd\n", + "from floris import TimeSeries\n", + "from floris.layout_visualization import plot_turbine_labels, plot_turbine_points\n", "from floris.utilities import wrap_360\n", "from matplotlib import pyplot as plt\n", "\n", + "from flasc import FlascDataFrame\n", "from flasc.data_processing import (\n", " dataframe_manipulations as dfm,\n", " energy_ratio_wd_bias_estimation as best,\n", @@ -40,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -50,83 +61,571 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 0**: Initial data pulldown\n", - "First, we import the data from the common_windfarm_information folder. This may take a while, so we keep these variables unchanged. These are df_scada_raw and df_metmast_raw. These variables are not manipulated throughout the script." + "## Load FLORIS model and show layout" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "def load_data():\n", - " root_path = os.getcwd()\n", - " f = os.path.join(root_path, \"postprocessed\", \"df_scada_600s_wspowfiltered.pkl\")\n", - " df_scada = pd.read_pickle(f)\n", + "# Load FLORIS model\n", + "fm, turbine_weights = load_floris()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the layout\n", + "fig, ax = plt.subplots()\n", + "plot_turbine_points(fm, ax)\n", + "plot_turbine_labels(fm, ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data set to illustrate operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity assume a fixed wind speed and turbulence intensity and uniform wind direction. Perturb the wind direction by random noise" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", + "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", "\n", - " # # Optionally: downsample to [x] minute averages to speed up things\n", - " # cols_angular = [c for c in df_scada if ((\"wd_\" in c) or (\"yaw_\" in c))]\n", - " # df_scada = fto.df_downsample(\n", - " # df_scada,\n", - " # cols_angular=cols_angular,\n", - " # window_width=td(seconds=600),\n", - " # )\n", + "# Apply noise\n", + "np.random.seed(0)\n", + "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", + "wind_directions = wind_directions + noise\n", "\n", - " return df_scada\n", + "# Set a FLORIS time series object\n", + "time_series = TimeSeries(\n", + " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1753918.68116782, 1753961.25195179, 1753974.02887594,\n", + " 1753984.64239339, 1753954.56987842, 1753926.45363424])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate FLORIS solution\n", + "fm.set(wind_data=time_series)\n", + "fm.run()\n", + "turbine_powers = fm.get_turbine_powers()\n", "\n", + "# Add random noise to the power output\n", + "turbine_powers = turbine_powers + np.random.normal(0, 25.0, turbine_powers.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the results to create a FLASC dataframe representing hypothetical scada data\n", + "df_scada = FlascDataFrame(\n", + " {\n", + " \"time\": pd.date_range(start=\"1/1/2020\", periods=len(wind_directions), freq=\"600s\"),\n", + " \"wind_directions\": wind_directions,\n", + " \"wind_speeds\": 8.0 * np.ones_like(wind_directions),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "FlascDataFrame in FLASC format\n", + "
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timewind_directionswind_speedspow_000pow_001pow_002pow_003pow_004pow_005pow_006wd_000wd_001wd_002wd_003wd_004wd_005wd_006
02020-01-01 00:00:000.8820268.01.300483e+066.782295e+051.062299e+061.753996e+061.753925e+061.753954e+061.753919e+060.4801630.5186760.8672501.3595690.6235290.2622910.860956
12020-01-01 00:10:001.2000798.01.336065e+067.107464e+051.097194e+061.753993e+061.753949e+061.753959e+061.753961e+060.2846841.0691041.8623820.6874901.3843951.0952821.194562
22020-01-01 00:20:002.4893698.01.464618e+068.748210e+051.233091e+061.753934e+061.753949e+061.753993e+061.753974e+062.0296912.9304302.2481663.1681702.6812702.3042222.469416
32020-01-01 00:30:004.1204478.01.588592e+061.075059e+061.396982e+061.753984e+061.753965e+061.753941e+061.753985e+063.1530604.6036433.4632093.6163384.6210693.8104295.051782
42020-01-01 00:40:004.9337798.01.633644e+061.164006e+061.466968e+061.753959e+061.753942e+061.753971e+061.753955e+064.0384525.6512415.7514675.1733014.9751734.2382874.768993
......................................................
17952020-01-13 11:10:00354.7220078.05.540390e+053.631354e+054.072392e+051.753920e+061.753931e+061.753940e+061.753937e+06354.104930355.603609355.974084354.515064354.500130354.891368354.998903
17962020-01-13 11:20:00356.0133698.06.913212e+053.469273e+055.184367e+051.753995e+061.753957e+061.753940e+061.753917e+06355.252039355.760453354.907418355.423705356.723372355.720953355.511321
17972020-01-13 11:30:00357.0917258.08.298914e+053.672902e+056.234497e+051.753920e+061.753938e+061.753954e+061.753921e+06356.338511357.314736357.075436357.273982356.775948357.175943356.677064
17982020-01-13 11:40:00357.7646298.09.225341e+053.940003e+056.904134e+051.753967e+061.753943e+061.753960e+061.753947e+06357.366234358.697730357.640309358.981603357.377406358.350070357.973726
17992020-01-13 11:50:00359.1363988.01.097052e+065.031428e+058.536198e+051.753960e+061.753918e+061.753974e+061.753920e+06358.308969358.423708358.505300359.747302359.438663359.130778359.271976
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1800 rows × 17 columns

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" + ], + "text/plain": [ + " time wind_directions wind_speeds pow_000 \\\n", + "0 2020-01-01 00:00:00 0.882026 8.0 1.300483e+06 \n", + "1 2020-01-01 00:10:00 1.200079 8.0 1.336065e+06 \n", + "2 2020-01-01 00:20:00 2.489369 8.0 1.464618e+06 \n", + "3 2020-01-01 00:30:00 4.120447 8.0 1.588592e+06 \n", + "4 2020-01-01 00:40:00 4.933779 8.0 1.633644e+06 \n", + "... ... ... ... ... \n", + "1795 2020-01-13 11:10:00 354.722007 8.0 5.540390e+05 \n", + "1796 2020-01-13 11:20:00 356.013369 8.0 6.913212e+05 \n", + "1797 2020-01-13 11:30:00 357.091725 8.0 8.298914e+05 \n", + "1798 2020-01-13 11:40:00 357.764629 8.0 9.225341e+05 \n", + "1799 2020-01-13 11:50:00 359.136398 8.0 1.097052e+06 \n", + "\n", + " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", + "0 6.782295e+05 1.062299e+06 1.753996e+06 1.753925e+06 1.753954e+06 \n", + "1 7.107464e+05 1.097194e+06 1.753993e+06 1.753949e+06 1.753959e+06 \n", + "2 8.748210e+05 1.233091e+06 1.753934e+06 1.753949e+06 1.753993e+06 \n", + "3 1.075059e+06 1.396982e+06 1.753984e+06 1.753965e+06 1.753941e+06 \n", + "4 1.164006e+06 1.466968e+06 1.753959e+06 1.753942e+06 1.753971e+06 \n", + "... ... ... ... ... ... \n", + "1795 3.631354e+05 4.072392e+05 1.753920e+06 1.753931e+06 1.753940e+06 \n", + "1796 3.469273e+05 5.184367e+05 1.753995e+06 1.753957e+06 1.753940e+06 \n", + "1797 3.672902e+05 6.234497e+05 1.753920e+06 1.753938e+06 1.753954e+06 \n", + "1798 3.940003e+05 6.904134e+05 1.753967e+06 1.753943e+06 1.753960e+06 \n", + "1799 5.031428e+05 8.536198e+05 1.753960e+06 1.753918e+06 1.753974e+06 \n", + "\n", + " pow_006 wd_000 wd_001 wd_002 wd_003 \\\n", + "0 1.753919e+06 0.480163 0.518676 0.867250 1.359569 \n", + "1 1.753961e+06 0.284684 1.069104 1.862382 0.687490 \n", + "2 1.753974e+06 2.029691 2.930430 2.248166 3.168170 \n", + "3 1.753985e+06 3.153060 4.603643 3.463209 3.616338 \n", + "4 1.753955e+06 4.038452 5.651241 5.751467 5.173301 \n", + "... ... ... ... ... ... \n", + "1795 1.753937e+06 354.104930 355.603609 355.974084 354.515064 \n", + "1796 1.753917e+06 355.252039 355.760453 354.907418 355.423705 \n", + "1797 1.753921e+06 356.338511 357.314736 357.075436 357.273982 \n", + "1798 1.753947e+06 357.366234 358.697730 357.640309 358.981603 \n", + "1799 1.753920e+06 358.308969 358.423708 358.505300 359.747302 \n", + "\n", + " wd_004 wd_005 wd_006 \n", + "0 0.623529 0.262291 0.860956 \n", + "1 1.384395 1.095282 1.194562 \n", + "2 2.681270 2.304222 2.469416 \n", + "3 4.621069 3.810429 5.051782 \n", + "4 4.975173 4.238287 4.768993 \n", + "... ... ... ... \n", + "1795 354.500130 354.891368 354.998903 \n", + "1796 356.723372 355.720953 355.511321 \n", + "1797 356.775948 357.175943 356.677064 \n", + "1798 357.377406 358.350070 357.973726 \n", + "1799 359.438663 359.130778 359.271976 \n", + "\n", + "[1800 rows x 17 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the turbine powers to the dataframe with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"pow_{t_idx:03d}\"] = turbine_powers[:, t_idx]\n", + "\n", + "# Set the turbine wind directions to be the true wind direction with some added noise\n", + "for t_idx in range(fm.n_turbines):\n", + " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", + " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + " )\n", "\n", - "df_scada_northing_uncalibrated = load_data()\n", - "df_scada_northing_uncalibrated[\"ti\"] = 0.06 # Assume a certain ambient turbulence intensity" + "df_scada" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 1**: Initialize FLORIS\n", - "and precalculate a large set of solutions using the parallel computing interface in FLORIS" + "#### Northing calibration error\n", + "\n", + "Add to the data two types of northing calibration error:\n", + "1. A constant bias on turbine 001\n", + "2. A change in bias on turbine 002 halfway through the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "df_scada[\"wd_001\"] = wrap_360(\n", + " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + ")\n", + "\n", + "mid_point = int(len(wind_directions) / 2)\n", + "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + "wd_change[mid_point:] = wd_change[mid_point:] + 30\n", + "wd_change = wrap_360(wd_change)\n", + "df_scada[\"wd_002\"] = wd_change" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind direction')" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the wd channels for the turbines\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"gray\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"gray\")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time\")\n", + "ax.set_ylabel(\"Wind direction\")" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 63, "metadata": {}, "outputs": [ { - "ename": "UserWarning", - "evalue": "Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUserWarning\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[49], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m df_fm_approx \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_feather(fn_approx)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mUserWarning\u001b[39;00m(\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease run \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m00_setup_floris_model/02_precalculate_floris_solutions.py\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor the appropriate wake models first.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 16\u001b[0m )\n", - "\u001b[0;31mUserWarning\u001b[0m: Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first." + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-19 13:33:42\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-11-19 13:33:42\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" ] } ], "source": [ - "# Now we calculate a grid of FLORIS solutions. Since our estimated SCADA\n", - "# data changes as we shift its wind direction, the predicted solutions\n", - "# according to FLORIS will also change. Therefore, we precalculate a grid\n", - "# of FLORIS solutions and insert that into the bias estimation class.\n", - "fm, turbine_weights = load_floris()\n", - "\n", - "# Grab the precalculated FLORIS model solutions from the 'setup_floris_model' directory\n", - "root_path = os.getcwd()\n", - "fn_approx = os.path.join(root_path, \"..\", \"00_setup_floris_model\", \"df_fm_approx_gch.ftr\")\n", - "if os.path.exists(fn_approx):\n", - " df_fm_approx = pd.read_feather(fn_approx)\n", - "else:\n", - " raise UserWarning(\n", - " \"Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' \"\n", - " \"for the appropriate wake models first.\"\n", - " )" + "# Finally compute df_approx for use in later algorithms\n", + "# Can compute only at 8m/s for this example\n", + "df_fm_approx = ftools.calc_floris_approx_table(\n", + " fm=fm, # fi=fi_pci,\n", + " wd_array=np.arange(0.0, 360.01, 1.0),\n", + " ws_array=np.array([8.0]),\n", + " ti_array=np.array([0.06]),\n", + ")" ] }, { @@ -134,8 +633,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **Step 2**: Cross-compare wind direction measurements\n", - "and see if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. The current functionality is limited and cannot account for this yet." + "# Cross-Check Northing calibration " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` is a function to check if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. " ] }, { @@ -147,67 +652,88 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-10-16 11:44:52\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-10-16 11:44:54\u001b[0m T006 T001 T002 T005 T003\n", - "0 18.0 16.0 -6.0 14.0 46.0\n", - "1 18.0 16.0 -6.0 14.0 46.0\n", - "2 18.0 16.0 -6.0 14.0 46.0\n", - "3 18.0 14.0 -6.0 14.0 46.0\n", - "\u001b[32m2024-10-16 11:44:54\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-10-16 11:44:56\u001b[0m T002 T006 T005 T003 T000\n", - "0 -22.0 2.0 -2.0 30.0 -16.0\n", - "1 -20.0 2.0 -2.0 30.0 -16.0\n", - "2 -20.0 2.0 -2.0 30.0 -16.0\n", - "3 -22.0 2.0 -2.0 30.0 -14.0\n", - "\u001b[32m2024-10-16 11:44:56\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-10-16 11:44:57\u001b[0m T001 T003 T005 T000 T006\n", - "0 22.0 52.0 20.0 6.0 24.0\n", - "1 20.0 52.0 20.0 6.0 24.0\n", - "2 20.0 52.0 20.0 6.0 24.0\n", - "3 22.0 52.0 20.0 6.0 24.0\n", - "\u001b[32m2024-10-16 11:44:57\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-10-16 11:44:58\u001b[0m T005 T002 T001 T004 T006\n", - "0 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "1 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "2 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "3 -32.0 -52.0 -30.0 -30.0 -28.0\n", - "\u001b[32m2024-10-16 11:44:58\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T002 T005 T001 T006\n", - "0 30.0 -22.0 -2.0 -2.0 2.0\n", - "1 30.0 -22.0 -2.0 -2.0 2.0\n", - "2 30.0 -22.0 -2.0 -2.0 2.0\n", - "3 30.0 -22.0 -2.0 -2.0 2.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T001 T006 T002 T000\n", - "0 32.0 2.0 4.0 -20.0 -14.0\n", - "1 32.0 2.0 4.0 -20.0 -14.0\n", - "2 32.0 2.0 4.0 -20.0 -14.0\n", - "3 32.0 2.0 4.0 -20.0 -14.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m T001 T005 T000 T003 T002\n", - "0 -2.0 -4.0 -18.0 28.0 -24.0\n", - "1 -2.0 -4.0 -18.0 28.0 -24.0\n", - "2 -2.0 -4.0 -18.0 28.0 -24.0\n", - "3 -2.0 -4.0 -18.0 28.0 -24.0\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -30.0 0.0 0.0 0.0\n", + "1 0.0 -30.0 0.0 0.0 0.0\n", + "2 0.0 -30.0 0.0 0.0 0.0\n", + "3 0.0 -30.0 -26.0 0.0 0.0\n", + "4 0.0 -30.0 -30.0 0.0 0.0\n", + "5 0.0 -30.0 -30.0 0.0 0.0\n", + "6 0.0 -30.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T002 T006 T005 T003 T000\n", + "0 30.0 30.0 30.0 30.0 30.0\n", + "1 30.0 30.0 30.0 30.0 30.0\n", + "2 30.0 30.0 30.0 30.0 30.0\n", + "3 4.0 30.0 30.0 30.0 30.0\n", + "4 0.0 30.0 30.0 30.0 30.0\n", + "5 0.0 30.0 30.0 30.0 30.0\n", + "6 0.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m T001 T003 T005 T000 T006\n", + "0 -30.0 0.0 0.0 -0.0 0.0\n", + "1 -30.0 0.0 0.0 -0.0 0.0\n", + "2 -30.0 0.0 0.0 -0.0 0.0\n", + "3 -4.0 26.0 26.0 26.0 26.0\n", + "4 -0.0 30.0 30.0 30.0 30.0\n", + "5 -0.0 30.0 30.0 30.0 30.0\n", + "6 -0.0 30.0 30.0 30.0 30.0\n", + "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 -0.0 -30.0 0.0 0.0\n", + "1 0.0 -0.0 -30.0 0.0 0.0\n", + "2 0.0 -0.0 -30.0 0.0 0.0\n", + "3 0.0 -26.0 -30.0 0.0 0.0\n", + "4 0.0 -30.0 -30.0 0.0 0.0\n", + "5 0.0 -30.0 -30.0 0.0 0.0\n", + "6 0.0 -30.0 -30.0 0.0 0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 0.0 0.0 -30.0 0.0\n", + "1 -0.0 0.0 0.0 -30.0 0.0\n", + "2 -0.0 0.0 0.0 -30.0 0.0\n", + "3 -0.0 -26.0 0.0 -30.0 0.0\n", + "4 -0.0 -30.0 0.0 -30.0 0.0\n", + "5 -0.0 -30.0 0.0 -30.0 0.0\n", + "6 -0.0 -30.0 0.0 -30.0 0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -30.0 0.0 -0.0 -0.0\n", + "1 -0.0 -30.0 0.0 -0.0 -0.0\n", + "2 -0.0 -30.0 0.0 -0.0 -0.0\n", + "3 -0.0 -30.0 0.0 -26.0 -0.0\n", + "4 -0.0 -30.0 0.0 -30.0 -0.0\n", + "5 -0.0 -30.0 0.0 -30.0 -0.0\n", + "6 -0.0 -30.0 0.0 -30.0 -0.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m T001 T005 T000 T003 T002\n", + "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", + "3 -30.0 -0.0 -0.0 -0.0 -26.0\n", + "4 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "5 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "6 -30.0 -0.0 -0.0 -0.0 -30.0\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['clean', 'clean', 'clean', 'clean', 'clean', 'clean', 'clean']\n" + "['clean', 'clean', 'bad', 'clean', 'clean', 'clean', 'clean']\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -218,19 +744,57 @@ ], "source": [ "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", - "df_scada_marked_faulty_northing_drift = df_scada_northing_uncalibrated.copy()\n", + "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", "\n", "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", - " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", ")\n", - "print(turb_wd_consistency)\n", + "print(turb_wd_consistency)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`crosscheck_northing_offset_consistency` detects that T002 contains a probable jump, one solution is to then remove T002's wind direction data from consideration however this is not done in this notebook as we next take advantage of HOGER recalibration. The code to do this is included below in comments" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# # Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", + "# faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", + "# for ti in np.where(faulty_turbines)[0]:\n", + "# df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homegenization with HOGER" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", + " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", "\n", - "# Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", - "faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", - "for ti in np.where(faulty_turbines)[0]:\n", - " df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" + " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "attachments": {}, "cell_type": "markdown", @@ -242,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1319,7 +1883,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2929,7 +3493,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -2982,7 +3546,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3090,7 +3654,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3114,7 +3678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -3124,11 +3688,9 @@ } ], "metadata": { - "interpreter": { - "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" - }, "kernelspec": { - "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", + "display_name": ".venv", + "language": "python", "name": "python3" }, "language_info": { @@ -3141,14 +3703,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.13.0" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "8f733c0fbb301080c2bcf96db7ac54d1ef0d7be04117d635d35c165c40504989" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 From c47984e8cdeda9eadcb740185834cd349ce0e84e Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 25 Nov 2024 21:33:12 -0700 Subject: [PATCH 28/31] Revert "update northing example" This reverts commit 062e3dab63407c3eb58c40d22b68e5633be50a01. --- .../01_northing_calibration.ipynb | 804 +++--------------- 1 file changed, 122 insertions(+), 682 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb index 89b03723..b8a8679c 100644 --- a/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb +++ b/examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb @@ -5,35 +5,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Northing Calibration in FLASC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Northing calibration, that is the detection of bias and changes in measurements of turbine yaw are important for many of the analysis in FLASC. This notebook demonstrates the use of several of these tools in FLASC for the calibration of northing measurements." + "# **Import dependencies**" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" + ] + } + ], "source": [ "# from datetime import timedelta as td\n", "import os\n", "import warnings as wn\n", - "from datetime import timedelta as td\n", "\n", "import numpy as np\n", "import pandas as pd\n", - "from floris import TimeSeries\n", - "from floris.layout_visualization import plot_turbine_labels, plot_turbine_points\n", "from floris.utilities import wrap_360\n", "from matplotlib import pyplot as plt\n", "\n", - "from flasc import FlascDataFrame\n", "from flasc.data_processing import (\n", " dataframe_manipulations as dfm,\n", " energy_ratio_wd_bias_estimation as best,\n", @@ -51,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -61,571 +59,71 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Load FLORIS model and show layout" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Load FLORIS model\n", - "fm, turbine_weights = load_floris()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MzFSbNm1CxsvLy1VVVSWPxyNJ+u///m9t2rRJGzdu1MaNG/WnP/1JkvTOO++osLAwvA0BABDBolu7gEhVUVGhkpISVVZWKi0tTfn5+UpPT1dRUZFGjRqlrKwsZWdna86cOTp48KDGjBmj+Ph4FRQUqKioSImJiXK5XBo/frw8Ho8GDRokSTrvvPNC3mfv3r2SpN69ex91LQ8AADg2Qs4pWLhwoW6//XY5HA4ZY+RwODRz5kx5vV6NHj1ae/bs0bRp0+T3+9W/f3+tXLnSuhh59uzZcjqdGj58uOrq6pSXl6d58+a1ckcAANiPwxhjWruIlhYIBBQfH6/a2lq5XK7T2lZFRYUyMjIUDAaPGnM6nSovL1evXr1O6z0AAMDpH7+5JucklZSUyOFwNDnmcDjk9XrDXBEAAGgKIeckVVZW6lgnv4wxqqysDG9BAACgSYSck5SWlnbcMzlpaWnhLQgAADSJkHOS8vPzj3smp6CgIMwVAQCAphByTlJ6erq8Xq+cTqeioqJCfnq9Xi46BgDgDMHdVado+/bt8nq91vfkFBQUEHAAAGhGp3v8JuQAAIAzEreQAwAANIGQAwAAbImQA+CMM3fuXKWlpSkuLk45OTlav369NXbo0CEVFhaqU6dO6tChg4YPH67q6mpr/KuvvtLVV1+t5ORkxcbGKiUlRePGjVMgEGiNVgC0ohYPOTt37tStt96qTp06qW3bturbt68++OADa9wYo2nTpqlbt25q27atcnNzVVFREbKNffv2aeTIkXK5XEpISFBBQYEOHDjQ0qUDaAVLlixRUVGRpk+frg0bNqhfv37Ky8vT7t27JUkTJ07U8uXLtXTpUq1Zs0a7du3SsGHDrNc7nU5dd911euWVV/Tpp5/qmWee0Ztvvqm77rqrtVoC0FpMC9q3b5/p0aOHGT16tFm3bp357LPPzOuvv262b99uzZkxY4aJj483y5YtM5s2bTLXXnut6dmzp/nmm2+sOVdffbXp16+fWbt2rXnnnXdMr169zIgRI064jtraWiPJ1NbWNmt/AJpfdna2KSwstH5vbGw0ycnJpri42NTU1Jg2bdqYpUuXWuOffPKJkWR8Pt8xt/nYY4+Z7t27t2jdAJrf6R6/WzTkTJ482Vx66aXHHA8Gg8btdptZs2ZZ62pqakxsbKx54YUXjDHGbN261Ugy77//vjXntddeMw6Hw+zcufOE6iDkAJGhrq7OREVFmZdeeilk/a9+9Stz7bXXmtLSUiPJfP311yHjqamp5tFHH21ymzt37jRXXHGFGTlyZAtVDaClnO7xu0U/rnrllVeUlZWlm266SV27dtWAAQP09NNPW+Off/65/H6/cnNzrXXx8fHKycmRz+eTJPl8PiUkJCgrK8uak5ubK6fTqXXr1jX5vnV1dQoEAiELgDPf3r171djYqKSkpJD1SUlJ8vv98vv9iomJUUJCQpPj3zVixAi1a9dO5557rlwul/70pz+1dPkAzjAtGnI+++wzzZ8/X+np6Xr99dc1duxY3XPPPXr22WclydopHWuHdmRO165dQ8ajo6OVmJh41E7tiOLiYsXHx1tLSkpKc7cG4Aw3e/ZsbdiwQS+//LL+93//V0VFRa1dEoAwi27JjQeDQWVlZemRRx6RJA0YMECbN2/WggULNGrUqBZ73ylTpoTs0AKBAEEHiACdO3dWVFRUyN1SklRdXS232y232636+nrV1NSEnM05Mv5dR+ZnZGQoMTFRl112maZOnapu3bqFoxUAZ4AWPZPTrVs39enTJ2Rd7969VVVVJUnWTulYO7Qjc47cVXHE4cOHtW/fvqN2akfExsbK5XKFLADOfDExMcrMzFRpaam1LhgMqrS0VB6PR5mZmWrTpk3IeHl5uaqqquTxeI653WAwKOnbj7IBnD1a9EzOJZdcovLy8pB1n376qXr06CFJ6tmzp9xut0pLS9W/f39J3551WbduncaOHStJ8ng8qqmpUVlZmTIzMyVJq1evVjAYVE5OTkuWD6AFVVRUqKSkxHr+W35+vtLT01VUVKRRo0YpKytL2dnZmjNnjg4ePKgxY8YoPj5eBQUFKioqUmJiolwul8aPHy+Px6NBgwZJkl599VVVV1dr4MCB6tChg7Zs2aJJkybpkksuUVpaWus2DSC8mvlC6BDr16830dHR5uGHHzYVFRXm+eefN+3atTPPPfecNWfGjBkmISHBvPzyy+ajjz4y1113XZO3kA8YMMCsW7fOvPvuuyY9PZ1byIEIVlJSYpxOp4mKigr5uXDhQmOMMU888YRJTU01MTExJjs726xdu9Z67TfffGPuvvtu07FjR9OuXTtzww03mC+//NIaX716tfF4PCY+Pt7ExcWZ9PR0M3ny5KPuyAJw5jvd43eLP6BzxYoVmjJliioqKtSzZ08VFRXpjjvu+G7I0vTp0/XUU0+ppqZGl156qebNm6cf//jH1px9+/Zp3LhxWr58uZxOp4YPH67HH39cHTp0OKEaeEAncOaoqKhQRkaG9RHSdzmdTpWXl6tXr16tUBmAMw1PIT8BhBzgzDFlyhTNmjVLjY2NR41FRUVp0qRJKi4uboXKAJxpeAo5gIhSWVmpY/23lTFGlZWV4S0IgG0RcgCEVVpamhwOR5NjDoeDi4MBNBtCDoCwys/PP+6ZnIKCgjBXBMCuCDkAwio9PV1er1dOp1NRUVEhP71eLxcdA2g2XHgMoFVs375dXq/X+p6cgoICAg6AENxddQIIOQAARB7urgIAAGgCIQcAANgSIQcAgLPc3LlzlZaWpri4OOXk5Gj9+vXW2KFDh1RYWKhOnTqpQ4cOGj58+FEP1pakZ555RhdddJHi4uLUtWtXFRYWhrOFJhFyAAA4iy1ZskRFRUWaPn26NmzYoH79+ikvL0+7d++WJE2cOFHLly/X0qVLtWbNGu3atUvDhg0L2cajjz6qBx54QPfff7+2bNmiN998U3l5ea3RTgguPAYA4CyWk5OjgQMH6sknn5QkBYNBpaSkaPz48Ro7dqy6dOmiRYsW6cYbb5Qkbdu2Tb1795bP59OgQYP09ddf69xzz9Xy5cs1ePDgZq2NC48BAMApqa+vV1lZmXJzc611TqdTubm58vl8KisrU0NDQ8h4RkaGUlNT5fP5JEmrVq1SMBjUzp071bt3b3Xv3l2/+MUvtGPHjrD3832EHAAAzlJ79+5VY2OjkpKSQtYnJSXJ7/fL7/crJiZGCQkJTY5L0meffaZgMKhHHnlEc+bM0V//+lft27dPP/3pT1VfXx+uVppEyAEAAKcsGAyqoaFBjz/+uPLy8jRo0CC98MILqqio0FtvvdWqtRFyAAA4S3Xu3FlRUVFH3S1VXV0tt9stt9ut+vp61dTUNDkuSd26dZMk9enTxxrv0qWLOnfurKqqqpZt4AcQcgAAOEvFxMQoMzNTpaWl1rpgMKjS0lJ5PB5lZmaqTZs2IePl5eWqqqqSx+ORJF1yySXW+iP27dunvXv3qkePHmHqpGnRrfruAAAgLCoqKlRSUmI9Ly4/P1/p6ekqKirSqFGjlJWVpezsbM2ZM0cHDx7UmDFjFB8fr4KCAhUVFSkxMVEul0vjx4+Xx+PRoEGDJEk//vGPdd111+nee+/VU089JZfLpSlTpigjI0NXXnllq/ZMyAEAwOYWLlyo22+/XQ6HQ8YYORwOzZw5U16vV6NHj9aePXs0bdo0+f1+9e/fXytXrrQuRp49e7acTqeGDx+uuro65eXlad68eSHb//Of/6yJEydq6NChcjqduuKKK7Ry5Uq1adOmNdq18D05AADYWEVFhTIyMhQMBo8aczqdKi8vV69evVqhsh/G9+QAAIBjKikpkcPhaHLM4XDI6/WGuaLwIeQAAGBjlZWVOtaHNsYYVVZWhregMCLkAABgY2lpacc9k5OWlhbegsKIkAMAgI3l5+cf90xOQUFBmCsKH0IOAAA2lp6eLq/XK6fTqaioqJCfXq/3jL3ouDlwdxUAAGeB7du3y+v1Wt+TU1BQcMYHnNM9fhNyAADAGYlbyAEAAJpAyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALZEyAEAALYUtpAzY8YMORwOTZgwwVp36NAhFRYWqlOnTurQoYOGDx+u6urqkNdVVVVp6NChateunbp27apJkybp8OHD4SobAABEqLCEnPfff19//OMfddFFF4WsnzhxopYvX66lS5dqzZo12rVrl4YNG2aNNzY2aujQoaqvr9d7772nZ599Vs8884ymTZsWjrIBAEAEa/GQc+DAAY0cOVJPP/20OnbsaK2vra2V1+vVo48+qquuukqZmZlauHCh3nvvPa1du1aS9MYbb2jr1q167rnn1L9/f11zzTX63e9+p7lz56q+vr6lSwcAABGsxUNOYWGhhg4dqtzc3JD1ZWVlamhoCFmfkZGh1NRU+Xw+SZLP51Pfvn2VlJRkzcnLy1MgENCWLVtaunQAABDBolty44sXL9aGDRv0/vvvHzXm9/sVExOjhISEkPVJSUny+/3WnO8GnCPjR8aOpa6uTnV1ddbvgUDgVFsAAAARqsXO5OzYsUP33nuvnn/+ecXFxbXU2zSpuLhY8fHx1pKSkhLW9wcAAK2vxUJOWVmZdu/erZ/85CeKjo5WdHS01qxZo8cff1zR0dFKSkpSfX29ampqQl5XXV0tt9stSXK73UfdbXXk9yNzmjJlyhTV1tZay44dO5q3OQAAcMZrsZAzePBgffzxx9q4caO1ZGVlaeTIkdaf27Rpo9LSUus15eXlqqqqksfjkSR5PB59/PHH2r17tzVn1apVcrlc6tOnzzHfOzY2Vi6XK2QBAABnlxa7Juecc87RhRdeGLKuffv26tSpk7W+oKBARUVFSkxMlMvl0vjx4+XxeDRo0CBJ0pAhQ9SnTx/ddtttmjlzpvx+vx588EEVFhYqNja2pUoHAAA20KIXHv+Q2bNny+l0avjw4aqrq1NeXp7mzZtnjUdFRWnFihUaO3asPB6P2rdvr1GjRum3v/1tK1YNAAAigcMYY1q7iJYWCAQUHx+v2tpaProCACBCnO7xm2dXAQAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAAAAWyLkAABa3Ny5c5WWlqa4uDjl5ORo/fr11tihQ4dUWFioTp06qUOHDho+fLiqq6tDXn/PPfcoMzNTsbGx6t+/f5irR6Qi5AAAWtSSJUtUVFSk6dOna8OGDerXr5/y8vK0e/duSdLEiRO1fPlyLV26VGvWrNGuXbs0bNiwo7aTn5+vm2++OdzlI4LxgE4AQIvKycnRwIED9eSTT0qSgsGgUlJSNH78eI0dO1ZdunTRokWLdOONN0qStm3bpt69e8vn82nQoEEh23rooYe0bNkybdy4MdxtoBXwgE4AwBmrvr5eZWVlys3NtdY5nU7l5ubK5/OprKxMDQ0NIeMZGRlKTU2Vz+drjZJhI4QcAECL2bt3rxobG5WUlBSyPikpSX6/X36/XzExMUpISGhyHDgdhBwAAGBLhBwAQIvp3LmzoqKijrpbqrq6Wm63W263W/X19aqpqWlyHDgdhBwAQIuJiYlRZmamSktLrXXBYFClpaXyeDzKzMxUmzZtQsbLy8tVVVUlj8fTGiXDRqJbuwAAgD1UVFSopKRElZWVSktLU35+vtLT01VUVKRRo0YpKytL2dnZmjNnjg4ePKgxY8YoPj5eBQUFKioqUmJiolwul8aPHy+PxxNyZ9X27dt14MAB+f1+ffPNN9bdVX369FFMTEwrdYwzHSEHAHDaFi5cqNtvv10Oh0PGGDkcDs2cOVNer1ejR4/Wnj17NG3aNPn9fvXv318rV660LkaePXu2nE6nhg8frrq6OuXl5WnevHkh27/99tu1Zs0a6/cBAwZIkj7//HOlpaWFrU9EFr4nBwBwWioqKpSRkaFgMHjUmNPpVHl5uXr16tUKlSHS8T05AIBWVVJSIofD0eSYw+GQ1+sNc0XAtwg5AIDTUllZqWN9KGCMUWVlZXgLAv4PIQcAcFrS0tKOeyaHa2bQWgg5AIDTkp+ff9wzOQUFBWGuCPgWIQcAcFrS09Pl9XrldDoVFRUV8tPr9XLRMVoNd1cBAJrF9u3b5fV6re/JKSgoIODgtJzu8ZuQAwAAzkjcQg4AANAEQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALAlQg4AALClFg05xcXFGjhwoM455xx17dpV119/vcrLy0PmHDp0SIWFherUqZM6dOig4cOHq7q6OmROVVWVhg4dqnbt2qlr166aNGmSDh8+3JKlAwCACNeiIWfNmjUqLCzU2rVrtWrVKjU0NGjIkCE6ePCgNWfixIlavny5li5dqjVr1mjXrl0aNmyYNd7Y2KihQ4eqvr5e7733np599lk988wzmjZtWkuWDgAAIpzDGGPC9WZ79uxR165dtWbNGl1++eWqra1Vly5dtGjRIt14442SpG3btql3797y+XwaNGiQXnvtNf3sZz/Trl27lJSUJElasGCBJk+erD179igmJuYH3zcQCCg+Pl61tbVyuVwt2iMAAGgep3v8Dus1ObW1tZKkxMRESVJZWZkaGhqUm5trzcnIyFBqaqp8Pp8kyefzqW/fvlbAkaS8vDwFAgFt2bKlyfepq6tTIBAIWQAAwNklbCEnGAxqwoQJuuSSS3ThhRdKkvx+v2JiYpSQkBAyNykpSX6/35rz3YBzZPzIWFOKi4sVHx9vLSkpKc3cDQAAONOFLeQUFhZq8+bNWrx4cYu/15QpU1RbW2stO3bsaPH3BAAAZ5bocLzJuHHjtGLFCr399tvq3r27td7tdqu+vl41NTUhZ3Oqq6vldrutOevXrw/Z3pG7r47M+b7Y2FjFxsY2cxcAACCStOiZHGOMxo0bp5deekmrV69Wz549Q8YzMzPVpk0blZaWWuvKy8tVVVUlj8cjSfJ4PPr444+1e/dua86qVavkcrnUp0+fliwfAABEsBY9k1NYWKhFixbp5Zdf1jnnnGNdQxMfH6+2bdsqPj5eBQUFKioqUmJiolwul8aPHy+Px6NBgwZJkoYMGaI+ffrotttu08yZM+X3+/Xggw+qsLCQszUAAOCYWvQWcofD0eT6hQsXavTo0ZK+/TLA++67Ty+88ILq6uqUl5enefPmhXwU9cUXX2js2LH6+9//rvbt22vUqFGaMWOGoqNPLKNxCzkAAJHndI/fYf2enNZCyAEAIPJE1PfkAAAAhAshBwAA2BIhB4AtzZ07V2lpaYqLi1NOTk7IV1H80IOBN23apBEjRiglJUVt27ZV79699dhjj7VGGwBOAyEHgO0sWbJERUVFmj59ujZs2KB+/fopLy/P+iqKH3owcFlZmbp27arnnntOW7Zs0QMPPKApU6boySefbK2WAJwCLjwGYDs5OTkaOHCgFUqCwaBSUlI0fvx4jR079gcfDNyUwsJCffLJJ1q9enXY+gDOdlx4DADfUV9fr7KyspAH/zqdTuXm5srn853Qg4GbUltbaz1cGEBkCMtjHQAgXPbu3avGxsYmH+y7bdu2E3ow8Pe99957WrJkif72t7+1VNkAWgBncgDgODZv3qzrrrtO06dP15AhQ1q7HAAngZADwFY6d+6sqKiokLulpP//4N/vPhi4qfHv2rp1qwYPHqw777xTDz74YEuXDqCZEXIA2EpMTIwyMzNDHvwbDAZVWloqj8dzQg8GlqQtW7boyiuv1KhRo/Twww+HtQcAzYNrcgBErIqKCpWUlKiyslJpaWnKz89Xenq6ioqKNGrUKGVlZSk7O1tz5szRwYMHNWbMmBN6MPDmzZt11VVXKS8vT0VFRda1OlFRUerSpUtrtgzgJBByAESkhQsX6vbbb5fD4ZAxRg6HQzNnzpTX69Xo0aO1Z88eTZs2TX6/X/3799fKlSuti5Fnz54tp9Op4cOHhzwY+Ii//vWv2rNnj5577jk999xz1voePXqosrIy3K0COEV8Tw6AiFNRUaGMjAwFg8GjxpxOp8rLy9WrV69WqAxAc+J7cgCcdUpKSuRwOJocczgc8nq9Ya4IwJmIkAMg4lRWVupYJ6GNMXykBEASIQdABEpLSzvumZy0tLTwFgTgjETIARBx8vPzj3smp6CgIMwVATgTEXIARJz09HR5vV45nU5FRUWF/PR6vVx0DEASd1cBiGDbt2+X1+u1vienoKCAgAPYyOkevwk5AADgjMQt5AAAAE0g5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAgGY3d+5cpaWlKS4uTjk5OVq/fr01dujQIRUWFqpTp07q0KGDhg8frurq6pDXV1VV6aabbpIknXfeeZo0aZIOHz58UjUQcgAAQLNasmSJioqKNH36dG3YsEH9+vVTXl6edu/eLUmaOHGili9frqVLl2rNmjXatWuXhg0bZr2+sbFRQ4cOVX19vSRpwYIFeuaZZzRt2rSTqsNhjDHN19aZKRAIKD4+XrW1tXK5XK1dDgAAtpaTk6OBAwfqySeflCQFg0GlpKRo/PjxGjt2rLp06aJFixbpxhtvlCRt27ZNvXv3ls/n06BBg/Taa6/pZz/7mcrLy5Wenq7a2lotWrRIkydP1p49exQTE3NCdXAmBwAANJv6+nqVlZUpNzfXWud0OpWbmyufz6eysjI1NDSEjGdkZCg1NVU+n0+S5PP51LdvX3Xt2tWak5eXp0AgoC1btpxwLYQcAADQbPbu3avGxkYlJSWFrE9KSpLf75ff71dMTIwSEhKaHJckv9/f5OuPjJ0oQg4AALAlQg4AAGg2nTt3VlRU1FF3S1VXV8vtdsvtdqu+vl41NTVNjkuS2+1u8vVHxk4UIQcAADSbmJgYZWZmqrS01FoXDAZVWloqj8ejzMxMtWnTJmS8vLxcVVVV8ng8kiSPx6OPP/5Ye/bsseasWrVKLpdLffr0OeFaIibkHO9+ewAAEH4VFRWaMmWKRowYoSlTpqiiokKSVFRUpKefflrPPvusPvnkE40dO1YHDx7UmDFjFB8fr4KCAhUVFemtt95SWVmZxowZI4/Ho0GDBkmShgwZoj59+ujOO++UJL355pt68MEHVVhYqNjY2BMv0ESAxYsXm5iYGFNSUmK2bNli7rjjDpOQkGCqq6tP6PW1tbVGkqmtrW3hSgEAODuUlJQYp9NpoqKiQn4uXLjQGGPME088YVJTU01MTIzJzs42a9eutV77zTffmLvvvtt07NjRtGvXztxwww3myy+/DNl+ZWWl+elPf2okmU6dOpn77rvPNDQ0nFSNEfE9Oce73/7+++//wdfzPTkAADSfiooKZWRkKBgMHjXmdDpVXl6uXr16nfb7nO7x+4z/uOqH7rdvSl1dnQKBQMgCAACaR0lJiRwOR5NjDodDXq83zBU17YwPOT90v31TiouLFR8fby0pKSnhKBUAgLNCZWWljvVBkDFGlZWV4S3oGM74kHMqpkyZotraWmvZsWNHa5cEAIBtpKWlHfdMTlpaWngLOoYzPuT80P32TYmNjZXL5QpZAABA88jPzz/umZyCgoIwV9S0Mz7k/ND99gAAILzS09Pl9XrldDoVFRUV8tPr9TbLRcfNIbq1CzgRRUVFGjVqlLKyspSdna05c+ZY99sDAIDwGz16tC699FJ5vV5VVlYqLS1NBQUFZ0zAkSIk5Nx8883as2ePpk2bJr/fr/79+2vlypVHXYwMAADCp1evXiouLm7tMo4pIr4n53TxPTkAAEQe239PDgAAwKkg5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFsi5AAAAFtqkZBTWVmpgoIC9ezZU23bttV5552n6dOnq76+PmTeRx99pMsuu0xxcXFKSUnRzJkzj9rW0qVLlZGRobi4OPXt21evvvpqS5QMAABspkVCzrZt2xQMBvXHP/5RW7Zs0ezZs7VgwQL95je/seYEAgENGTJEPXr0UFlZmWbNmqWHHnpITz31lDXnvffe04gRI1RQUKAPP/xQ119/va6//npt3ry5JcoGAAA24jDGmHC80axZszR//nx99tlnkqT58+frgQcekN/vV0xMjCTp/vvv17Jly7Rt2zZJ0s0336yDBw9qxYoV1nYGDRqk/v37a8GCBSf83oFAQPHx8aqtrZXL5WrGrgAAQEs53eN32K7Jqa2tVWJiovW7z+fT5ZdfbgUcScrLy1N5ebm+/vpra05ubm7IdvLy8uTz+cJTNAAAiFhhCTnbt2/XE088oX/7t3+z1vn9fiUlJYXMO/K73+8/7pwj48dSV1enQCAQsgAAgLPLSYWc+++/Xw6H47jLkY+ajti5c6euvvpq3XTTTbrjjjuatfhjKS4uVnx8vLWkpKSE5X0BAMCZI/pkJt93330aPXr0cef86Ec/sv68a9cuXXnllbr44otDLiiWJLfbrerq6pB1R353u93HnXNk/FimTJmioqIi6/dAIEDQAQDgLHNSIadLly7q0qXLCc3duXOnrrzySmVmZmrhwoVyOkNPGnk8Hj3wwANqaGhQmzZtJEmrVq3S+eefr44dO1pzSktLNWHCBOt1q1atksfjOe57x8bGKjY29iQ6AwAAdtMi1+Ts3LlT//Iv/6LU1FT953/+p/bs2SO/3x9yLc0vf/lLxcTEqKCgQFu2bNGSJUv02GOPhZyBuffee7Vy5Ur913/9l7Zt26aHHnpIH3zwgcaNG9cSZQMAABs5qTM5J2rVqlXavn27tm/fru7du4eMHbljPT4+Xm+88YYKCwuVmZmpzp07a9q0abrzzjutuRdffLEWLVqkBx98UL/5zW+Unp6uZcuW6cILL2yJsgEAgI2E7XtyWhPfkwMAQOSJmO/JAQAACCdCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsKXo1i4gHIwxkqRAINDKlQAAgBN15Lh95Dh+ss6KkLN//35JUkpKSitXAgAATtb+/fsVHx9/0q9zmFONRxEkGAxq165dOuecc+RwOJptu4FAQCkpKdqxY4dcLlezbfdMYOfeJHv3R2+Ry8790Vvkas3+jDHav3+/kpOT5XSe/BU2Z8WZHKfTqe7du7fY9l0uly3/Ykv27k2yd3/0Frns3B+9Ra7W6u9UzuAcwYXHAADAlgg5AADAlgg5pyE2NlbTp09XbGxsa5fS7Ozcm2Tv/ugtctm5P3qLXJHc31lx4TEAADj7cCYHAADYEiEHAADYEiEHAADYEiEHAADYEiHnNMydO1dpaWmKi4tTTk6O1q9f39olHVdxcbEGDhyoc845R127dtX111+v8vLykDmHDh1SYWGhOnXqpA4dOmj48OGqrq4OmVNVVaWhQ4eqXbt26tq1qyZNmqTDhw+Hs5UfNGPGDDkcDk2YMMFaF+m97dy5U7feeqs6deqktm3bqm/fvvrggw+scWOMpk2bpm7duqlt27bKzc1VRUVFyDb27dunkSNHyuVyKSEhQQUFBTpw4EC4WwnR2NioqVOnqmfPnmrbtq3OO+88/e53vwt5Vk0k9fb222/r5z//uZKTk+VwOLRs2bKQ8ebq5aOPPtJll12muLg4paSkaObMmS3d2nF7a2ho0OTJk9W3b1+1b99eycnJ+tWvfqVdu3ZFfG/fd9ddd8nhcGjOnDkh68/U3qQT6++TTz7Rtddeq/j4eLVv314DBw5UVVWVNR6R+1CDU7J48WITExNjSkpKzJYtW8wdd9xhEhISTHV1dWuXdkx5eXlm4cKFZvPmzWbjxo3mX//1X01qaqo5cOCANeeuu+4yKSkpprS01HzwwQdm0KBB5uKLL7bGDx8+bC688EKTm5trPvzwQ/Pqq6+azp07mylTprRGS01av369SUtLMxdddJG59957rfWR3Nu+fftMjx49zOjRo826devMZ599Zl5//XWzfft2a86MGTNMfHy8WbZsmdm0aZO59tprTc+ePc0333xjzbn66qtNv379zNq1a80777xjevXqZUaMGNEaLVkefvhh06lTJ7NixQrz+eefm6VLl5oOHTqYxx57zJoTSb29+uqr5oEHHjAvvviikWReeumlkPHm6KW2ttYkJSWZkSNHms2bN5sXXnjBtG3b1vzxj39std5qampMbm6uWbJkidm2bZvx+XwmOzvbZGZmhmwjEnv7rhdffNH069fPJCcnm9mzZ4eMnam9GfPD/W3fvt0kJiaaSZMmmQ0bNpjt27ebl19+OeSYFon7UELOKcrOzjaFhYXW742NjSY5OdkUFxe3YlUnZ/fu3UaSWbNmjTHm251UmzZtzNKlS605n3zyiZFkfD6fMebbfyhOp9P4/X5rzvz5843L5TJ1dXXhbaAJ+/fvN+np6WbVqlXmiiuusEJOpPc2efJkc+mllx5zPBgMGrfbbWbNmmWtq6mpMbGxseaFF14wxhizdetWI8m8//771pzXXnvNOBwOs3PnzpYr/gcMHTrU5Ofnh6wbNmyYGTlypDEmsnv7/sGkuXqZN2+e6dixY8jfy8mTJ5vzzz+/hTv6/44XBI5Yv369kWS++OILY0zk9/aPf/zDnHvuuWbz5s2mR48eISEnUnozpun+br75ZnPrrbce8zWRug/l46pTUF9fr7KyMuXm5lrrnE6ncnNz5fP5WrGyk1NbWytJSkxMlCSVlZWpoaEhpK+MjAylpqZaffl8PvXt21dJSUnWnLy8PAUCAW3ZsiWM1TetsLBQQ4cODelBivzeXnnlFWVlZemmm25S165dNWDAAD399NPW+Oeffy6/3x/SX3x8vHJyckL6S0hIUFZWljUnNzdXTqdT69atC18z33PxxRertLRUn376qSRp06ZNevfdd3XNNddIiuzevq+5evH5fLr88ssVExNjzcnLy1N5ebm+/vrrMHXzw2pra+VwOJSQkCApsnsLBoO67bbbNGnSJF1wwQVHjUd6b3/729/04x//WHl5eeratatycnJCPtKK1H0oIecU7N27V42NjSH/R0pSUlKS/H5/K1V1coLBoCZMmKBLLrlEF154oSTJ7/crJibG2iEd8d2+/H5/k30fGWtNixcv1oYNG1RcXHzUWKT39tlnn2n+/PlKT0/X66+/rrFjx+qee+7Rs88+G1Lf8f5O+v1+de3aNWQ8OjpaiYmJrdrf/fffr1tuuUUZGRlq06aNBgwYoAkTJmjkyJGSIru372uuXs7kv6tHHDp0SJMnT9aIESOshzpGcm9/+MMfFB0drXvuuafJ8Ujubffu3Tpw4IBmzJihq6++Wm+88YZuuOEGDRs2TGvWrLHqi8R96FnxFHIcrbCwUJs3b9a7777b2qU0ix07dujee+/VqlWrFBcX19rlNLtgMKisrCw98sgjkqQBAwZo8+bNWrBggUaNGtXK1Z2ev/zlL3r++ee1aNEiXXDBBdq4caMmTJig5OTkiO/tbNXQ0KBf/OIXMsZo/vz5rV3OaSsrK9Njjz2mDRs2yOFwtHY5zS4YDEqSrrvuOk2cOFGS1L9/f7333ntasGCBrrjiitYs77RwJucUdO7cWVFRUUddVV5dXS23291KVZ24cePGacWKFXrrrbfUvXt3a73b7VZ9fb1qampC5n+3L7fb3WTfR8ZaS1lZmXbv3q2f/OQnio6OVnR0tNasWaPHH39c0dHRSkpKitjeJKlbt27q06dPyLrevXtbdz4cqe94fyfdbrd2794dMn748GHt27evVfubNGmSdTanb9++uu222zRx4kTrjFwk9/Z9zdXLmfx39UjA+eKLL7Rq1SrrLI4Uub2988472r17t1JTU639yxdffKH77rtPaWlpVm2R2Jv07TEtOjr6B/cxkbgPJeScgpiYGGVmZqq0tNRaFwwGVVpaKo/H04qVHZ8xRuPGjdNLL72k1atXq2fPniHjmZmZatOmTUhf5eXlqqqqsvryeDz6+OOPQ/4xH9mRff8fSDgNHjxYH3/8sTZu3GgtWVlZGjlypPXnSO1Nki655JKjbvf/9NNP1aNHD0lSz5495Xa7Q/oLBAJat25dSH81NTUqKyuz5qxevVrBYFA5OTlh6KJp//znP+V0hu6KoqKirP+6jOTevq+5evF4PHr77bfV0NBgzVm1apXOP/98dezYMUzdHO1IwKmoqNCbb76pTp06hYxHam+33XabPvroo5D9S3JysiZNmqTXX3/dqjsSe5O+PaYNHDjwuPuYiD0+tMrlzjawePFiExsba5555hmzdetWc+edd5qEhISQq8rPNGPHjjXx8fHm73//u/nyyy+t5Z///Kc156677jKpqalm9erV5oMPPjAej8d4PB5r/MgtgkOGDDEbN240K1euNF26dDkjbrP+vu/eXWVMZPe2fv1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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the layout\n", - "fig, ax = plt.subplots()\n", - "plot_turbine_points(fm, ax)\n", - "plot_turbine_labels(fm, ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate data set to illustrate operations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For simplicity assume a fixed wind speed and turbulence intensity and uniform wind direction. Perturb the wind direction by random noise" + "# **Step 0**: Initial data pulldown\n", + "First, we import the data from the common_windfarm_information folder. This may take a while, so we keep these variables unchanged. These are df_scada_raw and df_metmast_raw. These variables are not manipulated throughout the script." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", - "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", + "def load_data():\n", + " root_path = os.getcwd()\n", + " f = os.path.join(root_path, \"postprocessed\", \"df_scada_600s_wspowfiltered.pkl\")\n", + " df_scada = pd.read_pickle(f)\n", "\n", - "# Apply noise\n", - "np.random.seed(0)\n", - "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", - "wind_directions = wind_directions + noise\n", + " # # Optionally: downsample to [x] minute averages to speed up things\n", + " # cols_angular = [c for c in df_scada if ((\"wd_\" in c) or (\"yaw_\" in c))]\n", + " # df_scada = fto.df_downsample(\n", + " # df_scada,\n", + " # cols_angular=cols_angular,\n", + " # window_width=td(seconds=600),\n", + " # )\n", "\n", - "# Set a FLORIS time series object\n", - "time_series = TimeSeries(\n", - " wind_directions=wind_directions, wind_speeds=8.0, turbulence_intensities=0.06\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1753918.68116782, 1753961.25195179, 1753974.02887594,\n", - " 1753984.64239339, 1753954.56987842, 1753926.45363424])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Calculate FLORIS solution\n", - "fm.set(wind_data=time_series)\n", - "fm.run()\n", - "turbine_powers = fm.get_turbine_powers()\n", - "\n", - "# Add random noise to the power output\n", - "turbine_powers = turbine_powers + np.random.normal(0, 25.0, turbine_powers.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the results to create a FLASC dataframe representing hypothetical scada data\n", - "df_scada = FlascDataFrame(\n", - " {\n", - " \"time\": pd.date_range(start=\"1/1/2020\", periods=len(wind_directions), freq=\"600s\"),\n", - " \"wind_directions\": wind_directions,\n", - " \"wind_speeds\": 8.0 * np.ones_like(wind_directions),\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "FlascDataFrame in FLASC format\n", - "
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timewind_directionswind_speedspow_000pow_001pow_002pow_003pow_004pow_005pow_006wd_000wd_001wd_002wd_003wd_004wd_005wd_006
02020-01-01 00:00:000.8820268.01.300483e+066.782295e+051.062299e+061.753996e+061.753925e+061.753954e+061.753919e+060.4801630.5186760.8672501.3595690.6235290.2622910.860956
12020-01-01 00:10:001.2000798.01.336065e+067.107464e+051.097194e+061.753993e+061.753949e+061.753959e+061.753961e+060.2846841.0691041.8623820.6874901.3843951.0952821.194562
22020-01-01 00:20:002.4893698.01.464618e+068.748210e+051.233091e+061.753934e+061.753949e+061.753993e+061.753974e+062.0296912.9304302.2481663.1681702.6812702.3042222.469416
32020-01-01 00:30:004.1204478.01.588592e+061.075059e+061.396982e+061.753984e+061.753965e+061.753941e+061.753985e+063.1530604.6036433.4632093.6163384.6210693.8104295.051782
42020-01-01 00:40:004.9337798.01.633644e+061.164006e+061.466968e+061.753959e+061.753942e+061.753971e+061.753955e+064.0384525.6512415.7514675.1733014.9751734.2382874.768993
......................................................
17952020-01-13 11:10:00354.7220078.05.540390e+053.631354e+054.072392e+051.753920e+061.753931e+061.753940e+061.753937e+06354.104930355.603609355.974084354.515064354.500130354.891368354.998903
17962020-01-13 11:20:00356.0133698.06.913212e+053.469273e+055.184367e+051.753995e+061.753957e+061.753940e+061.753917e+06355.252039355.760453354.907418355.423705356.723372355.720953355.511321
17972020-01-13 11:30:00357.0917258.08.298914e+053.672902e+056.234497e+051.753920e+061.753938e+061.753954e+061.753921e+06356.338511357.314736357.075436357.273982356.775948357.175943356.677064
17982020-01-13 11:40:00357.7646298.09.225341e+053.940003e+056.904134e+051.753967e+061.753943e+061.753960e+061.753947e+06357.366234358.697730357.640309358.981603357.377406358.350070357.973726
17992020-01-13 11:50:00359.1363988.01.097052e+065.031428e+058.536198e+051.753960e+061.753918e+061.753974e+061.753920e+06358.308969358.423708358.505300359.747302359.438663359.130778359.271976
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1800 rows × 17 columns

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" - ], - "text/plain": [ - " time wind_directions wind_speeds pow_000 \\\n", - "0 2020-01-01 00:00:00 0.882026 8.0 1.300483e+06 \n", - "1 2020-01-01 00:10:00 1.200079 8.0 1.336065e+06 \n", - "2 2020-01-01 00:20:00 2.489369 8.0 1.464618e+06 \n", - "3 2020-01-01 00:30:00 4.120447 8.0 1.588592e+06 \n", - "4 2020-01-01 00:40:00 4.933779 8.0 1.633644e+06 \n", - "... ... ... ... ... \n", - "1795 2020-01-13 11:10:00 354.722007 8.0 5.540390e+05 \n", - "1796 2020-01-13 11:20:00 356.013369 8.0 6.913212e+05 \n", - "1797 2020-01-13 11:30:00 357.091725 8.0 8.298914e+05 \n", - "1798 2020-01-13 11:40:00 357.764629 8.0 9.225341e+05 \n", - "1799 2020-01-13 11:50:00 359.136398 8.0 1.097052e+06 \n", - "\n", - " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", - "0 6.782295e+05 1.062299e+06 1.753996e+06 1.753925e+06 1.753954e+06 \n", - "1 7.107464e+05 1.097194e+06 1.753993e+06 1.753949e+06 1.753959e+06 \n", - "2 8.748210e+05 1.233091e+06 1.753934e+06 1.753949e+06 1.753993e+06 \n", - "3 1.075059e+06 1.396982e+06 1.753984e+06 1.753965e+06 1.753941e+06 \n", - "4 1.164006e+06 1.466968e+06 1.753959e+06 1.753942e+06 1.753971e+06 \n", - "... ... ... ... ... ... \n", - "1795 3.631354e+05 4.072392e+05 1.753920e+06 1.753931e+06 1.753940e+06 \n", - "1796 3.469273e+05 5.184367e+05 1.753995e+06 1.753957e+06 1.753940e+06 \n", - "1797 3.672902e+05 6.234497e+05 1.753920e+06 1.753938e+06 1.753954e+06 \n", - "1798 3.940003e+05 6.904134e+05 1.753967e+06 1.753943e+06 1.753960e+06 \n", - "1799 5.031428e+05 8.536198e+05 1.753960e+06 1.753918e+06 1.753974e+06 \n", - "\n", - " pow_006 wd_000 wd_001 wd_002 wd_003 \\\n", - "0 1.753919e+06 0.480163 0.518676 0.867250 1.359569 \n", - "1 1.753961e+06 0.284684 1.069104 1.862382 0.687490 \n", - "2 1.753974e+06 2.029691 2.930430 2.248166 3.168170 \n", - "3 1.753985e+06 3.153060 4.603643 3.463209 3.616338 \n", - "4 1.753955e+06 4.038452 5.651241 5.751467 5.173301 \n", - "... ... ... ... ... ... \n", - "1795 1.753937e+06 354.104930 355.603609 355.974084 354.515064 \n", - "1796 1.753917e+06 355.252039 355.760453 354.907418 355.423705 \n", - "1797 1.753921e+06 356.338511 357.314736 357.075436 357.273982 \n", - "1798 1.753947e+06 357.366234 358.697730 357.640309 358.981603 \n", - "1799 1.753920e+06 358.308969 358.423708 358.505300 359.747302 \n", - "\n", - " wd_004 wd_005 wd_006 \n", - "0 0.623529 0.262291 0.860956 \n", - "1 1.384395 1.095282 1.194562 \n", - "2 2.681270 2.304222 2.469416 \n", - "3 4.621069 3.810429 5.051782 \n", - "4 4.975173 4.238287 4.768993 \n", - "... ... ... ... \n", - "1795 354.500130 354.891368 354.998903 \n", - "1796 356.723372 355.720953 355.511321 \n", - "1797 356.775948 357.175943 356.677064 \n", - "1798 357.377406 358.350070 357.973726 \n", - "1799 359.438663 359.130778 359.271976 \n", - "\n", - "[1800 rows x 17 columns]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add the turbine powers to the dataframe with some added noise\n", - "for t_idx in range(fm.n_turbines):\n", - " df_scada[f\"pow_{t_idx:03d}\"] = turbine_powers[:, t_idx]\n", + " return df_scada\n", "\n", - "# Set the turbine wind directions to be the true wind direction with some added noise\n", - "for t_idx in range(fm.n_turbines):\n", - " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", - " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - " )\n", "\n", - "df_scada" + "df_scada_northing_uncalibrated = load_data()\n", + "df_scada_northing_uncalibrated[\"ti\"] = 0.06 # Assume a certain ambient turbulence intensity" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "#### Northing calibration error\n", - "\n", - "Add to the data two types of northing calibration error:\n", - "1. A constant bias on turbine 001\n", - "2. A change in bias on turbine 002 halfway through the data set" + "# **Step 1**: Initialize FLORIS\n", + "and precalculate a large set of solutions using the parallel computing interface in FLORIS" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "df_scada[\"wd_001\"] = wrap_360(\n", - " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - ")\n", + "# Now we calculate a grid of FLORIS solutions. Since our estimated SCADA\n", + "# data changes as we shift its wind direction, the predicted solutions\n", + "# according to FLORIS will also change. Therefore, we precalculate a grid\n", + "# of FLORIS solutions and insert that into the bias estimation class.\n", + "fm, turbine_weights = load_floris()\n", "\n", - "mid_point = int(len(wind_directions) / 2)\n", - "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - "wd_change[mid_point:] = wd_change[mid_point:] + 30\n", - "wd_change = wrap_360(wd_change)\n", - "df_scada[\"wd_002\"] = wd_change" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Wind direction')" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the wd channels for the turbines\n", - "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"gray\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"gray\")\n", - "ax.legend()\n", - "ax.set_xlabel(\"Time\")\n", - "ax.set_ylabel(\"Wind direction\")" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-11-19 13:33:42\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", - "\u001b[32m2024-11-19 13:33:42\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" - ] - } - ], - "source": [ - "# Finally compute df_approx for use in later algorithms\n", - "# Can compute only at 8m/s for this example\n", - "df_fm_approx = ftools.calc_floris_approx_table(\n", - " fm=fm, # fi=fi_pci,\n", - " wd_array=np.arange(0.0, 360.01, 1.0),\n", - " ws_array=np.array([8.0]),\n", - " ti_array=np.array([0.06]),\n", - ")" + "# Grab the precalculated FLORIS model solutions from the 'setup_floris_model' directory\n", + "root_path = os.getcwd()\n", + "fn_approx = os.path.join(root_path, \"..\", \"00_setup_floris_model\", \"df_fm_approx_gch.ftr\")\n", + "if os.path.exists(fn_approx):\n", + " df_fm_approx = pd.read_feather(fn_approx)\n", + "else:\n", + " raise UserWarning(\n", + " \"Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' \"\n", + " \"for the appropriate wake models first.\"\n", + " )" ] }, { @@ -633,107 +131,80 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Cross-Check Northing calibration " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`crosscheck_northing_offset_consistency` is a function to check if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. " + "# **Step 2**: Cross-compare wind direction measurements\n", + "and see if the relative offset between turbines is consistent. If the offset is consistent, then we know we can find a single offset value that would align the two turbine's northings. If this is not the case, one or both turbines likely experience jumps in their nacelle calibration throughout the timeseries. The current functionality is limited and cannot account for this yet." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T006 T001 T002 T005 T003\n", - "0 0.0 -30.0 0.0 0.0 0.0\n", - "1 0.0 -30.0 0.0 0.0 0.0\n", - "2 0.0 -30.0 0.0 0.0 0.0\n", - "3 0.0 -30.0 -26.0 0.0 0.0\n", - "4 0.0 -30.0 -30.0 0.0 0.0\n", - "5 0.0 -30.0 -30.0 0.0 0.0\n", - "6 0.0 -30.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T002 T006 T005 T003 T000\n", - "0 30.0 30.0 30.0 30.0 30.0\n", - "1 30.0 30.0 30.0 30.0 30.0\n", - "2 30.0 30.0 30.0 30.0 30.0\n", - "3 4.0 30.0 30.0 30.0 30.0\n", - "4 0.0 30.0 30.0 30.0 30.0\n", - "5 0.0 30.0 30.0 30.0 30.0\n", - "6 0.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m T001 T003 T005 T000 T006\n", - "0 -30.0 0.0 0.0 -0.0 0.0\n", - "1 -30.0 0.0 0.0 -0.0 0.0\n", - "2 -30.0 0.0 0.0 -0.0 0.0\n", - "3 -4.0 26.0 26.0 26.0 26.0\n", - "4 -0.0 30.0 30.0 30.0 30.0\n", - "5 -0.0 30.0 30.0 30.0 30.0\n", - "6 -0.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 13:36:21\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T005 T002 T001 T004 T006\n", - "0 0.0 -0.0 -30.0 0.0 0.0\n", - "1 0.0 -0.0 -30.0 0.0 0.0\n", - "2 0.0 -0.0 -30.0 0.0 0.0\n", - "3 0.0 -26.0 -30.0 0.0 0.0\n", - "4 0.0 -30.0 -30.0 0.0 0.0\n", - "5 0.0 -30.0 -30.0 0.0 0.0\n", - "6 0.0 -30.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T002 T005 T001 T006\n", - "0 -0.0 0.0 0.0 -30.0 0.0\n", - "1 -0.0 0.0 0.0 -30.0 0.0\n", - "2 -0.0 0.0 0.0 -30.0 0.0\n", - "3 -0.0 -26.0 0.0 -30.0 0.0\n", - "4 -0.0 -30.0 0.0 -30.0 0.0\n", - "5 -0.0 -30.0 0.0 -30.0 0.0\n", - "6 -0.0 -30.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T003 T001 T006 T002 T000\n", - "0 -0.0 -30.0 0.0 -0.0 -0.0\n", - "1 -0.0 -30.0 0.0 -0.0 -0.0\n", - "2 -0.0 -30.0 0.0 -0.0 -0.0\n", - "3 -0.0 -30.0 0.0 -26.0 -0.0\n", - "4 -0.0 -30.0 0.0 -30.0 -0.0\n", - "5 -0.0 -30.0 0.0 -30.0 -0.0\n", - "6 -0.0 -30.0 0.0 -30.0 -0.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m T001 T005 T000 T003 T002\n", - "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "3 -30.0 -0.0 -0.0 -0.0 -26.0\n", - "4 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "5 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "6 -30.0 -0.0 -0.0 -0.0 -30.0\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 13:36:22\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-10-16 11:44:52\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-10-16 11:44:54\u001b[0m T006 T001 T002 T005 T003\n", + "0 18.0 16.0 -6.0 14.0 46.0\n", + "1 18.0 16.0 -6.0 14.0 46.0\n", + "2 18.0 16.0 -6.0 14.0 46.0\n", + "3 18.0 14.0 -6.0 14.0 46.0\n", + "\u001b[32m2024-10-16 11:44:54\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-10-16 11:44:56\u001b[0m T002 T006 T005 T003 T000\n", + "0 -22.0 2.0 -2.0 30.0 -16.0\n", + "1 -20.0 2.0 -2.0 30.0 -16.0\n", + "2 -20.0 2.0 -2.0 30.0 -16.0\n", + "3 -22.0 2.0 -2.0 30.0 -14.0\n", + "\u001b[32m2024-10-16 11:44:56\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-10-16 11:44:57\u001b[0m T001 T003 T005 T000 T006\n", + "0 22.0 52.0 20.0 6.0 24.0\n", + "1 20.0 52.0 20.0 6.0 24.0\n", + "2 20.0 52.0 20.0 6.0 24.0\n", + "3 22.0 52.0 20.0 6.0 24.0\n", + "\u001b[32m2024-10-16 11:44:57\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-10-16 11:44:58\u001b[0m T005 T002 T001 T004 T006\n", + "0 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "1 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "2 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "3 -32.0 -52.0 -30.0 -30.0 -28.0\n", + "\u001b[32m2024-10-16 11:44:58\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T002 T005 T001 T006\n", + "0 30.0 -22.0 -2.0 -2.0 2.0\n", + "1 30.0 -22.0 -2.0 -2.0 2.0\n", + "2 30.0 -22.0 -2.0 -2.0 2.0\n", + "3 30.0 -22.0 -2.0 -2.0 2.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T003 T001 T006 T002 T000\n", + "0 32.0 2.0 4.0 -20.0 -14.0\n", + "1 32.0 2.0 4.0 -20.0 -14.0\n", + "2 32.0 2.0 4.0 -20.0 -14.0\n", + "3 32.0 2.0 4.0 -20.0 -14.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m T001 T005 T000 T003 T002\n", + "0 -2.0 -4.0 -18.0 28.0 -24.0\n", + "1 -2.0 -4.0 -18.0 28.0 -24.0\n", + "2 -2.0 -4.0 -18.0 28.0 -24.0\n", + "3 -2.0 -4.0 -18.0 28.0 -24.0\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-10-16 11:44:59\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['clean', 'clean', 'bad', 'clean', 'clean', 'clean', 'clean']\n" + "['clean', 'clean', 'clean', 'clean', 'clean', 'clean', 'clean']\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -744,57 +215,19 @@ ], "source": [ "# Create a copy in which we mark the wd measurements of turbines with northing drift as faulty\n", - "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", + "df_scada_marked_faulty_northing_drift = df_scada_northing_uncalibrated.copy()\n", "\n", "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", - " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True\n", ")\n", - "print(turb_wd_consistency)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`crosscheck_northing_offset_consistency` detects that T002 contains a probable jump, one solution is to then remove T002's wind direction data from consideration however this is not done in this notebook as we next take advantage of HOGER recalibration. The code to do this is included below in comments" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# # Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", - "# faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", - "# for ti in np.where(faulty_turbines)[0]:\n", - "# df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Homegenization with HOGER" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", - " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", + "print(turb_wd_consistency)\n", "\n", - " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" + "# Mark wind direction measurements of turbines with inconsistent calibration as faulty\n", + "faulty_turbines = [not s == \"clean\" for s in turb_wd_consistency]\n", + "for ti in np.where(faulty_turbines)[0]:\n", + " df_scada_marked_faulty_northing_drift[\"wd_{:03d}\".format(ti)] = np.nan" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "attachments": {}, "cell_type": "markdown", @@ -3688,9 +3121,11 @@ } ], "metadata": { + "interpreter": { + "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" + }, "kernelspec": { - "display_name": ".venv", - "language": "python", + "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", "name": "python3" }, "language_info": { @@ -3703,9 +3138,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.10.4" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8f733c0fbb301080c2bcf96db7ac54d1ef0d7be04117d635d35c165c40504989" + } + } }, "nbformat": 4, "nbformat_minor": 2 From 4b28fab549e43766b770d0e7177c3f80a5a056f5 Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 25 Nov 2024 21:41:26 -0700 Subject: [PATCH 29/31] Better function name --- .../03_northing_calibration_hoger.ipynb | 3112 +++++++++-------- .../northing_offset_change_hoger.py | 3 +- tests/northing_offset_change_hoger_test.py | 22 +- 3 files changed, 1592 insertions(+), 1545 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb index e5264a1b..d71f1451 100644 --- a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb +++ b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ " filtering as filt,\n", " northing_offset as nof,\n", ")\n", - "from flasc.data_processing.northing_offset_change_hoger import homogenize\n", + "from flasc.data_processing.northing_offset_change_hoger import homogenize_hoger\n", "from flasc.utilities import (\n", " floris_tools as ftools,\n", " optimization as flopt,\n", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -85,7 +85,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -549,7 +549,7 @@ "[1800 rows x 25 columns]" ] }, - "execution_count": 29, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -613,7 +613,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 31, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -645,15 +645,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:20\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + "\u001b[32m2024-11-25 21:39:22\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-11-25 21:39:22\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" ] } ], @@ -685,15 +685,15 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T006 T001 T002 T005 T003\n", + "\u001b[32m2024-11-25 21:39:22\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T006 T001 T002 T005 T003\n", "0 0.0 -30.0 0.0 0.0 0.0\n", "1 0.0 -30.0 0.0 0.0 0.0\n", "2 0.0 -30.0 0.0 0.0 0.0\n", @@ -701,8 +701,8 @@ "4 0.0 -30.0 -46.0 0.0 0.0\n", "5 0.0 -30.0 -44.0 0.0 0.0\n", "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T002 T006 T005 T003 T000\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T002 T006 T005 T003 T000\n", "0 30.0 30.0 30.0 30.0 30.0\n", "1 30.0 30.0 30.0 30.0 30.0\n", "2 30.0 30.0 30.0 30.0 30.0\n", @@ -710,8 +710,8 @@ "4 -14.0 30.0 30.0 30.0 30.0\n", "5 -16.0 30.0 30.0 30.0 30.0\n", "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T001 T003 T005 T000 T006\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T001 T003 T005 T000 T006\n", "0 -30.0 0.0 0.0 -0.0 0.0\n", "1 -30.0 0.0 0.0 -0.0 0.0\n", "2 -30.0 0.0 0.0 -0.0 0.0\n", @@ -719,8 +719,8 @@ "4 14.0 44.0 46.0 46.0 46.0\n", "5 16.0 46.0 46.0 44.0 46.0\n", "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T005 T002 T001 T004 T006\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T005 T002 T001 T004 T006\n", "0 0.0 -0.0 -30.0 0.0 0.0\n", "1 0.0 -0.0 -30.0 0.0 0.0\n", "2 0.0 -0.0 -30.0 0.0 0.0\n", @@ -728,8 +728,8 @@ "4 0.0 -44.0 -30.0 0.0 0.0\n", "5 0.0 -46.0 -30.0 0.0 0.0\n", "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T003 T002 T005 T001 T006\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T003 T002 T005 T001 T006\n", "0 -0.0 0.0 0.0 -30.0 0.0\n", "1 -0.0 0.0 0.0 -30.0 0.0\n", "2 -0.0 0.0 0.0 -30.0 0.0\n", @@ -737,8 +737,8 @@ "4 -0.0 -44.0 0.0 -30.0 0.0\n", "5 -0.0 -46.0 0.0 -30.0 0.0\n", "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T003 T001 T006 T002 T000\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T003 T001 T006 T002 T000\n", "0 -0.0 -30.0 0.0 -0.0 -0.0\n", "1 -0.0 -30.0 0.0 -0.0 -0.0\n", "2 -0.0 -30.0 0.0 -0.0 -0.0\n", @@ -746,8 +746,8 @@ "4 -0.0 -30.0 0.0 -46.0 -0.0\n", "5 -0.0 -30.0 0.0 -46.0 -0.0\n", "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m T001 T005 T000 T003 T002\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m T001 T005 T000 T003 T002\n", "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", @@ -755,13 +755,13 @@ "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:20\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -801,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -825,12 +825,12 @@ "The `homogenize` function implements the HOGER method for recalibrating northing measurements. HOGER was developed by Paul Poncet (https://github.com/engie-paul-poncet)\n", " and Thomas Duc (https://github.com/engie-thomas-duc) of Engie, and Rubén González-Lope (https://github.com/rglope) and Alvaro Gonzalez Salcedo (https://github.com/alvarogonzalezsalcedo) of CENER within the TWAIN project.\n", "\n", - " The `homogenize` will remove apparant jumps in northing correction (but does not confirm the final level is unbiased overall)" + " The `homogenize` will remove apparent jumps in northing correction (but does not confirm the final level is unbiased overall)" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -844,7 +844,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/pfleming/Projects/FLASC/flasc/flasc/data_processing/northing_offset_change_hoger.py:105: UserWarning: Encountered a tie, and the difference between minimal and maximal value is > length('x') * 0.05.\n", + "/Users/pfleming/Projects/FLASC/flasc/flasc/data_processing/northing_offset_change_hoger.py:110: UserWarning: Encountered a tie, and the difference between minimal and maximal value is > length('x') * 0.05.\n", " The distribution could be multimodal\n", " warnings.warn(\n" ] @@ -897,14 +897,14 @@ "0 1 wd_002 6 -45.005012 899.5 2020-01-07 05:40:00" ] }, - "execution_count": 35, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_scada_non_homogenized = df_scada.copy()\n", - "df_scada_homogenized, d2 = homogenize(df_scada_marked_faulty_northing_drift, threshold=10)\n", + "df_scada_homogenized, d2 = homogenize_hoger(df_scada_marked_faulty_northing_drift, threshold=10)\n", "\n", "# Show the search results\n", "d2" @@ -919,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -928,7 +928,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 36, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -980,15 +980,15 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T006 T001 T002 T005 T003\n", + "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T006 T001 T002 T005 T003\n", "0 0.0 -30.0 -46.0 0.0 0.0\n", "1 0.0 -30.0 -46.0 0.0 0.0\n", "2 0.0 -30.0 -44.0 0.0 0.0\n", @@ -996,8 +996,8 @@ "4 0.0 -30.0 -46.0 0.0 0.0\n", "5 0.0 -30.0 -44.0 0.0 0.0\n", "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T002 T006 T005 T003 T000\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T002 T006 T005 T003 T000\n", "0 -16.0 30.0 30.0 30.0 30.0\n", "1 -16.0 30.0 30.0 30.0 30.0\n", "2 -14.0 30.0 30.0 30.0 30.0\n", @@ -1005,8 +1005,8 @@ "4 -14.0 30.0 30.0 30.0 30.0\n", "5 -16.0 30.0 30.0 30.0 30.0\n", "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T001 T003 T005 T000 T006\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T001 T003 T005 T000 T006\n", "0 16.0 44.0 46.0 46.0 44.0\n", "1 16.0 46.0 46.0 46.0 46.0\n", "2 14.0 44.0 46.0 44.0 44.0\n", @@ -1014,8 +1014,8 @@ "4 14.0 44.0 46.0 46.0 46.0\n", "5 16.0 46.0 46.0 44.0 46.0\n", "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T005 T002 T001 T004 T006\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T005 T002 T001 T004 T006\n", "0 0.0 -44.0 -30.0 0.0 0.0\n", "1 0.0 -46.0 -30.0 0.0 0.0\n", "2 0.0 -44.0 -30.0 0.0 0.0\n", @@ -1023,8 +1023,8 @@ "4 0.0 -44.0 -30.0 0.0 0.0\n", "5 0.0 -46.0 -30.0 0.0 0.0\n", "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T003 T002 T005 T001 T006\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T003 T002 T005 T001 T006\n", "0 -0.0 -44.0 0.0 -30.0 0.0\n", "1 -0.0 -46.0 0.0 -30.0 0.0\n", "2 -0.0 -44.0 0.0 -30.0 0.0\n", @@ -1032,8 +1032,8 @@ "4 -0.0 -44.0 0.0 -30.0 0.0\n", "5 -0.0 -46.0 0.0 -30.0 0.0\n", "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T003 T001 T006 T002 T000\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T003 T001 T006 T002 T000\n", "0 -0.0 -30.0 0.0 -46.0 -0.0\n", "1 -0.0 -30.0 0.0 -46.0 -0.0\n", "2 -0.0 -30.0 0.0 -46.0 -0.0\n", @@ -1041,8 +1041,8 @@ "4 -0.0 -30.0 0.0 -46.0 -0.0\n", "5 -0.0 -30.0 0.0 -46.0 -0.0\n", "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m T001 T005 T000 T003 T002\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m T001 T005 T000 T003 T002\n", "0 -30.0 -0.0 -0.0 -0.0 -44.0\n", "1 -30.0 -0.0 -0.0 -0.0 -46.0\n", "2 -30.0 -0.0 -0.0 -0.0 -44.0\n", @@ -1050,13 +1050,13 @@ "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -1098,22 +1098,22 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:21\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-25 21:39:24\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -1127,762 +1127,760 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:27\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:28\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-19 15:07:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:33\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:34\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + "\u001b[32m2024-11-25 21:39:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", + "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", + "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", + "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", + "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", + "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -1892,7 +1890,28 @@ "Optimization terminated successfully.\n", " Current function value: -0.999863\n", " Iterations: 1\n", - " Function evaluations: 2\n", + " Function evaluations: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Turbine 0. estimated bias = 0.0 deg.\n" ] }, @@ -1900,14 +1919,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -2097,25 +2109,23 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" ] }, { @@ -2129,64 +2139,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -2196,7 +2201,29 @@ "Optimization terminated successfully.\n", " Current function value: -0.999863\n", " Iterations: 1\n", - " Function evaluations: 2\n", + " Function evaluations: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Turbine 0. estimated bias = 0.0 deg.\n" ] }, @@ -2204,21 +2231,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n" + "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n" ] }, { @@ -2233,147 +2255,149 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:07:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", + "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2387,16 +2411,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n" + "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n" ] }, { @@ -2411,149 +2435,149 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2567,18 +2591,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n" ] }, { @@ -2593,57 +2615,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" ] }, { @@ -2660,16 +2684,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" ] }, { @@ -2683,18 +2707,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n" ] }, { @@ -2709,64 +2731,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:45\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2776,7 +2793,28 @@ "Optimization terminated successfully.\n", " Current function value: -0.999876\n", " Iterations: 1\n", - " Function evaluations: 2\n", + " Function evaluations: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Turbine 4. estimated bias = 0.0 deg.\n" ] }, @@ -2784,21 +2822,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n" ] }, { @@ -2813,59 +2847,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" ] }, { @@ -2875,7 +2909,29 @@ "Optimization terminated successfully.\n", " Current function value: -0.999888\n", " Iterations: 1\n", - " Function evaluations: 2\n", + " Function evaluations: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Turbine 5. estimated bias = 0.0 deg.\n" ] }, @@ -2883,26 +2939,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n" + "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" ] }, { @@ -2917,64 +2963,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-19 15:07:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", + "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" ] }, { @@ -2984,23 +3025,30 @@ "Optimization terminated successfully.\n", " Current function value: -0.999892\n", " Iterations: 1\n", - " Function evaluations: 2\n", - "Turbine 6. estimated bias = 0.0 deg.\n" + " Function evaluations: 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-19 15:07:49\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" + "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-11-25 21:40:01\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Turbine 6. estimated bias = 0.0 deg.\n", " \n", "Wind direction biases: [ 0. 30. 44.9625 0. 0. 0. 0. ]\n" ] @@ -3267,7 +3315,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -3320,7 +3368,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -3347,13 +3395,13 @@ "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-19 15:07:57\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" ] }, { @@ -3426,7 +3474,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3435,7 +3483,7 @@ "Text(0, 0.5, 'Wind direction')" ] }, - "execution_count": 42, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, diff --git a/flasc/data_processing/northing_offset_change_hoger.py b/flasc/data_processing/northing_offset_change_hoger.py index 5b4046c2..c4ce70f7 100644 --- a/flasc/data_processing/northing_offset_change_hoger.py +++ b/flasc/data_processing/northing_offset_change_hoger.py @@ -144,8 +144,7 @@ def _plot_regression(y_data: pd.Series, y_regr: np.ndarray, date_time: pd.Series plt.show() -# TODO: Keep these defaults? -def homogenize( +def homogenize_hoger( scada: Union[pd.DataFrame | FlascDataFrame], var: str = "wd", threshold: int = 1000, diff --git a/tests/northing_offset_change_hoger_test.py b/tests/northing_offset_change_hoger_test.py index 67400eff..f7aae5bf 100644 --- a/tests/northing_offset_change_hoger_test.py +++ b/tests/northing_offset_change_hoger_test.py @@ -5,7 +5,7 @@ from flasc.data_processing.northing_offset_change_hoger import ( _discretize, _shorth_mode, - homogenize, + homogenize_hoger, ) @@ -30,8 +30,8 @@ def test_shorth_mode(): assert result == expected_result -def test_homogenize(): - """Test homogenize function.""" +def test_homogenize_hoger(): + """Test homogenize_hoger function.""" N = 100 df = FlascDataFrame( @@ -56,13 +56,13 @@ def test_homogenize(): df.loc[N // 2 :, "wd_004"] = 20 # If threshold is larger than number of points, df_hom should match df - df_hom, d2 = homogenize(df.copy(), threshold=N * 2) + df_hom, d2 = homogenize_hoger(df.copy(), threshold=N * 2) assert df.equals(df_hom) # If threshold is smaller than number of points, df_hom should homogenize wd_004 - df_hom, d2 = homogenize(df.copy(), threshold=10) + df_hom, d2 = homogenize_hoger(df.copy(), threshold=10) assert not df.equals(df_hom) - assert df_hom["wd_004"].nunique() == 1 # Test homogenized column + assert df_hom["wd_004"].nunique() == 1 # Test homogenize_hoger column # All columns besides wd_004 are unchanged assert df["wd_000"].equals(df_hom["wd_000"]) @@ -71,13 +71,13 @@ def test_homogenize(): assert df["wd_003"].equals(df_hom["wd_003"]) # If threshold == N should homogenize all columns - df_hom, d2 = homogenize(df.copy(), threshold=N) + df_hom, d2 = homogenize_hoger(df.copy(), threshold=N) assert not df.equals(df_hom) assert df_hom["wd_004"].nunique() == 1 # Test homogenized column -def test_homogenize_double_change(): - """Test homogenize function with two changes.""" +def test_homogenize_hoger_double_change(): + """Test homogenize_hoger function with two changes.""" N = 250 df = FlascDataFrame( @@ -103,7 +103,7 @@ def test_homogenize_double_change(): df.loc[2 * N // 3 :, "wd_004"] = 40 # If threshold is smaller than number of points, df_hom should homogenize wd_004 - df_hom, d2 = homogenize(df.copy(), threshold=N // 5) + df_hom, d2 = homogenize_hoger(df.copy(), threshold=N // 5) assert not df.equals(df_hom) assert df_hom["wd_004"].nunique() == 1 # Test homogenized column @@ -114,5 +114,5 @@ def test_homogenize_double_change(): assert df["wd_003"].equals(df_hom["wd_003"]) # If threshold is larger than number of points, df_hom should match df - df_hom, d2 = homogenize(df.copy(), threshold=N * 2) + df_hom, d2 = homogenize_hoger(df.copy(), threshold=N * 2) assert df.equals(df_hom) From 597f5ca6518940d3ce55109e5eb02355993525ef Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 2 Dec 2024 11:24:46 -0700 Subject: [PATCH 30/31] Update notebook --- .../03_northing_calibration_hoger.ipynb | 4273 +++++++++-------- 1 file changed, 2320 insertions(+), 1953 deletions(-) diff --git a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb index d71f1451..3b265f29 100644 --- a/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb +++ b/examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb @@ -46,17 +46,6 @@ "from flasc.utilities.utilities_examples import load_floris_artificial as load_floris" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# User settings\n", - "save_figures = True\n", - "plot_figures_in_notebook = True" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -66,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -85,7 +74,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -123,16 +112,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# Get an array that goes from 0 to 360 in 1 degree increments 5 times\n", - "wind_directions = wrap_360(np.arange(0, 360 * 5, 1))\n", + "# Set wind direction to step from 90 to 270, in steps that are 600 long and 10 degrees wide\n", + "\n", + "wind_directions = np.linspace(90, 270, 10)\n", + "wind_directions = np.tile(wind_directions, 600)\n", + "wind_directions = np.sort(wind_directions)\n", "\n", "# Apply noise\n", "np.random.seed(0)\n", - "noise = np.random.normal(0, 0.5, wind_directions.shape)\n", + "noise = np.random.normal(0, 5.0, wind_directions.shape)\n", "wind_directions = wind_directions + noise\n", "\n", "# Set a FLORIS time series object\n", @@ -143,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -158,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -227,122 +219,122 @@ " \n", " 0\n", " 2020-01-01 00:00:00\n", - " 0.882026\n", + " 98.820262\n", " 8.0\n", " 0.06\n", - " 1.300516e+06\n", - " 6.781713e+05\n", - " 1.062367e+06\n", - " 1.753991e+06\n", - " 1.753944e+06\n", - " 1.753939e+06\n", + " 1.754006e+06\n", + " 1.753879e+06\n", + " 6.357473e+05\n", + " 9.640375e+05\n", + " 9.666355e+05\n", + " 1.753524e+06\n", " ...\n", - " 0.423110\n", - " 1.448289\n", - " 0.878004\n", - " 8.074900\n", - " 8.146094\n", - " 7.801941\n", - " 7.823262\n", - " 7.834075\n", - " 7.960789\n", - " 7.994616\n", + " 99.644321\n", + " 101.107205\n", + " 99.348930\n", + " 8.011852\n", + " 8.162351\n", + " 8.159744\n", + " 7.899556\n", + " 8.076833\n", + " 8.185659\n", + " 8.064289\n", " \n", " \n", " 1\n", " 2020-01-01 00:10:00\n", - " 1.200079\n", + " 92.000786\n", " 8.0\n", " 0.06\n", - " 1.336040e+06\n", - " 7.107407e+05\n", - " 1.097192e+06\n", - " 1.753917e+06\n", - " 1.753948e+06\n", - " 1.753979e+06\n", + " 1.753913e+06\n", + " 1.753904e+06\n", + " 1.409497e+06\n", + " 8.553318e+05\n", + " 9.171484e+05\n", + " 1.753951e+06\n", " ...\n", - " 1.371773\n", - " 1.584267\n", - " 2.277361\n", - " 7.996819\n", - " 7.991431\n", - " 7.963854\n", - " 8.015349\n", - " 7.974499\n", - " 8.037995\n", - " 7.912931\n", + " 82.166085\n", + " 88.043464\n", + " 89.257707\n", + " 8.175628\n", + " 7.808788\n", + " 8.023940\n", + " 8.084883\n", + " 8.080681\n", + " 7.849506\n", + " 7.913113\n", " \n", " \n", " 2\n", " 2020-01-01 00:20:00\n", - " 2.489369\n", + " 94.893690\n", " 8.0\n", " 0.06\n", - " 1.464582e+06\n", - " 8.748157e+05\n", - " 1.233158e+06\n", - " 1.753936e+06\n", - " 1.753922e+06\n", - " 1.753934e+06\n", + " 1.753985e+06\n", + " 1.753891e+06\n", + " 9.842584e+05\n", + " 6.162996e+05\n", + " 6.600864e+05\n", + " 1.753942e+06\n", " ...\n", - " 2.450301\n", - " 1.912880\n", - " 2.967087\n", - " 7.748811\n", - " 7.829283\n", - " 8.108649\n", - " 8.055467\n", - " 7.914157\n", - " 7.978314\n", - " 7.977682\n", + " 94.712330\n", + " 92.252229\n", + " 90.985282\n", + " 7.991109\n", + " 7.892661\n", + " 7.928803\n", + " 8.081048\n", + " 8.073745\n", + " 8.091502\n", + " 8.053268\n", " \n", " \n", " 3\n", " 2020-01-01 00:30:00\n", - " 4.120447\n", + " 101.204466\n", " 8.0\n", " 0.06\n", - " 1.588553e+06\n", - " 1.075012e+06\n", - " 1.396981e+06\n", - " 1.753931e+06\n", - " 1.753918e+06\n", - " 1.753955e+06\n", + " 1.753950e+06\n", + " 1.753555e+06\n", + " 8.123594e+05\n", + " 1.319409e+06\n", + " 1.170321e+06\n", + " 1.751776e+06\n", " ...\n", - " 4.114914\n", - " 4.336638\n", - " 4.208488\n", - " 7.925097\n", - " 7.881330\n", - " 7.936178\n", - " 8.158323\n", - " 7.937358\n", - " 7.871846\n", - " 8.249129\n", + " 110.699134\n", + " 93.116417\n", + " 108.537493\n", + " 7.917771\n", + " 8.077458\n", + " 7.942418\n", + " 7.960415\n", + " 7.766181\n", + " 7.875381\n", + " 8.012211\n", " \n", " \n", " 4\n", " 2020-01-01 00:40:00\n", - " 4.933779\n", + " 99.337790\n", " 8.0\n", " 0.06\n", - " 1.633680e+06\n", - " 1.164021e+06\n", - " 1.466940e+06\n", - " 1.753928e+06\n", - " 1.753947e+06\n", - " 1.753943e+06\n", + " 1.754020e+06\n", + " 1.753852e+06\n", + " 6.487232e+05\n", + " 1.043126e+06\n", + " 1.030381e+06\n", + " 1.753318e+06\n", " ...\n", - " 5.448855\n", - " 6.091371\n", - " 4.720707\n", - " 8.168888\n", - " 8.213761\n", - " 7.982868\n", - " 8.000813\n", - " 8.009052\n", - " 7.944418\n", - " 7.949849\n", + " 95.656436\n", + " 92.461185\n", + " 106.783658\n", + " 7.850214\n", + " 7.973655\n", + " 8.028457\n", + " 8.225742\n", + " 8.147089\n", + " 7.934316\n", + " 7.798261\n", " \n", " \n", " ...\n", @@ -369,187 +361,187 @@ " ...\n", " \n", " \n", - " 1795\n", - " 2020-01-13 11:10:00\n", - " 354.722007\n", + " 5995\n", + " 2020-02-11 15:10:00\n", + " 263.345981\n", " 8.0\n", " 0.06\n", - " 5.540650e+05\n", - " 3.631716e+05\n", - " 4.072597e+05\n", - " 1.753983e+06\n", - " 1.753948e+06\n", - " 1.753977e+06\n", + " 1.753204e+06\n", + " 1.687681e+06\n", + " 1.753965e+06\n", + " 1.730570e+06\n", + " 1.753947e+06\n", + " 8.849585e+05\n", " ...\n", - " 355.064016\n", - " 354.808745\n", - " 354.445800\n", - " 8.138799\n", - " 7.746992\n", - " 7.900044\n", - " 7.845809\n", - " 8.072269\n", - " 8.101749\n", - " 7.899713\n", + " 261.464863\n", + " 265.654828\n", + " 265.006674\n", + " 7.867506\n", + " 8.118957\n", + " 7.879889\n", + " 7.999180\n", + " 8.046216\n", + " 7.930540\n", + " 7.880831\n", " \n", " \n", - " 1796\n", - " 2020-01-13 11:20:00\n", - " 356.013369\n", + " 5996\n", + " 2020-02-11 15:20:00\n", + " 271.762993\n", " 8.0\n", " 0.06\n", - " 6.913342e+05\n", - " 3.469324e+05\n", - " 5.184599e+05\n", - " 1.753937e+06\n", - " 1.753948e+06\n", - " 1.753917e+06\n", + " 1.435174e+06\n", + " 1.752561e+06\n", + " 1.753922e+06\n", + " 9.033320e+05\n", + " 1.753985e+06\n", + " 1.702120e+06\n", " ...\n", - " 356.362617\n", - " 356.601605\n", - " 355.963834\n", - " 8.093367\n", - " 7.981402\n", - " 8.074578\n", - " 7.871787\n", - " 8.043979\n", - " 8.021197\n", - " 7.848560\n", + " 271.559170\n", + " 267.387924\n", + " 271.051594\n", + " 8.028336\n", + " 8.219887\n", + " 7.878284\n", + " 8.086875\n", + " 8.154903\n", + " 8.149477\n", + " 8.080819\n", " \n", " \n", - " 1797\n", - " 2020-01-13 11:30:00\n", - " 357.091725\n", + " 5997\n", + " 2020-02-11 15:30:00\n", + " 267.310576\n", " 8.0\n", " 0.06\n", - " 8.298897e+05\n", - " 3.673236e+05\n", - " 6.234462e+05\n", - " 1.753946e+06\n", - " 1.753947e+06\n", - " 1.753962e+06\n", + " 1.728385e+06\n", + " 1.740995e+06\n", + " 1.753938e+06\n", + " 1.521491e+06\n", + " 1.753960e+06\n", + " 1.223109e+06\n", " ...\n", - " 356.966013\n", - " 356.288613\n", - " 357.729420\n", - " 7.978883\n", - " 7.944376\n", - " 7.938371\n", - " 7.767493\n", - " 7.957102\n", - " 8.015932\n", - " 8.007883\n", + " 267.398639\n", + " 263.998493\n", + " 270.306531\n", + " 8.020642\n", + " 8.044316\n", + " 8.031791\n", + " 8.170447\n", + " 8.253171\n", + " 8.056111\n", + " 7.903335\n", " \n", " \n", - " 1798\n", - " 2020-01-13 11:40:00\n", - " 357.764629\n", + " 5998\n", + " 2020-02-11 15:40:00\n", + " 271.967222\n", " 8.0\n", " 0.06\n", - " 9.225898e+05\n", - " 3.939484e+05\n", - " 6.904172e+05\n", - " 1.753973e+06\n", - " 1.753951e+06\n", - " 1.753965e+06\n", + " 1.408887e+06\n", + " 1.752647e+06\n", + " 1.753962e+06\n", + " 8.717409e+05\n", + " 1.753956e+06\n", + " 1.709693e+06\n", " ...\n", - " 356.899939\n", - " 358.256734\n", - " 357.815352\n", - " 7.894970\n", - " 7.789854\n", - " 7.936672\n", - " 7.996095\n", - " 8.129702\n", - " 8.021848\n", - " 7.820329\n", + " 270.043104\n", + " 273.116864\n", + " 281.039771\n", + " 8.063863\n", + " 7.936743\n", + " 8.230575\n", + " 8.071727\n", + " 7.996428\n", + " 8.013610\n", + " 8.109070\n", " \n", " \n", - " 1799\n", - " 2020-01-13 11:50:00\n", - " 359.136398\n", + " 5999\n", + " 2020-02-11 15:50:00\n", + " 271.432591\n", " 8.0\n", " 0.06\n", - " 1.097109e+06\n", - " 5.031884e+05\n", - " 8.536512e+05\n", - " 1.753939e+06\n", - " 1.753968e+06\n", - " 1.753903e+06\n", + " 1.475155e+06\n", + " 1.752267e+06\n", + " 1.753954e+06\n", + " 9.545584e+05\n", + " 1.753887e+06\n", + " 1.687766e+06\n", " ...\n", - " 359.319785\n", - " 359.078778\n", - " 359.000377\n", - " 7.791736\n", - " 7.844550\n", - " 7.966531\n", - " 8.194921\n", - " 8.053860\n", - " 8.003063\n", - " 7.963603\n", + " 269.690265\n", + " 269.465325\n", + " 269.938743\n", + " 7.972016\n", + " 8.079571\n", + " 8.001622\n", + " 8.017239\n", + " 8.017320\n", + " 8.013297\n", + " 7.983608\n", " \n", " \n", "\n", - "

1800 rows × 25 columns

\n", + "

6000 rows × 25 columns

\n", "" ], "text/plain": [ " time wind_directions wind_speeds ti pow_000 \\\n", - "0 2020-01-01 00:00:00 0.882026 8.0 0.06 1.300516e+06 \n", - "1 2020-01-01 00:10:00 1.200079 8.0 0.06 1.336040e+06 \n", - "2 2020-01-01 00:20:00 2.489369 8.0 0.06 1.464582e+06 \n", - "3 2020-01-01 00:30:00 4.120447 8.0 0.06 1.588553e+06 \n", - "4 2020-01-01 00:40:00 4.933779 8.0 0.06 1.633680e+06 \n", + "0 2020-01-01 00:00:00 98.820262 8.0 0.06 1.754006e+06 \n", + "1 2020-01-01 00:10:00 92.000786 8.0 0.06 1.753913e+06 \n", + "2 2020-01-01 00:20:00 94.893690 8.0 0.06 1.753985e+06 \n", + "3 2020-01-01 00:30:00 101.204466 8.0 0.06 1.753950e+06 \n", + "4 2020-01-01 00:40:00 99.337790 8.0 0.06 1.754020e+06 \n", "... ... ... ... ... ... \n", - "1795 2020-01-13 11:10:00 354.722007 8.0 0.06 5.540650e+05 \n", - "1796 2020-01-13 11:20:00 356.013369 8.0 0.06 6.913342e+05 \n", - "1797 2020-01-13 11:30:00 357.091725 8.0 0.06 8.298897e+05 \n", - "1798 2020-01-13 11:40:00 357.764629 8.0 0.06 9.225898e+05 \n", - "1799 2020-01-13 11:50:00 359.136398 8.0 0.06 1.097109e+06 \n", + "5995 2020-02-11 15:10:00 263.345981 8.0 0.06 1.753204e+06 \n", + "5996 2020-02-11 15:20:00 271.762993 8.0 0.06 1.435174e+06 \n", + "5997 2020-02-11 15:30:00 267.310576 8.0 0.06 1.728385e+06 \n", + "5998 2020-02-11 15:40:00 271.967222 8.0 0.06 1.408887e+06 \n", + "5999 2020-02-11 15:50:00 271.432591 8.0 0.06 1.475155e+06 \n", "\n", " pow_001 pow_002 pow_003 pow_004 pow_005 \\\n", - "0 6.781713e+05 1.062367e+06 1.753991e+06 1.753944e+06 1.753939e+06 \n", - "1 7.107407e+05 1.097192e+06 1.753917e+06 1.753948e+06 1.753979e+06 \n", - "2 8.748157e+05 1.233158e+06 1.753936e+06 1.753922e+06 1.753934e+06 \n", - "3 1.075012e+06 1.396981e+06 1.753931e+06 1.753918e+06 1.753955e+06 \n", - "4 1.164021e+06 1.466940e+06 1.753928e+06 1.753947e+06 1.753943e+06 \n", + "0 1.753879e+06 6.357473e+05 9.640375e+05 9.666355e+05 1.753524e+06 \n", + "1 1.753904e+06 1.409497e+06 8.553318e+05 9.171484e+05 1.753951e+06 \n", + "2 1.753891e+06 9.842584e+05 6.162996e+05 6.600864e+05 1.753942e+06 \n", + "3 1.753555e+06 8.123594e+05 1.319409e+06 1.170321e+06 1.751776e+06 \n", + "4 1.753852e+06 6.487232e+05 1.043126e+06 1.030381e+06 1.753318e+06 \n", "... ... ... ... ... ... \n", - "1795 3.631716e+05 4.072597e+05 1.753983e+06 1.753948e+06 1.753977e+06 \n", - "1796 3.469324e+05 5.184599e+05 1.753937e+06 1.753948e+06 1.753917e+06 \n", - "1797 3.673236e+05 6.234462e+05 1.753946e+06 1.753947e+06 1.753962e+06 \n", - "1798 3.939484e+05 6.904172e+05 1.753973e+06 1.753951e+06 1.753965e+06 \n", - "1799 5.031884e+05 8.536512e+05 1.753939e+06 1.753968e+06 1.753903e+06 \n", + "5995 1.687681e+06 1.753965e+06 1.730570e+06 1.753947e+06 8.849585e+05 \n", + "5996 1.752561e+06 1.753922e+06 9.033320e+05 1.753985e+06 1.702120e+06 \n", + "5997 1.740995e+06 1.753938e+06 1.521491e+06 1.753960e+06 1.223109e+06 \n", + "5998 1.752647e+06 1.753962e+06 8.717409e+05 1.753956e+06 1.709693e+06 \n", + "5999 1.752267e+06 1.753954e+06 9.545584e+05 1.753887e+06 1.687766e+06 \n", "\n", " ... wd_004 wd_005 wd_006 ws_000 ws_001 ws_002 \\\n", - "0 ... 0.423110 1.448289 0.878004 8.074900 8.146094 7.801941 \n", - "1 ... 1.371773 1.584267 2.277361 7.996819 7.991431 7.963854 \n", - "2 ... 2.450301 1.912880 2.967087 7.748811 7.829283 8.108649 \n", - "3 ... 4.114914 4.336638 4.208488 7.925097 7.881330 7.936178 \n", - "4 ... 5.448855 6.091371 4.720707 8.168888 8.213761 7.982868 \n", + "0 ... 99.644321 101.107205 99.348930 8.011852 8.162351 8.159744 \n", + "1 ... 82.166085 88.043464 89.257707 8.175628 7.808788 8.023940 \n", + "2 ... 94.712330 92.252229 90.985282 7.991109 7.892661 7.928803 \n", + "3 ... 110.699134 93.116417 108.537493 7.917771 8.077458 7.942418 \n", + "4 ... 95.656436 92.461185 106.783658 7.850214 7.973655 8.028457 \n", "... ... ... ... ... ... ... ... \n", - "1795 ... 355.064016 354.808745 354.445800 8.138799 7.746992 7.900044 \n", - "1796 ... 356.362617 356.601605 355.963834 8.093367 7.981402 8.074578 \n", - "1797 ... 356.966013 356.288613 357.729420 7.978883 7.944376 7.938371 \n", - "1798 ... 356.899939 358.256734 357.815352 7.894970 7.789854 7.936672 \n", - "1799 ... 359.319785 359.078778 359.000377 7.791736 7.844550 7.966531 \n", + "5995 ... 261.464863 265.654828 265.006674 7.867506 8.118957 7.879889 \n", + "5996 ... 271.559170 267.387924 271.051594 8.028336 8.219887 7.878284 \n", + "5997 ... 267.398639 263.998493 270.306531 8.020642 8.044316 8.031791 \n", + "5998 ... 270.043104 273.116864 281.039771 8.063863 7.936743 8.230575 \n", + "5999 ... 269.690265 269.465325 269.938743 7.972016 8.079571 8.001622 \n", "\n", " ws_003 ws_004 ws_005 ws_006 \n", - "0 7.823262 7.834075 7.960789 7.994616 \n", - "1 8.015349 7.974499 8.037995 7.912931 \n", - "2 8.055467 7.914157 7.978314 7.977682 \n", - "3 8.158323 7.937358 7.871846 8.249129 \n", - "4 8.000813 8.009052 7.944418 7.949849 \n", + "0 7.899556 8.076833 8.185659 8.064289 \n", + "1 8.084883 8.080681 7.849506 7.913113 \n", + "2 8.081048 8.073745 8.091502 8.053268 \n", + "3 7.960415 7.766181 7.875381 8.012211 \n", + "4 8.225742 8.147089 7.934316 7.798261 \n", "... ... ... ... ... \n", - "1795 7.845809 8.072269 8.101749 7.899713 \n", - "1796 7.871787 8.043979 8.021197 7.848560 \n", - "1797 7.767493 7.957102 8.015932 8.007883 \n", - "1798 7.996095 8.129702 8.021848 7.820329 \n", - "1799 8.194921 8.053860 8.003063 7.963603 \n", + "5995 7.999180 8.046216 7.930540 7.880831 \n", + "5996 8.086875 8.154903 8.149477 8.080819 \n", + "5997 8.170447 8.253171 8.056111 7.903335 \n", + "5998 8.071727 7.996428 8.013610 8.109070 \n", + "5999 8.017239 8.017320 8.013297 7.983608 \n", "\n", - "[1800 rows x 25 columns]" + "[6000 rows x 25 columns]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -562,7 +554,7 @@ "# Set the turbine wind directions to be the true wind direction with some added noise\n", "for t_idx in range(fm.n_turbines):\n", " df_scada[f\"wd_{t_idx:03d}\"] = wrap_360(\n", - " wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + " wind_directions + np.random.normal(0, 5.0, wind_directions.shape)\n", " )\n", "\n", "# Set wind speeds to be fixed with small noise\n", @@ -587,39 +579,39 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df_scada[\"wd_001\"] = wrap_360(\n", - " 30.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", + " 15.0 + wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", ")\n", "\n", "mid_point = int(len(wind_directions) / 2)\n", "wd_change = wind_directions + np.random.normal(0, 0.5, wind_directions.shape)\n", - "wd_change[mid_point:] = wd_change[mid_point:] + 45\n", + "wd_change[mid_point:] = wd_change[mid_point:] - 45.0\n", "wd_change = wrap_360(wd_change)\n", "df_scada[\"wd_002\"] = wd_change" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Wind direction')" + "Text(0, 0.5, 'Wind direction (deg)')" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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2zO9caWmpc926dc7S0tLTu9kG4Pe//70TcK5fv959rFu3bs6UlBS/sa+99pqzZcuWzvDwcOe1117rnDZtmjM2Njbotb744gtn9+7dnXa73Xn++ec7X3/9db8xf//7352tWrVy2u1250UXXeT8+uuvvc5PmTLFCfg9jObSc9FFFzlnzZrlfr99+3Yn4FyzZo2hnYCzqKjIWVpa6uzUqZNz5MiRXmOuu+4658UXX+ysrKx0Op1OZ15envPiiy92hoeHO2NiYpwXXXSR85VXXnGP37hxo/PSSy912u12Z/v27Z15eXlOwPmf//wnoM333HOP1z1GR0c7L7zwQuf7778f8D7Kysqc9957rzM2NtYZFxfnHD16tHPChAnObt26OZ1Op3P//v3OG264wZmamuq02+3O1q1bOydPnuysqqpyzzlw4EDn008/XePneSY5l3+fBEEQBEEQGgtKXjudy5fX77zl5drcu52wyAmn3GvVxPbtTudbbzmdrq/Pp02XLs5a1x050jMGnM5bbqmftc8mNelQPSanM9ju3/XPsGHDWLJkCfv27SM2NpauXbvyyCOPcOWVVwJQVlbGQw89xD//+U9OnTpFbm4uL730klfo+86dOxk9ejT5+flERkZyzz338Je//AWrNfh0/eLiYmJjYzl27JhfqHxZWRnbt28nPT39tHOYhTPDJ598wsMPP8wPP/wQsPK6AD/++COXX345mzZtIjY2tkFskN8nQRAEQRCEmqmogKNHQStBdOoUPPssXH01dOumjmkdfb/+Gvr0qb+1T57U6nc95jpyCaC0WU2qUbPnH/+AkSODX+/wYZg3D26/HZo39xxv1gy0YNtA644apdbT+NWvwNUJ+ZyhJh2qp0EL0b322ms1ng8LC2PmzJnMnDkz4JjWrVuzcOHC+jZNOIcYPHgwmzdv5qeffgq6oOAvkX379vHmm282mGAXBEEQBEEQaqdnT/jhB9iyBdq2hb/+FaZMUUXXysrA4Tgz65aWgk/TIeBQSHPk54cm2u+4Q4Xgz5sXet68tlEQ6H1TQtySQq107tyZqKgow8e8efMa2jwAHnzwQRHstTBgwAByc3Mb2gxBEARBEAShBn74QT3/+9/qWVfCiLAwePVVz/vTiZl+9lno0gW0ut7LlkFxse+o0BYINeh10SL1vHJlaNcZ0ZRFe4O3fBMaPwsXLqQiQEnG5OTks2yNIAiCIAiCIPxyGTGi9jH5+aq6+gsvQCC/1kMPqedu3WD//uA3AMaNgx074F//alhvt+8GgYh24RdN69atG9oEQRAEQRAEQWiyPPMMbNsGL73kOaY1RwpWTD/yiHr+619V5XVQIe+1NYU6cAB+/DHQOp6DiYnw5z+rNmsAq1ZB797eo4MVztXVNXvlg5lHwuMFQRAEQRAEQRCEs8L48TBrFnz4oefY00+r52D6sRcVwd/+ph579niO//STev75Z5g9G44dM75++/baNwcOH4b77/e8NwrELSmB119XY404fhyuvRaSk+G55wKvVZMtTie89RZs2FCzvU0JEe2CIAiCIAiCIAiNgBtv9D9WWlr7dfp+7fr+5Taber7uOlUg7t57ja+vqgraRDeaZ1u/qfDvf8PQoTBokP/4v/4VYmJgwQK1iTB2bHDrPPAAXHKJZ5Pg/ffhN7+BJUuM7WmKiGgXBEEQBEEQBEFohHz/vcpPrw29Z9pi8bzWhOzy5er5gw/Us+9GwA03qPZxBjPXuO5XX0FcnP/xb7+FXbtU9EDLlvDaazBhQo1TeVFdfQz4N7CXv/9dFcnr1w/uvBMWLza+RkS7IAiCIAiCIAiCcFZ54gnfI+uBDwCPa93p9A5H14v2sjK4/nrvGSorwagD8JQpodlmMinv/fHjxudnzoTRo1WI/vDhtc+3ebPywANUVPwL+A54xX3+66/h7bdVmH8ge37zG+XN37IlpFtp9EghOkEQBEEQBEEQhEaIv/f4XZT3uwi4Gkjm9tth/nzPCH2Bt3Xr1EPPwYPG+ejG1Oxpj4gIfC4mJtg1FO3bq+eFC6G0NLT+8KAiErR8/rvvVt75poJ42gVD5s6dS5xRrEsjZOPGjaSkpHA80DZfAHJycnjwwQfPjFEhYDKZ+ECLVTqHycvLo3v37lQHUy1FEARBEARBAFS7tdD4GfgMeJFly7wFO3h72o04nd7uvnz7beBzYWF1m/Pqq32L730CzAZqTrzXF+A7eLBuazdWRLQL9UJ+fj49e/bE4XCQkZHB3Llz/cbMnDmTNm3aEBYWRp8+fVi5cqX73JEjR/jd735Hhw4dCA8Pp1WrVjzwwAMcC1TiUsfEiRP53e9+R3R0tNsWk8nkfoSHh9O5c2deeeUVr+v+/e9/8/jjj5/ejdfC/v37+d3vfsf555+Pw+EgLS2Na6+9liW+lTOaAIMGDcJmszFv3ryGNkUQBEEQBOGcYfToUK84gQqTX8pDD5X7nX3ssZqvXrMm1PWMOXs55N8APwHBx7zX1E7uXKSJ3Y7QEGzfvp3BgwfTv39/CgsLefDBBxk+fDiffvqpe8y7777LuHHjmDJlCqtXr6Zbt27k5uZy0LUNtnfvXvbu3cu0adP44YcfmDt3Lnl5eQwbNqzGtXft2sWCBQu416AU5saNG9m3bx/r1q3jvvvuY/To0V5iOSEhwS30zwQ7duygV69efP755zzzzDN8//335OXl0b9/f+7X98toQtx777288MILDW2GIAiCIAjCOUNNAZdKGFcDPwLFujNaTru/S/mTT2pe79prtVf7ga9d8wcisFveM48vO4EvqK6uQ0n6eqKpFaUT0R4qTqdqQNgQjyBjWRYsWEBcXBxVrt4NhYWFmEwmJuhKNg4fPpy77rrL/X7u3Lm0atWKiIgIbrzxRg4Haq5owKxZs0hPT2f69OlkZmYyZswYbrnlFmbMmOEe8+yzzzJixAiGDBlCp06dmDVrFhEREcyZMweALl268K9//Ytrr72Wtm3bcvnll/Pkk0/y8ccfU6nvYeHD/Pnz6datG+edd57fuebNm5OSkkJ6ejoPPPAA6enprF692n3eNzz+//7v/+jduzfR0dGkpKRwxx13uDcVAIqKirjzzjtJSkoiPDycdu3a8frrrwe07be//S0mk4mVK1dy88030759ezp37sy4ceP42qc8588//8yNN95IREQE7dq146OPPnKfq6qqYtiwYaSnpxMeHk6HDh14/vnnva6/9957ueGGG5g2bRqpqak0a9aM+++/nwpdwtK+ffsYPHgw4eHhpKen8/bbb9OmTRue0zXJPHr0KMOHDycpKYmYmBguv/xy1q5d6z6/du1a+vfvT3R0NDExMfTq1YtvdXFR1157Ld9++y1b9b1GBEEQBEEQhNNgFfAeUN+OkVlAHrAY+BLQe+33udYN3G9OfU3eCxT6nHkdKGDPnhpi5+tE8NJVRPsvnZMnISqqYR4nTwZl4mWXXcbx48dZ44p9KSgoIDExkXxdv4iCggJycnIAWLFiBcOGDWPMmDEUFhbSv39/nvAvVRmQ5cuXM2DAAK9jubm5LHf1ligvL2fVqlVeY8xmMwMGDHCPMeLYsWPExMRgtQaul7h06VJ69+5do31Op5O8vDx27dpFnz59Ao6rqKjg8ccfZ+3atXzwwQfs2LHDy4P/pz/9iXXr1vHf//6X9evX8/LLL5OYmGg415EjR8jLy+P+++8nMjLS77xvvYCpU6dy66238t1333H11Vdz5513cuTIEQCqq6tp2bIl7733HuvWrWPy5MlMmjSJ+T4JTF988QVbt27liy++4I033mDu3LleaQp33303e/fuJT8/n3/961+88sorXpsSAL/61a84ePAg//3vf1m1ahU9e/bkiiuucNty55130rJlS7755htWrVrFhAkTsGkNQIFWrVqRnJzM0qVLA37OgiAIgiAIgi/fA1/5HV20CFRYeIXr4Ut9JKgvR+XIL9Id2wgcB36o5dpXUNXst/md2bv3SD3YpsdIiR/DKNe9qYl2qR7fBImNjaV79+7k5+fTu3dv8vPzGTt2LFOnTuXEiRMcO3aMLVu2kJ2dDcDzzz/PoEGDGD9+PADt27dn2bJl5OXlBbXe/v37SU5O9jqWnJxMcXExpaWlFBUVUVVVZThmw4YNhnP+/PPPPP7444wcObLGtXfu3BlQtLds2RKAU6dOUV1dzZ///Gf69esXcK6hQ4e6X59//vm88MILXHjhhZw4cYKoqCh27dpFjx493Ou1adMm4FxbtmzB6XTSsWPHGu3XuPfee/n1r38NwFNPPcULL7zAypUr3XniU6dOdY9NT09n+fLlzJ8/n1tvvdV9PD4+nhdffBGLxULHjh0ZPHgwS5YsYcSIEWzYsIHPPvuMb775xm3/q6++Srt27dzXf/nll6xcuZKDBw/icDgAmDZtGh988AHvv/8+I0eOZNeuXTz88MPu+9Jfr9GiRQt27twZ1H0LgiAIgiAIAP9yPWcAnu/Mym9yGCXomwOdXGfqsZqcG6PvbxVAGcojvx24EWhjMO4gSuCfhxLRp9i7t77t8/U37wSmApnAw15niotpUoinPVQiIuDEiYZ51NRTwYfs7Gzy8/NxOp0sXbqUm266iczMTL788ksKCgpo0aKFW3CtX7/ezwOdlZVVrx9bKBQXFzN48GA6derEY7VU0igtLSUsQGnKpUuXUlhYSGFhIa+++ipPPfUUL7/8csC5Vq1axbXXXkurVq2Ijo52b2rs2rULgNGjR/POO+/QvXt3xo8fz7Ia+kg4QyzL2bVrV/fryMhIYmJivLzgM2fOpFevXiQlJREVFcUrr7zitkujc+fOWHTlQlNTU91zbNy4EavVSs+ePd3nMzIyiI+Pd79fu3YtJ06coFmzZkRFRbkf27dvd4e7jxs3juHDhzNgwAD+8pe/GIbBh4eHczLIqBBBEARBEARBj9F3qI2uZ+274U7X68AppHVnCyrkXc+HqBD4Y8Bcn3NKoMMGYDXwMfAtsJLVq+ujhLv+O7Wv+3w+ysPvn8Rf/xsGDYt42kPFZAKDcOfGRk5ODnPmzGHt2rXYbDY6duxITk4O+fn5FBUVuQVpfZCSksKBAwe8jh04cICYmBjCw8OxWCxYLBbDMSkpKV7Hjh8/zqBBg4iOjuY///mPV+i1EYmJiRQVFRmeS09Pd4ehd+7cmRUrVvDkk08y2qBEZ0lJCbm5ueTm5jJv3jySkpLYtWsXubm5lJer/J6rrrqKnTt3snDhQhYvXswVV1zB/fffz7Rp0/zma9euHSaTKWAkgS++92kymdyt09555x3+8Ic/MH36dLKysoiOjuaZZ55hxYoVQc8RDCdOnCA1NdUrjUJD+xwfe+wx7rjjDj755BP++9//MmXKFN555x1uvPFG99gjR46QlJQU9LqCIAiCIAi/VIw9wpuABcBNGHu1j7qeQ2t3XDsngbcMjtf0ffZrVM/4k4DmDFJ58GVlO4ESYDfQnsD+4mIgGuPwdydqg2IbMBhI152rvctUU0E87U0ULa99xowZboGuifb8/Hx3PjtAZmamnwD0LZRWE1lZWX4tzBYvXuz21tvtdnr16uU1prq6miVLlnh59IuLixk4cCB2u52PPvoooAddT48ePVi3bl1QdlosFkpLjYtpbNiwgcOHD/OXv/yFyy67jI4dO/rlewMkJSVxzz338NZbb/Hcc8/5tZHTSEhIIDc3l5kzZ1JSUuJ3/ujRo0HZDPDVV19x8cUX89vf/pYePXqQkZERcqG3Dh06UFlZ6a5zACqEX7/h0bNnT/bv34/VaiUjI8Proc/db9++PWPHjmXRokXcdNNNXsX4ysrK2Lp1Kz169AjJPkEQBEEQhF8i3l9Nf0J51d9GCdk3arnaN7Izz/WoK0Ze/n2ofPtAUaRlwC68C9kVoYR2NfAq8A4qb96IZcCzQEGA805Ue7tT+HvUNZFfiooGOIVKJcgHyqihdNY5h4j2Jkp8fDxdu3Zl3rx5boHer18/Vq9ezaZNm7w87Q888AB5eXlMmzaNzZs38+KLLwadzw4watQotm3bxvjx49mwYQMvvfQS8+fPZ+zYse4x48aNY/bs2bzxxhusX7+e0aNHU1JSwpAhQwCPYC8pKeG1116juLiY/fv3s3//fncVfCO0gndGYw4ePMj+/fvZuXMn7733Hv/3f//H9ddfbzhPq1atsNvt/P3vf2fbtm189NFHfj3cJ0+ezIcffsiWLVv48ccfWbBgAZmZmQFtmzlzJlVVVVx00UX861//YvPmzaxfv54XXnghpPSDdu3a8e233/Lpp5+yadMm/vSnP/HNN98EfT1Ax44dGTBgACNHjmTlypWsWbOGkSNHEh4ejslVqWPAgAFkZWVxww03sGjRInbs2MGyZcv44x//yLfffktpaSljxowhPz+fnTt38tVXX/HNN994fQZff/01DoejQdMrBEEQBEEQzhU8GZUlKHH8EZ4WbE6f52r8xbP2vhRVEO4Daqr47n+dEXvxeLF/An52PYw4hhLrh/DYfQy1AXAUJeABAjnZtOJ3nwNb8Q/5dwZ4DSo0/xBwAFiDEv4voUT7QnRNos55RLQ3YbKzs6mqqnKL9oSEBDp16kRKSgodOnRwj+vbty+zZ8/m+eefp1u3bixatIhHH3006HXS09P55JNPWLx4Md26dWP69Om8+uqr5ObmusfcdtttTJs2jcmTJ9O9e3cKCwvJy8tzF6dbvXo1K1as4PvvvycjI4PU1FT3Y/fu3QHXvuqqq7BarXz22Wd+5zp06EBqaioZGRk88sgj3Hffffz97383nCcpKYm5c+fy3nvv0alTJ/7yl7/4hb3b7XYmTpxI165d6devHxaLhXfeeSegbeeffz6rV6+mf//+PPTQQ3Tp0oUrr7ySJUuW1Jhb78t9993HTTfdxG233UafPn04fPgwv/3tb4O+XuPNN98kOTmZfv36ceONNzJixAiio6PdEQ0mk4mFCxfSr18/hgwZQvv27bn99tvZuXMnycnJWCwWDh8+zN1330379u259dZbueqqq7yK5P3zn//kzjvvJCKE+guCIAiCIAi/VDyifROqb/r/UKJzH94itRLl0f4M477qpcBa12MZKgc9zzWfL/8BrsU/vNyJ6ge/AiW2K11rVeFdof2Ay5aNwAnd8e9d5/TzGb0G5Zl/HhXivwNVxO7/8Pem17S5sAu12aFxVGfnbkaNquHScwyTM9SKWU2Q4uJiYmNj3S3G9JSVlbF9+3bS09ODCtcWzj4zZ87ko48+4tNPP21oU84p9uzZQ1paGp999hlXXHHFac/3888/06FDB7799lvS09MNx8jvkyAIgiAIgod9+6BFi+3AlSiPtRklwDOBC4C/A2NQQvwIqpq7FSV2E1EivhXKO34xSmTfDNgAi+vh28o5x/VsBS7VHS9G9WbXqsi3QFWMNwFXAWkGdzDTZXes67wNJcIBrseT554C6FX0Y67nhUAEyqN/p+tYFlqIu9rM0IT5RcDfdHP0c9lqAu4FOqM2HQDi+e9/f8+gQQYmNyJq0qF6pBCdcM5z3333cfToUY4fP050dHRDm9No+fzzzzlx4gQXXHAB+/btY/z48bRp06bGNnihsGPHDl566aWAgl0QBEEQBEHwRrlP30AJ1DIgCpWbfQQlVk+gCrkdQnm+S13PlShvuOYt13q4l6G8zU5UyHhNDcsrUB55O2qTwNeDX4US5FUo8Wwk2nGtZZTOqu8rvwuV1+6bQnkSJdq1sSXA067XP7nOxRussdf1vthlP3iH1jctv7SExwu10rlzZ68WYPrHvHnzGto8rFYrf/zjH0Ww10JFRQWTJk2ic+fO3HjjjSQlJZGfn19rhf5g6d27N7fddlu9zCUIgiAIgvBLQIl2fc66RjFKlH+Bqpxe5DoGHvF6HP8K8ppwLdMdO4yxiN2NEu3bUKHlmsB3uh5FurVOUDN620tc12u2OVGRAhNR4l87tsfHZvDOx69CbUocd9mqv9cTLpudqE0O8OTHNz3E0y7UysKFC6moqDA8p+WkC40fraWdIAiCIAiC0DgInKhcjhKzX6K8ytWuhwVvAb4VFRbvi8k1zgS8AHRHhbBf5GsBHu+8GSWE9bnu5dQsGY1uQBPR1cB3qCgBs8uWxaj2b0dQPeGr8c6D/8FgvsOu540+x/UbBXvwFvU1RRice4hoF2qldevWDW2CIAiCIAiCIDQ5PKLdV/yedD1WoISzJlDLfcZpXmrfAOpiVNs1rW3vGtexbboxJ1zzhaOEtgVPD3g95Qb2gfKon8RfUmrji/BUjW+BCmPfgfK2H3fZ87PL9rAAa9SEfvxml/1G5859RLQLgiAIgiAIgiA0AJ7w+BKUcNane54yusRoFvxzyn/EU8DNCWxAebQPuY5V4skjP4qqOp8UYG7wbudmQeXef+szxpdAHaDeRnngj7jel6FEeyCceCrZaxx12VLlOr4TaFbDHOc2ItoFQRAEQRAEQRAaAI+nXRPdlSiRqvVkr60EWQXwD5Qgd+qOHUSJYivK870d5dUuQ3nW9SHpmhj+mZr7pJejPPagKtDr1/O9Tht/CuMWdUZCfwWeVneVLlvtqDz3ctc9HUSJ8ydRmxLaxoMTT5TAMeAwERHVNJUSbk3jLgRBEARBEARBaNT8/DN06ABP+HYg+wWjRPth3ZGTKKFbgXGoui//RAnyz/GIY30tqmJUEbi9ruMHXON861VpIlorIleJt/f+BPCV61yFa85DuvOlPs+4zpe67kd/L1tRleGP4V0wr9RlXwme6vnleFICSoCXXPdb5GP/Mdcau4CPgB+IiFhDU0FEuyAIgiAIgiAIZ5y//hU2bYI//amhLWk8KNG+SX8EALsdVN53bQXVdgN5KCGrhYrrw+o1Aa61jwNN3EdGVrjGasXofnad1zznemF/Es+mwAFgNf7h70UoT7kebYOgDLWBsA8Vun8KJdL1GwNlrvHa8ynXuvp7caJEvF7sg8fT/51rzp/4+edDNBUkPF4QBEEQBEEQhDNOgGZEv2iUaN+qP4LNBiYTKBEcX8sMmtiOweOl3+s1n79XXW0EVFZqQvcUqpBbuOu9UTh7Carae0vUZkIp3oXfQG0KFPsc07zkxa55v0BJUG1jQataD0rQ6z3roMS4JllLgAKgNcqzXuEzzuY6foSmlt8unnbBkLlz5xIXF9fQZgiCIAiCIAhCk6W0tBRPP3SjgnJH/K4xRi9gC3Wvq1xz6HPmfdFEcmmA89r8JaiwdoBqTCZfW4t1dvhuFGgbAVr4fSDbPdhsenu1zYdTwDsY956vQuW8lxP853ZuIKJdqBfy8/Pp2bMnDoeDjIwM5s6d6zdm5syZtGnThrCwMPr06cPKlSu9zpeVlXH//ffTrFkzoqKiuPnmmzlw4IDfPIIgCIIgCMK5R+Ce5L9cysrK8ISDg7Fo9hXHeioNxmjF7AAqsFpP4hHGJ4Kcz4hylOhWtirRrvfKVxjObbGAJ7TdiXc/df213uLd7KVUtRz7fcBmwsMDVdbXNiCaVliHiHbhtNm+fTuDBw+mf//+FBYW8uCDDzJ8+HA+/fRT95h3332XcePGMWXKFFavXk23bt3Izc3l4MGD7jFjx47l448/5r333qOgoIC9e/dy0003NcQtCYIgCIIgCPVMKKK9qgo+/xxOnKh97Nlm5UoYMQIO1UPKtNlcU7ZyMB+YlvOtKs7bbFoOu6dQW0KCb7h7oFZsvkI5EGo+q5fp+o0CDfVehfqXoULXtVx2Xw7jHVqvQvgt7gh8be4TQAlOZyV2u5b73/QR0d5EWbBgAXFxcVRVqd2uwsJCTCYTEyZMcI8ZPnw4d911F6DC4Vu1akVERAQ33ngjhw8fNpzXiFmzZpGens706dPJzMxkzJgx3HLLLcyYMcM95tlnn2XEiBEMGTKETp06MWvWLCIiIpgzZw4Ax44d47XXXuPZZ5/l8ssvp1evXrz++ussW7aMr7/+uj4+EkEQBEEQBOEc4Zln4IorIDcXdu6Ep56CI40k4rlPH3j1Vbj//tOfy2QyEVic1+T19sZqdWKxlLtEd02edFAC+SjgEcUWiwpHt9Za8awS75xzPb6bA8H2mdfwt9tqBZtN36O9EpPpJE6nE5NJ2xDQ0Dz5Wmh900FEe4g4nU7Ky8sb5OEMYXvysssu4/jx46xZo1odFBQUkJiYSH5+vntMQUEBOTk5rFixgmHDhjFmzBgKCwvp378/T4TQi2P58uUMGDDA61hubi7Lly8HoLy8nFWrVnmNMZvNDBgwwD1m1apVVFRUeI3p2LEjrVq1co8RBEEQBEEQfhm89pp6XrYMLrkE/vhHGDbszKz1449w9dXwzTeqLd3HH0NlEHp506baxwTirbcgKwtee6227/cV1OZxj4iA2NjAgvvkyZM+R5xYLGpOq1V5q61W5WU31Vas3p17H2qug9Z73h+zWe9R149XVFd7/zBsNuN59BEA2v01FaR6fIhUVFTw1FNPNcjakyZNwh5kDEhsbCzdu3cnPz+f3r17k5+fz9ixY5k6dSonTpzg2LFjbNmyhezsbKZMmcKgQYMYP348AO3bt2fZsmXk5eUFtdb+/ftJTk72OpacnExxcTGlpaUUFRVRVVVlOGbDhg3uOex2u1/xu+TkZPbv3x+UHYIgCIIgCELjpa457T+5ap8tXlx/tujJzVVr/Pe/cP75sG0b/O1v8PDDNV9nNkN5eegh2sePw29+o14bBZR6C+fadw9MpkqqqgKr7QqDsv3V1R4veO1C3Rdjm0wm35+xr7gO5KFXQruqtgABAJxUV1cbiHwje0K+sUaLeNqbMNnZ2eTn5+N0Olm6dCk33XQTmZmZfPnllxQUFNCiRQvatWvH+vXr6dOnj9e1WVlZDWS1IAiCIAiC0BQJRbQb6a3qQA7WWqiogK++CtxyTtsUACXYAd5/v/Z516wBhwPeeSc0e8p8W4y70HK0ve/dWOhaLGrTIDxctW5TVegD45mzLl7y2jGbVUi63e6fF6+/H5PJ49mvK77/Nmw2/NICmpJgB/G0h4zNZmPSpEkNtnYo5OTkMGfOHNauXYvNZqNjx47k5OSQn59PUVER2dnZ9WJXSkqKX5X3AwcOEBMTQ3h4OBaLBYvFYjgmJSXFPUd5eTlHjx718rbrxwiCIAiCIAgNz4kT0L8/XHstTJ58ZtYw0ly+or+gQIWFd+9e81y/+x384x9w330wa1a9mejm17+G22/3vN+2DVasUMc077OvcPXgDHBcoxytKJuG2ewRvVar8vbXhs0GTmd1wI0LPf4ec89a1dWezROTSR1zOr1FuMXivcGihb+bTN459MGkIGhjNS+8xeL0+5y0sH7952AOrqLeOUPTupuzgMlkwm63N8gj1B0jLa99xowZboGuifb8/HxycnIAyMzMZMWKFV7XhlL8LSsriyVLlngdW7x4sdtbb7fb6dWrl9eY6upqlixZ4h7Tq1cvbDab15iNGzeya9cu8foLgiAIgiA0Il57Db79FqZMCe26YD3tZWXGVePLy5V3e+lSlXeekwM9etQ+3z/+4f18pnA6lX1t28Idd8Abbyhh2qMH3Hjjac0c8Ex1LeEHms9PebirqSlEXX9NsCH/FktwXnOVZx7cnJoNGpp33uEAq7XccHPD95jT6QypHlhjR0R7EyY+Pp6uXbsyb948t0Dv168fq1evZtOmTW4h/8ADD5CXl8e0adPYvHkzL774YtD57ACjRo1i27ZtjB8/ng0bNvDSSy8xf/58xo4d6x4zbtw4Zs+ezRtvvMH69esZPXo0JSUlDBkyBFA5+MOGDWPcuHF88cUXrFq1iiFDhpCVlUXfvn3r70MRBEEQBEEQTotTBkXBjx+H555Tld6NqK729nLv3QsjR8Lq1dChA5x3nsdT2ry5Om80R8+e0K8fXHed97l331Xe/2BKIR05Avn5oefYv/AC9OoV+PzHHyv7NPLylMd97Vr44INAV52esKysxV1tNusLugWXX+BflV2bKyTTgLpXca8pxP6XiIj2Jk52djZVVVVu0Z6QkECnTp1ISUmhQ4cOAPTt25fZs2fz/PPP061bNxYtWsSjjz4a9Brp6el88sknLF68mG7dujF9+nReffVVcnNz3WNuu+02pk2bxuTJk+nevTuFhYXk5eV5FaebMWMG11xzDTfffDP9+vUjJSWFf//73/XzQQiCIAiCIAj1ztSpSgCPHaseF12khPtf/wpHj3rG+YZM/+Y3MHu2EsGbNimR/tJL6tzx46Hbcfvtyo6HHqp9bI8eSuC/9Vbw83/+Ofz+92qTIRC+861cGUxxtQAJ7meAUD3Pmmdc87zXRTjXdI02/+nkt9dEsJsU5wImZ1OKG6gjxcXFxMbGcuzYMWJiYrzOlZWVsX37dtLT0wkLC2sgCwWhaSC/T4IgCIJw7vPMM+BqOmRIairs2we33qo84KCJt2pgBdCapKQWHDrkfd3IkcqDfs01odmjzxkfMAA++kiF1ycl6ddWXHyxaiMHaq0PPzQWlhddpLzkGrUJVqcTbrkF/vUv7+O9e8O33zqBf7NkSTyXX345hw6paALFTkA50hwO72urqozzvrWib4EoL1f2+Iaum0wmysqc7vsJteo9qGJ+mhb2tVejutpT9C8Usa+P4Ag0dzDXW63gcFh57LHH+MMf/hD6RGeRmnSoHvG0C4IgCIIgCIIQNKtW1Xx+3z71rLVo85QsWgt8CrxiGGq9alXogh2guNjzuqoKMjKUKL7rLtV3XY8m2EHln7/yivGcK1fCqFEwYQLs2hWcHUaO3W+/BdgDfM+TT/7PYFzgynAWi6eifCgC22ZTD98ccr2vtq7h5tomwJkIVw8l5702nE4nP/74Y/1N2MBI9XihVjp37szOAAlK//jHP7jzzjvPskWCIAiCIAhCQ6F5z2tD7/1WeDoJGQm02jYDApGR4Xn99degdT+bNy9wezWA3btVRflAaIXrXn11E7AMyELziitOAj8AFwDhNeTIK2H++efqnbdorzknva4h6YGus9nUxkZdQ9Lr6qEPBq2N3eluCJhMUFVVZdif/lxFRLtQKwsXLgz4j16fky4IgiAIgiAIweARZtXAR0AroAdK4IamCvVh9r7tyn3D1UPnCw4fLgDygfeBN4EE17l3gF3ARqqrfxN0Ybvt2/Xvzm6mstlct4JyemoT1YHb29V+3ekIdq39nHZ/TSkLXES7UCutW7duaBMEQRAEQRCEc4wjR+DPf9Yf8Yion37SXq0DCl2Pja7H74BmZ8HCYCjQvS4D9uIR7Vrc/FaOHaupGr3nxO7dcMkl+nOhF0tr7C3Itf7tZ7viu2/0RnkwDezPERr5j1wQBEEQBEEQhMaFE/gfsK3WkbX3cte7xje6nusYJ9+AnDoFBw/WPq5VK98jtZaXd2O3KzHc2EU7eELdG5J9WnGFJsA58CMXBEEQBEEQBKExsGULKO/456hQcV+qge+AIoNzZ7vZdhmwhbp4swNj7E4vKvJUTA/2mtrPeWMyKTH8S+9ZHizH69I7sJEiol0QBEEQBEEQhKBQOshIkIMSx2uAfwPPG5w/BaxGVVPX0CvQSpSI9RWyJ4BvXdeHwpvAW8DSEK8LnWHD6lpIr+ZCdELdydBXKDzHkZx2QRAEQRAEQRCC4lRA3fwBsAlIq+HqdUCx62HEl0AMcDFQjspz7wDMQFVvvwUYGYK1e13Pa4HsEK6rCWPP+PLlRkfXoorqGUku7QKRY2eKCy+8sKFNqDfkX4kgCIIgCIIgCEFRUgLGwrXQ9bzd4JyGkVfZN9a72DXXz8BmYCGqcjvAe3iL9h+BSKBNDWsarXE2KAb+43o9EJW7H+56fwp4zfX63rNr1i8IUxPKI5DweMGQuXPnEhcX19BmCIIgCIIgCI2I7TVpcqDuArnU5/VbBmOqUBsG/wdMBMbgCcP/ERUOf8LgujJUFXgtzP59QikAZ8xOYAMqusBoTe1+yoF/Ait0506icu23AE0n77qxERER0dAm1Bsi2oV6IT8/n549e+JwOMjIyGDu3Ll+Y2bOnEmbNm0ICwujT58+rFy50uv8fffdR9u2bQkPDycpKYnrr7+eDRs2nKU7EARBEARBEGpjxIi6XlkOHEQJXKOKbYcMjhmxDdiKCi93onLoQXnht6LC9H1ZB3wBvAEsAH4Avg9yPV8+cT2/DuwH1gMf4l/xXtu8KMU/MqES9XmUc/qbB0IgRLQLgo7t27czePBg+vfvT2FhIQ8++CDDhw/n008/dY959913GTduHFOmTGH16tV069aN3NxcDup6Y/Tq1YvXX3+d9evX8+mnn+J0Ohk4cCBVVfLHTBAEQRAEofETsHw6SjBvR4W9/2Rw3lfYGnnsq4EXAaNWXtUocf4u8I6PLT+7ntfpjpXVYGtNY3yT+otca33sen4X5UHXMCqsV+GyZR0qBUA4EzQlDSGivYmyYMEC4uLi3P9YCwsLMZlMTJgwwT1m+PDh3HXXXYAKh2/VqhURERHceOONHD58OOi1Zs2aRXp6OtOnTyczM5MxY8Zwyy23MGPGDPeYZ599lhEjRjBkyBA6derErFmziIiIYM6cOe4xI0eOpF+/frRp04aePXvyxBNPsHv3bnbs2HGan4YgCIIgCIJwZtkDfIoKFzfiJ5TADSTOnShBXK17/xGq2nw1Ho/1Nxjnza9xndsPvIK3x11b48cAtjlRwl5v2zLgLxhvEATibeBlVNh+Ta3cvtK9/iaE+YVQENH+C8bpdFJeXt4gD6cz+D6Ol112GcePH2fNGhUyVFBQQGJiIvn5+e4xBQUF5OTksGLFCoYNG8aYMWMoLCykf//+PPHEE0GvtXz5cgYMGOB1LDc3l+WuMprl5eWsWrXKa4zZbGbAgAHuMb6UlJTw+uuvk56eTlpaTVVIBUEQBEEQhIahEvgfKrQ9HyV8f9CdXw0sQgnY48BR4Bgqp9uXIpRA3u96XwocQfV8PwQccD0XYdx3XcsNPwGUoMLY9d+da/Lk/w/lwc9zvT8APOeaZ6PrWAnGmw561rqed6IK5mkh//vw5L2fRFXD16jPHvKCnurqpvPZSvX4EKmoqOCpp55qkLUnTZqE3W4PamxsbCzdu3cnPz+f3r17k5+fz9ixY5k6dSonTpzg2LFjbNmyhezsbKZMmcKgQYMYP348AO3bt2fZsmXk5eXVsopi//79JCcnex1LTk6muLiY0tJSioqKqKqqMhzjm7P+0ksvMX78eEpKSujQoQOLFy8O+p4FQRAEQRCEs4EmXLcDn7se2jG9IP/I9dwelc+unSs3mPOwz7lqVBi5BU9Rt31AFErCxLuOVaEEtr53/EFUpfY41wPdWCf+ofdfoDYgVgBXoSq77wN2AFpe9CGXbQWoDYIK1zWlgMM1RosU2OeybzuQBex2Xb8WFUavJ3inXFPAZrNRUVFTGkX9rXPeeeed8XXOFuJpb8JkZ2eTn5+P0+lk6dKl3HTTTWRmZvLll19SUFBAixYtaNeuHevXr6dPnz5e12ZlZTWIzXfeeSdr1qyhoKCA9u3bc+utt1JWFkzOkSAIgiAIgnD2qEB5zTU08WnCW4hWoHLMSwLMowlo3wrsB13X6K9z6sYeRYXkH3G9PuAzBjwbACaUaN6NCt8/CPxXN/c+VI/43a73m1yvPbWXFKWua/6GCr/fgxLq2jx78OTSV6IiD4pdNpWhNiZexnvToGFF+9lui1Yf69lstlrPWywWWrVqddprNRbE0x4iNpuNSZMmNdjaoZCTk8OcOXNYu3YtNpuNjh07kpOTQ35+PkVFRWRnZ9eLXSkpKRw4cMDr2IEDB4iJiSE8PByLxYLFYjEck5KS4nUsNjaW2NhY2rVrR9++fYmPj+c///kPv/71r+vFVkEQBEEQBOF0KEOFvH+Gp+84KC92FUpArwXOA5qhKrqHuZ4DcRT/om+ad90oL/koSgDHua773PVcjfLmG1UN1/Lg97ke5a57uB5VAb4UT1G4ba7z2nfvoyjvuiawP8Uj1J0uG0/iCY8HtUlQgWoLZ3HZtgtojhLtmrjf62ep3W6noqIipNTYxorJZGqw+4iPj6990DmCeNpDxGQyYbfbG+QR6s6Ultc+Y8YMt0DXRHt+fj45OTkAZGZmsmLFCq9rv/7666DXycrKYsmSJV7HFi9e7PbW2+12evXq5TWmurqaJUuW1OjRdzqdOJ1OTp3yrdIpCIIgCIIgNAyfoXLVD/gcP4gStppw/xEl1DXxfRJvAf4uSsSCf6/yzSjRXE3NnmjtnDaPdo1R+D0ooayNNeEJ1z+MEtmaZ/2Q65x2vtB1H9rGwEHX2uUoT3s5Hi89unN6GzWRfhBVlE/b5NAqzZ9dj7fZbMZqPTP+W7M5NIlpsVjOiB1NCRHtTZj4+Hi6du3KvHnz3AK9X79+rF69mk2bNrmF/AMPPEBeXh7Tpk1j8+bNvPjii0HnswOMGjWKbdu2MX78eDZs2MBLL73E/PnzGTt2rHvMuHHjmD17Nm+88Qbr169n9OjRlJSUMGTIEAC2bdvG008/zapVq9i1axfLli3jV7/6FeHh4Vx99dX196EIgiAIgiAIp4GWO67ldINHxFbh3WrtAJ4idIdQ4fSacF8HTEWJ1gOu6ypc539ECf9AfcyN2qhpVLrm0Qt3TaAfxnuzwYkqdKd5zUtRmxLaRoP+XrSiZlpEQAmekP5qvAvKlRM4HUBb1+i1B7PZjMlkOmPCuj4wmUw4HA6/46GIdu0+a8L3M3A4HDWuoc3XFCIVNES0N3Gys7Opqqpyi/aEhAQ6depESkoKHTp0AKBv377Mnj2b559/nm7durFo0SIeffTRoNdIT0/nk08+YfHixXTr1o3p06fz6quvkpub6x5z2223MW3aNCZPnkz37t0pLCwkLy/PXZwuLCyMpUuXcvXVV5ORkcFtt91GdHQ0y5Yto3nz5vX3gQiCIAiCIAinicrtbtZsj+v9upoGA9+ixKkmqJ2otmdbUZXbtdz0EpTA11dp18RwFd7C+JRrjN4jrlGGCjvXh9xrXnTfTYCZutcVqNz22tDupdznmNFrDOwzqp7vLXa1SNtgBLDVaq11XDARu9o8VqvVUIz7YrFY3GP18wcjlu12OxaLJeT0X23dulx3LtN4t26EeuG5557jueee8zpWWFjoN27o0KEMHTrU69hDDz0U9Do5OTnu9nKBGDNmDGPGjDE816JFCxYuXBj0eoIgCIIgCEJDocSwcoD65qJX+oyLQInUU3hCwLWQ8p9R4eq+PdKNOI4n/F0T79pa+lB0vTgt0T3/pDuurVWGaulWhfJlav3g9cL+FP5C30h0H/J570R54o/g8dxr6DcfPHPbbDZOnTpFTEwMTqeTkpKSoASw1WqlsrIy5BZnZrPZq5e51WrFZrNRVVVFVFQUBw8edI8zmjsiIoLw8HCOHTuGw+EIqXh0REREnYtNx8TEUF1d7U6hNcqbdzgc4mkXBEEQBEEQBOGXiBZqXkVx8RFgqc95J6pV2nE8oegVunMaR13z6EPmMRinoYnGk7rzRpXlQYl5/ft1eG8m7Ha9r8Tj3ddC3VfgKTrnRBWp26i7Vl9ETo+vkNciCg6i7k+LMvBFCU+9Yzs2Npbo6Gi1Wg0ecqvV6tcaOVB+uNE8ZrMZh8PhviYsLIz4+HjS0tJo06ZNjddqx33P2e12v3B2X5vCwsKCasemXecbRRAeHu5e12w2+30GUVFRhIeH05QQ0S7USufOnYmKijJ8zJs3r6HNEwRBEARBaPSUlkJRUe3jzg1KgAoqKzVvuYbWW/0oKn+8lMAedKNicVpevFERYk20a3nvGloe/Sk8QtmJ2gzQhLrvfJV4NhQ0sW60cRDIxkr8Iwx8BbnvJkIl3i3yvDGbQytEZzKZaNasmZ+gtVqtREZG+glno3Bym81GWFgYNpsNh8NBRkYGLVq0wGw2uwW9RlhYmN/1RueNRLSvsI+MjCQ6Opo2bdrUKK7tdjspKSk1hsL75sRbLJYmmdMu4fFCrSxcuJCKCqOdQdw56YIgCIIgCEJgUlPh2DEl3OPioLISTpxQr89VlF48CkS7jmjC2okSymWoonJGYduleHq6n0QJZE1kVfqMPYW3CPadr0p3XBPoWgE8o7WPogrgLcNbfJvQNiQCV67Xbwbo2Ytq7abh26ueANf5F9qzWCxeYeugBGx5eTkmkwmz2ewWpGazGZvNRmVlpXucdn1lZSVWqxWn0+knnMPCwoiMjHSHqJtMJj+PuBaub7fbiY2NZf/+/e5zmh36saCEvD6UPiwszOteoqKi3C2fo6OjOXr0KHa7neLiYioqKtz3YTab6dmzJ3v37uX4ce/uAka2GpGYmFjrmHMFEe1CrbRu3bqhTRAEQRAEQTinOeZysq5eDZdfDhdeCIWFsHMntGoV3BxHj56eyK+uBpNJPepKx46wYUMJmmYymcpwOn/2GaXlr5vw5K/7Ug44UCK5Cm+RqxfS1Xgq1msEcwOnUMJca8umn78cFQmwCpVzrp9vM8YV68FigejoEo4eNTpbgrdo11OTx9cjcFNTUzl06BDh4eFe3mW9QDabzaSkpFBeXk5kZCTV1dWUlxu3uNOKylVUVGCxWIiMjKS0tBSbzUZMTAzx8fEcPHiQsLCwGvPLjQRyoKr24eHh2O12t9COioqisrLSnXOvbTLoiYmJ4dJLL2Xp0qXuPHqbzUYr1y/GwYMHsVgsOJ1O94ZAdHQ0x48fJz4+noqKCi/7Y2JiSE9PJ+5c3hHzQcLjBUEQBEEQBOEsoTkhtbrA//lPcNc9/jjEx8O8eXDkCHz9NYQS/VtRARdcANddF5K5fqhoZk+4udOp95Ar1KaAdixwz3Sz+SjNmh3A2AMdDMaRoB5O4GlN5/thlaFy27Vw+kqUWK+5OFppqW9ROb0twfxASgj0mSQnJ9OxY0d69epFt27dvM7Fxsa6hXJ4eDhhYWEkJSVhsVgCtk3TxkdERBAdHU1UVBR2ux2TyUSnTp24/PLLue666zjvvPNo1aqVX1h5REQEDofDMDw9MjLSLap90TYL9OH32hw2m81rE6Bly5bYbDbi4+O9PPcRERFkZGQQFxdHQkKCe0xcXBzt27cnOTmZ6OhoEhISiIiI8Fo/KSmJXr16Gdp2riKiXRAEQRAEQRDOEr5FuIMV3pMnq+eRI6F9e8jKgry84Nf9+mtYtw4WLPBf9/hx5UH//e9rn6e0dC960WmxaPnjvpzCv3+5njJMpjIqK49jt6tCbHY7hNbJK1AIu0Y5sD/AOU2ga574SvyrvGubFMFitDngy3Gs1sBjbDYbv/nNbwgPDyc6OtqdN671bDfKLY+JiSExMRGz2UxkZKT7uMPhICYmxk/UAu75Bw4cyNNPP82FF17oPqeJ57CwMCIiIrDb7cTExHjlsNtsNr+NApvNRnh4OGazmejoaKKjo73sAejWrRstWrSgWbNmdOvWDavVSkpKil9uvtaLXb8hoW0CpKWlYbFYaNOmDe3btyc1NdVrI6B58+ZBhc+fS4hoFwRBEARBEIQ6MGcOPPpoaNf4pCrXKNr/+U946SX/8YcPq9cffRTa2hp79qgce832N9+EjRvhhRfU+4qKwHbt3fu213uLBcLCnPjUHnOhxL3JpMS4UUR1ZWWlO1zfZNLy5I3D0xW+Qj2QJx+UKD9MMB5wta7T71h1dRm+Gw+BW6LXZLeHQOkJmjjVxPF5551HTExMwHEa8fHxnHfeecTFxREREeGVX64XvYGqwA8cOJC0tDTat29Ps2bN6NOnDwkJCSQlJeFwOIiKiqJ///5YrVZ3obcWLVrQtWtXoqOjsdlstG7dGovFQvPmzTnvvPNwOBzY7Xaio6M5//zziYmJITw8nIyMDNq2bcuVV17JmDFjSE1NJSkpyV0tX4/T6aRt27bY7XavgnU9evRw35+2vhZVYDKZguoxf64hol0QBEEQBEH4xTFjBvz738GNPXIELrkE/vEPz7HKShg2DJ58Er75Jvh1Q2mlfccdcP/9sH178NcEw+OPw4EDynZQ96Kxdy9ERcHddxtfW1XlXYldCXJnjXnyNpux0DWbcRceC56avPdqEyE0b71CXRPIFu8wfKtVjbdYwNehazKd8jumx2w2Fu1Op5P27dvTpk0bkpKSAOXpbt68ecC5TCYTzZs3p0OHDlitVncLuISEBKKjo91C19eLHYgWLVpwwQUXkJycTNeuXd1CWGsF5+vx/vOf/8yjjz7KFVdc4SWa9QK7c+fODB48mMTERJKSkoiKiuKhhx5i1KhRdOzYkTfeeIM//OEPhiH4119/PWFhYVx66aVeQrymNnha7ntTQ0S7IAiCIAiC8IuisBDGjYObb1Yh499/DyUlgcc//jgsWwajRnmOzZzpeW1cmMyYUES7xpluFafXOP/4B5SXw1tv1TTWWxQZFUKz2cBkchIdbSzow8MDe6x99VsojlOLRc1rt2vh9sF5v43wtds3IsBq9R5jtao1rVbje7PZtM/FeL0WLVp49UevCa3IWkREBImJifTu3Zv4+HhiY2Np0aIFiYmJpKam0qJFC1q2bOnOaTebzYbh8tdeey0AgwcPdh9r3bo1DofDHeJusVjc+fQmk4n4+Hhuu+024uPjA9oZHx/PNddcQ79+/dxV4/UC3W6306VLF26//XaioqK8ru3cuTMTJ06kV69eAfu6W61Wmjdv7g7Lr6k93LmMiHbBkLlz5zapiouCIAiCIAgaR454XnfuDF27Ku9yIM/7MYOU7Q8/DH69ggLP62BFu15I668JtfL7rl21h+Tr3+/Y4Xn9449wzz2wdat+dDWBwsA1oaqJVrsdnE7jYnHV1YE97EE6hv2wWLyFtRLXFSGJ/lDR26r3sGupAIG87qejLU0mE5GRkfTt25dOnTphsViIiooiPj6eCy+8EIfDQWxsrLtnutlsplmzZkRHR9O5c2fDAnK9evVi4sSJXrntGRkZpKamYjabOe+889xV7Y3C89U9++c/mEwmOnbsyH333cfFF18c8H46duzo198dPKkCejEeHR1Nx44dSU5OJiUlhY4dO7p7vptMJvG0C0Ig8vPz6dmzJw6Hg4yMDObOnes3ZubMmbRp04awsDD69OnDypUrDedyOp1cddVVmEwmPvjggzNruCAIgiAIvziM869VuLuejRuhWzeYP99/bCi6ICfH8zrYQnT6cTWt9d138PrraszJk7B4sfKUgypU17o1uJyoAefTr/Xmm57Xl1yi3l99tYpIyM6GkyfVxUabBzab+mz1Qta333iwaCH1gX5WdcVqVULabg8sqEPBZPJ49X2POxze3nj9Z6Z56n2PA36iUxPJYWFhREVFcf7557vfN2/enPvvv5+srCyGDh1KZmZmQM/3pZdeisVicXu8fdEEcseOHTGbzV7ifvjw4Vx33XVERES4w/c17HY7l156KW3atMFsNpObm+s+l5ycjMlkokOHDu4ieoHaxQVDcnIyMTExPPLII2RmZrqL1Wmfjfa6qSF92oXTZvv27QwePJhRo0Yxb948lixZwvDhw0lNTXX/0r777ruMGzeOWbNm0adPH5577jlyc3PZuHGjX67Oc889V2OuiiAIgiAIwukQSAj6fv24914lio0wEr56sVpeDi++CAMH+o+raR6jcSNHGo8BtakAEBOjQto/+AAeeACefx6ee06d++9/YdIkzzXB6mgtwmDTJrjhBti8GfTt3ozw/Qyr65IPgPos9Z+nyeT/WWnO14raOr/h+ZmH8hXTP1/deFxtc2p2+o7TQvl9iY+P5+GHH2b8+PHs2bPH7YG+6667iIiIIDIyki1btrjH2+12L6H81Vdfec134YUXUllZ6Q5z79SpE5s2bQLwE+AAt912G1VVVbz//vu6ezTxzDPPEBkZybJly/yusVqt5Obmct9997Ft2zb38aFDh7ru1cLVV19NZWWlXxh8bcTGxrpfa2HwRtECLVu2JCIigrZt23J3oKIM5yjiaW+iLFiwgLi4OPfuZmFhISaTiQkTJrjHDB8+nLvuugtQ4fCtWrUiIiKCG2+8kcNaWdIgmDVrFunp6UyfPp3MzEzGjBnDLbfcwowZM9xjnn32WUaMGMGQIUPo1KkTs2bNIiIigjlz5njNVVhYyPTp0/2OC4IgCIIg1BfBem+LiwOf09dP+/BD1UP9jjtUiP2bbyov60MPqd7oeoLVsPpxWk93X/QicM0aJdjBUwU+0Fr649u2wdSptdujBHvD4ZsL7nD4C/ua0MLlg8Vu94zXis7VNWy/prW1c06nkwceeICRI0cSHR1NmzZt6NSpE3379nV7pjWv8gUXXEDXrl2DXj8uLs5L+Nrtdvr37092djZ9+/Y1sEm1l2vXrh3gEc0Oh4PmzZv7Odd69uwJwKBBg/zEtD6s/aKLLgoYIg+BPfA33ngjffr0oV27dsTFxdGiRQs/G6xWK+effz4dO3Zk4sSJAXvIn6uIpz1EnE4nFcFs550BjHJIAnHZZZdx/Phx1qxZQ+/evSkoKCAxMZH8/Hz3mIKCAh555BFWrFjBsGHDePrpp7nhhhvIy8tjypQpQdu1fPlyBgwY4HUsNzeXBx98EFDFSVatWsXEiRPd581mMwMGDGD58uXuYydPnuSOO+5g5syZAcN2BEEQBEEQ6kplpQpJ/vln4/NFRTBrFowYETh0uqwMTpxQVdY1tKJ0//ynetTE8eNw/fWgKpWXAcZex1Ad1Fol+GDm0B9v2za0dYJpnxaIuopeUMLWag3Oq15XbDY1v2+BuVA2B06HhIQEv2NaCzethZrGb37zG57TQil80PRCeHg4nTt3Zty4caxbt46PP/6Yiy66CIBmzZrRv3//Gu3p0aMHw4cP9xLARvni11xzDX379nV77fWe9FCiZ8877zx++OEHQBXl07DZbPz1r3/lscceA/yr4Q8ePJiKigpsNhtdu3Ztcj3aQUR7yFRUVPDUU081yNqTJk0yLNBgRGxsLN27dyc/P5/evXuTn5/P2LFjmTp1KidOnODYsWNs2bKF7OxspkyZwqBBgxg/fjwA7du3Z9myZeTl5QW11v79+0lOTvY6lpycTHFxMaWlpRQVFVFVVWU4ZsOGDe73Y8eO5eKLL+Z69T+ZIAiCIAhCvTFjBjzyCOTnw5AhTuA9IAnwFi6jRyvRNny4ceh6Rgb89FPd7Xj2WVXgDWYCRZw8+XvAOwd5yxaYPLn2uV5+uebzwYj20Km7aNfyv+uKUcX2+sRsDq1S/dnAarXy0EMP0b59e1avXu0+HmzB6KioKMLDw+nVqxe9evVyC99gMJvN7qhcDSMRbjabvdJdHQ6H2xseimi32WxERkZSUVHhJdp98Z0zPT3d7dHXUgCaGg0aHv/0009z4YUXEh0dTfPmzbnhhhvYuHGj15icnBz3D1x7jNL32wB27drF4MGDiYiIoHnz5jz88MN16PnY9MjOziY/Px+n08nSpUu56aabyMzM5Msvv6SgoIAWLVrQrl071q9fT58+fbyuzcrKOqu2fvTRR3z++ecBdwwFQRAEQRBOh3HjlBe1f3/Ys2cHsA4oMBz797/Dr38N69f7n6tdsG8G9gc8u2sXKA+76uN28OBGvzHt2mke+yXAh9RVKOs3HS67zPP69ER72elcHHKYui+nE6buy9nwnp8OQ4cOpW/fvjzwwANcd911QV+n72n+61//+kyYVit2uz2kKGFQXnxtk6Fjx441jjWZTLRs2ZLExEQSEhK47bbbyMzMJDs7+3RNb5Q0qKe9oKCA+++/310cYdKkSQwcOJB169Z57ZKMGDGCP//5z+73+t6CVVVVDB48mJSUFJYtW8a+ffu4++67sdlsZ8QjbrPZmKSv5HEWCbXvYE5ODnPmzGHt2rXYbDY6duxITk4O+fn5FBUV1ds/6pSUFA4cOOB17MCBA8TExBAeHo7FYsFisRiO0cLgP//8c7Zu3eq3a3jzzTdz2WWXeYX1C4IgCIIg1BVVWb1m58533wUuQFczh4B5rtePGY44fvxn4EXdkZpEzVLXcxbQvIZx/hw8GLjg3OmJ9rr3PW9snGnP/enSqlUrw9xs3xB133DxpKQk0tLSSEtLo0OHDl7nEhISOHLkCF26dKmzTeC9MVAToYj2sLAwzGYzDoej1grzJpOJYcOG4XQ6MZvNZGZmkpmZGfRa5xoNKtp9w6/nzp1L8+bNWbVqFf369XMfj4iICJjjvGjRItatW8dnn31GcnIy3bt35/HHH+eRRx7hscceCzqcPFhMJlO9z3mm0PLaZ8yY4RboOTk5/OUvf6GoqIiHHnoIgMzMTFasWOF17ddffx30OllZWSxcuNDr2OLFi93eervdTq9evViyZAk33HADoCqJLlmyhDFjxgAwYcIEhg8f7jXHBRdcwIwZM7jWt0+JIAiCIAhCkFRVgavMzlkgmEK+q/yOvP8+zJsHc+eCrl6YDo9QPnkyOEt8shK9aKjw+DOFVlm+LqnM53L6c9++fTlx4oRfJyaTyUTbtm0NQ+hHjhzJoUOHaNmyZZ3WTEhIoE+fPkHroVBEe9euXVm7di3h4eFBz/1L6TjVqIJCjrn6SvgWYZg3bx6JiYl06dKFiRMnclL312r58uVccMEFXvnSubm5FBcX86NKGPLj1KlTFBcXez2aIvHx8XTt2pV58+aR42oQ2q9fP1avXs2mTZvcQv6BBx4gLy+PadOmsXnzZl588cWg89kBRo0axbZt2xg/fjwbNmzgpZdeYv78+YwdO9Y9Zty4ccyePZs33niD9evXM3r0aEpKShgyZAigvPVdunTxeoDazUtPT6+nT0QQBEEQhF8a77+vWq81LIuBuUC535kXX4Rf/UpVfo+LO7OF1jROT7Sf1sVnBK0//C9Ev7kjkgcNGsQtt9ziJ1w1Qd26dWu/a8PCwkhLS6uz2L3gggsIDw8nLS0tqPGhrBMeHk6LFi2CFu2/JBpNIbrq6moefPBBLrnkEq9wjTvuuIPWrVvTokULvvvuOx555BE2btzIv//9byBwETTtnBFPP/00U4PpbdEEyM7OprCw0C3aExIS6NSpEwcOHHCHy/Tt25fZs2czZcoUJk+ezIABA3j00Ud5/PHHg1ojPT2dTz75hLFjx/L888/TsmVLXn31Va9+kbfddhuHDh1i8uTJ7N+/n+7du5OXl+f3sxMEQRAEQahP9FXevakCNDfrPmA5cAVg6Oo+HQsArbR77Y4IjwPzzHm0g/XWG+O/8dDQNAWx7hvibsQtt9zCunXramybBjB69Gh++OEHLrzwwvoyz01iYiLjx48nLCwsqPGhiPYOHTqwdOlSnE5nyGnBTZ1GI9rvv/9+fvjhB7788kuv4yNHjnS/vuCCC0hNTeWKK65g69attA29RwUAEydOZNy4ce73xcXFQe8WnWs899xzfsXdCg2afQ4dOpShQ4d6HdPC54MhJyeHNWvW1DhmzJgx7nD4YDBqKSEIgiAIghAKxl7lAlS+eHfX+3+4nouAYfVswQ7d65MEH+i6GViNKvzWFRgIbAAuABwoUV83tWrc9706BNuE+iYYkaqPRq2J+Ph4LtNXHqxn9PXFaiMU0d6/f39iYmIoKipyt6YTFI1CtI8ZM4YFCxbwv//9r9b8Cq3K+ZYtW2jbti0pKSmsXLnSa4xW8CxQHrzD4Qi6eIIgCIIgCIJw7mIs2rUqc9t9jh86AxbonRAmgvegzwN2AhXAV67HOmAoqlXdGmA4EO1zXRnwb5TQNxZ4+/b5HilDFcfLAG6oxa5Q7kH4JZKYmOiuHh8KVqvVr6OVoGjQ7TSn08mYMWP4z3/+w+effx5U7rLmJU5NTQVUEbTvv/+egwcPuscsXryYmJgYOnXqdEbs/qXRuXNnoqKiDB/z5s2rfQJBEARBEIQGwrgBjRP4GTjien8C5cUOtZ2Z745AfYtZE54idKuAUuAtIB845nou8bmmANgEvB/COmtRn0FhEGPPjYLMQsORkpLC3XffzYgRIxralCZDg3ra77//ft5++20+/PBDoqOj3TnosbGxhIeHs3XrVt5++22uvvpqmjVrxnfffcfYsWPp168fXbt2BWDgwIF06tSJ3/zmN/ztb39j//79PProo9x///3iTa8nFi5cSEWAqiiSky4IgiAIQmPl2DHwaXDj4oTroX2/+Qo4jhLFwXIAeBW4DOhXw7jTEfJGXu3juteLUWL+t3hawoWasF4NHEVtDgSTry4h9GeCplQF3WQycddddzW0GU2KBhXtL7/8MoC7SJrG66+/zr333ovdbuezzz7jueeeo6SkhLS0NG6++WYeffRR91iLxcKCBQsYPXo0WVlZREZGcs8993j1dRdOD6PKk4IgCIIgCI2dn38OdMa317gWL741hNnzUCJ3MUq0nwgwTi+6vwb61jLvZpQ326l7gLLZhBLNpUA4KlogAeUpv9I1ribxtw6IANrojn0ELADWAzZgF+DfG9z4fgRBOBs0qGivrdBYWloaBQUFtc7TunVrvz7hgiAIgiAIwi+bKl9t7iaQsA21Mvo6VB78AuBboB1KXJcSuFDcEZ/3+jEnULnsZcAeVM69FeU9340S7G1Q3vVLddeVAQuBHjXYWgTMd71+THe80PW8DyXWv8NYtO92XX8WetIJQg38EivLN4pCdIIgCIIgCIJQ3wQW7cGwC1WlXUsFrEYJ7FKUmK4EDrqOvYMq4rYMVTzuBErQd0aJZT01bQxo+enfu+YoR31d1wrkaTn0lcAWPF7vlaj2dSvxVMT3RQurr8R/Q0Gfy/8dqu3dZajc+E+AG4F3CS19QBDqlyuvvJLDhw/XWri8KSKiXRAEQRAEQWiS1CzaT6AErB7t/XFgFkqkzkCJ57+j8sa1EPqVKG/4eXgXpNPC5NeiPO+rfdZw6p4DRZ2W+IzVz6/luW9Hhbp3QG0MJOrOG2FCeexXAm8Dd+rOFetelwNLUD3l33YdewNf2aD6ihuW5hfqgMViaWgTGj2XXHJJQ5vQYEglCUEQBEEQBKFJUm7o1HbiEexaGHs1cAqPSC5Gec3XoPqsf4/yOr/iGgeedmzHCSy+F6K85OWuOY/qzhWi8uE3ud5/icov14t6vc3648XAT6hieBBYqJ9y2bDLNWYPnigAXPaUAPvxFuBVqM0K/foajd/nZ7WeGRv1xeLqO0Q7lN7nwi+Pxv9bJwiCIAiCIAh1QHnaj6EEawSqHdpXwF7dqJN4hLev57gEFR7+KUool6I87Vpb4WKUwD3f5zonqqDcSeBHlPAvB/6nG7vWdWwmqjf6Z671TunmwDXGt8jdftSGga+9x4FFqPD2o8B/UT3bTwDPozYafka1t6sCngM+R+WrV/vM47sRcQr/yITGyZmqxG42m7FYLJSXl9f7Gk2perxQ/4hoFwyZO3cuDz74IEePHm1oUwRBEARBEOqEalk7HeUxj0EVcDuFErUVKBH6IEqMO/AUWTOh8rz3o0LDba4HGAtX31zvfajc8EjXPFobtiJUuLzWmeckqjDdEt21vkK8EhUKfxIlpLVCd6A2DQ7g+Ur/KSpMvhoVUHsK5WUHtWGwzXWPP+vu9bBrzjKXfcdd7/UBuXtQkQGRBvcePCaTqcZC1BaLharTK0QAKC94oHbFp4smrs1mM1arlcrK+tnISElJqZd5hKaJhMcL9UJ+fj49e/bE4XCQkZHB3Llz/cbMnDmTNm3aEBYWRp8+fVi5cqXX+ZycHEwmk9dj1KhRZ+kOBEEQBEFoapw8WYIStduA5Six6kQJ1iqUIN5ocOUJlGA/jhKzeiHtW/292vU4gkcMH3ettxFP6LkW4n4IT1i7RpnPe613OnjC+Y+7ng/qxjmBpaiNAL1HfrePrUWocPci1xijyvZO13WLUekAemH/GUrc6+0M3TNcW9i61Wqtl9D2+swPV7n7nnmjo6Pd81utVux2+2mvYbPZJDxeqBER7cJps337dgYPHkz//v0pLCzkwQcfZPjw4Xz66afuMe+++y7jxo1jypQprF69mm7dupGbm8vBgwe95hoxYgT79u1zP/72t7+d7dsRBEEQBKGJoJy6VXj6smteUb239yT+HNe9PoynUny5a7zeu1qKCn/PQ4nb7a5jFShxXum67hSe0PcNPuv5CuDdaB5ypQ/19mr5+Fq4+h7X/Kt196nNd9BlfxHe0QD6wnN6qnXnfsK7iF4Vnn72Z4aoqKh6Edy1tZWuKxaL5bQ2FvTX6e9TitAJtSGivYmyYMEC4uLi3CFGhYWFmEwmJkyY4B4zfPhw7rrrLkCFw7dq1YqIiAhuvPFGDh8+HPRas2bNIj09nenTp5OZmcmYMWO45ZZbmDHDU8Dk2WefZcSIEQwZMoROnToxa9YsIiIimDNnjtdcERERpKSkuB8xMTGn8zEIgiAIgvALpjpgcXO9SP7Z59zPqArr+sJvP6ME608o73UeHoEMKkdeE/NH8PbM673TpSjPe01oVd71wlO/lraJoD9fhMpXP47Kiz+Kii7YhfLsH/NZ4yj+efJG6IV+CUrQK++7loKt90QHwmq1YrPZ/MZq781mM5GRkdhsthpzu89UgbmaCGbNYIvS6e/farViMpmC+vwEQf6VhIjT6aS8vLxBHqHsGl522WUcP36cNWvWAFBQUEBiYiL5+fnuMQUFBeTk5LBixQqGDRvGmDFjKCwspH///jzxxBNBr7V8+XIGDBjgdSw3N5fly5cDUF5ezqpVq7zGmM1mBgwY4B6jMW/ePBITE+nSpQsTJ07k5Emj3W9BEARBEITaCe6rk6+yvwtPdXUnSrhqhdnKUQL+J7wrwWu55poHXguHrzSYf7trPs3rbsLf034S737u1QFeaxTjCYU/iNooOIgS82WutfTzlQP34Glfpy96dwpjqn3GuqzXiWy73Y7D4fA7b7VaiYiIIDIy0s/bbLVaCQ8Pp3PnzkRGqpx53zmMOBti12q1YjabMZvNbmEeFxfnFcpusVi8Nh8CERER4b4vh8PhTgXV5o2Pjz9TtyE0AaQQXYhUVFTw1FNPNcjakyZNCjpvJjY2lu7du5Ofn0/v3r3Jz89n7NixTJ06lRMnTnDs2DG2bNlCdnY2U6ZMYdCgQYwfPx6A9u3bs2zZMvLy8oJaa//+/SQnJ3sdS05Opri4mNLSUoqKiqiqqjIcs2GDJzzsjjvuoHXr1rRo0YLvvvuORx55hI0bN/Lvf/87KDsEQRAEQRD0eIv2Et3r4wSmDCVmj6EErM31bMUjWI/jKUwHyvvsW4xOHXM4zJzy08EH8Hjm9QXTSlAbAr67Db457xpaPn05njz1Y3gEdiWer/t6z7rmjf/BZy7weOHtroeGt03V1VVYLN6bDRaLxc/JpIlSk8lEUlISxcWe0HytMJ0mjJs1a8ZPP/2E1WrllP+H5oXVaqWioiIop5bZbKY6cNhFQOx2u5cQT01NJS4ujkOHDmGz2QgLC+PkyZPue7DZbG57yn36DUZGRtKsWTO2bdtGTEwMx48fd3vb7XY7F1xwQcj2Cb8cxNPehMnOziY/Px+n08nSpUu56aabyMzM5Msvv6SgoIAWLVrQrl071q9fT58+fbyuzcrKOuv2jhw5ktzcXC644ALuvPNO3nzzTf7zn/+wdevW2i8WBEEQBEHwQeknzfOsF4H616XuY2ZzhWvsJjwiVRPV5Xg86YEqhuvD2FWxu6oqvSjXz3nKNf4YSkQfR/VqX+01v0p3DiQ4Ne+/NpciOCf0Vozz+bV0gK2u5yr8NxEUMTExXg6l8PBwr/MWi4WwsDAsFgv9+vXzc+D4og+TN0IvvM1ms59HXl8gzndeX3zD8AOtqV8jLCzMvU5kZKR7Pa0VXEREBBEREURFRXnNoYn/sLAw2rRpQ1pamvu62NhYwsPDG1eY/MmTUByo7oHQEIinPURsNhuTJk1qsLVDIScnhzlz5rB27VpsNhsdO3YkJyeH/Px8ioqKyM7Orhe7UlJSOHDAuwrqgQMHiImJITw8HIvFgsViMRxTU3sLbSNhy5YttG3btl5sFQRBEAShZpxOT75yY0FzpoZq196924FvUMJcXzFd7xX3eLEtlgqqq/fjXbwuoFV4i1lfYesRmFYrqM5gp4Bw/EPQfwC+ReXGezy0dntdfhbOIK6pRIXUO1FyoNznvLapsAd4F+9oAEVkZBzh4WFUVVXhcDioqKhwf1fVRGxcXJxb2KakpJCamsqyZctqtEyrpB5M2+FQC7jZ7Xa3B9xkMmGxWNwt2ywWi7tNnL41XXR0NFarlerqaiIjI+nUqROnTp1i37597jntdjthYWHY7XaOH1dRHA6Hwx0toN/MiI2NdW8qaCHyjQqnE6Ki1PPJk+CzEdOocDqhoAA6dYLmzRvamjNKI9rSOTfQQlga4hHqL7WW1z5jxgy3QNdEe35+Pjk5OQBkZmayYsUKr2u//vrroNfJyspiyZIlXscWL17s9tbb7XZ69erlNaa6upolS5bU6NEvLCwEVCiSIAiCIAhnnpEj4YILoKxMPf7+d2gMAW+DBkHv3hBqC+9Dh/YGOKMX2L6CtBolamtbzLcifGC8v8IZec1PoQrdnUAT0Gaz57ra/Tba/VQBp4IQ7VoOfjXG3vYSn9cl+Ba/S01NxG6343Q63V5mvdfd4XB4eakDeZJ9c+L1z5onOlBrNd9q80bflfUh6xEREUF9n9Z76hMSEkhMTCQqKorw8HCaN29O7969ve6rTZs2pKamejmZEhISMJvNbieWZp/JZCI2NtZ9baPD6fTsku3YAT//DI89Btu3n31bKvw3i7z48EPo3x9+Ac69RvgvRagv4uPj6dq1K/PmzXML9H79+rF69Wo2bdrkFvIPPPAAeXl5TJs2jc2bN/Piiy8Gnc8OMGrUKLZt28b48ePZsGEDL730EvPnz2fs2LHuMePGjWP27Nm88cYbrF+/ntGjR1NSUsKQIUMA2Lp1K48//jirVq1ix44dfPTRR9x9993069ePrl271t+HIgiCIAjnMLt3wxNPwKFDnmPvvgsff1w/88+eDT/+CP/5D/z5z/DAA9C+ff3MXVecTli0CFavhnXrah+vx2Ix4xHletFpHO6ttEoRKmRdjVH6rRqPjtM888HlSPvrMqPrfqSmdmrBaDu7HazWCsxm3/GB8uFrQp8KUIL6DLXWdYro6Eg6duwIKDEaGRlJixYtiIyMdIvTlJQULrjgAnr06EFCQoJfL/Lw8HC3d/68886jb9++9O7d2y1mHQ6Hu1hdoBB3I9Gvx2KxYLPZsNvtbvFvs9n8Qvm1Ingmk8lL3MfFxREdHU1CQoL7WGxsrLvrUnx8vPu4Pvw/LS2NZs2aed2zZm9aWhpms/n0vOyFhcrLXN/oawScPAn33gtTp8LFF9f/WjWxeLH6Rz1zZuAxn3yink8E0wnh3EZEexMnOzubqqoqt2hPSEigU6dOpKSk0KFDBwD69u3L7Nmzef755+nWrRuLFi3i0UcfDXqN9PR0PvnkExYvXky3bt2YPn06r776Krm5ue4xt912G9OmTWPy5Ml0796dwsJC8vLy3H/c7HY7n332GQMHDqRjx4489NBD3HzzzXxcX99CBEEQBKEJkJMDf/oT3Hmnen/wINx+O1x3Xehe6JpwOuGLL9TrOtTvqlf0GiLUe6yo0Fd0V15ob0FqeBV6YW2xKO0QKBLbbtdEst7j7sRurym83deDqFWnrzs2m7LRZgu0ZqAPz2jdaszmSq/3Sg97j+3Ro4dbKGvCXS+ILRYLvXr1olu3bvzxj3/kyiuv9Lo+LCyMyMhI7HY7TzzxBEOHDqVTp05B3K0HTRRrAlgf2q7l1DscDrfoDw8PdxeA03vUzz//fAC3uIea273FxMTQvHlzEhISDM+bzWavTQStUrzJZOLRRx+lffv2oVeMP34c3noLjh6FHj3UH4S9gaJJ6oj+F753b48w3l9bq8J65vbb1fOYMcGNr6zpd/rcR3LamzjPPfcczz33nNcxLexcz9ChQxk6dKjXsYceeijodXJyctzt5QIxZswYxgT4xUtLS6PgTOwWCoIgCEITYts29bx4sXrW14qqrITPP4eXX4aXXoIaysbUSmPKa9driECivbwc/vUvFSmrv++KCt9cbajJ8xzIo13TZ2EyKbHsvblR5XWN97xGIb978PXA17amb9H02quj11SBXp93rzCbtXuq8rHH6XqvvMapqakcPHiQFi1acNFFF3Hy5Em2bduGw+GgvS5Mw2KxkJqa6uVdDgsLw2w2k5KSQnp6umvdwB7oyMhId844KMGurzIfGxvrzlUH3BXeS0tL3XNHRkbicDgoLy/3+sx8w+w127T3oPLb9e9Bif0dO3YY2hsREcHx48dp2bIlmZmZbN682b3WgAED2L9/P5999plf4To/duyAyy/3hKj37+85t3s3tGhR8/WhEEKLacNrz+YfDr2t0dEq+sDllGxqiKddEARBEAThHEUvBisrYeBAFdr+u9+d/tyNUbQH0qVPPw133AG9enkfNxJ/ERH+k5hMnhxyhyOwVz0Qp/9ZnUTzZlssno2AUKhJtCv7PALHbNaPNc4bVv+2qgzOe4u6mJgYOnXqRJs2bXTXmmnVqhXnnXee11i73e4lUG02G8nJySQmJnqN08LYw8LC3Pnf2nHf4nP6Qs1aVffo6GhiY2Np3749KSkppKWlERMTg9lspkWLFiQnJ+NwONx947U5tND56Ohod/91m81G37596dixo5+d2poa+n9vFouF1q1bExMTQ4cOHdypBNr9W61WWrZsSatWrWjWrBlXXHGF39xu/vAH75xyLQzmTFDX0Jonn1Q7Zg2R+w6qCEcIkcLnGiLahVrp3LkzUVFRho958+Y1tHmCIAiC8Itg7lz/Y3r9omo2lQDr2LOnHmPlgYkTYdiw03PC1ZVgPO0ffaSefSOFnU5NRHmEZ3W1v0i124Mp9uZB+9y1TZNgRHtgEX4CreK7yaQ83jVVja/LBoG6twqv90aR35qN2saByaSuMRrbrl07wBMGnpmZSVJSUo126HuqWyyWgKHl0dHRbq94RESE2wsfFhbmJYwjIiJo1qyZ17UOh4Pk5GRSUlKIiIggLCyM1q1bu73m3bt3p1WrVnTu3JmkpCTi4+OJiooiISGB+Ph4LrnkEpKSkoiNjSUpKcndWi4xMZGYmBi/+09KSiItLY327dt73V+rVq0wm80kJCSQlpbG3XffTcuWLf36sWuRBV6fXXExbNrkeV9Lz/o6s24dvPKK9y/WX/9at7kefVTl6/zxj6FfW9c/LL6/DI1lp/EMIOHxQq0sXLjQ3QLDl9r6bQqCIAiCUD888YTntfbd1NfTDv8AijlxYgBwaZ3X8o1y/ctf1PNDD6nuSsFSVQW//S1cdJES/XUhGNEe6Du/R0R5Lqyurp8NDZ8W4bWiCWEwSr81CuMPPI/v52AU1q+Ns1pxbwjoPyftvDrmCZHX26ltZBhpIU089+jRg6KiIkaOHElycjLvvfee13k9aWlp7rB6TZgboV2rdU/Se9s1wsLCaNasGQcPHnQfi4uL47LLLmPLli10796dyMhIKisrOXjwIM2aNcNisdCsWTOaNWvGd999525LDNClSxf2799Pt27d+N///ufejIiPj+fhhx9mz549/POf/3SvpdkUFhZG165dKSkpcbeBA7jgggvYvXs3a9asISsri4SEBDIyMgzvF3y+U7dqBceOwXffqXYOZ2q3rHNn9WwywYgRsHatqhR/Nlm8WBXpePVVVZxDIxgB7vu5NGHRLp52oVZat25NRkaG4UPL7REEQRAEof54913IyFDfoTWMhZPntdpfV0nupaUbzohd5QG0ZUkJPPOMt3MQlAf8lVdg+HBVQK+oyPv82LEqBVWXpuyHXrQ//7z3Z1IbKmS8AcIDDNBC3uva5UuLBvD12AeKEtA89tp4m02tbRxRUG0YNaCJ/UCkpKQQHR3tFux6jAS5w+HgoosuMqzyrhEREcHFF19MVFSUV8s4PWFhYe6cc6fT6Q6bT0xMJDU1lV69epGQkOAOgY+KiiIxMZEWutxv3zB7TaSbTCZ69OjBtddeS8+ePWnZsiWRkZG0bt3a794uuugiBgwYwMCBAwFV8DklJYWkpCTuv/9+LrnkEv72t7+Rm5vrtYmh98j37duXJ5980rso3bFj6vm//9UuCPh5eZ2bMUM9QOXITJ4c+Do9//qXeta3pTDihhvUL+vo0XDVVcah9KH+Ax84UK17/fXex+uyUdGERbt42gVBEARBEBoZWuHk226DDS79bfR9VP+dWe+99R3rdKo6VlFRSki/8Qb07AmBuqp6e9qdgGfCLVvg97+HSZPgkkvUscmT4dlnYfx4Za9WC+rIEc+cTzyhRP0VVyjPe/fuoNXKnTNHzWmE/h7fe0895s6Fe+4xvt+1ayEhAfbtgwMHoDYvtpGI1QrLnYk22pp4D7USfiABXVvBOv1r33s1mz12BKN3tGJ1agPAzJAhQ9i7d6+XoL3mmmt4/fXX/cLANbR88UCMGjWKt99+252bfvKkp4+8M4CQ00R7oHnNZjN9+/YlNTWViy++mG+++Qaz2czOnTv9xmqe/QcffJBPPvmEi12tzsLCwhgxYgQWi4XVq1ezcuVKLrnkEi699FLWuXoROhwOrrjiCiwWCw6Hg1tuuSXgfWpceOGFXKL9Ivmyf7/6ZVyxotZ5OHYMxo1Tr3Ny4MUX1evbb689PObTTz1r1cSHH4IuRYBvv1W/zHpq+4dUUqL+EHTvHnyrts2b1S/zpbVED4loFwRBEARBEM42xcXqe/S776rvrUpAn8RkUj2l9YJWHz6/ZYsqMP3225CaqqrO5+erc++8A0OGqNcVFZ58ZV8v+pdfgiqQ9grQBRiA0wm33KKE8cKFnu/4X33lua5jR9VTvUcPfw0wf756gPe5+fODE+0a997rLdr1dO+uf+dE72nXQr+1yuiBcsdNJqjBGdxk8M3ND2a8VrDPZDLhcDjc+eYaf/jDH0hPT+f777/H4XB4VXcHleudmJhIRkYGaWlpHDp0iL59+7rPx8TEMGrUKN555x0Ar/ZxRi3YfIV8IGFvMplITk6ma9eudO3alcOHD7tbCxuF8sfFxXGn1lvRhVZYLzc3ly5duvgV2gPvwni+62sESgvwY8YMJVQPH659rP5z1ovhe+6Bb75RfwReekmFtxjYzapVoRV2AOOdp127VC7MvffCZZd5bNOiJi66SOXSAwwYENw6WheCjRvV65ISFVKvtaPTaMKiXcLjBUEQBEEQGhF68bxvn3Ju/frX2pF84Bmczm8Ab0H7yive8+Tnqxx08BbIq1Z5Xuu7I02Y4HntGf81cBT40n181y6jcd4sXGh8PBDLlgWOzA2mmPXq1YHOeBuoz9Wuqdjb6VJDe+96nf907dcK34USURDMmjfddBN//OMfDWsf2Ww2cnNzGTx4MCkpKXTt2pVWrVoFnMtms3HZZZcRFRVFbGyslyjW8uM1AvVVv+222+jTpw+DBg1yH2vWrBlt27YFlIi+7rrrgu4Rb7FYaNWqVY0RA76YTCZ69epFZmYmCQkJXHrppdhsNnJyctSA4cOVQPfNIXn33eAW8C1YoKEJ+OxsmD4dbrwx8PWhhqQb/WP43/9U6Ey/fionf+pUCAuD5cvVeU2wA3z2WWhzf/ut2iF87jn1h9G3OEQTFu3iaRcEQRAEQWhEdOni/f6ZZ/TvPgK243SeoKTkQv7wh5rn2rEDbroJsrI8x/QiWOv7Dv6iX+H9Jf7gQe/vxYmJqi98fXxX/vlnSEpS38X//GeYN0957WsT7Vu31nQ2sAip7+/3mmc+1Hk1r38oQt9sbvhIgEC91LVzgTzOWjG2u+++m+nTpwecq1u3bmzevJkOHTpgsVjcnYtSUlIoLi4mPj6etm3bcuLECSIiIjCZTAGr0ScnJ3OpQWi1ViHeZrPRs2dPmjdvXut9G6HPve/Xrx8pKSmG46699lr36wEDBnD55Zd7vO6vvaaeA9xDQEwm1e5Mj34XRhPwe/ao52++UeL85pu9r6mLaAc4eRJeeMH43Kuvwt//rl7/9reh35svWuRDoIgAk0mlErRsaRxNcA4jol0QBEEQBKERocLgPSxbpr2qAr4CwoDveeop1ZO9JjTnln5cMLnUc+YYH7/qKu/3R46ovHv9poAeV4RzUGzYAGvWeL6X33GH8qArXbcAFar/K1R+fTWzZ5sZMUJtJATLmchR1whFrOu1kc3mX62/vtc7EwQT4m0kxu+8804qKysDinqNFi1aeBVnGzhwICUlJbRu3ZqtW7fSsWNHpk+fzsCBA7Hb7TgcjhoL3BkRGxtLZGRkSNcYcf7559OjRw+Sk5O9Qv1rI6gw+dp+0IsWQd++8MADnmN6T3txsX/xigUL/P94/POf6pc5FEwmVdDCtfnih96OwsLQ5q6JAF2tWLtWfRagdsIa+pekHhHRLhgyd+5cHnzwQY4ePdrQpgiCIAiCAMBS4CBgB1qwfn3dZtEKUtdEQUFocwb6brxkSeBrXn7Z+/1NN3m/16KEVbu5b11Hf0Zld85h5Mh0Lrjgllo88R51fCbD4U+XxmpXTdTkaa/tutoEO8CwYcNYtmwZcXFxfP7558TGxjJx4kT+9re/sWvXLtq2besW6YmJiZjNZpKTkwPmtBvRq1cvysrKSE1NrdO9aJhMJq73rX4eCv361f1arUK83tutF8u7d6uHHn1rNY1//tM7dD1YtJ1BI0LdJTt4UOXvFxUFl8fvyw8/eF7//e/eGxnnOJLTLtQL+fn59OzZE4fDQUZGBnPnzvUbM3PmTNq0aUNYWBh9+vRh5cqVfmOWL1/O5ZdfTmRkJDExMfTr14/S0tKzcAeCIAiC0Nj5zvWskt5DSKf1wteTX1QEv/qVqu3kT3DCzFe/BaPnfvvbms97onX1Iux74O9ACfADW7fWFj7vufZcFMbnOsEK+zZt2vgdS0xM5LrrriMuLs59rHnz5owfP57/+7//4yGtYAMQFRVFampq8AXeXFx//fX8+te/5v777w/punpn6dLA5+ryD7eufd1D6acIyjZPKJA/of6RSk5Wle4DVdMPheefP/05GhEi2oXTZvv27QwePJj+/ftTWFjIgw8+yPDhw/n000/dY959913GjRvHlClTWL16Nd26dSM3N5eDupi25cuXM2jQIAYOHMjKlSv55ptvGDNmTMh/gAVBEAShaeL95b2+/ntMSID33z+9OfTV4+uLnTtVAWolvMuBU8AXXmNmzFCdqgJzZnq0ywZACBXQa+DBBx/kpptuomfPngHH+Ar/5s2bk5aWZtjD/corrzScI1DYfFhYGFlZWcTo25g1BULtJ1hXast/acjv8E1MPzStuxHcLFiwgLi4OKpcv7SFhYWYTCYm6ErDDh8+nLvuugtQ4fCtWrUiIiKCG2+8kcMhhKTMmjWL9PR0pk+fTmZmJmPGjOGWW25hxowZ7jHPPvssI0aMYMiQIXTq1IlZs2YRERHBHF3S3NixY3nggQeYMGECnTt3pkOHDtx6662Gf5QFQRAE4ZeHyet1XT3t5xJvvqm9WgYsB7yrRa9aBbfeqr0zEuj1K9qtVk/bs186dc1p1xMXF0fXrl3rZQOgdevWXHLJJV7h8YMGDaJ37941Vqdv9NRlh6hPn/q3wwjdd31DGvKPVBPbWZM/OSHidDopLy9vkEcoOTqXXXYZx48fZ82aNQAUFBSQmJhIvtak1XUsJyeHFStWMGzYMMaMGUNhYSH9+/fnCX2z11pYvnw5A3z6LObm5rLcleNSXl7OqlWrvMaYzWYGDBjgHnPw4EFWrFhB8+bNufjii0lOTiY7O5svVZNYQRAEQRB8CKYVWt3ZDhT6HAs+Xe1vfwt1vU+Aj32OVaNEt3ajTlROu1ERqgPANOAbgznqD4tFCfcmpgfqRH2I9vpYp0ePHqSmptLFt+0C0LdvX6655pp6sUOoAw25u9XEfuZSiC5EKioqeOqppxpk7UmTJgVdFTM2Npbu3buTn59P7969yc/PZ+zYsUydOpUTJ05w7NgxtmzZQnZ2NlOmTGHQoEGMHz8egPbt27Ns2TLy8vKCWmv//v1+fTiTk5MpLi6mtLSUoqIiqqqqDMds2LABgG2unjOPPfYY06ZNo3v37rz55ptcccUV/PDDD7Rr1y4oWwRBEASh6eL9JTTY9s3KO23xu75m3nA964XQC8AjQV197Fgo35lP4RHbOUA0qlL+S0AU8BtUAb5SoAyIBXzL1X+IynP/BLhQd/yM7mzUGasVyssb1hF5ugQj2i+88EK2bdtG69at67xOhw4dSElJIS0tzfB8bGysX//2JsX27Q1tQd05cqTh1g6lrcQ5gHjamzDZ2dnk5+fjdDpZunQpN910E5mZmXz55ZcUFBTQokUL2rVrx/r16+njE0aTFah3yxmi2uUuuO+++xgyZAg9evRgxowZdOjQwSuEXhAEQRCaMjV/P6+L56gUeBp4s7aBLj4FXsMjdrXqdGVAHao5++FEFdQ76HNMQ1v3gGu9nSgBf9JlS7nr8SMqZL7cYA49e+rB5vrHZAKHI7Te7I0NaxDGa2mTd99992mtM2rUKAYPHhzU+FAiUxsFP/9c8/mvvz47dpwJtm49u+vpN5KaWAesc/hPRcNgs9mYNGlSg60dCjk5OcyZM4e1a9dis9no2LEjOTk55OfnU1RURHZ2dr3YlZKSwoEDB7yOHThwgJiYGMLDw7FYLFgsFsMxKSkpAO5WG506dfIak5mZya5du+rFTkEQBEFo7LRtW9PZuoj2jSjRG6y3TmvfdAiIAT4DIoENruO7gTTgCEp8XwREuM4dAPYB3Qxs3YJqWZcJaJF8j7muece1XhIe8a2/3shbrgmdTXhHA/jSOD3tTYFg89ATExPPsCXe9OnTh6VLl9KhQ4ezum6dOVtF4xoCXVHqs8KZzRlqUES0h4jJZAo6RL2h0fLaZ8yY4RboOTk5/OUvf6GoqMjdKiMzM5MVK1Z4Xft1CLt6WVlZLFy40OvY4sWL3d56u91Or169WLJkCTfccAOgPOtLlixhzJgxgGr10aJFCzZu3Og1z6ZNm7jqqquCv2lBEARBOIfxOAl3A18DuSjxDErYnnELUEJ3Kx7vthVwuJ6LUKJ9NsqLvx+43XWt1njdgRLnet5yPf+ACn/XeB91Xz+iQuONvKRVruPBFprTNgACnRfqg8aaJ96/f38yMjJo0aJFQ5sSHOdaZMC5xKuvwvDhDW1FvSDh8U2Y+Ph4unbtyrx588jJyQGgX79+rF69mk2bNrmF/AMPPEBeXh7Tpk1j8+bNvPjii0HnswOMGjWKbdu2MX78eDZs2MBLL73E/PnzGTt2rHvMuHHjmD17Nm+88Qbr169n9OjRlJSUMGTIEED94X/44Yd54YUXeP/999myZQt/+tOf2LBhA8OGDau/D0UQBEEQzgleQoW0j0aJZwjda1xVyzXlrjF6VqE84kdQ3uwSYC/+YeZaUbqdBvPW1IOtzOf9KZ/3gUR7Mepz0O7nGEqcG3kpZ+psqL+kcWlBe25gNptp3br1uZPjLqL9zHHyZO1jzhHkr08TJzs7m6qqKrdoT0hIoFOnTqSkpLjDhvr27cvs2bN5/vnn6datG4sWLeLRRx8Neo309HQ++eQTFi9eTLdu3Zg+fTqvvvoqubm57jG33XYb06ZNY/LkyXTv3p3CwkLy8vK8itM9+OCDTJw4kbFjx9KtWzeWLFnC4sWLaVtzrKAgCIJwjiPfWY1Y5XosB/IJ3WNcCfwN/4rsGqeAp4DnXO/XAz8BJ1zvj6BEsZZbrq/grnEYWIl3BMA+oADf1myBKUJtCGjz1uRpByXeT7muK8F/02APKmx/I55ohXObxio+z7nc8SBokI2ZJvg5NhrOP7+hLag3JDy+ifPcc8/x3HPPeR0rLCz0Gzd06FCGDh3qdUwLnw+GnJwcd3u5QIwZM8YdDh+ICRMmePWSFwRBEJo2L74IU6fC55/DBRc0tDWNiXKUZ1kruqalilWjWp4F8qAfQYnvCsCGEtF2VOG3U6jQdVzHAY6jvNK+peg1j7jmCTda73vUV8n3gN+6jm10XbMG7yruGvqQ6pMor36Vbp1Aol2jVGc7KGGuZ4vreROwIsB85w52ux2z2UxFhVGbu4alqpHlYtfHJsLQoUPJy8vzcjwJ5zCNNIWjLoinXRAEQRCEBuN3v1PFk5tI2mE98SPeldpL8YjPEtf7w65ju4EFwOfALpSX+TBKUGt8j/KY/8/1/iCwTjenfq0KlICuxlss1yTQjEJQjXq6l6NC7rUNgGk+64K/yC5DFarTqHbN49SdrwCOGqyl99CHjuVc7sd2FoiOjq590FmgWbNmAIZ92kOlZcuWDB8+PGB7uTOCeNqFIBBPu1ArnTt3ZudOo5w1+Mc//sGdd955li0SBEEQmhqNzGnXwPwR5S3XqEYVbNuDRzyXoIq6zQPWAglAbelkxa7nl1Aitxxojrf3W1u3BI+QBuWRr0R58rUQ+gqUCD9aw5qbUH3VAVbjyZPvYTB2LyqPf5TP9VpbtyqUSDcD4aiogQOu+9FvEpQCXwJda7CrdqxWa6PwJgcbsu1wODh1yrdGwJnBarUSFRV1VtaqjREjRrB3717S09Mb2pS6IaJdCAIR7UKtLFy4MGBYlj4nXRAEQRDqinxv1aN5ozWv8h7X62U+435Aecm3ogR5bfmbJlQuuPZ/+hY8Xu8Kav5aWOm69l2UV38Nqo3cKdf7Kjwh+Fpo/T7gbdfrKtd9HdDZ4CuqN6Eq5b8INEN5yU9h7C2vRIn27aic+ja6+ziA+ty64r35ERr66ugN6XVvjFXaTSYT7du3b2gzAAgLC+P8czl3Wf74nTka4e9OXRHRLtRK69atG9oEQRAEoYkj31v1HHQ9l6OE9j4gFU/It97zut51/KDrUe1zXp+LfgwVkj4fSEflspcDm4GvUG3SSvD0XfflpGvsLlR4/REgzDXPYlTBvGMoT3cp3gXqNuHZjChFbTT4VpIHJdC/QbWF01rdaXn8vuO0fPxIlGivQnn99fd8mLrgK5TDwsKorKx0e95NJlNQOdQOh8N9TWVlsMX5vElJSWHr1q21jjOZTFgsljpHB4Ryrd1uJzw8vE7rCD7IH78zRxMS7ZLTLgiCIAiC0Kgo93l/CuVJL0WJ13KU4P1GN+Y4SkhrQrkC2OAaownX3XiK1BW75jkCfOE6v811TssTN7JrMyocX6MaJaD/jaevO6gQer0YOaB7f8JlL3hEtr56/AZUeLsWhq/Pr9cEfKXr2GHdtTtc8x3HE/Jf93BxrWq7yWQiPDwcq9Xj69KLepvN5lXh3WKxuEPaTSYTsbGxREZG1uqt9z1vNpuJjo4mJSUlaJuDCaWvS9SAw+HwO6b/PITTQET7mUNE+y+PptjWQhDONvJ7JAhCoD8D59qfh9JSePhhWLr0bKy2BxWOrq+wruVta17lk7rj5ajQ969RYlkTyAWuRylKvB9FifcjunkhcGX6MpRQPq47Vo4Kd/f1aJtQmwDLUd73k3i88FpuOi4bjrmena6HZrNWTE8vvDWxXqG7/hDKy78N7970wfVoNhKkABERnogDk8lEVFSUWxSbzWZsNhtms9kt0n3FsCZqTSZT0AJXP85isbg3B+ozPL8uHnLfyIOqqioR7ULjp1Wrhrag3pDftlqw2WyYTCYOHTpEUlJSo8wrEoRzAafTyaFDhzCZTI2256wgCGeWxx+HmTNhxQrwzbw610T7M8/AtGnqceZtP4US1kZiugglnDUPPKiCbkZUokLUzSjRrH2nWQa0xFuMG1HumkMT1xplAdb8FE/7N60Xe5XrYdPNU+a6tx1AputeKlAiXDvvi379YlSxu4MoT38VSsj/j8AbEAqHw4HJZMJut1Ne7hvhoMLAKyoqsFqtVFdXu///0orUxcTEEBUVRXFxMSdPnqS6uhqLxUJlZaWfqDWbzYbh5/qwdJPJhMPhwOl0ennrgyGY8HaHw0FkZCQnTpzwOm61WmvdWLdare4QfxHt9ci59sfvXKJTp4a2oN6Q37ZasFgstGzZkj179rBjx46GNkcQzmlMJhMtW7aUNjqC8Atl8mT1/Kc/wZtvep871763btx4NlfTCsgZUYLHo6wJ6nLUVzzfv7VO3TjtK2AZqhK7NhcEbu+m9X7XPPq+aOLaieqRrrHdZaNeQFeiwt+P6ewyoTzzGtUoT7qRaNcoddlfhXc4vZYe4G+nXqCbTCYiIiI4edLfK5+SkkJFRQUlJSXY7XZOnTrlFtCaALfZbKSmphITE8PmzZsxmUzuMVoevJEY1gtsm81GVVWVl1DXh7lHRkbWcP+ee9LGHj16NOC4mjYA9PdWXV3tlbcfGxvLsWPHqK6uprq62m13nVi3TnlAG0n1+QZH+/dhNkN1zZtM9c4f/qB2HoVGj4j2IIiKiqJdu3YBK6gLghAcNptNBLsgCIbUJtorKsBII3z4IfzwA0ya1KTSF3U4USJUC02viSqU8NbEa6LP+UAfkNPndU0iWfsu5JsrXuK+zuEo59SpHUA8KtT9mM4+7f+ASrwL1ZWjqsEfwrP5YHedC2SPE889Hca7FkCx63r/4m+aONXC22NjYzl58qRbrIISrna7HYvF4vYod+/enfz8fENLjDzcYWFhAO7vj3rBHEg8m0wmYmJUAb7i4mLMZjMmk4mwsDBKSkrc4/S2au9BfWfVRHtYWBhlZWV+YwKhRQhYLBaqq6u9IgP0n1lVVRVRUVHujYKQWLoU+vWD886DPXtCv74pov3xs1i8Rfuvfw3//OeZXXviRBHt5wgi2oPEYrGI2BAEQRCEM0RNov2bb+Cii2DqVI+3XuOGG9RzVhZcfvkZM+8so4WQaxzFO487EEfw9iyfwONFB0+fdl/0nm6tYn2oaDn1Jlq3rmbTpmOo6u/bXefDUJsPRnPrw+1NeIS29hkcNbimGrVxYEXdcwnemw+1eywtFgt2u91LkEZFRXHixAm3WNZEfXp6OkOHDg0o2jUBHSjE3LdwndE4bc2UlBS3KDebzWRmZrJmzRov0W6EzWYjOTmZyspKqqurKS8v9xLtmmdcy5XX2xAeHl6rc0qLDrBarV6fW0j861/q+ae6t+Jrcug97Xruv//Mi/aEhDM7v1BvSCE6QRAEQRAanJqiQh94QD1PmRJ4zP799WtPbZw5r/46lNdYL6A0z7MefQ57bZxCiefdAFit+g0BXy97sHkKvuOK0MT2yZOlqBz3Tbrz2g9Yv7YWPVCtG6P3jDtR91mTTZWoDYNK/IW6/+djtVqxWq1uL7gerdK7PsRdu8bhcNCtWzePZU6nVwE7X8eO/r3T6SQ+Pt5rHSO0TYSIiAjCwsLcHn6n00lUVJT7vdls9iomZzabiYyMxOFwYLFYaN26Neeff77fOloV/NTUVD8vua8XPiwszKsQHygvvnbP7du3lzpP9YXe067xt7/BJZc0jD2hsG9fcON6967/tX9hNRVCEu3V1dV88cUX/PnPf2bYsGH8+te/5oEHHuD1119n9+7dZ8pGQRAEQRCaODV52oPoYnVOh8bfdhsMGKB9BvPxF5+V+Id5V6HEuDZWyzP3DdPW+qJrReCqXZ+V9oFrwjbUHuK+glgfvu9EefX1nv1ALeS09m3ae6NceaNjvmhz6D87452giIgI4uLi3FXd27Vr546ovOiii4iOjja8riaRGqXLz9bnpqelpXHppZfSo0cPL2FsVLHeZDKRmppKhw4d3HOaTCbS09OJiooiOjraLer1tlgsFnfbuQ4dOhjaGRYWhsViITExEavV6jdGX1TObrcTGRnpl0vfp08fMjIySE9PJz09vW6i/VwrXhGI/fth3jw4VfeWgn7o/9BlZdXfvACHDtU+RuOpp4If60rlqJXx44OfU8/LL0NenvG5DRtgxAgYMqRuc59jBCXaS0tLeeKJJ0hLS+Pqq6/mv//9L0ePHsVisbBlyxamTJlCeno6V199NV9//fWZtlkQBEEQhCZGTd/lz2VBXhvV1TB/PixZUpfidqdQFd81weoJn1aOVE3Yg0cQ60W91h/9FLCLmkLKfZ1aNW2kVFRUkJQE3qLd6Adcm4CrQuXD15bLD+oeqrBYvMdqdlutJrdo1nvZw8PDCQsLIzo6mujoaHfrNk2w6oVpoNZwvuO09xEREXTo0IFmzZqRmOhbX8CD3ivfpk0bUlNTAUhMTKRNmzZER0dz+eWX06xZM8MNBb1deg+6PoQ9IiKCli1bct1117kr1BvZ4HA4DKMQAH71q19hs9mIjY31akf3i+Sii+Cuu1RLjLpSXa0e2h+/M/l5JiaCazPITUqK/7g9e1See7AEa3NtRQsD/UEZNQpyc6F7d+/j110HbdvCK6/ArFnB2XCOE5Rob9++Pd999x2zZ8+muLiY5cuX869//Yu33nqLhQsXsmvXLrZu3cpll13G7bffzuzZs8+03YIgCIIgnKMYfc87XdEejDe+Pqmv79c1pQUYfc/1jsLWWrRVusSp50NU9um928pbbbVqn1U5Hm95CZ5+6WCx1JwPbrEYRaZ6rjl16hQqstpjj/F3diMvpW9eejBedu0+VRSB3jaLRW1gWK0QExNDZGSkW0CbTCaio6OJjY0FcAv2sLAwkpKSyMjIIEUnbOLi4oiOjg5arIaFhdGyZUv3WvrrfKvDa6HrenHfrl072rRpQ5cuXXA4HCQmJtKyZUvatGnjnkMrUhcTE+O3MaCFs1utViwWC/Hx8dhsNi655BK/Xu2aaNeOOxwOr+iBtm3b0t1HOJ1xT/tPP6kwlK++gk2bVKXzAwdCX/NMoEUYf/xx3a53OuHCC9VD+yOg/zxD/Wx//BGOH4dLLw08xvePTXa2ev78c1UY8OOP1XNN3H473Hqr532wf3hrGzdihCpSmJZmfP43v/F+r+VMgbZD6c1f/3r2c6bOMEF90osWLWL+/PlcffXVAds7tG7dmokTJ7J582YubzqVYARBEARBqGdOR7QfD9BKvC76oaSk4aN19UXHTSbQpxH73pPN5ivaPcZr4lR7DoRxTV0tP97pHmO1BhqrzplMxuvExKjw8KqqKsLCPEXQTCY1n6+oNrYl1J+nR4wYXacdCwsLIyEhwR0mbrfbSU5OJiIigjZt2pCWlkZCQgIdO3bk5ptvplWrVoSFhdG2bVuuvPJKd864Vt3dt095TEyMewzg5RV3OBxuL7jFYvESzWFhYdjtdtLT07283Oeddx59+vTx8ooPHDiQoUOHEhkZSWxsrDv3PSEhgbi4OC979NdpGxMAEydOJCMjwytHHpSX3+FwEB4eTtu2bUlMTHTn2WdnZ5ORkUHXrl11n+sZFu3DhqkwlEsvVTnR06cr7/aZYN8+1YIi1PbOdd0t/PlnWL1aPbTQ9dPZCezUSbXQ+/xz2LnTeIzvZ6+9799fedivucZzbuhQ5Z335frr4fzzPe99bf7gA+O1a/ucTCb1cw6QmhIyvXpBcnL9zNVICOpfWmZmZtAT2mw22rZtW2eDBEEQBEE4t/n0U/UdzGQKvkh0sKL9kUdqHxMMu3ap77j676l15ccfVQRnTRmCWiSs0XENkwmqq1UbLJvN/57MZt9jviHZHkFdG7V9h7ZYgvue7YvT6amiHhPj/UO1WpXQr80+m81/QyC4n28VJlPNdoeHh7urxEdFRREfH09sbCw9evSga9euhIeH8+abbzJel4Or5b0D7nxyi8VCq1atiIiIoF27dqSnp2M2m93e79jYWK/CdXa7neuvv54ePXpwiU+BsVatWhEXF4fdbqdbt26kpqbSunVr9/mMjAxat25Ny5Yt3RsOWVlZxMXF0axZM9Jc3skuXbp45aFrr31D3qOiorj00ktJSEjwcsYNGDAAu93OeeedR1paGiaTyd0WLyMjA5PJRIKu2vgZD4/fts3zWtutW778zKx1yy3w9NNKwIaCxRJ4JzFY5sxRz/rPMxjBOWAAjBsHa9Z4jtls0KqV8fhAot2I114L7KkOFBHw/fdK1Hfp4n/NhRcGXisQ8+d7XvtEhtTK2e53fxYIuezed999Z3hcC89p1apVjTk/giAIgiA0bQYN8rz+7W9VL/WPPqr5mmBFe6Dv7IG894F0xdy56nnhwprtCkRFxQngLaAXubkX8tNPsGiR8X1UV0OfPsqWFSu8bfL1tFdWbjFcz/g+amsBByrfPbive5p+Ox0tVl5ejt1up6KigrKy0It06b9C2u1QXu79vroaKl0p/Gaz57u5tsmg2W4UBZCUlITFYqFv377YbDa++eYbHA4HJpOJSZMmsXDhQrKzs92edIALL7wQk8lEf5eYa968OceOHSMxMRGHw0Hz5s0577zzKCoqcl8TExNDs2bN3BXjb7/9dmJiYli5ciUtWrRg165dXnaZTCZatGhBSkoKt912GzExMezTVeW2WCwMGfL/7J13nBTl/cffM9v3em8cB0fvVSkiggVQsWGvwRYL2HvUqIm/WGKNRmPsPfYYNYoFsSIiBrGh0qXX47hedn9/PDO7M7Oz7W4PDnzer9exU555nmeWvb35PN92OisNFlQ9Vn7u3Lkhy355eTnbtm1j2LBhuN1u1q5dy/Lly2lpsU8yqCff00u/nXjiicyfPz+iHFw0ce786iux8hVNJLYXu3GtYmzhQjGHnj1j9/XTT/B//wfXXhsZ2w3w+efidcUK+OgjePhhuOsuKCyM3e+CBcK95OGH4ayzore7/XYRQ37aaZHnHn9cvCoKvP22sLzHu58rrhB9JoOhggEQ3+vBztVGX43VcTqhtBSqq6F3b3FszhwRZ37ddeF2RUWwZEn4vvbeG778MrJ/45yOPTa8PX26SPz32Wfh/mIhRTsMHTo05sqay+Xi+OOP56GHHoqayEIikUgkEslvA91Y87vfhY/ZPSsmKtqjPYtZH02ammDYMBgwwGyw0TE+j771Fhx6aPTx7Vi/fjawHniLNWtiW5E2boSvvhLb774rrPI6Vku79X1wuYSwb3t1o8RFe6ryAjRpSjsYDMZdCHA6wyLcivUa3cXeTrSrqnn+kdcqpKWl0dDQgNPpRFVVBg4cSGlpKQcffDAFBQX8zvgh1UhLS6OsrMyU4C0rK4u8vDy2bNlia6hyOp2m2Pfs7GyGDBnCTz/9xLp168jIyAjNIRAIhMrH9e3b17RgYKVbt26sWLGCkSNHsm7dOhRFoWfPnmzcuNHWFV5VVZxOJ4FAwDa8taysjObmZnbs2BHKLq/PWU9W53Q68fv9JhFfUVFB10CAnvr7lYzLe3vjUYJB8XPGGeIX4+mnE+t3//1h7Vp47734ZcomTBCvLS3wr3+Zx472y3j22WbRfv31sGiRcBf/8cewi5CdaNdRFPOKZyzaIkqfflqEFyxYIPbb+n9htbSvWCHmo/+O5OXB0UebRTsIca/z1FNQXy++pBPB54NPP4U33xSu/FZr/owZ8Pe/h/f3QNGe9Nfza6+9Rq9evfjnP//JwoULWbhwIf/85z/p06cPzz33HI8++iizZ8/mOut/lEQikUgkkj2aLVuEccqIXY6lp56KfF5cujR6v20R7XPmwA8/wEsviWfvjz6CurrweaPAmzpVPMsnwp13wqRJ0NSUeIk04wLBlCnQbDCQt1oSowctb4yq2rvKC+zqtxtJ3GQeLX491FMCXRl1YXNzs1b6LPZiQLxxExnT5Yq/4BAwfHB0YXrRRRdxySWX0Ldv36THHTBgAF27djUlZ9Ozz1uTvJVpyb0OPfRQ+vTpw7Rp0ygpKYmaJwrggAMOCF2jc9ppp3HJJZdQWVnJmWeeyZFHHsn+++9P//79GTFiREQfwWAQt9uNy+WioKCAcePGcZUhvsTpdFJRUUF6ejr5+fn06NGDsrKyUMK9Ll26kJmZSUlJielz2b17d6b6/fYioqoqxrtG4kLxlltE8jm76//3P+Eqowt2nS1b4P337b8k1q4Vr8kkJ1u+3Lx/1FHQpQvU1MS+bvNmuPlm4V70yCNiXqkmGcF98snitW/f8Ophon08/LCID9exWtpB/AJaF6/s+jb+smdmiozw114rvBkS1Y1Tp4qM8lbuu88cTrEHivak123/7//+j3vvvZfJhmXiQYMG0aVLF66//nq+/PJL0tLSuOyyy7jjjjtSOlmJRCKRSCSdl5NPFvHsRvRnN6uoWrs2sm00jM+I0TSBoojY8pISyM01X3PggUK0H3KIsKpDpFj85BM46CAh7Pv1E/NbsybSO/byyxOb8+rVIhHyCSfA/febzzU1hQVupKU9/gO+w6FXimoBoqtepzNIa6seKx/9AT2RqEY9VjyaeFfVoOn/OBgMRnXLTiVt8Q7QRbue2b0tFBYWmjLAA+Tl5VFfXx86fumll1JTUxPK6p6RkcGJJ54YcnNXFAW32012djabN282Ja7bd999GTt2rKkcnKqqISt6eno6Q4cOZcGCBRTGcOEuLi6mtbWViy66iNGjR0csKLhcLnr27Mm+++5LRkZGKHa/qqqKsrIyBg8eTLdu3SIWk2x57TWYNk3U5L7sMvHLdtxxoMfZn3ACvPCC/bWTJomVs7VrxS/xH/5g366x0SwijYwYIZKwPfKISGKXal5/Xbware9WGhrg4ovD+y+8IKzuiZBMXEoyov3RR9vex1lniR/j3BL5wjD2rX+G3W644w7xJauVNeTmm0XZPL3/9lj/u3cP7++Boj3pr7pvv/3WlBxDp6Kigm+//RYQLvTr4rmeSCQSiUQi2aOwE+HRShDvtVfiz9WBQC3wMrCc1asj+wYh2AcOFJ6Z338Pv/wSPvfRR+LVGL9uFe26vnzpJZGkrqVFVA3SWb8eTj89sflC2Av2X//CNGcQcfnNzXDJJeaKUUK01xIPPaGbIHoNcz2TvKpGPggna+FWFLHQYPUMTiwjfGRfRvRr45Vybi+VlZVUGjNfJ4hRsI4ePZphw4ZFCGW/Ie2/1+ulf//+ZGZmUmp0CTb0p/fpcDjo06cP3bt350zLL4SjnW4IiiJq0/v9fkpKSkxJ6ozk5eWZyrsBnHrqqRx33HEMGTIkIit9VKZNE6+33y7c0c84Q3zIdayCvaFBrKh16RJ2dbn55ujxEvHQY/7t4mFSydlnRz93++0i9lrngw8i3WnAXpwmI9oTFaVdu0YX2UaRmwwXXCC+bG+8MXob4/0Zs5FedlnkIkZHJDSUoh369u3LrbfeGopXAuECdeutt4bci9asWUPRHpZmXyKRSCQSiZn164WHozGU0Eqke/wq4FdLWGnshGVbtrwNfAc8aTpufC7UhTkIHTBjRswuIyy0usu68VnvrruEDrjwQmEY0pPXCVqB1Yga52aCQfjww/B+dbX5/EEHwXPPwT33mBcCxP1EF+FtpbW1EasbvaoKQR+rPFwiJJoR3oheLk4f2+EQuiJVMfXRmDp1KgcffHC7sp6fdNJJTJ48ma6W5Gsej4eRI0cycOBA+vXrF9P6HQwGQ+76aWlppKenc+GFFyYuji19teWcTnZ2NuPGjeOCCy4wHc/JyWHAgAGh9yohS7uR778Xry+/LF6Nrss611wjRK1R1LW0xP/ljYcu+v/zHxg+XMTJGFm8WCSaeOut2OLuyy+F9X769Ohl1KxoBkwTDz0UecxuXLvPpe7ansj1dtj1OXs2/P73cMMNifVhJC8PsrLEfca63vh5MVQciMtBB4nXNvwumNgDRXvS7vF///vfOfzww+nSpUuoVuO3335La2srb775JgDLli3j/PPPT+1MJRKJRCKRdCpuugm++QZmzoz+nG0W7Y2AVt6I6xGu3bOAT4EzALPlp6VFT1RWZdu30YBlfFZOJGw1mmi3MnUqfPed3ZmlQGSm9+Zm8UxrxE7vbN4ceUwsZNQDHWF8irzBeGOoatiSnmqS7dPuPUxW5E+bNo1Vq1axNFYCBRuMseeDBw9m8ODBbN26lXfeecfULj09nQEDBpCbm8tpMRKOGQWw0+nk6quvTkn5tIEDB/LJJ5+E9vU+M+LUvi4qKoqwwscV6cZqUmecET+W3a4MmDUmHUS8+vz5sfuywzjflhaxWKBnHz/xRHNbYynro48WK2jR0GupW/6vo2InFo2W/w8+gAMOSFy0P/64cLf/97/Fl+FNN0Ufx8hhhwk3HqOrvs7EicmXtnvqKfFFuP/+ibU3zi8Zj5FbboFeveDww5ObX6zx9xCSFu1jx45l+fLlPPvss/ysJYg49thjOemkk0JfCqeeempqZymRSCQSiaTT0dAQv43ZPb7ecEaPx/4n8C3QTEvLrdTUCCPLAw+Iqkb//W/ks+zNN0OPHmFvXIANG5Kbu/U58t57RalmK/aCHWCr7dG5c0VSZCN23r5ffx15TDxHtzO7doppe9b61KBniDe6zusW+o4uE37UUUfxySefcNhhh0Wcy83N5bLLLuPOO+80HXe5XIwcOZKCgoKYfesiWVGUlNU7nzBhAvn5+bz22msAnHfeedx3331UVFTQo0ePpPrSs+WPHj2aL774gv2tYm327PC2XrLMDv0LwPpLAfYJ2toi2EEkItNpaTGXC4u1oPDKK4nFaCf6BRNvsePAA8MZ8K3YfQ5cLhg5UvxAWLTHW7F6+WXx5ZVodvZ4JKvtjPeXzOpaWppwv28v1vJ2ewBt+irOyMjgXLvMfRKJRCKRSH4zJCLovv1WxLpHf+bVyg/xNnvtdSsLF4qYct1yP20aFBcHgM2AKIelh0RGy0cVj02bzLHkOvvu27b+jFg0HCAMa1bsjHs7IW/bbofLJZ7/rVWmdgZDhgxhyJAhUc/Hs2BHw+v1UlhYSGlpKfvtt19bpxeBw+Fg8ODBfPPNN7S0tDBq1CgqKyvx+/22pekgsgb7IYccwpYtW0JJ+qZMmcIBBxwQM9N9p+Cii8LbVreZVat23jwSCSM47rhw+ECy3HADPPaYCC2IhdstQgN2FX37gt8PWhLGncbjj4vFimQ9CXYD2iTan376aR566CGWLVvG3Llzqaio4O6776ayspIjjjgi1XOUSCQSiUTSCUnUCmtfetiqvIIsXCi2Xn01fHTrVti6dRkipt0sPPRyw8kSI9w4CYzzfxGYBGTzn/+0pa9mIID1/iSCnSXSU0U8y3lJSQmDBg0iIyOj3TmgrG7siqKEPF4VRYkZW2/H3nvvHXHMTrAn7PBcVWUf092RtNVa316CQXNdyWi89JL98UQ+6DfeKIR7Z/+l8HiEJ8XOdtWZPn3njrcTSTrlx4MPPsill17KwQcfzLZt22jVAspycnK45557Uj0/iUQikUgknZT2Gd+sFqnwfmQYpp61LnbCul3HD8Arbbw2CNwK3IIIGZDs6SiKwtChQ20zyyeLnl/KKP6TdblPpu24cePolptLn8SnaF9Xe0+kqQmWROa5SJhE/x86u2DX8Xp3fXzNHkTS7+R9993Hww8/zJFHHsmtt94aOj5y5EguT7R4qUQikUgkkt2edlakSoLUPaSuXZuyrixsa8M1zYhHMT2jXlv6kHQ2UhWjngh77703hYWFlOh1rzuYAw88EIqLd8pYux1eb/uul+WyJTFI2tK+fPlyhtkkNfB4PNTWxq8tKpFIJBKJZM/AaET5058Svcqa1bcFkaAu1QnYgsBc4EPT0bKyeNcFgNeAL1M8Hysrgf8D3rMcT33JN0kYd3tr3MWgWBOz/fv3j9tWf5a2lo5LFkVR6N69O952CMacZJN2Gco+SySSnUPSlvbu3buzcOFCKioqTMffeecd+hlLKEgkEolEItmjMYr2xEr+fgB8AuxlOFajve6IcV0sy+UO4HVgLdAHUYqti7a/CWGfWK+N2dNy7WbABRhrtP0EfKP9RMb3hmnRxsgAvgcGx2hrxyzt9XPDMYVkQgBcLpG8rrPnCOsM6HHZRUVFjBkzhszMzJSPcfbZZ9PQ0BBRPs2OkpISLr/8cvx+f8rnkShDhgxBURQmJpu0a6t95QSJRNJxJC3aL730UmbMmEFDQwPBYJAvv/yS559/nltuuYVHHnmkI+YokUgkEomkExJdLM4DfgROJJxcrRWYj7CAr7S5pq3x3K8Aer3n/wITgCeBkcAabbwGhBi/0XBdHXC/tm08bqxj16r92FlnVwJNwBaEcP/Wpk0soi1EJF5fWFXD5c8ksdEt0YqiMHny5JT12717d5YvX05paSkOhyMhwa6Tnp6esnm0hZycHHr06JG8lf6ggzpmQhKJJCpJi/azzjoLn8/HddddR11dHSeddBKlpaXce++9nHDCCR0xR4lEIpFIJJ2Q6DHtb2uvHwP6A/6cKG2Dhp+2sIZIl/Kg4TWa5ToRa+H9iDjzqwCf5Zx1zDptvPVAPsKCH40m4Gdt2yjy7BYzJKmgo+LMjz32WBYuXBhKCLe7UV5evqunIJFIEqBNKf1OPvlkTj75ZOrq6qipqUm6nIREIpFIJJLdn/iJgT8DegCVCCu7EV1YN2nbltrKCRPtuniLAIksEuiJ4VaBbb7sVsLpgRoQrv+zgULgUKDC5hoQcfY/AdUIzwAdm+Lxkk6N3+9n7Nixu3oaSTNz5kyWLFnCyJEjd/VUJBJJAiSdiM6I3++Xgl0ikUgkkt8oiVXzma29RrN0GsW7cd9IAKgivgt9ACG0g1p7nQZEnfdfbcZdDTxF8osGtYh4+hpteznwZ+3ci8Dj2FvOWxEx8DuP3aVClGTnkZ+fz+jRo3HKklwSyW5BQr+pw4YNS9it6Ouvv27XhCQSiUQikeweRI9pb0LUVs+3HI9n3X4d+AWYCRjjbBciBLBdDPByhEt6EbABkUCuEZFkzo0Q1Bu0sR8lHL+uz2UJInHdfGAosBghrB2WdgGtbTnCVX67drwVkfDOgbDI76Md3wo8AVwBGJONPYKwsBup1e47mmW+fTidImHdzivRJ5FIJJJUkpBoP/LII0PbDQ0NPPDAA/Tv358xY8YA8MUXX/D9999z/vnnd8gkJRKJRCKRdD4iRWAt8DUiKdsOhKDWy0lZF/8XAN0sx/6nvX4DjDIc1+PP6y3t9aR2DYiYcj1+fTti4aAKWKHNy1pay7qA0AQ8o82rCuiLiJdfCkxECPb5QAEww+ZaazKvRdprLnCB4fg6y9hB4E3t3jqmTruiyAzzgwYN2tVTkEgkkjaTkGi/wVDH5ayzzuLCCy/kz3/+c0SbX3/91XqpRCKRSCSSPRQ1IsjuFYTo3YwQozWEXcEVREZ5EIL+v0RP1rYaYT2fgsg+r2j9tWjHi7X9HQih24pZCAcQCwcbtOsdCNf45ZY2mwi7xQcRJdwCiIWATYQzub+n9fETsNFwP/qYehI6J2KBIEh4keId4AzCCeeWafdgfATTQwOaEZGLiWeQ70gURSEYbGuCwM5Ffr7V60MikUh2H5IOZHnppZf46quvIo6fcsopjBw5ksceeywlE5NIJBKJRNK5MZeYDiLixr9BWJN11hjOL0YI3B42vRnF4dsIl3U/4ezzAYRo/wdwFvAsQuxuR4j3IsP1LdpPK2LhQAXyEGK8BfH4sxKxoLAa4a7+PULUf6a16WborwnhQaC72uvoln2P1qYeIfDrgRLtnjYBxwC3auP8jBDnDsJu8/XamJ1DrHcEqmGFJxCIvE+n00lLS1vL/sVHinaJRLI7k7Ro9/l8fPbZZ/Tq1ct0/LPPPku+zqNEIpFIJJLdFnMOq5cQ2dOtbvANwFzgI8JWat3d3eomXo+5TrrRXVwXepsQid7qESXlWrRrrK7zaOdUwtb0VoQ3wHDgc+26HQj39DLMlngj/0OI9RqEGDfOB4R4D2r35UAsTBQhYtx3IGLgDwFKEe76aYj4/AZEnfcGrb+1UcbfOXg8HlpbWztEPLtcrpDlvrHRXIZPVVWcTietra0dYtlXFEWKdolEsluTtGi/+OKLOe+88/j666/Ze++9AZg3bx6PPfYY119/fconKJFIJBKJpHNi1lc/EHZZb0GIaB8iPnwWQoDr1m9dFBqt1gDztNcchIv9d4CxJJUew15pOFalva4l7IKuGNob66lvQLjoP6z1X0U4Fj5WiF8LQrCD8CKoQiweGGkyjNuMuN9qxPvQSthKr9/7NkR4gDGBb3jhQVGEiLYK3ERxuVw0N9tnxI91zoqiKCEreFsFtdPpxOFwEAgEbBMbu7SA+7a44ydioVcUpcPqtEskEsnOIOmSb1dffTVPPvkkCxYs4MILL+TCCy/k66+/5vHHH+fqq69Oqq9bbrmFvfbai4yMDAoLCznyyCP56aefTG0aGhqYMWMGeXl5pKenc/TRR7Nhg/mP/KpVqzj00ENDJeiuuOKKDnWxkkgkEolEAq2tAcIx7EZqCLum6yJsI0KwBhECth4hmI0EEVbnlQgB/yXwe5uRjRnd9b/3TYTLvUUrG6fHjoefEZxOKClBGzeaYNTj6Wu1fhZi9giwzku3uge07RWI92O7oY0uIo3vXdsEuh1qZMKBEMkIWLfbjSOBtPOxSoc5HI6QMG/vfFJ5rUQikewutKk443HHHcdxxx3X7sE/+ugjZsyYwV577UVLSwt/+MMfmDRpEj/88ANpaWK1/JJLLuGtt97ipZdeIisri5kzZzJt2jQ+++wzAFpbWzn00EMpLi7m888/Z926dZx22mm4XC7+8pe/tHuOEolEIpFI7Fm5cgHwluFILCtpjWG7AZGJ3SjkgghRW4WwtNdrP8uIFMgrtFejYNMt6j8jSr1Z2YJdCTpVhdpavXybDxGfrgt/Y//6QkQt9vXXrRgFeA1hF309lDCAsMRnJNBXYugu6K2traSnp9ta6X0+H62trTZXR5KoIHY4HKEf45gdncjO6XRG9K9b1Y1x87EWMCTt5KWX4Nhjd/UsJJI9noREezAY7JCVzHfeece0/8QTT1BYWMiCBQsYP34827dv59FHH+W5555j//33B+Dxxx+nX79+fPHFF4wePZp3332XH374gffff5+ioiKGDh3Kn//8Z6666ipuvPFG3G67P9wSiUQikUjay6ZNKxACNhchPmNZqu2syAst+1sRQtZjONag/bQa9n9CiOAdNn3uQAhjn+W40S1fR7iji0ecIMLy36rN9SegO2ERD2Hh/YFhOxrGsayx+40Iq78u3NsvbI15hVRVxePx2LZTVTWqaHc4HKZziqLg8XhQVdU2Fh3EQoHREu92u2lqagr1Z/V89Pv9NDY22op5p9MZulafq13SOuP8rP24XC78fj9VVVWhPtLT06P2IWknRUXx20gS4/jjd/UMJJ2YhJYeBwwYwL/+9S/TF6kdv/zyC+eddx633nprmyazfbtY6c7NzQVgwYIFNDc3c+CBB4ba9O3bl65duzJ37lwA5s6dy6BBgygyfGlMnjyZ6upqvv/+e+xobGykurra9CORSCQSiSQ5Nmz4BZG87QPC1mdjPLnuHj4Xs2gPItzCmyzHdBG+HSGKWxFC2ijcjOXd7OqaGzPHW2lElFtr1OYpBKUQo3r7JsI13ldFGWeZTd9GYmWBbyRseY/mym+Pw+GIajW2ivRo7uiqquLzWRc0BOnp6RFu7j6fLyLRsC7m3W53xHxUVSU3Nxe32207B0VR8BvKDhiNQvpig75Q0FYyMzND2w6Hg6ysLNOYEkmn5J57dvUMJJ2YhCzt9913H1dddRXnn38+Bx10ECNHjqS0tBSv18u2bdv44Ycf+PTTT/n++++ZOXMm5513XtITCQQCXHzxxeyzzz4MHDgQgPXr1+N2u8nOzja1LSoqYv369aE2RZZVPn1fb2Pllltu4aabbkp6jhKJRCKRSMLU1W1HiNCtCPG+lbCFuxkhXhsRCeWMQrYekfjNeMwoYHXLeisiG7uReoT7uzFG3Q67bPKNiLJzKxEWdDG+sNa2IIS8wzCPWm0OWyz9WBcSjOjzctnMXaeZ8OKGStiGEturMT09nbq6Olvrs560Tk8yV1paysqVkW78/fv3Z+nSpRHH9QUBl8tFQ0NkvL4xKZ7uhq//eL1e6uvD77fb7cbn85mMPTk5OaHnsqysLBoaGggGgxGLBIqikJWVRSAQYOvWrcQimteA7mXpcrlIS0uja9eubXo2lSRA1667egZ7DsXFu3oGkk5MQqL9gAMO4KuvvuLTTz/lhRde4Nlnn2XlypXU19eTn5/PsGHDOO200zj55JPJyclp00RmzJjBd999x6efftqm65Phmmuu4dJLLw3tV1dXU15e3uHjSiQSiUSyJ1FfX42oc74F4UreghDGegk0EALcKkatWd2txEomu5awsI4m2hswu9jr6GJ3G0L4W+eg10+39tWI0wlhT+/lNtfq6B4FRvd5O2u6fkxfnECbU3TLu9PpJDs7my1btkS4kns8HrKyssQMGhtxu90mV3WdHj16UFtby+bN5uSBuou70+m0zS5vFNe6dTwjI4O6ujo8Ho9JtIOw0Bv7MHoCTJgwgVdffdXUPisri7q6OrxeL126dOHXXyOz+SuKYutyb9cOIDs7G1VVycrKIiMjdbkD2sw338CWLaCFfO4SqqrA4wE7b4tAAJL1cKiosD8+ZIi4X4lEkhKSSkQ3btw4xo0bl/JJzJw5kzfffJOPP/6YLl26hI4XFxfT1NREVVWVydq+YcMGirXVqOLiYr788ktTf3p2+eIoK1a665VEIpFIJJK2s2PHBsJu8MsRFuON6MLT7YampkbMCefAXvDaJX+zo4XoFmwjdjH0rYRd9o1iOR6tKErQtB99YcEoXhMrq2bX3ul0kpOTw9atWwkEAvh8PpxOZ6jmeHFxMYsWLQq1z87OprW11SSe7VzMu3btisvl4scffzRZqT0eD4WFhfz6669x8xjpMe5OpzMid1B2dnYoW3xNTTj5YHp6Og6Hg/T09FCyYSP6mLEyzevPbrFEe3p6esi9Xl+IKCgoiHk/O42hQ8Xr8uXQrRt8+y38+CMkmtw5EIClS6FnTyG+f/kF9tpLT8oQn+pqyMkBvx9qa83ntm+HQYNgyhT45z9j99O/P/zwA8ycKfaHD4evvza3qazcM0X7tdfCp5/CRx/t6plIfmPs0nSawWCQmTNn8tprrzF79my6d+9uOj9ixAhcLhcffPBB6NhPP/3EqlWrGDNmDABjxozh22+/ZePGjaE27733HpmZmfTv33/n3IhEIpFIJL9BFMW49m8WqC6XriUasXdVF5h1pZ3QDrvNi7bbbdokgx6frpefi8d6IEAwaHRJ15PIRUdRIIFKaRbC80lLS2PKlCnk5uaSl5dHWlpayHXcTiyDiOUuKiqKKbqtidlcLhdFRUV4vV78fj/FxcVkZGSEXOV1T0SjpTojI4Ps7GxKSkoikrw5nU66dOlCUVERaWlpuN1uMjIyUBSFjIwMcnJyImLk7dAFdzwjS1paGi6Xi5ycHPLz80NGHrfbTdeuXSktLWWfffaJO95ORQ9PGDxYJB+bMyex684/H3r3FrHPvXvDqFEwa1bi4+oius5aahF48kn49Vd4+OH4/VRWild9EaI9ZZYHDwZD7qpOydSp4e0uXcT/l03oiUTSkexS0T5jxgyeeeYZnnvuOTIyMli/fj3r168PrRJnZWVx5plncumll/Lhhx+yYMECTj/9dMaMGcPo0aMBmDRpEv379+fUU0/lm2++YdasWVx33XXMmDFDWtMlEolEIulAFMX4GKFneQ/gcITFeNvyiRkt4EJcq6qoqS7O6THXbcm6bpdxPhotCNf/JgKBRK3mYk4Oh/gRc7afp71BWV+gEAndsrOzQyJcVVVOPvlkysvLI4S50aqemZlpusZ4TreS+/1+/H6/KWkbCLd23aVct5oD5OfnhzwVy8vLqaiooKKiwtS3y+WiV69eFBYW0r9/fzIzM+nXrx/5+fnsu+++ZGdnhwS7dVwr+qJErAUIr9dLSUlJKB6/tLSUgoIC07337NmTKVOmxBxrp2PNSZCoRfqhh8TrddeBHt7w+uupmVMypfmsbS+6yL5NIh4AZ58Nzz0n7mnJElixIvF5RKOtGe0tVa0AKC8HYyiH/nnv2hWuvrpt40gkbWCXivYHH3yQ7du3M2HCBEpKSkI/L7zwQqjN3XffzdSpUzn66KMZP348xcXFpjgoh8PBm2++icPhYMyYMZxyyimcdtpp/OlPf9oVtySRSCQSScqproYnnoA4ebkSYulS4d2ZCsyivQUhiJtMFua2VYy1lkgT/YT7qgfq8PmacTiiid9omF3i9T7tFxdaiGdRj0XY2m5viVRVEV5sN3ZGRgYOh8NkgCgvL2fgwIGMHDmSvn37mtpXGGKL9dh2n8+H3+83lWQ77LDDACF4MzMzKSsro6SkJCSS3W43+fn5uN1u09jDhg2je/fuZGdnhxLR9ezZ0ySqKysrycjIYMCAARQVFXHIIYfQpUsXrr32WgYOHGgKdYyWHT4/P9/yHkW2czgcoQz2xvH1xHjGMnDGe4tLgvXrTdx7r3ATb4/o7QxY5/TWW7AsSpUEva3+3p9+OixaBMaSgNb+nnsOTjoJTjvNfPyEE6CgAP78Z+jRQ8TIG3RAXAYNijx27bUwb17ifehMnhx57LTTzF8w7ahqIJG0h13uHm/3M3369FAbr9fL3//+d7Zu3UptbS2vvvpqRKx6RUUF//3vf6mrq2PTpk3ccccdEdlIJRKJRCJJNW+9JZ4zP/+8Y8c580zxXHzUUe3vq2dP2HdfEZLaXlTV6P+tl2gzC/V4f46NbcXzcNAi9AMoSiDC1VxV6wkGRYK45J6j7S3tLpfeT+Q9JEqi2tCK3XvUu3dvxo4dG6qIk5GRQWVlJYWFhWRkZETEaRufjbp27cqIESPo1atXhPv6kCFDmD59eihxcGFhIRMnTmTfffcFhDt6nz59yM7OpqKiIiSK8/Ly6NatG3379mX48OFMnTqVK664gj59+uDz+UIZ4yFsHff5fJx11lkce+yxCb0PiqJEzDc9PT1Ue934qi8o2JWvKy8vNyVGTki0P/KIiPWePTuhuQKwcSNcfDH8/e/wxReJXxej9nwE//sffPJJ4u0TpaFBLDa8+67YN4rsd98VLuE9ethfaxXtiiLEs/V9Nv5fnngiPPssnHJK+FhjI1gWaYDEY/zB/r1UFNh7bzAmH9Rd+pMl8osn8Wtvuy3ymP57KqsZSJJELhdJJBKJ5DfH0qXC8JPMs7MdU6cKY9SkSebjNTWwzz72z2xt4eWXxevHH6emP2h7jqivvhJeusEgqGr8BXJFEdZkK7rYDj8TC7Hu8zXjconnf1UltG0V0eYY87bEj0c+f4t94Qbvcpn7tHtWt85J7AdszsW2rOrvkVG8FxQUcMABBzB+/HhKSkrIzc1lypQpDNXjiE3zVikvLw8J3pKSEgoLC6Nas/Py8kLC2uVyUVFRQUlJCQDdunXjyiuvZNSoUQwYMAAg5KI/dOhQxowZw1133cXll19OZmYmAwYMYMCAASEruqIoXHjhhaGxdCt/0Ma6bMxYb34/lFDmdz2hnKqqoRh5I3rMvJHS0tJQn4cddlj8GPq//EW4aTc1wdFH27cJBoX1dtu28LHLLgtvG5LuxSWZL57hw2H8+PYJd7uY87vvFosNunXZ+P/z2Wf2/TQ2wn/+I1x/IP7K1jvvQPfu4hod4zixFlOsVZ1efNG+nZ3Xgr6Qs369+KJ7/33het8W7L8kBBZvlxAXXQRPPWV/7rnnxPty993w5puRc5ZIotAmc3QgEGDJkiVs3Lgxolbo+PHjUzIxiUQikUg6ivPOg/feg59/hhtvbH9/1rxODz0krO+ffw5XXRU+vn49LFgABx/cPi/LmhrhaXrMMZHeph3NXnuJ13PPBT3Tu6LE9vhVlEiRosd8m9uJZwxdCyTj9q7H0SuK0F46brfQSGHdEj4Zq39FESLa6DGta4zWVjGWff6ttlvqVTV8D7qgPfTQQ/nll1/Iz8/n2GOPjSrEL730Unbs2EFzc3PUnD5GwduvXz8WLlxInz59OOecc7j22mtD5/Lz8yktLaW6uppevXqZMtKffPLJpj67deuG2+0OZbkvLS0lJyeHzMxMqqur6WFjrdUFfFpaGg6HI5TFXhfaY8aMoba2lrVr14au8fl85OXl4Xa7qaqqAggtNGRlZeH1eunduze//vorOTk5ZGVl0aNHj9DCgy033gglJcKdOh5vvw2HHira6/Nasyb+dTp//Wt42yraE3GXb+vz9bffwsiRIv7amPDN6vpunEO0BYirrxZJ8HRifciDQRg7NrqLfTwWL4Y+fWD1ajjySIjmrWH33unWfL9fJLqLxogR4gs5FrEs7aeeKj4L++4rvuh1lyv9Pbr77sj+hgyB3Fyxfeih4kvklVfEeyWRxCBp0f7FF19w0kknsXLlyohVU0VRTOVDJBKJRCLpjLz3nnh9662waH/lFXjjDbjzTsjLC7etrhbVlbp2jd6f9dm1oSGyzX//K57RAB5/HAyRYLS0iJ8EkmrT1AR33SXm+sYbbRftbYs1t6ISvVa6QDwrBE3i2Tq2yyWevdtiLdcxx7ybjxv7bWmxs4QnPgbEdvnXFwKsIQItLWIesR6TFCV8vS7OPR5PSJwaMQpwvX65qqohwW5n2db7DAaDIbf2888/PyLZm56AzufzkZGRERLJAL169TK1PfPMM6mpqaG5udkk0s8//3zWrl0bURnIOp+KigpWrFhBRkZGqK672+0OZa7XhbvL5WLffffF4/Gwbt06mpubKSoqorq6mvz8fAYNGkReXh6//vorDoeD4uJik/dBBF99BTfdFHk82gdDz6e0bl34mPU9rq+PtJhu2CBitq+8MnwsUUt7KmLfr7hCfGn86U9w0EHR52Ac6667wttffSW+JG+7TYQQGIn1S9SeX2YQgnvhQnjtteiCHeD//g+mTTPvx0tEffPN4v/qmmtEtvxLLoneVremezzC08C4eKKqog8Q3gn//S+UlYXPn3UWPPooHHYYXHqpGFMX7DoOR3LhAJLfLEmv85977rmMHDmS7777jq1bt7Jt27bQz9ZUZMiRSCQSiWQnYbTIHnOMqHqUny9KGP/wgzB+ZGWJ3EirVkXvx95N2owu2EEsFugsXCiEWmamfSUmK/n5wvgUi+bmyDLMYXYgypilggDQmJC20MWz2x1p4Ta7ySeOnTCFcF/GcXTLvh67bmdlj6VBEvWM0K3l1vm43fHj+4107do18QRqbUBRFCoqKuhqsxrlcDi4+uqrOeecc1AUJap1X2+blZWFy+UiLy8v1Nbr9VJZWRlaEDBmi7f+vw0cODBkRTei72dkZFBUVBTKPt+tWzd69eoVNQO9oihMmTKFyy+/PGrNd7YnUTowGDSL3K++EjErxj4ee0wIzSefDB/75BMRw3zkkeb+WluFm3R4wvbjJiLuFQVuv13EcFdXw/z5QiQuXhy7D+Pxjz4yu2obGTVKfGEZBX+sed91lyiLdscd8ecej7w8IXy1xIqm9wxETgFroo9Yv6g//AD/+hf84Q9CuKelxW5/553hcImNG2H5cujWLXr7Qw4RlnSdjAz47ju45RaxcBNr5VciiUPSlvZffvmFl19+mZ49e3bEfCQSiUQi2Wk0RTES//yzcD8Xz73fApu44YaJPPigEtUabszNFE/gGTXLsGHitblZ5JyKV1J6xw6hGazogt/vFyWcV6wQz/CWcF/gTq2fC4C8iLknx9r4TSykxsLfTKys7k6nEMp2Y9mJauM5p9N8na4j2zvvZK/Xa4tbs6Pb9x1OFrdlyxZyc3MjwheB6FZnA7qgdrlcIQt/WVkZlZWV5BldUJLkwAMP5JlnnqG4uNjkvj9jxgwWLVpEMBjk008/ZciQIRx//PFs3LiR2tpaampqqK6uZpj2i6Lfa35+PpMnT+bRRx+1Hc/r9ca+340bE5v4//4nBOuWLeFjeoyIkX/9S7xOny5W1a69VmSWB+ESY+Sll+D558P70Va9oglua3s9BkcXtyAE6tKlifU9YYJ9G2M7o4dBLC65RCTni/aBb4/3wIknCren888X+3oyxoULwzXjY/2i9esnfoxY2196qVh4uPBCsa2TmSl+JJJdRNKifdSoUSxZskSKdolEIpHs9jTHKL0dfqZ/BYAnnuhGXV1l1GpE48cL0TdnTuRzYCIWdEj8udhKU5N4nvT5RJ4svdTxggXRn8e3bVsL5FFbK5I8l5QIbwKnU3iBxvMwFeyqkLj447ZVZNvF2cdqax/X3n6SqYKjW6RPPfVU5s6dy+jRo3nyyScZMmQImzdvZvv27TQ3N4f6tPNQsFsQcLlcXHvttaFkcIkQrZ3f76d///6AuLfCwkIAjjnmGL7//nsAJk6cyB//+EcURaFfv358+OGHpKenk56ebkoml5eXx4ABAygzuCIfeeSRLFq0iMFaDLOtd0AwKETfBx+IVTk7tm0Tq126QDv1VLNgT4TrrhOx1K+8Yn8+kZqLv/4qEuTFI9qXhh5LbhTnxv/39mbhhOi/HLF+afbZR3y5WMVzokyfLizuRsu/0bqdbLIQ61xvv13ExMeKhZdIdgFJi/YLLriAyy67jPXr1zNo0KAIt6PB8kMukUgkkt2EaJZ2e+p48UX7EsKtreHn8KqqyOfA/fbTtwJADWBvsTnvPOGmH8k64G3gQCDSxXLVKjGHmhp4+unw8Vi674EHglx5Zbgq07p1IqfSG2+I5Mf//rfIjh8blV0n3Hc9utU+FfrHSrRkckZ0sZ6WlgaIWPSDDz44dD4nJ4f8/HzWrl2bsOi2EtW93MIRRxzBnDlzONLqCm5DZmYmfr8/4riqqnG9CfSxjj/+eL7++mvy8/Opra1lv/3244ADDuBGLUmFvihg4pJLwtbvWGRlibrjS5eCtqCQNIcfHv3cr7/Gvz5RV+p//zv2eeOHc8cO++NtpS0rY+np4kuyraEfPl/sTPrJzskulkV3f5JIOhFJi/ajtdiOM844I3RMURSCwaBMRCeRSCSS3YrGRvvjdonkdH7+WeRGmjnT/vxXX0U+N4bd2Z8BllFTcxoQWTd48+Zooz4F1AOPATeaDGZbtpjna6xspIv2xkYRmrr33uFzK1dGjvLuu+H7uvFGUQ3qs89g9GiRJO+dd/SWQWAxUA20xM0ev6cSLfldKtAty9FELAjrtV4KLRbZ2dmmvEN2gtlItFwBsRg2bFjIhT0eGRkZDBs2LGT5nzhxIh9++CFTY6wSGedkTLinZ4fX+zrrrLNYtGgR+++/f2QniQh2nc5shDJk84+LUZwbE2uk4nm9rR/+RDJuJstxx4nScslm5mxPGQ+JZCeStGhfvnx5R8xDIpFIJJKdTjRLu331HSEa+vQRexs22F87aVKs50bhsrp+/XzsRHt0oj+k5+eb940u/6oq8mOdeabYt9Zmnz3bvH/22eHtYFB4+d5+u8jFdPfdolSd4HvgZWAbkuQwZpKPhV1tcauAz8zMpKmpyd6qbKBbt25UV1dTXFwMCPfygw8+2BTzbXQn78gEeDpZWvy1oijst99+7L333vhi1Ko2vh+x4vy7dOlCly5dIjtIpjzbzuaRR8QvaWQCivaxaFFiiejaSketWLWFf/1LfPkl+9mdNEm85uSkfk4SSQpJWrRXVFR0xDwkEolEItkpGMsMx4ppj4ddCV6dp56Kfe033yhs327/jB4MwuWXi7Dbzz8XieXs2kTDGGP9z3+KikPROOCA6Of8/rBh8pVX4LLLjGdXRLTXy5m1t9JTPPTa6cnUcO9MGOvJxyKWNXzMmDEsW7aM4uJigsEgGTHE3ogRI2htbaV///7kGITJqFGjTO3Gjh2Lqqocf/zxO0W0W4km2AcNGsTq1asZM2YMv/zyC9XV1fRLNh566VLozLmYvv9euLh07y5cZ5LxCIiFMdbbSipcYzqTaFeUtrncV1aK+CJrKTaJpJORtGgHWLp0Kffccw8//vgjAP379+eiiy4K1eaUSCQSiaSzcsklrcCzQFeamiYkcWXbHnKjPRtnZ9sf/+abcJnk116Dk0+ObNPYGAA+RljrzbGvxoWIWII93v34fOIZWA8hiOdFGitjeyrRS7ftziTyHuki1s49fvLkyQC8EC0rooGMjAz69esXemaLxsEHH2yKh+9IYrn8W8nLyyMvL4+0tDTOPfdc1q9fH7P2uy3Rypl1Jowrffff3/Hj7WmW9vZQXr6rZyCRxCXpQI5Zs2bRv39/vvzySwYPHszgwYOZN28eAwYM4L333uuIOUokEolEkhKEe/gPwCLgIZqbRWKmjkgkpnPuucm1N4YFe732wnvx4gXAHESMuxlD6HK7qK01J7KL9Xze9pJxuz/6QkWqFxJiuYpbSUYE7874/X5T7feE6chf8N2VV1/d1TOQSCRJkLSl/eqrr+aSSy7h1ltvjTh+1VVXcZCxBINEIpFIJJ2IE04AaAEWICzNLzN16um89FKsq35BJFyLkQ06Bv/8Z5suA0SCubPOsjuzARFXXhBx5rnnEul5C6L+fHT32c8/N+9bvKk1fhtiMRZt9cqNR6JZ28E+cZzx2IQJE/jxxx8jXOJ3J9q1MBEtAYWkffxGFoskks5A0pb2H3/8kTP1jDYGzjjjDH744YeUTEoikUgkko5g+3Z9Sxc0q3nrLfu48TBrgB2ATbr1DuaUU/StOoRIr9H2fwQ2IbwG2sK3wKeIe5N0RvQSbamwohcVFXHdddftNPf3aORrWRP76Nkc28nQoUNJS0uLnbW+oQFuuy0l40ksSNEukew0kra0FxQUsHDhQnr16mU6vnDhwrjZSyUSiUQi2ZVEVkpKtOxRMzAX2AdI9d+6txGW75OIvpa+CGhAZJ+fBUSpVZc024GyNlzXBHyHWEyQdASJiPWSkhLWrVtHT5ska5MnT+bFF19kzJgxQLgk2q7k3HPPZceOHVRXVyd9rd09+nw+LrvsMlPm+wjWrUt6LEmCSNEukew0kv4GP/vss/n973/PsmXLGKvVxPnss8+47bbbuPTSS1M+QYlEIpFIUkVVVayz6xFx4vsTKcw3IjKmPwxc24aRv0Av9xbJPITl/1cgWoUWvRD7FsTiwY42zEFnBcK9Ppq181eEBX8iEM3v+0dgM4kvekiSJRHR3rt3b7p27RqyYBvp378/V111lW3puF2F0+kkJycnYdF+zTXXUF1dTUtLCyUlJbZtYgp2kPHsHYkU7RLJTiNp0X799deTkZHBnXfeyTXXXANAaWkpN954IxdeeGHKJyiRSCQSyc7hMYQFeRVwpeVcM0JYt7VG3Dsxzn2PsFifEqMN2tzqtTm0o1YdixEW9irsM8jrme9UwC5PzUpgiXZtqiz+EiNGIRpLvI8aNYovv/ySAw880PZ8MsnsOiMej4eCgsi8DUkhRXvHIUW7RLLTSFq0K4rCJZdcwiWXXMKOHWKlP1Z9UIlEIpFIOgsTJ8KHH0Y7uxYhZK3lS1u1nyqtjZF6RKK6voCLxBOztQLPICzZmwzj97K0+wnhhh7Qzjdq4xUnOI4d6xGLBDXELvu2Ocrxx7U5bIlzfWpRFMU24VoqcLvdNDU1dUjfbcGYhC6WW/vBBx/MuHHjyMzM3BnT2j1pld4gHYYU7RLJTqNdAU5SrEskEolkdyK6YA8gXOObiPzT+CvCHX0TwsK9nrBofh4hYD1ALnCOdn28+l8/IUT4z3HaPa+9bjccqyG5rO11iKRzQzG7/TchRPd24J/AAcBwm+trgTTLsfXAOname7zD4aClpaVD+u7MJdOKi4sZNmyYrTBXFEUK9nh00GdGIpFIdiYJifbhw4fzwQcfkJOTw7Bhw2L+cfv6669TNjmJRCKRSHYOnxPOzB4t9ly3xO4gLNo/QriL6xbyCwAfMBN4DzgY6B6lv02W/VhW5PaI47cQLvifA2Ntzj+mtXkLsXBh5BNEHP/RwBGG48knEpO0DUVROOKII+I3lNjTiTwo9jg68WKXRLKnkZBoP+KII/B4PKHtzrwiLZFIJBJJ8hhLp+kCuQkhdhsim4fQy8D9gsjCrlvOb9f2nwRuTGD8GkRW+BKiJ4jTCWo/DYQTxQURCe0KEYsETwPpwDTMLv3G4uuKdt2CGGPdBWwD/o5ZtO9ZeDweGhtlfP4eiXSP7zikHpBIdhoJifYbbrghtH3jjTd21FwkEolEItlFGK3cehKwNxHW53rM7uiJPKg2IES0MYlWHUIAl2hjGMfcjHCFb0CI/EVAnuG8Mf58I5CBcFHXM4M/gLD69weORbjDlwBHxpmn7h4f7dw2bexdk9BMVVUCWiKxjopnB0KGCckeSAd+bn7zyAURiWSnEadORiSVlZVs2bIl4nhVVRWVlZUpmZREIpFIJB1LC7AaYUnXH+pbEYneNgMPAi8DSwm7xeusJxwPHo2NCLG/ynDsE2A2QpBvQcSKVyNiw/UM19UIa/0HwIvaMf2cHpu7XesfhMh/F2FZX4OIuX8KUZJNT1yHdk9LY8y5QbunbdrPYm1/q3btRm0ej2t9dzxxS3mliIyMDFPit44k0XtKT0/v4Jn8htgTRHtnzSElRbtEstNIOhHdihUraLX5JW1sbGT16tUpmZREIpFIJB3LSoQ4XYKwUrcQrn3uRgjejQjRqz/0Gy3dfwF6Ey57ZrXU2gmFTYiFgcWEM9E3Iiz3jYSt2XXaGEGgCJhv06fR2v8eQqxXG/r7FSHu9f3vtLbWpGVrEMJ8sdbuceAb7dwPhOP8GxGLCysRNd47FqfTidPpTDiju6qqBIPBCGu8y+WiuTl2eTy/3095eTlr15orA1ivbW/2erfbnVAiPa/Xi8MRL5Hh7k1ZWRl+v5+cnJyOH2xPEO0VFfDdd/HbSSSSPZaERft//vOf0PasWbPIysoK7be2tvLBBx/QvXu0ZDsSiUQikXQmtiOE+jqEmNYXnZsRVvAdCGFtV/ZMf3j+n3a+lkgxDMLS7iQsthsRgrwVIbC3asfTCbupL0dYtPUY+1zCtdmtLvoB7XW9YbyvgX6GdkHteADhXGeN235NG1efozHBXK1lTN1rIGDTjxm3W8Tat6WMmsvlwuv1xhXbVuxEdXty8KTC0m907/f7/dTX1ycUO7+zvAx2FU6nk8suu2zn3OeeINp3kidI0siYdolkp5GwaD/yyCMB8Qfwd7/7nemcy+WiW7du3HnnnSmdnEQikUgkHYMuCHXPsVrDvoIQzdFivTcjxPQXCIu4FyF29QRxCkIor9eOP6ldt0M7X6X9eBALBwGEK3qDNq5ROLdoc6olHL+uH9fbbUdY1lsQme/7aX0GEa7s/0EsShQBP5HDFpqoo5ZmhKfBKq29cYEBwosCOrO01zXEDg0QolNV1QjRnoi1WlEUfD6frWiPZzl3uVwEg8GQRTsRUehwOGzFvfWY0+mMGFtVVRRFCXkgejweWlpaQvsOhyMk2r1eb6eqBb+r2WneBIFA/DadnX33hf/9b1fPQiKR7EISXuIMBAIEAgG6du3Kxo0bQ/uBQIDGxkZ++uknpk6d2pFzlUgkEomkXRRqZcpVWnHRiCMkijcaWikI8WsNBdMf/lsRVvalQCMutiIWAeYg6q83IgR4K0LcrgACKARQqUMI9GbMcexG3tdem7RxAtrPdq3PVsKeAo3aPOoIW78btPObgYcQgn07Qpz/hJManARI5xfD2Pq9BgnH60cT1zsIx9cnhm5570hUVcXhcOB0JmaPcDgcuFwuSkpKEu5fj32Pthjg8/l2Wny8JEE6g6X9scdg5Mi2X5+XF7/NrkBa2iWSnUbSfknLly8nPz+/I+YikUgkkt2AzvAM3Fb0uXtoRgUyqEa4u6/ESRMOmhECtln7ETHtCvUIYV+FEPQrgTp81OKgDh+btGMNCJFcTwbbtf4A5uNjBy5a8NBE2BKONkarYb9GG3s1ugeAQj0eGlCp19rVIYRzECG8Fa2POoTbfSMiZv8NrT+9bF1DKK2eYhLlukj/Rbu/n4CFhBcJarRz+vwChmvsPxB+v9+0r2doN4pql8sVNXO7VXzHEuOKokRdGDBadK3W82REfrdu3UJ96aLdKt67du1qO47Onu723ilJ9gvrqKOin2urd0Dv3vDNN/HbRaNv3+SvueWWto8nkUg6HUn/9bjwwgv529/+FnH8/vvv5+KLL07FnCQSiUTSSfnDH6CkBCw5u3YbxPN7+CG+lSAiIVwTfupxEkCllXBSuno8NOGmCSGgVyOyv1cBECBAEHCb3O2rSGcTXmpIow7hKj/XZLd3WizVLmq0MXThvEKbw2ZgM26tvSvUSwuKSejr6JJcF9O6a7045nA00xxxlXHxYA1C4OvWerTrWxDl74SFX6UFj/beeGwy6aelpZGWlhYhmBVFsXWLtlqng8EgaWlppmOFhYX4fPal5zIyMqIKYqfTicvlClnio2HM2K6LeV18q6qKz+cLtXE4HDgcDrxer6mPCRMm4PF4cLvdoWv1RQGv1xuxkLGnJ5zrFCQr2s86K/q5tmZLr66GBJIQ2nLUUXDMMclfl+BiVLuQlnaJZKeRtGh/5ZVX2GeffSKOjx07lpdffjklk5JIJBKJmc5i3b7lFtiwYfc14oj3MZyFWTyCrzO1UUIu4uZYWCG0dXFulr5iawe6hb6RbdSiS+j6KLMJ96EibOVOmhEWcl1oG93eBQ6a8LAdd8hqb5XhCi5NTDtUo1BoAIIGP4I6whZ4/S6MMddV2mvAcP5TPDQbFg8EquXZXRfhTqcTh8OB3+8PiVg7K7RRcGdnZzN48GAURTGJ3GHDhtmK3LS0NEpLSyOO6yiKEurfeL3H4yEzMzM0H12A6yLf6AWgLzgYhbzexng/Xq8Xn89nWiDw+/1kZmYybty4CJFvXZjodFRVwYUXwpdf7uqZtJ1kvzw7YiGlthZyc9t27ZNPQls8NOSCkESyR5H0t8CWLVtMmeN1MjMz2bzZLsuuRCKRSNpDXZ3wjoxlAJIkhnh+nxeSuM02rt1OAoTd1cOi10EQP7UIybuDyAzqYQEcIGCwg7diXQAQiJh0V0Qd+K2EE9pF4mcdjlApNgi7yutsD/1xdzqMJcvCz/Hh2YgM9k6atHnUacf198U47xbDeTN2Ydy5ubkhsetyuciIUWtaF8QOh4OioiIGDhxIeXk5+fn5OJ1O3G53yJpudYOvqKhg8ODBlJWVaXNx4Xa7UVWVzMxwVn+rwPZ4PKbFgqysrJAngJ2rvXH+1gUF43H9VbfE+/1+/H4/Xbp0iVh00K3wSVncW1vh2293zkre5ZfDfffBqFEdP1ZHkez7ZGc9vu028frPf4aPDR2aeJ/DhsG99yY3DxBf+vrnzuAJEpW//jW8PWBA8uMlS1FRx48hkUiANoj2nj178s4770Qcf/vtt6msrEzJpCQSiUQS5pVX4OefN/Poo/aCSZI44vm9ynJUWLXNzqvCrVyNsHLrceNNNoassPXZLBPmQoQwD6MahHEwlHDOGvduHiVokwjOSTNe6hBl4wRG+eH1tpj0SGsoXj6AgyAq4KYGJ7VkUK0l6TPekXD9t0O3QOsWdjuBrqpqSKTaxZHbCVpFUcjKysLn81FWVkZaWppJaCuKgsfj4dhjjyUvL49+/fqRk5MTEvf6YoHf76e4uNg0rtPpJM+Q4Et3bddFdnFxsUn06wYLXZi73e6IORur66SlpcWtQ15YWIjb7Y54Pwr1jIl2nHceDB4Mf/pT+Nj558PVV8ccK2GqqmDZMrH9ww8xm+4WJJs93q79lVfCli1w9tnhY4ksBqxfD4sWQY8e0JZnZOPvUSL/F5dfDnPnisWFgw5KfjwrF1wQ+/zYse0fQyKRJETSov3SSy/lyiuv5IYbbuCjjz7io48+4o9//CNXX301l1xySUfMUSKRSH7T7NixFbgfuL3dfQWDUFMTv11n5o03YMYMaEv1LPGcvSO0L+SXSO5mjVZ1U4fLIo7FkKJl4iGjK7G3tEcSCNVUDyIs+dXk5zeY2ljt9ooiZLyDgOa6Hs6EHwTc7iBud5BAU6MpJtepJd3zGdz3WzSx3gT41G2Erf764oU1073Aoap4PJ6YSd+MOJ3OCFfxaLhcrpDL+fjx40PH09LSSE9Px+VykZuby8CBA/F4PAwbNgy3243H48HlcuFwOEJWdd2C7nK5cDqd9O7dG5fLRUVFBQUFBaY5jxgxgq5du+J2u/H5fLYC3BpjbzVepKWlkZmZaVoc0OPdMzIyQgn0fD5fKJt9Wloa++23X/Q35OGHxeuNN4rXFSvgwQeFNbilRYi7P/+57b/oOTlCZK5c2bbrO4qFC4UgTZZkLe3RRH4s9/Zo4rWoCAYNEtuJVlCINk55eWLXjx4tFhdSEW/eu3fs8zKmXSLZaSQt2s844wzuvPNOHn30USZOnMjEiRN55plnePDBBznbuAIpkUgkkpSwdeuqlPV1/vnCePP55ynrcqdz+OHwwANmT9VEEc/vv8ZtJ8Rv9MRRblekEHDRhNMmKRs04aE5or1tv05dMARQtPGbmxsi2gUMY3i9QVxO45gG74BAAKWpCaW5WQh2QzIsMVKrqS/TrAMBwln0rfMzo4t1YyZ4fVuv2V5aWhqyKOsWcuO1enI3Y1I6a3I5o0t6RkYGaWlpZGRk0Lt3b3Jzcxk7dixnnnkmfr8fn88XYcF2uVz4/X7cbjcVFRX06dOH5557jiOPPBIwx5jrcen6/Lt27UpWVpbJdd4ua31JSQnp6enk5eUxfvz4kPgHKC4uxuFwkJmZSVpaGsFgkPT0dLxeb2iBQRfutnzxReQx4+pVICDcov/4R7j2Wvs+EuXzzzuXKBs2TIjjaKGYa9bAYYfBu++ajycr2hMt2Wds9+678NBDsdsn6k7/l7+EM9ife25i16SKv/zFvH/OOeb9Bx7YeXORSCQm2lR75LzzzmP16tVs2LCB6upqli1bxmmnnZbquUkkEomE2BbLZPnHP8TrTTe1rx/jlGpra3nggQeYm4AVbNEi+DW+Zk6I1auTvyYQCAIbQvvh24i0SkazjbtpQmluisgkrRLEEbW2eYJootpDM24tS3swnuhobg4lghM54w3t9TlG6cPFdtOMPab3IQgE8FCPQiuqEsTRYh+ioapqKAmbLpQPOeQQ8vLyQvXN3W43Xq83lInd4XCgKArp6ekh8Z2WlmaKPS8oKMDj8ZhKzep96QwcODCU1d3qsm60hHu9XsrKysjIyGD48OGhGPiCggKOilLmS5+HPt/09PSoGexD76HHE/rJycnhyiuvZPDgwYCw3mdmZob66NWrV8y+Ijj55MhjxoUN42dy/vzk+rYSDHYe0W60fq9ZY9/mvPPgzTdh8mTz8WRF+377waRJ8dv97nciTOHKKyEtDX7/+9jtHQ4YPjx2m+7dhVB+5RXYsSO6pfuIIyBG8sWoGOPc7ZIL9uhh3jcuTEydKt5jnbaML5FI2kybRHtLSwvvv/8+r776auhhYu3atdTs7j6XEolE0glJ5rk5EIBfftm52eY//vhjNm7cyKxZs2K2W7kShgwBzei4S2ht/RZosZHWkWI0mmhX9DMGq3VidnQr0Wuch2miKU4cQKClJTQXUVU+8f98pyUowGmJ4XfTjAK4acHpDMZ08i8tLaWkpITc3Fx69epFcXExXbp0Cbl/A6HkcF6vNyTo9Vh4RVHo06cP3bp1C7nOZ2dnhyzXOpWVlRHWbY/Hw7nnnsuMGTMYPXo0qqpGxNVXVFTgdrvJysqKasmO5rKvu7S7XC66du1Kr1696Nq1K/n5+aG5GxcLjB4Cffv2ZdKkSZx44omAeRHuPKMISgS7LOKmRAVtLEnW0bzxBnz3Xfx20TCK9oMPhueei2wTTczbfRnazeXGG4VYdrth1qz4K5tpaaL2up6kLhGOPz72eT28QlHsE889/zwceKAIkejfP3x8773hhRdi9/3vf5szyu+1V2SbWJ8fa9hAZylpIpH8RkhatK9cuZJBgwZxxBFHMGPGDDZt2gTAbbfdxuWXX57yCUokEslvnWSq/ZxzjjDO/O1v4WNLlogQxyVLwseqquD//k+Ew7YV/ZmtNUGhsHBh28eKNb6VWHmnWlt/QLFY1Z00afHdbccUY55A/LqDIJ5QxnYrxlJyCsFgpAyP+Eh0wAO0LgWDln07HA4H2dnZodhxr9dLIBDgL3/5C5WVleRqcbrZ2dmha6wC2ePxUFBQwDkGl1w7LwM9KV1onppoLS4upqCggLS0NMrLy0Nx5Lo1u3v37qSlpYXmYofRoj9t2jTGjBlDWloaHo8nNBefz8eECRPo2bNnqDSdnvTO2E96ejonnHACiqKwzz770KdPn4jxJkyYQHZ2NkVFRXg8nsjFhNpa877dCl400d5eK7nV0r5yZdvcZObPFzEtemx3WzD+Uq9bJzwOYiVm27gx/N7Z/W4UF0ceu+EGmDat7XNMhEsvFeJ540b72PV42dhPOAHeew8KCszH582D446LbK+HSLzwgrDO614I0TLR5+bC++8LK/obb5jPWb/npWiXSHYqSYv2iy66iJEjR7Jt2zaTi9hRRx3FBx98kNLJSSQSiSS5Z+9HHhGvN9wQPnbQQeL4AQeEj335JVx3nchZ1BY2bxZ5kRRFJIVrtFY/s2FneNpWVQlL/hln2J9vbf0Er6VuuotW1Pa6tRtw04LDIuOjYf9HuH1zUVJ4L4n+lwVtVkoCgQD77LMP3bt3DwlrOwu3fq6oqIjJkyczcODAuOPZlZ6161NRFMaOHRuKRzda/O3QY88zMzNDrvm6Jd1oIU9LSwslqdNLxRlxuVwMHDiQvn37xpynqqpkZ2fj8/nw+XyhuHZAJJNLT4f//td4gd3Nhrfjifann4aXXhJJ6iZOhHvuiTk/Pv00vN2tm/jl2r7dvu3KlbDPPsJabeSbb8Lb338vBPhrr8HSpeLYAw/AMcfEzixptzA4bFj09kVFoC/A2InL3FwYNy68/3//F9mmI0Sp0ynEc0EBXHVVx495882wbVtY0N90E/z976JkIMDixeb2kyaJPxRr1gh3eCNStEsku5SkRfsnn3zCddddF+Ga1q1bN9ZEc02SSCQSSZtJVOwaI5S2bw8/U+nW9FU2+ew2bIg8lgjPPx/2Rl21Cr76SjyD68eeeUY83//vf9H7aGwUIag1NcIzYJ99ousBgIYG83Nic7MwWIEQ68EgPPWUmMPjj0frRbW1g6d6PcG4COBopxWfYDCp+bUnrj7mlc3R76OhqclkFVcUhUAMlweHw0FOTk4osVxZWRmqqoZqu0dDL8FmTHgXTYAb51KuZd4+7bTTKCgooFu3bgSDQZN1HCAvL4/CwkJycnIi5mFXps5YEs7qjm83L2OfdgsPbrebIt3a+sc/ildjMrJ4lvZbbw1vf/YZnH66EG0gfllOO00IuHvvhTlzIFbVn2iibPlyqKsTv2zGpHDnnSeS1x1zTPQ+Bw4ULtrTpkHPnuLYjBlC6D/9dPTr7D5L8cpHNDQIYWzt95lnxHs2Z4744qivhz/8IfL6RBPSpZJkhLCde7sdBu8WfD6RjbRbN7Hfp4/ZQm/3+dKTP+px/rdrVUwefTTxuUokknaTtGgPBAK2rpCrV6+2rcsqkUgkktSzYwc8+yxUGypwWa3mH36YmrEWLowtvkF4VD7zDHTpIjw1Tz1VeNKedFL0ay6/XCR7PuYYuOgi8bx/5532bTdtElnvjcafu+8WBrUXXxRVqqIZ61pahIHpk0+gd+uPce831biIzP4eiwhLeVtq2yWIVQq1eXnBRmzYPSv0798fr9dLYWEhw4cPp3v37nTp0iUkngcNGmQStlb3eKfTybhx49h7771Dx2KJdr2vXr16cd1113H44YczYMAAMjMzOfTQQ7n00ksjrvF4PKY55OTkkJubG1E7XVVV+vbtGxrfWrPdatywHtNDBXTvA4/HQ2lpqWlBAggvlgSD9pZ243v017+azz3xRFiQGlfEqqrC29dcE9lnLIJB4eb9u98Ji7E+v61bw21Gj06+5JydtVsnkVrrdTZJEm+/PTL+XV8scTjEdrTSg+efD/36wfXXxx/bDjsXfCN2Aj0Z0X799cKSvmhRcvOyMmZM7POLF4uFj4svFvtXXCEWOg49tH3jSiSSpEhatE+aNIl7DO5UiqJQU1PDDTfcwCGHHJLKuUkkEslvnvfeg88/j7R+TJ8Op5xiFsXff29uo1uh28P69cILdfhw/Zl4FTAHTAnMzPO7+ebwtlFvWo04Dz4oXo3566KFob7wghDfRk9hHT2306uv2uvbxx4TOabGj4fM4EeRDTqYZO3ebkOpOXeMsnOdDd21PC0tjS5dutha2jMzMykuLsblclFeXo7L5aKwsJB//etfPPDAAxHZ1O1i2p1OJy6Xi+zsbLKyskzx7Tp9+vQJZYs3XmfE6C4Pwgo/ZsyY0DFduNuNMWrUKM4//3ycTicFlvhiPZleuk3c8PTp0ykuLsbpdDJIi/EeNmwYQ4cODd173759GWyMd16/XqxaFRXZx3HHK6Ngl7jC+H9jtM5bY+jtCAbFSpmO7hFg/AWfNy9cquL99+P3CcKCv26d/bmvvop9bV1dpKt3NBIVxtnZ4v3+058Saw/CcyE/X4Q22GVnjzYP/fOVTEy9zydi1tuTKwCEp8N990X+AdEpLxd/bIyeB9EWOiQSSYcR6esVhzvuuIMpU6bQv39/GhoaOOmkk/jll1/Iz8/n+eef74g5SiQSyW+S9eujVx569VXx+tZb0a9XlPjPujr/+Ifwbs3OFvpA1zfGClNbtgA8pu35gb2x4803ExvTjrvuEoax9euF16aeEDnRZHzG2PqmJrG4YfzTtHw3EsG7E0GgR48e5ObmsmTJEnw+H0O1utSZmZmsW7eO3NxckzV66tSprF+/ntGjR1NcXEyxZpnUxXJZWRnbt28nLy+P4uJiFEVh2rRpZGVl8e2339Lc3ExTU5MpuZ1OSUkJJSUl/PzzzwnfQ2VlJbW1tRQWFlJVVUW/fv14/fXXbdsefPDBUfspKSmJei47Oxuv10uXLl1CifKcTifZ2dkcfvjh7L333hQWFlJsXYiwWPlDzJ5tTlZhR0uLqL+tu6ND9CQUF10Uuy+AtWvN+/fdB7fcErkqp48RL6u5kaoq0N+/QCD8i7///vbtL7sM7rgjcWtzQUH0vlLBXXcJd6FEYpoM1Sf49lshmvfZp+PmFg2XC2bO3PnjSiSSpEhatJeXl/PNN9/wwgsv8M0331BTU8OZZ57JySefHLd2qUQikUgSRyvOYeKEEyLzF1VXgyG0NsQ//iHCNhNBrzxVVSUs03rJ4dmzw23MVuzNJIKiCIOSEPxhVq+OXl1o/Xrx+uKL4ef9ROP6jXOcPdss2AHqUGhvojdJJIHWVhRFISsri6FDh3L66aeH4sj3339/mpubKSkpoWfPnvz000+UlZVRUVHBddddF+EOrlvEKysr8fl81NXV4XA4uP7660Ou6F27dmX+/PkRMelW7AS9TpFNpm6fz0d/rZRWtPJvRmLF30drn5eXx5YtWyjV6lx3796d5cuXM3bs2ISS8JkwlomIxvvvR1q7//73yHZffWWOU45W7uGww8z7tbWiv1RkmtSF/sMPCzfsWbNEObNo3HWXyIgeLRu6lTVrOj5WPdH3wZgjIifHnBhPIpFILCQl2pubm+nbty9vvvkmJ598MicbTTASiUQiSSl2XpwvvBBpuDrlFPjPfyLbJirYdS9WHb003EsvmY83RA3N1h9Sm4ClQCVGl/mzzxZa4Pzzw1ckalBqaRFW/0Qt7UbRruffMmKXpC2BaFlJEjgcDioqKkL7fr8/5Drv9/vZy5BAKyJ+Wzt2xBFHAPD999+zRPtAWmPXx44dy+eff87EiROjzsVOVJ977rksX76cESNGRJzr0aNHyPJvJDMz07ZUnPE+E0FRFPr168eGDRs4SYttOeWUU9i6dWuEm31CJFMPMhZpaZEx4dESTNgxc2bkL3Ui4tVaL/2EE4Sbu75qeMIJ8WtFbt4cTpYWj12RXC4aLdLrRyKRJE5Sot3lctEQ/alNIpFIJCkk0dBLazndZNGt7EZaW+G4494CGoGjgBYGDowSbxpiPuDW2g8BhEVdN9498EC4pV0meztcLhFSGqfCVwijaE+VnpEkhl38eVsZppXzqqmpCYl2K5MmTeKAAw6ISABnpFevXrS2tjJ8+PDQMaMrvhVFUUyu7zNmzGDdunUUFRXh9/sZPXq0aby9996bXr16sXz58tCxI444gtdffz208GAkNzeX/v37M3z48FDyXofD0TbBLi5u23VW7JK47Qys8dg//WR291+xwpz93I5gULjJ7yz23VdktbR6HCRLjGoMEolEYiVp9/gZM2Zw22238cgjj9iWP5FIJBJJakgkYbKOMRl0e2lthYaGAEKEA0wE/gv8EufKZoTb/BPALYA3ofrt8dDzXCWC8Tn4qafaP7YkMWKVd2sPY8eOxefzUVlZaXs+lmAH4e7++9//Pmk3dp2CggKOOOIINm7cSEVFRYTLvKqqFBcXm+YxbNgwBg4caOu6ryhKyMKeElIl2lOBneW4vj75fowxOYkwf378pG+pZM4c4cbf3pBQaWmXSCRJkLTqnj9/Ph988AHvvvsugwYNCpUq0XlVz44kkUgkknYRLym0kQsuSN24d90FL7xgtJoGiC3YjYLoOyAf+BiIkkWvA3noofC2Xab5KGH0kvbSQQLE4XAwcuTIdvXRVsGuo1fGWZ3EL2S8WPuUsTmx3BI7hXnzzPuLFyfutt4e7r2348cwoqrtF+wSiUSSJEmL9uzsbI4++uiOmItEIpFIDJx+ur4VX3Q880yyvVcDHwKjgEhX4TVr4l0fb04JlI7aBUiH1A6igyzt7aW9gt1IWVkZw4cPJycnp8PGSJoPPth1Y8fj6ad39Qw6N1ddBa+9BmecsatnIpFIdgOSFu2PP/54R8xDIpFIJBasGdeToxlYq716gS6W868AK4H/ATe2Z6Ak2Q54tDmlkiCJLG7I3PEdR7SY9jbHa3cyFEXh8MMPtz2eUlKYG0DSiSkqErXpJRKJJAFkULpEIpF0eoLAz0BGEtc8DywG1gEVwFWA0aVzQwJjthVdQG8E6oBu2vEdwN3a9o3t6N9KNfAIMBIYb3O+AXgKGGRzTtLR7LXXXtTX19OzZ08+++yzXT2dlJPyeP6zz05tfxKJRCLZ7UlItA8fPpwPPviAnJwchg0bFnNV+euvv07Z5CQSiUQCsAJhNU+GZYgSbGsRov02YAowOsHr44n2VuB1oGeMNnq6+IuAHCCuz30b+RAh3GcjRPtGIB3Q42k/R7wPyb6HklTgcDhilmXrSHaG63rKxzDWSpdIJBKJhARF+xFHHBGqpXrkkUd25HwkEolEEkFT/CYAfKH9TI9y/h0SF+3xWAJUIdzr4/W5BSHajdQALwAjgKHtmEcQ8/uzkfBiwY3aqx7JXpeAA72kLXQ2h269hvuBBx64q6cikUgkEkm7SUi033DDDbbbEolEIulMvAPUA3fR/jzp8WRYvFpu0eRxC8Jl/zXgV+1nFTCYsBs9iOzzjcBBhmPfIBYAJhr6fxp4C6hExMqviDJuAPhfpxOXewpqgu18Oynr9qRJk5gwYQJut3unjCeRSCQSSUciY9olEomk0xPPPhxEWK6DCAGbD+QhRHAjsBAhajMN19QCXwIlWhtPkuMbj32K2dodTRoHgU8Qse3lhuNfaz83GtrptZr3ArK17de0117aeM8Bm7RjGy19GlmGWASoCc9EVVE6acbz3REn0RPRGTnwwAPZtm0bw4cP7/A57SzBvkuzx0skEonkN0FCoj0nJyfhP0pbt25t14QkEolEkiwfAXOA7xHCVP/ppp2vQojiCcB/ENb4BYgEbQuB64Azgb6GPqMJsDpgM8LdPV3r40vAYWln9zdjLbCasBdALSLBXnfCwtw6tl397zrgeoTFfhMwxHCN3bw/114NNa1VR6ctU9YRpDJrvoNIP45EZWtaWhrTp09P0Uw6B2VlZcDO8yKQSCQSyW+PhET7PffcE9resmULN998M5MnT2bMmDEAzJ07l1mzZnH99dd3yCQlEonkt00LQvBGs4bPRiRiW2E4tlU7ZiQIvItICLeCsHW+BmGh70s8cvkvsIKtDETEo/+CEMONCKt9NPmmx54b5d4iwp4AEyxtdaJZ+esQCwDViEWItBizbkYsLsTpdjfATjB3hjnE8tPY03G73VRUVOzqaUgkEolkDyYh0f673/0utH300Ufzpz/9iZkzZ4aOXXjhhdx///28//77XHLJJamfpUQikfymWY1KHYGQ8NwCPAuMA4YjXM71muy6CM8i0mV9A6IMXEDrowUhv3SRXE24rNyPiGRzZpfzgXzPSsDJKjbSC2Ht3gy4EC7qRVp/dYjFgWJELPqriNrsOwhb5eMl2GvS2ucZ7sGIvl+DCAlAu6evCSe+awB+IHIBYxficEDrLpTeTie02HkwJIjPB/X1qZvPpk3w2WcwdaqY226GoijSRV4ikUgkHUqiuWNCzJo1iylTpkQcnzJlCu+//35KJiWRSCQSOPRQ8ZrFNspZQzm/amfeQljS/6PtL0dYrAMIsWwnCLcjEtWtJCzUje02IhLYvYRwJ38ZYcn+3tTLRu3VHcrIrlu6m7RXnXcRVvil2nwXAW9rY9qJ9Rpt/gu1sYPaPB5ALEYsAl5ECPJvEAns7By+l2hzWqnt32rTRr8Jd2IicScIsoT/GDusYQjRiZi1ooDLlcCFinhvok1BjZxt4rOyYdQoOOoouPPO9vQikUgkEskeS9KiPS8vj9dffz3i+Ouvv05eXp7NFRKJRCJpC1oEEj1ZChi/sGNZSZsN27qoDSBEfguwTdtuxCzal2uvPyDi443W9zD1oTNGSRjUxm1EWPC3ay13IIT794gFgPWWuQYILx68h1gw+DfCyq/HmysIQf+M1ue3iBj+OsIZ7ANam2bC740ee28fye1xufF4vWAjQCOwEbAOQPX5EhPBViyLACpx3N5ijOFKT8cRS8gbr3U6E7tfAEXB7Yni9G5Z6FABR3sWNpZrn71XXml7H7uC1lZ44gnYsGFXz0QikUgkezhJ+6HddNNNnHXWWcyZM4dRo0YBMG/ePN555x0efvjhlE9QIpFIfqtET8Zdj3CHz0MIbJ0mhGh1GNqBSNamW7L1483avi7iAog49wJElvlWw/nFiAzs3dmOkMpZoTj7Fq1dIyKxHIjEd8UIAe8EcrXjRlf8JsRCgYqIiG4EngBGGe5nC0L0mxPG9eR1WlnDcvK0/nZoY1Yh3OQXa+Per50LE3pLFZus306nSE5nk6DOGMftAJxOJy2KYhbBbXR7t5PcTgxLMzGEdkZGBnUOB601NfZj24npBIW7PyODpkZLaT+3O6JPl348EQIB2LIFCgrsz+1OPPwwnHeeWL7ab79dPRuJRCKR7MEkbWmfPn06n332GZmZmbz66qu8+uqrZGZm8umnn+5xGWElEolkV6KLdl0iid3XgVcQZdbeAvSwpFbC1nRdvK3SXuu0q4O4aCSfVViFsHB8XwHM1/Y/164PINzm5wLfUa8d2U4dYjGg1tKXcXwrrWRSTRo1CEFejRDamxCLCNsQ4v077fwOhHX+ZoQQrwcaCLKaaoKkUafd26+IRYAq7b1pwFjezTw3QWZ6utiwilobq7bT6TStcDscDpS0NPM1TmdM13Xj9U5LOyfg13c08RutJ5/DYfJxcOpWb6dTzMN6P4oCDgcOzcquqiout1uME82Cr/dht6hhid9WCX8+jzvuOACmTp0aZfaImI/CQpg7N/JcAiXjOhWffAL8tpPwSSQSiWTn0KaML6NGjeLZZ59N9VwkEolEYiAQCALPs4llBNHt5nchBKwPIWr1cmZGoWwU0TvQxS4ouGigDnDSRAsuQ3tjBnZd5EM4c30z4ThxCMYtILYs4kg6NSiAkwAi1t3ovl+LEOjfI9zrV2hj1yFKyq3VjntC/gRhWhALFnVEJt9TCbv4h4W83+ulpsGcUd7jdkcuOSgKbrcbd3MzjU3mWHyv10tLSwuNBmu0yUJu7Maw7fR4cDQ2hpz7FUXBG1qhie1mXuDzUWuYhy6gVVXF5fHQWF8PzeEQCZ/PR319ParbjUdR8Pl81NbWinGiiWR9IUBVw9txYumzHA769+/PddddF15IsOOdd8TrAw+E4z90djdLu/beVwJFRUVkZWXt2vlIJBKJZI8laUu7RCKRSHYOLS1NwCIaqQ0VTBM0E45d18WxVYDpCem24KAWLw0oNNOqHXUQ1ORvEJHkzch8RAy6no1+tXY8PINWApgXB3SpG9Da/RLn7jYhrOrGGPwGbS7rCbv06+jCO2CTB74JYzk5D/U4Qv1WYSejrcJSQQhgl8uFE3BrP6qikJubi7e4GNCc+TWx5nK5UBTF1JdTt+Db4XCA04nP70cxCjzNXd2lzcHO0VxBWHQzPR7bP9x9+vQhLS0N1XJfLoM1XVEUsrKy4mc61877fD4xZ4tgd9lY6Eu0+PeYgt1mDBO7m6VdQwH69etHaWnprp6KRCKRSPZQpGiXSCSSTsrWrcuAD6hlk+WMHj9u54YeRKUF1SB406kDwG0jXtOoRQhno4hagrC8b9fGaNb2t5jGEfutCFGsCy59Ti2k+1oMba1W1CrtVRf/emy6fn/GcRIhAOzARQ1emrR7XoFIaqe/F+H797jd5OTk4DbGYmvi1KkoOBF/IIvcboYPHx4SmQqQpYlWj8eDx+PB6XTi9Xrxer2iD0vcd2jL6kKvJ4bT+nNofVr/MCuKQjYi04DX4cCjCWRVm7vL5SInJ4fc3FxbQa33AZCenk5GRoaYu9st4vMtbd1uN4qiRL4/oWk7caO5hSsKWYA7FRn2d1PRXh6/iaQzYJNEWSKRSHYXpGiXSCS/GYJBWLAALF7RnZblyz8ENtGoiW4hh/UY8iAOGlGVcD53ACfNeGgmg1qcJpFtT0soiZyRWoRIb0FkYN+GEPDbTa3S2IGHGs1iH8BBC4o2Ny/15NavIY0diPrwy0P3IN7+7YTFfCMq9bhowLwQ0YqItW8gbJFvDuWqbzEtQohxFZPl/mfDdiPQYkhEp9C1a1dGjx4NbjcOvz8stDMyQleVer30798fVbOGOwCXZpFXFIWMjIyQCFaMseAWsasiXNgdDkeoLxyOiDh0j03Gdo/Hg1sb2+t0kl5YSGZ+PnmFhVRUVOD3++nXrx95eXkRVvSCgoKQCDfi9Xpxu1y4EKLdeDY9PR2/32+6Tp+Xbq3XY9nznE5ygSF+P+0mlnt8MAhffAG1tZHn7rgDjj++TUkAU0EJcMYZZ3DJJZfskvHbxIUXtr+PvfeOfm748Pb3n0qWLIHDD9/Vs5BIJJI2I0W7RCLZrdmyBd58E1piVUHTeOghGDkyXP88GXZFuG1Dw3Z0C3UTumxdAgTxUYeTBrxsISMjvArhMAh4B9sSHGkbZhdya/12HePiQKtmpRd4aNaO7QjtbwfcbEK4tu+ghXqDpA6g0oqHZlxKCy6aUQngZANhC79e+z1cUktVw47wAQKoKii0kskO/OxAwZyjXk/AZ64hL1AUhYsvvhi3x4PD6URVVSFSbazG5eXCnuoAXB4PJSUlYt/hwOFwCFdyA6rBgq5oP263G5fLRVlZmblzj4eirCzwem3dy/Py8lBUlRwgp0cPSktL6da9O9nZ2aE5p6enM3DgwIhrzz33XFvru8/nw2tYIDA6wDudTgoLC03tjRZ3l8tFBuAF8rKyGAGkJ1E/PiqxLO2PPCJi4CdOjDx3xRXw4ovii2AX0bVr1/bFtH/8Mbz9duomFI+TTmp/H7H+v7QkfZ2Cd9+FHj129SwkEomkXUjRLpFIdmvGjIHDDoO7747f9u9/F6+zZyc3xqefQm4uPP548vNrD/X1oqZ6K00GsSsEtUsT2a3BVgIBoxu6QFii63FSHXI+j4bDUQdU4+UnMvgKazZ4t9KsxYibLZl26xhino2hc3WhaPwArSaLfhNOrT81GO4pSCtQRz5rcIdSzu0AmnArTbiUsLU8SACXI4CDRq3qfJAggVDFeDHDJdqe+R3QLcglJSUh4ev3+yMs0k5t/7jjjsPlcpGuKLiKisLWcgPBYBCv1xuKjUdVUVwucoHuhnZ+vx+/3y/c6QGX241n8GCyCgsjxDKKwv777w9lZWSNGIFSXIzX68Xv90fUZ9fnrst+r6Jw4oknkm6Jsw/qYsvlEh4BHg8uvS9FQVVVMjMzTdf4fD6RME8LA3CUleEtKkLR+m5DtfpIYonAxx4Tr/PnR29TV5eKWSROKkICQNz3fvvBIYfAJksozCGHJNaHXRm/yy6zb3vOOVBUlNwc7Yi1kpkKz4u2MnKkef+gg3bNPCQSiSSFJJQxZtq0aQl3+Oqrr7Z5MhKJRGLl66/hlVfgD38AY5UtnV+0fGcvvigMbh3B0UfD9u1wxhlw+ump6fOUU4Sb/ksvRX/2X7duPioNqLSG7OAqjQRQzPI5qLvLi1b6o3SQVpxsw5zzPBJFaQbWEaQOFXDioSWUDi2IEgzgRIhinQAQTSI5qacJYYkNhlo3EcARd6VYJYhKE3WYXbZVWlCCQZMLtIMANAdMVuIgQcN1VeiCXaGOoGH0vOxsjjz2WAYMGBAewyB8gg4HztZWlNJSTjzxRPr06cPtt9+OoigoaWkEtQztxcXFBINBfv31V0pLS1G0DO1VVVXaJB24s7Ppv/febP7mm5CwdrvdqKpKMBgM0ZhgCAAAjzVJREFUudbrrvYY7l3xeCgtLSUjKwss4lsX/TrTp0/n9ddfJ1hdTV0wyNCSEvLy8hg8eDBffvmlfZI4o4dATQ04HAwePJihQ4eydetWfvrpJ3HPiiJK3ekfVqcTnE4GVVSQuWIFJ+XmRvZdVQXZ2ZHHwf5DH0sEJiKQE6w/3+kw3vcWS0jLSy/Zf/FZKS4WGfmPPFLsjxghwgbuvDOy7T/+0eapmoiXg+Cqq+C221IzVjJcfTUcc8zOH1cikUg6kIT+wmVlZYV+MjMz+eCDD/jqq69C5xcsWMAHH3wgy51IJBJb1q0LP5du3w7PPAM7dsS+RmfEiGb+8pd1XH997AfERJ7poyWsvuUW+O9/29d3LPRnW/09qK+HZ58VixGrVkW/rqm+ChetqISjzl0046LJ5MzuaNgB1Gml1MKotrbwSFS1BYcSlvZBmtEzxTtNMeLm/4NoEcSOhMYNYve2qiZpDcLS3mzTMlqvxjnWoFCHi2bctOChKXS2qKCAE0880STUu3btitfrFZbpjAy8hYXk9elDnz59gHBmdKMV2u12s99++zF16lTGjRtHz549GThwoOnvoTM7G7KyTPHs5eXlFBcXk56eHuo3LS3NNB9FUfB4PPTt25e8vDzb+/VqLvUhC7jDgbO0lLKSErJHjkTRYvf79OlDSUkJPXr0iLDQq6oqPuRaHfbhw4ejqmrovfB6vRQUFISuUxSFsrIyCgoKqCwp4TigizVh3f33Q04O3HdfzP8vE7FEYGcU7amytMeKxbdbyDjgAPu2RxwBa9fCn/8Mb7wRf9zevaOfmz8/0lr+73+b948/Pvo8AG691b5UYPfukcdSSU4OSAOSRCLZw0joL9zjjz8e+ikqKuK4445j+fLlvPrqq7z66qssW7aME044gfz8/I6er0Qi2c147z0oLRXWahDPeaeeCtOnJ9rD48BDvPjitzFbJfL8bKcJZs0SVvxYce5tCdcNBsXz82OPifufMAHKymDjRvM8YsXiqwF7sRqRXTzQisMRaU93xHSKN1xPEGfQbiJBUx+uJMQzxJbanjj2f12qqJGV02OiGOarsA03Lag274OqZ4PXEqtlZmbSq1cvysvLqaioICcnB6clTr1Es1wXFhaGLM66m3peXh6lpaUMGzbMJG51V/mg4T99wIAB7LPPPvTu3Ru/30+axZLqU1VcgNvjoayszGQh724RPKNGjaKsrAxFUQgGg+Tk5AjxnZeHqsWyDxo0CFVVSU9PJycnh6KiIsrKykx/s+3KwDkcDkpLS8nKygqJdz0Bn8/nE/NWFLH4YhWXF1wgXpNJeLazRXtVFQwYAH/8Y3LX2fHJJ0KMtiWu3vjeKQoYDSBGbwpVFXFAL7wQ2Yf+3pWUwHXXidd4xErMVl4O27bB55+Hjxm/CP/9b+F+/8wzkdf+85/h7TPPDG+/8Yb4WbYs8ho7DMahpHA6xfwlEolkDyLpZenHHnuMyy+/3LRS73A4uPTSS3lMjzmTSCQSjTvuEK+6kWbWLPGauCFkLQBr1iyM2aqtRq9Ylm4dqxa4+Wa4997Y11xwgRDpZ54J69fDRx+J17/9zaxNWlujewW3tlizukcn0fLYtjSFBXQLumt2MEJYJ/sHwyq13UkEPutvkYvWkNt/IqiqUbRHLhuEPiaaoAbhTZaWlkZ2djY9evQICVMrTqeTjIwMFEVh/PjxFBcXc9ppp4XO5+TkmNq73e5QjLuRoqIifD4fZWVllJWVhazrerI3T1kZ/YcMoUCzZhsxJpULBoOhhQF9X2/v9/sZPHiw7XuUnp5OUVFRhMVdp2fPntpbJBYc3G4348aNE8nrvF6OPvpoDjjgAPr16xf+xUskU+MXX4S32+MeHy12PdnVtX/8A374QVim28v48bBihUiwEY0zzoCzzoo8bhXtutfC7NnmX+yePeHiiyGK10XSxPrSzM4W8zC+p8b2Rxwhzp18cuS1xrwMf/pTeHvECJg6VWxPnhx/fiNGmP9QTJ0qxnvlFXO7SZNEjL7+RT10qIhrf+ghsWoskUgkewBJi/aWlhYWL14ccXzx4sUEdkV6ZYlE0qlJlQfprqK11fzc+uuvcP314tk5llernvTOiiUsm2uvhYICWL48SuNE5phQq8RoIbVexkHtX6cziKu5bYnCnARwteEuY4l9RbtJRVHIzc2lqKgIRVHo0aMHPeJkmg4Gg+Tm5jJlyhQOMSQKCwaDHGOIpdXFtNfrpbS0lPT0dNLS0hg0aBCnnnoqaWlpQvhqZGVlcfHFF7P/gQdS0rdv1Jrrseb19NNPM3HiRKZMmUK2Fk8+dOhQFEWhtLQ01LZXr14x+7nwwgsZP348ubm5FBQUhO5FURT23ntvrrnmGoqKipIT7UcdFe8Gop8zfpFcd519G+sHt6VFZJufOdO+/51RIm72bHjrLZFg7vHH4dFHYevW6PNQ1fB7qSeLu/deUYbw6adTOze7L+e1a2HNGtArCxjbtOXL3Og1YExueP310a+58kqYO1dsGz8zfr+w7FvzLL3zDqxeLXIybNsWHuf3v4cDD0x+zhKJRNIJSdo2c/rpp3PmmWeydOlS9tZqdM6bN49bb72V01OVoUkikewR1NSELesgYts7irYuDsTSCb/+CgMHQnV1+FhNjX3bbdvE82k8wRsImPXNyy+L1xtvhCefjGgduzONZkhY4CdCKtdZVDRX+JZkItNTN3Y0FMLu6x6Ph8bGxpAVXS/vZk30phMMBrniiitobm42lXprbW1lwIABTJw4kfXr11NVVYXP52PcuHGkp6fz0ksvsWHDBsaOHYvb7eaGG27gq6++Ys2aNVRrH7IjjzyShQsX8uOPP7bpnouKiqioqAAIud1nZ2ez7777oqoq69evB0Q2+JEjR7JixQqcTidNBm8LfVFi7NixfPTRR8ybNy/GG5mAaF+xArp1g8Y4niOJivYPP4zfBmDOnPDPSScJ6/Ddd4sskNC+1alEft9aW8Px5999Fz4+YoRwEbd77xQlvK/P78ILYcaMtsXpxMKwiBMillt9IvdsnaPXK8pvBIPmhHrR+ioqip68zu5L/pFHwvkYnE5zYkWJRCLZg0j6L9Ydd9zBlVdeyZ133sn48eMZP348d911F1dccQV//etfO2KOEolkN+XGG837S5aktn9jWWO757lly0SY6f33R29jpGdPePDB8P5tt5kFO5ifNfXt774TJeGmTIk/50DA3sCn99XaKqo/TZ8O/toNkQ2j0RQvR3wSBAJxY87b1G3Ke7QbJLFRjJb2UaNGMW7cOFPs+IABAygqKooswYYQtbo7vZHW1lYURSE7Oxuv10t2djb77LNPqF2/fv2YMGFCyA1eURT22muvkFt90CJk/FoisJHWElZR0K8/6aSTqKio4CiDlVJ3wR8wYABDhgxh3333DdV3T09PN1nSQ++RYds6N0AsDigK/SH2+37eeXqH4WOKIko/nHpq+Fii7vHGdsZtVRVW7f32E7/8xl+0o46CzZvN47VVBJ99thjL6qZtxZiwYvv28PaKFWYXf+M87US73VynTxfxNzptWbQ791wRv5No+bf99xex4tYvurvvhn79RJjBzz9HXrfPPjBunPmYltwRgEsvFbXUhw2LnRHU7h4tpQklEolkTyVpS7uqqlx55ZVceeWVIcuAtZ6rRCKRBAKwcGHHjmEMl7RyxhnhuuoXXBD2kLWiW7oBli6F888Pawy7Z3qjRtCfIfW8S4mETwYCIrbdyn//KxLi3XKL2P/4Y+gdtaiapL0YBamqqqiqis/no0ZzpRg7diwbNmyw/ftmJ2BBiHb9/MiRI9m6dWtMN3SdyspKVq5cSZcuXULH0tPTQ1nfrcnndDy6C7NlXr1796Z3lMzgF+gJ4oBly5bxzjvvUFBQQEFBAYsWLSIjIyPUj9vtDrno28X4T58+neayMtxPPx1bcG/bhtZJ+NgTT4gPuTEp2YoV0fuIJtqNwlhVw3XNf/c7c4K5jRsj+4wm2hsahPDfd99wUg4jjzwiXuPVhbda0I2sXh0WrtZ2+ucrlifA44+L6+ItPLhc0BzFx8XjEffy4IPii88OzWsDEFbsZcsix7z4YvGTDAUFsHix+NKdPFn02ZZ66jIsUyKR/EZoV+RiZmamFOwSicSW8ePhgw9it6mpEc9sds9dK1eKPEJGjK72VubOFeGYEA4ftRIMtgD/A8Lm8/ffj2z35z8LY5rd87BRrz33XKS7ezxeeEG43FvZsiUs2HXsnbMlqUBR1QghetBBB9G1a1dOOOEEioqKyMrKMrWprKwEYKDlP7Bv374AoZAxEKL7oIMOCp2LxdVXX80pp5zCueeeGzpWVlbGoEGDuOiii8zzVhSGDRtGSUkJxcXFpnPJxsCfccYZPPHEEzz99NPk5ORQUFCA1+sNiXafz8e0adP44x//aCpXpy8IKIqCW1840H8JPv9cWF2N6OeswjXRLOLWa42/cEYrdVVVeHvduviuNdEE78svw5dfihrn33wTPt7QEOl6E43Nm2O33X9/8Tp7tjkbZjBob2m3IxH3/iVLRH3JWAnyzj5blGf78svIc4WFovybHq6hlQVMCX36iEWWRD0e7LyJdkZeAolEIukEJG1p37BhA5dffjkffPABGzdujLA4tMovUInkN8u338Jrr8Hll8Nnn0Wetz7rHXVUWDQvXy7CXltbW3n99f9yyindqa83i6MpU4QB7c9/Fl6a1v4OP1xUCYr2NVRbOwf4FEijoeEKooQsx6wAZQzLnT5d/Bi109atwlU+TADj+uivv0bv24qMzty5ZGZmcsYZZwCivNt7773H6NGjQ+e7du1Kly5d6N+/v+m6o48+mjVr1oRi4XUyMjI48cQTefPNN2Na3D0eD2dZsoqrqkrPnj0psYkxzsrKIisri19++QWAHj160Ldv34h5xcPhcLDffvuF9r1eLw0NDey1116hY8OHDwfg448/5sgjj6Spqcm8WGCNy544MVJc6c8J7RF7RoFq/AU3/kKedFJ4u6VFZIZPtM85c0RdxkAgLFBBiMo1a8R2ovHSVVXCkmzE6p2xdq0Y01pzPRnRHqt/na5dxftidCmy4nTCVVdFP59geEaHY7T660hLu0Qi+Y2QtGifPn06q1at4vrrr6ekpMTWZS5RPv74Y/7617+yYMEC1q1bx2uvvcaRRx5pGutJS2amyZMn884774T2t27dygUXXMAbb7yBqqocffTR3HvvvaSnp7d5XhKJpG3oFaZuuCGx9kYr9zXXwPPPw/vvf8MxxywgGFwAGEW7eCh98klh/Pr000gNsGCBeHaN5pa/fLkeb1nL1KmJh3IaMeiZEEZ397w84/NvHXAf0BuwZs5+HZGa7WiipX5LXWo5iRXVxtJuJCsry5QJHuDcc89l4cKFjB8/3nTc5XLRrVs3236cTqfp71oqqaioIDc3lwMOOIB99903artE/k6PHDmSpqYmunfvzoABAyLO9+vXjx9//DHkbRBCF5a6eLKzhrZHWAUCYgzjPfz0U3g7Wib5NWvA4qUQgdHCO3GisHq/8ILZxUd334nnCm9k0aLIY3ai+pNPIo99+214rFSWcdid+fJLkTnf7g9LChNwSiQSSWcmadH+6aef8sknnzB06NB2D15bW8uQIUM444wzmGYt4aExZcoUHjf4uVpj+E4++WTWrVvHe++9R3NzM6effjq///3vee6559o9P4lE0l6CJJqLXDeevfRSTdznsIULIT0d7AyLb78Nhx4af7x4rvvtIZz8eCFQD3yDWbQ3I9z0AQ4CDGWRDEgbUgdiI2SjxarrFBcXMyWRbIMdTGVlJcuWLWPQoEGceOKJcdsnItqLi4vZZ599orY98sgj6dOnT6S7v1W02xHNPd6O2bPh66/hsstE3cQrrrCPYamqgr/9LXptRWOsux3V1fDYY+ZjV19t7yL+1FPCxSdR7O7T7rNld1/GEme7e73MVLHXXpGrpWedBR99BEcfvWvmJJFIJDuZpEV7eXl53AebRDn44IM5+OCDY7bxeDwRcXs6P/74I++88w7z588PZde97777OOSQQ7jjjjtMNWklEsnO5lVgBTATcCd8VTJfL8L7NYgQxiLT9t/+lvj1u45glO3orSSpxeFwtMtTLBa+Di47deyxx7J48WJTjfdY9OjRwzYG3sioUaN444036Nmzp+15j8djv1ifiGgPBkWsu10yOCu6u3iXLiKDJIh4bD2ZnY6Wcb/NZNkslNkJdkhOsAM88EDksenTI499/HHsfqSlPToPPyw+V3JhQyKR/EZIWrTfc889XH311Tz00ENR3QFTyZw5cygsLCQnJ4f999+fm2++mby8PADmzp1Ldna2qRzOgQceiKqqzJs3z1TuxkhjYyONhji46kQTy0gkkiTQXUR/AIYCwv09GrpYT35N8E1gAXAy0CtqsrqOzmTfdmqAzUAPrF4J0tLecThUNaIOeyoXpGtra02J6VKJz+dj2LBhCbd3OBycc845MdsMHz6cLl26kJ+fn9xkdGEZ67375htR9isZjB4EVsHe2fnXvyKPLV2afD+pFO17orjdE+9JIpFIopC0aD/++OOpq6ujR48e+P3+iGy1W7duTdnkpkyZwrRp0+jevTtLly7lD3/4AwcffDBz587F4XCwfv36iBq6TqeT3Nxc1tvVVNK45ZZbuOmmm1I2T4lEkggtPPDAJqCYRF3mE2OB9vohED3Zl9A4nfEhTysfxdHAINOZatIRol6Sao445JDQ36+BAweyadOmlC1EGxPapZqO8g5QFIWitiR5MFrajdnbOzPGRHOdGWlpl0gkEolGmyztO4sTTjghtD1o0CAGDx5Mjx49mDNnDgdYM64mwTXXXMOll14a2q+uro7I+iuRSFLN88BSYCoQPRvx7pFX6HOEy38yWZVXAN20betNBoA3gCIgvBApRXvHUWRY8D3mmGMIBoMdJoj3aIyi3a6WYWckVvmzzoQU7RKJRCLRSFq0/y7Z2K4UUllZSX5+PkuWLOGAAw6guLiYjZYYuZaWFrZu3Rozds/j8UQktJNIJB2N7h76JXZiVxfrkaGxzSRmIU+V4NqGSAxnfGBuAX4EemrzeVc7PtzSzkotQnSnI8IEukVptwoRl/8AcKPh+G6xgrFbolgEUWcX7IqiEAwGbUvA7VKMol3PtN7ZaYur+q4gFSXfJBKJRLJHkNBfBGPMd3V1dcyfjmT16tVs2bIl9NAyZswYqqqqWLBgQajN7NmzCQQCjBo1qkPnIpFIEsX6MGnd3wEsJhAI8sorIlFzmHrgfeAZ4CeiUwXMApIogm5iDvAKQljfC1irTzwFXIAQ1DYlrVgFzCXy3p4HvgIaiJ18TubV2Nm4O/HCbffu3QFMMfFjxoxhyJAhlJWV7app2aMLS2PtdElqSES066UqHn20Y+cikUgkkl1KQpb2nJwc1q1bR2FhIdnZ2bYWCd21sDWJP9w1NTUsWbIktL98+XIWLlxIbm4uubm53HTTTRx99NEUFxezdOlSrrzySnr27MnkyZMBUTd2ypQpnH322fzjH/+gubmZmTNncsIJJ8jM8RLJbsPfgGZee+0IXnttGGZBuxqRpC2AEMXRmI0Qxn8EHo/Rzo5vEWK8m7YNsMTS5mXtdR721m+9dFQWYFOHjhoiRXut9uoGVgL5CIv+34A04MDEb0GSFA7AZZc9vJNw6qmnsn37dnIMGdLdbjdut7vzeQQ4tceIDRt27Tz2RBIR7VdeKbLsd3DFAolEIpHsWhIS7bNnzyY3Nze0naqHhq+++oqJEyeG9vU489/97nc8+OCDLFq0iCeffJKqqipKS0uZNGkSf/7zn02u7c8++ywzZ87kgAMOQFVVjj76aP62e9R8kkh+o1hFb7P2ugSwZsRWEOK2AYhWLqoRIezTgeVxxq5GxKN3Nxz7j/a6guju64l+523RXu3q0xvvuxWYr20XEL7HUoRw/wL4laB0j085DsQfPsWZdHTYTkNVVZNg79RYktFKUkii7vFSsEskEskeT0JPLfvttx/Lly+ne/fuTJgwIWWDT5gwIWaJnVnRajcZyM3N5bnnrK6sEomk85KsENXd0e0SsgUR4rcuwX7nIVzgf0YI/O6xm5toRQjr5hhtGhEu+v/S+v4WMe8umAu4GT2SdiCEuhNxD41IOg6VzllDIFH0BfROgxTtHUdn86qQSCQSyS4j4SwnPXr0oHv37pxxxhk888wzrF69uiPnJZFI9liWAjcgYr2NJPKA+jDwjrYdRMSat2j7upiuAf6BSHhn5TuENXw98KR2rD6BcesQYnwTIvZdx7hQsAp4FPidtn0/IqldLWKRwNg2gIjDr9XatiCTzu1E0tNxdmJLux3Tp09n3Lhx7LXXXrt6Kmbc7l09gz2XVGaPv+02yMmBP/0pdX1KJBKJZKeR8FPL7NmzmTNnDnPmzOH555+nqamJyspK9t9/fyZOnMjEiRPbVuNVIpH8BqjSXmuBVxHrhauIH39uFPLNwBrtZwrCXd6u3vJHCFH+X2Bvy7kdRIrjzQhR7o0yh3pgnWH/FWC8dg+bgA1an8u0/rcQtqrXELaqW2P1q7Rt64P5JoRFH3x42BFlVpI24naDw9H5YsPj0K1bt5TVkU8p0tLecaRStPfsCZs3yzJyEolEspuSsGifMGFCyDW+oaGBzz//PCTin3zySZqbm+nbty/ff/99R81VIpHsNmwxbP8MvK5t667gbuLHn8fjZ+AXosfI29Fgc2w1YgHAKtprAB9inroI18f6BfAgSrSpCOHfghD3Vstji+VaEAsBrdoxo+hpQsTcg7DSS+LickFzrP9zexwORwdM5jeIFIEdR6rfW/l/JZFIJLstbfIP9Hq97L///owbN46JEyfy9ttv89BDD7F48eJUz08ikex2VAH3GfaN1vB44qoVc+y3Tgth8QvCYv88wspda2kbQAhtFViEcJPfB+hn0w6EZRvMgr4WuAMoAfbVjjUhxHYDYct7gbatEj8W3a7UHYgEek6EtV+vc12PWBhIAFUVNZoTqdPsduNUFFoak4ub9/r9NNTVJXUNAA5Hm0qBuYj/SQnRBiHi8/l2O0t7p6UTl87b7ZEiWyKRSCQaSYn2pqYmvvjiCz788EPmzJnDvHnzKC8vZ/z48dx///3st99+HTVPiUSy29DWfBetwF0It/lcdNd4lUYUGmg1uZH/FViIEN56ObUgQsQvQoj2VQgL+g7gDeBVFJpx0EQrDoJ8DHwWZS7rEIsEG4F8FBpxs4Um3AS1Y2J+/0EI98oE7m89ojTdBCIzyTu1+9iOEP+NiDAAeyHuwJDKzuEQgr2lxbatCUUBVUXFfmkkGv6sLHvRHk+Uq2rkebtrDNZyN+LdsBXtipLY4oRk55GevqtnsOciRbtEIpFINBIW7fvvvz/z5s2je/fu7Lfffpxzzjk899xzlJSUdOT8JBLJbocuqurRLdMq2wnQlXCMum5RXwR8A/RBWJprESI7R2uzBT91NAMuGm2c25tQqQfqCJCpjfc6os45CNHtRGSYvxgvDQQI4qCFRmqA96Lcw1ZtPuuAWbhZjZcAHpqoJo9w+bkqhMBOpOTSHIRVPxvYhkoTCgqt6KKnERFWsI2wq35s0e5SFJrthLGGm3Du/RCKgoPkRHtUYol2lyuh7NdJLSDofTY34wkE2pVnX1raJZ0e+RmVSCQSiUbCov2TTz6hpKSE/fffnwkTJrDffvuRl5fXkXOTSCS7JbrVex5QhYs1FLAZPyUs4RCEBbsOYZG/Efgfwi28D0JiNiFqps/HWOZNpRVhf92CqLe+HsiglLVsIohCLQ1kIIS/ByEFqxHytg74FpWAQSBuRohuo+hcjxDVaxALB/rCg7hKCd1fHeYkeZss/VhpJGw73ga8hIt6XEANWdrxFsxx7A00We3NmuBWdWuz1wuB8B3pM9JFfTQ7nUO7C902ryhKzPKbUYklKqKdsywyqEDA0DaNBIreORx42yDajTNKS0uL2k4i6RRI0S6RSCQSjYR9r6qqqvjnP/+J3+/ntttuo7S0lEGDBjFz5kxefvllNm3aFL8TiUTyG2AhMBchnlfjYhu1QBnrELKpHiEZAwh3cBCCdgUiCZteR70e+DkkslsJIDLPf4uIk98MbMChWaMVk005iBDsWzDGqkfK0oUIa3odQiouRiw2RLYMGLbc7MBlyutepV1vtBkb73OT1jeIhYilofmoJrFvFOnV1GFxSXe5UDWx6QJclmRqCmK5Ip2wr4FRyGdnZ5OuXa8ATlUlMyMjrtU5ojyaqoq5WN13E3HnNbRRtHkZSWTpQHU6yezWDaffHzE3B+I9sEO/S6fTiUfGYkskEolEItlNSFi0p6WlMWXKFG699VbmzZvH5s2buf322/H7/dx+++106dKFgQMHduRcJRJJCqmqguefh5qauE2TYAfwGELArjCd0SW1SjOZVOO1ClJmG1rpyeWWhM4GCCJi1TdiFOJ6lXWz2NPFs0I852uFBrLYRDobEOI7wqGcoGn+LfhoxE+DdqaZ8CKBPottwGL8bMRPtXZuM8JF/ittX6/OXo+9VA3QbGNpB8DpxKEoFGjhSYpFLLsBp8djctpXEaLd5wsfVX0+PFlZogyaFu8e+f5Afn6++ZzLBaqKy1ruKwHRbsza7nA6UTIzI+Yf7/q0tDTKKipQbTLAuzBb1CG8MOACctLTmThxIm5ZX1yi8+ab0GBXWUIikUgkks5Bm7OcpKWlkZubS25uLjk5OTidTn780a5mskQi6YwccwycdBKcc05q+hPe1WsR1u01WDO+i/R03+HWnJpzMXrnbEeI8bWYaQzZoVXDMaMDdaSLtIJVtrlc0KWLtZ2Q4W6aaAXNYr8SWBNTeyomgd2kzb1W+9EXIlbioppWtNhz6ggvHqw29eGghixWEbm4ECRoHMsoMtPSICNDuMs7HDhcLhFfrotvwJmWRr6xO6+X4uJie4GsKOJapzNCvDsAVVVDFm3VsO9yucDpRFUU3OnpOOOJb7db9ON2i/8UjwcUJUJk+20uVRGVS1wuV8gzIBgMRrr1qyr4LDkGXC4cHg+K201GURHFxcWx5yn5bbH//rLevEQikUg6NQmL9kAgwJdffsntt9/OwQcfTHZ2NmPHjuWBBx6guLiYv//97yxbtqwj5yqRSCy0J5H2Bx+I1+eeS+VcFIS1ehvWmujCtvw9IIS2MPDr8eLWzOdbESLYzg0gqI0htu3fgqBpO2BrbK/W5hikGV0y69fZx6cLm3prKPI+PPdGQwuB8ctVJYiX7RSwEd2+ruMgSABwhmz5QbzU4mRruJ2qhsR4yFKu7Xu9XoLBIKrbLYR7RgYUFeHLzSVa1pFoyUwUVRUu5wbRqwBdunRB1d3JHQ7cuvjW9vF6UR0OnEbLtza/0BFtQSAtLU0sHKhq2Opucc+3m5/b5cKvufbrVvLQPByO8MJDRgaejAzzxfqihs8HihLp1i/5baN9HiUSiUQi6awknIguOzub2tpaiouLmThxInfffTcTJkygR48eHTk/iUQShepqGDYMDj0U/va3XT0bXbQvBqLnt1BoCslVIXPXIQS+Ahitn7qruV3ccQPQgMMRwNfaYpHXzQhr92bCCfEaCQQCtLRY67m3AD8QJIiitXTSRCsNBAI1iER0QTyGxQeR8367jcN9ACVkGY90rwdQCVDJj6hksNESGmC8wqEEUYMtWoZ3xXZlNSMjg2AwyJAhQ5g3bx4Oh4OGhgYCgUBIvObk5ACE7g0gKysrZNlWEbHhaWlpbN26VbRVlJAYDy2jqCpHHXUUS5YsYcWKFbhcroST1lnd1z0eD/3792f+/Pmh8RLG78flcpGRkUFWVhZjxoxh3bp1uFwuVq1apf0ft6AoCl6vN5QtwUowGJSZ4yVmEhXsF18M99zTkTORSCQSicSWhEX7X//6VyZOnEjv3r07cj4SiSRBnnwSli2D++7rHKJdWLNfjDgeJEgTusXVLPYcbMZBDc04CVJH+CtJt1ibv6KcNOFgC404ARUXzSH56yCIk220kA60kEE1LmAbGQSDjWzfvsJGbDdpJeCEzTyNeuqppwkPCovJIQsP29ga595VWvHTqFngbeqZa/2vB4IsI4i9K65CK46gdSEikq5duwJQUFCAy+WiQYvHNYrR3r17s3r+fLwIAV5aXs6wYcPIzc3l/fp6mmtqKCoupqCggMbGRrZu3Worxh2KEoqF9/l8OJ1Ompub8Xg8eDweamIkRVAMru9uza3d5XKRlpZGnV3dd0DNzIT6epFh3uIioWjhAKqq4vF4yM/Px+VysWbNmtC99+nTh23btpFGODMCgN/vp7m5GUVR6BIZKyFpD2PGwNy59ufmz4e99mpbv243NNkvgqWUREW7XOyRSCQSyS4iYX+wc845Rwp2iaQTEa08dipZtgw2bEisrVXvOWkmQ4vrDoA1pRoAfmpQADcthFPKGYWa+YHdoVnPXTa9BQAv9dpIDaErM9mBi2aCwfqIa0AkxgNhRQ+iR8QHcLMFL8ts563j0kZxaXbp8CN9bKd9JUpyPDctEfHdAG6LxdrhcIRiu8vKyoBwCbOsrCxKSkrIzc2FPn1QnE5yy8vJ0izvpaWlODwesnJzycvLw+Fw4PF4yMrKwuv1mhLV6XTp0oWysjKysrJwu91UVlZSWVlJaWkpHo8nHO9uI2p0qz6EFxUqKyuF1V/D6/Xi8fnweDyoGRlQWmobYzx06FA8Hg8jRowAwOVyMW7cOHJycsjKyiIvLw+v14uiKFjvwul0UlRUxNixY2W5t1Rzxhmxz7f12aE98T/JIEW7RCKRSDo5MohLIunkfPSRcIP/4gvzcePz7Pr1sG0bKWXzZujRAxLN2RU2igZxOMBBAJUgjjjZ23Xc7CA7uwm0UmoqLXiwF9r6o7M1ol0I7LA7u27LVQkSDLZYksjpfUUe08uw1WK/2BBuB25HK8EI23jkVUHEsoTCdjJIbCVEvz+HquL1egHhGq/HZCuKEhLCLpcLp9OJoigMHDiQ8vJyKC7GkZNjEiV6EjmHwxES23qfPp+PgoIC+vXrF7aQa5b24uJi3G43Xq8Xv9+Pw+GgoKCA/v37o6oqWVlZeAzl15xOZ6h/F+Bzu8nQyss5nU66deuG0+kkMzMTVVVR3G4U7R7tyMjIYPDgwRx33HFUVFSYxnG5XJSVlYl7BtGn5frc3FxKSkpE4j6brPOSdhBNzKalwYAB8UX7739vf9xalu+ii6KXu/jDH2KPEYto8//uO+jePbyfbNz7SSfBtGltn5dEIpFIJBpStEskSfDll/Dppzt3zAkTYOFC2G+/6G1KSiA3N7Xj/vxzcu2DQejGAtw0QTCaUI9lqQrQ0lJHZmYVbqUJF61xv6CabC3a4bFj2+mChuRvAj2O2xljocFjEeRKq9k6rtBItER2jUALzQnVIhcz1GvQCyHq8Xgi8oiMGTOGAQMG4HK52GuvvTjqqKMoLy+nZ8+eDB06NCRwS0pKOP744+nfvz8DBgygvLw8JPh79eqF2+1GURRyc3PxeDy4EaXjfA4HlZWVDBs2jMzMTAoLC8PzCwZD9c69Xq/pvjIyMkQCO23+LqcTVVXJyxPp8XRXeX0BIjMzE7/fH+Gi7wK8wEEHHYRqTF5nwWjxt4tZ93g8+P1+AoGALE+6MzjnHNi4UST/O+mk2G2N3h39+4e3337b3C4YFAsBdkybJr4s28Pkyeb9AQOEu5FOsqL91FOhqCh2mw0b4JVX4Pzzk+tbIpFIJL8ppGiXSBKktRVGjYJ99xU1zo28+CIMHpyc0N28GR58UDwTHnCAiFGPhR7auWoVvPBCx7vHG4ymCXmpBgKQy2IUwBkIC9tYdnZzzvggigKO5kYUbcCY0t8yKb2tM6Zt3NhHQHO3D9Oet1Qf301DzDnY+w4QMRcrqqpGFaN9+/alS5cuZGdnh9zODzjgALKzsxk3bhyTJk1iyJAh9OvXj0MOOYShQ4fSt2/fUB9+v5/S0lLKysrwer306dMn5NauW/MPO+wwunTpEhLNejI8neLiYvLT003z8vl8oQWNzJwcKioqwqXi0Oq/IzLip6enU1BQEJHZ3QNkI+L40w39H3LIISE3eSsDBgwwvZter5eioiKGDx8eun9JCjniiMhjFRXg14r3nXACnHJK9OuNn2ujgB83ztzu0EPN+2PGCGv4W2/BiBHtd1+3LhLEmqeOVeh//HF42+kUHgAlJdH7LCwUCw7S9V4ikUgkMZCiXSJJkFpDVqsHH4TDDgu7pB9/PHz7bfzQTiNHHCGMKz16wOzZMH16Ytd16yaegR97LPGx2oJRtLdYK7LZEAiISutGGiz7Vld0W1GeYByrM9BkaxG3jhFNiDvaJdEjMfYWS4C3JUo33uN8QUEBAwcOZOrUqaFjmZmZgIhxLy0tDQl+O/FvdLEfPXo0JTYio7S0lHHjxjFq1ChGjBgRIXwVRSHT50MlnD7Q6/XiLyjAm5ODy+8PjXPooYcyZcqUUIZ7h8PBLbfcwi233BJaFPB4PLg9HvFHShN/RtG+9957h67X4/CLioo466yzuPPOO01z070USktLOfjgg2X2+FSTnw/WxIIXXxzeVpRIwR2NQw4RP3/8o9h/5hnx/3///TBpkrmt2y2s4YccErvP449PbGxFAe33Jup5nS5d4OmnI63v++4b3nY4RLs1ayCed8fOSFIikUgkkt0WKdolkgS59trw9h/+AG++CTfeaG4TI5F2BJ9/3rZ56Jr2xx8jz33/vbD6t4cHHxTP20Yv5OYEjNfBYLhaeTQ8UcqhGWlK4uE1mgBWDRLaOHVj+3iW7c6AvgARgFC5M9t2ikJBQYFJ1EbDrkZ5LBFrPFNUVITP5yMjIwOHwxGytHs8HtLT08kvKMCNOee/2+/HaxFCubm5XHXVVQwYMCB0rHfv3gwcODAkwBVFwZuRQUmfPkIU2jBz5kwOO+ywkHfBtGnTOOWUU8jJyUEtKBCNnE6ys7M55JBDOP/880Pu/JIUY01gaN0fMyaxflwuYTm/6Saxf/LJYsV0xoz41151lXg9+mjz8eefh02bwpZ/I+eea97/618j2xxyiFgtNbrf//qr8B6I5TKvn1MU4QI/eTJ88ol92/a69kskEolkjybhkm8SyW+dF16IPLbRalrepVQxcOAmoBc5OXDQQW3rRQ+t7NcvfCyaaP/lF7FIcMEFwtIezfVbJ5arfIAgwWCAlkBiievAviK6ilm0h2iDJSt61fW2EST2e2BFX1jwOZ2UlZWxYsUKPB4PTVHKYPXp04dFixbhtxMnGnYCvX///vxsie1QbeYarT67z+dj5MiR/O9//xPJw7QVH+tY3bp1w+FwMHnyZBRFMS0g5Obm0tjYSEFBAfX19QQCAcrLy1FVle2//mo7bnZ2NiNGjGDgwIFs376d7oakYYrfLyyxWok3l002eslOpKJCfGGceWbYhfzww+E//xHx783N7Y/tnjwZ1q4VceT6quPw4UI05+fD9u2wbh1oJRM57TSxSmnEpnoCb74pvuACAREH1adP+Fws0d6rV3i7d294553obY87Tnxe/X6xUGENDZBIJBLJbxop2iUSjWBQxKprHrcR2GnJWPrym2/EM+oxx0Seu+iiNk0xjsX7Hu31NBYurExYtEczshrDAaKNO3CgiLV/+214443ExotFU1NjXFfwNpOIj7/1Etrmzh6NtjrAqorC0KFD2bJlC8XFxezYITLsW0Vx//79mT59eihZ3GmnncZTTz1l7stGZIwYMYItW7awevVqm8GjixJbEW/IAG9dPOjWrRsTJ06kSEvOlZOTQ0lJCa2trRQVFbF+/XpUVSUtLQ1VVXG5XLS2tpqS39lRUFBAQUFBxPvh9ngIBAIhN3rJLqZnT3Nitn//W7gnZWQI9/e//S35ZG9WrKEdRpchpxO0CgMAnH565PV2X4iKIvpxOERWUGMbu/kuXgzV1aJ0YaIoChx1lNhev948b4lEIpH85pHu8RKJxgkniAzs8+bZn7fTJ3bHHn4Ybr8dhg6FY4+Fzz6LbPO3v7VtjldckUirVabnvf/8R9zXlVeKfEe1teaEyIlgFe2ffCIS5+kG388+g7lzk+vTioMgSlPTzqvNnACpjjJt650pCKvysGHDTOXOItopCt26dQuJZT3ru7WNlbFjx3LUUUdx1llnhQ/6/aCqBA2C1yrSo1neASoqKsjJySEnJwefz0eXLl0ixp8+fTp9+vThzDPPBEQyOz1WXi9xV1BQELqfZF3b9fJ28eYq2Yncfruwut91lxCqxpCP9gp2I3/7m/jie/jhyHPPPiti5u1KcsTLd2A9bzfnPn1gr70Sn6sVp1MmppNIJBKJCWlpl0g09Fjwu+4SZYP/+lfhOal73CZiaf/mm8iSw999B/vsE96fM6ftc7z33sTaNTYKL9CSknBiZz1UMyPDrIujPRsa21g9y8ePj2z/738n5/odlSTc43c39DtL1uVe/y+qrKzksMMOw+/38/7773Pqqafy1ltvxbx20qRJqKoaEsO6aNZfi4qKcDgcDB8+nGAwiKIolJeXc//99xNwuUwZCSPKscVwOZ8wYQJdu3Zl/fr1ZGVlhdpmGuLbe/bsyQMPPBCKYwe4+eab+eqrr3j00Uf5VXOLHzduHNdccw2zZs1i5cqVMe/XDinYOxHdusGKFanpK5awveACmDnTvk2sEnTJiu0rroDXXkvuGolEIpFIkkRa2iUSC4EAHHggzJolQh6Nx60kogXOPRcuuSS8P2tW4nNpm9YI8oc/CM/M5cvj9xntudd4v4EAzJ8v2t56a/S5yvzHHYTlP+nSSy/l9ddfZ68EBIbP5+Owww6jXHMLNlq6R40aRT9D8gJFURg7dizl5eX4/X5cLpdtJnkQJde6desGELJmm6esmGrKH3rooYwdO5bBgweb2vkNWeVBZIifMGECPXr0CFnbnU4nHo+Hgw46iCFDhnCa8RfTgFGcOxyOUChAYWGhFO6/Rdpire7dW3zZrVqVWPsxY+Dmm5MfRyKRSCSSJJCWdonEwssvh7fXrg1vJxvTbuSee+BPf4I//xm+/jqxaz791D4nUjIks0Bg5corw9vr14sa9QDXXGPf/pFHRE1tyc6hrYnVdIHt8XhiupvrseXGNkbhu/fee1NSUsLLL7/MChvL6YgRI9hnn30YOXIkO3bsYOLEifTs2bNNc9bx+Xwcpcf9xqGoqCiUcT9a1n3Jbk5lZcf0O3Jkcu1lzgSJRCKRdDBStEskCfDJJ/bl3P79b7jhhsT6iFX+18rGjeZyv2ZagCeAbsCBMftJJEQ0kaTq06bFbyPpOFoMYjk/SvmzRFEUhWuuuYbW1la+/vrrqEnePB5PKJu7zujRo1m6dCm9tKzY5eXlXHLJJdyo1T4sLCwMxZEfdthhgLCcp6en2ybASzVGi73b7aa4uLjDx5TsAubMgSeesC/PtjswapRInqJnsZdIJBKJJA5StEskQF2d/fFgEBYssI/h1vnTn6KdCSIEdvIW0TVrYp39Hlit/cQW7bETEC8F3gaOAMpjNYwzHx3pftxRKMEgv//979m6dStlZWWmc5MnT2bWrFlMnTo14f506/m4GGWliouLaW5uNmVe79WrF5dccklUy3V6ejoZGRkx674nQzAYtHW9j9W+LeckKaJ7d/uYnFSz3372SeR2F159VSTKs9aIl0gkEokkClK0S37zBAKQlmZ/bvXq5D0lwzwBrASuAKIMAEAzMAvox3PP9eDTT6MvIgjimcbD4iS2cfNp7fVJ4LoobRq08WLNX9LReOvrKS0tpdSmhNSYMWMYNmxYKP47VSiKgtvtjjielZUV9Zrs7Gzy8vJsr0tWyB977LH89NNPobh5yW7Af/8rErP98Y+7eiY7l2QXhEpLoycHkUgkkv9v787j7KjK/I9/qm7dfel9786+kj0QQgKEhC0GCDsKAgZGRRRRBERQJKKD2zgDjgO/UUeRcQAVBxkXBJVVkJ2AIDuEJUBC1u4kvXfX74+6t27drft20p10p7/v16tft7uWU6c61cpTzznPEclDhehk1LvttsL7+l4XvT+pKtcv9nPcw8CTwM8580ynYv1NNw30Wo8DH+Rs/cY38h27EWj2/NzNI484VeZff917XC/wEeCfcIL3/iiTOWT6CXgHO2AH3Gx6X0vMZfP7/SQSiUHpz4wZM1iwYMGAl3lLmTlz5qBl/KVI06bB7363e8udjUSlpXu7ByIiso9Tpl1GvdNP39s92LaL5/UA1wP1wLPAC0Dm/OTckao7k+ekzt8KlLF4sbMlc/32d3GC+2ZgM5A5LFv2nL3xOmTu3Lm0tra666v35bjjjmPt2rV0dhZezC611vpA7M6Q9nPPPZff/va3rFu3brfbEunTRz4Cf/hD3/OoREREdoOCdpG9bleDibeAv+JkxA8v8pwtnu9fwAnG08W6nn9+F7sCKNM+dEp3sVL87vD5fMTj8YJLvnkdcMABHHDAAfziF7/I2XfyySezfft2ampqBtyH3Qm0999/f2bMmMG3NQxZhpplwS237O1eiIjIPkxBu4xaO3c6y5QNjl6crHVFnn2FhujaOEHzrgYm25KfAxnD7+3L5uTn+iKO1TDjvaliLwTt559/Pi+++CIHH3zwbrWTvS77QOxuxXnvMP1oocIVIiIiIsOc5rTLqPXlL8NFFw30rG7P996CcL8CfgA808e52UOH7wb+A6cafH/XbAPew3k5AM48+fcLHF/oJcBjwK1k3kNfvIF6oTY/AP6S7J8y7UPFzFOAbqjV1taybNmyvEXlChnsOeQf/vCHicVinLwbaw6eeeaZTJo0aUDV9UVERESGE2XaZdS6557sLR042ec68meWXwP+B2coejtOEPxpoBJ4KXnMI8DcPOeuA/4LWAAcm9z2aPLzeWB6Hz39LvAcMAk4DAgBG3Dmmof7OC/bH4EWYG2y/21FnN+N87vIDsh7cd753ZD8uQVYOYC+SLFMwD9hwt7uRlF2ZQh8XxobG7nkkkt262XA5MmT3XXlRUREREYiBe0yauXGAT/CCdpPB8YBbwMTgdRi579Lft7rOed+4EM4QXRVoSsljwN4gnTQ7tVJ7nruf8JZaq0TJ6tehpOdP95zzK5kt18HygnwKrW0sIUp7Mh7XDvOywZIZ/gB7sGpVv8pz7b3dqEfUgwLqK6t7fe44WDRokUcdthhTJo0adDaVAV4ERERGe0UtMuolVt0LTXH+zngAZxA+TBgWXJ7O05WvA4owanE3gP8BGe4+o7kvmK8BWzCmQPfDPwN8Fbp3pzcltIFPAQkcF4SQPHD3LM5gX6QrbQCZbxYIGjfnHerU/wO4Pa87crgmzBCMu3BYJDVq1fv9lz0XbluR0fHiPk9iYiIiAyEgnaRvFLzxf9OOmh/Dmc9dYCpOMPpG4HtyW2bcDLu68gMwPO5EWd4fBinEFwD6aw2ZBaX24BT6T2M8+KgHSfz3YoTuHuz4OCsw34rcBTO0P38OnHGAPQUPMKb4cy+BjgjAfYrePZQ89FX3/MwTejNdx/DS77JCEOxDvtQ2dMBO8DFF19Ma2srZWVle/zaIiIiIkNNhehEXDuBd3AC5p1Z+3qBV0gXk2vFmRP+evLnbpyMeQ9OQO7VgRPwtyR/fiL5uSl5Xn8Z6heSx+3ECdh7cAL8dpyXC9lD0+8EXgb+u592nZZyw9ge4Dc4c/hT1pK/8F2rpwV72NeYH+798wH5yr5Zlt6v9iUYDCpgFxERkX2W/ktQxJUKprfjVEWfgzOPHODnOEu6teKEVW/gBM1xoBoniLaAN3HmwXckz+sGvg28C8SApThz41Pvy3roO1+8HSe478IJ6VpwgmlvqG0X+L7F830H8CrOsHxIZfK7Sc/YT3s2+fV+8jwD+HecYnlf9bTXgjO3vRZnPv8eHh7v90NX8cvdBS0Lu7Mzp4b/cGIBRp77isfje6dDIiIiIrLXKWgXKehZIFUN+8msfalM/DukA+HUz1XAbcmfN+LMdd+JE7SDEwyX4oTM7yf3dZKZY90IbAHuJ8gmeumgCz9Osbr7cdZo7yJEK12Y9NCbvE4Hue7DmaP/LukXBBvdvblhb+rebJyXFOAM4e8EvoTzIqMnua00+bm5wLUHgc8HPQMaCJ9XvmHnfQoEoLO4EN8wTewhHHp/9NFHD1nbIiIiIjK8aXi8SJ/+jpNh30b+jPYOnKHjXk+THlqevdb5uzjZ8yeS5/Ykj2lPHpPKYN+UvHYLtXyACQTo8rQB0EUNG6llA05xuHXAU+SGpvcmt231XCcdYOaGmt7zO5JfNs4ogr+TDtLXk87mvwl8K/N/UEzTCbj7k70OuM+HP9z/Una+AVQV9wWDTmZ+IIpo3x8IOP23rAH9j6kv6xOSWfYCxzc1NQ2gdRERERHZlyjTLpKhF6dCvB8no9wJfB9njngXAdoJsY0WbJwQy8nERthBBVvZgE0nUdLrmHttxxmivpX0sPvsAPtfcDLwuRlewz3eyYSbGeH2M559TwLjk9d/GCegr8OZc9+dvPZ2ClvjXtGkGxsD253nX4UzXD9VNG8LTkX7NzOb8PnAsvrPkOcL6i2LcCxGV1tbn6dGAwE6i8mEBwJYwaAThEci0Nqa3pcKzO0ic/CGgc+2MyY0BIJBent66M3qi0Xh+v5+wBcKQXu725Y3YM8YFTCCitCJiIiIyOBTpl0EcJZTAycg3YgT4PaSGVR3E6aDMndfSjsRNtNNLyW8jRPQetdyT0kFdTbpYertZM5p78bJbL9J/ortkB6y7oTRLW6b4BSlex8n4w7w5+Tnq6RfBtj0PZR9CwBxtuGnh0BG6PkBzkiCDeTmhVsHPqvdsjKLrPn9BINB/H5/Ogudpxr5gHLmycDc5/MRyZ4bnsqU5+H3+8Hvz1wn3LKwfL709Q0D0zQJhcOFRxXkq6aeSGAGg3jHE/hwfqPZ65IHgsFCdyYiIiIio4CCdhEA/pL89AbQ2UFzvuHxNrCeLpxa8m30Js9bhxMYZwf+4ATOb2LSTZgWfHQxjRep593ksU8Db+Nk5HPL1Jl0YyVfAKTmo0fYQZidOAG90ydHm+eoHTgvCfKtyp7q4ybgIXy8jo9NeY57L9kv70z4luT2TRlhvGma+QPWLD5vsOsJWI1IBDMYzGnDxAlw/XnaLjS83O/3U1NTU2AvzqgAb5+S/TJ8PqKequSGaUI0mlO8LxqNFh6u7/eDZ3h+IBDIO/Q+CNQDiayXCIGBDusXERERkX2KgnYZxXYAt+MUj/NKDQXf5tlmA+1uaBugixgtFJ4b3gv8EngMJ/PtHeq9E9hOnJ1AL1FaidDOZF5L9ue55HFORj17IHsD7xOlDZ8nnLfowaQbJ4BOLSPXiTM0/o2s3qUCe6+bktv+yAL+xgH8D90ZS8mllrNL3Vt78jpdyfY/wDtE3jQMJ4NuGG422wqF8mbIo5FI3v8hCoXD2J7g1oez9JnfMAiGw0XNOfdasWJFxs8ZZ/t8TmCd+jHZtt/vp7621t1eEo/nZMJJ9qustDRnu+F9IWGaEAhg9jFfvwLwRyKY3msUUxdARERERPZZmtMuo9jvcOaqr8HJjG8nHcx2JX9OBdtbgC66cELWIF2YQJSd7CThtmi457eSXsvdxHlBEHCPSunFycen975Behm4aN5ep0JyM2tbD+D39dLT00Mvm4DfJ++rh8wXEKnicg6nx2+SGmjfTTvrAD8bPfn0NtJV5TuTPe8lM+Pe7b4a8Jkm4XDYmXNuGASDQYxuZ5h9drV60zTxkztgPyM49vsxACscpqKykurqat578cX0/iIqzMfjcaqqqtixYQM2zkuA1IQF0zTp7e11AuveXoxkYB2JRDKG7+eE68lsv2EYEA5jBoP02rbzQsE0CVgWHZ657kby+Egkgm3b9BoGbNvm7Cwpwaiuht5e2Lgx5xoiIiIiMjrpvwZlRNu4Eb7+dXj77YGfu2TJluR3j+Isi7Yh+ZUK/rzZ8Xas5OD3VtLBXsDKzFjb2Mmh671AN3624Qwn78DJRqfWes/MdW/HycdnznXPvwb5TjIDXO8MebOnCz/dWGzFedHQgxPS7yQ3u+7VibPm+hu8ihO+t2Xcf/a5TgBu0EOEZvw0Ax+4RxmpTLvn53yZccuy3EAWnOA5XyY7laXGMAiFQgSDQSfbPgA+nw/LsrBw5sR7r9LY2EgsFnNeDgSDmIEA0WiUUCiU0e9IOIxlWYQqKpwh9ckseKrPAe/xpul+b5om0WjUGRqPM0TelyrWl1ReWYlRX5/T76DmtIuIiIiMagraZUQ74wxYvRoOP3zg5wYCqRDTCYF9tOLLCMltnBz4owCYyZA0N5S23Znr3UCQNqCHIF34acdwz+jCGWbejo/2IlY1dzLi/RV3y1ehPBHpJb3+u+05slBxu0eB7wD3ee4vXfDOyHOeSScxdtBDap655zfjmYedCt5DnrnavnAYAgFKSkooKy+HeBwCAWcJtSz+ZHG6FDtZ6T1UWelWqe9v+TfDMJzCcpAebu4NmMvLnaAdJxtumib19fXOvHZP2+FQiFgshuH348+q6l5TU+Nm5r3nWJaF3+8nHA5nzN/3+XzpPkHBlxBVVVV93puIiIiI7NsUtMuIds89zufrr+9uSzspYysW3VgZpd/eIT3E3OFd2KvHtoE2Oj3bu4GS5FBzZxX2dNhtml2YdGFlhew7Sa+gnuYUjctcSCw7s58/f27bNpGId69NgFbi7MSkOzPABpxh+a9hmtvyXsl2A/j0Vj89eRamc2QHreAMmXf3J7PQlmVRV1fnZKTzBN7+ZPV2X5553cFQyM12W8kh9l7e8D+RSDBt2jSi0ShEoxAOO19J+dr3+XzU1NRkBNZe0WgUwzCIRCJEo1Gi0Shnn302lmW5AXo0GnWDeDtrWTnDMCivrMQfDGZk7SE9b8nw/P5EREREZHRS0C5DbuNG+OCDvd2L/nSl5zcbvRj0EHaz7jtIz+fOnPXuBGL958wdvfj9OzHy5MadMndePclrd2UEzyEy1y4vdGUbMM0efD4w6aGEje5w8AitWDmZ8y5gKz7ftgItduAM92/BotOtXl9IOBIhHA67w8G9TNJBvWEYzJ49O+9xgUAA0zSJx+POMPU8+1PBdsiyciq6p+43GAy6X1OmTHGL41l+P36/P6OdlFR2PxAIMHvmzHTfPf22LMsd8l5SUuLcdzhMJBKhvr6eMWPGMNNzrjf4Nk2T6dOnM3fuXHyhEIFwOONFRyIUIkihqgYiIiIiMpooaJdBZdvQ7YlJu7uhuhpqaqCj2Ni2D2vWwKmnwiuv7H5b6cxnZgBqAAl2EKWVEJuTW3tzipD1AJ29PRieQNr27Evx0YNBDz5fL0Z3sb+E1C8xMzvrz1kALr/e3l66upxsepDOjLOyc+zOfe3IuVY2g47kMHgbXz/HmoZBZWWlO0c9FApRngxsU+FxKkidN2+eO4Q8paysjFAoRFNTE42Nje6+QCDgnldbW5s3Q56arx5Mfe+ZVw5Ohjw1/D2RSBCPx919qX6VlJSkz/NcY+yYMcTjccqSy8AZySJ7XoFAgMrKSmbNmsXs2bPd7dFo1A3u6+vrWbhwIYlEwu1bxtD5ykqMQICop3K9iIiIiIxOGnc5QvX0wIYNkKdu1V61dKkzVP31150VtFpa0vu2bIG6ut1rf/585/O55+Dll3evrfRo5fWEPPO3UztswKIb2EGE1oLhciDPLHfvFhMI0A09qTZzdWZtd4qh9zebvbDuZCV0w0jXee9bF2ARCtl05q1/139fvNl7fzJLnsq2V1ZW0tuWfrkRCoWcyvLA0qVLAWc4um3bzvJpZWVUVlZSW1tLLBajpqYG0zTZtClz7XjTNAkEAkQMwx114MN5aRImPRLBGxT7/X4qKio4/PDDufvuu2lra8Pn8xGPx1m/fj1+v5/S5PJtdXV1TJ42zfkHsW3qJ0xge1cXO3bsoLm5mbKyspx57z5PcbpYLEZTUxM+n4/Gxkaam5vZuXMnCxYsAJyXKykzZ87kzTffBKC0vJxtLS0YKkInIiIiMuop0z6CdHbCP/7hxJTHHw8NDek53bviBz+AZctgx47cfbYN3/oW/OlPsG4dHH00/P73hdu67z740pfgwQfh3Xfh8cdzjxngstp9evXV3W/Dic2d8nFWVuCdWoqtFxuL5j3yh5LKrTtJZZsgnQT7GYZeUE8PdHVhmpnBdqHWgsFewuEeurq25d9PV/IFRnGOOuoolixZ4g5tDwaDBOJxCIUgFHLnedfX12dk2H0+H5FIhCVLlrB48WKmTZtGY2Mj8+fPZ+7cuRxyyCFYlpWRcTdNM3Nd83gcTJMyYCowdepUli5d6gbRfr+fqVOnEgwGqaqqoqamhrPOOovy8nKCwSCWZVFbW8vcuXOZPHmy0/9olGAsRjQaZe7cuW5fLcvKGLp/3nnn0dDQkOxGnIMPPpiKigr3JUDq/lIZd2+G35uxr6iocL+vVbZdREREZFRTpn0EOekkuPNO+PnPnU+Af/932LTJCfROPnlg7X3uc+k2vvzlzH1/+EN628qV8Oc/O192gYTrrlRvH6h33kl/X6gf+XR3w6pVsHgxXHBBdhtPY7GFLsiYE92J80arF/DRXWDxtaFgY2LTW+D+sjPyBSUzuEY/a5dnntKDYRTOyQ/kxcWcOXPYvHkzBx10EO3t7ViWRWdnJy+//DJhz/z1BQsWEAqFmDlzJq+99hqBQIAFCxZw1lln8ctf/jKn3ZUrV2KaJpZlsXnzZgzDIBAIYBsGNDcnO2oS8vk4ureXEMDy5ZimyZgxYygtLWXSpEmcdNJJPPvss+5ogGnTpmEYRsaLgFSgbZqmW1k+VQUenEz6tGnT8Pv9rFu3DnCGvcdiMVpbW5k3b54b4Bfi8/nctk3TZObMmXR0dBAMBonFYgSDQa666qoB/OZFREREZF+joH0ESQXq116b3rZ1K5x+uvN9W5uTyMy2Ywc0NcHUqfDII7kZ7507c89566309++/v+t9Hkhw3Z8xY3btvNtvh1tucb4yg3YbeJ8IbXRAzvD3/oeUD74gndDV9x9mMflu914GELQDORXOd1VTUxObN29mwoQJALS2trqBrXcoeWpu+Pjx4+nq6qKnp4dJkyZlZKAXLVrEfvvtR3d3N1u2bHGL1lmWRSKRcNozTYhEoLubSCRCV2cnkeScfpJtTZo0iTPOOIPPfe5zVFRU8Oyzz7rXmDp1ap/3k11Bfty4cWzfvp3S0lLa250Sgqml2ebPn09bWxtNTU0Zv89p06bx1FNPufec4i6JFwpRUlLivixIVdZXpl1ERERkdFPQPgJ5C7p5A+7OzvxBezzufD72GJx5prPi1ZIl6f2/+Y0z1H7lSie4BzfOKcoddxR/7K7KjiX7Gmpv25n7UwnYbD09PcCwL2u/5/T2DtochkQiwWmnncZtt93mbksNky+G97hIJEJT8sHcsmVLxnHeFwD4/VjJOe5WRYXzME+blnHs9OnT855bXl4OONXfu7u7M6q+p4ayA6xatYrKykpuuOEGNm3ahM/no6mpibFjx3J4criJZVnE43G3oF1KPB5nyZIlPPDAA+4227apqamhq6vLPfaggw7ipZdeUrAuIiIiIoCC9hGp3bM22NNPp7/3BradnXDooblzy2+91fn8r/8C+CvwAi++uIoLLgjxta+ll2YbSNB+0kmF93n7VGw82NPjFK1LJi4Bd7R3v047Dd58Ex59NL3sdaHrtrVtAjYXvWDbPq2zc1CHRZimyYwZM2hoaOC6664jkUiwefNmwuEwbW1t7pJpkBmgT5kyhYULFzJ27Fh3mzdbbfTzEPl8PhYtWkRzczMceOCA+nzooYfy9ttvM3/+fMrLyzn22GNpbW1l3LhxXHPNNUSjUWpqajLmm4MTpH/ta1/L22YwGGThwoVuFf18/Q+Hw+6Qe3BGHxx66KG8vztDXERERERkn6GgfYR4++3096+/3v/xf/5z/mJwmVJV7B4FlrJxo/NTczPccMPA++iVL7b65Cfh85+HI47o+9zjjoO77nIq47/7rrMte5S3bcP/+3+55/76187nk0/CwoV9X6etbRt7ZxD8MDSY8xhIB9elpaV88YtfZPv27Xzzm9/E7/djmia2bbtD4AOBAMcddxz33nsvpaWlrFixYkDXyDZ16lReeOGFAff5K1/5Clu3bnWz7lOmTAGgs7OTU045BSDvEnOFpCrDewPybIXuITWPvrOz0x12LyIiIiKjk4L2fYg37io0JDy/zIj4vPPg739P/7wrI6YPPRRefBG8Scnf/c75uugi+OxnYeJEZ/ubbzpzzXfudIrF3XWXs/2995x5+uFw/kz7Zz6T+fP//m/6+2Ji0K7NawjQ3v+BMmDeOenRaJSdO3e6a6Pbtk04HGb79u3uMfX19e5c7mzFzLPvKzAuZPr06Tz66KPuMPRQKERlZWXOcX6/n2g0Snd3N/HUXJOkvjL/PVlvmuLxOM3NzW7hOYBjjjmGF198kUmTJmUce9JJJ7Fjxw6WLl06oBcFIiIiIrLvUdA+Qgz0v9s7BjTm+6/AYpyVreG3vwVnKbQ/A4309o4H7gPmAA1Ft/qpT8GvfpW7/brr4Je/dIJycCq7P/ig871nui8AXV1O0F5MPbVTT01//+Uvw913Oy8OCgltfJpBXIVOPAoFs6ntkyZNcgvTQd9Bd6Gg3Vsczrvs2owZM6itrWX9+vV99rGiooIlS5ZkvGDI13/DMLj00kuxbTsjgG5sbARg//33z9t+Kmi/+OKLaWtrY/v27fzP//wPCxYscO+ppqaGRYsW5fy+ampquOSSS/qdDiAiIiIi+z4F7SPEQIP2gcxJdwL0O4CT2bw5mMxSvwj8DYBt2+YCzwCP8/Wvf40//AHuvdcpaJfWCmwGmtwt7e2F56J7p+t6Yrcc7e2QSBQ/pz3lvvvAs7KY7GHZgXB/2fLp06czf/58t+CcV6FMc2NjI5FIhHA4zOTJk3nrrbfw+/2cdtpp/PrXv84I2gN5HoalS5e6c9j7k68PEydOZOnSpSzxVnX0SA2PTyQSJBIJampquOKKK3jrrbe45ZZb3JcO/b3gEBEREZHRTUH7COHEDDuBt4Gp5Fs12xvYWgP6l30beAvYybJln0gG7Tvcva2t6Qrrq1c7n9dfD5dd5m3jOpxVxM8CJrn9KWaYel8vGB5+2BkyX2TR8X49+SRceCH8y784rypkaOQLOLO3eQN50zQ5/vjjM/YfccQRvPDCCxxYoKCcz+ejuroacCrMe4ede82bN49FixblbI/FYnwme45FH/3Pd0xtbW3Blwo1NTU524LBIJMnT+bcc8+lsrKSt73FKpJC+ZaAEBEREZFRS0H7COHEBT8CmoGjgINzjkkF7S+8AE88MZDW1wM1wDqeey43Q52viPWXvuQUHL/yytSWzuTnK6SC9p6evjPkn/iEU8W+r/jo5JOdz82b+72Johx+OGzf7sy5n2UobB8q2Uu7eQPbQlXUsx166KEceuihBfcXmgOf7YQTTijquF2R7z7OP/98XnvtNQ466KCC56Sq40+dOpXZs2fT2NhIIpHgwQcf5OTUQy8iIiIigoL2EcOJeVLV5V4kX9CeSlzOmDEYV/QGIzawBUh4fl7HV79ax5VXFn6E1qxxMtuF/OQn8Nxz8Oqr/ffGWxhvd3hqn+G3OwsfKLtlzpw5GT9XVFQwY8YMNm/enDe7vCtOO+001qxZw/7778+f//znQWlzMNTW1ha9xrppmhlB+jTPuvIiIiIiIpBvjLUMS8XMaX/llb72bgf6Wvc5HaR3dmb+DGuBvwOpReEfBn4C/LLfPvW1hjsUsyydY9my4o4bCIXsQyd7yLhhGCxfvpwJEyZkbNsdwWCQf/3Xf+WjH/2o224kEgGKqzjfl2L7VlJSslvXERERERHpjzLtI4QTA+0ANgL5s3iHHAIbNhRq4V+TnxcAb+RpIzvI8QYtycXSaU1+Ppb8fDXvWumDoyfZh6F7r7RtyFqWfLKHzO9uYO21cuVK1qxZ465pXmh++2A566yz2LZtG3V1dUN6HRERERERBe0jhFOsLTXWvHB279xz+2vpb8CaAV69cEBfoI7XbuoFrgX8wOegz4XZbOADoIriA3wbsBlgQXrZTdXV1Rx88MG88847g972nDlzuPrqq6mvrwecyvDNzc05w/QHS/a66iIiIiIiQ0VB+wiROdq4cFW2O+/0/tQDZI+r31bkFQc6dHkbzrz3/GtWD7ytVPX6bpzgvZAHgPuB+cDxfRzn9XNgE+272DvZdUcddRQPP/wwmzdvxhrYEgdAevh7NsMwmDVrlvtzOBzm9NNP3+V+ark1ERERERkuFLSPEOmgvRcnGO/Py8A3gWNxAulunH/uQsFI9vbsn7vo+3F5Jvn5AFAKFK763T9vZr+/4On+5OfTFB+0vwE4dyR73qmnnspf//pXamtrGTdu3IDOnTp1KgceeCANDQ1D0rdJkybx2muvFVxmTkRERERkT1PQPkI4ib+NOPPLe3BCzi1ANfkD228AzwLP4QSzzwHzCrVeYHtzct+25HXzZzkz3Q28BEym0Nz7wbUdp3+Nnm3P4RTPO5bckQayt82cOZOZM2fS29s74Iy2YRgcc8wxQ9QzOP300/nggw80V11EREREhg1Vjx9RUgXhXgFuBP4fToCcTyvwTnJ/F04A/i7pKvL9FQHrwJn7/jTOy4JUm30dn1qSbgdO4JzyD+D55PdtOIXsdlAcbz87gU3AI8l2AJ4CXgfe8xz3v8l+P1vkNWRvME1z2A1DtyyL+vr6YdcvERERERm9lGkfsVJB6jPA9Dz7vUPoO4CdOMHyRtLz2rOziVuB14BJpIPibPcBLXm2p5aTC+AE938AjsJ5YXBbct8k4Dc4Lx2eAT5V4BqFXih8M3l9P3AMsIz0vP3US4B3cUYglNP3S4Z9lGHAIFZl74+P4iZriIiIiIjIrlGmfZ/wAPBQ1jbb8/k+TjD7DpBaE64FWI+TBe9Nfj0L/A9OwG7naSt1rb504lRzT5V56/bs68IJ2KHvNeO9sl8+vIUTjP8K+CLp+0n5Mc6a8h3Jc9+gUFi550LbPcfKWlZtyK+HJiCIiIiIiAwlBe0jXjtO9vkvOAFzind4bzNOpr2D9BB7cIbOb8QZcv4a6ex6B04Qv9PTZi9OmJsvy55PZ55thUq/9eIE1x2ebduS13o6+bONMyy+B2dEwHqcFwKdnv3ZQ+kfAP4b53eTq2MwBpr4+6psX8S5gYDzae7Gn2IgADj/4kay3f5aK2rwdxFDxAPFtiUiIiIiIrtEQfsIkj+u2+L53hu0eoeGt+EEzL3kHzK+GSdo9mat1+ME9O8lz29Jfr46wF57+5QaDZCd+X4IJ7i+3nO9Z3AC9tRLhlQ7Oyg8Hz4V0O/EebTfSm5/rMDxBcJN3x7KHZumExjvTsAOmcF1qr1w2H2hsMtBdRFBu4EzEUFERERERIaGgvYRb5Pnext4EXiczPXY38MJ2PF8eu0gd7D4GzjBbw9O8bpenEx4vkCumIHm3TjF6DYAfyUz4/90sv3fAtdm9b0ZZzRBqt/Z1+qljncJ0I7zYuEenKH3bTgvNDqTfe5Nfr/RPdPMcy+mZcEurB9etN0N0FPNFNjutywMw3AKqZkmWBYBPEPYB1JgrYhjg+EwgbIyAsW3KiIiIiIiA6BCdCNIZn2xbuBNnAHK9Z7tv0x+diaPsckclt6Jk23fSIhmErTzQcZVUlnw3+EE6204S711Zu33eiK5fwchegiSriPvXH8nTiX6KM58982k57xDOqgG+HPy7LeBGuBPyXMu8Vx/OxAE3qWcjYRpZzzv8jI3kc7Ev4nzsqIdWACci5OFn4VTyA4MjJxXAD7TxDYM7EEu6BZI3mF31nbLsujuzt7aPx9Zr1+SowOq6+pYv2FDRpvusHnILVTn80GP59/UNKG3N72vt9c5pzffyx4I+f2UhMNs2roVg32zToCIiIiIyN60VzPtDz74ICtXrnSXWLrjjjsy9tu2zVVXXUVdXR3hcJgjjzySV1/NHJ69ZcsWzjzzTBKJBKWlpXz84x9nx45ilxMbqXqBP+PjVeBlnOHlD5EZCLeSDhG3ZZ37FvAWNTzDdF7CT3vy3A6cqu//wAmce3ECfu9c87ez+mIDdyav14lNK9BKmEdxgu0enKA+1Zf1OAH+etLB++ZkO5twMvG/xcmSv5E8p5NUOGjSRpRmTDYDG+minU3AFtqAn5LOyr+GE7y3Jfv8N5xM/NasvqcZgBkYQM54APPZTcAXi7nZawsIhUIDXlos9QdrpwrO+f34fD6wLHyhEIFgkGg0mj4h68WDL/t6luXMiU+NMMgeap/al8WfvIeyCRPoTQb0GrYjIiIiIjL49up/Z+/cuZM5c+Zw/fXX593/3e9+l3//93/nP//zP3nssceIRqMsX76c9vZ0cHrmmWfyj3/8gz//+c/8/ve/58EHH+S8887bU7ewx7S1tYHdiZkMSifyMrN5nDhbSReQuzX52U7fRd96SGXOWzOO34aTob4NP60E6SKYU4guu1r7d/JexaALJ1vvLRSXaiPlPOCfcYbzv0A6yG7zHJ/iBO5+eugB/PQAHe54AidXvC15L53JzxacrPyjOC8EUi8f3iP9QiAtEI9jDGQ+u2mmv4pgBQL4/X4Mih/ikioslz3P3gqFIBjE5/djJYPqeDzOvHnzGDNmTPq4vu4nFaAbhtN+1rE+n895IQDp4e9+v1PoLhzGKimhoraWxkSiyLsREREREZGB2qvD41esWMGKFSvy7rNtm+uuu44rr7ySE044AYD//u//pqamhjvuuIPTTz+dF198kbvuuosnnniCAw44AIAf/OAHHHPMMXzve9+jvr4+b9sjUWdnJxZtmEA3PbTRjQ+o5F22UwaMwZknbuFktr0ZVW+g7LSQ2vqBuzcVUDthvEWzGzL7aMeiK5l37wFexxm6HiWd2c43MDqVHX8L6KQ3Z134zTgF52yclwXeofddeMNx+DapefDpKznf9ZAaY5A9hLsNJ0APEKKVEOvZxpvAOqAcO7vPySA2EAjQ2dHR91Bvnw+/309XV5cTyHZ05B7j90N3NwHbhmgUwzDwBYP4fD5My6IXCIfD+P1+2lta0r33DGE3gZ7UiwHPMPZoNEp7ezuWZRFMthkMBikrK2PGjBm8+eabbN26Nbeont8PhoHV00NvKERP1rB30++nt6fHfRHh9/uxfD5inZ1sTh4TCIXAMPD5fIwdOxZz507ir7zCdrRmu4iIiIjIYBu2I1rXrl3L+vXrOfLII91tJSUlLFy4kEceeQSARx55hNLSUjdgBzjyyCMxTZPHHitUMRw6OjpoaWnJ+BoJUv9YFt3sxMl5b6OD3OXOeshd3zy1Fnv6uHZSOXobgx7PvvaM1qxk1t65fjfOEnM/S37CJH5JnBa84X8PbcB6DHZQzpvE3ex2B7nLwW1Ofm0jXTAu1dedOFnyR6nNGZqfLTto7wLaCNFGiE6qeBOnEJ3T13xBud/vJxaL9V9x3bIwTZNAoeH0Pp8T+AYCmIkEJAvEAURLSmhoagLAMAzi8TiWN1ufWgauz8tb+Hw+AoEAlZWVxONxd19jY2N6iLxpQiyGv6QEgkHnZ5+PQFkZZp4svD8QwIpGCcZiRCIRSktLqaiooJz0ayAz2ddoNIplWcQWLMDnHZIvIiIiIiKDZtgG7evXrwegpqYmY3tNTY27b/369VRXV2fstyyL8vJy95h8vvWtb1FSUuJ+NSUDqOFs4/vv593eQQ+wiQDbPVt7ge3JQLwXk67kUPcuwu689y5acUJoHz0E6cLvBtNP0O0JrDPD2+7kWd04c8XfYwevALhD6Z0h6y3Ag8Rp8QzU78V5TdBK5kuGVJX6Lrzz153jDeBZSA7XT/cpU0/yt0HGEU7g76MHm+zieGvp9Qb5yeA7EokwZswY/EUMeY/FYoTDYSzLwo+nQrtlYSSDbsszT9zv99PY2MiYMWOoqKigsbGRyspKJ5jPLnpnms589VgsvSl5jQBOsO/3+ykrK8M0TfeFQCQSAaCqqopoNEooFAKfj2Ao1O/9gDMk3kq+YJgxYwZ+v58xY8eydO7c9BJ1QCKRwDRNbNumtqGB8ilTsEpKnEYGOE9fREREREQKG7ZB+1C64ooraG5udr/eeeedvd2lfnW8nc4yp0IiZwB5D0FaaeLZZJC+FWd+eAcBurPmpTsCfhvDaM4ZQJ9+GLYUyEPncy9t5ObOw2zHz2Yys9+257MNJ1h/O3l2oYHV24B3yCwgV8za4+05W5yB/2/jFLlrSS/5Zpr4PEulpeaduwpkvi3LIhQKEQqFSM3qNgB8PsLhMIlEwp0T7mz2MW/ePMaPHw9AZWUlU6ZMIRwO570D07IIRyJuZtswDIKk/52CwSCGYVBWVkZTUxMNDQ1u5r+hoYGxY8cS8wT9BUcFeARTBe6Sx6cy+OXHH08okcBKrf2eDMwNTxBvhUJFjRIQEREREZHiDdugvba2FoANGzILn23YsMHdV1tbywcfZC5Y1t3dzZYtW9xj8gkGgyQSiYyv4c70BEKpwfDOgHYnEG4lNYz9MWAHpidYzh40Hu5qwW930J1nn6ML27Mnc0Z8NyGaPVvzL1dmA1W8TK9nv98d9t4JdBAOv48zq76TkFt8zjk7QAcWnaSD9daca/iyiu356CBAe/LlRXrKg51xl62kivQZydDcn5yf7h5v25mBZ3LNcx/pqummadLU1ERJSQnRaJTU0SZOsGsYBuFwOKM6vGmaGddJqaqqyq3qDu6xbhuhkNOvaBSfz0ckEmHixIn4kxXkvW37fD4mTpyY8dIgFYCntqXa9fl87lD/VKY+GAxSVVVFXV0d48ePxzAMYrEYoVAo40VAb29vRqCPYSjTLiIiIiIyiIZt0D5+/Hhqa2u555573G0tLS089thjLFq0CIBFixaxbds2nnrqKfeYe++9l97eXhYuXLjH+zyUfHmW3YL0P2A6TNrUZzupYN+ixw2fswVpTVZnz7evEz9dyRcEhdcX7wa2084Xv/i0uy1COwm2kw7OW4B2YmwiSAuB5ND4IJ0YgM9dBg4qynMz/1bWK4cYrYSTIwzSryM66aA1Y8Z+Nl+eofBWJOIs/5YMwCsrK7FID4EPBAJYlkVDQ0PBZduyt0ejUerq6pg+fbq7ze/3Y5pmRtAeTQbl5eXlLFmyxK0Oj2FAIkEkkaCxsTFnakjG7yIWwzAM5wVEUjAYJBKJuMXrQskh88FgkJKSEhobG92lE8PhMD6fjwULFjB27Fg+/vGPM2PGDCorK2loaHDb7O3tZerUqVRUVOR9ISEiIiIiIrtnrwbtO3bs4JlnnuGZZ54BnOJzzzzzDG+//TaGYXDRRRfxz//8z/z2t7/lueee42Mf+xj19fWceOKJAEyfPp0PfehDfPKTn+Txxx/n4Ycf5rOf/Synn376PlU5HqDbE3zlCxFtwHCLzRVm4+SZ+xr8Xpo1FD37fCdf3okzzLyvwL2XR9fclrHNcFuBrq7UKwNnZr4Bnnn1Kc78fJ/VVXARO2/fUvcVpBMfXQRpx6DL84oh1Uov3Z4XE6mh8dXV1cyePZtINOoUk0utq25ZGMk+en//M2fOdL5JFmJLLRmXGvKeymqnguW5c+dy+eWXM2PGDEpKSjIKLaZUVFQQiUSorq4mGAy6w+NTAoEAS5cuda+R76VBQ0MDs2fPpq4uXbG/oqICy7IIh8M0NTURDoedof2JBMFgkOrqahobG93jvcP2m5qaqK2tpba2Nmc4v8/nY9asWZSXl2NZVlHD8EVEREREpDh7NWh/8sknmTdvHvPmzQPg4osvZt68eVx11VUAXHbZZVx44YWcd955LFiwgB07dnDXXXe5GUKAm2++mWnTpnHEEUdwzDHHcMghh/CjH/1or9zPUOroTgfHNpkl1yC18vr/4FRHt/H18y9bOLTP3VN4sHMvztrqBRjw8KOP5mwO0Jkc+g7QQ7dnBn1ut7uxrG7a29rYUvhKQLo8XkoqE+/L+4oi8+VAJBIhkUi4FdO9gbDlGeWQ3b/99tvPKcpWWYkVDuOLRqmtrXWnXKQqrANMnTqVE044Ab/fT1VVFfPmzWPy5MnMmzePoCcwr6urY+rUqW5wnBqO7u1T6ns7q4DdMccc4/TTNFmyZAnl5eXO78DnIxqNcuCBB9LU1ER9fT2GYVCSKh4HLFmyJKOt2tpaDjvsMFatWpVzrbKyMvx+PxMmTHC3BYNBrGRVfRERERERGRx7dZ32pUuX5gQdXoZh8PWvf52vf/3rBY8pLy/nlltuGYruDSs+77zhPFqBcKidtvZuLLowe4stJJcpSFdOiJtvxXcfPZg+H909hbP2vbZNd0+P+5D14AS9ztD3dnoIklr5vXBvOwCDnp78oXe2XV0nPJFI0NaWXAAv65n0ztmO4ryqmDBxIgDHHXccN910E2VlZXR0dREOh6mvr2fnzp20trZimqYbfHuHxaeMHTuWkpIS93fsx3nuU4F+ah58at450Ocw9NLSUvf7xYsXM3PmTDZs2EBnp/OSIhqNEk2uGR8MBqmtrWXLli3utbwBt2EYLFu2zP2517Omezwep6ysjDFjxqR/N9EokUgkYx69iIiIiIjsHqXERoie3r6HvYNTo8zvL5RZHlwm4OtxAsHsrL9XT3c3HaSz4NmD3/uvWebcS3d34WH4A2NjGpnr1Uf9frcAG8CyZcvcwNPv9xMOh93A3QTCkLEuOjgBbkNDA42NjW5m3FsMsaSkJCMgvuiiizjrrLPSmepAIGPO/KRJk0gkEpx66qmce+65VFZWUlZWRllZWc7Uj0KZbcuymDFjBpFIJL1ue1KqwNy4ceNYtGgRlZWVgDOEvhDvPc+fP5+GhgaOO+449yWHYRgEAgEF7SIiIiIig2ivZtqleF09xeWQTXP3A/b+5o4PrLG+WzMM+p5gP8iCvh7o6aHb874qHgqxYsUKnnjiCaqrqznooIM48MADefTRR+nq6iKaHPLe8vbb0N5Ooqwsb9uTJk3ivffec39OTeOIx+OZFdZxMuLerDixGLS1QfK4U045hTVr1nDYYYfh9/u544472Lx5Mz6fL2OYvG3blJeX09nZ2WfAnXr54M3Wn3POObz00kusX7/ePS5VdG/Tpk0cd9xxGW0sXLiQrVu3Ul9fz4EHHsiUKVMyKsmDM2Kh0BJ2IiIiIiIycAraRwjLM4+/T31MNyjWoAbtfTCNXrB7+xzuEaQLe7CS7ADJlx8WvfQkr1wXj/PP//zP/PrXv6a3t5dYLMbs2bN5//33iUQi7Ny5E4Dxc+ZQ5fezJfk7DoVCGUPpKyoqMAyD5uZmd7m3YDBIZWUlra3OknVWnlUADjjgAG688UZ84TAkM/YrVqxg+fLlmKZJeXk5sViMzZs3572lsrIyamtrM18CJE2cOJFgMEhHRwdVVVU0NjZSUVHBxIkTOfbYY3nppZcyjjdNk1gsRiQS4YADDsjYt2LFCtauXet+n5q3P3PmTB588EFisVjGEHoREREREdl9CtpHCLOf5bQMoKe1FQahCNhAVtmO+9vo3MUo3+jqopiB1EO16reRLFQXSAbSp556qruvvr6egw46iGAwyLp16+jo6ODdd99l3tKldHR0MG/ePA4++OC87abmhh966KE89thjdHR0sHLlSi644IK8Q9mPO+44vv71r9Pc3Aykh6Gnjj3ggAPc7Hr2+bZts3TpUkpLS1m4cCEbN27M2B8IBDjooIMwDIOxY8fy1ltvEQgEWLlyZc4Qf4Djjz+eH/7wh8yfPz9nX77jAaqrq7nkkku44YYb+OMf/5j3GBERERER2TUK2kcIXz/LaHWCk0UehEz7QHR35VvpfWRIvQwotM56aph3qtjau+++CzgZ9g9/+MMAdHZ2Ul1dzY4dOzj99NO5/fbbaWxs5LHHHiMQCHDRRRfx6KOPcumll+bMKy+Wz+djv/32Y+PGjZR5huaPGzeOrVu3cswxx7hZ73zLreW7v+xie6mfjznmGGbOnElTU9OA+hiPx9lvv/144okn3PnxIiIiIiKy+xS0jxB7srjXQCqwj+TB0KlQ1rcboxMCgQA33nijWyW+paWFxx57jMWLF3PZZZcRCARYsWJFv+14g+iqqqqc/atXr+ZjH/sYHR3psn/jxo1j5syZbsAOTgX3Sy65xA3eq6ur814jtRRc6h5SfD4f48eP77e/+fj9/vS69SIiIiIiMihUPX6E6GuZrwyDMKd4V5dNG6kKZdqLFY1G3ax8KsscCATyZr0LSRWRCwaDLFiwIGd/TU1NRoX7lHx99xa+a2pq4rTTTuP888/POCa1PntpaSllZWUceOCBnHzyyX320VtML19fpk6dWnCfiIiIiIjsGmXaRwgtozV0OvO86NjVQH7//fdn586dA85Wh8NhZ712w2DlypV5j8ke0l5oW7YZM2YU3HfOOefwzDPPsGDBgn6H75umyeWXX45t23kL6o0fP55PfepTGUP4RURERERk9yjTPkLkC5JkcAxmFQDTNFm6dCljx44d0HljxowhFAq5Fejz8QboqWHohYrhFau0tJSlS5cWPd8+FAr1uaRbXV2du9SdiIiIiIjsPkWCI4Qy7UNnoFn1YrLbA1VfX09dXV3R1z355JM59NBDM+as9+fYY4/lpz/9KUuWLNnlfoqIiIiIyJ6loH2EUNA+dHanEN1gGT9+PNOmTXPnmuczY8YMHnnkESZMmIBpmtTU1AzoGjU1NVx++eW7PYdfRERERET2HAXtI4SC9qFj9hPEWpZFd3e3u/TbUAgGg9TW1vZ5TGVlJUuWLMm71nuxFLCLiIiIiIwsez/FKEVRsDV0Otra+tz/la98hc985jO7vBRaMQ4//HCqqqr6XB5u9uzZmKbZ7zB6ERERERHZdyjTPkL4fD4sw6B7COZTj3ZmZ2fOtgkTJrB27VpM08QwDKqrq4f0xUk8HueCCy7o85hly5bR2NjIuHHjhqwfIiIiIiIyvChoHyEMw9CwiCHSnWe4+eLFi4nFYkOaXR8oy7KYPn363u6GiIiIiIjsQQraR4jdmccsfctXiM7n8zFv3ry90BsREREREZE0RYIjhOa0D53hUD1eREREREQkH0UrI4TP5wPNZx8SViy2t7sgIiIiIiKSl4L2EcI0TXx7Kdu+r8+hqNIweBERERERGaYUtI8QhmFgGgbBPXzdwB6+3p5mAP7Avn6XIiIiIiIyUiloHyFM0wTTxAB8u9oGYFgW+IpvYTQ8IMXWCwiHwwDU1tYOZXdERERERERc+/rI531LNAo7d0J39663YVnO3PiensHrV3b7u9O/XWEYBef7+wAb6O3z9OKC9o9//OM89thjHHLIIQPuooiIiIiIyK4YDYnUfYJhGE6GPBoFT7XzgcxyN0gWtAMnuC7SbpW/sywnqC60bw/o83fk99Pe3l5UO5WVlRx77LGUlJQMSr9ERERERET6o6B9BIlEIrt1vi8cxufzEQgEnBcAfr8TOCfndOcEt1nBdjEvCELBYObwfdPcpeA85wy/f8BtFGzLyzQpKyvb5bZFRERERESGkoL2EcIwDCfY7kueueoGOEF5IOAGz2623TSdcwwDAgHs7PMNI+MBSe3tK3gP+v3Eq6qcIDsQwDBNLE/QXuwDl9ETnw+rj3s3+xne3tfeQDDItGnTiuyViIiIiIjInqWgfQQJBtO14wPk+cfzBt2WhQ8oTSScoNwwMAyDUChEPB7PCKQBZ392Njs5DN9M7ncP9fQhH38w6JxrGPh8PqeInnuZ/vP1JlmBdrLvwC5Xz0/dWfZrjfLSUmXaRURERERk2FLQPkIYhsG8efPcIfImzrBvNwhNZsyNUAiCQXyBAJHKSoKegNQwDCZOnMhHPvKR3KAdnG3eoNo0SdTUYMbjWJEIRjAzZDbyVaHPKggXzDrH277f789bCT/fQPh8wb4FhICwz+e+YChUF9+HE/BnP/A+y6JnqIryiYiIiIiI7CYF7SPIuHHjmDBhAuFkwG0CEZJZ6eS21BB6w5Od9krNac8XtGcf7w8EaJo8mXgiQTweJ+Z5AWACRp459sFoFCOZYQcnMM+4vuca0WAQw5OFx7Iw82T8TdPMyNZnMw2jqDnvBrlD5Q3DoLq6ut9zRURERERE9gYF7SOEYRj4/X4OO+ywjO35iqxlB9/eAH3atGkccMABBa/jDapN08Tn89HY2Ehtba0bOAeBiGURicUyHyCfj3hpKaZpEggE3BcI0eT65gCEQvhxMt8+yyLuycQHIxH88ThkvQzw9RGwpwQ8xfT6HICfZ3RAU1NTv+2LiIiIiIjsDQraRxi/358RlNaTO887ld1OBe/eoH3OnDmceOKJ6WJ0HoZhYJmmOzw9FAq5n95lzgzACIcJBAL4Y7H0fHGfj2g0SigUIhKJuEG+6bmWYRiY4bBbqC7oGSKfmnOfvIkifyOOQCBAKJEg6p0OkH2QZUGq/aRwOFz0Ou0iIiIiIiJ7moL2EaJQYBmzLCIlJW4QbpomJSUlBINBotEofr/fDaD9fj+rVq3KGTrv9/uxLMvZjjNPPIYzHD/FnZseCBCtqYHUz6bpBsepIH3+/PmUlJQQj8edvmfeCPj9mMEgsVgMEgn8qSr2yb7E43HwZOdj4TCNjY19/n6i0SixeLxgsG8CIZ8Pw+fD9GT3tea6iIiIiIgMZwraR7rFiyEScQNvcDLeqSrxDQ0NRCIRAoEAtbW1brDuDdp9Pp97brS8nKhhECspyVhibs6cOUSjUcrKy/GFQiQSCSorK0kkEhndCQQC+Hw+EolEOpuf54WDaZpMnTqVcCwG8bg7Jz+VqQ97hsg3lJdTWVnZZ7E5wzCwLCuzP55h9TEgmJwbH7AsAjgjFGbMmNHnr1dERERERGRvUtA+Avm9Q9uz1m8PJzPUtqeKe2lpKSUlJU5mOykWi7nz5FOi0SgNY8dCUxOUlmYMoY9EIpSVlbnbgslMuc/nczPpIb+f8vJySkpKmDdvXsH+pwrVVVZW5hSqM02ThoYGN7Nv4Kz9DjiBfTyOPxLB9NxzKNmnWCxGeXm5sya9aeZk3Q1P/1PLylVUVPRZ5E5ERERERGRvUrQyAlWOHet8kwxcq6qqiEQiRCIRJyONM8w8FfjOnDmTmpoagsGgGyQHg0FCoVDGsPq5c+c652RlxisqKtzvswPceDzuBNY4VdzHjRvHySefnLc6PThZfcMwaGxsJBaLUVVV5e4zDCNjzfQAzlB9f/I+zOSydtHSUvyhkJtxj1gWdXV17L///hx++OEZAbt7J3V1hBOJnJcEhmlqTruIiIiIiAxbCtpHoJLKSkgkMuZ9BwIBQqEQdXV1jB8/nsbGRjfrHg6H3fXdU4F8WVkZNTU1Ge0GAgE36Acn8F+yZAkzZ87kkksuYc6cOTlBe3l5OeAEx/UVFVRVVbFo0aKMY7xBccDvp6KigvLycj772c9mjAiorKx0Rw2UlZVhhkKYwSDlEya4/UkkEpSUlFBaWuoG5EYoRDAYJB6PEwwGKSkpwe/3O20ZBkHTxB8Ou0vH+bNeKChoFxERERGR4UpB+wjy0Y9+lBNPPJGxY8e62XAja4m2o48+msMOO4xEIuHuq6ysJBQKsf/++7vHNjQ0UFtb6xZiMwwjI4AGGDt2LGYyEx2Px9OV3UlXlvdev2HSJM444wz3ZUGKYZpuhfumpib33OwXAN62SkpKIBjEiMWoq6tz95umSSgUco6NxZxq8MnsfGr4/5IlS4hGoyRKSjBjMcKNjdTX1wNO4B/2FKJLtSkiIiIiIjIc5R/DLMPSlClTACfwDQQC7lDvWbNmce+991JXV5c3a+zz+Vi4cGFG0D5lyhSqqqrYvHkzzc3N7nG9vb2MHTuW7u5uGhoaMtqZMWMGv/3tbwGoq6ujtLSUuro6nlmzxjkgEHAz+imp4e4t0Sh0dmJaFnR0AE7A3NDQwNNPP71Lv49oIkFnZycnnnQSPT097suAD33oQ7S3t+P3+3nllVcwDIP58+fz0ksv8dZbb9He3k6E9NB5ZdpFRERERGS4UopxBDJNk0RyfnZ5eTljxozhwx/+MEcddVTe40877TQmT57MEUcckdFGRUUFhmEQiUTw+XxMnjzZXQ7O7/fnBLOHH344DQ0NhMNhLMti8uTJnHjiiU7W3zCorq52M9q1tbWAMx9+3rx5ThG5rPnjtm1nzJdPmT17dsbPhYLq8ePHs3TpUhYsWEA0Gs2431WrVnH11Ve7Q/HD4TClpaWpBgmAu768gnYRERERERmuFLSPQIZhUFtbS1lZGdOmTQNw12LPZ8aMGZx11lkZga1XIBCgtLSUUCjE+PHjATKGuB933HHudf1+v1t5HuCQQw4hEokQCoVYunSpe84nPvEJxowZg2VZHHfccW6gn93H+vr6jKr25eXlrFq1qt/fgWVZWJbFpEmTcoLueDzOKaecQlNTU8553iH+qXtS0C4iIiIiIsOVhsePQJMmTeKhhx5yC6557U4AOmnSJBYvXsz111/vBu3nnHMO48aN6/O8pqYmuru7MyrB19TUMGvWLAKBANOnT2fChAmsXbs259zp06eTSCTcIfrjxo3LGMafLXV/DQ0NHHzwwTn37+WtYG9ZFrZtM3bsWPy2zcb16932vBXrRUREREREhhMF7SNQeXk5Bx98ME888cRutxWPx2lpaaG6upqzzjoL27YZM2YMW7duZcmSJU7Ru36kAvx4PJ6x/ZprrmHr1q2Ew+F0Nfc891JdXU1zczMVFRXMnDkz5xjvi4jUEnXZa8x/7GMf4ze/+Q0rV650twWDQfbff39s23YDeMMwsCIRqKykobqa8847r+AIBBERERERkb1NQfsI1VeGedGiRTz++ONFt7NkyRImJJdVMwyDCy+8EMit7l7I3LlzaW9vp7q6OmN7aWkppaWlbNy4MadvCxcudAvklZWVUV5ezn777ecuSZftwAMP5NVXX6WsrCxvkD1hwgQuueSSjG2WZXHRRRfxk5/8JLfBaBR/VZVbmV5ERERERGQ4UtC+D1q+fDk/+9nPaG1t7ffYefPmMWPGDFasWOFuG+gSaKkCb9lLxqWEw2EmTpxIS0sLoVCII488kkmTJrlLys2ZM4etW7fmVJ73ikQifOhDHyIQCBCPx+nu7i6qb/nmtQeSVe6bmpo0n11ERERERIY1FaIbwfIFyakg9FOf+hTRaJTjjz++zzZKSkr4xCc+kbO822Brampizpw5JBIJvvCFL3DmmWe6Lwc+8YlPsGTJEsrLy1mwYAEAU6dOxTAMYrGYW6iuqamJf/3XfyUSiTBmzJgB9yHVjpGsdD9x4sRBujsREREREZGhoUz7CFRMdnjp0qXMnj07vcxZlv32248XXniByZMnF33dfMuzFSM1533s2LFccsklGQXiABobG1m9ejUtLS1uUbi6ujp3uPyZZ56J3+8nHA4TiUS4/PLL8fv9fOMb3wByK8JnO//883nttdf405/+xJYtW3bpHkRERERERPYGBe37sPLy8oL7TjzxRKZPn86UKVOKbq+srIy5c+fS3NzM1q1biz7P5/PxpS99ySkCZ+V/5Hw+X04V98WLFxMMBnOK06WC9JNOOok1a9ZkLDWXT21tLbW1tfzlL3/J2afh8SIiIiIiMpwpaN/H9FWgzisQCDBr1qwBt5/K3A8kaIfMdd+LFY1G+1yObc6cOcyZM2dAbRaady8iIiIiIjIcaU77COYNQE899VQqKio49dRTh/y6JSUlTJ8+fUivddJJJxGLxTjttNMGrc1zzjmHKVOmsN9++w1amyIiIiIiIkNJmfYRKN889ZkzZ+Zd43woGIbB9OnTh/R6c+bMYfbs2YM6fH3cuHF84xvf4Lbbbut3HryIiIiIiMhwoKB9BFq0aBEtLS0cfPDBPPzwwyxfvnyPXdvn89HT08P48eOH/FpDMd88FAqxbNkyXn/99UFvW0REREREZLApaB+BLMvi2GOPBWDZsmUFi7sNhQsvvJC1a9cye/bsPXZNERERERGR0UpB+wi3JwN2cIbmz5s3L+8+FXkTEREREREZXCpEJyIiIiIiIjJMKWgXERERERERGaYUtIuIiIiIiIgMUwraZdSprKzc210QEREREREpigrRyagTCoW49NJL93gRPxERERERkYFS1CKjUiwW29tdEBERERER6ZeGx8ug0ZJvIiIiIiIig0tBu4iIiIiIiMgwpaBdBo1hGHu7CyIiIiIiIvsUBe0yaDQ8XkREREREZHApaJdBEwqF9nYXRERERERE9imqHi+77dhjj+WDDz5g3Lhxe7srIiIiIiIi+xQF7bLbFixYsLe7ICIiIiIisk/S8HgRERERERGRYUpBu4iIiIiIiMgwpaBdREREREREZJhS0C4iIiIiIiIyTCloFxERERERERmmFLSLiIiIiIiIDFMK2kVERERERESGKQXtIiIiIiIiIsOUgnYRERERERGRYUpBu4iIiIiIiMgwpaBdREREREREZJhS0C4iIiIiIiIyTA3roP1rX/sahmFkfE2bNs3d397ezgUXXEBFRQWxWIxTTjmFDRs27MUei4iIiIiIiAyeYR20A8yYMYP333/f/XrooYfcfV/4whf43e9+x2233cYDDzzAe++9x8knn7wXeysiIiIiIiIyeKy93YH+WJZFbW1tzvbm5mZ+8pOfcMstt3D44YcDcOONNzJ9+nQeffRRDjrooD3dVREREREREZFBNewz7a+++ir19fVMmDCBM888k7fffhuAp556iq6uLo488kj32GnTpjFmzBgeeeSRvdVdERERERERkUEzrDPtCxcu5Gc/+xlTp07l/fff5+qrr+bQQw/l+eefZ/369QQCAUpLSzPOqampYf369X2229HRQUdHh/tzS0vLUHRfREREREREZLcM66B9xYoV7vezZ89m4cKFjB07ll/96leEw+Fdbvdb3/oWV1999WB0UURERERERGTIDOugPVtpaSlTpkzhtdde46ijjqKzs5Nt27ZlZNs3bNiQdw681xVXXMHFF1/s/tzc3MyYMWOUcRcREREREZE9IhV/2rbd53EjKmjfsWMHr7/+OmeffTb7778/fr+fe+65h1NOOQWAl19+mbfffptFixb12U4wGCQYDLo/p35ZTU1NQ9d5ERERERERkSzbt2+npKSk4H7D7i+s34suvfRSVq5cydixY3nvvfdYvXo1zzzzDC+88AJVVVV8+tOf5s477+RnP/sZiUSCCy+8EIC//e1vA7pOb28v7733HvF4HMMwhuJWBkVLSwtNTU288847JBKJvd0dGSH03Miu0HMju0LPjUhx9Lciu0LPzb7Htm22b99OfX09plm4RvywzrSvW7eOM844g82bN1NVVcUhhxzCo48+SlVVFQDXXnstpmlyyimn0NHRwfLly7nhhhsGfB3TNGlsbBzs7g+ZRCKhP1QZMD03siv03Miu0HMjUhz9rciu0HOzb+krw54yrIP2X/ziF33uD4VCXH/99Vx//fV7qEciIiIiIiIie86wX6ddREREREREZLRS0D6CBINBVq9enVFET6Q/em5kV+i5kV2h50akOPpbkV2h52b0GtaF6ERERERERERGM2XaRURERERERIYpBe0iIiIiIiIiw5SCdhEREREREZFhSkG7iIiIiIiIyDCloD2Pb33rWyxYsIB4PE51dTUnnngiL7/8csYx7e3tXHDBBVRUVBCLxTjllFPYsGGDu//ZZ5/ljDPOoKmpiXA4zPTp0/n+97+fc63777+f+fPnEwwGmTRpEj/72c/67Z9t21x11VXU1dURDoc58sgjefXVVzOOueaaa1i8eDGRSITS0tKi7/3vf/87hx56KKFQiKamJr773e9m7P/HP/7BKaecwrhx4zAMg+uuu67otvd1em4KPzcA1113HVOnTiUcDtPU1MQXvvAF2tvbi77Gvmq0Pjft7e2cc845zJo1C8uyOPHEE/P21zCMnK/169cXdY192Wh9bu6//35OOOEE6urqiEajzJ07l5tvvjnjmB//+McceuihlJWVUVZWxpFHHsnjjz9eVPuy7xnpfytvvvkmH//4xxk/fjzhcJiJEyeyevVqOjs7+227v/48+OCDrFy5kvr6egzD4I477ui3zdFCz03h/vT09PDVr341o+1vfOMbqLb5ELMlx/Lly+0bb7zRfv755+1nnnnGPuaYY+wxY8bYO3bscI85//zz7aamJvuee+6xn3zySfuggw6yFy9e7O7/yU9+Yn/uc5+z77//fvv111+3f/7zn9vhcNj+wQ9+4B7zxhtv2JFIxL744ovtF154wf7BD35g+3w++6677uqzf9/+9rftkpIS+4477rCfffZZ+/jjj7fHjx9vt7W1ucdcddVV9r/927/ZF198sV1SUlLUfTc3N9s1NTX2mWeeaT///PP2rbfeaofDYfuHP/yhe8zjjz9uX3rppfatt95q19bW2tdee21RbY8Gem4KPzc333yzHQwG7Ztvvtleu3atfffdd9t1dXX2F77whaKusS8brc/Njh077PPPP9/+0Y9+ZC9fvtw+4YQTco657777bMB++eWX7ffff9/96unpKeoa+7LR+txcc8019pVXXmk//PDD9muvvWZfd911tmma9u9+9zv3mI9+9KP29ddfb69Zs8Z+8cUX7XPOOccuKSmx161bV9Q1ZN8y0v9W/vjHP9rnnHOOfffdd9uvv/66/X//9392dXW1fckll/TZbjH9ufPOO+2vfOUr9u23324D9m9+85uB/Gr3aXpuCvfnmmuusSsqKuzf//739tq1a+3bbrvNjsVi9ve///0B/Y5lYBS0F+GDDz6wAfuBBx6wbdu2t23bZvv9fvu2225zj3nxxRdtwH7kkUcKtvOZz3zGXrZsmfvzZZddZs+YMSPjmI985CP28uXLC7bR29tr19bW2v/yL//ibtu2bZsdDAbtW2+9Nef4G2+8sej/GLrhhhvssrIyu6Ojw932pS99yZ46dWre48eOHaugvQ96btLPzQUXXGAffvjhGeddfPHF9sEHH1zUNUaT0fLceK1atarPoH3r1q0DbnO0GY3PTcoxxxxjn3vuuQX3d3d32/F43L7pppt2+Rqy7xjJfysp3/3ud+3x48cXvsld6I+C9r7puUn359hjj7X/6Z/+KeOYk08+2T7zzDP7bFt2j4bHF6G5uRmA8vJyAJ566im6uro48sgj3WOmTZvGmDFjeOSRR/psJ9UGwCOPPJLRBsDy5cv7bGPt2rWsX78+47ySkhIWLlzY53nFeOSRR1iyZAmBQCCjPy+//DJbt27drbZHIz036edm8eLFPPXUU+4Q1TfeeIM777yTY445ZreuvS8aLc/NQMydO5e6ujqOOuooHn744T123ZFkND832X3O1traSldXV5/HyOixL/yt9PfM72p/pDA9N+l2Fy9ezD333MMrr7wCONMAHnroIVasWNFn27J7rL3dgeGut7eXiy66iIMPPpiZM2cCsH79egKBQM4cvJqamoJzLf/2t7/xy1/+kj/84Q/utvXr11NTU5PTRktLC21tbYTD4Zx2Uu3nO29353muX7+e8ePH57Sb2ldWVrZb7Y8mem4yn5uPfvSjbNq0iUMOOQTbtunu7ub888/ny1/+8m5de18zmp6bYtTV1fGf//mfHHDAAXR0dPBf//VfLF26lMcee4z58+cP+fVHitH83PzqV7/iiSee4Ic//GHBY770pS9RX1+f8x+iMvrsC38rr732Gj/4wQ/43ve+1+e97kp/JD89N5n9ufzyy2lpaWHatGn4fD56enq45pprOPPMM/tsW3aPMu39uOCCC3j++ef5xS9+scttPP/885xwwgmsXr2ao48+uujzbr75ZmKxmPv117/+dZf7kG3GjBluu3ozNvj03GS6//77+eY3v8kNN9zA008/ze23384f/vAHvvGNbwxa3/YFem4yTZ06lU996lPsv//+LF68mJ/+9KcsXryYa6+9dtD6ti8Yrc/Nfffdx7nnnsuPf/xjZsyYkbeNb3/72/ziF7/gN7/5DaFQaND6JiPTSP9beffdd/nQhz7Eaaedxic/+Ul3u7fd888/f8DtSt/03GT61a9+xc0338wtt9zC008/zU033cT3vvc9brrppgH3TYqnTHsfPvvZz/L73/+eBx98kMbGRnd7bW0tnZ2dbNu2LeMN24YNG6itrc1o44UXXuCII47gvPPO48orr8zYV1tbm1FlMtVGIpEgHA5z/PHHs3DhQndfQ0MD77//vntcXV1dxnlz584t+t7uvPNOurq6ANy3eIX6k9onxdFzk/vcfPWrX+Xss8/mE5/4BACzZs1i586dnHfeeXzlK1/BNPX+cLQ9N7vqwAMP5KGHHtqtNvYlo/W5eeCBB1i5ciXXXnstH/vYx/Ke/73vfY9vf/vb/OUvf2H27NlFX1f2TSP9b+W9995j2bJlLF68mB/96EcZ+5555hn3+0QiUVR/pDh6bnKfmy9+8YtcfvnlnH766YDz33RvvfUW3/rWt1i1ahUyRPb2pPrhqLe3177gggvs+vp6+5VXXsnZnyo+8etf/9rd9tJLL+UUn3j++eft6upq+4tf/GLe61x22WX2zJkzM7adccYZRRWf+N73vudua25uHtSCYp2dne62K664QoXoiqTnpvBzM3/+fPuyyy7LOO+WW26xw+Gw3d3dXdR19lWj9bnxKlSILp8jjzzSPumkkwZ8jX3NaH5u7rvvPjsajdr/8R//UfCY73znO3YikeizIJSMDvvC38q6devsyZMn26effnrR/5850P6gQnQZ9NwU7k95ebl9ww03ZBzzzW9+0548eXJR15Bdo6A9j09/+tN2SUmJff/992csM9Ta2uoec/7559tjxoyx7733XvvJJ5+0Fy1aZC9atMjd/9xzz9lVVVX2WWedldHGBx984B6TWlbhi1/8ov3iiy/a119/fdHLPJSWltr/93//Z//973+3TzjhhJyldN566y17zZo19tVXX23HYjF7zZo19po1a+zt27cXbHfbtm12TU2NffbZZ9vPP/+8/Ytf/MKORCIZS3d1dHS4bdXV1dmXXnqpvWbNGvvVV18d0O94X6TnpvBzs3r1ajsej9u33nqr/cYbb9h/+tOf7IkTJ9of/vCHB/Q73heN1ufGtm37H//4h71mzRp75cqV9tKlS93zUq699lr7jjvusF999VX7ueeesz//+c/bpmnaf/nLX4r99e6zRutzc++999qRSMS+4oorMvq8efPmjGsHAgH717/+dcYx/T2Psm8a6X8r69atsydNmmQfccQR9rp16zKu35di+rN9+3b37w6w/+3f/s1es2aN/dZbbw3od7wv0nNTuD+rVq2yGxoa3CXfbr/9druysjInOSODS0F7HkDerxtvvNE9pq2tzf7MZz5jl5WV2ZFIxD7ppJMy/hBWr16dt42xY8dmXOu+++6z586dawcCAXvChAkZ1yikt7fX/upXv2rX1NTYwWDQPuKII+yXX34545hVq1blvf59993XZ9vPPvusfcghh9jBYNBuaGiwv/3tb2fsX7t2bd52DzvssH77va/Tc1P4uenq6rK/9rWv2RMnTrRDoZDd1NRkf+Yzn9FSXvbofm7Gjh2b97yU73znO+4zU15ebi9dutS+9957++3zaDBan5tC53j/P6jQc7V69ep++y37npH+t3LjjTcWvIf+9Nef1LKa2V+rVq3qt+19nZ6bwv1paWmxP//5z9tjxoyxQ6GQPWHCBPsrX/lKxtK/MvgM27ZtRERERERERGTYUfUnERERERERkWFKQbuIiIiIiIjIMKWgXURERERERGSYUtAuIiIiIiIiMkwpaBcREREREREZphS0i4iIiIiIiAxTCtpFREREREREhikF7SIiItKnc845hxNPPHFvd0NERGRUsvZ2B0RERGTvMQyjz/2rV6/m+9//PrZt76EeiYiIiJeCdhERkVHs/fffd7//5S9/yVVXXcXLL7/sbovFYsRisb3RNREREUHD40VEREa12tpa96ukpATDMDK2xWKxnOHxS5cu5cILL+Siiy6irKyMmpoafvzjH7Nz507OPfdc4vE4kyZN4o9//GPGtZ5//nlWrFhBLBajpqaGs88+m02bNu3hOxYRERlZFLSLiIjIgN10001UVlby+OOPc+GFF/LpT3+a0047jcWLF/P0009z9NFHc/bZZ9Pa2grAtm3bOPzww5k3bx5PPvkkd911Fxs2bODDH/7wXr4TERGR4U1Bu4iIiAzYnDlzuPLKK5k8eTJXXHEFoVCIyspKPvnJTzJ58mSuuuoqNm/ezN///ncA/uM//oN58+bxzW9+k2nTpjFv3jx++tOfct999/HKK6/s5bsREREZvjSnXURERAZs9uzZ7vc+n4+KigpmzZrlbqupqQHggw8+AODZZ5/lvvvuyzs//vXXX2fKlClD3GMREZGRSUG7iIiIDJjf78/42TCMjG2pqvS9vb0A7Nixg5UrV/Kd73wnp626uroh7KmIiMjIpqBdREREhtz8+fP53//9X8aNG4dl6T8/REREiqU57SIiIjLkLrjgArZs2cIZZ5zBE088weuvv87dd9/NueeeS09Pz97unoiIyLCloF1ERESGXH19PQ8//DA9PT0cffTRzJo1i4suuojS0lJMU/85IiIiUohh27a9tzshIiIiIiIiIrn0altERERERERkmFLQLiIiIiIiIjJMKWgXERERERERGaYUtIuIiIiIiIgMUwraRURERERERIYpBe0iIiIiIiIiw5SCdhEREREREZFhSkG7iIiIiIiIyDCloF1ERERERERkmFLQLiIiIiIiIjJMKWgXERERERERGaYUtIuIiIiIiIgMU/8fW3k0NEDtWwwAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -631,29 +623,30 @@ "source": [ "# Show the wd channels for the turbines\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001\", color=\"blue\", ls=\"--\")\n", - "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002\", color=\"red\", ls=\"--\")\n", + "\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_001\"], label=\"wd_001 (Fixed Bias)\", color=\"blue\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_002\"], label=\"wd_002 (Bias Changes)\", color=\"red\")\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_000\"], label=\"wd_000\", color=\"k\", alpha=0.5)\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_003\"], label=\"wd_003\", color=\"k\", alpha=0.5)\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_004\"], label=\"wd_004\", color=\"k\", alpha=0.5)\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_005\"], label=\"wd_005\", color=\"k\", alpha=0.5)\n", + "ax.plot(df_scada[\"time\"], df_scada[\"wd_006\"], label=\"wd_006\", color=\"k\", alpha=0.5)\n", "ax.legend()\n", "ax.set_xlabel(\"Time\")\n", - "ax.set_ylabel(\"Wind direction\")" + "ax.set_ylabel(\"Wind direction (deg)\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:22\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", - "\u001b[32m2024-11-25 21:39:22\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" + "\u001b[32m2024-12-02 11:23:06\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 361 cases.\n", + "\u001b[32m2024-12-02 11:23:06\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" ] } ], @@ -685,83 +678,328 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:22\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T006 T001 T002 T005 T003\n", - "0 0.0 -30.0 0.0 0.0 0.0\n", - "1 0.0 -30.0 0.0 0.0 0.0\n", - "2 0.0 -30.0 0.0 0.0 0.0\n", - "3 0.0 -30.0 -40.0 0.0 0.0\n", - "4 0.0 -30.0 -46.0 0.0 0.0\n", - "5 0.0 -30.0 -44.0 0.0 0.0\n", - "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T002 T006 T005 T003 T000\n", - "0 30.0 30.0 30.0 30.0 30.0\n", - "1 30.0 30.0 30.0 30.0 30.0\n", - "2 30.0 30.0 30.0 30.0 30.0\n", - "3 -10.0 30.0 30.0 30.0 30.0\n", - "4 -14.0 30.0 30.0 30.0 30.0\n", - "5 -16.0 30.0 30.0 30.0 30.0\n", - "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T001 T003 T005 T000 T006\n", - "0 -30.0 0.0 0.0 -0.0 0.0\n", - "1 -30.0 0.0 0.0 -0.0 0.0\n", - "2 -30.0 0.0 0.0 -0.0 0.0\n", - "3 10.0 40.0 40.0 40.0 40.0\n", - "4 14.0 44.0 46.0 46.0 46.0\n", - "5 16.0 46.0 46.0 44.0 46.0\n", - "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T005 T002 T001 T004 T006\n", - "0 0.0 -0.0 -30.0 0.0 0.0\n", - "1 0.0 -0.0 -30.0 0.0 0.0\n", - "2 0.0 -0.0 -30.0 0.0 0.0\n", - "3 0.0 -40.0 -30.0 0.0 0.0\n", - "4 0.0 -44.0 -30.0 0.0 0.0\n", - "5 0.0 -46.0 -30.0 0.0 0.0\n", - "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T003 T002 T005 T001 T006\n", - "0 -0.0 0.0 0.0 -30.0 0.0\n", - "1 -0.0 0.0 0.0 -30.0 0.0\n", - "2 -0.0 0.0 0.0 -30.0 0.0\n", - "3 -0.0 -40.0 0.0 -30.0 0.0\n", - "4 -0.0 -44.0 0.0 -30.0 0.0\n", - "5 -0.0 -46.0 0.0 -30.0 0.0\n", - "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T003 T001 T006 T002 T000\n", - "0 -0.0 -30.0 0.0 -0.0 -0.0\n", - "1 -0.0 -30.0 0.0 -0.0 -0.0\n", - "2 -0.0 -30.0 0.0 -0.0 -0.0\n", - "3 -0.0 -30.0 0.0 -40.0 -0.0\n", - "4 -0.0 -30.0 0.0 -46.0 -0.0\n", - "5 -0.0 -30.0 0.0 -46.0 -0.0\n", - "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m T001 T005 T000 T003 T002\n", - "0 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "1 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "2 -30.0 -0.0 -0.0 -0.0 -0.0\n", - "3 -30.0 -0.0 -0.0 -0.0 -40.0\n", - "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:23\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-12-02 11:23:06\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-12-02 11:23:07\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -16.0 0.0 0.0 0.0\n", + "1 0.0 -14.0 0.0 0.0 0.0\n", + "2 0.0 -16.0 0.0 0.0 0.0\n", + "3 0.0 -16.0 0.0 0.0 0.0\n", + "4 0.0 -14.0 0.0 2.0 0.0\n", + "5 0.0 -14.0 0.0 0.0 0.0\n", + "6 0.0 -16.0 0.0 0.0 -2.0\n", + "7 0.0 -16.0 0.0 0.0 0.0\n", + "8 0.0 -16.0 0.0 0.0 0.0\n", + "9 0.0 -16.0 0.0 0.0 0.0\n", + "10 0.0 -16.0 0.0 0.0 0.0\n", + "11 0.0 -16.0 0.0 0.0 0.0\n", + "12 0.0 -14.0 0.0 0.0 0.0\n", + "13 0.0 -14.0 0.0 0.0 0.0\n", + "14 -2.0 -16.0 0.0 0.0 0.0\n", + "15 0.0 -14.0 0.0 0.0 0.0\n", + "16 2.0 -14.0 0.0 2.0 0.0\n", + "17 0.0 -14.0 0.0 0.0 0.0\n", + "18 -2.0 -16.0 0.0 0.0 0.0\n", + "19 0.0 -16.0 0.0 0.0 0.0\n", + "20 0.0 -14.0 8.0 0.0 2.0\n", + "21 0.0 -14.0 46.0 0.0 0.0\n", + "22 0.0 -14.0 46.0 0.0 0.0\n", + "23 0.0 -14.0 46.0 0.0 0.0\n", + "24 0.0 -14.0 46.0 2.0 0.0\n", + "25 0.0 -16.0 44.0 0.0 0.0\n", + "26 0.0 -16.0 44.0 -2.0 0.0\n", + "27 0.0 -14.0 46.0 0.0 2.0\n", + "28 0.0 -14.0 46.0 0.0 0.0\n", + "29 0.0 -14.0 46.0 0.0 0.0\n", + "30 0.0 -14.0 44.0 0.0 0.0\n", + "31 0.0 -16.0 44.0 0.0 0.0\n", + "32 0.0 -14.0 46.0 0.0 0.0\n", + "33 0.0 -16.0 44.0 0.0 0.0\n", + "34 0.0 -14.0 46.0 0.0 0.0\n", + "35 0.0 -16.0 44.0 0.0 0.0\n", + "36 0.0 -14.0 46.0 0.0 2.0\n", + "37 0.0 -14.0 46.0 0.0 0.0\n", + "38 0.0 -14.0 46.0 0.0 0.0\n", + "39 0.0 -14.0 46.0 0.0 0.0\n", + "40 0.0 -16.0 44.0 0.0 0.0\n", + "41 0.0 -14.0 46.0 0.0 0.0\n", + "\u001b[32m2024-12-02 11:23:07\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-12-02 11:23:07\u001b[0m T002 T006 T005 T003 T000\n", + "0 16.0 14.0 16.0 16.0 16.0\n", + "1 14.0 16.0 14.0 16.0 14.0\n", + "2 14.0 16.0 14.0 16.0 16.0\n", + "3 16.0 16.0 14.0 14.0 16.0\n", + "4 14.0 14.0 16.0 14.0 14.0\n", + "5 16.0 14.0 14.0 16.0 14.0\n", + "6 14.0 14.0 14.0 14.0 16.0\n", + "7 16.0 14.0 16.0 16.0 16.0\n", + "8 14.0 16.0 14.0 16.0 16.0\n", + "9 14.0 14.0 14.0 16.0 16.0\n", + "10 16.0 14.0 14.0 14.0 16.0\n", + "11 16.0 14.0 16.0 14.0 16.0\n", + "12 16.0 14.0 14.0 14.0 14.0\n", + "13 14.0 14.0 14.0 14.0 14.0\n", + "14 16.0 14.0 16.0 16.0 16.0\n", + "15 14.0 14.0 14.0 16.0 14.0\n", + "16 16.0 16.0 16.0 14.0 14.0\n", + "17 14.0 14.0 14.0 16.0 14.0\n", + "18 16.0 14.0 14.0 16.0 16.0\n", + "19 16.0 14.0 14.0 14.0 16.0\n", + "20 22.0 14.0 14.0 16.0 14.0\n", + "21 60.0 14.0 16.0 14.0 14.0\n", + "22 60.0 14.0 16.0 14.0 14.0\n", + "23 60.0 14.0 16.0 16.0 14.0\n", + "24 60.0 16.0 16.0 14.0 14.0\n", + "25 60.0 16.0 14.0 16.0 16.0\n", + "26 60.0 16.0 14.0 14.0 16.0\n", + "27 60.0 14.0 16.0 16.0 14.0\n", + "28 60.0 14.0 16.0 14.0 14.0\n", + "29 60.0 14.0 16.0 16.0 14.0\n", + "30 60.0 14.0 14.0 14.0 14.0\n", + "31 60.0 16.0 14.0 14.0 16.0\n", + "32 60.0 16.0 16.0 14.0 14.0\n", + "33 60.0 16.0 16.0 16.0 16.0\n", + "34 60.0 16.0 16.0 16.0 14.0\n", + "35 60.0 14.0 16.0 14.0 16.0\n", + "36 60.0 14.0 14.0 16.0 14.0\n", + "37 60.0 14.0 16.0 14.0 14.0\n", + "38 60.0 16.0 14.0 14.0 14.0\n", + "39 60.0 14.0 16.0 14.0 14.0\n", + "40 60.0 16.0 14.0 14.0 16.0\n", + "41 60.0 14.0 16.0 14.0 14.0\n", + "\u001b[32m2024-12-02 11:23:07\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-12-02 11:23:08\u001b[0m T001 T003 T005 T000 T006\n", + "0 -16.0 0.0 0.0 -0.0 0.0\n", + "1 -14.0 0.0 0.0 -0.0 0.0\n", + "2 -14.0 0.0 0.0 -0.0 0.0\n", + "3 -16.0 0.0 0.0 -0.0 0.0\n", + "4 -14.0 0.0 0.0 -0.0 0.0\n", + "5 -16.0 0.0 0.0 -0.0 0.0\n", + "6 -14.0 0.0 0.0 -0.0 0.0\n", + "7 -16.0 0.0 0.0 -0.0 0.0\n", + "8 -14.0 0.0 0.0 -0.0 0.0\n", + "9 -14.0 0.0 0.0 -0.0 0.0\n", + "10 -16.0 0.0 0.0 -0.0 0.0\n", + "11 -16.0 0.0 0.0 -0.0 0.0\n", + "12 -16.0 0.0 0.0 -0.0 0.0\n", + "13 -14.0 0.0 0.0 -0.0 0.0\n", + "14 -16.0 0.0 0.0 -0.0 0.0\n", + "15 -14.0 0.0 0.0 -0.0 0.0\n", + "16 -16.0 0.0 2.0 -0.0 0.0\n", + "17 -14.0 0.0 0.0 -0.0 0.0\n", + "18 -16.0 0.0 0.0 -0.0 0.0\n", + "19 -16.0 0.0 0.0 -0.0 0.0\n", + "20 -22.0 -6.0 -8.0 -8.0 -8.0\n", + "21 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "22 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "23 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "24 -60.0 -46.0 -44.0 -46.0 -44.0\n", + "25 -60.0 -44.0 -46.0 -44.0 -44.0\n", + "26 -60.0 -46.0 -46.0 -44.0 -44.0\n", + "27 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "28 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "29 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "30 -60.0 -46.0 -46.0 -44.0 -46.0\n", + "31 -60.0 -46.0 -46.0 -44.0 -44.0\n", + "32 -60.0 -46.0 -44.0 -46.0 -44.0\n", + "33 -60.0 -44.0 -44.0 -44.0 -44.0\n", + "34 -60.0 -44.0 -44.0 -46.0 -44.0\n", + "35 -60.0 -46.0 -44.0 -44.0 -46.0\n", + "36 -60.0 -44.0 -46.0 -46.0 -46.0\n", + "37 -60.0 -46.0 -44.0 -46.0 -44.0\n", + "38 -60.0 -46.0 -46.0 -46.0 -44.0\n", + "39 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "40 -60.0 -46.0 -46.0 -44.0 -44.0\n", + "41 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "\u001b[32m2024-12-02 11:23:08\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-12-02 11:23:08\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 -0.0 -16.0 0.0 0.0\n", + "1 0.0 -0.0 -16.0 0.0 0.0\n", + "2 0.0 -0.0 -16.0 0.0 0.0\n", + "3 0.0 -0.0 -14.0 0.0 0.0\n", + "4 0.0 -0.0 -14.0 0.0 0.0\n", + "5 0.0 -0.0 -16.0 0.0 0.0\n", + "6 0.0 -0.0 -14.0 0.0 0.0\n", + "7 0.0 -0.0 -16.0 0.0 0.0\n", + "8 0.0 -0.0 -16.0 0.0 0.0\n", + "9 0.0 -0.0 -16.0 0.0 0.0\n", + "10 0.0 -0.0 -14.0 0.0 0.0\n", + "11 0.0 -0.0 -14.0 0.0 0.0\n", + "12 0.0 -0.0 -14.0 0.0 0.0\n", + "13 0.0 -0.0 -14.0 0.0 0.0\n", + "14 0.0 -0.0 -16.0 0.0 0.0\n", + "15 0.0 -0.0 -16.0 0.0 0.0\n", + "16 2.0 -0.0 -14.0 2.0 2.0\n", + "17 0.0 -0.0 -16.0 0.0 0.0\n", + "18 0.0 -0.0 -16.0 0.0 0.0\n", + "19 0.0 -0.0 -14.0 0.0 0.0\n", + "20 -2.0 6.0 -16.0 0.0 -2.0\n", + "21 0.0 46.0 -14.0 0.0 0.0\n", + "22 0.0 46.0 -14.0 0.0 0.0\n", + "23 0.0 44.0 -16.0 0.0 0.0\n", + "24 0.0 46.0 -14.0 0.0 0.0\n", + "25 0.0 44.0 -16.0 -2.0 0.0\n", + "26 0.0 46.0 -14.0 0.0 0.0\n", + "27 0.0 44.0 -16.0 0.0 0.0\n", + "28 0.0 46.0 -14.0 0.0 0.0\n", + "29 0.0 44.0 -16.0 -2.0 0.0\n", + "30 0.0 46.0 -14.0 0.0 0.0\n", + "31 0.0 46.0 -14.0 0.0 0.0\n", + "32 0.0 46.0 -14.0 0.0 0.0\n", + "33 0.0 44.0 -16.0 0.0 0.0\n", + "34 0.0 44.0 -16.0 0.0 0.0\n", + "35 0.0 46.0 -14.0 0.0 0.0\n", + "36 -2.0 44.0 -16.0 -2.0 -2.0\n", + "37 0.0 46.0 -14.0 0.0 0.0\n", + "38 0.0 46.0 -14.0 0.0 0.0\n", + "39 0.0 46.0 -14.0 0.0 0.0\n", + "40 0.0 46.0 -14.0 0.0 0.0\n", + "41 0.0 46.0 -14.0 0.0 0.0\n", + "\u001b[32m2024-12-02 11:23:08\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 0.0 0.0 -16.0 0.0\n", + "1 -0.0 0.0 0.0 -14.0 0.0\n", + "2 -0.0 0.0 0.0 -16.0 0.0\n", + "3 -0.0 0.0 0.0 -16.0 0.0\n", + "4 -0.0 0.0 0.0 -16.0 -2.0\n", + "5 -0.0 0.0 0.0 -14.0 0.0\n", + "6 -0.0 0.0 0.0 -16.0 0.0\n", + "7 -0.0 0.0 0.0 -14.0 0.0\n", + "8 -0.0 0.0 0.0 -14.0 0.0\n", + "9 -0.0 0.0 0.0 -16.0 0.0\n", + "10 -0.0 0.0 0.0 -16.0 -2.0\n", + "11 -0.0 0.0 0.0 -14.0 0.0\n", + "12 -0.0 0.0 0.0 -16.0 0.0\n", + "13 -0.0 0.0 0.0 -16.0 0.0\n", + "14 -0.0 0.0 0.0 -16.0 0.0\n", + "15 -0.0 0.0 0.0 -16.0 0.0\n", + "16 -2.0 0.0 0.0 -16.0 0.0\n", + "17 -0.0 0.0 0.0 -16.0 0.0\n", + "18 -0.0 0.0 0.0 -14.0 0.0\n", + "19 -0.0 0.0 0.0 -14.0 0.0\n", + "20 -0.0 8.0 0.0 -14.0 0.0\n", + "21 -0.0 44.0 0.0 -16.0 0.0\n", + "22 -0.0 44.0 0.0 -16.0 0.0\n", + "23 -0.0 46.0 0.0 -14.0 0.0\n", + "24 -0.0 46.0 2.0 -14.0 0.0\n", + "25 2.0 46.0 0.0 -14.0 2.0\n", + "26 -0.0 44.0 -2.0 -16.0 0.0\n", + "27 -0.0 46.0 0.0 -14.0 0.0\n", + "28 -0.0 44.0 0.0 -16.0 0.0\n", + "29 2.0 46.0 2.0 -14.0 0.0\n", + "30 -0.0 46.0 0.0 -14.0 0.0\n", + "31 -0.0 44.0 -2.0 -16.0 0.0\n", + "32 -0.0 46.0 0.0 -14.0 0.0\n", + "33 -0.0 46.0 0.0 -14.0 0.0\n", + "34 -0.0 44.0 0.0 -16.0 0.0\n", + "35 -0.0 46.0 0.0 -14.0 0.0\n", + "36 2.0 46.0 0.0 -14.0 0.0\n", + "37 -0.0 46.0 0.0 -14.0 0.0\n", + "38 -0.0 44.0 0.0 -16.0 0.0\n", + "39 -0.0 44.0 0.0 -16.0 0.0\n", + "40 -0.0 44.0 0.0 -16.0 0.0\n", + "41 -0.0 46.0 0.0 -14.0 0.0\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -16.0 0.0 -0.0 -0.0\n", + "1 -0.0 -14.0 0.0 -0.0 -0.0\n", + "2 -0.0 -14.0 0.0 -0.0 -0.0\n", + "3 -0.0 -14.0 0.0 -0.0 -0.0\n", + "4 -0.0 -16.0 -2.0 -0.0 -2.0\n", + "5 -0.0 -14.0 0.0 -0.0 -0.0\n", + "6 -0.0 -14.0 0.0 -0.0 -0.0\n", + "7 -0.0 -16.0 0.0 -0.0 -0.0\n", + "8 -0.0 -14.0 0.0 -0.0 -0.0\n", + "9 -0.0 -14.0 0.0 -0.0 -0.0\n", + "10 -0.0 -14.0 0.0 -0.0 -0.0\n", + "11 -0.0 -16.0 0.0 -0.0 -0.0\n", + "12 -0.0 -14.0 0.0 -0.0 -0.0\n", + "13 -0.0 -14.0 0.0 -0.0 -0.0\n", + "14 -0.0 -16.0 -2.0 -0.0 -0.0\n", + "15 -0.0 -14.0 0.0 -0.0 -0.0\n", + "16 -2.0 -16.0 0.0 -2.0 -2.0\n", + "17 -0.0 -14.0 0.0 -0.0 -0.0\n", + "18 -0.0 -14.0 0.0 -0.0 -0.0\n", + "19 -0.0 -14.0 0.0 -0.0 -0.0\n", + "20 2.0 -14.0 0.0 8.0 -0.0\n", + "21 -0.0 -16.0 0.0 44.0 -0.0\n", + "22 -0.0 -16.0 -2.0 44.0 -0.0\n", + "23 -0.0 -16.0 0.0 44.0 -0.0\n", + "24 -0.0 -16.0 0.0 44.0 -2.0\n", + "25 -0.0 -14.0 0.0 46.0 -0.0\n", + "26 -0.0 -14.0 2.0 46.0 2.0\n", + "27 -0.0 -16.0 0.0 44.0 -0.0\n", + "28 -0.0 -16.0 0.0 44.0 -0.0\n", + "29 -0.0 -16.0 0.0 44.0 -0.0\n", + "30 -0.0 -14.0 0.0 46.0 -0.0\n", + "31 -0.0 -14.0 0.0 46.0 -0.0\n", + "32 -0.0 -16.0 0.0 44.0 -0.0\n", + "33 -0.0 -16.0 0.0 44.0 -0.0\n", + "34 -0.0 -16.0 0.0 44.0 -0.0\n", + "35 -0.0 -16.0 0.0 44.0 -0.0\n", + "36 2.0 -14.0 0.0 46.0 -0.0\n", + "37 -0.0 -16.0 0.0 44.0 -0.0\n", + "38 -0.0 -14.0 0.0 46.0 -0.0\n", + "39 -0.0 -16.0 0.0 44.0 -0.0\n", + "40 -0.0 -14.0 0.0 46.0 -0.0\n", + "41 -0.0 -16.0 -2.0 44.0 -0.0\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m T001 T005 T000 T003 T002\n", + "0 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "1 -16.0 -0.0 -0.0 -0.0 -0.0\n", + "2 -16.0 -0.0 -0.0 -0.0 -0.0\n", + "3 -16.0 -0.0 -0.0 -0.0 -0.0\n", + "4 -14.0 2.0 -0.0 -0.0 -0.0\n", + "5 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "6 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "7 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "8 -16.0 -0.0 -0.0 -0.0 -0.0\n", + "9 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "10 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "11 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "12 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "13 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "14 -14.0 2.0 2.0 -0.0 -0.0\n", + "15 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "16 -16.0 -0.0 -2.0 -2.0 -0.0\n", + "17 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "18 -14.0 -0.0 2.0 -0.0 -0.0\n", + "19 -14.0 -0.0 -0.0 -0.0 -0.0\n", + "20 -14.0 -0.0 -0.0 2.0 8.0\n", + "21 -14.0 -0.0 -0.0 -0.0 46.0\n", + "22 -14.0 2.0 -0.0 -0.0 46.0\n", + "23 -14.0 -0.0 -0.0 -0.0 46.0\n", + "24 -16.0 -0.0 -0.0 -0.0 44.0\n", + "25 -16.0 -0.0 -0.0 -0.0 44.0\n", + "26 -16.0 -2.0 -0.0 -0.0 44.0\n", + "27 -14.0 -0.0 -0.0 -0.0 46.0\n", + "28 -14.0 -0.0 -0.0 -0.0 46.0\n", + "29 -14.0 -0.0 -0.0 -0.0 46.0\n", + "30 -14.0 -0.0 -0.0 -0.0 46.0\n", + "31 -16.0 -0.0 -0.0 -0.0 44.0\n", + "32 -16.0 -0.0 -0.0 -0.0 44.0\n", + "33 -16.0 -0.0 -0.0 -0.0 44.0\n", + "34 -16.0 -0.0 -0.0 -0.0 44.0\n", + "35 -14.0 -0.0 -0.0 -0.0 46.0\n", + "36 -14.0 -0.0 -0.0 2.0 46.0\n", + "37 -14.0 -0.0 -0.0 -0.0 44.0\n", + "38 -16.0 -0.0 -0.0 -0.0 44.0\n", + "39 -14.0 -0.0 -0.0 -0.0 46.0\n", + "40 -16.0 -0.0 -0.0 -0.0 44.0\n", + "41 -14.0 2.0 -0.0 -0.0 46.0\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 002 seems to have one or multiple jumps in its WD measurement calibration. [BAD]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:09\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -787,7 +1025,7 @@ "df_scada_marked_faulty_northing_drift = df_scada.copy()\n", "\n", "turb_wd_consistency = nof.crosscheck_northing_offset_consistency(\n", - " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=2)\n", + " df=df_scada_marked_faulty_northing_drift, fm=fm, plot_figure=True, bias_timestep=td(days=1)\n", ")\n", "print(turb_wd_consistency)" ] @@ -801,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -830,14 +1068,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[899.5]\n" + "[2999.5]\n" ] }, { @@ -884,20 +1122,20 @@ " 1\n", " wd_002\n", " 6\n", - " -45.005012\n", - " 899.5\n", - " 2020-01-07 05:40:00\n", + " 44.982446\n", + " 2999.5\n", + " 2020-01-21 19:40:00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Class Turbine Count Jump Knot Knot_date\n", - "0 1 wd_002 6 -45.005012 899.5 2020-01-07 05:40:00" + " Class Turbine Count Jump Knot Knot_date\n", + "0 1 wd_002 6 44.982446 2999.5 2020-01-21 19:40:00" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -919,22 +1157,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Wind direction')" + "Text(0, 0.5, 'Wind direction (deg)')" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -947,28 +1185,56 @@ "# Show the effects of homogenization\n", "# Show the wd channels for the turbines\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", "ax.plot(\n", " df_scada_homogenized[\"time\"],\n", " df_scada_homogenized[\"wd_001\"],\n", - " label=\"wd_001\",\n", + " label=\"wd_001 (Fixed Bias)\",\n", " color=\"blue\",\n", - " ls=\"--\",\n", ")\n", "ax.plot(\n", " df_scada_homogenized[\"time\"],\n", " df_scada_homogenized[\"wd_002\"],\n", - " label=\"wd_002\",\n", + " label=\"wd_002 (Bias Changes)\",\n", " color=\"red\",\n", - " ls=\"--\",\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_000\"],\n", + " label=\"wd_000\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_003\"],\n", + " label=\"wd_003\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_004\"],\n", + " label=\"wd_004\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_005\"],\n", + " label=\"wd_005\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_006\"],\n", + " label=\"wd_006\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", ")\n", "ax.legend()\n", "ax.set_xlabel(\"Time\")\n", - "ax.set_ylabel(\"Wind direction\")" + "ax.set_ylabel(\"Wind direction (deg)\")" ] }, { @@ -980,83 +1246,181 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:23\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T006 T001 T002 T005 T003\n", - "0 0.0 -30.0 -46.0 0.0 0.0\n", - "1 0.0 -30.0 -46.0 0.0 0.0\n", - "2 0.0 -30.0 -44.0 0.0 0.0\n", - "3 0.0 -30.0 -46.0 0.0 0.0\n", - "4 0.0 -30.0 -46.0 0.0 0.0\n", - "5 0.0 -30.0 -44.0 0.0 0.0\n", - "6 0.0 -30.0 -44.0 0.0 0.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T002 T006 T005 T003 T000\n", - "0 -16.0 30.0 30.0 30.0 30.0\n", - "1 -16.0 30.0 30.0 30.0 30.0\n", - "2 -14.0 30.0 30.0 30.0 30.0\n", - "3 -16.0 30.0 30.0 30.0 30.0\n", - "4 -14.0 30.0 30.0 30.0 30.0\n", - "5 -16.0 30.0 30.0 30.0 30.0\n", - "6 -16.0 30.0 30.0 30.0 30.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T001 T003 T005 T000 T006\n", - "0 16.0 44.0 46.0 46.0 44.0\n", - "1 16.0 46.0 46.0 46.0 46.0\n", - "2 14.0 44.0 46.0 44.0 44.0\n", - "3 16.0 46.0 44.0 46.0 46.0\n", - "4 14.0 44.0 46.0 46.0 46.0\n", - "5 16.0 46.0 46.0 44.0 46.0\n", - "6 16.0 44.0 46.0 44.0 44.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T005 T002 T001 T004 T006\n", - "0 0.0 -44.0 -30.0 0.0 0.0\n", - "1 0.0 -46.0 -30.0 0.0 0.0\n", - "2 0.0 -44.0 -30.0 0.0 0.0\n", - "3 0.0 -46.0 -30.0 0.0 0.0\n", - "4 0.0 -44.0 -30.0 0.0 0.0\n", - "5 0.0 -46.0 -30.0 0.0 0.0\n", - "6 0.0 -44.0 -30.0 0.0 0.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T003 T002 T005 T001 T006\n", - "0 -0.0 -44.0 0.0 -30.0 0.0\n", - "1 -0.0 -46.0 0.0 -30.0 0.0\n", - "2 -0.0 -44.0 0.0 -30.0 0.0\n", - "3 -0.0 -46.0 0.0 -30.0 0.0\n", - "4 -0.0 -44.0 0.0 -30.0 0.0\n", - "5 -0.0 -46.0 0.0 -30.0 0.0\n", - "6 -0.0 -44.0 0.0 -30.0 0.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T003 T001 T006 T002 T000\n", - "0 -0.0 -30.0 0.0 -46.0 -0.0\n", - "1 -0.0 -30.0 0.0 -46.0 -0.0\n", - "2 -0.0 -30.0 0.0 -46.0 -0.0\n", - "3 -0.0 -30.0 0.0 -44.0 -0.0\n", - "4 -0.0 -30.0 0.0 -46.0 -0.0\n", - "5 -0.0 -30.0 0.0 -46.0 -0.0\n", - "6 -0.0 -30.0 0.0 -46.0 -0.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m T001 T005 T000 T003 T002\n", - "0 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "1 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "2 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "3 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "4 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "5 -30.0 -0.0 -0.0 -0.0 -46.0\n", - "6 -30.0 -0.0 -0.0 -0.0 -44.0\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-12-02 11:23:09\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m T006 T001 T002 T005 T003\n", + "0 0.0 -16.0 44.0 0.0 0.0\n", + "1 0.0 -16.0 44.0 0.0 0.0\n", + "2 0.0 -14.0 46.0 0.0 0.0\n", + "3 0.0 -16.0 44.0 0.0 0.0\n", + "4 0.0 -16.0 44.0 0.0 0.0\n", + "5 0.0 -16.0 44.0 0.0 0.0\n", + "6 0.0 -14.0 46.0 0.0 0.0\n", + "7 0.0 -16.0 44.0 0.0 0.0\n", + "8 0.0 -14.0 46.0 0.0 0.0\n", + "9 0.0 -16.0 44.0 0.0 0.0\n", + "10 0.0 -14.0 46.0 0.0 0.0\n", + "11 0.0 -14.0 46.0 0.0 0.0\n", + "12 0.0 -14.0 44.0 0.0 0.0\n", + "13 0.0 -14.0 46.0 0.0 0.0\n", + "14 0.0 -14.0 46.0 0.0 0.0\n", + "15 0.0 -16.0 44.0 0.0 0.0\n", + "16 0.0 -16.0 44.0 0.0 0.0\n", + "17 0.0 -14.0 46.0 0.0 0.0\n", + "18 0.0 -14.0 46.0 0.0 0.0\n", + "19 0.0 -14.0 46.0 0.0 0.0\n", + "20 0.0 -14.0 46.0 0.0 0.0\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m T002 T006 T005 T003 T000\n", + "0 60.0 16.0 14.0 16.0 16.0\n", + "1 60.0 16.0 14.0 14.0 16.0\n", + "2 60.0 14.0 16.0 16.0 14.0\n", + "3 60.0 14.0 16.0 16.0 16.0\n", + "4 60.0 16.0 14.0 16.0 16.0\n", + "5 60.0 14.0 14.0 14.0 16.0\n", + "6 60.0 14.0 14.0 14.0 14.0\n", + "7 60.0 14.0 14.0 16.0 16.0\n", + "8 60.0 16.0 16.0 14.0 14.0\n", + "9 60.0 14.0 14.0 14.0 16.0\n", + "10 60.0 14.0 14.0 16.0 14.0\n", + "11 60.0 14.0 16.0 16.0 14.0\n", + "12 60.0 16.0 16.0 16.0 14.0\n", + "13 60.0 16.0 14.0 16.0 14.0\n", + "14 60.0 14.0 16.0 14.0 14.0\n", + "15 60.0 14.0 14.0 14.0 16.0\n", + "16 60.0 16.0 16.0 16.0 16.0\n", + "17 60.0 14.0 16.0 16.0 14.0\n", + "18 60.0 14.0 14.0 16.0 14.0\n", + "19 60.0 14.0 14.0 14.0 14.0\n", + "20 60.0 14.0 16.0 14.0 14.0\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m T001 T003 T005 T000 T006\n", + "0 -60.0 -44.0 -46.0 -44.0 -44.0\n", + "1 -60.0 -46.0 -46.0 -44.0 -44.0\n", + "2 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "3 -60.0 -44.0 -44.0 -44.0 -46.0\n", + "4 -60.0 -44.0 -46.0 -44.0 -44.0\n", + "5 -60.0 -46.0 -44.0 -44.0 -46.0\n", + "6 -60.0 -46.0 -46.0 -46.0 -46.0\n", + "7 -60.0 -44.0 -44.0 -44.0 -46.0\n", + "8 -60.0 -46.0 -44.0 -46.0 -44.0\n", + "9 -60.0 -46.0 -46.0 -44.0 -46.0\n", + "10 -60.0 -44.0 -46.0 -46.0 -46.0\n", + "11 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "12 -60.0 -44.0 -44.0 -44.0 -44.0\n", + "13 -60.0 -44.0 -46.0 -46.0 -46.0\n", + "14 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "15 -60.0 -46.0 -46.0 -44.0 -46.0\n", + "16 -60.0 -44.0 -44.0 -44.0 -44.0\n", + "17 -60.0 -44.0 -44.0 -46.0 -46.0\n", + "18 -60.0 -44.0 -46.0 -46.0 -46.0\n", + "19 -60.0 -46.0 -46.0 -46.0 -46.0\n", + "20 -60.0 -46.0 -44.0 -46.0 -46.0\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m T005 T002 T001 T004 T006\n", + "0 0.0 44.0 -16.0 0.0 0.0\n", + "1 0.0 46.0 -14.0 0.0 0.0\n", + "2 0.0 44.0 -16.0 0.0 0.0\n", + "3 0.0 44.0 -16.0 0.0 0.0\n", + "4 0.0 44.0 -16.0 0.0 0.0\n", + "5 0.0 46.0 -14.0 0.0 0.0\n", + "6 0.0 46.0 -14.0 0.0 0.0\n", + "7 0.0 44.0 -16.0 0.0 0.0\n", + "8 0.0 46.0 -14.0 0.0 0.0\n", + "9 0.0 46.0 -14.0 0.0 0.0\n", + "10 0.0 44.0 -16.0 0.0 0.0\n", + "11 0.0 44.0 -16.0 0.0 0.0\n", + "12 0.0 44.0 -16.0 0.0 0.0\n", + "13 0.0 44.0 -16.0 0.0 0.0\n", + "14 0.0 46.0 -14.0 0.0 0.0\n", + "15 0.0 46.0 -14.0 0.0 0.0\n", + "16 0.0 44.0 -16.0 0.0 0.0\n", + "17 0.0 44.0 -16.0 0.0 0.0\n", + "18 0.0 44.0 -16.0 0.0 0.0\n", + "19 0.0 46.0 -14.0 0.0 0.0\n", + "20 0.0 46.0 -14.0 0.0 0.0\n", + "\u001b[32m2024-12-02 11:23:10\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m T003 T002 T005 T001 T006\n", + "0 -0.0 46.0 0.0 -14.0 0.0\n", + "1 -0.0 44.0 0.0 -16.0 0.0\n", + "2 -0.0 44.0 0.0 -16.0 0.0\n", + "3 -0.0 46.0 0.0 -14.0 0.0\n", + "4 -0.0 46.0 0.0 -14.0 0.0\n", + "5 -0.0 44.0 0.0 -16.0 0.0\n", + "6 -0.0 44.0 0.0 -16.0 0.0\n", + "7 -0.0 44.0 0.0 -16.0 0.0\n", + "8 -0.0 44.0 0.0 -16.0 0.0\n", + "9 -0.0 46.0 0.0 -14.0 0.0\n", + "10 -0.0 44.0 0.0 -16.0 0.0\n", + "11 -0.0 46.0 0.0 -14.0 0.0\n", + "12 -0.0 46.0 0.0 -14.0 0.0\n", + "13 -0.0 44.0 0.0 -16.0 0.0\n", + "14 -0.0 46.0 0.0 -14.0 0.0\n", + "15 -0.0 44.0 0.0 -16.0 0.0\n", + "16 -0.0 46.0 0.0 -14.0 0.0\n", + "17 -0.0 44.0 0.0 -16.0 0.0\n", + "18 -0.0 46.0 0.0 -14.0 0.0\n", + "19 -0.0 44.0 0.0 -16.0 0.0\n", + "20 -0.0 44.0 0.0 -16.0 0.0\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m T003 T001 T006 T002 T000\n", + "0 -0.0 -14.0 0.0 46.0 -0.0\n", + "1 -0.0 -14.0 0.0 46.0 -0.0\n", + "2 -0.0 -16.0 0.0 44.0 -0.0\n", + "3 -0.0 -16.0 0.0 44.0 -0.0\n", + "4 -0.0 -14.0 0.0 46.0 -0.0\n", + "5 -0.0 -14.0 0.0 44.0 -0.0\n", + "6 -0.0 -14.0 0.0 46.0 -0.0\n", + "7 -0.0 -14.0 0.0 44.0 -0.0\n", + "8 -0.0 -16.0 0.0 44.0 -0.0\n", + "9 -0.0 -14.0 0.0 46.0 -0.0\n", + "10 -0.0 -14.0 0.0 46.0 -0.0\n", + "11 -0.0 -16.0 0.0 44.0 -0.0\n", + "12 -0.0 -16.0 0.0 44.0 -0.0\n", + "13 -0.0 -14.0 0.0 46.0 -0.0\n", + "14 -0.0 -16.0 0.0 44.0 -0.0\n", + "15 -0.0 -14.0 0.0 46.0 -0.0\n", + "16 -0.0 -16.0 0.0 44.0 -0.0\n", + "17 -0.0 -16.0 0.0 44.0 -0.0\n", + "18 -0.0 -14.0 0.0 46.0 -0.0\n", + "19 -0.0 -14.0 0.0 46.0 -0.0\n", + "20 -0.0 -16.0 0.0 44.0 -0.0\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m T001 T005 T000 T003 T002\n", + "0 -16.0 -0.0 -0.0 -0.0 44.0\n", + "1 -16.0 -0.0 -0.0 -0.0 44.0\n", + "2 -14.0 -0.0 -0.0 -0.0 46.0\n", + "3 -14.0 -0.0 -0.0 -0.0 46.0\n", + "4 -16.0 -0.0 -0.0 -0.0 44.0\n", + "5 -14.0 -0.0 -0.0 -0.0 46.0\n", + "6 -14.0 -0.0 -0.0 -0.0 46.0\n", + "7 -14.0 -0.0 -0.0 -0.0 46.0\n", + "8 -16.0 -0.0 -0.0 -0.0 44.0\n", + "9 -14.0 -0.0 -0.0 -0.0 46.0\n", + "10 -14.0 -0.0 -0.0 -0.0 46.0\n", + "11 -14.0 -0.0 -0.0 -0.0 46.0\n", + "12 -16.0 -0.0 -0.0 -0.0 44.0\n", + "13 -16.0 -0.0 -0.0 -0.0 46.0\n", + "14 -14.0 -0.0 -0.0 -0.0 46.0\n", + "15 -14.0 -0.0 -0.0 -0.0 46.0\n", + "16 -16.0 -0.0 -0.0 -0.0 44.0\n", + "17 -14.0 -0.0 -0.0 -0.0 46.0\n", + "18 -14.0 -0.0 -0.0 -0.0 46.0\n", + "19 -14.0 -0.0 -0.0 -0.0 46.0\n", + "20 -14.0 -0.0 -0.0 -0.0 46.0\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -1098,22 +1462,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:24\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" + "\u001b[32m2024-12-02 11:23:11\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Constructing energy table for wd_bias of -180.00 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.798, 8.248)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.798, 8.248)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -1127,760 +1491,762 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.794, 8.243)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.831, 8.243)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.202)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df: (7.806, 8.315)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.831, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.315)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df: (7.802, 8.274)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.802, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.814, 8.202)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.805, 8.243)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df: (7.815, 8.243)\n", - "\u001b[32m2024-11-25 21:39:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.759, 8.243)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.773, 8.202)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df: (7.791, 8.202)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.189)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.793, 8.202)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.210)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.815, 8.230)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df: (7.825, 8.230)\n", - "\u001b[32m2024-11-25 21:39:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.826, 8.230)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.230)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.817, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.210)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:31\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.209)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.734, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.175)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.789, 8.179)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df: (7.800, 8.212)\n", - "\u001b[32m2024-11-25 21:39:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.212)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.187)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df: (7.789, 8.222)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.789, 8.260)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.824, 8.260)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df: (7.806, 8.215)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:35\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.248)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.801, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.794, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.817, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df: (7.818, 8.250)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:36\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.818, 8.193)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.193)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.811, 8.196)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df: (7.754, 8.199)\n", - "\u001b[32m2024-11-25 21:39:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.178)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.754, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.767, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df: (7.804, 8.193)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.786, 8.193)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.193)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.203)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df: (7.770, 8.172)\n", - "\u001b[32m2024-11-25 21:39:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.794, 8.213)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:40\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.800, 8.243)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Constructing energy table for wd_bias of -175.00 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.709, 8.248)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.709, 8.248)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Constructing energy table for wd_bias of -170.00 deg.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.709, 8.243)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df: (7.709, 8.243)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:11\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Constructing energy table for wd_bias of -165.00 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.799, 8.243)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.799, 8.243)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Constructing energy table for wd_bias of -160.00 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.799, 8.230)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.799, 8.230)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Constructing energy table for wd_bias of -155.00 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.788, 8.230)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.788, 8.230)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Constructing energy table for wd_bias of -150.00 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.788, 8.227)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df: (7.788, 8.227)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:12\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Constructing energy table for wd_bias of -145.00 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.794, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.794, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Constructing energy table for wd_bias of -140.00 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.794, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.794, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Constructing energy table for wd_bias of -135.00 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.811, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.811, 8.227)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Constructing energy table for wd_bias of -130.00 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.811, 8.219)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df: (7.811, 8.219)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:13\u001b[0m Constructing energy table for wd_bias of -125.00 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.244)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.244)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Constructing energy table for wd_bias of -120.00 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.244)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.244)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Constructing energy table for wd_bias of -115.00 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.192)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.192)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Constructing energy table for wd_bias of -110.00 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.198)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.795, 8.198)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Constructing energy table for wd_bias of -105.00 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.803, 8.198)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df: (7.803, 8.198)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:14\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Constructing energy table for wd_bias of -100.00 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.802, 8.219)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.802, 8.219)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Constructing energy table for wd_bias of -95.00 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.219)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.219)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Constructing energy table for wd_bias of -90.00 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.249)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.249)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Constructing energy table for wd_bias of -85.00 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.249)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.795, 8.249)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Constructing energy table for wd_bias of -80.00 deg.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.725, 8.227)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df: (7.725, 8.227)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:15\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Constructing energy table for wd_bias of -75.00 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.725, 8.227)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.725, 8.227)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Constructing energy table for wd_bias of -70.00 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.810, 8.227)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.810, 8.227)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Constructing energy table for wd_bias of -65.00 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.773, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.773, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Constructing energy table for wd_bias of -60.00 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.773, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.773, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Constructing energy table for wd_bias of -55.00 deg.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.810, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df: (7.810, 8.234)\n", + "\u001b[32m2024-12-02 11:23:16\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Constructing energy table for wd_bias of -50.00 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.798, 8.234)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.798, 8.234)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Constructing energy table for wd_bias of -45.00 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.222)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.222)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Constructing energy table for wd_bias of -40.00 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.235)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.235)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Constructing energy table for wd_bias of -35.00 deg.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.235)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df: (7.783, 8.235)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:17\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Constructing energy table for wd_bias of -30.00 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.766, 8.240)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.766, 8.240)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Constructing energy table for wd_bias of -25.00 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.766, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.766, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Constructing energy table for wd_bias of -20.00 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.787, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.787, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Constructing energy table for wd_bias of -15.00 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Constructing energy table for wd_bias of -10.00 deg.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:18\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.789, 8.231)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.789, 8.231)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Constructing energy table for wd_bias of 10.00 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.803, 8.222)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df: (7.803, 8.222)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:19\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 10.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Constructing energy table for wd_bias of 15.00 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.803, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.803, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Constructing energy table for wd_bias of 20.00 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 20.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.222)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.193)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.193)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.230)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df: (7.794, 8.230)\n", + "\u001b[32m2024-12-02 11:23:20\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Constructing energy table for wd_bias of 40.00 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.796, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.796, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 40.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Constructing energy table for wd_bias of 45.00 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 45.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Constructing energy table for wd_bias of 50.00 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 50.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Constructing energy table for wd_bias of 55.00 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.795, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df: (7.795, 8.239)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:21\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 55.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Constructing energy table for wd_bias of 60.00 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.220)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.220)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 60.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Constructing energy table for wd_bias of 65.00 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 65.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Constructing energy table for wd_bias of 70.00 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.795, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 70.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Constructing energy table for wd_bias of 75.00 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.798, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df: (7.798, 8.329)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 75.000 deg.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:22\u001b[0m Constructing energy table for wd_bias of 80.00 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.329)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.329)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 80.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Constructing energy table for wd_bias of 85.00 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 85.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Constructing energy table for wd_bias of 90.00 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.798, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 90.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Constructing energy table for wd_bias of 95.00 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.789, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.789, 8.235)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 95.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Constructing energy table for wd_bias of 100.00 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.789, 8.204)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df: (7.789, 8.204)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:23\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 100.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Constructing energy table for wd_bias of 105.00 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.789, 8.204)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.789, 8.204)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 105.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Constructing energy table for wd_bias of 110.00 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.195)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.195)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 110.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Constructing energy table for wd_bias of 115.00 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 115.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Constructing energy table for wd_bias of 120.00 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 120.000 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Constructing energy table for wd_bias of 125.00 deg.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df: (7.776, 8.224)\n", + "\u001b[32m2024-12-02 11:23:24\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 125.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Constructing energy table for wd_bias of 130.00 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 130.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Constructing energy table for wd_bias of 135.00 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 135.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Constructing energy table for wd_bias of 140.00 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.785, 8.227)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 140.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Constructing energy table for wd_bias of 145.00 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:25\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 145.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Constructing energy table for wd_bias of 150.00 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 150.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Constructing energy table for wd_bias of 155.00 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.804, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.804, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 155.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Constructing energy table for wd_bias of 160.00 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.256)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 160.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Constructing energy table for wd_bias of 165.00 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 165.000 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Constructing energy table for wd_bias of 170.00 deg.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:26\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 170.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Constructing energy table for wd_bias of 175.00 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.798, 8.247)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 175.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Constructing energy table for wd_bias of 180.00 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.798, 8.248)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.798, 8.248)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 180.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:27\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" ] }, { @@ -1888,38 +2254,24 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999863\n", + " Current function value: -0.893931\n", " Iterations: 1\n", - " Function evaluations: 2\n" + " Function evaluations: 2\n", + "Turbine 0. estimated bias = 0.00025 deg.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:41\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Turbine 0. estimated bias = 0.0 deg.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-11-25 21:39:42\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:28\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:28\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -1931,7 +2283,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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5brH5hIGv9lXi5arw6ihtuwtEnIELn/mZRYJQLwUXtX2fBRd+bGirTJs2jYCAAEfLqEZFRQVvvPGGDDZbS8um0WjkxIkTNabr9Xo++eQT7rvvPps21RuNRlJTU4mNjW1Qa+6Gzxh2YmGN6SYBOfgTymnOfzaYhMIqzyu5rPQXVMq508soFH73vZVxhd/WmP5b6EMAjM/+d415y92v55ryZdWmm4TCb/53gMnIuMJvUFXbPqSKcOJVJ2tozjP5cEQVy0Vij7krmTB314sfdU+9x8Aa7OGrxvjJmWyeOnmMS9beUs0v9bFZ6cvFpl0X+FHhR+/bcHV1Y/ypz2qeM363M/7M1zXPMf/bGXe65vQ/gu5DIBiX92mNc2mbqg+XiF116hMCFIU6z5fW6idbUJCVRu6JwxScTGVg6V/0UKXVutxmpQ8Xm3ZX87FRKPwedC8oKsblflzDZ99wBbfzu9W+r+8+0tjz5feztsY1wlZTtl/n9JCHqNBVcP2Zz63X3IR9qWsdW+5LU7b/k/sNXFv+Q43pKzpMRhEmxhd8XuM63qLqx2DxD9ZiECq2X/otAWHRVq9zIa3lOnWG51NBVhpF2an4hMZSovKiYt27XGVcWWM5k4AC4UOgqqjGvI3KAAaZdlj9bDGh4tg1P6HTBrZbP4H9z9PC3BNc/Nct1a7J+vjTZTQ6twDGFX1f4zpe4TaO8RUrarzX7VIS8TedJlaVXcPe3/Rjhfct/HVSTbQqhzRTCNcMiOWGfhFtxk+RkZFN0u8MLZsOHbPZWEJDQ8nJyak2LScnBx8fn1oDTQCtVltrwVWtVouLiwtardamB785Nrt06VJjmk6nIyAggM6dO9u8cKzJZKr95CvMhIKjAOT+OZ8umX/WXF8oHL91NTGJ/Vm75HWGpryFRjFhECo2xD/F2Ltm1pyeMIMJdz7L2iVRNaZfeaf5A8LaJb41bCX0vZq/d8Uz9MjbNWyZ16lpL37wdRgXX4L6vJuYEBCkKiKI3XD2JqZWBGOz/022y0QiYpvXjdkevqrXT05qUxFGKn54qNaXgb9C72N41qfV/GIQKiLv/jd/b/pfDd937ns1iYmJ/P1NcK3nzNolkXWcYzWnjz97vvz1RQDDjr7T4PliEgrJSie6iHTLx5S6zpfW6Cdb2Fy9+BUuTX3n3MuCCsqFC1oqq/nf7OOPavq42nUcUG3e+rgnOXrMwLqYnjX8VZfv67yPVK3zZUSt95G6zqN6bV24TvxTXHnXzMatU8/0cbfNICkpib93BTdKc7378qWP1bYaPJYXrLM+7kmuvPu5Rh/LWrd/9tr/e1dcDb1XWWyFWHXfNwoV+0f+h6JD6xiW82W181ejmMg/eZRBI5ue86E1XKfg+OfT2iWvMyzlTdSKwJQE5WjxVHQ1ljMKhbSb/iQrL59Ba2+t8ayIuntBjfvIqoDbKDVq6H7mLxJV1XNfqDDhSRnRXbq0Wz+Bfc/ThIQE1vz6Zq2B5l/R0xl+7N0afux622tExHZl7ZK4Gtd+dN+r+XtXzxrX/sg7nyUj9VCNaxxgODsZXrwT4XXuw/Bz++/DbfQsIjp4tXs/2TPesYZW1bL59NNPs2LFCvbt22eZdvvtt1NQUMDvv/9u1XaKiorw9fWloKCAzMxMEhMTbXrwk5KSbGpTp9Pxxhtv8Mwzz9j8hK5V684vEMsfRcGEwByTGYXCXhFPL+UIakVUu/CryEg9xMmje9FrfBg0/DKLzYzUQ2Sn7ic0tke1l/O6pl84LywqwaIzKz3FqnWq5tUIdmOn41KWzZCcr2ocjx1DP2XAmBubdUzt4St7nFP2tNklLorDC26hR8lGS2tgFQahInviZo7UEnBUnUt1+V6tVjf6XKpruk6nY/47rzJiQDc6JvSp+3xJmIFnSBwDN95fY38vPF9am59sYTMj9RDhiy+u0Rqddt1yju/faJWP67uOgzrGWK6nvMxjzbqPVM0zGo1s/nsVroYiwuN6NctW1fTa7nsNrWPN9PN9Vde9r7HbsMf+Z6bsZt2Ogzz65HOW+15Tbdnlvn/23Es/coCIL4fUeEnNFn78L3wGI4cNpSTzMGEXHJeGcPbrtApHPp8yUg8RVkuAkEMgGZ496FPyN+rz/DXs7IeW/F3Law04qmxe6Ps/129i1J/jqwU9BqFi07jfGTJwQLv1E9jvPN2zdz8Zaz7k6uJva8y35pkPjb/2L7zGdwRdh2fxUXpU7K7xzmEv37c2P9nLZseOHQkICHDuls2SkhKOHDli+X3s2DF2795NQEAAnTp14tlnnyUzM5MvvvgCgIceeogPPviAGTNmMHnyZNasWcPSpUv59ddfHbULbYvCTEzLp6HCfKNWMLcGzg/+F5Mm3k9Wbrrlwh95wcM4Irar5SZx4fTaHtx1Tb9wntFobPQ6VZi/gl1XTbP5q9jXNVqwAjo5b3KmVoOuhPT3r6RH2W50QsN6z8sYWfpH9QdMbFciYmv6pYq6fH/hvOZMByg3qOkz8rpqD4k6z5cNSo0Wco2Hd+OOTRskactvRFzQeq1SBAX5ubUeyyqsvY7PH/7Q3PvI+fgFRdT60G2Krbrue03R3NTtN9aWrfc/qGMMv285bNN9sfV9HyAipisrO/4fl2V+gEYxYRQKRYo3ocoZHs6aiek7UClg3KCwNuHpai/DkuaRdXQvEbW0ep0Y8joDLru5WrAx8uyHFoBhtz9NVvr1Vt9HErp0Z+bv9/K65hNL69bzhnt5uJm9liQ1yUg9xJH9WxG7v+Zq0xYAdmsH0qPin0Y986Hx1/6F1/glsV3J3vUbyk+3VltOo5jorD1t612XNAGHBps7duzg0ksvtfx+/PHHAZg4cSKff/45WVlZ1cqBxMTE8OuvvzJ9+nTmz59PREQEn3zySasve+IsZKfuJZTqDwRFgdsHd8bfyx1/r7of+s7KhTeriNiurI2fYflaCiCApau3MCO+m01T4rcXMlIPkXNwPUG73qeTMZ0S4cYfia9zw62Ta7xEVFHfC6QjqfV8SXja8hW1qrXWsOZ1jBeNQa1pnWnIbYHX0Zof+QxCRWhsD8B5fSxp+9R17kUOvoVMt1vJS0syt5ZERHLk03uJz/nN0kKvVgRDU94iI/U6ef7aiEoXz1p7uoTG9QKa9nGiNqKCfYkZeQ/r/t7GSPVePjJcSczIiUQF+9b4eClpOlVdoqs+IJgEHOj1HH1umNFiz/wL7YXG9sKEChUmyzQTCh2iutlsm5Km49Bgc+TIkdTXi/fzzz+vdZ1du+pO5CFpOkeLXAip5YGQrPMn1HGybM6w259m89/9cK0sJOTQYqIKNnJb1pss/LUbU68a7Gh5rYoLHzolQssvPeZz603mL4xtIeA4/yuqChPd109hgGEXv3/xGldMnuVoeQ5h8+r/Mci4HZMAgXKue338UzW+WkskzkRETFei4rtbfp+Ovx5yfqu2jEYxkZ26v9Xfu5wBg8GI8vccFOVcorXzW71szYOX9eS7g4Pg9F5GeKbT/bKeNt9GeyYj9ZBl7G0VAgX/vlcDDnzm+3ZEdfV8xM+PoQjzhwW9UPPRyn08clPHltcjqYZsxpFYiMj50/JAAPMD4XnDvUS1wQeuX1AE/UffSNT9X1PoGkKUKpeQba/z265jjpbWaqjtoeOOnqH9+zhOlJ2IiO3KgDE30m/MzeyIfgCAoekfsHf3dgcra3mMBgP+G14GYJvP5WRN3MK2IR+zZeQ3DLv9aQerk0gaR1hcL4yien9wo1AsLfSS5rHqyzcYZPoHvdBw8NJP2TH0U7InbrZrN+XAnpcBEKdPgsoKu22nPZJ1dG+NsbdqRZCdut9Bis6j390oj+2Du38mz6srboqBngfeZtOhTEcra/fIYFNipvwMQUnmsbHPVU7iVv3zDNfNt3RBabO4++F92yJMqLhR/Tdr/vcJq/ems3J3Gum5hY5W59Q49UPHjgy66yWSNN3wUiow/jSN0vL29TKz6b/z6CqOUiLcibv1bSJiu9J/1A34BUU4WppE0mgiYruyPuFpDOLc69AJEYxrgGwNaS5HDu1lWNr7AOyMvp/uI29kwJgb7d7y1W/gUHKEH25UkrV3tV231d4Iiu7OhR0Szx8+4XB8O0LscDrc9TmVaBil3sXK7/9DmV52o3YkMtiUAJC3ai4eopTDpgj6THiQe2+5mW8ev4YH20EXFFXMUIxDzOOFZymf8sE3/+OzpUu5de5PfLRqXwNrt1/C4no590PHTqg0LgTe+QllQktfcZDVHz7GP6u/50xehqOl2Z3SwgK6Hl4AwNaOdxHUMdqxgiQSGzDyzmfJnriZnf3eoBxXolU5rFz8mqNltWqMBgMl/52Cl1JOkroLF931cott29/bnb0a87tL7p4/Wmy77YGc1H01esBtiH/K6bqcq0IS0Q+eDsBjps/55O8jrNqbLhsRHIQMNiVQfgbPXZ8A8Iv3Ldw8JJHLeke17RbNC3AZ9SzFgX3wUcpZ5jqbb1xfZYN2GsfWLpY3pzqIiIhEr5wb9u2sDx17EBTdnSO9ngDgqqJvuGjTAwxaeyvrv37Twcrsy56vnyOI05wghEtuf8HRciQSmxER25V+Vz9MZh/zdX1NwWes3bzVwapaL+uXvEIf4z7KhSvuNy5EpXFp0e0XBA4EwDt7S4tut82zawkAm91HOv3wCc9RMyj2iaeDUsy1pxay+L/fy0YEB+HQBEGOpCozmS0zlNnLpkajwWg02k1r3u/vECpKSTZ15JIJE5u8ndZ0TGvaVNjf9TEuWX+PJUGSWhG8ovmUDUduIKLDRVbZtbWvnPmYpq1ZTBwGMkyBJA94CVfvQAYPGeXU+15lzxZ+8u01AbH3tWrny9Ajb5N+5BoiYpofcDub708dP0T/7P+CAoe6TWeUu0cNe85+73O2Y9rSdqWfGrYZM+FxjiX9jxjdIdR/PEtRz1/wdK+7jp70U02de9f/zCXHPgAFtkQ9xPDOfRq1TVvsv1/iKMiZSyd9Csay0xg1ns22eSGtxU9Vds//tykU5mbQt3wTKOA27BF6DBxBcnKy0+m0oKgpGv0WnsuuZ7x6O+PV2zEKhefW3kdqz5lEBTW9QcWZ/eSMNhVRXzrYNsSCBQtYsGABRqOR5ORkNm/ejJeXl6NlORyVvphOP9+Alyhljsd0xo6/seGV2ij6tC302/FEjen/DJyLNupiByhyblyXP0hn/X6+c7+V7hP+z9FyWpzMAxsYm1Tzi+7Kbm8R3m2IAxTZjzN5GQRsmE034yG2Kz1xuWYBmnZc9kXStjHkpdB97b24KEYWBz5F/5HXOlpSq+HExq+54uQCS1fL38IfodOQ21pcR4nOQOhPNxOtymF3n1fQxF/a8EqSeslb/wmX5nzGQeIwXP95qygVd+joMa7feaeltBGYe2H91P8LusTGOE5YG6GkpIRBgwZRWFiIj49Pncu1m5bNqVOnMnXqVIqKivD19SU+Pp7s7Gw6d+5co7h1U6kKZG1pU6fTMW/ePKZPn16tAH1zqdLqc+xnvM62ao647n4SY5te5MQe+9+iNjv6YNpxYZ0mFX0GXwY+DSeLsIevnPWY6vOO4K7fj0ko+F58J507d3ZKnbVhKz95uykYDyrVkiQZhIou/YbarGXTGY7p+q/fZPiRt1ApAiHglH8vLutZfVxua7n3OcsxdZRd6ScrbSYmknRiEj1SP+GqvIVs2+9P4oCRtV7XDtdqJS3hp4xjhxh78kNLbw9FgctPfkim2+2Nuifaav9/+60X0YZVuOTtIWH8g+3WT2ADrcKE/w/mEkGpEdcyrnv3VnHuB+jSUV1QLVGjmBjUUU1IYmKT7Tqtn1rYZnx8vFXLt5tg80KqDrharbbpC4KtbarVagwGg110qvTF+O3/HICV/rfzSIJtsu85+zGt06Z/J7h6PmL5NBQEJgFlY9/By7+T1fbs5Stns3ls1cd0A7bSg9FDB58riO5kOuuyZws/RcV3Z23C0wxPeROVYj5fNsQ/xcjzavjZAkce04zUQww7G2iC+eVxTME3ZKVPq3Vsbmu597UWm/ayK/3UsM0et79G/ivfE6ic4YpDMzEmKaxPeLrOkh3Ovv8t4afcYweIuiBDuUYxkZeWVK22aWPtNpXC4Ivg5Cr8crfJd77z7DfF7ontvxEpsikW7vQad181G858TMPj+2CiZiNCeHxvaIZ9Z/WTI2xag/O3gUvsRuXWj/ASpaSaQhl5/f2OluMc9Lsb5ebFAJThxvelfR0syAkxmQg69iMAx8LG49KOu1OOvPNZ/kp8CYBCxYthtz3lYEW2pbbyNlUF7yWStkzG8WMEiHPJ4dSKYGjKW2SkHnKgKufGp0OoU2Uo79Dd3HW2Y2U6lOQ6RENboXD9fwDYoB1Op47hDlbTCHw7wpXzEGfDHSFgR5cnzNMlLYYMNtspm5b8i17Z/wMgWskhf/0iBytyIrpeRaFLEF5KBbm7VzhajdNRsO8Pgkx5FAkPuo++w9FyHE7fK+6hRLjhTwk5h9pW5sOwuF6YLih43x7K20gkWUf3Wlr0q5AfWuon9+D6mmUxEmY4LEP5JX16csAUZda253eHaGgLGIty6FK0AQBDr9sdrKbxiL53cXjc95SofFAU2Jjv4WhJ7Q4ZbLZDMlIPMSR1vmVchUp+sa2OSkVl1+sA6FW0lpwzpQ4W5FzkrDOXyVmrGULvhCgHq3E8AX6+7FGbg6/sf352sBrbEhHblZ2u/S2/Hf3yKJG0FGFxvTDKDy2Nwv/YrwCs8b+FHUM/JXvi5jq7HbcEfp5uHHTtBUDRgT8dpqO1k7ryQ1wwskfEceno8Y6W0yQMniGcjrwMgND8zRiMpgbWkNgSGWy2Q+QX24YJHHI3AJeqdvPzxt2OFeNMVBQSW7AOgPKuNzhYjPOQFWDOVux9cqODldgeF1MFAGt9r3P4y6NE0lJExHZlfcLTGMW516Q/vK+XH1rqICs9mW6GgwBEj3uEAWNudIpjVRxivjd3yN/mYCWtFJMJn0PfAbAnYAJe7q4OFtR0ggdcA8BQsYtNSRkOVtO+kMFmOyQkprtTjatwSkJ6kOcWhVappGTfr45W4zSk/fU5WipJMXVk1GVXO1qO0+DRdQwAMbokqChsYOnWQ3l5OfGGFAA6jrrfKV4eJZKWYuSdz5I1cTMHtb0BMOgrHKzIeTn852eoFME+VVfiuvRytBwLoT1HUinUBBpzcSnJdLScVkdZ8hpCDFkUC3eiRrTuYTOa+EvR40KkKo9dO9vWkBdnRwab7ZByfaVTjatwShQFVc+bARhQuo4T+UUOFuQk7P4agO0+lxHk5+lgMc5Dv34DOWYKRYORgn1/OFqOzdi7bS2eio4i4UFcD1lrVtL+iIjtit+IKQAM0G3mRF7b+ZhkS8IzzWMi8yKvcLCS6gzumcBuYS7PYDy+1cFqWhmFmRT+9goAf6iGMqxXZwcLaiaunuT69wNAe3ydg8W0L2Sw2Q5J2bYSgKOqaLYN+Vh2jauDDoPvBGCQ6gC/rZddcHQnDxCtO4RBqAi4uOWLdDszQb4e/KPpA8CpNpRU6tSh9QAc03ZFpW63lbIk7ZzwgddQhhsdlVOsXy17ulxI6sEddDalYhAqul02ydFyquHn6cYh154AKJnbHaymFbHzC8S7PQgrNBepDAwMRaVq/SGDd68rAeij/4fj8sNRi9H6zxxJo1Fl7gAgz683/UfdIFs068I/mpOe3VErAmNS20r80hSOLH8LgO10Y/SggQ5W43xk+5m/mAbkbKRGP/VWimfebgDKQ/o5VohE4khc3DnRYTAAmiNt52OSrchYvwSAvS69CY2IdqyYWigLvwSA8KI9bebebFcKMzEtfxRFnEuiMzzvKyhs/d2QfXtfBcAA5TB/7jjgYDXth3b7qdpoNFb715ltajQajEajTeyeLqkgVncQVKCN7Ncq9t+RNrV9b4YNsxlUsZ6UzFPEhvrVa9eWvmqsVnvaXP/Va4zI+hEUuJgD/P3Nmwy7/Wmn02mtXXv4SRMxAN0pDR0MuRhzD0NgQrNtnv+vLWiMzXJdJQmVh0CBoMRh9a7TGu59VTbP/9dZbdrLrvRT020GDrwZfl/DIP0WDqTn0DUi0CZ2a6M1+clkNBKba870Whw7wSa2bb3/HbsPpSxdiy+FVGbvh7CeNrHbWvxUZff8f+sj58huwqmerVWFiZNH9hDSJ7RJNu2hs0k2fTtRoI0kSHeCwoOrMV7etOEhzuCn1mRTEaJ9fOZZsGABCxYswGg0kpyczObNm/Hy8nK0rBZn06EM7tt3KypFkHTlzxjdAhwtyalRVxTQ+ZdrUGNibqd/c/lFtnlItSbO5GUweO2t1TIYG4SKLSO/wS8owoHKnIvTZZXw8yMMU+8nNfERyrq37q7Gh1NTuWHnXRiEiqRrfkdxlWN0Je0XxVBB3I/jcUPHu+HvMGbwIEdLcgqyU/cyZufDVAgX9k34GXcPb0dLqkGZ3kj5j48wQrWX4xHXUNbrHgwewY6W5bQcOnqM63behfqCZ/5P/b+gS2yMA5XZBtct8+ic8T3LTMOJvv5VXDWyk2dTKSkpYdCgQRQWFuLj41Pncu2mZXPq1KlMnTqVoqIifH19iY+PJzs7m86dO6NWq22yjapA1pY2dTod8+bNY/r06Wi12mbbW/3XH6gUQZ5LOEa3AKfff2ewmbF+AFGF2/A7uYbExJvrXM7WvmqKVnvY/OfkgVpL5bgaikhMTHQandZiTz+tc+3DMON+3HL/IerGf9nEpqOOadIWc3fBEy4xdOs9oMW1ttXryZF2pZ+aZ/P4xiHE5K0hIOtvunS5B5VK5bRaL8Refsr+3Ty8Yq/bQPr3v8hmdm29/xt/9QAjdMr4CZH5M2LCPETfu5xOpz38BI3T6hEYzuvbbud5l6/M6woVzxvu5YGLhhAV5Nskm/bQ2VSbQnsHfPU9w5TdbK10Y1zPuEbbdAY/OYPN+Ph4q5ZvN8HmhVQdcLVabdMXBFvbVKvVGAwGm9g0Gk145u8BBXRhAy32nXn/ncGm38V3wMptjNCvZ/HaJEb3jCQq2LfGcrb0VVO12sNmeHxvxEZQzqtvbhAqwuN61Vi/Ney7Pf1UGDIITi4hsGAnapMeXNybbdNRx1SbuxOA4sA+Vm/fWe99tdluDTbtZVf6qWk2gy+5FX5ew3DjVv45msslXTvaxG5dOLufDAYDPYrXgwLGbtc57/4XZjLUeC4TrSJMiF+mo064DHw71rOidTi7ny6035Dd2NAA4rv0gtSvyDQFcJP+JSaO7ktsaO094Zz93K9hM3YY5YoHQRSRsnsDVw5sfJZdZ/CTs9i0Btl23I7YkpJFT3EYgKDulzpYTevBt9/16HAhTpVFyupF3Dr3Jz5atc/RslqMiJgulHLuy50slVM3nbr0JUsE4Cp0kL7J0XKaTLmukpiKQwD4dR3uYDUSiXPg2WMCelyJVuWwceMaR8txOLv+Xk6oUkCxcKfvZbc7Wk6dZKfuRUX13jkqTGSntp/neGNJdDkJwCFVAt88fg0PXtaGhhCpXcgPNneD9z65AZPJ1MAKkuYig812xOo9x+itHAVAEzPYwWpaD+lFgsMm89jE110/ZYN2GsfWLiY9t32kzT6Zuh8vRYdOqNkw4ANZKqcehnfryN9Gc0HzsoOtt97m1gNHSFTSAYjoPcrBaiQSJ0HrRV7IEAB8j/+Jwdi+X1J1u38AYJ/XENw8nDcHxuGKAIxCqTbNIFQk6/wdpMj5ETkHASj2jqu1J1drp0O/qwG4yLiTgxmnHKym7SODzXZE7tFduCmVlLv4QUDj+6i3V9KOHqKHkmb5rVYEr2g+JT31kONEtSCpO81f8I+qYhh65V2yRbMeOgZ4s0/bB4DK5JWOFdMM0veuR6OYOKUKROXfydFyJBKnIegSc+KvUWILf+5Jc6wYB3L00G76lfwFgLb3DQ5WUz/RcV2ZabjPUvXEKBSeN9xLlHyW1YlPiblhQhWc6GAl9sGj+3gAeqmO8ff23Y4V0w6QwWY74UR+EeGlZ2sKdbqk+gA8Sb10cSuoNUFOZ+1pBylqWQwnzHVZ8727OVhJ66Ai7BKMQsG35BgUZjhaTpNQZZnHa57y7+1gJRKJc+HabTyVuBCrymbb1vWOluMQ1i55nZhvRuKtVCAEFGcfdbSkeokK9iVmxN38ZjInMPrIcCUxIye2yRY7m2AyEm44AYB/TB/HarEXXsFke5o/NlSmyC7x9kYGm+2EFbvSGahKBsA9bqiD1bQuQmN7YbrgUjGhIjS2DY1hqIeg4iQAXDrVn5FUYqZ352h2i7MZ2o6sdqyYJlCuqySy3NyFyithiIPVSCROhtabM2HmZ2iHk3/x685j5BTrHCyq5chIPcSwlDctH2AVBYYenUuGk/f0uX90dwo9ogC4KMjQtsYg2pjCjIO4UUmZ0NK1Rz9Hy7Eb6i5jAehSup3/bU1pN0OjHIEMNtsJ6w7n0l9lTg5EJ1kfrFH4dkR19XwE5tZgAaiunm+TLHbOTt6p08SbjgEQ20+O3bOGEYkdWWc0twhW7vwKCjMdrKhxbDqUQV8lBYDQntLnEsmFdLjoFgDGK5v55n/LeP6nA3y8+oCDVbUMWUf3Vqu/COaePtmp+x2kyHpMnmEAeFecdLAS5+b4gS0ApCoRBPk571jc5hLU/1oAhqr28cNP37e75I8tiQw22wHl+krOZB0lQCnBqHaD0F6OltT66Hc3JRP+DUCO8CMz6joHC2oZ9u9Yi1YxcBpvQmJ6OFpOq6BTkA/+rgYAXDK3wrs9YOcXDlZlPYf3bcNXKaNCcUMVKr/+SyQXkhE4lEqhIkaVzTeur7JBO420dV+0i5aRsLhetSbbCY11/ueD8DF/IPbVZztYiXNTesIccOVqox0rxM6ka2IoFu54KRUscX2j3SV/bElksNkO+Gv/CXphbtVURQwAjauDFbVOvHtfhQE1ocoZ/tm109FyWoSiI+YvnBluXeQ4X2spzGSi+Oncb2GCnx9rNS2cpox/gLNjdNXtthSzRFInxzJOouFcJtr2lDQuIrYrG72vsPw2CBUb4p9qFYnjtAHmrPKBpjwwGhysxnnRnjYPuarwS3CwEvuSlpqMF+WW3+3pOm5p2u2bhNForPavM9vUaDQYjcYm2111IIshZ8drisiLMZ1nqzXsv9PYVLuR6RpHlD6ZM8nrMV5afeyrLXxlM602sulVYO4aVRnap97lHa2zsXbt6qf8FNQX1HRDGDHmHwGv0KbZtBEN2SzXVRJeehDUoIkeZPW2nfXeV5vN8/91Vpv2siv9ZBubCa75Nb69aRQT8S6nbGLf6f2kcQNgm8tA9IOfZPCQUU7rq/Nt+ncIQSdc0CqV6E6loekQ02yb5/9rC+xxPVXZPf/f+giqMA+dcevYo0089+uy2Zzr2Bn81JpsKkII0fBirZ8FCxawYMECjEYjycnJbN68GS+vttsX/XzuXXaC7wyPEq3KIW3oXEpCL3a0pFaLfu3b9Mv/kR9VlxF//YuOlmNXyvRGgn+6iWglh5393sQ1ViaWsoYzeRkMWntrtXFNBqFiy8hv8AuKcKCyhtmdWcKQjXcTo8ohdfA7lIXL8d0SyYVoynLpvOJ6VOd9VDKhInn8Dxg8gh2orGVQLbuHbqYUVkQ8QadLrne0HKsxGE14/3AzcaosdvR/B7cYeX+7EJOulB7Lx6JSBGuG/0BwsPUfSFsb7f06tgUlJSUMGjSIwsJCfHx86lyu3bRsTp06lalTp1JUVISvry/x8fFkZ2fTuXNn1Gq1TbZRFcja0qZOp2PevHlMnz4drVbb6PXX7D+OqewU0W45CBQiB10Pbj520doebOYXjYWVP9LFcJjgyBj8vNws85rrK1trba7N1Vt2cZGSA0Dv0TeDW91p4p3NT/Vhbz+tqfTgWcN9vKH5BJUiMAmYabiX0X6dGZQY1SSbLXVMf9v/JzGqHEwoRA25EdzqfnjYW2tbu56cwa70k61sJgJzECseR8H8gmoaP4eE/iNsoNS5/aQrL0FjTAUFYgaOwwit6tzPUYcQJ7JwMxaSmNi8GpLO7KcLsVZryo4/USmCfOHD0KEjUKvrHm3n/NdpQzZrXsdcOY+Evg1fx472k7PYjI+Pt2r5dhNsXkjVAVer1Ta9SdraplqtxmAwNMnmR6v28frq44w724X2lGccgZ7+dtPaHmyG9BwNK6GLcoKVSUe44pJzdQib4yt7aG2uzZxDmwDI0nQkzDPAJjabgq1t2ttPiR078LDxUrxEOS+4LmGbqSvfGy9lascOTdpeSx7TsmPbAMhziybkgntFc+w2hbZ2PTmTXeknG9i86F6y/nyPMH0aS3zu547+E51XK7bzU+qeDfRQjOQLX+ITe3M4OblVnfuFrmGg201lfppNj62z+ak++/XZPZW6C4ATmij6urrYxGZTaDGbF91L1u9vEWbK5o+4mYwbcI/VthzpJ2eyaQ0yQVAbJT23kDdWHwdg4NmSJysKY2SWrebiHUKuOhSVIsg50LYLemty9gBQFCCzFzeGqGBfnhndiQPCPB4oRDnD06M7OX0B8Q9+30Noqbmm6p8lsTIFvETSACUdzPdGdVmug5W0HKcPm597R127oLLDS7a9qfAMB0BVeMLBSpwTU475GXDGM9bBSlqOU+7mfS0/k+dgJW0XGWy2UQ6fPG3phT7gbLC53dSF5KwzDtPUVjjdoS8A2py2m5G2XFdJRIX5vPGJl+NaGsuDl/VkYPfOAESo8nlwdHcHK6qf9NxC5qzNYJDKXCswxRTOm6uPy49TEkk9uHcyF7yP0KdiNJoaWLpt4JFrbvkqDOjdwJLOieJnHsrgVd46soO3NL4lR83/CW5eF+PWRKV/HAAexWmOFdKGkcFmG6VLuD8K4EEF3ZR0AP4xdaFzmJ9DdbUFvBKGARBTcZByfaWD1diHzYcz6K2YHzoh3Yc7WE3rJDomHoNQ4YIBSnIcLadeDp88ze3qVfRU0gCY5bKEG9V/yY9TEkk9hCaaP8QlKukcyT7jWDEtgRBEVZjLQnjGD3awmKbhEWxuxQowOPc92RGYTCYiKs3vix1i+jpYTcvhGmYOrIP0xx2spO0ig802SlSwL3f0DWSUaicaxUSW8Gfi6L5O35WvNRDWaxQAvZSjbD/cNrviHN6/Ex+lDD2uqEKdv1i3MxIdGkCW6GD+cca5H2LdfCp4WfO5JQ28WhG8pvmURO/y+leUSNoxmvBeGFEIVs5wKLnt1+bLO36IQM6gF2q69B/paDlNIqhTFwD8KQJdiYPVOBdZGccIUIowCYW4Xpc4Wk6LERxnDqyjRCalFXoHq2mbODzYXLBgAdHR0bi5uXHxxRezbdu2epd/99136dKlC+7u7kRGRjJ9+nQqKipaSG3rYpzpL95z+QCAUOU0D3bY5WBFbQNVcFeKFW/cFT3H9m5ytBy7UJlhPlfyvLqA2rokAZLqxIX4kSGCACjJOeJgNfUToeSiUqpXwdIoJiIUOYZFIqkTVw9yNOZyRiXpbf/5mr5zFQApqhgCA6xLGudsxHaK5LQwl707nXHYwWqci/QDWwDIUELx9PZzrJgWpEN0TwAClSKOpDr3s7q14tBg87vvvuPxxx9n9uzZ7Ny5k969ezN27Fhyc2sfbP/111/zzDPPMHv2bJKSkvj000/57rvvmDlzZgsrbwUUZjLo8JuozrZUKAA/PwaFcpxCs1EUcn3MNyeRsd3BYmxPpcFIUPFBANSdBjpYTevF38uNHJU52DyTmeJgNQ0QEEeNEWeKGgLaT5IIiaQpFPqYW8pc8pMcrMT+GI6bn3cnPZ17DHp9eLm7chJzDcXc9IMOVuNclJ4wJ4XLdYt2rJAWRuXmTbZiflbnp+51sJq2iUODzblz53L//fczadIkunXrxr///W88PDxYtGhRrctv2rSJIUOGcPvttxMdHc3ll1/Obbfd1mBraLuk4CiqC18fhREKUh2jp42hiTaP1elYdqDNJYbYfiSbnmfHawYlDnOwmtZNoWsIAPr8Yw5W0gC+HdlDl3O/FTVc9S74dnSYJImkNaAON2ekDSpv+y0iQYXmYMQU3t/BSprHKRfzfbk4q+37rDFoT5vL5JX7JjhYScuT5xoJQEVW2/9o5AgcVmdTr9fzzz//8Oyzz1qmqVQqxowZw+bNm2tdZ/DgwSxZsoRt27Zx0UUXkZqayooVK7jrrrvq3I5Op0On01l+FxUVWaZXVlai0+lsWuTU1jartJ+/D1bhFYkGBTXnusYJRY3eKwJ0OrtobU82g7qPgD1z6Mthtidn0Dc2pOm+srPWxtrcfPAYjyrmMYaGkN4YrNgfZ/VTbbSkn3TuoVAJSuGJRm+vJY+pwWiizKQBFZzq/RBeQx8Cn3CwUrNT3fvqoTWdp61Fa3v3k39sf9gPnU3HyMw/Q6C3e7NtOqOfDOXFRBvTQIGgroPR2ek9Auzv/xK3MCgBU0Fas85bZ/RTXVijNajc/FFUG97dqu23luvUGpslXjGg24nrmaNW7bsj/eRsNq1BEUKIhhezPSdPnqRjx45s2rSJQYPOlVaYMWMG69atY+vWrbWu99577/Hkk08ihMBgMPDQQw+xcOHCOrfz4osv8tJLL9WY/swzz+Dm5tb8HXFShIAgQypTNT8CYELhF8awS+npWGFtBLUwMIOFuFLJLI8X0ZQXOVqSzShx8eBt/SucUXyZLyZjyRojaTQaNzXPlb/NSSWEj7nD0XLqpNLFi/sqPiJalcOn3EyGEuFoSRJJq8BNlPM05neQ593/hUvFGccKshNhmtM8UPkZJ0UH/qO6BwWHvDrahCjXXO7RLeEfVW9+EaMdLccpMKHiGdN7uCt63tU8TKGx+R9NWhOdXU9ym+5btik9+Y3LHC2n1VBRUcEbb7xBYWEhPj4+dS7XqoLNtWvXcuutt/LKK69w8cUXc+TIER599FHuv/9+Zs2aVet2amvZjIyMJCsri6ysLLp06WLTSP/w4cM2tanT6Zg3bx7Tp09Hq9Vavd7R7DN8/NE85rkuxBjUHcPNX5lbKuyotb3ZzJ4/iqiy/Xzs83/cPXVWk33VElqttWk0mljwxhM8pfqK/PBReE/81il1NoeW9NPSP9Zy186bqUSD6ZkMUKwfudCSx3TdgeOM/OliXBUjuqm7wKdx3Wed6d5XH63pPG0tWqWfjBS/04sQUx7fJszluhvvtIlNZ/PT7iUzufjEJ/ytGczFT/1oN532snu+zXW/fsO4A4+Troog9Omm18t2Rj/VRUNad/+zhYtXXk25cEX1TDqoGt6f1nKdWmPz6I4/6LbqLrJEB/yeOYBKVf+z2lF+cjabYWFhhIWFNRhsOqwbbWBgIGq1mpyc6rWOcnJyCA0NrXWdWbNmcdddd3HfffcB0LNnT0pLS3nggQd47rnnaj05tFptrSeCVqvFxcUFrVZr04Nva5tV1LUfdZGaX0qsKgsAdaeLUAfF2F1re7Np6jgQUvYTWLQfF5dzGVsb66v6aOn9X7HzKN2EOZmNX5chaKzcD2f2U120hJ9CI+Iw/KPCRTGA/gz4hDXbpj10ns7NxFUxYkCNtkOUVS8a9tZaRWu+npzNrvSTffx01C2OkLI8lNwDNjkGzugn97w9ABQG9Las31rP/YBOXeEAhJhy0bq6Nrn3jjP6qS4a0no6zZwYJ1MTSby7h01s2kOnvWxGdbsYVkGYcorswsI645ALaWk/OaNNa3BYgiBXV1f69+/P6tWrLdNMJhOrV6+u1tJ5PmVlZTUCyqoD56AGWqclJbuQOOWk+UdgZ8eKaaOE9hoDQC/TIY62gYLeH63ax5Slh+ijMicHWpEX5GBFrZ/Y8A6WWpuGgjTHiqmHslxz4rDT6qBGB5oSSXun3M+cUMW/ONnBSuyEyURUhTlxikfcxQ4W03wiY7tiFApu6Kk4fdLRcpwCU67Zv2c822cGck//EPIx16E/cfgfB6tpezg0G+3jjz/Oxx9/zOLFi0lKSuLhhx+mtLSUSZMmAXD33XdXSyB01VVXsXDhQr799luOHTvGqlWrmDVrFldddZVdWj5aM6n5pTLYtDPucYMBiFNlsX1v606hnp5byBurj9ODVDoqpzAKmLnDi/TcQkdLa9VEBfmSiTloLzjhvDXdxJkTAObEGRKJpFFogrsCEG04RoXe4GA1tqco4wC+lFAuXOnad4Sj5TSb0A7+ZGP+CJiVut/BapwDn+KzmXmDEx0rxIFkacwZaYszDjhYSdvDYd1oAW655Rby8vJ44YUXyM7Opk+fPvz++++EhJjTUh8/frxaS+bzzz+Poig8//zzZGZmEhQUxFVXXcWrr77qqF1wWk6cKiVayTb/CIx3rJi2ikcA2a6dCNUfp/DIZri09dakPHzyNDep/+INzSeA+SvUePVmkrMGEBXs61hxrRiNWkW+OggEFGUdOVvdzflwLTV3ua/0lqVOJJLG4hbWDXZDnJLJnmMn6NclpsF1WhPpu1bTEzioxNE/OMDRcpqNSqUiRxVCR5HP6YxkYgaMdbQkh2I0muhoSAcFAmL7OlqOwzjjEQVF+1HynbwudiukWcFmVddVpRnZKh955BEeeeSRWuetXbu22m+NRsPs2bOZPXt2k7fXXjAVnUSrGDCqtKh9Ix0tp81SFtwfMo7jmb+b9LzWm5G2m08FozWfoFKqrml4TfMp2d73OVhZ66dYGwoVYChId7SUOvHRm4NNTUCUg5VIJK0Pk0cQpxVf/CkkM2l7mws2DelbAMjybDutXoWuoaA7gC5f1h5PPp5JF8z5UyK7XeJgNY7D6B8PReBV6uR1sVshTepG+8UXX9CzZ0/c3d1xd3enV69efPnll7bWJmkiRWU6OugzARABcXIMlh05rDF3n+puOszYD7aR00rHO0QouaiV6uOeNYqJCCXPQYraDpWe5izQmuIMByupHYPRRKDR7GfvUNkLQiJpNIpCtlscAPrMPQ4WY3sCC81dTQ1h/R2sxHZUeJl7caiLTjhYieM5nrQDlSI4gzcuvu13KIV7x+4AhOrlOWFrGh1szp07l4cffpjx48ezdOlSli5dyhVXXMFDDz3EvHnz7KFR0kiSMk4Re3a8piZYjte0F+m5hbx+2NwxspdylEiy+T0/oHW2cAbEIS68HShqCGidwbMzofbvBIBXRbaDldRO5qliOmIONv0juzhYjUTSOikP6AaAZ6Hzjs1uCqay00QazS/fod2HO1iN7VD5mXtxeJXJBEGlJ/YBkK2Nadd1tUPj+gAQLnLQlZc4Vkwbo9HB5vvvv8/ChQt58803ufrqq7n66qt56623+PDDD3nvvffsoVHSSJJlJtoW4fDJ01yiOogQ4KKYWKt9ghvVf3E0pxUm1fHtyIqQB879VtRw1bvgK8fwNRfvUHPAHmDMBZPJwWpqkppTQLhyCgBNQLRjxUgkrRTPqH4AdNSlYnLC67ypnNy3FoA0EULv7m2nG63n2ftyB4NzfgRsSTzydwNQ7h5e/4JtnIioeIqEB2pFkJHc9nooOJJGB5tZWVkMHjy4xvTBgweTlZVlE1GS5nEkp5i4szU2ZbBpP7r5VPC65hPLh0CVInhN8yldvMocK6yJ/FNhDixLNAHw2D7od7eDFbUNQiJiMQgVrhigJKfhFVqYvMw0XBQjlWjA27raYhKJpDoR3c3vRZ05TkZugYPV2I6ifb8BkK6Owd3VpYGlWw9BUeaW6CBRgKmywsFqHMfaJa8zVm8uQdj79B+sXfK6gxU5DrVGzXFVBACnjslg05Y0OkFQfHw8S5cuZebMmdWmf/fddyQkJNhMmL0xGo3V/nVmmxqNBqPRaLXdY6fKLN1ojQGxUMt6rWn/ndVmmMiudZxjuMi1md6W3H/PInN9zdIOPXD3Cq31vGmszeZgD5tV9hp7TVlj8/x/zyc2NIAs0YFIJY+ik8l4elqXk7aljqmlxqYmiA6CRvm9PrvNpaX95Ew27WVX+sl+fnIJjKEUdzyVclL2b6dj0OXNtuloPym7viQx4zsAhpm2YtrxOaLvXXbVaS+7F9qMiIimTGjxUHScTEsiJLaX0+i09fVUZff8fwEyjh1iWMqb1T6YD015i/QjVxMR07VJNu2hsyVtntJGQkUyldlJ9S7fkn5qCzYVUZVS1kp++OEHbrnlFsaMGcOQIUMA2LhxI6tXr2bp0qVcd911jZTcMixYsIAFCxZgNBpJTk5m8+bNeHl5OVqWXXj6pyR+rTRnET147SpMGg8HK2qbaMpy6bLiBhTOdZkSiorD437A4OGsRS5qp6LSSMoPs7lN8xfpcXdS3PdhR0tqU5T9934uUg6yI/FZ3Lpf6Wg51di08lseKHqfFLde6K5c6Gg5EkmrRf3j/SQaDrI06FG6jbjZ0XKahaYsl84rrkfFuVdEEyqSx7e+51tduH9/C3Fk8Fe31wjq1vrrhzaWzAMbGJv0dI3pK7u9RXi3IQ5Q5HiOrf6Yq05/zjbtYDyuetvRcpyekpISBg0aRGFhIT4+PnUu1+iWzRtuuIGtW7cyb948fvzxRwASExPZtm0bffs6b32eqVOnMnXqVIqKivD19SU+Pp7s7Gw6d+6MWm2bbK1Vgawtbep0OubNm8f06dPRarVWaDDhUf4naEDvEUKXnrVnj7OH1vZnMxGhmge/TkcRJoSAnzRXcnmPS6zyVctqrd/m9iPZdFGZk0B07DkcJbFxY3Oc20/Vaew1ZQ0NaV2lCQbjQbSVZ0i08ti21DHd87M5OZDJr5PV2lpCqyP85Cw27WVX+sm+ftrj1xXyD+JTerTJ15K9tDbWTzm706sFmgAqTPioKwg5u2+t/dz/RxNCnCEDV31Bk/xVm82MY4fISd1PSGwPq1oHL8Qe11NdWr3dFEwHFUvJMwCDUNGl31CrWzZbw3XaGJtZh7rDaQiuzCCynnOiJf3kzDbj463LYN+kOpv9+/dnyZIlTVnVaag64Gq1utaDbzQaqaysbJRNo9GIyWSisrLSZgkCDAYDWq0Wg8GAStXwENuM/CJ6eBZT4RKJqeNATHXsgz20tkubPW6FjoMxfXU9KuCwdgijrPRVi2utx2Zq1inGekOFEglBPcAJzn172ITGX1N14erqWmP9uu4npW5hUArizIlG3+zrstkczrfprTcnyHAJiG72dmypVa1WYzAY7L7/zmzTXnaln+zjJ5eI3pC/jJCyozbZhiP9lKIPJFTUDESOVHYg/IL1W+u5X+IWDiX/IM4cb9Z2qmyuXfI6w1LeJEoRGDcqrE94mpF3PttoW/a6ns7XChAV3501bqMYpTOP2TQIFRsSZjAyvnuTbdpDZ0vaDIjuDfsh3HgSNQLUtYdJLeknZ7dpDVYFm0VFRZbm0aKi+ss61NeM2hoQQpCdnc2ZM2eatK4QgvT0dBQbpY8WQjBkyBAyMjKsslmuNzBu5EiOcRFoveFY7cVp7aW1vdo0DnkXNUZGKD5W+8oaWmr/Yz0ryR36GgIFpVCBosYVNW4tfqqy25hrqi5UKhUxMTG4uro2uKzJOxxKwbU0s8nbswcGo4kgYy6owCssztFyJJJWTXi3wbAb4kQaxWXleHu4O1pSk4mO68pKY3+u0OwAzIHI84Z7eTi28a11zorRJxJKwK2k+TWQM1LN4x+r8jioz45/zEi9jggnPmalroGggz1uA+lw8weMdGKtLUFsl56U/+yKu6KnMPMQvp16OFpSm8CqYNPf35+srCyCg4Px8/Or9QVNCIGiKDYfKN7SVAWawcHBeHh4NOplVAiBTqdDq9Xa7OXYZDKRn59PYGCgVa0w+cXlaMs0eCvl4BkKngEtprU929TlgVaUU4AffoGhNmvZbKn9z83LJVgoVCquuAQ1vrZma/ETNP6aqsvGyZMnycrKolOnTg0u79IhCrLBR+dcafbPr7EZECFrbEokzSE4ri864YKPUs72A7sYOLBm5v7WQlSwL+keHqCHrw2X8oHheiaO7ktUsK+jpdkMl8AYOAk++ubfl7OO7iWiloSB2an7nTrY9C43B9rGjhc7tc6Wwt/bgyTCSSSNk8k7ZbBpI6wKNtesWUNAgDlo+euvv+wqyJEYjUZLoNmhQ4dGr1+Va8nNzc2mwaZGo8HNzc2qF2NTiQFvjRE3RQFPL3BzazGt7dqmmztulRW4nbVry2AT7L//bmoTbiioXT1wqeOccQadtqCx11RdBAUFcfLkSau64/qFx8EBCDTlmWtt2uj8aC7Hsk8xRDGXaZA1NiWSZqJ24bimEwnGo5xK2Q6tONgEiDSZA5HS8KF8c+M1bSrQBPCP6Ax7IcTY/JJUYXG9MG5QqmWoNwgVobHOHawEVZ4EBTzCZJm8KrJdIkg0pFGWedDRUtoMVgWbI0acy9IVExNDZGRkjZc/IQQnTpywrboWpmqMpodH683eWmk04srZ8XaaxgcNkqahuLhBJbiIxo11dAYMRhOuQg8KqFxb77nf0lR1nzUajQ0Gm+FRnc21NhUDxqIs1H4dW0Jig+SdPFdj08U7zNFyJJJWT4FXZyg8imfaKjJSx7be1iKTkXCD+Z2uc8+BbS7QBOgYax6b6KuUUnw6F2//pmfZjYjtyhZNfy4xmrsdCwF/RU3jMif2f3FZBZGYW3WbUvqlrVLkGQOFG1AXpDhaSpuh0Z/XY2JiyMvLqzG9oKCAmJgYm4hyNLZsRWlphFGPSgGBCtRtpwCzs6NxNY/NcaESo6lR1YQcTrnegBt6ANSurXeMUUvTmPtEVEgA2Zh7h+QdP2wvSY2mLNc8NrdAE+Q0ra0SSaumshyAYfr1hC2+hLVLXnewoKZRkZeKlkp0woWYzm0zEAkMDOKUMOcZOXn0QLNs6SoNBBmyLL8VBdTBzj00ITX1KD5KOSah4B/Z9OzJbQ0RYM6w6lOa5lghbYhGv11Ujc28kJKSEtya0P1OYjuMJhMak7llTWi00IqD5tZGVbCppRJdpcHBahpHhb7yvNZwGWzaA62LhhwlCIBTGc4TbJrOmFsuSt1kq6ZE0lwyUg8xsHSd5fe5JDGHHKiqaWQcMrfQHSOciKC216pZRY4qBICCZt6XN61fTZySiV5oOOBmLjlXkbq52frsyanj5m6ieaoO4CLf36vwijS3eIcZTpibqCXNxurSJ48//jhg/po/a9asal1NjUYjW7dupU+fPjYXKLGeCr0RrWIOGhTZhbZlUbtgwpwmvlJXDu62q7tkb4z6ClSKuWC3SraG240zLiFQmUR5buMy/doT19KTAFR6RThYiUTS+mmtSWJqo+j4PgCyNBF0bcO9Hs5ow6AiBX1+8+7LxbuWAXDYsz8VHQdDyj90OLPPFhLtRnl2MgD5LuGEOFiLMxEe14PKdWrcFR3G08dRB0Q5WlKrx+o7yK5du9i1axdCCPbt22f5vWvXLg4dOkTv3r35/PPP7ShV0hAVlQa0Z1uoFJfWE+zYgpEjR/LYY481ap0XX3zRdh9IFHMmVwBTpc7q1YYPH87XX39tGw1NxVABgFHVulvD165di6IolrJFv//+O3369LFpjc7mUO5+tvWw0HnGtvvozYkxNB0azqgrkUjqJyyuF0ZR/R7aGpLE1Eq+ORAp8mobw6PqQucZDoCq8HiTbZTrKulWtB4ATY9riOg9GoCuxsMUlZY3X6SdUJ9JA6DUU97/zyehYzBpIhSA3KO7HSumjWB1y2ZVFtpJkyYxf/78Vl9Ps6pEy/mlWoxGo6W2n2hC03nVOk1Ztz6biqI0qGnSpEksXryYyXfezKdvPoNQay3N/1OnTmXhwoVMnDiRzz77zK5aHWnTWr9duExj9KpUKpYtW8a1115bY55J5QpGHYpRZ5XN5cuXk5OTwy233FLn8i1xTNUmHSggXNyavJ2m6ly7di2jRo2ioKAAPz+/Ztk8f3khBGPHjmXWrFksWbKEu+66q9py1lxT1mxPCFEtQVB9pZ+ETwQUgVvZSatKRNV2j2ou59s0GE0EVtXYDIlr1nbspVWj0WA0Gm1m197H1Ja0Fq3ST+fshkUlsD5+BiOOvImigEkobIh/imFRCY3arjP4ybdqvFqHurW3iXPfLwpOgWd5VqO3V7X8unWrmaBkokdD3NBbUbt7UYYWX6WMv7etZ8jw0Y2yaevr6Xyt59v0Kjd/+DT5RTdpW63lOm2sTZUCGeqOJIhMCo7tJrjflbXabCk/tQWbVgebVVQFK62NBQsWsGDBAsuBOXLkCF5eXiQnJ1uWMZlMltp+zaFqfb1eT0VFBW5ublYVfq8LPz8/9Hp9vcsYjUbCOkbww/IVfPDiYyjeCqKigoqKCr755hsiIyMxGo1UVFTUqtWWNNWmXq+v8zg1ZNNkMtW6f/VhMBgwmUyNWqdKZ23rmFQuYAS1qfb5FzJ//nzuvPPOBn0L9vOTwSTQnk0OJFSuteo2Go0oilIj42pt/mqszqp9rzh7rtals6m2br/9dubPn89NN91UbVlrrqmG0Ol0VFZWcvToUcuxOf9+UkOf1pwgyE+fQ1JSktXbqc9mU0lOTianWEfnszU2iwyu5DVCU312bck111xDamqqTW2C/Y6pPWgNWqWfztkN7Hs1WzM3c0nF3/zhfgWRfa9u1PVem01bYbWfhCDmbCZag2d4g/pb87lf6Wq+L3cwZDfZT+V7/gfAYbe+KMfNSYIUlwS6V+4nd/8akoLCG2XPXtcTVN//oErzMIoKbVCT9/1Cm7bC0TbzXSNBt42KzP11HpuW8pMz2zxy5Ih1C4omsH37dvHUU0+JW265RVx33XXV/pydwsJCAYi8vDyxb98+odPphMFgEAaDQZSUlIgDBw6IsrIyYTKZhMlkEkajUVRWVlr1p9frRXFxsdDr9SIjI0OsXbvW8peRkWG1nao/o9EoDAaDOHnypDAYDBZNtf1NnDhRjBk7TvToGi+WvP+KMBnNyy9ZskT06tVLXHPNNWLixImW5SsrK8VLL70koqOjhZubm+jVq5dYunRptfmTJk2yzO/cubOYN29etW2uWbNGDBw4UHh4eAhfX18xePBgcejQIWE0GsXEiRPFNddcU235adOmiREjRlh+jxgxQkyZMkVMmzZNdOjQQYwcOVKYTCaxd+9eccUVVwhPT08RHBwsbrvtNpGTk2NZr7i4WNx1113C09NThIaGirfffluMGDFCTJs2rd5j9Nprr4ng4GDh5eUl7r77bjFjxgzRu3dvy/ytW7eKMWPGiA4dOggfHx8xfPhwsWPHDsv8qKgoAVj+oqKihMlkEikpKeLqq68WwUGBwtPDXfTr3V388ccf9WrJyckRiqKIffv2WaalpqYKQOzcudMy7dSpUwIQq1evthxzQKxatUr0799fuLu7i0GDBomkpKRq9n/66ScxYMAAodVqRYcOHcS1115rmZefny9uv/124efnJ9zd3cXlI4eI5PU/ClNFkTCZTGLRokXC19dX/PjjjyIxMVGo1WqRmpoqoqKixEsvvSTuuusu4e3tbTmf/v77bzF06FDh5uYmIiIixCOPPCKKi4st2ysvLxdPPfWUiIiIEK6uriIuLk58/PHHlv09/6/KpsFgEK+++qqIioqq9fw0mUzil19+EQkJCcLNzU2MHDlSLFq0SACioKDAskxaWpoAREpKimWatddUQ39lZWXiwIEDoqSkROh0uhr3kwv/tm3fLMRsH1HxQgdhqNTXuVzVnzU2G/t3vs3Vu1JE5Qt+Qsz2EYaC4zazayutpaWl4pVXXhGlpaV22X9nttmatEo/1bS7bfFMIWb7iLUvX+E0Whvjp9Lso0LM9hH6F/zF0Yxspzim9rKZmrRbiNk+QveCnzh6aE+jbW7buVukvJAoxGwfkbziQ8u8rf/5PyFm+4jVr1xlNz81Z/9LSstFyQtBQsz2EZmHtzu9n1ra5lcfviLEbB+R8UovYShId5ifnN1mXl6eAERhYWG9sVejWza//fZb7r77bsaOHcvKlSu5/PLLzV/Jc3K47rrrGmvOYajVasu/5/9fURTLH5hbzDZu3Njs7R05csT6LwBnGTp0qKW73/ma6sbE5Fuu5rOlP3PHI88B5pboSZMmsXbtWuBcuYY33niDr7/+moULF9K5c2f+/vtv7rrrLoKDgxkxYgRCCCIjI/nvf/9Lhw4d2LRpEw888ADh4eHcfPPNGAwGrrvuOu6//36++eYb9Ho9W7duraGztv+fP+2LL77g4YcfthzjwsJCRo8ezX333ce8efMoKytjxowZ3HrrraxZswaAGTNmsG7dOn766SeCg4OZOXMmO3fupE+fPnUeo6VLl/LSSy+xYMEChgwZwmeffcbChQuJjY21rFNSUsLEiRN5//33EUIwZ84cJkyYQEpKCt7e3mzfvp3g4GA+++wzrrjiCsv5Ulpayvjx4/nXC8/hUZ7F59//yjXXXMPhw4fp1Kn2sRAbN27Ew8ODbt261Tgu5x/DC6dV/X7++eeZM2cOQUFBPPTQQ9x7772WY/jrr79y/fXX89xzz/HFF1+g1+tZsWKFZd3JkyeTnJzMTz/9hFHR8OYLTzL+rv/j4KHDuJzdRllZGW+99RaffPIJHTp0ICTEnD5gzpw5vPDCC8yePRuA1NRUxo0bx8svv8yHH35IUVER//d//8f//d//WXpBTJw4kc2bN/Pee+/Ru3dvjh07Rn5+Pp06deKHH37ghhtu4PDhw/j4+ODu7o6iKLzxxhssWbKE9957j+7du7N+/fpq5+eJEye44YYbmDp1Kg888AA7duzgiSeeqHH8oqKiCAkJYcOGDcTHx1vmW39N1U3V+hfeQ6r+fyFRcYkYhAqtUknp6Sw8g6wbJ1OfzaaiVqs5lZ2ORjGZa2z6drRJ6RNbalWr1RgMBrvtf2uwaS+70k/29ZNvRFdIhaDKrGZty1F+yk7ZRSyQTiixYUEN1hFuzed++rblRAtwVUxEfT2c9QlPM/LOZ622d+jQfu6q6kI74lZUZ20HdhsBmYuJ1yeZexC5WPe6bc/rqcq+Wq0m88RhOis6jEIhNKaHRXdzbNoSR9vs7GLu9dOxMg3xXm+Uq+ZDv7ur2WoJP7UGm9bQ6GDztddeY968eUydOhVvb2/mz59PTEwMDz74IGFhMn2+ozCaBCoEd94wgWff+ID09HTAHNR8++23lmATzN3/Xn/9dX799VdGjBiBoijExsayYcMGPvroI0aMGIGLiwsvvfSSZZ2YmBg2b97M0qVLufnmmykqKqKwsJArr7ySuLg4ALp27droLqkJCQm89dZblt+vvPIKffv25bXXXgPM4+KqAuLk5GTCw8P59NNPWbJkCaNHm8dBLF68mIiI+rNpvvvuu9x7773ce++9CCF48cUXWbduXTW9o0aNqrbOf/7zH/z8/Fi3bh1XXnklQUHm0hV+fn6EhoZaluvduze9e/fGZDSgytnHqzMe5n8rN7B8+XIeeeSRWvWkp6cTEhLS4EO8Ll599VVGjBgBwDPPPMOECRMsXbZfffVVbr311mr+6927NwApKSksX76cNWvWMGzYMHLy8vnq/VeJHDieH5f/YuluWllZyYcffmhZ7/xjVBXUAdx3333ccccdPPbYY5btv/fee4wYMYKFCxdy/Phxli5dyqpVqxgzZgwAsbGxlvUDAsxdmIKDgy1jNnU6Ha+99hqrVq2ib9++uLm5ERcXV+38XLhwIXFxccyZMweALl26sG/fPt58880axyo8PNxyPTiSQF9vsgigI/mcPJZEgpXBpr04v8ZmSBvONimRtDTh8b3hb4ggm9wzJQT7eTlaUqM4nb4XgJOaSOLb8L0hI/UQw47OseTFO1eq5jqrswdr08wfwY96DSDRw98yParPKEwrFTopuWzbv5+L+vaxtfxmkXtsP52BHCWIcFn2pDqFmfTL+NzyUxEmTMsfRRU3Gnw7Ok5XK6bRwebRo0eZMGECAK6urpSWlqIoCtOnT2fUqFHVXnDbAiqViqFDh1q1rBCCiooKFEVhx44dNeYPHDgQrdb6LLEqlcrqBCZGkwkVJoI6+DPh8tF8/vnnCCGYMGECgYGB1ZY9cuQIZWVlXHll9UHPer2evn37Wn4vWLCARYsWcfz4ccrLy9Hr9ZbsrQEBAdxzzz2MHTuWyy67jDFjxnDTTTfh7+9PY+jfv3+133v27OGvv/7Cy6vmw/no0aMWHRdffLFlekBAAF261F88OSkpiYceeqjatEsuuaRaEJ6Tk8Pzzz/P2rVryc3NxWg0UlZWxvHj9WepKykp4cUXX+TXX38l62QmBoOB8gpdveuVl5c3qy5tr17nimxXfeTJzc2lU6dO7N69m/vvv7/W9ZKSktBoNAwcOBAAxVhBhwA/OsfHVBuX4OrqWm0bVQwYMKDa7z179rB3716++uoryzQhBCaTiWPHjrFv3z7UarUlMLaGqvPz8ssvrzb9/PMzKSmp2jkAMGjQoFrtubu7U1ZWZvX27YVKpSJPFUxHkc+ZkynAWIfqEWcyAChxk2nvJRJb4hXWGRMKPko5+44eIbh/H0dLahx55pqTRZ7RjtVhZ5pbqqaguJx+5ZtBBR59rq82T+3pT7omiihjGll7/wInCzbLc1IAyHMJp3EjSts+2al7CaX6eaHCRHbqPkL7ymCzKTQ62PT396e4uBiAjh07sn//fnr27MmZM2ec4oXO1lR1k7MGIQRqtRo3NzdLS1wVnTt3rlab1FqsDTZNJoH67MUxeeKdPPKkuRvtggULaixbUlICwLJly4iJianWlbAqGP7222958sknmTNnDoMGDcLb25u3336brVu3Wpb97LPPmDZtGr///jvfffcdzz//PL/88gvDhw+vNVCurKysocXT07OGtquuusrSQiXOJmzSarWEh4c3uityY5g4cSKnTp1i/vz5REVFodVqGTRoUIOJZJ588klWrVrFW2+9RaSfhgA3uO7BmfWuFxgYyOnTp6tNq2rlPP+41XbMAFxcztXDPL/LN5iDK2sQQqAx6UEBlOpfr6u6s15Ibf568MEH+b//+z+Ln6rW69SpU5P8VXV+/vLLLwQGBlaz2ZiPNVUUFBRYWqUdTaE2BCoOostLc7QUS41Ng7essSmR2BQXN/KVDgSLfPLSDkArCza9S829HkwBCQ5WYl/C4nph3KCgPi/gbEypmnXrVnOjytyFNmrorTXmn/LvRVR+Gprsf2ym2VaoTpt9XOIR6WAlzsfhigCCRM3zIlnnT2g960nqptH9I4YPH86qVasAuOmmm3j00Ue5//77ue222yzdGiXm1qZLLrmE3r17c8kll9i9i7FRCFSYg40rxo1Hr9dTWVnJ2LE1W0+6deuGVqvlxIkTxMfHV/uLjDTfeDZu3MjgwYOZMmUKffv2JT4+nqNHj9aw1bdvX5599lk2bdpEjx49+O677wAICgoiKyur2rK7d+9ucD/69evHgQMHiI6OtmiKi4sjPj4eT09P4uLicHFxqRb0nj59usEsW4mJidXWAWr83rhxI9OmTWP8+PF0794drVZLfn5+tWVcXFxqpHreuHEj99xzD9dddx1du3UjNDiQ4yfqr6XYt29fsrOzqwWcVQHR+cfNmmN2Ib169WL16tW1zktMTMRgMLB9+3YqjSa06DlVcIbkI6l069at0dvq168fBw8erOanqj9XV1d69uyJyWRi3bp1ta5flc32/GNadX4eP368hs2q8zMxMZFt27ZVs7Vly5Ya9isqKjh69Gi1FntHovcwf0NWF2c4WAn46LMB0ATIGmsSia0542b+iKPLrfncdGqEIKzS/PzyjuzuYDH2JSK2K+sTnrbURhUCNiTMsLoLrTHpFwCOeQ8AN98a8z3ihgAQWXbQaeo9V+FZavax8G/bdVSbQnRcV2Ya7sN0NtY0CYXnDfcSZeV5IalJo4PNDz74gFtvNX/Bee6553j88cfJycnhhhtu4NNPP7W5wNaMVqvFz8+vSa0xjaVqzCaAWutBUlISBw8erLVV1tvbmyeeeIKnn36axYsXc/ToUXbu3Mn777/P4sWLAfNYyh07dvDHH3+QnJzMrFmz2L59u8XGsWPHePbZZ9m8eTPp6emsXLmSlJQUS3fWUaNGsWPHDr744gtSUlKYPXs2+/fvb3A/pk6dSkFBAbfddhvbt2/n6NGjrFq1ismTJ2M0GvHy8uLee+/lqaeeYs2aNezfv5977rmnwbGPjz76KIsWLeKzzz4jOTmZl19+mQMHDlRbJiEhgS+//JKkpCS2bt3KHXfcUaOVMDo6mtWrV1cLFBMSEli2bBm7d+9mz4EUbp86E9HAg6Vv374EBgZWSz7l7u7OJZdcwhtvvEFSUhLr1q1j1qxZDR6zC5k9ezbffPMNs2fPJikpqdpYxoSEBK655hqmTp3KmrXrOHxwH3dOe56O4eFcc801jd7W008/zaZNm3jkkUfYs2cPKSkp/PTTT5axqtHR0UycOJHJkyfz448/cuzYMdauXcvSpUsBcwIfRVH45ZdfyMvLo6SkBG9vb5588kkef/xxlixZUuv5+dBDD5GSksJTTz3F4cOH+frrr/n8889r6NuyZYulhdoZUPzMgZ1HeVYDS9oXc41NcwIE79B4h2qRSNoiOu8oANSFjh8v3hh0pzPxphSjUIjq2s/RcuzOyDufZf+YJQBUomLgtbXnWbiQf45m06/M/Pz27HtDrcvE9DcPBUkkjaQ0x39gPJ/As2VP3ELbdut1U4gK9iV25EQ+NowH4BfjxcSMnEhUcM0PChLraHSwGRAQQHi4+eu8SqXimWeeYfny5cyZM6dZY9AkTSevqMwS3BiFirwSAz4+Pvj4+NS5zssvv8wzzzzDG2+8QWJiIldccQW//vorMTHmr1wPPvgg119/PbfccgsXX3wxp06dYsqUKZb1PTw8OHToEDfccAOdO3fmgQceYMqUKdx3330AjB07llmzZjFjxgwGDhxIcXExd999d61azic8PJyNGzdiNBq5/PLL6dWrFzNmzMDX19cSUL799tsMGzaMq666ijFjxjB06NAaYz8v5JZbbrHoGTBgACdOnKgxhvPTTz/l9OnT9OvXj7vuuotp06YRHBxcbZk5c+awatUqIiMjLa1lc+fOxd/fn6FDh3LbxPsZO3IQfXok1tsFWq1WM2nSpGpjHQEWLVqEwWCgf//+PPbYY7z88ssNHrMLGTlyJP/9739Zvnw5ffr0YdSoUdVaARctWkTfvn259cbrGXb1RIQQrFjxa7WuudbSq1cv1q1bR0pKCpdddhn9+vXjhRdesNwjABYuXMiNN97IlClT6Nq1K/fffz+lpaWAuSv+Sy+9xDPPPENISIglSH355Zd5/vnneeedd+jWrVuN87Mqk+2PP/5I7969+fe//21JKnU+33zzDXfccUeTurDbA88Qc3Ik/8och+rILCim49kamwGR9Y93lkgkjUcbbP6I46tzriCjITIPmfNNHCeU6DDnGH5gb3oOHkeh8MRVMZG8Z1ODy3+0ah9zPllMgioTnVCzsqL2brfaoBhOKQG4KEaO7FxrY9VNx2Aw0FGYe7YER1vXZbi98eBlPXELM/f26uxRyoOX9XSwolaOLWpXVlRUiDlz5oiQkBBbmLMrVXU2CwoKxL59+4TBYLDMKy8vFwcPHhTl5eVNsl1Ve89kMtlKrjAajSIzM1MYjcZa51foK8WeE6dF2olMITJ3itKMA2LvidOiQl/Z4lrbs00hzL7KyjwuROZOYczYKUordPUun5WVJQICAkRaWlqLaq2ymZ2dJUTmTqE/ud9mNp3JT3l5eSIgIECkpqZWm97QNWUt598vDAZDjftJbSQf2GWptWk01H+NWmuzMVTZPL/Gpig8aTO7ttRaUVEhXnzxRVFRUWEzm/Y8pra0aS+70k8t56f8bd8LMdtH7JvVS5RV6G1iszlY66d/vn5JiNk+Yv2/Rjdosy2d+7tfHmqui/n5v+q1k5ZzRsyY+YQwvuAjxGwfYXrBRzw98wmRlnOm1uX3zblSiNk+4oc5U6zSaY/rSYjq+38s5YAQs31E5Qt+olLX9O20luu0qTaXff+1ELN9RO5LsTXmtYSfWoPNgoICq+psWt2yqdPpePbZZxkwYACDBw/mxx9/BMxJYmJiYpg3bx7Tp0+3T0QsqZOKSvNYN61iTkajwwUB6CqN9awlsRcm1JhQUClQqau/DExoaCiffvppg9lu7YXKqANAqO3fzdsRpKWl8eGHH1paQ52ByNiullqbuSfTHKbjVNYxS41NvGQuWonE1vh3MreKRCk5JGXkN7C082A6m4m20CPasUJamELfRABccvfVu1za0UO8pvkE1dn8eYoCr2g+JT31UK3LK5HmrOnBRQdqne8I8o6ZhzRlKcFoXNvm898W+Iabuxh3MJ0CQ/2JIiX1Y3U22hdeeIGPPvqIMWPGsGnTJm666SYmTZrEli1bmDt3LjfddJNdCpvai6qEJOcnJjEajQghLH+NpWqdpqxbn82qIvS12XVzMR9zLeaspTpcUACti7peHfbS2l5tVtlTFIVK4YIWPUZ9OUJ417tO1TjJurTYa/+FEGiEOROt4urebPvO6Kf+/fvTv3//Gus3dE01Rp8QAqPRaOnifWHyqAtxcXEhRwkgnHxOph4kKLzuQLi2e1RzqbJVmpMGQIEmmEAhoJnbsJdWjUaD0Wi0mV17HlNb2rSXXemnFvSTXxQmFLyVctKOHaV3dHAtazfSZjN1WuMnr2JzllKjf3yD229L5766Y2849R3BZcn1bjfBNb9allIwl0uJdzlV63ode42CA2/TUxzmWNYpOgX7NajT1tdTld2qf0uzzB8U8lzCCW/GNlrLddpUmxGRUZQJLR6KDn1+Kuqgc+NbW8JPbcmmIqx824qNjeXdd9/l6quvZv/+/fTq1Yt77rmHTz/9tNYSCc7GggULWLBgAUajkeTkZDZv3lyjlqPJZEIIYSl70VrIKqokzHACd0VPpikQlYcffu6NrmojsRGiMAMPUwkFqg64+zrnmBe9wYRrURruih6dZ0dMrvUHxZKa6HQ60tPTURSlwQRV52Nadj+9TAf5w+dGPPvehF9Qy5ce2bLya+4rWkCyW2/0V37Y4tuXSNoDIcuuJciUx4dhrzF8iPW1hh1JxPdX4EcxP3R7ny7d2n6CoCrOZKcxdMMd6IWa/VevxFVbew4STVkunVdcb0nICGBCRfL4HzB41PJBwWQgbtnluKNjSZcP6dOzt712wWpy/niL0cU/scZzAsHjZjpajtNiMJpw/+F2uqhOsLPfm7jGDnW0JKejpKSEQYMGUVhYWG+eGKsjkoyMDEsSlh49eqDVapk+fXqrCDTBnOV06tSpFBUV4evrS3x8PNnZ2XTu3NnSIltRUUF6ejparbZJyY7EeTUhbXVcTCYTubm5BAcH1/lC61dcgBvmJv5wVT5o3cHNq9Zl7am1PduEc77yddGCvgS1Sd/spFn22v8SXSneZ1vDXT18QNO8jyut0U/1XVPW4uLiQlRUFC4uLiQnJ1e7n9TFIQwAjC36HuPaH1gfP4Nhtz9dY7mqD2PW2LSWKpueOnNyIOEXRWJios3s2lKrTqezDM+w1cc/ex5TW9q0l13pp5b103GPSIJK8nAty27UdeYoP1UW5eBGMSah0POiS4mLDK91OXvqtJfdhmyaOnemcL0nvkopSkU+iX0uq8NSIkm/xdNdpAAgUGEaP4eE/nV/TDi6ogudK/aiyT9IYmLNepznY4/rCarvf8Xys9nQO8Q16/7fWq7T5tjc+L9gunACN8MZupx3rFrCT86w/w3ZjI+3Lpu91cGm0Wi01MQD0Gg0NVoGWxNVB1ytVlf7v6Iolr+m0tz1L7RV1e2vVpsGPf6GXKpmKQCFJ0DrAxrXmsvbUWt7t1nlK5WLO+hBIypteh7YUquxUodKEebxpRotOKlOe9hs8JpqpK4L7yH13cwzUg+RaEw+e6GCWhEMPfI2WenX11nbrSGbTcG7qsZmh2ib2ralVrVajcFgsMv+txab9rIr/dQyfjL5x0LJTtxKTjRpmy3tp+PJu4gBMgkkLrIjarV1H+PawrmvVqs55hJHH8NeCo/uQD3kilrXP11cRoDpFChw6pLnyAsYQEL/EfXq1IcNgGN78Tu9t8H9sef1VGW/w3llT2yxjdZynTbFZqE2FHRQmZ9Wbd2W8JMz7L81Nq3B6mBTCME999xjieArKip46KGH8PT0rLbcsmXLGiFT0myMOmp9XTbqrAo2JbZHo3WHUnClkkqDEReNE45lPpscyKBocW0lvRPaAllH9xJxweHWKCayU/dbXUi8uVhqbKrAO0zW2JRI7IVnWGc4AQGVJzEaTVYHb46iIG2POdhURxLp5FrtQaFPVyjYi7qeJEH/7NjMGKUAPRr8Rk4h60hag3ZDeoyEY4voajjM6ZJy/L3cG1zHXpiMBsJNOaBAYCdZ9qQhdJ4RoAN1kWMSObYVrL6bTJw4keDgYHx9ffH19eXOO+8kPDzc8rvqT9KyCLWWWkfdttEMo60BlYu566yLYkSnd74MZnlF5WiM5ky5ZSY1eUVlDlbUfgiL64VJVI82DUJFaGzLPfRPlVUSocgamxKJvQk8W8OwEzmk5xU5WE3DmHLNGVVPe0Q5WIlj0ET0ASC4NLnOZQqT/gIgTdsVXKwLGoO6j8SEQrQqhy9WrCc9t7DZWptK7okUtEoleqEmMr75QyjaOoq/+VrwqshysJLWjdUtm5999pk9dUiaiAE1ZXjgy3kBg2+kbNV0JIqKStS4YMSgKwcPx33FvBBdpQF9ySk6Yn7x8aWUzOJT6Nxd0brIpFL2JiK2K39HPsjwjH8DYBQqNiTMYGQLtWoC5BSWM5JTAGgColtsuxJJe0NzNntltJLN2oxTxIb6OVZQA3haMtEmNLBk2yS82xDYC9Gm41SUl+Hm7lFjmYBT/wBQGnqJ9YbdfElXIokRxzHs+ZZbd2Zzz+i+PHhZT1tJt5q8YwfoCJxUQoh2c553E2fFKzQOjkKgIQuEsNmQo/ZG++sn0cbQGYwYq9zo0QGCu4NnoGNFSTAq5mDfVFl/rc2WRq+roCP558b4KtCRfPQN1ASV2I7hk19HL8xdq7cOW8TIO59t0e3n5WSiVgR6XMDT+nIMEomkkfhHYUKFl1JBRsYxR6tpkBC9uaugZ0Q3BytxDFEJPSkUnmgVAyl7NtaYf6aknJ4Gc43KkD5jrbabnltItsE85OwJlx/YoJ3GsbWLHdLCWVX2JFcT1uLbbo0ER5l7/3hRDuWnHaym9SKDzVaO3mDC9Wx2S1y92m2L5siRI3nssccatc6LL75Inz597KLHdLYbs8pYfzfa4cOH8/XXX1t+K4rCjz/+WOfyaWlpKIrC7t27m6TLTWWs8WFOUczT2zvR0dG8++67AOj1eqKjo9mxY4ftN6RSka0KMW+nomW7MH+8+gDJh83jkU6a/PlotfMUGpdI2hwaLWdczOWvyrOPOFhM/RhKCujAGQA6dm4/JU/OR3U2SRBAQcq2GvN379hAoFJEBa6E97S+lE3a0UNcrDpk+a1WBK9oPiU99VA9a9mJ0+aPHsXuLV9yqzUSFxFOjvAD4EyGA/zVRnB4sLlgwQKio6Nxc3Pj4osvZtu2mhf4+Zw5c4apU6cSFhaGVqulc+fOrFixooXUOh86gxFXKrnnsdkonh146KGHaiwzdepUFEXhnnvuaXmBbYyGgkHLcmdLiaiFrs5lli9fTk5ODrfeWn8q9POJjIwkKyuLHj2aNsbPxdWdC4f4irPTWyPnB4i2xNXVlSeffJKnn65ZksQWnHYxf1WuyE21i/3aSM8t5Ni6L3jf5QMAopRch31dl0jaC+Ve5jFfypl0Byupn8xk84e1k6IDsZ0iHazGcZzxMQ9pUOfsrTGv+OAaAI5quzWqXFgXtwJUSvUnr0Yx0Vnb8i1lHqUnADD4Rrf4tlsjPh5aTmL+OJybnuRgNa0Xhwab3333HY8//jizZ89m586d9O7dm7Fjx5Kbm1vr8nq9nssuu4y0tDS+//57Dh8+zMcff0zHjh1bWLl1ZBWWs+loPlmF5XbbRqXBiAvmVqnIyEi+/fZbysvPba+iooKvv/6aTp062U2DrdA7YTKdpqLWmoM3F1GJqDWDE7z33ntMmjSpUbUe1Wo1oaGhaDRNHF+pcaVUda7wrgAUO4/xraysrDGtNfj6jjvuYMOGDRw4YPvWv3JP8z1LKWy5DHdpRw/xmuYTy0uPouC4r+sSSTtBHWhuKfMqz3SwkvrJO7obgAx1BJp2mIm2CnV4HwCCymomCQo4ZQ7Iy8IubpTN0NhemC6oG2BCRWhsy4/Z7KA/V/ZEYh2nXMzBZqmT905wZhr9xlpaWlqj3ElTmTt3Lvfffz+TJk0C4N///je//vorixYt4plnnqmx/KJFiygoKGDTpk24uLgA5paN+tDpdOh051qXioqKLNMrKyvR6XSWOjF6vR4hBCaTCZPJBJhLvpRXWtfF0FyE3oABPct2neSlnw9iEqBSYPZV3bihX+OCYneXcyUzqnTV2KZBj6KYg4a+ffuSmprK999/zx133AHA999/T6dOnYiOjq5mw2g08tZbb/H555+TnZ1N586dee6557jxxhst8x988EH++usvsrOz6dSpEw8//DDTpk2zbHvt2rU888wzHDhwABcXF7p168aiRYtISEhg8uTJnDlzhv/973+W5adPn86ePXtYs8b8dXDUqFF0794djUbDV199Rc+ePVm9ejX79+9nxowZbNiwAU9PT0aNGsW7775LUJC5O1JpaSlTpkzhf//7H97e3jzxxBP1HqMq3nzzTd59913Kysq4/vrrCQkx30Cq1tm+fTvPPfccu3fvprKykj59+jBnzhz69TN3KYqNjQXguuuuAyAqKorU1FSOHj3KE088wdatWykpKaFbt2688q+XGNs7DFcqqdBX1ki+k5eXx5o1a5g3b14NzZmZmVxxxRWsW7eOsLAwXn75ZW699VYURSEtLY24uDj++ecf+vTp0yQ/dUmIZ+mClwiN6oxLQCeE2gXO05CRkcGMGTNYuXIlOp2OxMRE3n//fS6+2PyAXbhwIXPnzuXEiRPExMQwc+ZM7rzzTsvx12g0fPDBB/z++++sWbOGJ598EoCffvqJKVOm8Prrr5Oeno7BYODMmTM89dRTLF++HJ1Ox4ABA5gzZw69e/dGCIEQgp9++olXX32Vffv24eXlxdChQ1m2bBmjRo0iPT2d6dOnM336dMt5C7Bhwwaee+45duzYQWBgINdeey2vvfYanp6eCCHIz8/ngQceYPXq1YSGhvKvf/2rxjnk6+vLkCFD+Oabbyzzz8dkMiGEQK/XYzKZatxP6sPkEwGnwaMss9r96XyMRmOjbDZErDoHdS1f12NUuXVqsBZbawUsmpqr7XzsodMeNu1lV/qp5f3kFZoAKRBmyuJk/hk6eDfci8QRfjJkmz86FbhHWe3LtnjuB3e5CPZDjPE4RYWn0bqZkwSVlFfQ3XAAFOjQbTQ6nc56nW6BqMbPRVkxHQVzoGkcP4dKt0C44Fjb43qCs/uvryD0bNkT77DOTnnfd0abJW7hUALidFoN/9jFT062/w3ZtIZGB5shISHcfPPNTJ48maFDhzZaYBV6vZ5//vmHZ589lxxDpVIxZswYNm/eXOs6y5cvZ9CgQUydOpWffvqJoKAgbr/9dp5++uk6D+Drr7/OSy+9VGP6/PnzcXNzqzbN09OTIUOGkJ+fb2k5Kq80Mnphze4UjcEkYPbyg8xefrBR661+uJcl4MzJyal1GeXs1zKBgk6n44YbbuA///kPo0ePBuCjjz7i+uuvZ/PmzZSXl5OdbS7mPn/+fJYtW8arr75KTEwMW7Zs4e6770atVjNo0CAqKyvx8/Pjww8/xN/fnx07djBjxgzc3Ny4+uqrMRgMXHfdddx+++28++67VFZWsmvXLoqLi8nJyaG8vBydTmfZHpiDRL1eb5mm1+tZvHgxd999t6U+6+HDhxk1ahS33XYbM2fOpKKigldffZXrr7+e//73vwA8++yz/PXXX3z66acEBgbyxhtv8M8//xAfH19te+ezfPlyXnrpJV599VUGDhzIDz/8wMKFC+nUqZNlnfT0dK655hpeeOEFhBB89NFHjB8/ng0bNuDl5cXPP/9Mr169mDt3LpdeeilqtZrs7GzS0tIYMmQIjz32GK6urnz//fdce/0NJP29jOiOoZwpOFWjdfO3337D3d0df3//GppnzZrFzJkzee655/jhhx+YOHEiERERJCQkkJdnLlmRn59PdnZ2k/yUtGsziqJQblJzKu9UtW2XlpZy2WWXERoayqJFiwgKCmLfvn3k5eWRnZ3Nb7/9xvTp03nxxRcZNmwYf/75J/feey8eHh4MGTLEYufFF19k5syZzJw5E41Gw7fffktKSgrffvstH330ESqViuzsbG699Vbc3Nz44osv8Pb2ZsmSJYwePZr169fj7+/Pn3/+yeTJk5k2bRrvvPMOer2eNWvWkJ2dzYIFC7jsssu44447LB9Xqvwxbtw4ZsyYweuvv86pU6d4/vnnue+++5g3bx5g/vCRnZ3N0qVLcXFxYdasWeTk5FBUVFTNH926dWPNmjVMmTKlxjllMBgoLCxkxYoVlJaW1nre1UW0SzGDAf/KbN54441GrdtUvEUxj6GgOq8jtREV/12xjuLfdraIhqZQ5TOJcyP9VDsJIpXbgWglh3c//QbPitp7brUUdfnpKsXckpdl8G2xe5IzIoTgMeGJn1LKvz98i9N683uit9bE40opJcKdJSs2IX7b0mjbd9ORGDL5Uns7ab+dgN/qPs72uJ68VeX0VQzohAs//bKKn3/53ebbaIuEuZo/OGjOpNW4Ntr7fa+iwrrkko0ONpcsWcLnn3/OqFGjiI6OZvLkydx9992Eh4c3yk5+fj5Go9HSulRFSEgIhw7V3q0rNTWVNWvWcMcdd7BixQqOHDnClClTqKysZPbs2bWu8+yzz/L4449bfhcVFREZGcmjjz5KVlYWXbp0sQSqOp2OjIwMAgMDLYFomd7QqP2yJSEhIbi7qMnJySEkJATlgswuQkB2dgYooKjUaLVaHn74Yd544w3L14YdO3bwww8/sHPnTtzd3QkNDUWn0/HBBx/wyy+/MHz4cBRF4eKLL2b//v18//33lpa7d955x7Ktiy66iKSkJFatWsUDDzxAQUEBRUVF3HzzzVxyiTkF+NChQ9HpdGi1Wtzd3dHpdISGhlpseHp64urqapnm6upK586d+eCDDyzLvPrqq/Tr14/58+ef3UdBTEwMnTt3pqioiPDwcL799lu++OILbrrpJgB69+5Np06d8PT0rLa98/niiy+YPHky06dPRwjBgAED2LJlSzWNVa26VQwZMoSAgAAOHTrElVdeaVkuKiqKXr16WZYLDQ1l9OjRCCHIyclhzpw5/Pnnn/y4cgOPTboRtVrBxy8QV825DyJFRUWEhobWet3cfPPNlnN28ODBbNq0iW+//ZYFCxZYLuzAwECLnsb4yWgycXF8B9yUSip9AvBx96m27f/85z8UFBSwY8cOAgICABg0aJBl/qJFi5g4caKl58GQIUM4ePAgn332GRMmTECrNY9jueOOO3j00Uct6/3yyy9UVlbyzTffWFqoN2zYwJ49e8jOzrasN3jwYP7880/Wr1/P/fffz4cffsgtt9xSbR+rPqSEhobi6upKeHh4NX/MmjWLO+64g1mzZlmm+fv7c+mll/LZZ5+Rnp7OmjVr2Lx5MxdddBEAnTp1onv37vj4+FQ7hxISEvj1119rPa8qKiooKSnhgQceQKPRcPjw4Wr3k/rITd4GP3xJmMjlsSefxK2WsjNGo7FRNq0hb0MMwX/PNPeGUFSYxs1hau87mm3XHlp1Oh3z5s1j+vTplvOjudhDpz1s2suu9FPL+0k5dQT+8yPRSjaxfQYxaWTDtQ0d4aczry8GILLnMG6/7FqrbLbVcz/prT/pY9xH746eXHLLUwCs/vR5qICjbt14+vGZTdKZNH8LlGUS4OvNrffW7L0H9rmeqrRu+2URHIQMgpn5zFM2sdkartPm2lz5y39h3yKCRb7l3ceefnK2/a/PZlhYmFUfpxodbF577bVce+215OXl8eWXX/L5558za9Ysxo4dy+TJk7n66qubPp6sAUwmE8HBwfznP/9BrVbTv39/MjMzefvtt+sMNrVaba0nglarxcXFBa1Wazn4QggURUGlUlnG0XlqXTj4L+tSXAshqKjQcUYnuGze35jOa8xSKfDn4yMI9XWr28AFuLuoLS1iVbrOR1dpsGSiVVRqFMVESEgIEyZM4IsvvkAIwYQJEwgODkZRFIuN1NRUysrKuOqqq6rZ0+v19O3b17KdBQsWsGjRIo4fP055eTl6vZ4+ffqgUqkIDAzknnvuYdy4cVx22WWMGTOGm266CX9/f1QqVbXtVVEVLJ8/rX///tV+7927l7Vr1+LjUz0IAjh27Bg6nQ69Xs+gQYMs6wUGBtKlS5daj1EVSUlJPPTQQ6hUKoufBw0axNq1ay3r5OTk8Pzzz7N27Vpyc3MxGo2UlZWRkZFRze755wdASUkJL774Ir/++isnT57EaDRSXl5OWqY5CFdVlnAsV0OgjwdBPuYvZBUVFbi5udWqd/DgwZbpQgjLh4Dzt3v+/xvjp2EjRnLnqJ6Ehwah0XqgXLD9vXv30rdvXwIDay+fk5SUxAMPPFBN99ChQ5k/f3614z9w4MAavo+Kiqr2cWnfvn2UlJRYgs8qysvLOXbsGCqVir1799bY3oVc6Pe9e/eyd+/eall+q7rHpqenc+jQITQaDQMGDLCs161bN/z8/GrY8vDwoKysrNbtV53nrq6uuLi41Lif1EfHePNYnQ5KMYdycukaF1NjGaPR2Cib1pAceBkhivlFSXloAy4h3W1i1x5aq6jrHt4U7KHTXvvemrSC9FOddoMTMKHCU9FxKjcTrbaPw7RC7X4ylJ0h5Gzt3ejISKv92FbP/dM+iXB6H+q8/ZZjEXjeeM2qaY3VqffqCGXgWnqywWNsy+sJzg4xKcwAIEcTTpwNbDvaTy1ls0NUIuyDQJGPRqMCtYtlnj385Gz735BNa2hyVBgUFMTjjz/O448/zvvvv89TTz3FihUrCAwM5KGHHuKZZ57Bw6NmQdwqAgMDUavVNbqH5uTk1Nk6FRYWhouLS7WDlZiYSHZ2Nnq9HldX2yc5URQFD1frDpMQApXJQICPG69f35OZy/ZjFAK1ovDa9T2IDfJq9PbrSi4D5zLRmoWq4ez/J0+ezCOPPAKYA5ELKSkpAWDZsmXExMRUazGtOnG+/fZbnnzySebMmcOgQYPw9vbm7bffZuvWrZZlP/vsM6ZNm8bvv//Od999x/PPP29pLa0K6s6ntkQxF47/LSkp4aqrruLNN9+07H9Va2l4eDhHjthvgPbEiRM5deoU8+fPJyoqCq1Wy6BBgxpMZvPkk0+yatUq3nrrLfz8/IiMjOSmm25Crzfvb6BSTAeKySwOROfuitZFQ2BgIKdPNz8TXWP99P3S73j5xRf4/Zt/M/jKPjXsubvbJittbeO6a/N1WFgYa9eurbGsn59fk/WUlJTw4IMPVhu3WkWnTp3q7DlRGwUFBTWCYVugcvfjDN74UUxO+qFag017UJBpvn5K8cAzuH3W0pNIWhSNK8XaEHx1WVTmt1z26cZQuPItOpz9f9yPE8A4H/rd7VBNjkTVsTecXkpQqblrcWlZGd0MSaBASG/r62teiOIXBbngVZFlK6mNQlNszkRbJMueNIqo6HgqhAtuSiW6/HS0IfGOltTqaHLKsZycHN566y26devGM888w4033sjq1auZM2cOy5Yt49prr613fVdXV/r378/q1ast00wmE6tXr67Wbe98hgwZwpEjR6olVElOTiYsLMwugWZzuGVgJzY8cynf3H8JG565lFsG2j4brL7yvBqb57W8XHHFFej1eiorKxk7tuaNsVu3bmi1Wk6cOEF8fHy1v8hIc8rzjRs3MnjwYKZMmULfvn2Jj4/n6NGjNWz17duXZ599lk2bNtGjRw++++47wPwxIiur+g3VmtqQ/fr148CBA0RHR1s0xcXFER8fj6enJ3Fxcbi4uFQLpk6fPk1ycs3MceeTmJhYbR2gxu+NGzcybdo0xo8fT/fu3dFqteTn51dbxsXFxZKE5vz17rnnHq677joSExMJDQ0lPT0dF84F14oCHclHrzN3g+3bty/Z2dm1BpxbtlQfC7Jt2za6du1a63411k+//7qcHl3i+OrHP2q116tXL3bv3k1BQUGt8xMTE9m4sXqx640bN9KtW+MDl379+pGdnY1Go6lxHla1rPbo0cOSUKo2XF1da/ijX79+HDx4sIbN+Ph4XF1d6dq1KwaDgX/++ceyzuHDhzlz5kwN+/v376dv376N3jdryNeYW3mLT6bYxX5t6PJSz237woKrEonELlSeLTPhUpzhWCG1UZhJwO4PLT8VYcK0/FEodO7sufYkPHEwADHGdCp15ezfuhovpZwi4Ul0zyENrF03nqHmzMQdjI4Zt+tdYc5Ea/CNcsj2WythAd5kYv7onJ0ma1M3hUYHm8uWLeOqq64iMjKSr7/+milTppCZmcmSJUu49NJLueuuu/jpp59qba24kMcff5yPP/6YxYsXk5SUxMMPP0xpaaklO+3dd99dLYHQww8/TEFBAY8++ijJycn8+uuvvPbaa0ydOrWxu9EihPm6MyiuA2G+9qlhqDcYzwWbyrnWXrVaTVJSEgcPHqy1ybwqg+vTTz/N4sWLOXr0KDt37uT9999n8WLzuI2EhAR27NjBH3/8QXJyMrNmzWL79u0WG8eOHePZZ59l8+bNpKens3LlSlJSUujSpQtgzjS7Y8cOvvjiC1JSUpg9ezb79+9vcJ+mTp1KQUEBt912G9u3b+fo0aOsWrWKyZMnYzQa8fLy4t577+Wpp55izZo17N+/n3vuuafB8iGPPvooixYt4rPPPiM5OZmXX365RkmLhIQEvvzyS5KSkti6dSt33HFHjZa16OhoVq9eXS1QTEhIYNmyZezevZsDBw5wxx13YDJdmOjc/G7vpjIHRlVdVS8M3AD++9//smjRIpKTk5k9ezY7duywtFRfSGP99NfqVaQcO0HnzrWnPb/tttsIDQ3l2muvZePGjaSmpvLDDz9YknY99dRTfP755yxcuJCUlBTmzp3LsmXLLBmBG8OYMWMYNGgQ1157LStXriQtLY1NmzZZssgCzJw5k2+++YbZs2eTlJTEvn37LK3eYPbH33//TWZmpuXDwNNPP82mTZt45JFH2L17NykpKfz000+WY9ilSxcuvfRSHn74YbZu3co///zDfffdV2sr6vr167n88ssbvW/WUOJmzk5tPN1y5U+qSq0UuzdujL1EImk62mDz/TZAf5Jyfc0ePo4kO3UvygXVl1WYyE7d5yBFjie2S2/OCE9cFQNH9m6m+NBfAKS49UClcWlg7boJijR/NA4XuVQ4IB9Ih0pzA0DV+SixDpVKRa7K/HH4TEb9DRuS2ml0sDlp0iTCw8PZuHEju3fv5pFHHrF0easiPDyc5557rkFbVYk/XnjhBfr06cPu3bv5/fffLeO6jh8/Xq11LDIykj/++IPt27fTq1cvpk2bxqOPPlprmZT2gMFoQK2cbeVVqrvSx8en1nGPVbz88ss888wzvPHGGyQmJnLFFVfw66+/EhNj7s734IMPcv3113PLLbdw8cUXc+rUqWoZOT08PDh06BA33HADnTt35oEHHmDKlCncd999AIwdO5ZZs2YxY8YMBg4cSHFxMXff3XC3nKpzy2g0cvnll9OrVy9mzJiBr6+vJaB8++23GTZsGFdddRVjxoxh6NCh9O/fv167t9xyi0XPgAEDOHHiBA899FC1ZT799FNOnz5Nv379uOuuu5g2bRrBwcHVlpkzZw6rVq0iMjLS0uI1d+5c/P39GTp0KPfccw9jx46lX9++XNgBWgAuruaARq1WM2nSJL766qsaWl966SW+/fZbevXqxZdffsnixYvrbDlsrJ8ef+pZpt5zE5PvuadWe66urqxcuZLg4GDGjx9Pz549eeONNywfLa699lrmz5/PO++8Q/fu3fnoo4/47LPPGDlyZB1Hvm4URWHFihUMHz6cSZMm0blzZ2699VbS09Mt94Dhw4ezdOlSli9fTp8+fRg1ahTbtm2z2PjXv/5lKQdT1d21V69erFu3juTkZIYNG0bfvn154YUXqiVjmjt3LmFhYYwYMYLrr7+eBx54oIavN2/eTGFhYY3EUbbC4GPuRaAtabnWDvdSc2uFwav9Fm2XSFoaz3BzkBGlZJN8svnDJ2zJ4YoALhytYxAqknX+jhHkBKg1atI05lJn+Ye30CG/arzmJc2y26FTZwB8lDJOZLTcR0YATAZCTOYWVf/IhpNUSapTqA0DoDL/mIOVtFJEIyktLW3sKk5FYWGhAERBQYHYt2+fMBgMlnnl5eXi4MGDory8vEm2TSaTKCsrEyaTyVZyhdFoFJmZmcJoNNaYl56VI0TmTmE4ubfRdu2htT3bFKIWX5XkCVPmTiEyd5r/LcmrtnxWVpYICAgQaWlpLaLVZDKJ8oz9QmTuFCUFOU5/TFvMT3Vw8803i1dffbXO+effLwwGQ437SUPsWfa2ELN9xKYXh9c6vyk2G+LvF0cJMdtH7P3v6zazKYR9tFZUVIgXX3xRVFRU2MymPXTaw6a97Eo/OchPh38XYraPODCrp/h24yHb2GwkdfkpLeeMODkrSojZPkLM9hGVL/iJp2c+IdJyzjhEp73sNtbm6nmThZjtIza+db0ofSFIiNk+4vDOv5utM392pNnuXytqnW+P60kIIYqOHxBito+oeCFA7Nvzj01sOoOfWsrmj+8/KcRsH7Hr7SuFEPbzk7Puf102CwoKBCAKCwvrXb7RLZsGg4GioqIaf8XFxQ0mUpHYFpXJ3B1HqJxrvKrkLJ6BVGLuclPsGgqe1TO8hoaG8umnn3L8eMt84dSfl1BK5WK77GltEb1eT8+ePZk+fbrdtuEfYe5yHmzKwWg0NbB08zGZTASbzAnZvMNlNyqJpMUIMI/Vi1KySckqdLCY6kQFeuOvmJMGTtNPZbhuPjEjJxIV7OtgZY7l/9m77/CmyvaB49+TpE33opPuUjayRBCVIYIIgoqIqCBTccCLgKDiAhfgQFF/OF5FQF8Vt+JEREBBQFH2ptCyOqB7t0nO74/QSOigI2kSen+ui0uTnHOf+5wnSfOcZ2madwKgc8HveCmlZKl+JHasej6RusjUmnvQFKbZb7LDquz+yjz8RK8YaPtFP9b9b36jHt/VaQPN41wrxr2KuqnzbLQVywNUJyoqinHjxjFnzpwLjqMT9WcyqWhVg3mNTZ1UHJyVQeOOu6kco8lY5esXmkjLlspLitErKiYU0NR/3ElT4O7uzuOPP27XY4THmZcdieI0x07nEB8eZNfjnckrIhpzN6qQGOlGJUSjCYixLH+SlXEC6O7ojCxyUpMIUMopV7X0v3EMD8ZHNPmKJkB42ytgL3gp5jXLD3lcQg9tw5f1y/OIgMJDGBpxrP6JI/vpkfkNFRNJaBWVqw69wIkjw4hKqHryQWHNJyIRDkGwIc3RqbikOtcGly1bRvPmzXn00Uf5+uuv+frrr3n00UeJjIzkzTffZNKkSbz22mu1WuRT1F+ptFK5hIpWZ8Xo+EkhjGXFAJThLjOROgG3oBgMaNArBo4n2/8u9/GUI3gppZhUBY/QxllqRQgB6Nwp8jSP+TI1YiWjNk4c2mH+L2Hc0KONVDTPSmzbhRz13+W6CgJb2yRuqbd5Yjj3Rhyrn5q0E41iPTBXp5hIO3LhSRuFWVic+QatPwWYipxr3LUrqPNtmuXLl7Nw4UJuvfVWy3NDhw7lkksu4e2332bNmjXExMTw3HPP8eijj9o0WVuqWC7h3GUTjEYjqqpa/tVVxT712bemmIqiVMqptNzw70y0Wvc6H9NeuTbVmBXxzi8rRecOBtCq5Q5/T6kG87IrRo07GhvFtMR28XKqbxxVVTEajZZeHOcvw1IzhTOaEMJN6WSdOIDRaD3JVVXfUQ2RfeIAAKeVZgSiBRvFBdvnWhFLp9NhNBptFtdeedo6pr3iSjk5rpxMgQlQfBLPohMcPpVJfFhAg2PWNc+qyinv5D4ATrtFEFPH4zn6mtozpqJAtuJPAIUA9E1dyroPQuh1x8MNylMTaF5r07ckrcr97PF5Co1vj7rB+h6zQdUQEte2QcdwhnJqrJgxEeGcUf0IVvJIT96Df1xXm5fTuTlebDEVtY6/tjw9Pdm5cyctW1qP+Tl06BCdOnWiqKiIo0eP0r59e4qKiuoS2q4WL17M4sWLMRqNHDx4kE2bNuHj42O1jclkQlVVYmNj0eudu7Uwp9hAUHEyesVAqU8MJjcvR6ckqmAsycOn+BTFqjsEJTg0F0POCXzVAvJ0zXDzDXFoLheD0tJSUlJSUBSl3kMGNCvvo13ZTv4X9B8697vNxhlaO7DhC4anvcweXXvUm/5r12MJIayVrJlHt+zvedMwlBcMt3PvpX5c39bxM76eWfU8ffNXssprKJGDm+bM/lXJOX2CK9aNRHNeBW1z348JCImqd9zT+37j6j2zOUIkRbd8aoNMa6fZ5zcSgXl5MIOqYXXkFKKvGNlox78YmD4fR0cO8Xvrxwm8ZJCj03EKBQUF9OzZk9zc3BpXwKhzy2Z0dDRLliyp1E12yZIlREebp9PPzMwkMNDxX6Lnmjx5MpMnTyYvLw9/f38SExNJS0ujVatWlmUdSkpKSElJQa/X4+HhUedjqKpKaWkper2+xnGtdWEymcjIyCA0NNTqB62pqMDSsunu5QPauk0SZI9cm3JMqLqsTBoVisEdAyY3d3TaulVKbJlriWqewEt7dvkVZ7+mjVlO9eXm5kZsbCxubm4cPHjQ6vukNnavi4YzO/EqyaBtW+txlBU3xuoaszrH1pjHa+bpw7nURjEr2DpXMFfmX3nlFaZPn26zm3/2yNMeMe0VV8rJMeWUcjqXLzKC6OYGcUoaKvD233mM6NWe2JDK3VYbs5x2rjRPeKI0a1HpO+hCLub3/t+n9lhVNMHc9dTdkGe5TvXJM1BvgD0QoZ5GbZGI3t16/gR7fJ4A8jGPPf0+6kE69B3OtfENH6vpDOXUmDF/14WD4RD6siwSEhLsUk7OfP5VxUxMTKzV9nWubL700kuMGDGCH3/8kcsuuwyArVu3sn//fj7//HMA/vrrL0aOdO47JhUXXKvVWv2/oiiWf/XV0P3Pj1XR7e/cmCZjOYoCKgqKtv5j8GyZa1OPWVVZac+Op9UqJkrKy3DTeTokV5PJhLtaDgq46b0w2iBmVVwhZnWfqfrmdf53SF2+zLVB8XAGfEpSq92vrjGr41Vk/lFZ4hlhs5jns2VcrVaLwWCwS66uEtNecaWcGrecDqflcVQNByBOMc8IbQKS0vNJqGFisMYop+By89q7nhGt632si/G93zyxE8aNCtpzxjoaVA3NW3SstH9d8gyPbYNJVfBUyjh66hjxLVpVimXrz5OpJJ8A8gGI7zOK2MQWNolbwVU+pw2NWejVHPJAyTlm1+89cM7zry5mbdT5tv4NN9zAgQMHGDx4MFlZWWRlZTFo0CD279/PkCFDALjvvvt4+eWX6xpa1IXR3Epl0rjJZC/OTKPFgPnDaCgrcVgaZaUlaBUVkwo6ff0qvML2fCLMwxGaGdMwmey7/ElgWSoAJt9Iux5HCGGtdfNAjp2tbMYq6YCKBmgVEeDItDCWlRJuMvd4CEu4xKG5OJuohDb83vJhDGdXCDSoGja0fKjBs7dq3T04rZhvMJw+tr/BedZG9inzBHS5qhcxkfL9X1+qfwwAHkUnHZyJ66lTZbO8vJxrrrmG8vJy5s+fz5dffsmXX37J/PnziYuLs1OKoiraijU269h9VlRPURS+/vprAJKTk1EUhe3bt9c7XkWMv/eYv+hN5Y5bh9ZQah4/Xa6427zlUdRfeHw7AKLIICPXvmPcw86usakLrP94IyFE3cWG+nN730sxqApeSimXcISHr4lx+Myvacn7cFOMFKvuxCbYZrbVi0nf0bNJG7uJrVctIW3sJvqOnm2TuGfOrrVZkJZkk3gXcjrFXKlNIwRPvSx7Vl+eIeZ5NwLLZPmTuqpTZdPNzY2dO3faKxdRS0ajCV3Fsidn19gcN26cVRfgin+HDx+2vF7Tmo7FxcXMmTOHVq1aodfrCQ4OZsSIEezZs8dqu7lz51p1H4yOjmbSpElkZWVZbRcXF8eiRYssj3fs2MENN9xAaGgoHh4exMXFMXLkSDIyMmxwRWwvOjqa1NRUOnToUKvtx40bx7Bhw6qM0batuUKBsdTWadaaqdzcqlquyM0JZ+IRah7vEKbkkHQi1W7HycnNIRTzdO3ewTF2O44Qomp3h+yydMn8Rv8k9zTb5uCMIPWI+ffcCSUcvbv8bahKVEIbuvW/xabrUeZ7NO4yOAXpRwA4o5WJARsiMNrc5TnMlAHVrJ0uqlbnbrSjR49myZIl9shF1JJ5jU3z5EDnrrF53XXXkZqaavUvPv7C6+mVlpbSv39/3nvvPZ599lkOHjzIDz/8gMFgoEePHmzevNlq+/bt25OamsqxY8dYunQpP/30E/fdd1+18U+fPs0111xDUFAQq1atYt++fSxdupTmzZtTWFhYz6tQtfJy26xnqdVqCQ8PR6er/yLOFTG0evNMwRqT49ba1Jyt6Jq0zj3LcpPjGUgh5vdHxrGDdjtM6tF9aBSVQtUDD99mdjuOEKIKuSfh2weo6FOiUVTUb6eZn3eg4lTzckiZ7s0dmkdTU1ax1mZh45S/8WylNt89tFGOd7GKS2hDmarFTTFScDrF0em4lDpXNg0GA2+++SbdunXjnnvuYcaMGVb/xDlyT8LR32z+B6W03HjOGpv/Vh70ej3h4eFW/2ozeHfRokVs2rSJ7777jltvvZXY2Fi6d+/OF198Qdu2bZk4caLVeoQ6nY7w8HAiIyPp378/I0aMYPXq1dXG37hxI7m5ubz77rt06dKF+Ph4rr76al555ZUaK8NxcXE888wzjB07Fh8fHyIjI1m8eLHVNoqi8Oabb3LDDTfg7e3Nc889B8A333xD165d8fDwICEhgaeeegqDwWDZ7/Dhw/Tp0wcPDw/atWtXKf+qutHu2bOHIUOG4Ofnh6+vL7169SIpKYm5c+eyfPlyVq5cSWRkJFqtlnXr1lli7Np/CDCvtbl+/Xq6d++OXq8nIiKCRx55xCqvvn37MnXqVB566CGCgoKIiIjg2WefraH0akdrMlc2FZ1UNp2KopDpZh7LVZRxxG6HyT5hrsimasJkjLcQjS0rCVTrMdmKaoQs+33ma0PJSQag0DvWoXk0Ndog8/X2LbFfb5ZzuRWYJ4cr9ghrlONdrIL8fTiFuXU4/eheB2fjWurcbLN79266du0KwMGD1nfiL8qxYKoK5bUcS6WqUFYCGiPs+Bh+fMj8B0bRwKAXoPMddTt2NWtnlhpM+J7tRlvXJU+q8tFHHzFgwAA6depk9bxGo2H69OmMGjWKHTt20Llz50r7Jicns2rVKtxr6IITHh6OwWDgq6++4pZbbqnT++Sll15i1qxZPPPMM/z888888MADtGrVigEDBli2mTt3LgsWLGDRokXodDp+//13xowZw2uvvWapEE6aNAmAOXPmYDKZuP322wkPD2fLli3k5uYybdq0GvM4efIkvXv3pm/fvvz666/4+fmxceNGDAYDM2fOZN++feTm5rJgwQJCQ0MJDg7m1CnzF7xWZ742GadOMnjwDYwbN47333+f/fv3c/fdd+Ph4cHcuXMtx1q+fDkzZsxgy5Yt/PHHH4wfP54+ffpw7bXX1vq6WVFVy0y0WndZj9XZFHlFQu4RsGOXqrLT5h+1WW7hyDtAiEYW1ML8O+CcCqcJDRoHr73sW2j+zlGCLtwDStiOV3gL2A/BxvRGOZ5P6dnJ4XzCG+V4F7MMbRhxpjTyG2m87cWizpXNtWvX2iMP51VeBPNq18VEAaqc51M1wQ8zzf/q4tFTUMVSGQaDAZ1y9o+W7t9K3nfffYePj4/l8aBBg/jss88ueJiDBw9y9dVXV/laxXpSBw8etFQ2d+3ahY+PD0ajkZIS81jAmmYfvvzyy3n00Ue54447uPfee+nevTv9+vVjzJgxhIXVfKftyiuvZObMmXh4eNC6dWs2btzIK6+8YlXZvOOOOxg/frzl8YQJE3jkkUcYO3YsAAkJCTzzzDM89NBDzJkzh19++YUDBw6watUqIs/OzDZv3jwGDap+kd7Fixfj7+/PihUrcHMzD7Bv1erfKcs9PT0pKSkhNDSU8PBwq/UbtWdbE999/2OioqL5v//7PxRFoU2bNpw6dYqHH36YJ5980rJPx44dmTNnDgCJiYm8/vrrrFmzpt6VTYOhDJ1iQlXB3UNmonU2akAs5P6Opx1nuNPmmX9UFno2l8qmEI3NPxKGvgrfPgCq+bt4RcgU7vB37MygIQbzDVGfyLqtrykaJjTGPP4zTD1DeVkZbnYeL9vMcBoAnb90l26ofH1zKN6BMSsZkJbi2qr3iuaHDx9m1apVFBcXA1h1sxT2pVYse4IWNP/eL7j66qvZvn275d9rr71W+5h1KL/WrVuzfft2/vrrLx5++GEGDhzIf/7znxr3ee6550hLS+Ott96iffv2vPXWW7Rp04Zdu3bVuN/ll19u9bhnz57s27fP6rlu3bpZPd6xYwdPP/00Pj4+ln933303qampFBUVsW/fPqKiomjevLlV3Jps376dXr16WSqadaHRuaEC+w8f4bLLulm17F555ZUUFBRw4sQJy3MdO3a02j88PLxBEymVl5jHxZbh1qAxqMI+PELMrQqB5fab4a6iImv0i7bbMYQQNeg6Bu7fAph7sn9f0tmh6ZQUFRCuZgIQmdjxAlsLWwqPbkm5qsVdMXLq2GG7HstUVkQzcgDwaibLnjRUma95Nnf3/BMX2FKcq86/PDMzM7n11ltZu3YtiqJw6NAhEhISmDhxIoGBgSxcuNAeedqc0Wi0+m/F/6uqavkHmFsWZ9euxUFVVUpLS9GXZqK8eTnKOV1mVEUL928GvzrcWdJ5WhafPzcnpaKyqXVDOaeS6O3tTYsW1ov1nl+JrHh87n9btWrFvn37qqxw7t1r7pfesmVLSw7u7u6W48yfP58hQ4Ywd+5cnn766Uqxz40ZFBTELbfcwi233MJzzz1H165deemll1i2bNkFL0VVeVfw8vKyelxQUMDcuXO5+eabK8XR6/8ds3juPufnfP5jT0/PSvtU5dyyssQAyjFXUlWTqcbjgnlM7LnPK4qC6bz96sJYVjETrRvu552brbhKzIp453+m6htHVVWMRqOlVfrc75PaCopuA1shQs0gM6+QAG8Pq1j1iXm+ioqsLijOZjHPZctcz42p0+kwGo02i2uvPG0d015xpZwcXE5BLSj3jsKj8AQe+Uer3acxyil5/3baKCp5qheh4VH1OpZTXFNXjKnRkK40My95lbKPqPh/l52x9ecp89hBQoEC1YNmgcHOcf4uHFMTGAsZ4Ft6Cp2uh02/98D5z7++Metc2Zw+fTpubm4cO3bM0sUSYOTIkcyYMcNpK5uLFy9m8eLFlgtz+PBhfHx8rMadVvygLy09f4mKC0+yY+Guo9TdG+3Al3BbNQtFNaIqWsoHvojRJxrqsm772TwCAgIoKzNXMFVVRXt2/J1JcaP8bDfWijd8RbfW81X3emlpKcOHD2fu3Ln8+eefVq1qJpOJl19+mbZt29K6dWtKSkowGAyYTCarOLNmzWLQoEGMHz+e5s2bU1paiqqqGAyGavMB8wRAeXl51W6jqip//PGHJU+AP/74g1atWlntU1ZWZvW4c+fO7N27l6lTp1aKWVZWRosWLThx4gTJyclERJinIP/tt9+sYlUcr7S0lJKSEtq2bcuHH35Ifn5+la2bGo2G8vJyq7I6N4ZR8aRtYjyf/rie4uJiS+vmunXr8PX1JTg4mJKSEkwmU5XldP41r5Nyc+8Dg8bdKkbl93nDuUrMc8upvkpLSykvLycpKclS2Tx/HHttuBdp8AeilQxWbd1Nq1Bvq9frE9OKqhJvTAcFStwCbBOzGraOe+ONN3LkiO0nUbHH+bvKNbVHTCmn2seN9InBo/AEkYZjbPpnJwGe1feWsWc5ndi9iTbAKU04hgYex9HX1BVjGrRhRBkzOHN0F/vCrNc4teXnKXv/H4QCaQTj7qZ1mvN31Zjl+iAAQozp3DjMPt974Lznf76K5RUvpM6VzZ9//plVq1YRFWW9MHjLli1JSXHeqYAnT57M5MmTycvLw9/fn8TERNLS0mjVqpVlxtaSkhJSUlLQ6/V4eHjU+RiWlk29HqXHBGg7EDXrCAQl4OYXSX2W0jWZTGRkZBAaGmqu0BhNuJ2diVan98TtbJ5arRatVltt3lqtloKCAvbv32/JtaysjIiICGbNmsUPP/zAiBEjeOmll+jRowfp6enMnz+fAwcOsHr1akvLnk6nQ6PRWB2nT58+dOzYkZdffpmXXnrJfP6Kgk6nw8PDg++++45PPvmEkSNH0qpVK1RV5dtvv2XVqlW899571easKAqbN2/m5ZdfZvjw4fzyyy98+eWXfPfdd1b7uLu7Wz2eM2cOQ4cOJT4+nltuuQWNRsOOHTvYvXs3zz77LIMGDaJly5bcc889vPjii+Tl5VlaZStiVbSAVrwXpk2bxltvvcX48eN55JFH8Pf3Z/PmzXTv3p3WrVvTokUL1qxZw9atW2nVqhWBgYFWMVSNO/ePvZVF737MrFmzmDJlCgcOHOC5555j+vTpeHmdXR5Fo7Eqx4qWt/OveV2U5JytVOk88PDwsH6f2mhSL1eJCZU/Uw3h5uZGbGwsbm5uHDx40Or7pNYMCZhWKXgrpaiqwXITz2g01j/mOYqzU/FWSjCpCh0uvYKczMwGxzyfrXI9V2lpKa+88grTp0+36pHQEPbI0x4x7RVXysnx5aSkXg7pf9BaOUGpPpC2bSt3bW+Mcjq9cSkAWfooerSt35hNZ7mmrhjzjx8joHAXnmWZVg03tv48bd39LQCZuhC8wWnO31Vjevv4wE4IIo//fvUZUx982Gbfe+D8539+zMTExFptX+fKZmFhoeWH8bmysrJsesHtreKCV1TSKv5fURTLv/qy7O8fZf7XABXd/SpilpUbcT87E61Gp6+0jEFNea9bt84yk3CFCRMmsGTJEn799VfmzZvHY489RkpKCr6+vlx99dVs3ryZDh06VIp//nGmT5/OuHHjmDZtmuXNV5Fz+/bt8fLyYubMmRw/fhy9Xk/Lli159913GTNmTI3nP2PGDP755x/mzZuHn58fL7/8Mtddd12lcz43n+uuu47vvvuOp59+mhdeeAE3NzfatGnDXXfdhaIoaLVaVqxYweTJk+nRowdxcXG89tprXHfddZXKv+L/g4OD+fXXX5k1axZ9+/ZFq9XSuXNnrrrqKhRFYdKkSaxbt45BgwZRWFjI2rVriYuL+/daad2JjAjlsw/eYe7zr9K5c2eCgoKYOHEiTzzxhFX+Vb3/GvKedFPPvl/cPC94nIZyhZjnf6Yamtf53yF1/jLXepGpbUYz4xkK0o6g1V5p/XJ9Yp7jdMp+fIB0AgkLDiYnM7PBMatjy7harRaDwWCXXF0lpr3iSjk5sJzC2wPQSnOcv9LyuKZj9fvYs5zczk4aVuob2+BjOPyaumDMct8oKAT3ghNW+9r682TMMY8tLNCH413PXC+kKcWMjo4hW/UiUCkiSJsn7/1axqtzZbNXr168//77PPPMMwCW8WQvvPBCtTOaCtspNRjxtqyx+e8MZhca97hs2TKrbVRVpaSkxNJa5uXlxbPPPnvBNR3nzp1rtUxHhdtuu42RI0daumkmJydbXktISOC///1vjXGr4+fnx//+9z88PDyqrBhUN+Zu4MCBDBw4sNq4LVu25LfffrOKeW6suLi4SrE7duzIqlWrqowXEhLCqlWrSEtLs5qNtiJGSe5pKIO+Pbvy559/VpvXunXrKj336aef1rtVUzWWo8PcddxNLzPROqt8fQTNis5gyEq2eezcU+auM2maMEIb2JIrhGigUHMrVmvlBB+n5TssjYCS4wDoQlpcYEthD9qAGEgDv1L7TQwH4HZ2Ipsyb5mJ1hY2fPw8fTAvh3hX6VLWfxLM1WMec3BWzq/Olc0XXniBa665hq1bt1JWVsZDDz3Enj17yMrKYuPGjfbIUZyjzGAisKKyqXOdluSmTufuAYXmVkaTydTg7pu1ZSgtwg0oU3Xo7Ty9uqi/ct8YKNqFe4Htlz+pWGMz203WWBPC4Zq1xKRo8aOI3DOOm9EyzJAKCgREt3dYDk2ZdyOttelTao6vDYix63GaghNH9tPr0POWDoUaRaVX0kucODKcqIQ2jk3OydX5F2+HDh04ePAgV111FTfeeCOFhYXcfPPNbNu2rdJMqML2jIYyNIqKCqCtzyhQ4Qhad3PLpJtipLTc0GjHNZSaJwcqU9zQaGzbvVXYji7YvPyJ79nFt22pYo3NIi+Z9l4Ih9O5U+wbB4BnnmMWhs/OzCBEyQEgqlVnh+TQ1IXGmisnIWoWhrJ6Tv5XC0EG87JpXqFxdjtGU5GatBOtYt3jTaeYSDuy20EZuY56Lbrn7+/PY49Js7FDGM3j70yKG1rl4u4Sl5ycbOnu6+oUjQ4jClpUyktL8NQ3TiujWm6+dgZFWjWdWUBkK9gDYcYMisvK8XS33Y0k7yLzwu2yxqYQzsEtoj3kJRFRfpzsgmICfRp3iMOxAzsIBDJVP5o1C23UYwuz5tEtKFHd8FDKOZlygMiWnWx+DFN5CcFqFigQHNMKQ3ldlkMQ54to0RHjBsWqwmlQNYQndKhhLwH1rGzm5OTw559/kpGRgclk/ea90IQvomE0prNLoGil8uBSFAUDbmgpw1Ru+6U8qqMxmo9l0kqXa2fmH2me+j5ak0FSWg4dYkJsFjuo3Nxa6h6SYLOYQoj6c29+CRxYSRvNcXalnKF3+8a9EZRz3Lx+dpquOc0a9ciigk6n5bgSQjynOH1sv10qm1mnjhCsqJSobkRFJ5B8pHbLVIiqRSW0YV3Lh+l16Hm0ioqqwu8tZnK1dKG9oDpXNr/99ltGjRpFQUEBfn5+lWa3vBgqm7ZeRN5WzGtsGkABRSeVTVdj1LiBqQzV2HiVTZ1qPpaiq98EQ6J6tvye0ASZu9E2J5Otp87YrrJpKCNYzQTAv3lL28QUQjTM2UmCWinH+eNUTqNXNg1nzJWOXI+GzZYvGiZTF0a84RRF6fZZqzEj5QDBQKoSQoxehl3ZQt/Rs/nzt650//VWVKDHTfc7OiWXUOd+mA8++CATJkygoKCAnJwcsrOzLf+ysrLskWOjcXMzfxiLioocnEnVygznLHviJpUHV6NqzDcIlLNdoe3OZPh3Jlo3+UNja2Vl5l4GNplK3CeUUtzRKCpZJ21397k8KxkNKkWqnqjoeJvFFUI0QGg7AFoqJzmcntPoh/fIN4/jNvjHNfqxxb/y9REAmHKO2Sd+mnlMcJbWdj1lBHS6vB/5qicaBU4f2+/odFxCnVs2T548ydSpU6tca9PVabVaAgICyMg4O6Day6tO6/BVLEIPNa93WRcmkwmDwUBJSQlFpQYUQxklikp5mYpbA8Yy2iPXphwTrMuqqtlmy0watAYVIyW1HofakFxN+RloDObWN232YUrKI8Ar0GWuqaPKqbYxTp8+jZeXFzqdrtJwgjpTFLLdIwgvS6HkdHLDYp3jdMpemgPH1VASm/kBztlrQ4gmJTAOg8YdD1MZRRnJQK/GPXypedZr9zDp7eBI/661aftZyAEMWeZKbJ5eZiK3Jb27G0eUCNpyhDMpe4jt0NPRKTm9Olc2Bw4cyNatW0lIcO3xP0aj0eq/FUJCQjCZTKSn12866vLycpu2IqmqSn5+Pvn5+WQXlhFgzESLkQy1HE+PDHw86n8sW+fa1GOeW1ZVVY6MpUXoSjIpR4euoPY/+uuVq2pEyTtl/RRnwC8CFK3LXFNHlFNtaTQaIiMjMZlM1X6f1EWJdxSUpVCWmcyRtCyignwaHDP3xEGaA+maUBJRbZJnVewR12g0otPpMBqNNotrrzxtHdNecaWcnKecivxa4JezD8+8I5X2tWc5lZeVEWk6CQoERbdt0DGc7Zq6WkxNQPTZtTZTrWLZ6vOkq1hj0yvCKc/flWNmukVA+RFK0g85fa7OEFNR6zjwaMmSJTz99NOMHz+eSy65pNIPwRtuuKEu4RrN4sWLWbx4MUajkYMHD7Jp0yZ8fHyq3V5VVacau5lZWM7Tv57iR/1sAG4unUsePjzTL4Rm3tJF0iXkHKPVn49QoHpysO/b+OjrNT9XrZSf3EH7Pc9Xen5Ph0dwa97RbsdtKioqqbZscT390zyuLvietwxDeN5wB/de6sf1bQMbFLN43UIuO/MlX7sNJvFGmUFcCGcRuulpQk+uYmH5LVw1fCre7jbojl8L2Zmn6bX2JgC2D12NTn/x9VJzFSkHtnH9rilk4k/qLT/YPL761SQuMe5hZeSDJPS82ebxm7KUn17h+oLP2eh9Lf6D5jg6HYcpKCigZ8+e5Obm4ufnV+12df61e/fddwPw9NNPV3pNURSb3+GylcmTJzN58mTy8vLw9/cnMTGRtLQ0WrVqZZsxV2CpyNoyZmlpKa+88gqdr70VpeAfPMqPU6y6kVVaSBqelHkE0r59rFPk2pRjwr9lNX36dPT6KmZ/LY9H++txPIAjeg/at29tt1x/KyhEn3+cc+tCBlVDvl8LrmjTxiWuqcPKqR4ammvK6Vx+yw7iajforBwmjEze/hu6RXnR69JL6n3+B382Dwko9o6ibdu2drum9vzuc6ZyaqyY9oor5eQ85aRk94STq2itOUG5ZxBtWzW3a64V5dTnMvPfnXSCuKTzpQ2K6WzX1NVienh6wC5oRi6+8dFoPXxs+nlK/9z8/R8S14FWrVo53fm7csxjf7WAAggoO0Wbtm1tEhNc5/wrYiYmJtZq+zpXNhs8NslJVFxwrVZr0y9JW8fUarUYDAZaRgQwWvsLAJ5KORv1D/CY4S7aRF7VoGM5+/m7UsyKsqo2rtaXLMWfIDWXrBOH0HZsZ7dcExLbkbPKm0AKAXNF83HDRO5LbOcy7317xbxgOTUwdn1iHk7LI0Yxd92/XLufjZqpzDbcxam8WxqUp3exuSu1yT/GKoY9zt3WcZ2xnBo7pr3iSjk5QTmFm9fma6Uc5/fUPK5qW3lGWnuUU3HqIQAydJGE2TC2U1xTF4sZGxNHvuqJr1LMmROHad76Upt9nkyGMkLOrrEZEvNvJcOZzt+VY3qFt4QUCClPbfLv/dqo3+wYotHF6gsYr1tleaxVVOa5LSFWX+DArERdZenMA/WLM5LsepxYPwhUzBXNCWUP0rv0VeL7jiU21N+uxxX1086vhDHa1ZbHWkVlnm4J8e459Q+qqjQrTwNAHywz0QrhVM4ufxKvpHEkNbPxjpt9FIB878ZdbkVU5uamI1UxzxSbedy2s5qeOXkUN8VImaolMrZ2rU+i9oJj2wMQSiamUvkdfiG1rmwOHjyY3Nxcy+MFCxaQk5NjeZyZmUm7drVvqRF1o2QfQTlvJkkNJsiyz/pMwj4KPc92lco9btfjGNL3AZCuBtD72uF8PONG7hlwiV2PKeovSslAq1h/vnWKiUg1o/5Bi7PxxryMU2CkzDophFPxi6RU64ObYqQkw3bLHV2Id5H5b48aIDegnEGmLgyAogzb/pZLTzFXXtOVZnh6yFJ5thYTHUOuah7vnH50j4OzcX61rmyuWrXKsgwBwLx586zW1TQYDBw4cMC22QkLNTABlPOKS9FCkGvPCtzUGP3Nd5M9Cu0z1XmFjEN/A3BYjWJ0r7bSounsglpU+nyripZSn/ovum7MNP94SVMDiQmXddaEcCqKQnGA+SaQZ17j3TRuVmbuWu8V0abRjimqV3B2rU01O8W2cdPNLdhntKE2jSvM9O5unFDMPdUyju52cDbOr9aVzfNnZnWmmVqbBL/mMPRVcwUTzP8dugj8Ix2alqgbfbD55kDg2e6N9lJ4fBcAqW4x6LTSW97p+UfC0FctfRdMKKjXv4zBq/4/FHJPmG/+HVdDiQmpfpY4IYRjuEeYu+JFlB8jv7j0Als3nElViFJTAQhLkJ4uzqDc13xDUW/jG9DlWckAFLiH2TSu+Fe65uywqLRDDs7E+cmvUFfSdQxM2wVjvzP/t+sYR2ck6iggqhUAocYMDEb7TbalzTwIQKGvdJVyGV3HkNn6dgC+MvbC0HFUg8LlnTK/B9I0Yejd7LfMjhCifryiOwHmSYL2Hrf/uE0PvYK3UopB1RAeb7sZNEX96YJiAPArte0NaF2+uQW7zFsaJOwlS2vuMaTJOergTJxfrSubiqJUWlPOVmvMLV68mLi4ODw8POjRowd//vlnrfZbsWIFiqJw00032SQPl+AfCfG9pEXTRYXFme9kRyqnOX46x27HCSwyf/kpIRdeXkU4D/+WPQEIJ5OUM3kNiqVm7AWgzE1aNYVwSmcnCWqtHGffqRy7H85XKQYgXROCxk3G8TkD7zDz5D1hxlOcOGK7SYK8S8wt2JpAmQjKXnI0QQD4FNl3Do6LQa1vd6uqyrhx4yzr/pSUlHDvvffi7e0NYDWesy4++eQTZsyYwVtvvUWPHj1YtGgRAwcO5MCBA4SGVt+FLDk5mZkzZ9KrV696HVcIR9AFRGFAi14xcCL5EPHhPWx/kOIcAk3m8dRBcR1tH1/YjVu4+WZEouYkf57MJt6znoH+eZ+49J8BGFb+PfzzvvSEEMLZnK1sxmhOk3wqHbDvJIteJvMNrAxdJHK72jnkH/gNAB9K8Fx+Ob+1mGWTuEGG0wB4hUrvJnspUMw3ckPKTzk4E+dX68rm2LFjrR6PHj260jZjxtT9x8zLL7/M3Xffzfjx4wF46623+P7773nvvfd45JFHqtzHaDQyatQonnrqKX7//XerWXHPV1paalURzsvLszxfXl5OaWmpTRc5tXXMitzrW5mvjj1ybcoxofZllaUJJsKUTtaJA5SWdq5x2/rkajy2HS8gVQ0iPqp5pXxc5Zo6upzqwma5+saiB8KUHJJSjhPVIqDuMfNO4f7tA1T0O9Ggon47jbLoXhi9w+xyTV3lu8+V3qeukquUUwPi6nwx6ALxNmRTln6A0tIr7JbrO7/uIaDoJOjgYLEvm1ftYELfhk0S5JTX1IVinkw+wICTr1PxZa1VVHolvcRf7v9p0OfJZDQQpp4GBfzD4yktLXXK83flmKWlpeSZzHeDQ8imOPcMGg/fBsd1lfM/N2ZtKKoDZ/opKyvDy8uLzz//3Kor7NixY8nJyeGbb76pcr85c+awc+dOvvrqK8aNG0dOTg5ff/11ldvOnTuXp556qtLzjzzyCB4yHbRwgGHKT3Q07eW/+vGklgXaPH5H7WGGGVbyu6kjazT9sVFvd9FI/sNSgtRsHtM/jntZUZ33j1OPMZbPKz2/jBGkKNKlSghncruyklamwzzDJEyKj12OUe7mTXlhGgt076Ao5omCHjXchc47HLfyQrscU1xYqHsh95W+Xen5N/X3klHmVe+4HhoDDxtfw6gqPKtMQ34E2IeKwlTT2wQpBfyf7m4yjQ2vbLqakpISFixYQG5uLn5+1Q/ZceisEWfOnMFoNBIWZj1bVlhYGPv3V913fcOGDSxZsoTt27fX6hizZ89mxowZlsd5eXlER0fzwAMPkJqaSuvWrW1a0z9w4IBNY5aWlvLKK68wffp0SxdmW7BHrk05JtS+rPa8vR+y9hLqVszYGc/bPNejH0yGE5DmHsPsWZV7B7jKNXV0OdWFLXM98+ZvkLOVYDLpe8PIusfMO4W6+AurdXlNaLh98mMYvcPsck1d5bvPld6nrpKrlFPD4pZ8mwW7DxNhOM4tD7+Lp7ubzXPd+OdW+vxyvaXOoVFUntUt4ffe33HFZd3qHddZr6mrxDyZfADjR/+1WmPZoGrIN+kb9Hnau3kVrIUMpRmPzJ5tk1yr0pRjVnzvnVQiCOIQV3ZuSbsB4xoc11XOvyJmREQECxYsuOD2LjVFYX5+PnfeeSfvvPMOwcHBtdpHr9dX+YHV6/W4ubmh1+ttevFtHbNCdedRX/bItSnHPNeFykoTGAtZ5gH8FyrT+uTqnm1eILzQN6HK+K5yTR1dTnVhy1y1YW0hZyuBxcfqFTNFDeJ3Qz9G69YA5h8vjxsmcp8aRJQdvvfAdb77XOl96kq5gpRTfeO6xXWB3ctopZwgKaOAS1uE2zzXdj55VhUaAJ1ioq13foPKzFmvqavETGjdkXUtH6b3oefRKCqqCr+3mEnxUUODPk+FGckAnNGEEHE2hjOevyvHrJDp3hzKDlF2Oskm33+ucv7nxqwNhy59EhwcjFarJT093er59PR0wsPDK22flJREcnIyQ4cORafTodPpeP/991m5ciU6nY6kpKTGSl2IevMMbQFAUHkaKRm5No8fVJwMgDZUZqJ1RQGx5kmdYtSTZBeV13n/A6eySVfN3bPXGztyVemrrDBezcHUHFumKYSwAU2YeVKw1prjfL01xS5/E8ITOmLCuiulCQ3hstamw/UdPZtv4h4FIE0TyhUjZzY4piErBYB8feXf0cK2Cr3M66Rqs2X5k5o4tLLp7u7OpZdeypo1ayzPmUwm1qxZQ8+ePStt36ZNG3bt2sX27dst/2644Qauvvpqtm/fTnS0jEcSzu/vbPMMznGc4raXv+Ht1btsF7woiwBTNgDN4mUmWlfk0dz847OlcpLk7LpPEtG6eSAtNObZ8Tab2pFGMzRAq4gAG2YphLCJs8tThSk5fLvtKH1f3sA7a/bY9hj+kez0vtLyUFW0aG54VZZQcxIh7fsBEKqeBkNJg+NpCyrW2IxocCxRMzXQPNuvT7Esf1ITh1Y2AWbMmME777zD8uXL2bdvH/fddx+FhYWW2WnHjBnD7LN9zj08POjQoYPVv4CAAHx9fenQoQPu7u6OPBUhLiglI5fDu/4AoJmSzwb9VI6uW26zu9mlp8w/Uk6owbSNj7FJTNHIzv74jFTOkJpV97U2Y0P96aw3LxB+WG2OBnj4mhhiQ/1tmaUQwgZS8lROqOZhQa2UE6jAC78eJz3ftjPQ55aaANgXNgRl2i5ZCsmJtGzZhlzVCy0qRacavtZmxRqbBMhvAHvzCmsJyPInF+LwMZsjR47k9OnTPPnkk6SlpdG5c2d++ukny6RBx44dQ6NxeJ1YCJtITtrPY7r/WR5rz07U8MeREcSGNnzNzYzDfxMNHFEjuSqk+pnBhBPzDqZA64+PMRdDVnLd9zeZaG48CUBQ80TW3nqVVDSFcFIHTmWjNUUTpT3DQO1fHDOEkkYzTuXVvQt9dQxGE3HlSaCAtv1N0qLpZMICffiHaLpygPTD/zQ4XmB5BgDeIXENjiVqFhLXDjZDM3IxFeeg8QxwdEpOyeGVTYApU6YwZcqUKl9bt25djfsuW7bM9gkJYSetPbKqnKihlT7bJvGLTpi75KbrY+QmjQvL947DJ28H7vnH6r5z7nHc1VJKVR3t2l0iFU0hnFjr5oEcwdzqOFH3E+O0q3jMcBfN/W632TH2H06ig2KeGyOy/ZUX2Fo4QoZ7NJQfoPjUPqBZveOYjEbCz66x2Sy6le0SFFWKjYrkjOpHsJJH+tE9RLSTz1dV5NeoEI3IPFGD9cfOlhM1uGUdAqDIt4VN4gnHUIPNPxICSuoxDuTMQQCS1XDaRNVu1m4hhGPE6gvoq91peaxVVOa5LSFSa7uJgo7tNg/dOEUoOp/6V2SE/eT7mMf+uWUfalCc06kn8FTKAGge16bBeYma6d10nFTMEzGdSd7t4Gycl1Q2hWhM/pFobngV9ezMgKoKDF1ks25Nzc7ORKsLkz8yrswvxnzzIcp4kpyCuk0YkXfM3Lp9RG1O+2ipbArh1LKSrNbEBdBgQl9wwmaHMJzcBsAxrYzhc1bK2bH6gcUpDYqTlrIPgAwC8fDybnBe4sIy3c2/34rTDjo4E+cllU0hGlvXMZTe/gUAxbiT0nyIbeIWnsFfNd8ND46XKe1dmU+0ufxaKifYezKrTvvmHTffXT2ljcTHUyZNE8KpBbUAxfqnmKpoKfWJstkhAnLNFZA0bXObxRS2FRhr/s4PM6aiVQ31jpObal4C8IwmxCZ5iQsr8javhKHNadiNgouZU4zZdASj0Wj1X2eOqdPpMBqNLpFrU41ZEa+2ZeWW0IsiPPBSSji4cwsxoQMbnGvZ8R14AsdNIbSOjax2H1e5ps5QTnWJee5/GywoES0Qq6Sz7ngGV7Su/Y9ETab57mq+d2ylfOx5TW0d1yXKyU4x7RVXyskJy8knHOX6V1C+ewAFMKFgGvQSBq9Qm+RaWm4g3mCeHCiLZk5fTvaK6+wx4xNakqd64acUEaIrqHc5lZ8y92wp1Ppb7e/s5++KMSu+95TAeMg2L3/S0GO40vnXJaaiqqp64c1c3+LFi1m8eDFGo5GDBw+yadMmfHx8HJ2WaMK030yibfkePmk2hfZXN3wyCMP2FXQ+/Drr1C4EDX9NJghyZapKiy8H4KkWMy/8VW64qlutd4378jp8TPk8H7aQ63tdbsckhRC20mzzs0Sc+JFPDH2IuelpfD1s0xaQfCqNIX8MB2DXkB9QPGTCMGdkMpkwfjGBLsoh/m73GPp2g+sc4/gfnzDw5GtoFDCpsCpyKtFXjLRDtuJcB/f+w817/0M2fpy85UdHp9OoCgoK6NmzJ7m5ufj5Vb8CQpNp2Zw8eTKTJ08mLy8Pf39/EhMTSUtLo1WrVmi1Wpsco6Iia8uYpaWlvPLKK0yfPh29Xm+TmGCfXJtyTKh7WW3b0BHS9hBYcJC2bds2ONdDf5jH+Jx2j6FX+/bVbucq19RZyqk27JFrxk+xeBbuR19wnLZt76zdTkWZaE35AIQkdKr0vrLXNXWV7z5Xep+6Sq5STraJq5TfCCd+pIUmldOKL74U2yTXo3s3A3BSE8HKVeucvpzsFdcVYq76JpouxkOcObyVXkOm1KmcThzdz7UnX0djng4CjQIDTv4fJz1uIyq+jUucvyvFPPd7T+vpBXshkDx8YiPQeAU4Va72jJmYmFir7ZtMZfN8FRdcq9Xa9EvS1jG1Wi0Gg8EueVbEd+bzd6WYdS0rz/jukPYx0aUHL7h9bWLqsw8DUOSXUKvju8I1tUdMe36mbBnTGNQKCvfjW5hS+5hZ5vfACTWY1rHNq93PFb5PXKWc7BnTXnGlnJywnKIuBaC9kszrxzOJTvSySa7qqR0AnPZuhaHAdcrJXnGdOWa+TzzkQpDxdJ1jZhzdQ2wVy6qdTt5HbOK/N5+d+fxdKea533sJMTFkqAGEKjlkHttLePteNonvzOd/bszakH52QjhIbOe+ALRUj3EyPaNhwVSVZiXJAOjC2zUslnAK3lHmHwjhhhMUl9Zugff84+bxOodNkbSPliUOhHAZzRIp1XjiqZSRc3yvzcIG5u0HwBgqk8Y5O7WZecmrcFNanfeNaNERo6pYPWdQNYQndLBJbqJ65y5/clqWP6mSVDaFcBCf0HjOEIBOMZG0/feGBSs8jZ+aj0lVCJOZaC8KvtHmHwktlZPsO5lZq31yU8yVzRPaSAK8PeyWmxDCxjQaCgLNNwq9svbZJGRxaTktjObZSZu1kvHbzs4vxvydH6Gmg7GsTvtGJbThN/+hlscGVcOGlg8RlSDLoDWGiuVPStIatk7qxUoqm0I4iqJw3MN8J7M4+a8GhSo+Ya5kHFNDaZ8Q3eDUhOMpIeYfCQlKKvtPnKnVPurpAwDkecXaLS8hhH14xponAosuTyK/pP7LX1TYvW8fzZVM803I1j0aHE/YV1xCa/JVT3QYMZ45XOf9je7myZ+26zqRNnYTfUfPtnWKohrFPmeXP8lNdmwiTkoqm0I4UEGQuRXSJ6thXS/SDv0NwFElioggmWX5ohAQQxnu6JVyTh+r3d1S74JkAEyBCXZMTAhhD17x3QHoqDnKoTMlDY6Xum8TAKe0zdF4yiy0zq5l8yCSVHMLWeaRHXXePzDb/DuiIKaftGg2tsB4APyKjjs4EecklU0hHKjix0Vs6YEGxSk9tQeADH1Mg3MSTkKjtXTNMZ6pRWWzrJAgg3nsr29k1bMbCyGcWPMuALRTUkg6U9jgcGqqucKS6du6wbGE/bnptJzUmVvIKsbf15bBYKSFwbzGckjbq2yem6iZT3PzZyzEcMrBmTgnqWwK4UAJnfsAEEUGp9OO1TuOPsdcGSn1b2GTvIRzKPQxd4f1yj964Y3PVkgzVV/iYuPsmJUQwi4C4ynReKNXyik9ndTgcM0Kzt7EjOjc4FiiceScHQKhZB6s034Hdv9JgFJIiepGi05S2WxsFRMx+VOAqbB2cyw0JVLZFMKBAkMiSCECgOTt6+sXRFUJLkkBwD1cWrQuJurZrjkhZScoNxhr3LbwhLkL1WFVZqIVwiVpNBQEmWeh9str2EQj+cWlJJ6dHCi87ZUNTk00DlOged1C/4Ja3GA8R/oe8ySDR3QJ6NxlcrjGFh8ZQaoaBEDGEZmR9nxS2RTCwY55mLtflNR3kqCCdHzVAoyqQlhCRxtmJhxN18w89rKFcoJDqdk1bpuVbO52dVyJJNjPy+65CSFszzPWvN5mTHkS2QX1H7e5a89uwpVs89+FNjITravwijTPSBxqOAXG2i15BaBJ3Q5Apl/7mjcUduHhruPU2eVPMlOksnk+naMTcBSj0Wj1X2eOqdPpMBqNLpFrU41ZEa8+ZVXc7BI4uQ7frN2V9qtNrkUp2/EFUtQw2sRGXPDYrnJNna2cLhTz3P/aKqYhwNyy2UJJZdXxM7RuHljt9uXp5vX0cjyiq83DntfU1nFdqZxsHdNecaWcnL+cPGO7wd9wieYoW5PSGNAprl5x0ismB9JF0VznibG01CXKyV5xXSVmZEwiBZs88FFKKM84iCa0dhP9RBSa12bVRV9aZT6ucv6uFPP8z1Ome3Mo24sueT3G7KHgF+k0uTo6pqKqqmqzozuxxYsXs3jxYoxGIwcPHmTTpk34+MisncLxjuzdyg17HyAbX04O/xEU5cI7nUPd8haXHP+AjWpH/Ee8aacshSMopnJaf3kNOow8H7eE67tV/8Oj2de3EWE4zmtBj9Ov36BGzFIIYStuBSdp/dOtlKla5id+yvAu4fWKc+D7RQwv/oytPlfjcd2zNs5S2EuZwYTmy3F01iSxp/Nc1MQBF9ynuKiQzt8Pwk0xsrnvJ/gERzVCpuJ8pd8+yKWlmwFQ0XDq0ofIjh96gb1cW0FBAT179iQ3Nxc/P79qt2syLZuTJ09m8uTJ5OXl4e/vT2JiImlpabRq1QqtVmuTY1RUZG0Zs7S0lFdeeYXp06ej1+ttEhPsk2tTjgn1L6tmoWGU7ZlBoJIP/jr8IlvVOldl2wcoxz8A4AplJ2rJVtQud9Z4PFe5ps5WTjWx5/ln65sTUnoct/wTtG07rOqNTQZMhlQA/GIuoW3bqsfu2uuausp3nyu9T10lVyknG8dV21C82hdPYz6mnKO0bXt1vcLkfmEer+kecylt27Z1mXKyV1xXiVlaWsoaTSSdScK9OI2Ear7Lz/XX2q9xU4ycIYDLrupf5Q1rVzl/V4lZ6fOUdxKldIvldQUTEX+/QPhVo+rUwukq518RMzExsVbbN5nK5vkqLrhWq7Xpl6StY2q1WgwGg13yrIjvzOfvSjHrW1YRoSHsUeJoTxIndv/GJTGV/7hUGTP3JKbvplPxZ0UB1O+mo205APwv/OXmCtfUHjHt+ZmyR8xivxZw+jj6vKPVx84+ihYDRaqeqPjWF8zBFb5PXK2cXOGa2iOmlJPt4xYEtsPzzBY8sw/UK2Z2fjGtTEmgQGSHXpbcXKmc7BXX2WNqtVoydM3BAKbTB2sVt+CIuZKTrG9NsK7mn/XOfv6uEvP8z1Na8h7Cse4oqsFEWvJewrvUfUk6Zz//c2PWhkwQJIQTOOFhbs0sTdla633SjuxEg8nqOQ0m0o7UbX0u4dzczs4w3KzkGEfTq54kqPiUeUKCI2oEHWJCGi03IYTtecZ2AyCm7DDZBcV13n/n7p2EKLkY0NAssbut0xN2lq0LA8A3v3bL3/ic2QlAYXAnu+UkanagJAijat2ibFA1HCytfp6FpkQqm0I4geLgSwDwy679LGYHSoIwyZfbRW9XsXkZk3jlFP1e+YO3V1e+mXA6ybx4e4oSSXigjEUXwpV5xl8GQEfNUbYmZdR5/8yD5nFjJ3Ux4C4zU7uaXI35b3hI+UkwGmrc1mQyEVdqXk/VP1FmHXaUuBZteNRwl+U3marC44aJxCZceIKnE0f289fqTzlxZL+903QYqWwK4QR8Esx3n2PKDl/wj0uFuBZt2KG2sDw2qJpaf7kJ15CeX8qiPeYfi22VZMLI5Pk1x0jJyLXarixtHwCZ+uhGz1EIYWMRXQBorRxn19HUOu9uSjW3dGV4t7rAlsIZlZvcKFL1uGGArCM1bnv8yEHClSyMqkLLS+s3vlc0XGyoPwl9xzKqbDYAZWhpddUIYkP9a9zvl+XPELH8ci7beDcRyy/n94+eb4x0G51UNoVwAq0vuYw81RMPyig8saNW+8QG+5KgMd/1nlt2J71LXyW+79gLfrkJ13Eyt5wumkOoKvgpJWzUT+UW7VoOpuZYbafPNXe3KvNPcECWQgib8o+iQOOHm2Ik//jOOu369updRBbuAWDdmYAqe0II5+Zensch1TzvQt6xmn8PHNu5DoBkTQzefkH2Tk3U4J4Bl/DQXWM4YopArxi51qvmlsoTR/bT78hCtIp5rKdWUbnq8IvknD7RGOk2KqlsCuEEokMC2Iu5lfLkzt9qtY/x5D/4k0ee6ol399F8PONG7hlwiT3TFI0sQZ/D07pllskFtYrKPN0S2vqeM45LVWlWehwA9/DWDshSCGFTikK2r7lV0jO79l3rUjJyObJuOZdrzD0dHtR9xtF1yyv1hBDOTWsq55jGvHxJ5pGaK5uG438BkOpz4Vlrhf11aRHBds8eAORs+6rGbU8d3olGsZ5USKeYKDqdYrf8HEUqm0I4iVOe5h8XZcf+qtX2J7d8DcBmtQP/GdxNWjQvQpFquuWuZwWdYiJCPWccV34qXmoxBlVDWFy7Rs5QCGEPaoi58hBblkR2QUmt9klO2s883buWm1MaReVZ3RJSLuKxYBerTI9YAIzpe2vcrlmu+XU1oqvdcxK1o7S+DoC4nM1gKKt2u8zCyp9rg6rBKyTWbrk5ilQ2hXASJWcnCfLP2VO7HZLWAHDM/zI83JvsKkYXtVKfaFTF+mvapMLXSf9WQEtTzT82UtQw2sXWbwF4IYRzUUPMN446ao7w95H0Wu3T2iOryptTrfRVz2ItnFfFkAivvOrHbJaWlpBoNA+hiGjfu1HyEhd2Zb8hZKgB+FBMyl/fVbudsv8HwDyZEJgrmhsSZxEQEtUYaTYqqWwK4SR8W5i7XjQvT4aDP0Puyeo3LsoistjcVcqnbf9GyE44gsErFPX6V0Axr2WlAhoFdm/6geKycgAyDm8DIFmJJDJIZqIV4mJQHGie6K2lcoJdR2v4W3CuoATLD9cKJjSEJ8jwClejPzskIrjsRLWTBh7YvhEvpZR81ZP49pc1ZnqiBqEBvmzTmyd9PPPXF1Vuczg5hatK1gKwu/vzbL1qCWljN9HrjocbLc/G1GSbQ4xGo9V/nTmmTqfDaDS6RK5NNWZFvIaUVZvWbchZ502AUggfjUBVNKiDFoJX90rxzvzzHWGoHDRF0rvHZXU6nqtcU2ctp+pinvtfW8Y0dLwDbYt+kHWUsr0/4Pn3WzxsWsKnX3Vl1C23UHTK3LJ52j0KVVVrzMGe19TWcV2tnFzlmtojppST7XM1eIZQoAvEx5DNqYP/cCStDbEhNQ+VWL3pH+5UzDelFEBVtHD9yxh9wjGWlrpEOdkrrivF1Ol0hEW3pHi7O55KGcadn0BcL/CLtNr2zL6NACS5teQSRWn07/6mHrOmz1N5i2th38/EZW3AaCiH83oo7fruDRKVEo5romg38C4q+r670vnXJaaiquffB7s4LV68mMWLF2M0Gjl48CCbNm3Cx0daAYTz0Bak0ebH4ZbxNgCqouHAoC8weIVabWtcPYdOub/wmW4IbW+a3ciZCodRTXismk5iwVaOqyGcGriUZr/NJrFkF+/6/YfLr73N0RkKIWxE/XYql5T+zZPlY/nAOJB7L/Xj+rbVr6N88KtnuNn4E/u9u6O/9E5KfaIq/e0QriG7qJz474YTpckEQEXDqUsfIjt+qGWbrG8fp3fpWlb7jyBiwDQHZSqqkltYRMcfbsBXKeaPbq/jF/fvmNqi0nJCV95GnJLGhtjJBFx2hwMzbZiCggJ69uxJbm4ufn5+1W7XZFo2J0+ezOTJk8nLy8Pf35/ExETS0tJo1aoVWq3WJseoqMjaMmZpaSmvvPIK06dPR6/X2yQm2CfXphwTbFBWyWesKpoAimpCX3CChM5X/ZurqpL7xd8AlEX3om3bus1C5yrX1GnLqQqNef7l0R9zatGVRCsZFG58ipBS88x1QSHhF3wv2Ouausp3nyu9T10lVykn++T6+9+72FUYyyW6v7lGs42fjd14+28Y0at9lS2c+1NS6WvYAAoE95tMYOehVq+7SjnZK66rxKwopxl3jcRDybQ8r2Ci+T8vEn7VKEsL5/HPDwDgk3iFQ777m3LM2nyeNv5yKb3LN0DyBtoOGmV5/vvP3qW7kkYBnnQfMQut57+VNFc5/4qYiYmJtdq+yVQ2z1dxwbVarU2/JG0dU6vVYjAY7JJnRXxnPn9XitnQsjqhhBOhKlYTPBhUDSeVUGLOiVmU8g9BajZFqp4OVw6u9zm4wjW1R0x7fqYaI6Y2IJR9V7xM0MbxtCn8d+biYUlPoOzwg65jHJKnreO6ejk5c1wpJ+cvp5O55fhTAEAf7U42aqYy23AXSekdSAivvJ7izrWf0V4pIFsJILjzEDgvF1crJ3vFdfaYlnLKTani5rMRbU4KBMaQmZFKHKcASOzWv9bHd/bzd5WYtfk8lSYMgAMbiM78Da1GA4qCyWQi5MDHABwMHUxXn6p7Kjj7+Z8bszZkgiAhnMTePA9mG+7CpJr/wqgqzDWM4WhZgNV2SRvNA87/VtpxScLFN2uZuLCr+1/PBx7WXW8UTPDttJonlhJCuIQEfQ5jtKstj6tcY/csk8lE+PHvATgVNRi0TbYd4aJxQg3BqFrXNlUVTpabW8EO/f2reTvCCImIafT8xIV17n87paqOaDWVw7s2A7Dxjw30MG0HoMXQBx2YXeOSyqYQTqJ180A+M15Nn9JXSDMFoCgQQBHN/dystnNPWQ9AWrPL0WjkI9wUaTQael7Rt/ILqhGyqp8qXwjhGqpbYzdKOV1p2y0793Kl+g8AcQMmNUp+wr7253sx23AXBtX8N15VzXPIePz4HyjKovjoFgCOe7ZxZJqiBiEhYexy6wjA8T8+BSB/43/RKCp7PLvhH93ekek1KvmlKoSTiA3155FrYjhJKPMMowG43+1bmrv/eyfbWJRDQol59tHgToMckqdwDgHR7Srd+TaoGk6oIQ7KSAhhK1WtsWtUFZKNzSpte+L3D3BXjKS4JeAd06WxUhR2lBjuz2fGq7mq9FVuK3uc+8oeIE/1oln2DnIW9yP6jPmmc4lvnGMTFTUqjDUvTReRvp4jx09wVdEvAHhdea8j02p0UtkUwoncM+AS1s64itBLb2C3KQ4vinHf9p7l9cObVuKmGElWw7j88iscmKlwtIpu1xV3vg2qhkcNE9mX7+ngzIQQDXX+GrsAqWoQ835Ns9qutNxA2zM/A5Df8uZGzVHYT2yIH49cE0MGzdhsascqtQfjmUOqGkRA4VESTckA9El/n3X/m+/YZEW12l0zCpOq0EZNYufHc/BTijmpiSD+iuGOTq1RSWVTCCcTG+rP48N78HXAWABiTnyDKSsZgLzdPwFwwOtSPN3dqgshmoCKbtcVd76vKn2Vz41X0yoiwNGpCSFsQO1yJ0zbBbd+gEHnQ5Qmk9iUz/nnyL8Vzg2//UoH5QjlqpbWA6UL7cWk4ubzO7d3YN2Mq/jfYxP5PPoJzl2wUKOoXHXoBU4c2e+4REW1QprHcUDXGoAbCj8H4EzrO6CJDYFyirNdvHgxcXFxeHh40KNHD/78889qt33nnXfo1asXgYGBBAYG0r9//xq3F8JV3Xb7eDaaOuCGgZTPHwVVJTr77Hs94WrHJiccrqLbdcWd7wya8fA1McSG1rzwuxDChfhHQrsb0A02t17N0H3GkpVrLC8X/2Oe2XK/92W4+Yc5JEVhP7Gh/gzoFEtsqD+e7m5cHutTaZZanWIi7chuxyQoLijfzdz1XaOYx97mlRgdnFHjc3hl85NPPmHGjBnMmTOHf/75h06dOjFw4EAyMjKq3H7dunXcfvvtrF27lk2bNhEdHc21117LyZMyA6O4uCRGBLKv1f0AJKStImXdMsI5TanqRqfeNzk2OeEUzr3zvXbGVdwz4BJHpySEsIcud1IYcTmeShkjM99k7a4UcvILuaxgLQBuXW5zcIKiMUS06FjlWP3whA4OykjU5MSR/Vxa/IflsaJAzyOvNbmWaIdXNl9++WXuvvtuxo8fT7t27Xjrrbfw8vLivffeq3L7Dz/8kPvvv5/OnTvTpk0b3n33XUwmE2vWrKlyeyFc2W3Db2G10hONohK6fhYA+zQtCA8LdXBmwlmce+dbCHGRUhS8b3mDctzord3F1m//y+ZVnxCmZJOHN636SGWzKYhKaMPvLR+2Gqu/oeVDRCXIrLTOKDVpZ5WzSje1lmiHLsZUVlbG33//zezZsy3PaTQa+vfvz6ZNm2oVo6ioiPLycoKCKi9yDFBaWkppaanlcV5enuX58vJySktLbbbIqdFotHnMitzPPQdbsEeuTTkm2KesNKqRnA4TMezcjKdSDkBH037Wvv8cV4ycWa+YrnJNXamcXOX87XVNXeW7r6lfUyknFy8nnygKuk+j2Z8vcnfpexzbFQEK7A/sRydVAzWUgauUk73iukrM2pRTzxEzOJZ8PaeT9xIS146eca0vWK6ucv6uErO2n6dmMW0wblCsKpwGVUNQdKsq93WV8z83Zm0oqqqqF97MPk6dOkVkZCR//PEHPXv2tDz/0EMPsX79erZs2XLBGPfffz+rVq1iz549eHh4VHp97ty5PPXUU5Wef+SRR6rcXghn46kzMrPsVTTn9JwxqBpedv8PxQbb/YEXQgjh3DSqkXv4gFCyLM99qxvEP8a2DsxKCFGdFu5p3FayAp1iwqBqWOFxG0ll4Y5OyyZKSkpYsGABubm5+Pn5VbudQ1s2G2rBggWsWLGCdevWVVtxnD17NjNmzLA8zsvLIzo6mgceeIDU1FRat25t05r+gQMHbBqztLSUV155henTp6PX620SE+yTa1OOCfYpK6PRyPpvlqE5YP28TjHRp1s7OvcdVq+YrnBNXa2cXOH87XVNXeW7r6lfUykn1y+nk8kHCP7oNTjn5uOg8lW0v+NRIuNaVxvTVcrJXnFdJab85nONmHUtp2PJ91laokfU8Dl1lfOviBkREcGCBQsuuL1DK5vBwcFotVrS09Otnk9PTyc8vOZa/0svvcSCBQv45Zdf6NixY7Xb6fX6Kt8Ier0eNzc39Hq9TS++rWNWqO486sseuTblmOeyZVkZjUZ8wxMw7q/cDSOyZed6HcdVrqmrlZMrnL+9rqmrfPc19Wsq5eT65ZR5bD8JVYwByzp+kITW1f8WquDs5WSvuK4Ss4L85nPumBVqW04JrTvW6vPpKud/bszacOgEQe7u7lx66aVWk/tUTPZzbrfa873wwgs888wz/PTTT3Tr1q0xUhXCYQJCovg98SGZEEAIIZo4mY1UCOFqHN6NdsaMGYwdO5Zu3brRvXt3Fi1aRGFhIePHjwdgzJgxREZGMn++eY2p559/nieffJKPPvqIuLg40tLMixv7+Pjg4+PjsPMQwp563fEwqSk3k3ZkN+EJHegrFU0hhGhyohLasK7lw1x16AXLGLANLR+SvwlCCKfl8MrmyJEjOX36NE8++SRpaWl07tyZn376ibAw8+LEx44dQ6P5twH2zTffpKysjFtuucUqzpw5c5g7d25jpi5Eo4pKaCOtmUII0cT1HT2bE0eGyc1HIYRLcHhlE2DKlClMmTKlytfWrVtn9Tg5Odn+CQkhhBBCOCm5+SiEcBUOHbMphBBCCCGEEOLiJJVNIYQQQgghhBA2J5VNIYQQQgghhBA25xRjNh3BaDRa/deZY+p0OoxGo0vk2lRjVsSzdVm5yvm7SsyKeFJOtr+mto7blMvJXnGlnKScnL2c7BXXlWLKbz7XiCnlVPuYiqqq6oU3c32LFy9m8eLFGAwGDh06xC+//IK3t7ej0xJCCCGEEEIIl1JYWEj//v3JycnB39+/2u2aTGWzwokTJ4iOjnZ0GkIIIYQQQgjh0o4fP05UVFS1rze5yqbJZOLUqVP4+vrSvXt3/vrrL5vGv+yyy2waMy8vj+joaI4fP46fn5/N4oLtc23qMe1VVq5y/q4SU8rJ9jHtEbepl5O94ko5STk5eznZK64rxJTffK4RU8rJHPPPP/8kPz+f5s2bo9FUPw1QkxuzqdFoLLVvrVZr8zeJPWIC+Pn5uUSuTTlmBVuXlaucv6vErCDlZFuu8t3X1K+plJOUk7OXk73iukpMkN98rhATpJz8/f1r7D5boUnPRjt58mSXiGkvrnL+rhLTXlzl/F0lpr24yvnb65q6Slk19Wsq5WR7rpSrrTX1a+oq5QSuc/6uEtNeXOX86xKzyXWjdTV5eXn4+/uTm5trtxYeYRtSVq5Bysk1SDm5Bikn1yDl5BqknFyDlFPdNOmWTVeg1+uZM2cOer3e0amIC5Cycg1STq5Bysk1SDm5Bikn1yDl5BqknOpGWjaFEEIIIYQQQtictGwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYRwSuvWrUNRFD7//HNHp1Ir6enp3HLLLTRr1gxFUVi0aFGjHHfZsmUoikJycnKjHO9iM3fuXBRFcXQaQghxUZLKphBCNGEVFRUPDw9OnjxZ6fW+ffvSoUMHB2TmeqZPn86qVauYPXs2H3zwAdddd1212yqKYvmn0Who3rw51157LevWrWu8hIG9e/cyd+7ci66iGhcXZ3WNPTw8aNmyJbNmzSIrK8vR6QkhRJMhlU0hhBCUlpayYMECR6fh0n799VduvPFGZs6cyejRo2nTpk2N2w8YMIAPPviA5cuXc++997Jz50769evHjz/+WKfj3nnnnRQXFxMbG1vnnPfu3ctTTz110VU2ATp37swHH3zABx98wP/93//Rv39/Fi1aVOkmwOOPP05xcbGDshRCiIubztEJCCGEcLzOnTvzzjvvMHv2bJo3b+7odBpVYWEh3t7eDY6TkZFBQEBArbdv1aoVo0ePtjweNmwYHTt2ZNGiRQwaNKjWcbRaLVqtti6pujyDwYDJZMLd3b3abSIjI62u71133YWPjw8vvfQShw4domXLlgDodDp0Ovk5JIQQ9iAtm0IIIXj00UcxGo0XbN1MTk5GURSWLVtW6TVFUZg7d67lccVYuIMHDzJ69Gj8/f0JCQnhiSeeQFVVjh8/zo033oifnx/h4eEsXLiwymMajUYeffRRwsPD8fb25oYbbuD48eOVttuyZQvXXXcd/v7+eHl50adPHzZu3Gi1TUVOe/fu5Y477iAwMJCrrrqqxnM+cuQII0aMICgoCC8vLy6//HK+//57y+sVXZFVVWXx4sWWrpt1dckllxAcHMzRo0ctz/3666/06tULb29vAgICuPHGG9m3b5/VflWN2YyLi2PIkCFs2LCB7t274+HhQUJCAu+//77VfiNGjADg6quvtuRd0ZV369atDBw4kODgYDw9PYmPj2fChAkXPI+KY//888907twZDw8P2rVrx5dffllp25ycHKZNm0Z0dDR6vZ7ExESef/55TCaTZZuK99xLL73EokWLaNGiBXq9nr1799bqup4rPDwcwKpyWdWYTUVRmDJlCl9//TUdOnRAr9fTvn17fvrppzofUwghmjKpbAohhCA+Pp4xY8bwzjvvcOrUKZvGHjlyJCaTiQULFtCjRw+effZZFi1axIABA4iMjOT5558nMTGRmTNn8ttvv1Xa/7nnnuP777/n4YcfZurUqaxevZr+/ftbdX389ddf6d27N3l5ecyZM4d58+aRk5NDv379+PPPPyvFHDFiBEVFRcybN4+777672tzT09O54oorWLVqFffffz/PPfccJSUl3HDDDXz11VcA9O7dmw8++AD4t2tsxeO6yM7OJjs7m2bNmgHwyy+/MHDgQDIyMpg7dy4zZszgjz/+4Morr6xVt9fDhw9zyy23MGDAABYuXEhgYCDjxo1jz549lrynTp0KmG82VOTdtm1bMjIyuPbaa0lOTuaRRx7h9ddfZ9SoUWzevLlW53Lo0CFGjhzJoEGDmD9/PjqdjhEjRrB69WrLNkVFRfTp04f//e9/jBkzhtdee40rr7yS2bNnM2PGjEoxly5dyuuvv86kSZNYuHAhQUFBNeZQXl7OmTNnOHPmDCdOnODbb7/l5Zdfpnfv3sTHx1/wHDZs2MD999/PbbfdxgsvvEBJSQnDhw8nMzOzVtdACCEEoAohhGiyli5dqgLqX3/9pSYlJak6nU6dOnWq5fU+ffqo7du3tzw+evSoCqhLly6tFAtQ58yZY3k8Z84cFVAnTZpkec5gMKhRUVGqoijqggULLM9nZ2ernp6e6tixYy3PrV27VgXUyMhINS8vz/L8p59+qgLqq6++qqqqqppMJrVly5bqwIEDVZPJZNmuqKhIjY+PVwcMGFApp9tvv71W12fatGkqoP7++++W5/Lz89X4+Hg1Li5ONRqNVuc/efLkWsUF1IkTJ6qnT59WMzIy1C1btqjXXHONCqgLFy5UVVVVO3furIaGhqqZmZmW/Xbs2KFqNBp1zJgxlucqyvDo0aOW52JjY1VA/e233yzPZWRkqHq9Xn3wwQctz3322WcqoK5du9Yqv6+++sryvqirimN/8cUXludyc3PViIgItUuXLpbnnnnmGdXb21s9ePCg1f6PPPKIqtVq1WPHjqmq+u97zs/PT83IyKhTDuf/u/LKK9UzZ85YbVvxnjgXoLq7u6uHDx+2PLdjxw4VUF9//fXaXQghhBCqtGwKIYQAICEhgTvvvJP//ve/pKam2izuXXfdZfl/rVZLt27dUFWViRMnWp4PCAigdevWHDlypNL+Y8aMwdfX1/L4lltuISIigh9++AGA7du3c+jQIe644w4yMzMtrVmFhYVcc801/Pbbb1bdMgHuvffeWuX+ww8/0L17d6uutj4+PkyaNInk5OR6deWssGTJEkJCQggNDaVHjx5s3LiRGTNmMG3aNFJTU9m+fTvjxo2zasHr2LEjAwYMsJx7Tdq1a0evXr0sj0NCQqq9xuerGHv63XffUV5eXudza968OcOGDbM89vPzY8yYMWzbto20tDQAPvvsM3r16kVgYKClzM6cOUP//v0xGo2VWrmHDx9OSEhIrXPo0aMHq1evZvXq1Xz33Xc899xz7NmzhxtuuKFWEwL179+fFi1aWB537NgRPz+/Wl0/IYQQZjIiXgghhMXjjz/OBx98wIIFC3j11VdtEjMmJsbqsb+/Px4eHgQHB1d6vqouihUTuVRQFIXExERLV9JDhw4BMHbs2GpzyM3NJTAw0PK4Nt0oAVJSUujRo0el59u2bWt5vb5Lw9x4441MmTIFRVHw9fWlffv2lomKUlJSAGjdunWVx161atUFJzY6/7oDBAYGkp2dfcHc+vTpw/Dhw3nqqad45ZVX6Nu3LzfddBN33HEHer3+gvsnJiZWGgfZqlUrwDwGMzw8nEOHDrFz585qK5AZGRlWj2tbZhWCg4Pp37+/5fH1119P69atueWWW3j33Xf5z3/+U+P+Dbl+QgghzKSyKYQQwiIhIYHRo0fz3//+l0ceeaTS69VNfGM0GquNWdVMqdXNnqqqai0z/VdFq+WLL75I586dq9zGx8fH6rGnp2edj2NrUVFRVpUhW2vINVYUhc8//5zNmzfz7bffsmrVKiZMmMDChQvZvHlzpetZHyaTiQEDBvDQQw9V+XpF5bSCLcrsmmuuAeC33367YGXTlu9RIYRoqqSyKYQQwsrjjz/O//73P55//vlKr1W0Dubk5Fg9X9ESZw8VLZcVVFXl8OHDdOzYEcDS1dHPz8/mlbfY2FgOHDhQ6fn9+/dbXreHirjVHTs4ONgmy7VcaNbcyy+/nMsvv5znnnuOjz76iFGjRrFixQqrrtFVOXz4MKqqWsU/ePAgYJ6tFszlVlBQYNcK9/kMBgMABQUFjXZMIYRoymTMphBCCCstWrRg9OjRvP3225bxdRX8/PwIDg6uNJ7ujTfesFs+77//Pvn5+ZbHn3/+OampqZa1KC+99FJatGjBSy+9VGUl4vTp0/U+9uDBg/nzzz/ZtGmT5bnCwkL++9//EhcXR7t27eoduyYRERF07tyZ5cuXW1Xsd+/ezc8//8zgwYNtcpyKCuv5Nw+ys7MrteBVtBqXlpZeMO6pU6css/UC5OXl8f7779O5c2fL8iO33normzZtYtWqVZX2z8nJsVQMbenbb78FoFOnTjaPLYQQojJp2RRCCFHJY489xgcffMCBAwdo37691Wt33XUXCxYs4K677qJbt2789ttvllYrewgKCuKqq65i/PjxpKens2jRIhITEy1Llmg0Gt59910GDRpE+/btGT9+PJGRkZw8eZK1a9fi5+dnqWTU1SOPPMLHH3/MoEGDmDp1KkFBQSxfvpyjR4/yxRdfoNHY757tiy++yKBBg+jZsycTJ06kuLiY119/HX9/f6v1TBuic+fOaLVann/+eXJzc9Hr9fTr14+PPvqIN954g2HDhtGiRQvy8/N555138PPzq1VFt1WrVkycOJG//vqLsLAw3nvvPdLT01m6dKllm1mzZrFy5UqGDBnCuHHjuPTSSyksLGTXrl18/vnnJCcnVxrXWxcnT57kf//7HwBlZWXs2LGDt99+m+Dg4At2oRVCCGEbUtkUQghRSWJiIqNHj2b58uWVXnvyySc5ffo0n3/+OZ9++imDBg3ixx9/JDQ01C65PProo+zcuZP58+eTn5/PNddcwxtvvIGXl5dlm759+7Jp0yaeeeYZ/u///o+CggLCw8Pp0aMH99xzT72PHRYWxh9//MHDDz/M66+/TklJCR07duTbb7/l+uuvt8XpVat///789NNPzJkzhyeffBI3Nzf69OnD888/X+fJcqoTHh7OW2+9xfz585k4cSJGo5G1a9fSp08f/vzzT1asWEF6ejr+/v50796dDz/8sFbHbtmyJa+//jqzZs3iwIEDxMfH88knnzBw4EDLNl5eXqxfv5558+bx2Wef8f777+Pn50erVq146qmn8Pf3b9C5bd++nTvvvBMw35AIDg7m5ptv5plnniEyMrJBsYUQQtSOospIdyGEEELYSFxcHB06dOC7775zdCpCCCEcTMZsCiGEEEIIIYSwOalsCiGEEEIIIYSwOalsCiGEEEIIIYSwORmzKYQQQgghhBDC5qRlUwghhBBCCCGEzUllUwghhBBCCCGEzTW5dTZNJhOnTp3C19cXRVEcnY4QQgghhBBCuBRVVcnPz6d58+ZoNNW3Xza5yuapU6eIjo52dBpCCCGEEEII4dKOHz9OVFRUta83ucqmr68vYL4wfn5+Ds7mwsrLy/n555+59tprcXNzc3Q6ogZSVq5Bysk1SDm5Bikn1yDl5BqknFyDlJNZXl4e0dHRlrpVdZpcZbOi66yfn5/LVDa9vLzw8/Nr0m9oVyBl5RqknFyDlJNrkHJyDVJOrkHKyTVIOVm70LBEmSBICCGEEEIIIYTNSWVTCCGEEEIIIYTNSWVTCCGEEEIIIYTNOdWYzfnz5/Pll1+yf/9+PD09ueKKK3j++edp3bq1ZZuSkhIefPBBVqxYQWlpKQMHDuSNN94gLCzMZnmoqorBYMBoNNosZn2Vl5ej0+koKSlxinxE9aSsXIOtysnNzQ2tVmvDzIQQQgghLi5OVdlcv349kydP5rLLLsNgMPDoo49y7bXXsnfvXry9vQGYPn0633//PZ999hn+/v5MmTKFm2++mY0bN9okh7KyMlJTUykqKrJJvIZSVZXw8HCOHz8u64I6OSkr12CrclIUhaioKHx8fGyYnRBCCCHExcOpKps//fST1eNly5YRGhrK33//Te/evcnNzWXJkiV89NFH9OvXD4ClS5fStm1bNm/ezOWXX96g45tMJo4ePYpWq6V58+a4u7s7vNJgMpkoKCjAx8enxgVTheNJWbkGW5STqqqcPn2aEydO0LJlS2nhFEIIIYSoglNVNs+Xm5sLQFBQEAB///035eXl9O/f37JNmzZtiImJYdOmTVVWNktLSyktLbU8zsvLA8xd6crLyyttazQaiYyMxMvLy+bnUx+qqlJWVoZer3d4xVfUTMrKNdiqnJo1a0ZBQQHFxcXo9XobZigAy/fz+d/TwrlIObkGKSfXIOXkGqSczGp7/k5b2TSZTEybNo0rr7ySDh06AJCWloa7uzsBAQFW24aFhZGWllZlnPnz5/PUU09Vev7nn3+uVKHU6XSEh4dTVFSEwWCwzYnYSH5+vqNTELUkZeUaGlpOZWVlFBcXs379eqf7vriYrF692tEpiFqQcnINUk6uQcrJNTT1cqrtkEOnrWxOnjyZ3bt3s2HDhgbFmT17NjNmzLA8zsvLIzo6mmuvvRY/Pz+rbUtKSjh+/Dg+Pj54eHg06Li2oqoq+fn5+Pr6SmuZk2vSZZW2s/Jz4R0dd/wajm2rciopKcHT05PevXs7zffFxaS8vJzVq1czYMAAWTTbiUk5uQYpJ9cg5eQapJzMKnqLXohTVjanTJnCd999x2+//UZUVJTl+fDwcMrKysjJybFq3UxPTyc8PLzKWHq9vsoubm5ubpXeIEajEUVR0Gg0TjPmzmQyAVjyEs6raZeVWvmpRr0G5x2/hmPbqpw0Gg2KolT5XSJsR66va5Bycg1STq5Bysk1NPVyqu25O9UvYlVVmTJlCl999RW//vor8fHxVq9feumluLm5sWbNGstzBw4c4NixY/Ts2bOx0xVOpG/fvkybNq1O+8ydO5fOnTvbJZ/a6t27Nx999JFDc7hYrPtjK0pkV3Jyzd1jf/rpJzp37mypXAohhBBCiMblVC2bkydP5qOPPuKbb77B19fXMg7T398fT09P/P39mThxIjNmzCAoKAg/Pz/+85//0LNnzwbPRHsh69evt2v88/Xp06dO248bN47ly5dzzz338NZbb1m9NnnyZN544w3Gjh3LsmXLbJhl06MoCl999RU33XRTg2OtXLmS9PR0brvttoYn5qLWrVvH1VdfTXZ2dqWx2A113XXX8cQTT/Dhhx9y55132jS2EEIIIYS4MKdq2XzzzTfJzc2lb9++REREWP598sknlm1eeeUVhgwZwvDhw+nduzfh4eF8+eWXDszaeURHR7NixQqKi4stz5WUlPDRRx8RExPjwMxqp6yszNEpNKrXXnuN8ePHO32XW6PRWGXroCuU17hx43jttdccnYYQQgghRJPkVL9yVVWt8t+4ceMs23h4eLB48WKysrIoLCzkyy+/rHa8ZlPTtWtXoqOjrSrfX375JTExMXTp0sVqW5PJxPz584mPj8fT05NOnTrx+eefW143Go1MnDjR8nrr1q159dVXrWKsW7eO7t274+3tTUBAAFdeeSUpKSmA+Uf++a1/06ZNo2/fvpbHffv2ZcqUKUybNo3g4GAGDhwIwO7duxk0aBA+Pj6EhYVx5513cubMGct+hYWFjBkzBh8fHyIiIli4cGGtrs+CBQsICwvD19eXiRMnUlJSYvX6X3/9xYABAwgODsbf358+ffrwzz//WF6Pi4sDYNiwYSiKYnmclJTEjTfeSEREBFFRUfTo0YNffvmlxlxOnz7Nr7/+ytChQy3PJScnoygK27dvtzyXk5ODoiisW7cOMF9zRVFYs2YN3bp1w8vLiyuuuIIDBw5Yxf/222+57LLL8PDwIDg4mGHDhlley87OZsyYMQQGBuLl5cWgQYM4dOiQ5fVly5YREBDAypUradeuHXq9nmPHjhEXF8czzzzDmDFj8PPzY9KkSQBs2LCBXsMm4NmiJ9HdBjH1iRcoLPr3hkdpaSkPP/ww0dHR6PV6EhMTWbJkCcnJyVx99dUABAYGoiiK5bN+ofcnwA8//ECrVq3w9PTk6lsmkXz8VKXrPHToULZu3UpSUlKN5SGEEEIIIWzPqSqbouEmTJjA0qVLLY/fe+89xo8fX2m7+fPn8/777/PWW2+xZ88epk+fzujRoy3dhU0mE1FRUXz22Wfs3buXJ598kkcffZRPP/0UAIPBwE033USfPn3YuXMnmzZtYtKkSXWe3XP58uW4u7uzceNG3nrrLXJycujXrx9dunRh69at/PTTT6Snp3Prrbda9pk1axbr16/nm2++4eeff2bdunVWlcKqfPrpp8ydO5d58+axdetWIiIieOONN6y2yc/PZ+zYsWzYsIHNmzfTsmVLBg8ebFki46+//gJg6dKlpKamWh4XFBQwePBgVq9ezfr16xk4cCBDhw7l2LFj1eazYcMGvLy8aNu2bZ2uV4XHHnuMhQsXsnXrVnQ6HRMmTLC89v333zNs2DAGDx7Mtm3bWLNmDd27d7e8Pm7cOLZu3crKlSvZtGkTqqoyePBgq/WSioqKeP7553n33XfZs2cPoaGhALz00kt06tSJbdu28cQTT5CUlMR1113H8MHXsHP1J3zy5gI2/LmdKY8tsMQaM2YMH3/8Ma+99hr79u3j7bffxsfHh+joaL744gvAPPY6NTXVckPjQu/P48ePc/PNNzN06FC2b9/OXXfcxCPzX690nWJiYggLC+P333+v13UWQgghhBD151RjNkXDjR49mtmzZ1taGDdu3MiKFSssLWNgbmmaN28ev/zyi2VipYSEBDZs2MDbb79Nnz59cHNzs1qfND4+nk2bNvHpp59y6623kpeXR25uLkOGDKFFixYA9ao4tWzZkhdeeMHy+Nlnn6VLly7MmzfP8tx7771HdHQ0Bw8epHnz5ixZsoT//e9/XHPNNYC5wnrurMVVWbRoERMnTmTixImW4/zyyy9WrZv9+vWz2ue///0vAQEBrF+/niFDhhASEgJAQECAVWt6p06d6NSpEyaTiby8PJ5++mm+/vprVq5cyZQpU6rMJyUlhbCwsHp3oX3uuecs43ofeeQRrr/+ekpKSvDw8OC5557jtttusyq/Tp06AXDo0CFWrlzJxo0bueKKKwD48MMPiY6O5uuvv2bEiBGAeVrvN954w7LfudfowQcftDy+6667GDVqFNPuHgVAy4QYXntmFn2G382bJSUcO3aMTz/9lNWrV9O/f3/A/F6rEBQUBEBoaKhlzGZt3p9vvvkmLVq0sLRqt/YdzK79h3l+8bJK16p58+aWz4MQQgghhGg8Utm8yISEhHD99dezbNkyVFXl+uuvJzg42Gqbw4cPU1RUxIABA6yeLysrs+puu3jxYt577z2OHTtGcXExZWVlltlbg4KCGDduHAMHDmTAgAH079+fW2+9lYiIiDrle+mll1o93rFjB2vXrsXHx6fStklJSZY8evToYXk+KCiI1q1b13icffv2ce+991o917NnT9auXWt5nJ6ezuOPP866devIyMjAaDRSVFRUYwslmFs2586dy/fff8+pU6cwGo0UFxfXuF9xcXGD1mbs2PHfdSQrrnlGRgYxMTFs376du+++u8r99u3bh06ns7p+zZo1o3Xr1uzbt8/ynLu7u9UxKnTr1s3q8Y4dO9i5cycf/u8Dy3OqqmIymTh69Ci7du1Cq9XWacKr2rw/9+3bZ3UOAD0vrXptTU9Pz1ovPCyEEEIIIWxHKpsXoQkTJlha1BYvXlzp9YKCAsDc3TIyMtLqtYo1SVesWMHMmTNZuHAhPXv2xNfXlxdffJEtW7ZYtl26dClTp07lp59+4pNPPuHxxx9n9erVXH755Wg0GlTVeu3Dc7tpVvD29q6U29ChQ3n++ecrbRsREcHhw4drcwnqZezYsWRmZvLqq68SGxuLXq+nZ8+eF5wIZ+bMmaxevZoXXniB8PBwQkJCuPXWW2vcLzg4mOzsbKvnKlo5z71uVV0zsF7bqKLrcsUkPp6enjXmWxuenp5Vdomuqrzuuecepo68ptK2MS1a1Ku8avP+rIusrCxLq7QQNZrrX8VzuY2fhxC1Ie9XcbE4/70s7+OLilQ2L0LXXXcdZWVlKIpimXTnXOdO+lJdi1NFN8v777/f8lxVk6x06dKFLl26MHv2bHr27MlHH33E5ZdfTkhICLt377badvv27RdcALZr16588cUXxMXFodNVfnu2aNECNzc3tmzZYplhNzs7m4MHD9bYeta2bVu2bNnCmDFjLM9t3ry50jm/8cYbDB48GDCPCzx3YiIwV/KMRmOl/caNG8ewYcPIy8tDo9GQnJxc43l26dKFtLQ0srOzCQwMBLBUiFJTUy0teOdOFlRbHTt2ZM2aNVWO1W3bti0Gg4EtW7ZYutFmZmZy4MAB2rVrV+djde3alb1795IYX/lYuLtzySWXYDKZWL9+vaUbrfUm7gBW17Q278+2bduycuVKq+c2/7Or0nYlJSUkJSVVmiBLCCGEEELYn0wQdBHSarXs27ePvXv3otVqK73u6+vLzJkzmT59OsuXLycpKYl//vmH119/neXLlwPmsZRbt25l1apVHDx4kCeeeMIyIQ7A0aNHmT17Nps2bSIlJYWff/6ZQ4cOWcZt9uvXj61bt/L+++9z6NAh5syZU6nyWZXJkyeTlZXF7bffzl9//UVSUhKrVq1i/PjxGI1GfHx8mDhxIrNmzeLXX39l9+7djBs37oJjHx944AHee+89li5dysGDB5kzZw579uyx2qZly5Z88MEH7Nu3jy1btjBq1KhKrYRxcXGsWbPGUlGs2O/LL79k+/bt7Nq1i1GjRlW5VMi5unTpQnBwMBs3brQ85+npyeWXX86CBQvYt28f69ev5/HHH7/gNTvfnDlz+Pjjj5kzZw779u1j165dlpbili1bcuONN3L33XezYcMGduzYwejRo4mMjOTGG2+s87Eefvhh/vjjD6Y8toDtuw9w6Mgxvlm1zjJBUFxcHGPHjmXChAl8/fXXHD16lHXr1lkmmoqNjUVRFL777jtOnz5NQUFBrd6f9957L4cOHWLWrFkcOHCAj776kWWfflspv82bN1taqIUQQgghROOSls1aqsuYM2fg5+dX4+vPPPMMISEhzJ8/nyNHjhAQEEDXrl159NFHAbjnnnvYtm0bI0eORFEUbr/9du6//35+/PFHALy8vNi/fz/Lly8nMzOTiIgIJk+ezD333APAwIEDeeKJJ3jooYcoKSlhwoQJjBkzhl27Krc+nat58+Zs3LiRhx9+mGuvvZbS0lJiY2O57rrrLBXKF1980dLd1tfXlwcffJDc3Jq7XIwcOZKkpCRLPsOHD+e+++5j1apVlm2WLFnCpEmTLEvIzJs3j5kzZ1rFWbhwITNmzOCdd94hMjKS5ORkXn75ZSZMmMBVV11FUFAQjzzyiGUG2+potVrGjx/Phx9+yJAhQyzPv/fee0ycOJFLL72U1q1b88ILL3DttdfWGOt8ffv25bPPPuOZZ55hwYIF+Pn50bt3b8vrS5cu5YEHHmDIkCGUlZXRu3dvfvjhhwu2OlelY8eOrF+/nsdmTqXXzRNRVZUWsVGMvOHfnN98800effRR7r//fjIzM4mJibG8zyIjI3nqqad45JFHGD9+PGPGjGHZsmUXfH/GxMTwxRdfMH36dF5//XW6d27HvEcmM2HGU1b5ffzxx4waNQovL686n5sQQgghhGgYRT1/YN1FLi8vD39/f3JzcytVyEpKSjh69Cjx8fENmrzFlipmOPXz86v3zKWicdS1rNLS0mjfvj3//PMPsbGxjZChHZ3aVvm55o3YdfX84zfvwpkzZ2jdujVbt24lPj7e8pKtPlPO+H1xMSkvL+eHH35g8ODB9boRUi8yBq7OHFJOwqwO71cpJ9fQZMvJxcZsNtlyOk9NdapzSe1FCAcJDw9nyZIlF5ztVtRPcnIyb7zx+7FvcwAATThJREFUhlVFUwghhBBCNB7pRiuEA910002OTuGi1a1bt0pLtQghhBBCiMYjLZtCCCGEEEIIIWxOKptCCCGEEEIIIWxOKptCCCGEEEIIIWxOxmwK1+LoWU+F/UkZ258rzrrqYrMVOrWmfi2b+vnXVX2ul1xjIcRZ0rIphBBCCCGEEMLmpLIphBBCCCGEEMLmpLIphBBCCCGEEMLmZMxmLcU98n2jHi95wfWNejxX17dvXzp37syiRYtqvc/cuXP5+uuv2b59u93yupDevXtz7733cscddwCgKApfffVVtetvJicnEx8fz7Zt2+jcuXPjJdoExMXFMW3aNKZNm0ZZWRmtWrXi888/l7U6hRBCCCHqSVo2LxLjxo1DURTuvffeSq9NnjwZRVEYN25c4yd2kVEUha+//tomsVauXEl6ejq33XZbrfeJjo4mNTWVDh062CQHVxcXF1enGwy15e7uzsyZM3n44YdtHlsIIYQQoqmQyuZFJDo6mhUrVlBcXGx5rqSkhI8++oiYmBgHZlY7ZWVljk6hUb322muMHz8ejab2H0OtVkt4eDg6net0SigvL6/0nCuU9ahRo9iwYQN79uxxdCpCCCGEEC5JKpsXka5duxIdHc2XX35pee7LL78kJiaGLl2sl44wmUzMnz+f+Ph4PD096dSpE59//rnldaPRyMSJEy2vt27dmldffdUqxrp16+jevTve3t4EBARw5ZVXkpKSAphbWs/vCjpt2jT69u1redy3b1+mTJnCtGnTCA4OZuDAgQDs3r2bQYMG4ePjQ1hYGHfeeSdnzpyx7FdYVMyYqU/g0/JKIrpcy8KFC2t1fRYsWEBYWBi+vr5MnDiRkpISq9f/+usvBgwYQHBwMP7+/vTp04d//vnH8npcXBwAw4YNQ1EUy+OkpCRuvPFGIiIiiIqKokePHvzyyy815nL69Gl+/fVXhg4dWum11NRUBg0ahKenJwkJCVblkpycjKIolq6/DS2nqpw4cYLbb7+doKAgvL296datG1u2bLG8/uabb9KiRQvc3d1p3bo1H3zwgdX+SmRX3lz+GTfccAPe3t4899xzzJ07l86dO/Puu+8SHx+Ph4cHADk5Odx1112EhITg5+dHv3792LHnoFW8b39ez2WXXYaHhwfBwcEMGzYMML9/UlJSmD59OkpkV5TIrpZ9NmzYQK9evfD09CQ6OpqpU6dSWFhodf1vuOEGPD09iY+P58MPP6x0HQIDA7nyyitZsWJFtddKCCGEEEJUTyqbF5kJEyawdOlSy+P33nuP8ePHV9pu/vz5vP/++7z11lvs2bOH6dOnM3r0aNavXw+YK6NRUVF89tln7N27lyeffJJHH32UTz/9FACDwcBNN91Enz592LlzJ5s2bWLSpEkoilKnfJcvX467uzsbN27krbfeIicnh379+tGlSxe2bt3KTz/9RHp6Orfeeqtln1nPLGL95r/55r2X+fmjxaxbt86qUliVTz/9lLlz5zJv3jy2bt1KREQEb7zxhtU2+fn5jB07lg0bNrB582ZatmzJ4MGDyc/PB8yVUYClS5eSmppqeVxQUMDgwYNZvXo169evZ+DAgQwdOpRjx45Vm8+GDRvw8vKibdu2lV574oknGD58ODt27GDUqFHcdttt7Nu3r8o4ti6ngoIC+vTpw8mTJ1m5ciU7duzgoYcewmQyAfDVV1/xwAMP8OCDD7J7927uuecexo8fz9qNf1nFmfvy2wwbNoxdu3YxYcIEAA4fPswXX3zBl19+aaksjxgxgoyMDH788Uf+/vtvunbtyjUj7yUr27wm2/e//M6wu2YyePBgtm3bxpo1a+jevTtgvpESFRXF008/Teq2n0nd9jMAScnHue666xg+fDg7d+7kk08+YcOGDUyZMsWS3/3338+JEydYu3Ytn3/+OW+88QYZGRmVrkf37t35/fffqy5EIYQQQghRI9fpiydqZfTo0cyePdvScrVx40ZWrFjBunXrLNuUlpYyb948fvnlF3r27AlAQkICGzZs4O2336ZPnz64ubnx1FNPWfaJj49n06ZNfPrpp9x6663k5eWRm5vLkCFDaNGiBUCVFacLadmyJS+88ILl8bPPPkuXLl2YN2+e5bn33nuP6OhoDh48SHOliCUrvuZ/rz3LNb16AOYKa1RUVI3HWbRoERMnTmTixImW4/zyyy9WrZv9+vWz2ue///0vAQEBrF+/niFDhhASEgJAQEAA4eHhlu06depEp06dMJlM5OXl8fTTT/P111+zcuVKqwrOuVJSUggLC6uyC+2IESO46667AHjmmWdYvXo1r7/+eqXKMWDzcvroo484ffo0f/31F0FBQQAkJiZaXn/ppZcYN24c999/PwAzZsxg8+bNvPTWB1x95WWW7e646bpKNznKysp4//33Lddxw4YN/Pnnn2RkZKDX6y3xv/7iEz7//hcmjR7Oc68t4bYbr7U6x06dOgEQFBSEVqvF19eX8NBgy+vz/28po0aNYtq0aYD5Pfbaa6/Rp08f3nzzTZKTk/nll1/YvHkzPXqY30NLliyp8ro0b968xlZgIYQQQghRPalsuqJT26wfN/+3i2xISAjXX389y5YtQ1VVrr/+eoKDg602P3z4MEVFRQwYMMDq+bKyMqvutosXL+a9997j2LFjFBcXU1ZWRuf2reDUNoKAcbcOZeDAaxkw4Fr69+/PrbfeSkRERJ1O5dJLL7V6vGPHDtauXYuPj0+lbZOSkijW5VFWVk6Prv9OkBMUFETr1q2rP8ipbezbu5t7Rw4yX7uz16tnz56sXbvWsll6ejqPP/4469atIyMjA6PRSFFRUY0tlGBuDZw7dy7ff/89p06dwmg0UlxcXON+xcXFlq6k56u4AXDu45pmzK2ynM7OVBsUFMS4ceMYOHAgAwYMuGA5bd++nS5dulgqmufbt28fkyZNsnruyiuv5NWXX7R6rlundpX2jY2NtVQ0wVzWBQUFNGvWzGq74uJiklJOmPPZc5C7Rw2r9tyrsmPvQXbu+8Gqa6yqqphMJo4ePcr+/fvR6XRW7702bdoQEBBQKZanpydFRUV1Or5wInP9z3uc27SOL8TF7PzPF/z7GZPPXtWawnWx5TnW9B5zZCwXI5XNi9CECRMsLWqLFy+u9HpBQQEA33//PZGRkVavVbQwrVixgpkzZ7Jw4UJ69uyJr68vL774Ils2rLNsu/SVp5g68XZ++juZTz75hMcff5zVq1dz+eWXo9FoUFXVKnZVE8V4e3tXym3o0KE8//zzlbaNiIjg8B/f1eIK1M/YsWPJzMzk1VdfJTY2Fr1eT8+ePS84mc3MmTNZvXo1L7zwAuHh4YSEhHDrrbfWuF9wcDDZ2dkNzrnacjpnjOXSpUuZOnUqP/30U6VyOp+np2eDcwLw9qocp6qyjoiIsGp1ByB9DwH+vuZ8PPR1PnZBYRH33HMPU6dOrfRaTEwM+/fvr3WsrKwsqwqyEEIIIYSoPRmzeRG67rrrKCsro7y83DLpzrnatWuHXq/n2LFjJCYmWv2Ljo4GzN1vr7jiCu6//366dOlCYmIiSUlJlWJ16dCG2bNn88cff9ChQwc++ugjwNzCmpqaarVtbdaz7Nq1K3v27CEuLq5Sbt7e3rSIi8LNTceWf3Zb9snOzubgwYM1RIW2ifFs2bbL6rnNmzdbPd64cSNTp05l8ODBtG/fHr1ebzUxEZi7rRqNxkr7jRs3jmHDhtG+fXvCw8NJTk6uMZ8uXbqQlpZWZYXz/Lw2b95cbdfXWpdTly5VltP5OnbsyPbt28nKyqry9bZt27Jx48ZKObRrGV/l9jXp2rUraWlp6HQ667KOjyE4KNCcT9uWrNnwZ7Ux3N3dK5VH10vasnfv3krvn8TERNzd3WnTpg0Gg4G///7bss+BAwfIycmpFH/37t2VJtcSQgghhBC1I5XNi5BWq2Xfvn3s3bsXrVZb6XVfX19mzpzJ9OnTWb58OUlJSfzzzz+8/vrrLF++HDCPc9u6dSurVq3i4MGDPPHEE5YJcQCOHjvJ7Pmvs2nrDlJSUvj55585dOiQpVLUr18/tm7dyvvvv8+hQ4eYM2cOu3fvrpTL+SZPnkxWVha33347f/31F0lJSaxatYrx48djNBrx8fZi4m03MevZRfy64U927z/MuHHjLrh8yAMTb+e9T1ay9JNvOHjwIHPmzKm0pEXLli354IMP2LdvH1u2bGHUqFGVWvri4uJYs2aNVUWxZcuWlklvdu3axahRoywT6lSnS5cuBAcHV6q4AXz22We89957ljz//PPPasd+XrCcjh5l9uzZbNq0qcpyOt/tt99OeHg4N910Exs3buTIkSN88cUXbNq0CYBZs2axbNky3nzzTQ4dOsTLL7/Ml19+ycx7x9R4vlXp378/PXv25KabbuLnn38mOTmZP/74g8cW/B9bd+wFYM6MSXz89SrmzJnDvn372LVrl1Wrd1xcHL/99hsnUzM4k2Uuj4fvH8sff/zBlClT2L59O4cOHeKbb76xXMPWrVtzzTXXcN9997Flyxb+/vtv7rrrripbdX///XeuvfbaOp+bEEIIIYSQbrS1lrzgekenUCd+fn41vv7MM88QEhLC/PnzOXLkCAEBAXTt2pVHH30UgHvuuYdt27YxcuRIFEXh9ttv5/777+fHleZlVbw8Pdh/OJnln31LZnYeERERTJ48mXvuuQeAgQMH8sQTT/DQQw9RUlLChAkTGDNmDLt27ao2JzBPyLJx40Yefvhhrr32WkpLS4mNjeW6666zVChffGIaBYVFDB03DV8fbx6c9TC5uTX3ex9540CSUk7w0LOvUvLkQoYPH859993HqlWrLNssWbKESZMmWZaQmTdvHjNnzrSKs3DhQmbMmME777xDZGQkycnJvPzyy0yYMIGrrrqKoKAgHnnkEcsMttXRarWMHz+eDz/8kCFDhli99tRTT7FixQruv/9+IiIi+Pjjj2nXrvIYSKihnH78EQAvLy/279/P8uXLyczMrFRO53N3d+fnn3/mwQcfZPDgwRgMBtq1a2fpjn3TTTfx6quv8tJLL/HAAw8QHx/P0qVL6XtFhyrj1URRFH744Qcee+wxxo8fz+nTpwkPD6f3ZR0ICzaPGe17RTc+e/t5nln8IQsWLMDPz4/evXtbYjz99NPcc889tLjyBkpLy1BP/kPHdq1Yv349jz32GL169UJVVVq0aMHIkSMt+y1evJgZM2bQp08fwsLCePbZZ3niiSes8tu0aRO5ubnccsstdT43IYQQQggBinr+wLqLXF5eHv7+/uTm5laqkJWUlHD06FGrdQAdrWKGUz8/v39b72qYIMjuzj+2Kxy/ka5XlWVVg7S0NNq3b88///xDbGysXXJqNLZ8X9i5jGtbTiNHjqRTp06WGzDnc8bvi1pzgYkKysvL+eGHHxg8eDBubm71m/ShrvvY+ro462QcNsyrUjm5Amctl7qqw/u1QeXUGJ+9+rgIJwiy++fJWa+LPSf1scMEQfUqJxf4u1tXNdWpziXdaIVwkPDwcJYsWXLB2W5F4ysrK+OSSy5h+vTpjk5FCCGEEMJlNbiyuXz5cr7//nvL44ceeoiAgACuuOIKWZ9OiAu46aab6NWrl6PTEOdxd3fn8ccft9nsvEIIIYQQTVGDK5vz5s2z/CDbtGkTixcv5oUXXiA4OFhaBYQQQgghhBCiiWrwBEHHjx8nMTERgK+//prhw4czadIkrrzySvr27dvQ8EIIIYQQQgghXFCDK5s+Pj5kZmYSExPDzz//zIwZMwDw8PCguLi4wQmKOnDkxEH1Of7FMNlQffZpyDlWF8vRebmitJ3AOfOj2eP8bTkhQH1iOfr4TUFTv8bzo6DTf83/NZXIRDAVnHVSHWfVWO99R09EU90+Fc9rPP79PD2ZXv/cbMUVv5Maw/nfeyDXpQYNrmwOGDCAu+66iy5dunDw4EEGDx4MwJ49e4iLi2toeCGEEEIIIYQQLqjBYzYXL15Mz549OX36NF988QXNmjUD4O+//+b2229vcIJCCCGEEEIIIVxPg1s28/LyeO211yqtVzd37lyOHz/e0PBCCCGEEEIIIVxQg1s24+PjOXPmTKXns7KyiI+Pb2h4IYQQQgghhBAuqMEtm6qqVvl8QUEBHh4eDQ3vPKoaJG3X48lA48amRHblq6++4qabbiI5OZn4+Hi2bdtG586d6xXPEmPVx3Tu0Nq2yQohhBBCCOHk6t2yOWPGDGbMmIGiKDz55JOWxzNmzOCBBx5g5MiRdf6R/ttvvzF06FCaN2+Ooih8/fXXVq+PGzcORVGs/l133XX1PYWLyrhx41Aiu1r/UxQOHz5sef2mm26qdv/i4mLmzJlDq1at0Ov1BAcHM2LECPbs2WO13dyFb1nia6O7Ed1tEJMmTSIrK8tqu7ge17No0SLL4x07dnDDDTcQGhqKh4cHcXFxjLz3YTLOWO/nLKKjo0lNTaVDhw612n7cuHEMGzas6hhtWtgjRSGEEEIIIZxavVs2t20zL5ugqiq7du3C3d3d8pq7uzudOnVi5syZdYpZWFhIp06dmDBhAjfffHOV21x33XUsXbrU8liv19cj+4vTdVdfwdKX5/77RPglhISEXHC/0tJS+vfvz7Fjx1i4cCE9evQgPT2d+fPn06NHD3755Rcuv/xyy/btW7fglxVvYjSa2HfoKBMemkdubi6fvPJIlfFPnz7NNddcw5AhQ1i1ahUBAQEkJyez8qN3KSyy7fI45eXluLm5NTiOVqslPDzcNjFOpTY4HyGEEEIIIVxNvSuba9euBWD8+PG8+uqr+Pn5NTiZQYMGMWjQoBq30ev1Da4EXKz07u6Ehwb/+0Qtr9OiRYvYtGkT27Zto1OnTgDExsbyxRdf0KNHDyZOnMju3btRFAUAnVZrOU5kRCgjRow4ewOg6srmxo0byc3N5d1330WnM7/l4uPjubp1QI15xcXFMXHiRPbu3cvKlSsJCAjg0cl3MnncSMs2SmRX3njjDX788UfWrFnDrFmzmDt3Lt988w1PPfUUe/fupXlYMGNHDOGxqRMtxz906BAT75zIn9v3kBATyatPz7I6dlXdaPfs2cPDD0zlty3bUFWVzu1bseyVp/jgv9+wfPlyAAIDAwHz5yMuLq5SN9r1m/5m1vP3sGPHDoKCghg7dizPPvusJa++ffvSsWNHPDw8ePfdd3F3d+fee+9l7ty5FypGIYQQQgghnEqDx2ye28rYGNatW0doaCiBgYH069ePZ5991rLcSlVKS0spLS21PM7LywPMLWDl5eVW25aXl6OqKiaTCZPJZPVag2dSqqOK41eMia3Iy0w5f2NUVUUFTOe+ds45qKp6Xox/ffTRR/Tv359LLrmk0usPPPAAd955p6XSpZ6NX3Gc5OOnWLVqFe7u7tbHPifn0NBQDAYDX3zxBbfccoul0lrpPM7LGeDFF19k9uzZzJkzh59//pkHZswgMSGOAb3/bWmdO3cu8+bN4+WXX0an07F+/XrGjBnDokWL6NWrF0lbf+Heh55BReHJGfdgMhi4+eabCQvwYtO375ObX8CMOS9Zrvu55V/x/ydPnqR37970ubwzv3z6Nn4+Pmzcup0yg4kZM2awd+9e8vLyePXVV/Hx8aFZs2acOnXKcq1MKJxMzWDwnf9h7LjxLFu2jP3793PPPfeg1+uZM2eO5XyWL1/O9OnT2bRpE5s2bWLChAn07NmTAQMGVFn2tb2WlVUXqz7qc3xbxqr9uVg+U2gwcc52dczXdPZzV15ejlarrXojTRXj1s/73qm1+sSqbh9bxrKTiu9ny/f0+cevzbHruo+tr0tjHL8+6nMtq1F+NlbFfxsl33OP0xjvi/pwsvdrpc9TXTjZudRqn+qOb8uyt+V1Oe9zVK7xcI73pQt+J9k11tl4lb73anOcRv4b2hhq+32iqNXN8FNLhYWFLFiwgDVr1pCRkVGpsnLkyJF6xVUUxTJZS4UVK1bg5eVFfHw8SUlJPProo/j4+LBp06Zqf+zNnTuXp556qtLzH330EV5eXlbP6XQ6wsPDiY6OtuoWDBCwKLZe51FfOdNS6rT9/fffz6effmo1KVP//v1ZtmyZ5fXc3Fw+/PDDSvtGREQwbtw45s+fX+m1nTt30qdPH9577z2GDRvGggULePHFF/H09MRoNFJSUgLAc889x/3332/Zr2PHjtx3333cd999APx/e3ceH1V1/3/8PYGskEUgkER2AihCWIVvRFkECVQp1H5bwOWLSlEQvyCgIFr2tiwqVfyhtHUB+3W3ghULlC0gCAGUFAQEElFQCMiWEEJIYM7vD5yRycZk5k4mE17PxyMPM+fc8znn3DP3mg/3zp2ZM2dq/vz5ioyMVIcOHdStWzcNHjxYdevWLXVOSUlJatGihT788ENn2UMPPaSzZ8/qgw8+kHT5SuLIkSP1pz/9ybnNwIED1a1bN40bN85Z9t5772natGnau3ev1q5dq0GDBmnnzp2Kj4+XJK1evVq/+c1v9H//93+68847dejQIbVt21YbNmxQmzZtNGPGDH300Ufatm1bibfplrR/i8aYOXOmPvnkE6WlpTkT7ldffVXTp0/Xd999p6CgIN111126dOmSli9f7ozTq1cv3XbbbVzdrGQKCgp0+PBhZWVl6eLFi/4eDgAAQIXJy8vTPffco+zs7DLvcPX6yubvfvc7rV+/Xvfff7/i4+OvuGplvcGDBzt/b9OmjZKSktSsWTOlpqaqV69eJbaZNGmSS9KRk5OjBg0aqE+fPsV2TH5+vg4fPqyaNWv6/Um6jrEZY3T27FlFRkb+vG+zdrpuHJek4OBg9bilk16e9bSzuEbjjs44wcHBql69evE3Q9ZOSUbBhdmKyvtWiktyqa5Ro4YkKTw8XFFRUQotPKOWzRpp6RsvKP9Cgd766FOlHziiJ554QtVP7HG2CzIXFRYW5uzv2Wef1VNPPaW1a9dq69atWrx4sf487zml/uM1tbmxuctcnDGCgnTbbbe5jLlbu0S9+Opbl8f6k+TkZJdtdu/erbS0NM2bN+9ygbHrkt2u/PwLqn5yrw4dOqQGDRqoZfQF6ac4vVrXc5lnzZo1nfOPiorS3r171a1bN9Uu/EEq+g85P+1/x62wjrVyxjh/RFF5kfpmb7puaX+joqOjnfu+V9sGejI3VzmZaWp4fbyqV6+upKQkl/ldXyfy5wO5hLX/eR2Lj6tMpcXyhCf9WxmrHHNxHlN5h2S78spmOfvIv2gUHh6ubt26lX6+mFW/eNmk70uuc5SXxpNYpbUpK5Yv+7/auK5QWFioVatW6Y477rj8Dzzl3V9l9e/u9t62qYj+PWHhviyck6hVbebrjl2jFWzPd2+NrXy/Wvm+qKyx3N2+jDbFjqfy9F/J5uJWGyvn4m7/FrxfCoPCfj6eJmZ41k9l/f9Oefqp6HVxN9ZP8Yqd99zpp6LO7xXIcbfo1XidbC5fvlyffvqpunbt6m2ocmvatKnq1KmjjIyMUpPN0NDQEh8iFBwcXOyEe+nSJdlsNgUFBSkoqKJvnHXl6N9xpdgxrstM0Y0vJzcR4WrRpMHP5QnXO391PL23+LyMWjRpqK8PHFSQjFSkft++fZKkG264QUFBQbLJKCQ42NlP0tOjdefw32vmzJmaOdL1oU5F+4uNjdWgQYM0aNAgzZo1S+3b3Kh5C9/U4hdnuMylrBi2n+YedMU+iIyMdNkmNzdX06dP//khU8d+fqJuRGiIM2m/Mobjd8faO+I5fo+IiLg8lqL7/qcxX/mPLEXfQ0EyCpKR7aefK9fR8ZtjG+nyA7ZcxmazyRhHu+Jr74hV0rjKVlosT3jSv5Wx3J+L85iS3XU9y9lHkIxsNluJ55KfO8svXubYtmjd1R5s5Ums0tqUFcuX/V9tXCVw7t/y7q+y+nd3e2/bVET/nrByX/5UHmzPv/xHlztrbOX71QdzqXSx3N3ejTblOl9V8rlYek7yhA/fL8H2/J/XqSL+X2FlLCvWsqLXxd1YjnhFz3vu9FNR5/cK5O4DOb3OqK677jrVqlXL2zAe+f7773Xy5EnnrZDwzOABKVr9WZr+s3u/S7ndbtef//xntWrVyvngoJL8/ve/13PPPacjWT+63WdISIiaNap/1afRbtmyxfX1l7t0Y/MmZbbp0KGD9u3bp8TExMs/TRo6f4KCgnTjjTfq8OHDOnrs5/Fu+XJXmTGTkpL02WeflXp/ekhIiC5dulRmjBsTm2jzF7tcvpt207Z0Rdasofrx9cpsCwAAAAQar5PNmTNnasqUKcrLy/N6MLm5uUpPT1d6erok6eDBg0pPT9ehQ4eUm5urJ598Ulu2bNG3336rNWvWaMCAAUpMTFRKSorXfV8LsrOznfvX8XP4hyyNHX6vOre7Sf0feFwffPCBDh06pG3btunXv/619u7dq9dee63M26OTk5OVlJSkP730Won1y5Yt03333adly5Zp//792rdvn5577jn9a+0mDUjpXuaYN23apLlz52r//v1asGCBPli2WmOGDSmzzZQpU/Tmm29q+vTp2r17t/Ye+EbvfrxSv5+zQNLlz7K2aNFCQx+fqv/s3q/P0r7UMz/Vleaxxx5TTk6OBj86Sdv/s0cHvjmkv3+4TPsyvpV0+cm5u3bt0oEDB3TixIkSk9JHh/5Wh49k6X//93/19ddf6+OVqZr6/EKNe/hev19JBwAAAKzm9W20zz//vDIzM1WvXj01bty42CXVL7/80u1Y27dvV8+ePZ2vHZ+1HDp0qF555RXt3LlTixcv1pkzZ5SQkKA+ffpo5syZFfNdm9Oyfd+Hj6Wmpqp9+/YuZcOGDNSrz03R2g/+oj/Nf11PP/20vvvuO0VGRqpnz57asmWLWrdufdXYY8eO1QMPDNXERx9Qg+tdv3KlVatWioiI0Pjx43X48GGFhoaqefPmevXZybr/v+8qM+748eO1fft2TZ8+XVFRUZo3dZxSetxSZpuUlBQtW7ZMM2bM0Jw5cxRcvZpuSGys3w0ZKOnyrbFLlizRsPsHqfNd96tx/QTNn/mk+t77WKkxa9eurbVr1+rJ0Y+o+69/p2rVqqndTS3U9eZ2kqThw4dr3bp1uv3225Wbm+v86pMrXR9fV//6+0t6cs5f1bZtW9WKidKwIQP1+zG/K3M+AAAAQCDyOtm88mmx3urRo4fKejjuypUrLeurqlm0aJF0ZEeZ9Y4n07r4qU1EeLj+MHGU/vDiq2X2M238CE0bP6JY+eDBgzW4W0vn62/TPpUSLie2TZs21V//+tdS+y5LVFSU3n///VLbmB++dPZzpZSUlJ+veJfQT4sWLfTZktdLjdW4ceNi78WkpCStfPvlEscZGxurlStXKicnR1FRUc4rlcYYl/67J3fU1q1bSx1Xampqsbqlr88rcY4AAABAZeZ1snnl9wMCAAAAACBZ8JlNAAAAAACK8ujKZq1atbR//37VqVNH1113XZkPjzl16pTHg8O17dtvv/X3EAAAAAB4yKNk889//rMiIyMlSS+88IKV40FVVNJnM6vaZxCP7JBkkyIaS1k7JZmqN0ep+FpeC3OUrj7PadFFXgf+A8VQyZT2Hita7qgrrRzl5+996Un/Rds8c8K68VRFZe3j8p7f/f1+qUqs3JdWrnF5+mHtPUs2hw4dWuLvVUVZDykCAEniNAEAAFA2rx8QJEmXLl3S0qVLtXfvXknSTTfdpF/+8peqVq2aFeErjONrW/Ly8hQeHu7n0QCozArsl/8baOc5AACAiuJ1spmRkaFf/OIX+uGHH9Sy5eWvvpg1a5YaNGigTz/9VM2aNfN6kBWlWrVqiomJ0fHjxyVJERERZX4etSLY7XYVFBQoPz/f+XUauljkkkp+ftnlZSlvm6Lbu9N/aW3KiuVJ/+62udq4POn/opFdurxWF42CZDzfL+VpY+VcvHm/eNK/u324E6scc3EeU451KtrGjfer3Ug/Zucr4rraql7dkn+zAwAAqHK8/itp9OjRatasmbZs2aJatWpJkk6ePKn77rtPo0eP1qeffur1ICtSXFycJDkTTn8zxuj8+fMKDw//OfE986PrRucOll1elvK2Kbq9O/2X1qasWJ70726bq43Lk/7P/Cgjm86H2BVecFI2Gc/3S3naWDkXb94vnvTvbh/uxCrHXJzHlGOdirZx6/1qFHT+lBq26uT3f5ACAACorLxONtevX++SaEpS7dq1NXv2bHXt2tXb8BXOZrMpPj5edevWVWFhob+Ho8LCQm3YsEHdunVz3uar//cb140e2152eVnK26bo9u70X1qbsmJ50r+7ba42Lk/6/3+/UaEtVBtumKFuX09RsLng+X4pTxsr5+LN+8WT/t3tw51Y5ZiL85hyrFPRNu68X+2XFHL+uIJuv6fscQEAAFzDvE42Q0NDdfbs2WLlubm5CgkJ8Ta831SrVq1SfBarWrVqunjxosLCwn5ONnMPu24UFlZ2eVnK26bo9u70X1qbsmJ50r+7ba42Lk/6zz2sakFhl9fq3PcKtud7vl/K08bKuXjzfvGkf3f7cCdWOebiPKYc61S0TXnerwAAAChVkLcB7rrrLj388MNKS0uTMUbGGG3ZskUjRozQL3/5SyvGCAAAAAAIMF4nm/Pnz1ezZs2UnJyssLAwhYWFqWvXrkpMTNSLL75oxRgBAAAAAAHG69toY2Ji9PHHHysjI8P51Sc33nijEhMTvR4cAAAAACAweZxs2u12Pfvss/rnP/+pgoIC9erVS1OnTuX7KauKadFFXmfTf6Dy91xK69/f46qsrvX94ph/UJjU9q/ub+9SVsn3WWVd40Dcl4HG3/t4Vn3JXuSroSqyf3/PvyqprOeR0li99oE2/2uYx7fR/vGPf9TTTz+tmjVr6vrrr9eLL76oUaNGWTk2AAAAAECA8jjZfPPNN/Xyyy9r5cqVWrp0qT755BO99dZbstvtVo4PAAAAABCAPE42Dx06pF/84hfO171795bNZtORI0csGRgAAAAAIHB5nGw6vvvxSsHBwSosLPR6UAAAAACAwObxA4KMMXrggQcUGhrqLMvPz9eIESNUo0YNZ9lHH33k3QgBAAAAAAHH42Rz6NChxcruu+8+rwYDAAAAAKgaPE4233jjDSvHAQAAAACoQjz+zCYAAAAAAKUh2QQAAAAAWM7j22gRQKZFl1CWXfHj8LWi86yKc6wIlfn9whr73rWwjyvzexwAKtq1cN6vzKr4/ufKJgAAAADAch4lmx06dNDp06clSTNmzFBeXp6lgwIAAAAABDaPks29e/fq3LlzkqTp06crNzfX0kEBAAAAAAKbR5/ZbNeunR588EHdeuutMsboueeeU82aNUvcdsqUKV4NEAAAAAAQeDxKNhctWqSpU6dq2bJlstlsWr58uapXLx7KZrORbAIAAADANcijZLNly5Z69913JUlBQUFas2aN6tata+nAAAAAAACBy+uvPrHb7VaMAwAAAABQhVjyPZuZmZl64YUXtHfvXklSq1atNGbMGDVr1syK8AAAAACAAOP192yuXLlSrVq10tatW5WUlKSkpCSlpaXppptu0qpVq6wYIwAAAAAgwHh9ZfOpp57S2LFjNXv27GLlEydO1B133OFtFwDgmWnRxcueOVHx40DgK/pempbtn3HAPSUd+6yZtSpqH3PsAQHN6yube/fu1bBhw4qVP/TQQ9qzZ4+34QEAAAAAAcjrZDM2Nlbp6enFytPT03lCLQAAAABco7y+jXb48OF6+OGH9c033+iWW26RJG3atElz5szRuHHjvB4gAAAAACDweJ1sTp48WZGRkXr++ec1adIkSVJCQoKmTZum0aNHez1AAAAAAEDg8TrZtNlsGjt2rMaOHauzZ89KkiIjI70eGAAAAAAgcHn9mc0rRUZGepVobtiwQf3791dCQoJsNpuWLl3qUm+M0ZQpUxQfH6/w8HD17t1bBw4c8HLUAAAAAACrWZpseuvcuXNq27atFixYUGL93LlzNX/+fC1cuFBpaWmqUaOGUlJSlJ+fX8EjBQAAAACUxevbaK3Ur18/9evXr8Q6Y4xeeOEF/f73v9eAAQMkSW+++abq1aunpUuXavDgwRU5VAAAAABAGSpVslmWgwcPKisrS71793aWRUdHq0uXLtq8eXOpyeaFCxd04cIF5+ucnBxJUmFhoQoLC307aAs4xugy1qCwohuVr9yTNlbGctRZGas8bXw0l8Kf6hz/Dbi5+GK/+Kv/MmI5j6lr/P1qWazytClHLOfxxH7xrE0FzcXt8543/VeFdfHzfin1vOdJ/6yxz2K5HE8BPpdidVVhjX+qK3be81X/lZy7eZTNGGO86aRv375auHChmjdv7mmYEtlsNi1ZskQDBw6UJH3++efq2rWrjhw5ovj4eOd2v/3tb2Wz2fTee++VGGfatGmaPn16sfK3335bERERlo4ZAAAAAKq6vLw83XPPPcrOzlZUVFSp23l1ZTM4OFg7d+70JoTPTZo0yeX7PnNyctSgQQP16dOnzB1TWRQWFmrVqlW64447FBwcfLlwVn3XjSZ9X75yT9pYGctRZ2Ws8rTx0VwKg8K0qs183bFrtILt+YE3F1/sF3/1X0aswicOXj6mHOvkbv9VYb846gJgLs7jyXHuY7+Ur00FzaVwTqJ7570AmEvAxHLUlSNWqec9T/pnjX0Wy+XviIkZAT2XYnVVYY1/qit23vNV/5Wc427Rq/H6Ntr77rtPr732mmbPnu1tqDLFxcVJko4dO+ZyZfPYsWNq165dqe1CQ0MVGhparDw4OPjn5C0AuIy36P8oylvuSRsrYznqrIxVnjY+nkuwPf/yySfQ5uKL/eKv/t2I5Vwnd9tUhf3iqAuguTjPfeyX8rWp4Llc9bznTf9VYV0qyX4pdt7zpP9KMpdKty4Wxgq25/M3n1X9+3AuLseTL/qv5NzNo7xONi9evKjXX39dq1evVseOHVWjRg2X+nnz5nnbhSSpSZMmiouL05o1a5zJZU5OjtLS0jRy5EhL+gAAAAAAWMPrZPOrr75Shw4dJEn79+93qbPZbOWKlZubq4yMDOfrgwcPKj09XbVq1VLDhg31+OOP6w9/+IOaN2+uJk2aaPLkyUpISHB+rhMAAAAAUDl4nWyuW7fOinFIkrZv366ePXs6Xzs+azl06FAtWrRIEyZM0Llz5/Twww/rzJkzuvXWW7VixQqFhYVZNgYAAAAAgPcs++qTjIwMZWZmqlu3bgoPD5cxptxXNnv06KGyHo5rs9k0Y8YMzZgxw9vhAgAAAAB8KMjbACdPnlSvXr3UokUL/eIXv9DRo0clScOGDdP48eO9HiAAAAAAIPB4nWyOHTtWwcHBOnTokMv3Vg4aNEgrVqzwNjwAAAAAIAB5fRvtv//9b61cuVL167t+R0zz5s313XffeRseAAAAABCAvL6yee7cOZcrmg6nTp0q8fstAQAAAABVn9fJ5m233aY333zT+dpms8lut2vu3LkuT5YFAAAAAFw7vL6Ndu7cuerVq5e2b9+ugoICTZgwQbt379apU6e0adMmK8YIAAAAAAgwXl/ZbN26tfbv369bb71VAwYM0Llz53T33Xdrx44datasmRVjBAAAAAAEGEu+ZzM6OlrPPPOMFaEAAAAAAFWAJcnm6dOn9dprr2nv3r2SpFatWunBBx9UrVq1rAgPAAAAAAgwXt9Gu2HDBjVu3Fjz58/X6dOndfr0ac2fP19NmjTRhg0brBgjAAAAACDAeH1lc9SoURo0aJBeeeUVVatWTZJ06dIlPfrooxo1apR27drl9SABAAAAAIHF6yubGRkZGj9+vDPRlKRq1app3LhxysjI8DY8AAAAACAAeZ1sdujQwflZzSvt3btXbdu29TY8AAAAACAAeXQb7c6dO52/jx49WmPGjFFGRob+67/+S5K0ZcsWLViwQLNnz7ZmlAAAAACAgOJRstmuXTvZbDYZY5xlEyZMKLbdPffco0GDBnk+OgAAAABAQPIo2Tx48KDV4wAAAAAAVCEeJZuNGjWyehwAAAAAgCrE668+kaQjR45o48aNOn78uOx2u0vd6NGjregCAAAAABBAvE42Fy1apEceeUQhISGqXbu2bDabs85ms5FsAgAAAMA1yOtkc/LkyZoyZYomTZqkoCCvv0kFAAAAAFAFeJ0d5uXlafDgwSSaAAAAAAAnrzPEYcOG6YMPPrBiLAAAAACAKsLr22hnzZqlu+66SytWrFCbNm0UHBzsUj9v3jxvuwAAAAAABBhLks2VK1eqZcuWklTsAUEAAAAAgGuP18nm888/r9dff10PPPCABcMBAAAAAFQFXn9mMzQ0VF27drViLAAAAACAKsLrZHPMmDF66aWXrBgLAAAAAKCK8Po22q1bt2rt2rVatmyZbrrppmIPCProo4+87QIAAAAAEGC8TjZjYmJ09913WzEWAAAAAEAV4XWy+cYbb1gxDgAAAABAFeL1ZzYBAAAAACjK6yubTZo0KfP7NL/55htvuwAAAAAABBivk83HH3/c5XVhYaF27NihFStW6Mknn/Q2PAAAAAAgAHmdbI4ZM6bE8gULFmj79u3ehgcAAAAABCCffWazX79++sc//uGr8AAAAACASsxnyeaHH36oWrVq+So8AAAAAKAS8/o22vbt27s8IMgYo6ysLP344496+eWXvQ0PAAAAAAhAXiebAwcOdHkdFBSk2NhY9ejRQzfccIO34QEAAAAAAcjrZHPq1KlWjAMAAAAAUIX47DObvjJt2jTZbDaXH66gAgAAAEDl4vGVzaCgIJfPapbEZrPp4sWLnnZRqptuukmrV692vq5e3esLtAAAAAAAC3mcpS1ZsqTUus2bN2v+/Pmy2+2ehi9T9erVFRcX55PYAAAAAADveZxsDhgwoFjZvn379NRTT+mTTz7RvffeqxkzZng1uNIcOHBACQkJCgsLU3JysmbNmqWGDRuWuO2FCxd04cIF5+ucnBxJUmFhoQoLC30yPis5xugy1qCwohuVr9yTNlbGctRZGas8bXw0l8Kf6hz/Dbi5+GK/+Kv/MmI5j6lr/P1qWazytClHLOfxxH7xrE0FzcXt8543/VeFdfHzfin1vOdJ/6yxz2K5HE8BPpdidVVhjX+qK3be81X/lZy7eZTNGGO87ezIkSOaOnWqFi9erJSUFM2aNUutW7f2NmyJli9frtzcXLVs2VJHjx7V9OnT9cMPP+irr75SZGRkse2nTZum6dOnFyt/++23FRER4ZMxAgAAAEBVlZeXp3vuuUfZ2dmKiooqdTuvks3s7Gz96U9/0ksvvaR27dppzpw5uu222zwN55EzZ86oUaNGmjdvnoYNG1asvqQrmw0aNNCJEyfK3DGVRWFhoVatWqU77rhDwcHBlwtn1XfdaNL35Sv3pI2VsRx1VsYqTxsfzaUwKEyr2szXHbtGK9ieH3hz8cV+8Vf/ZcQqfOLg5WPKsU7u9l8V9oujLgDm4jyeHOc+9kv52lTQXArnJLp33guAuQRMLEddOWKVet7zpH/W2GexXP6OmJgR0HMpVlcV1vinumLnPV/1X8nl5OSoTp06V002Pb6Ndu7cuZozZ47i4uL0zjvvlHhbbUWIiYlRixYtlJGRUWJ9aGioQkNDi5UHBwf/nLwFAJfxFv0fRXnLPWljZSxHnZWxytPGx3MJtudfPvkE2lx8sV/81b8bsZzr5G6bqrBfHHUBNBfnuY/9Ur42FTyXq573vOm/KqxLJdkvxc57nvRfSeZS6dbFwljB9nz+5rOqfx/OxeV48kX/lZy7eZTHyeZTTz2l8PBwJSYmavHixVq8eHGJ23300UeeduGW3NxcZWZm6v777/dpPwAAAAAA93mcbP7P//zPVb/6xBeeeOIJ9e/fX40aNXJ+VrRatWoaMmRIhY8FAAAAAFAyj5PNRYsWWTgM933//fcaMmSITp48qdjYWN16663asmWLYmNj/TIeAAAAAEBxHieb/vLuu+/6ewgAAAAAgKsI8vcAAAAAAABVD8kmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMsFbLK5YMECNW7cWGFhYerSpYu2bt3q7yEBAAAAAH4SkMnme++9p3Hjxmnq1Kn68ssv1bZtW6WkpOj48eP+HhoAAAAAQAGabM6bN0/Dhw/Xgw8+qFatWmnhwoWKiIjQ66+/7u+hAQAAAAAkVff3AMqroKBAX3zxhSZNmuQsCwoKUu/evbV58+Zi21+4cEEXLlxwvs7OzpYknTp1SoWFhb4fsJcKCwuVl5enkydPKjg4+HJhQYjrRidPlq/ckzZWxnLUWRmrPG18NJfCoJDLa1UQomC7PfDm4ov94q/+y4hVePKk6zq5239V2C+OugCYi/N4cpz72C/la1NBcykscPO8FwBzCZhYjrpyxCr1vOdJ/6yxz2K5/B0R4HMpVlcV1vinumLnPV/1X8mdPXtWkmSMKXM7m7naFpXMkSNHdP311+vzzz9XcnKys3zChAlav3690tLSXLafNm2apk+fXtHDBAAAAIAq7fDhw6pfv36p9QF3ZbO8Jk2apHHjxjlf2+12nTp1SrVr11bnzp21bds2S/u7+eabLY2Zk5OjBg0a6PDhw4qKirIsrmT9WK/1mL5aq0CZf6DEZJ2sj+mLuNf6OvkqLuvEOlX2dfJV3ECIyd98gRGTdbocc+vWrTp79qwSEhLK3Dbgks06deqoWrVqOnbsmEv5sWPHFBcXV2z70NBQhYaGupTFxMRIkqpVq2b5m8QXMSUpKioqIMZ6Lcd0sHqtAmX+gRLTgXWyVqCc+671fco6sU6VfZ18FTdQYkr8zRcIMSXWKTo6WtHR0VfdNuAeEBQSEqKOHTtqzZo1zjK73a41a9a43FbrjlGjRlk9PJ/E9JVAmX+gxPSVQJl/oMT0lUCZv6/2aaCs1bW+T1kn6wXSWK12re/TQFknKXDmHygxfSVQ5l+emAH3mU3p8lefDB06VH/5y1/UuXNnvfDCC3r//ff19ddfq169ev4enqVycnIUHR2t7Oxsn13hgTVYq8DAOgUG1ikwsE6BgXUKDKxTYGCdyifgbqOVpEGDBunHH3/UlClTlJWVpXbt2mnFihVVLtGULt8GPHXq1GK3AqPyYa0CA+sUGFinwMA6BQbWKTCwToGBdSqfgLyyCQAAAACo3ALuM5sAAAAAgMqPZBMAAAAAYDmSTQAAAACA5Ug2AQAAAACWI9ms5BYsWKDGjRsrLCxMXbp00datW/09pGvatGnTZLPZXH5uuOEGZ31+fr5GjRql2rVrq2bNmvr1r3+tY8eO+XHE14YNGzaof//+SkhIkM1m09KlS13qjTGaMmWK4uPjFR4ert69e+vAgQMu25w6dUr33nuvoqKiFBMTo2HDhik3N7cCZ1H1XW2dHnjggWLHV9++fV22YZ18b9asWbr55psVGRmpunXrauDAgdq3b5/LNu6c6w4dOqQ777xTERERqlu3rp588kldvHixIqdSpbmzTj169Ch2TI0YMcJlG9bJt1555RUlJSUpKipKUVFRSk5O1vLly531HEuVw9XWiWPJcySbldh7772ncePGaerUqfryyy/Vtm1bpaSk6Pjx4/4e2jXtpptu0tGjR50/GzdudNaNHTtWn3zyiT744AOtX79eR44c0d133+3H0V4bzp07p7Zt22rBggUl1s+dO1fz58/XwoULlZaWpho1aiglJUX5+fnObe69917t3r1bq1at0rJly7RhwwY9/PDDFTWFa8LV1kmS+vbt63J8vfPOOy71rJPvrV+/XqNGjdKWLVu0atUqFRYWqk+fPjp37pxzm6ud6y5duqQ777xTBQUF+vzzz7V48WItWrRIU6ZM8ceUqiR31kmShg8f7nJMzZ0711nHOvle/fr1NXv2bH3xxRfavn27br/9dg0YMEC7d++WxLFUWVxtnSSOJY8ZVFqdO3c2o0aNcr6+dOmSSUhIMLNmzfLjqK5tU6dONW3bti2x7syZMyY4ONh88MEHzrK9e/caSWbz5s0VNEJIMkuWLHG+ttvtJi4uzjz77LPOsjNnzpjQ0FDzzjvvGGOM2bNnj5Fktm3b5txm+fLlxmazmR9++KHCxn4tKbpOxhgzdOhQM2DAgFLbsE7+cfz4cSPJrF+/3hjj3rnuX//6lwkKCjJZWVnObV555RUTFRVlLly4ULETuEYUXSdjjOnevbsZM2ZMqW1YJ/+47rrrzKuvvsqxVMk51skYjiVvcGWzkiooKNAXX3yh3r17O8uCgoLUu3dvbd682Y8jw4EDB5SQkKCmTZvq3nvv1aFDhyRJX3zxhQoLC13W7IYbblDDhg1ZMz86ePCgsrKyXNYlOjpaXbp0ca7L5s2bFRMTo06dOjm36d27t4KCgpSWllbhY76Wpaamqm7dumrZsqVGjhypkydPOutYJ//Izs6WJNWqVUuSe+e6zZs3q02bNqpXr55zm5SUFOXk5LhcKYB1iq6Tw1tvvaU6deqodevWmjRpkvLy8px1rFPFunTpkt59912dO3dOycnJHEuVVNF1cuBY8kx1fw8AJTtx4oQuXbrk8qaVpHr16unrr7/206jQpUsXLVq0SC1bttTRo0c1ffp03Xbbbfrqq6+UlZWlkJAQxcTEuLSpV6+esrKy/DNgOPd9SceSoy4rK0t169Z1qa9evbpq1arF2lWgvn376u6771aTJk2UmZmpp59+Wv369dPmzZtVrVo11skP7Ha7Hn/8cXXt2lWtW7eWJLfOdVlZWSUec446WKukdZKke+65R40aNVJCQoJ27typiRMnat++ffroo48ksU4VZdeuXUpOTlZ+fr5q1qypJUuWqFWrVkpPT+dYqkRKWyeJY8kbJJtAOfTr18/5e1JSkrp06aJGjRrp/fffV3h4uB9HBgS+wYMHO39v06aNkpKS1KxZM6WmpqpXr15+HNm1a9SoUfrqq69cPpuOyqe0dbry88xt2rRRfHy8evXqpczMTDVr1qyih3nNatmypdLT05Wdna0PP/xQQ4cO1fr16/09LBRR2jq1atWKY8kL3EZbSdWpU0fVqlUr9kSyY8eOKS4uzk+jQlExMTFq0aKFMjIyFBcXp4KCAp05c8ZlG9bMvxz7vqxjKS4urtiDty5evKhTp06xdn7UtGlT1alTRxkZGZJYp4r22GOPadmyZVq3bp3q16/vLHfnXBcXF1fiMeeog3VKW6eSdOnSRZJcjinWyfdCQkKUmJiojh07atasWWrbtq1efPFFjqVKprR1KgnHkvtINiupkJAQdezYUWvWrHGW2e12rVmzxuX+cfhXbm6uMjMzFR8fr44dOyo4ONhlzfbt26dDhw6xZn7UpEkTxcXFuaxLTk6O0tLSnOuSnJysM2fO6IsvvnBus3btWtntduf/UFDxvv/+e508eVLx8fGSWKeKYozRY489piVLlmjt2rVq0qSJS70757rk5GTt2rXL5R8HVq1apaioKOdtafDO1dapJOnp6ZLkckyxThXPbrfrwoULHEuVnGOdSsKxVA7+fkIRSvfuu++a0NBQs2jRIrNnzx7z8MMPm5iYGJcnXaFijR8/3qSmppqDBw+aTZs2md69e5s6deqY48ePG2OMGTFihGnYsKFZu3at2b59u0lOTjbJycl+HnXVd/bsWbNjxw6zY8cOI8nMmzfP7Nixw3z33XfGGGNmz55tYmJizMcff2x27txpBgwYYJo0aWLOnz/vjNG3b1/Tvn17k5aWZjZu3GiaN29uhgwZ4q8pVUllrdPZs2fNE088YTZv3mwOHjxoVq9ebTp06GCaN29u8vPznTFYJ98bOXKkiY6ONqmpqebo0aPOn7y8POc2VzvXXbx40bRu3dr06dPHpKenmxUrVpjY2FgzadIkf0ypSrraOmVkZJgZM2aY7du3m4MHD5qPP/7YNG3a1HTr1s0Zg3XyvaeeesqsX7/eHDx40OzcudM89dRTxmazmX//+9/GGI6lyqKsdeJY8g7JZiX30ksvmYYNG5qQkBDTuXNns2XLFn8P6Zo2aNAgEx8fb0JCQsz1119vBg0aZDIyMpz158+fN48++qi57rrrTEREhPnVr35ljh496scRXxvWrVtnJBX7GTp0qDHm8tefTJ482dSrV8+EhoaaXr16mX379rnEOHnypBkyZIipWbOmiYqKMg8++KA5e/asH2ZTdZW1Tnl5eaZPnz4mNjbWBAcHm0aNGpnhw4cX+8c11sn3SlojSeaNN95wbuPOue7bb781/fr1M+Hh4aZOnTpm/PjxprCwsIJnU3VdbZ0OHTpkunXrZmrVqmVCQ0NNYmKiefLJJ012drZLHNbJtx566CHTqFEjExISYmJjY02vXr2ciaYxHEuVRVnrxLHkHZsxxlTcdVQAAAAAwLWAz2wCAAAAACxHsgkAAAAAsBzJJgAAAADAciSbAAAAAADLkWwCAAAAACxHsgkAAAAAsBzJJgAAAADAciSbAAAAAADLkWwCAAJKamqqbDabzpw541WcBx54QAMHDrRkTFbGqsx9v/baa+rTp0+Fj2fFihVq166d7Ha7pXEBAL5FsgkA8IuFCxcqMjJSFy9edJbl5uYqODhYPXr0cNnWkWBmZmbqlltu0dGjRxUdHe3T8Tn6tNlsCgoKUnR0tNq3b68JEybo6NGjLtu++OKLWrRokU/H8+2338pmsyk9Pb3C+5ak/Px8TZ48WVOnTvV5X0X17dtXwcHBeuuttyq8bwCA50g2AQB+0bNnT+Xm5mr79u3Oss8++0xxcXFKS0tTfn6+s3zdunVq2LChmjVrppCQEMXFxclms1XIOPft26cjR45o27ZtmjhxolavXq3WrVtr165dzm2io6MVExNTaoyCggKfje9qfVvlww8/VFRUlLp27erzvkrywAMPaP78+X7pGwDgGZJNAIBftGzZUvHx8UpNTXWWpaamasCAAWrSpIm2bNniUt6zZ0/n71feRrto0SLFxMRo5cqVuvHGG1WzZk317dvX5erjpUuXNG7cOMXExKh27dqaMGGCjDFujbNu3bqKi4tTixYtNHjwYG3atEmxsbEaOXKkc5uit4726NFDjz32mB5//HHVqVNHKSkpkqSvvvpK/fr1U82aNVWvXj3df//9OnHihLOd3W7X3LlzlZiYqNDQUDVs2FB//OMfJUlNmjSRJLVv3142m8159bdo3xcuXNDo0aNVt25dhYWF6dZbb9W2bdtc9qXNZtOaNWvUqVMnRURE6JZbbtG+ffvK3A/vvvuu+vfv71Lmzn612+2aNWuWmjRpovDwcLVt21Yffvihyzb//Oc/1bx5c4WFhalnz55avHhxsVul+/fvr+3btyszM7PMcQIAKg+STQCA3/Ts2VPr1q1zvl63bp169Oih7t27O8vPnz+vtLQ0Z7JZkry8PD333HP6+9//rg0bNujQoUN64oknnPXPP/+8Fi1apNdff10bN27UqVOntGTJEo/GHB4erhEjRmjTpk06fvx4qdstXrxYISEh2rRpkxYuXKgzZ87o9ttvV/v27bV9+3atWLFCx44d029/+1tnm0mTJmn27NmaPHmy9uzZo7ffflv16tWTJG3dulWStHr1ah09elQfffRRif1OmDBB//jHP7R48WJ9+eWXSkxMVEpKik6dOuWy3TPPPKPnn39e27dvV/Xq1fXQQw+VOe+NGzeqU6dOLmXu7NdZs2bpzTff1MKFC7V7926NHTtW9913n9avXy9JOnjwoP77v/9bAwcO1H/+8x898sgjeuaZZ4r137BhQ9WrV0+fffZZmeMEAFQiBgAAP/nb3/5matSoYQoLC01OTo6pXr26OX78uHn77bdNt27djDHGrFmzxkgy3333nTHGmHXr1hlJ5vTp08YYY9544w0jyWRkZDjjLliwwNSrV8/5Oj4+3sydO9f5urCw0NSvX98MGDCg1LEV7edKy5cvN5JMWlqaMcaYoUOHusTq3r27ad++vUubmTNnmj59+riUHT582Egy+/btMzk5OSY0NNT87W9/K3E8Bw8eNJLMjh07XMqv7Ds3N9cEBwebt956y1lfUFBgEhISnPN3zGv16tXObT799FMjyZw/f77Evk+fPm0kmQ0bNriUX22/5ufnm4iICPP555+7tBs2bJgZMmSIMcaYiRMnmtatW7vUP/PMMyXu+/bt25tp06aVOEYAQOVT3U85LgAA6tGjh86dO6dt27bp9OnTatGihWJjY9W9e3c9+OCDys/PV2pqqpo2baqGDRuWGiciIkLNmjVzvo6Pj3dedczOztbRo0fVpUsXZ3316tXVqVMnt2+lLcrRrqzPjXbs2NHl9X/+8x+tW7dONWvWLLZtZmamzpw5owsXLqhXr14ejckRp7Cw0OVzlcHBwercubP27t3rsm1SUpLz9/j4eEnS8ePHS9zP58+flySFhYU5y9zZrxkZGcrLy9Mdd9zhEq+goEDt27eXdPkzsTfffLNLfefOnUucX3h4uPLy8kqZPQCgsiHZBAD4TWJiourXr69169bp9OnT6t69uyQpISFBDRo00Oeff65169bp9ttvLzNOcHCwy2ubzeZxIukOR+LWuHHjUrepUaOGy+vc3Fz1799fc+bMKbZtfHy8vvnmG0vHeDVX7jNH0lzaV4vUrl1bNptNp0+fLlcfubm5kqRPP/1U119/vUtdaGhouWJJ0qlTpxQbG1vudgAA/+AzmwAAv+rZs6dSU1OVmprq8pUn3bp10/Lly7V169YyP695NdHR0YqPj1daWpqz7OLFi/riiy88inf+/Hn99a9/Vbdu3cqV+HTo0EG7d+9W48aNlZiY6PJTo0YNNW/eXOHh4VqzZk2J7UNCQiRdfihPaRxP6920aZOzrLCwUNu2bVOrVq3cHmtJfbdq1Up79uxxlrmzX1u1aqXQ0FAdOnSo2JwbNGgg6fKDoq58IrEklwcaOeTn5yszM9N5RRQAUPmRbAIA/Kpnz57auHGj0tPTnVc2Jal79+76y1/+ooKCAq+STUkaM2aMZs+eraVLl+rrr7/Wo48+6vKk07IcP35cWVlZOnDggN5991117dpVJ06c0CuvvFKuMYwaNUqnTp3SkCFDtG3bNmVmZmrlypV68MEHdenSJYWFhWnixImaMGGC3nzzTWVmZmrLli167bXXJF1+Km54eLjzwULZ2dnF+qhRo4ZGjhypJ598UitWrNCePXs0fPhw5eXladiwYeUab1EpKSnauHGjS9nV9mtkZKSeeOIJjR07VosXL1ZmZqa+/PJLvfTSS1q8eLEk6ZFHHtHXX3+tiRMnav/+/Xr//fed3xt65W3KW7ZsUWhoqJKTk72aBwCg4nAbLQDAr3r27Knz58/rhhtucD55VbqcbJ49e9b5FSneGD9+vI4ePaqhQ4cqKChIDz30kH71q1+VmLAV1bJlS9lsNtWsWVNNmzZVnz59NG7cOMXFxZVrDAkJCdq0aZMmTpyoPn366MKFC2rUqJH69u2roKDL//Y7efJkVa9eXVOmTNGRI0cUHx+vESNGSLr8ecj58+drxowZmjJlim677TaXr41xmD17tux2u+6//36dPXtWnTp10sqVK3XdddeVa7xFDRs2TJ06dVJ2draio6MlubdfZ86cqdjYWM2aNUvffPONYmJi1KFDBz399NOSLn+ly4cffqjx48frxRdfVHJysp555hmNHDnS5Vbbd955R/fee68iIiK8mgcAoOLYjC8/1AIAAKqM3/zmN+rQoYMmTZrk037++Mc/auHChTp8+LAk6cSJE87bbR3fNwoAqPy4jRYAALjl2WefLfFput56+eWXtW3bNn3zzTf6+9//rmeffVZDhw511n/77bd6+eWXSTQBIMBwZRMAAPjV2LFj9d577+nUqVNq2LCh7r//fk2aNEnVq/NpHwAIZCSbAAAAAADLcRstAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACw3P8H6XbzleWnBCAAAAAASUVORK5CYII=", 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", 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", + "image/png": 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", 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" ] @@ -2109,23 +2461,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n" + "\u001b[32m2024-12-02 11:23:29\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -2139,59 +2490,62 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:44\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.789, 8.231)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df: (7.789, 8.231)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:29\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n" ] }, { @@ -2199,228 +2553,210 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999863\n", + " Current function value: -0.893931\n", " Iterations: 1\n", - " Function evaluations: 2\n" + " Function evaluations: 2\n", + "Turbine 0. estimated bias = 0.00025 deg.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:30\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:30\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Constructing energy table for wd_bias of 9.00 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 9.000 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Turbine 0. estimated bias = 0.0 deg.\n" + " \n", + "Initializing wd bias estimator object for turbine 001...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 25.00 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 25.000 deg.\n" + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 9.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 9.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Constructing energy table for wd_bias of 14.00 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Constructing energy table for wd_bias of 19.00 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 19.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 19.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 19.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Constructing energy table for wd_bias of 14.00 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.000 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Constructing energy table for wd_bias of 14.70 deg.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:31\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.700 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.700 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.700 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Constructing energy table for wd_bias of 15.40 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Constructing energy table for wd_bias of 15.05 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.050 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.050 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 15.050 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Constructing energy table for wd_bias of 15.40 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 15.400 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Constructing energy table for wd_bias of 14.88 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.875 deg.\n", + "\u001b[32m2024-12-02 11:23:32\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.875 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.875 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Constructing energy table for wd_bias of 15.23 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 15.225 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 15.225 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 15.225 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Constructing energy table for wd_bias of 14.96 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.963 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.963 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.963 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Constructing energy table for wd_bias of 14.96 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 14.963 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 14.963 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " \n", - "Initializing wd bias estimator object for turbine 001...\n" + "Optimization terminated successfully.\n", + " Current function value: -0.999869\n", + " Iterations: 4\n", + " Function evaluations: 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 25.000 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:45\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 35.00 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 35.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 31.50 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 31.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 28.50 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 28.500 deg.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:46\u001b[0m Constructing energy table for wd_bias of 29.25 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.175)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.250 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 30.75 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.750 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 29.62 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.625 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 30.38 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.375 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Constructing energy table for wd_bias of 29.81 deg.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df: (7.836, 8.164)\n", - "\u001b[32m2024-11-25 21:39:47\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 29.812 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.19 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.188 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.09 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.094 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 30.00 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 30.000 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df: (7.734, 8.274)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:48\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 14.963 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:33\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Turbine 1. estimated bias = 30.0 deg.\n" + "Turbine 1. estimated bias = 14.9625 deg.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 39.00 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 39.000 deg.\n" + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Constructing energy table for wd_bias of -49.00 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -49.000 deg.\n" ] }, { @@ -2435,172 +2771,171 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 39.000 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Constructing energy table for wd_bias of 49.00 deg.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:49\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 49.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 44.00 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.175)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.000 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 41.80 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 41.800 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Constructing energy table for wd_bias of 45.10 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-25 21:39:50\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.100 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 46.20 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 46.200 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 44.55 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.550 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 45.65 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.650 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 44.83 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.825 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Constructing energy table for wd_bias of 45.38 deg.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:51\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 45.375 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 44.96 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 44.963 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df: (7.774, 8.210)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:52\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -49.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -49.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Constructing energy table for wd_bias of -44.00 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Constructing energy table for wd_bias of -39.00 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.787, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.787, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -39.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -39.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -39.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Constructing energy table for wd_bias of -44.00 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.000 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Constructing energy table for wd_bias of -46.20 deg.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:34\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Constructing energy table for wd_bias of -41.80 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -41.800 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -41.800 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -41.800 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Constructing energy table for wd_bias of -45.10 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.100 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -45.100 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -45.100 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Constructing energy table for wd_bias of -46.20 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -46.200 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Constructing energy table for wd_bias of -44.55 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.550 deg.\n", + "\u001b[32m2024-12-02 11:23:35\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.550 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.550 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Constructing energy table for wd_bias of -45.65 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.650 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -45.650 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -45.650 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Constructing energy table for wd_bias of -44.83 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.825 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.825 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.825 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Constructing energy table for wd_bias of -45.38 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -45.375 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -45.375 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -45.375 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Constructing energy table for wd_bias of -44.96 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Constructing energy table for wd_bias of -44.96 deg.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:36\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -44.963 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.802, 8.239)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Turbine 2. estimated bias = 44.962500000000006 deg.\n" + "Turbine 2. estimated bias = -44.962500000000006 deg.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n" + "\u001b[32m2024-12-02 11:23:37\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n" ] }, { @@ -2615,59 +2950,60 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:53\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:37\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.787, 8.231)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.787, 8.231)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:38\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" ] }, { @@ -2675,7 +3011,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999854\n", + " Current function value: -0.861142\n", " Iterations: 1\n", " Function evaluations: 2\n" ] @@ -2684,16 +3020,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:54\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:54\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n" ] }, { @@ -2707,16 +3043,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n" + "\u001b[32m2024-12-02 11:23:39\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n" ] }, { @@ -2731,59 +3067,58 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:55\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.221)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df: (7.776, 8.221)\n", + "\u001b[32m2024-12-02 11:23:39\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" ] }, { @@ -2791,7 +3126,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999876\n", + " Current function value: -0.890643\n", " Iterations: 1\n", " Function evaluations: 2\n" ] @@ -2800,15 +3135,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:56\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:40\u001b[0m Determining energy ratios for test turbine = 002. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n" ] }, { @@ -2822,17 +3159,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:56\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n" + "\u001b[32m2024-12-02 11:23:41\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.253)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" ] }, { @@ -2847,59 +3184,58 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:57\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:41\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" ] }, { @@ -2907,7 +3243,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999888\n", + " Current function value: -0.900444\n", " Iterations: 1\n", " Function evaluations: 2\n" ] @@ -2916,16 +3252,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:58\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:58\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:42\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 003. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:42\u001b[0m Determining energy ratios for test turbine = 006. WD bias: 0.000 deg.\n" ] }, { @@ -2939,16 +3275,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing a bias_estimation() object...\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Estimating the wind direction bias\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.779, 8.220)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing a bias_estimation() object...\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Estimating the wind direction bias\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Constructing energy table for wd_bias of -5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: -5.000 deg.\n" ] }, { @@ -2963,59 +3299,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df: (7.800, 8.163)\n", - "\u001b[32m2024-11-25 21:39:59\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Evaluating optimal solution with bootstrapping\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 000. WD bias: -5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Constructing energy table for wd_bias of 5.00 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.231)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 5.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:43\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Evaluating optimal solution with bootstrapping\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" ] }, { @@ -3023,7 +3359,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: -0.999892\n", + " Current function value: -0.884975\n", " Iterations: 1\n", " Function evaluations: 2\n" ] @@ -3032,30 +3368,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-25 21:40:00\u001b[0m Initializing energy ratio inputs.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Interpolating FLORIS predictions for dataframe.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df: (7.800, 8.164)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:00\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", - "\u001b[32m2024-11-25 21:40:01\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" + "\u001b[32m2024-12-02 11:23:44\u001b[0m Initializing energy ratio inputs.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Constructing energy table for wd_bias of 0.00 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Interpolating FLORIS predictions for dataframe.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df: (7.776, 8.248)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m minimum/maximum value in df_approx: (8.000, 8.000)\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 001. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 005. WD bias: 0.000 deg.\n", + "\u001b[32m2024-12-02 11:23:44\u001b[0m Determining energy ratios for test turbine = 000. WD bias: 0.000 deg.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Turbine 6. estimated bias = 0.0 deg.\n", + "Turbine 6. estimated bias = 0.00025 deg.\n", " \n", - "Wind direction biases: [ 0. 30. 44.9625 0. 0. 0. 0. ]\n" + "Wind direction biases: [ 2.50000e-04 1.49625e+01 -4.49625e+01 0.00000e+00 0.00000e+00\n", + " 0.00000e+00 2.50000e-04]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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5brH5hIGv9lXi5arw6ihtuwtEnIELn/mZRYJQLwUXtX2fBRd+bGirTJs2jYCAAEfLqEZFRQVvvPGGDDZbS8um0WjkxIkTNabr9Xo++eQT7rvvPps21RuNRlJTU4mNjW1Qa+6Gzxh2YmGN6SYBOfgTymnOfzaYhMIqzyu5rPQXVMq508soFH73vZVxhd/WmP5b6EMAjM/+d415y92v55ryZdWmm4TCb/53gMnIuMJvUFXbPqSKcOJVJ2tozjP5cEQVy0Vij7krmTB314sfdU+9x8Aa7OGrxvjJmWyeOnmMS9beUs0v9bFZ6cvFpl0X+FHhR+/bcHV1Y/ypz2qeM363M/7M1zXPMf/bGXe65vQ/gu5DIBiX92mNc2mbqg+XiF116hMCFIU6z5fW6idbUJCVRu6JwxScTGVg6V/0UKXVutxmpQ8Xm3ZX87FRKPwedC8oKsblflzDZ99wBbfzu9W+r+8+0tjz5feztsY1wlZTtl/n9JCHqNBVcP2Zz63X3IR9qWsdW+5LU7b/k/sNXFv+Q43pKzpMRhEmxhd8XuM63qLqx2DxD9ZiECq2X/otAWHRVq9zIa3lOnWG51NBVhpF2an4hMZSovKiYt27XGVcWWM5k4AC4UOgqqjGvI3KAAaZdlj9bDGh4tg1P6HTBrZbP4H9z9PC3BNc/Nct1a7J+vjTZTQ6twDGFX1f4zpe4TaO8RUrarzX7VIS8TedJlaVXcPe3/Rjhfct/HVSTbQqhzRTCNcMiOWGfhFtxk+RkZFN0u8MLZsOHbPZWEJDQ8nJyak2LScnBx8fn1oDTQCtVltrwVWtVouLiwtardamB785Nrt06VJjmk6nIyAggM6dO9u8cKzJZKr95CvMhIKjAOT+OZ8umX/WXF8oHL91NTGJ/Vm75HWGpryFRjFhECo2xD/F2Ltm1pyeMIMJdz7L2iVRNaZfeaf5A8LaJb41bCX0vZq/d8Uz9MjbNWyZ16lpL37wdRgXX4L6vJuYEBCkKiKI3XD2JqZWBGOz/022y0QiYpvXjdkevqrXT05qUxFGKn54qNaXgb9C72N41qfV/GIQKiLv/jd/b/pfDd937ns1iYmJ/P1NcK3nzNolkXWcYzWnjz97vvz1RQDDjr7T4PliEgrJSie6iHTLx5S6zpfW6Cdb2Fy9+BUuTX3n3MuCCsqFC1oqq/nf7OOPavq42nUcUG3e+rgnOXrMwLqYnjX8VZfv67yPVK3zZUSt95G6zqN6bV24TvxTXHnXzMatU8/0cbfNICkpib93BTdKc7378qWP1bYaPJYXrLM+7kmuvPu5Rh/LWrd/9tr/e1dcDb1XWWyFWHXfNwoV+0f+h6JD6xiW82W181ejmMg/eZRBI5ue86E1XKfg+OfT2iWvMyzlTdSKwJQE5WjxVHQ1ljMKhbSb/iQrL59Ba2+t8ayIuntBjfvIqoDbKDVq6H7mLxJV1XNfqDDhSRnRXbq0Wz+Bfc/ThIQE1vz6Zq2B5l/R0xl+7N0afux622tExHZl7ZK4Gtd+dN+r+XtXzxrX/sg7nyUj9VCNaxxgODsZXrwT4XXuw/Bz++/DbfQsIjp4tXs/2TPesYZW1bL59NNPs2LFCvbt22eZdvvtt1NQUMDvv/9u1XaKiorw9fWloKCAzMxMEhMTbXrwk5KSbGpTp9Pxxhtv8Mwzz9j8hK5V684vEMsfRcGEwByTGYXCXhFPL+UIakVUu/CryEg9xMmje9FrfBg0/DKLzYzUQ2Sn7ic0tke1l/O6pl84LywqwaIzKz3FqnWq5tUIdmOn41KWzZCcr2ocjx1DP2XAmBubdUzt4St7nFP2tNklLorDC26hR8lGS2tgFQahInviZo7UEnBUnUt1+V6tVjf6XKpruk6nY/47rzJiQDc6JvSp+3xJmIFnSBwDN95fY38vPF9am59sYTMj9RDhiy+u0Rqddt1yju/faJWP67uOgzrGWK6nvMxjzbqPVM0zGo1s/nsVroYiwuN6NctW1fTa7nsNrWPN9PN9Vde9r7HbsMf+Z6bsZt2Ogzz65HOW+15Tbdnlvn/23Es/coCIL4fUeEnNFn78L3wGI4cNpSTzMGEXHJeGcPbrtApHPp8yUg8RVkuAkEMgGZ496FPyN+rz/DXs7IeW/F3Law04qmxe6Ps/129i1J/jqwU9BqFi07jfGTJwQLv1E9jvPN2zdz8Zaz7k6uJva8y35pkPjb/2L7zGdwRdh2fxUXpU7K7xzmEv37c2P9nLZseOHQkICHDuls2SkhKOHDli+X3s2DF2795NQEAAnTp14tlnnyUzM5MvvvgCgIceeogPPviAGTNmMHnyZNasWcPSpUv59ddfHbULbYvCTEzLp6HCfKNWMLcGzg/+F5Mm3k9Wbrrlwh95wcM4Irar5SZx4fTaHtx1Tb9wntFobPQ6VZi/gl1XTbP5q9jXNVqwAjo5b3KmVoOuhPT3r6RH2W50QsN6z8sYWfpH9QdMbFciYmv6pYq6fH/hvOZMByg3qOkz8rpqD4k6z5cNSo0Wco2Hd+OOTRskactvRFzQeq1SBAX5ubUeyyqsvY7PH/7Q3PvI+fgFRdT60G2Krbrue03R3NTtN9aWrfc/qGMMv285bNN9sfV9HyAipisrO/4fl2V+gEYxYRQKRYo3ocoZHs6aiek7UClg3KCwNuHpai/DkuaRdXQvEbW0ep0Y8joDLru5WrAx8uyHFoBhtz9NVvr1Vt9HErp0Z+bv9/K65hNL69bzhnt5uJm9liQ1yUg9xJH9WxG7v+Zq0xYAdmsH0qPin0Y986Hx1/6F1/glsV3J3vUbyk+3VltOo5jorD1t612XNAGHBps7duzg0ksvtfx+/PHHAZg4cSKff/45WVlZ1cqBxMTE8OuvvzJ9+nTmz59PREQEn3zySasve+IsZKfuJZTqDwRFgdsHd8bfyx1/r7of+s7KhTeriNiurI2fYflaCiCApau3MCO+m01T4rcXMlIPkXNwPUG73qeTMZ0S4cYfia9zw62Ta7xEVFHfC6QjqfV8SXja8hW1qrXWsOZ1jBeNQa1pnWnIbYHX0Zof+QxCRWhsD8B5fSxp+9R17kUOvoVMt1vJS0syt5ZERHLk03uJz/nN0kKvVgRDU94iI/U6ef7aiEoXz1p7uoTG9QKa9nGiNqKCfYkZeQ/r/t7GSPVePjJcSczIiUQF+9b4eClpOlVdoqs+IJgEHOj1HH1umNFiz/wL7YXG9sKEChUmyzQTCh2iutlsm5Km49Bgc+TIkdTXi/fzzz+vdZ1du+pO5CFpOkeLXAip5YGQrPMn1HGybM6w259m89/9cK0sJOTQYqIKNnJb1pss/LUbU68a7Gh5rYoLHzolQssvPeZz603mL4xtIeA4/yuqChPd109hgGEXv3/xGldMnuVoeQ5h8+r/Mci4HZMAgXKue338UzW+WkskzkRETFei4rtbfp+Ovx5yfqu2jEYxkZ26v9Xfu5wBg8GI8vccFOVcorXzW71szYOX9eS7g4Pg9F5GeKbT/bKeNt9GeyYj9ZBl7G0VAgX/vlcDDnzm+3ZEdfV8xM+PoQjzhwW9UPPRyn08clPHltcjqYZsxpFYiMj50/JAAPMD4XnDvUS1wQeuX1AE/UffSNT9X1PoGkKUKpeQba/z265jjpbWaqjtoeOOnqH9+zhOlJ2IiO3KgDE30m/MzeyIfgCAoekfsHf3dgcra3mMBgP+G14GYJvP5WRN3MK2IR+zZeQ3DLv9aQerk0gaR1hcL4yien9wo1AsLfSS5rHqyzcYZPoHvdBw8NJP2TH0U7InbrZrN+XAnpcBEKdPgsoKu22nPZJ1dG+NsbdqRZCdut9Bis6j390oj+2Du38mz6srboqBngfeZtOhTEcra/fIYFNipvwMQUnmsbHPVU7iVv3zDNfNt3RBabO4++F92yJMqLhR/Tdr/vcJq/ems3J3Gum5hY5W59Q49UPHjgy66yWSNN3wUiow/jSN0vL29TKz6b/z6CqOUiLcibv1bSJiu9J/1A34BUU4WppE0mgiYruyPuFpDOLc69AJEYxrgGwNaS5HDu1lWNr7AOyMvp/uI29kwJgb7d7y1W/gUHKEH25UkrV3tV231d4Iiu7OhR0Szx8+4XB8O0LscDrc9TmVaBil3sXK7/9DmV52o3YkMtiUAJC3ai4eopTDpgj6THiQe2+5mW8ev4YH20EXFFXMUIxDzOOFZymf8sE3/+OzpUu5de5PfLRqXwNrt1/C4no590PHTqg0LgTe+QllQktfcZDVHz7GP6u/50xehqOl2Z3SwgK6Hl4AwNaOdxHUMdqxgiQSGzDyzmfJnriZnf3eoBxXolU5rFz8mqNltWqMBgMl/52Cl1JOkroLF931cott29/bnb0a87tL7p4/Wmy77YGc1H01esBtiH/K6bqcq0IS0Q+eDsBjps/55O8jrNqbLhsRHIQMNiVQfgbPXZ8A8Iv3Ldw8JJHLeke17RbNC3AZ9SzFgX3wUcpZ5jqbb1xfZYN2GsfWLpY3pzqIiIhEr5wb9u2sDx17EBTdnSO9ngDgqqJvuGjTAwxaeyvrv37Twcrsy56vnyOI05wghEtuf8HRciQSmxER25V+Vz9MZh/zdX1NwWes3bzVwapaL+uXvEIf4z7KhSvuNy5EpXFp0e0XBA4EwDt7S4tut82zawkAm91HOv3wCc9RMyj2iaeDUsy1pxay+L/fy0YEB+HQBEGOpCozmS0zlNnLpkajwWg02k1r3u/vECpKSTZ15JIJE5u8ndZ0TGvaVNjf9TEuWX+PJUGSWhG8ovmUDUduIKLDRVbZtbWvnPmYpq1ZTBwGMkyBJA94CVfvQAYPGeXU+15lzxZ+8u01AbH3tWrny9Ajb5N+5BoiYpofcDub708dP0T/7P+CAoe6TWeUu0cNe85+73O2Y9rSdqWfGrYZM+FxjiX9jxjdIdR/PEtRz1/wdK+7jp70U02de9f/zCXHPgAFtkQ9xPDOfRq1TVvsv1/iKMiZSyd9Csay0xg1ns22eSGtxU9Vds//tykU5mbQt3wTKOA27BF6DBxBcnKy0+m0oKgpGv0WnsuuZ7x6O+PV2zEKhefW3kdqz5lEBTW9QcWZ/eSMNhVRXzrYNsSCBQtYsGABRqOR5ORkNm/ejJeXl6NlORyVvphOP9+Alyhljsd0xo6/seGV2ij6tC302/FEjen/DJyLNupiByhyblyXP0hn/X6+c7+V7hP+z9FyWpzMAxsYm1Tzi+7Kbm8R3m2IAxTZjzN5GQRsmE034yG2Kz1xuWYBmnZc9kXStjHkpdB97b24KEYWBz5F/5HXOlpSq+HExq+54uQCS1fL38IfodOQ21pcR4nOQOhPNxOtymF3n1fQxF/a8EqSeslb/wmX5nzGQeIwXP95qygVd+joMa7feaeltBGYe2H91P8LusTGOE5YG6GkpIRBgwZRWFiIj49Pncu1m5bNqVOnMnXqVIqKivD19SU+Pp7s7Gw6d+5co7h1U6kKZG1pU6fTMW/ePKZPn16tAH1zqdLqc+xnvM62ao647n4SY5te5MQe+9+iNjv6YNpxYZ0mFX0GXwY+DSeLsIevnPWY6vOO4K7fj0ko+F58J507d3ZKnbVhKz95uykYDyrVkiQZhIou/YbarGXTGY7p+q/fZPiRt1ApAiHglH8vLutZfVxua7n3OcsxdZRd6ScrbSYmknRiEj1SP+GqvIVs2+9P4oCRtV7XDtdqJS3hp4xjhxh78kNLbw9FgctPfkim2+2Nuifaav9/+60X0YZVuOTtIWH8g+3WT2ADrcKE/w/mEkGpEdcyrnv3VnHuB+jSUV1QLVGjmBjUUU1IYmKT7Tqtn1rYZnx8vFXLt5tg80KqDrharbbpC4KtbarVagwGg110qvTF+O3/HICV/rfzSIJtsu85+zGt06Z/J7h6PmL5NBQEJgFlY9/By7+T1fbs5Stns3ls1cd0A7bSg9FDB58riO5kOuuyZws/RcV3Z23C0wxPeROVYj5fNsQ/xcjzavjZAkce04zUQww7G2iC+eVxTME3ZKVPq3Vsbmu597UWm/ayK/3UsM0et79G/ivfE6ic4YpDMzEmKaxPeLrOkh3Ovv8t4afcYweIuiBDuUYxkZeWVK22aWPtNpXC4Ivg5Cr8crfJd77z7DfF7ontvxEpsikW7vQad181G858TMPj+2CiZiNCeHxvaIZ9Z/WTI2xag/O3gUvsRuXWj/ASpaSaQhl5/f2OluMc9Lsb5ebFAJThxvelfR0syAkxmQg69iMAx8LG49KOu1OOvPNZ/kp8CYBCxYthtz3lYEW2pbbyNlUF7yWStkzG8WMEiHPJ4dSKYGjKW2SkHnKgKufGp0OoU2Uo79Dd3HW2Y2U6lOQ6RENboXD9fwDYoB1Op47hDlbTCHw7wpXzEGfDHSFgR5cnzNMlLYYMNtspm5b8i17Z/wMgWskhf/0iBytyIrpeRaFLEF5KBbm7VzhajdNRsO8Pgkx5FAkPuo++w9FyHE7fK+6hRLjhTwk5h9pW5sOwuF6YLih43x7K20gkWUf3Wlr0q5AfWuon9+D6mmUxEmY4LEP5JX16csAUZda253eHaGgLGIty6FK0AQBDr9sdrKbxiL53cXjc95SofFAU2Jjv4WhJ7Q4ZbLZDMlIPMSR1vmVchUp+sa2OSkVl1+sA6FW0lpwzpQ4W5FzkrDOXyVmrGULvhCgHq3E8AX6+7FGbg6/sf352sBrbEhHblZ2u/S2/Hf3yKJG0FGFxvTDKDy2Nwv/YrwCs8b+FHUM/JXvi5jq7HbcEfp5uHHTtBUDRgT8dpqO1k7ryQ1wwskfEceno8Y6W0yQMniGcjrwMgND8zRiMpgbWkNgSGWy2Q+QX24YJHHI3AJeqdvPzxt2OFeNMVBQSW7AOgPKuNzhYjPOQFWDOVux9cqODldgeF1MFAGt9r3P4y6NE0lJExHZlfcLTGMW516Q/vK+XH1rqICs9mW6GgwBEj3uEAWNudIpjVRxivjd3yN/mYCWtFJMJn0PfAbAnYAJe7q4OFtR0ggdcA8BQsYtNSRkOVtO+kMFmOyQkprtTjatwSkJ6kOcWhVappGTfr45W4zSk/fU5WipJMXVk1GVXO1qO0+DRdQwAMbokqChsYOnWQ3l5OfGGFAA6jrrfKV4eJZKWYuSdz5I1cTMHtb0BMOgrHKzIeTn852eoFME+VVfiuvRytBwLoT1HUinUBBpzcSnJdLScVkdZ8hpCDFkUC3eiRrTuYTOa+EvR40KkKo9dO9vWkBdnRwab7ZByfaVTjatwShQFVc+bARhQuo4T+UUOFuQk7P4agO0+lxHk5+lgMc5Dv34DOWYKRYORgn1/OFqOzdi7bS2eio4i4UFcD1lrVtL+iIjtit+IKQAM0G3mRF7b+ZhkS8IzzWMi8yKvcLCS6gzumcBuYS7PYDy+1cFqWhmFmRT+9goAf6iGMqxXZwcLaiaunuT69wNAe3ydg8W0L2Sw2Q5J2bYSgKOqaLYN+Vh2jauDDoPvBGCQ6gC/rZddcHQnDxCtO4RBqAi4uOWLdDszQb4e/KPpA8CpNpRU6tSh9QAc03ZFpW63lbIk7ZzwgddQhhsdlVOsXy17ulxI6sEddDalYhAqul02ydFyquHn6cYh154AKJnbHaymFbHzC8S7PQgrNBepDAwMRaVq/SGDd68rAeij/4fj8sNRi9H6zxxJo1Fl7gAgz683/UfdIFs068I/mpOe3VErAmNS20r80hSOLH8LgO10Y/SggQ5W43xk+5m/mAbkbKRGP/VWimfebgDKQ/o5VohE4khc3DnRYTAAmiNt52OSrchYvwSAvS69CY2IdqyYWigLvwSA8KI9bebebFcKMzEtfxRFnEuiMzzvKyhs/d2QfXtfBcAA5TB/7jjgYDXth3b7qdpoNFb715ltajQajEajTeyeLqkgVncQVKCN7Ncq9t+RNrV9b4YNsxlUsZ6UzFPEhvrVa9eWvmqsVnvaXP/Va4zI+hEUuJgD/P3Nmwy7/Wmn02mtXXv4SRMxAN0pDR0MuRhzD0NgQrNtnv+vLWiMzXJdJQmVh0CBoMRh9a7TGu59VTbP/9dZbdrLrvRT020GDrwZfl/DIP0WDqTn0DUi0CZ2a6M1+clkNBKba870Whw7wSa2bb3/HbsPpSxdiy+FVGbvh7CeNrHbWvxUZff8f+sj58huwqmerVWFiZNH9hDSJ7RJNu2hs0k2fTtRoI0kSHeCwoOrMV7etOEhzuCn1mRTEaJ9fOZZsGABCxYswGg0kpyczObNm/Hy8nK0rBZn06EM7tt3KypFkHTlzxjdAhwtyalRVxTQ+ZdrUGNibqd/c/lFtnlItSbO5GUweO2t1TIYG4SKLSO/wS8owoHKnIvTZZXw8yMMU+8nNfERyrq37q7Gh1NTuWHnXRiEiqRrfkdxlWN0Je0XxVBB3I/jcUPHu+HvMGbwIEdLcgqyU/cyZufDVAgX9k34GXcPb0dLqkGZ3kj5j48wQrWX4xHXUNbrHgwewY6W5bQcOnqM63behfqCZ/5P/b+gS2yMA5XZBtct8+ic8T3LTMOJvv5VXDWyk2dTKSkpYdCgQRQWFuLj41Pncu2mZXPq1KlMnTqVoqIifH19iY+PJzs7m86dO6NWq22yjapA1pY2dTod8+bNY/r06Wi12mbbW/3XH6gUQZ5LOEa3AKfff2ewmbF+AFGF2/A7uYbExJvrXM7WvmqKVnvY/OfkgVpL5bgaikhMTHQandZiTz+tc+3DMON+3HL/IerGf9nEpqOOadIWc3fBEy4xdOs9oMW1ttXryZF2pZ+aZ/P4xiHE5K0hIOtvunS5B5VK5bRaL8Refsr+3Ty8Yq/bQPr3v8hmdm29/xt/9QAjdMr4CZH5M2LCPETfu5xOpz38BI3T6hEYzuvbbud5l6/M6woVzxvu5YGLhhAV5Nskm/bQ2VSbQnsHfPU9w5TdbK10Y1zPuEbbdAY/OYPN+Ph4q5ZvN8HmhVQdcLVabdMXBFvbVKvVGAwGm9g0Gk145u8BBXRhAy32nXn/ncGm38V3wMptjNCvZ/HaJEb3jCQq2LfGcrb0VVO12sNmeHxvxEZQzqtvbhAqwuN61Vi/Ney7Pf1UGDIITi4hsGAnapMeXNybbdNRx1SbuxOA4sA+Vm/fWe99tdluDTbtZVf6qWk2gy+5FX5ew3DjVv45msslXTvaxG5dOLufDAYDPYrXgwLGbtc57/4XZjLUeC4TrSJMiF+mo064DHw71rOidTi7ny6035Dd2NAA4rv0gtSvyDQFcJP+JSaO7ktsaO094Zz93K9hM3YY5YoHQRSRsnsDVw5sfJZdZ/CTs9i0Btl23I7YkpJFT3EYgKDulzpYTevBt9/16HAhTpVFyupF3Dr3Jz5atc/RslqMiJgulHLuy50slVM3nbr0JUsE4Cp0kL7J0XKaTLmukpiKQwD4dR3uYDUSiXPg2WMCelyJVuWwceMaR8txOLv+Xk6oUkCxcKfvZbc7Wk6dZKfuRUX13jkqTGSntp/neGNJdDkJwCFVAt88fg0PXtaGhhCpXcgPNneD9z65AZPJ1MAKkuYig812xOo9x+itHAVAEzPYwWpaD+lFgsMm89jE110/ZYN2GsfWLiY9t32kzT6Zuh8vRYdOqNkw4ANZKqcehnfryN9Gc0HzsoOtt97m1gNHSFTSAYjoPcrBaiQSJ0HrRV7IEAB8j/+Jwdi+X1J1u38AYJ/XENw8nDcHxuGKAIxCqTbNIFQk6/wdpMj5ETkHASj2jqu1J1drp0O/qwG4yLiTgxmnHKym7SODzXZE7tFduCmVlLv4QUDj+6i3V9KOHqKHkmb5rVYEr2g+JT31kONEtSCpO81f8I+qYhh65V2yRbMeOgZ4s0/bB4DK5JWOFdMM0veuR6OYOKUKROXfydFyJBKnIegSc+KvUWILf+5Jc6wYB3L00G76lfwFgLb3DQ5WUz/RcV2ZabjPUvXEKBSeN9xLlHyW1YlPiblhQhWc6GAl9sGj+3gAeqmO8ff23Y4V0w6QwWY74UR+EeGlZ2sKdbqk+gA8Sb10cSuoNUFOZ+1pBylqWQwnzHVZ8727OVhJ66Ai7BKMQsG35BgUZjhaTpNQZZnHa57y7+1gJRKJc+HabTyVuBCrymbb1vWOluMQ1i55nZhvRuKtVCAEFGcfdbSkeokK9iVmxN38ZjInMPrIcCUxIye2yRY7m2AyEm44AYB/TB/HarEXXsFke5o/NlSmyC7x9kYGm+2EFbvSGahKBsA9bqiD1bQuQmN7YbrgUjGhIjS2DY1hqIeg4iQAXDrVn5FUYqZ352h2i7MZ2o6sdqyYJlCuqySy3NyFyithiIPVSCROhtabM2HmZ2iHk3/x685j5BTrHCyq5chIPcSwlDctH2AVBYYenUuGk/f0uX90dwo9ogC4KMjQtsYg2pjCjIO4UUmZ0NK1Rz9Hy7Eb6i5jAehSup3/bU1pN0OjHIEMNtsJ6w7n0l9lTg5EJ1kfrFH4dkR19XwE5tZgAaiunm+TLHbOTt6p08SbjgEQ20+O3bOGEYkdWWc0twhW7vwKCjMdrKhxbDqUQV8lBYDQntLnEsmFdLjoFgDGK5v55n/LeP6nA3y8+oCDVbUMWUf3Vqu/COaePtmp+x2kyHpMnmEAeFecdLAS5+b4gS0ApCoRBPk571jc5hLU/1oAhqr28cNP37e75I8tiQw22wHl+krOZB0lQCnBqHaD0F6OltT66Hc3JRP+DUCO8CMz6joHC2oZ9u9Yi1YxcBpvQmJ6OFpOq6BTkA/+rgYAXDK3wrs9YOcXDlZlPYf3bcNXKaNCcUMVKr/+SyQXkhE4lEqhIkaVzTeur7JBO420dV+0i5aRsLhetSbbCY11/ueD8DF/IPbVZztYiXNTesIccOVqox0rxM6ka2IoFu54KRUscX2j3SV/bElksNkO+Gv/CXphbtVURQwAjauDFbVOvHtfhQE1ocoZ/tm109FyWoSiI+YvnBluXeQ4X2spzGSi+Oncb2GCnx9rNS2cpox/gLNjdNXtthSzRFInxzJOouFcJtr2lDQuIrYrG72vsPw2CBUb4p9qFYnjtAHmrPKBpjwwGhysxnnRnjYPuarwS3CwEvuSlpqMF+WW3+3pOm5p2u2bhNForPavM9vUaDQYjcYm2111IIshZ8drisiLMZ1nqzXsv9PYVLuR6RpHlD6ZM8nrMV5afeyrLXxlM602sulVYO4aVRnap97lHa2zsXbt6qf8FNQX1HRDGDHmHwGv0KbZtBEN2SzXVRJeehDUoIkeZPW2nfXeV5vN8/91Vpv2siv9ZBubCa75Nb69aRQT8S6nbGLf6f2kcQNgm8tA9IOfZPCQUU7rq/Nt+ncIQSdc0CqV6E6loekQ02yb5/9rC+xxPVXZPf/f+giqMA+dcevYo0089+uy2Zzr2Bn81JpsKkII0fBirZ8FCxawYMECjEYjycnJbN68GS+vttsX/XzuXXaC7wyPEq3KIW3oXEpCL3a0pFaLfu3b9Mv/kR9VlxF//YuOlmNXyvRGgn+6iWglh5393sQ1ViaWsoYzeRkMWntrtXFNBqFiy8hv8AuKcKCyhtmdWcKQjXcTo8ohdfA7lIXL8d0SyYVoynLpvOJ6VOd9VDKhInn8Dxg8gh2orGVQLbuHbqYUVkQ8QadLrne0HKsxGE14/3AzcaosdvR/B7cYeX+7EJOulB7Lx6JSBGuG/0BwsPUfSFsb7f06tgUlJSUMGjSIwsJCfHx86lyu3bRsTp06lalTp1JUVISvry/x8fFkZ2fTuXNn1Gq1TbZRFcja0qZOp2PevHlMnz4drVbb6PXX7D+OqewU0W45CBQiB10Pbj520doebOYXjYWVP9LFcJjgyBj8vNws85rrK1trba7N1Vt2cZGSA0Dv0TeDW91p4p3NT/Vhbz+tqfTgWcN9vKH5BJUiMAmYabiX0X6dGZQY1SSbLXVMf9v/JzGqHEwoRA25EdzqfnjYW2tbu56cwa70k61sJgJzECseR8H8gmoaP4eE/iNsoNS5/aQrL0FjTAUFYgaOwwit6tzPUYcQJ7JwMxaSmNi8GpLO7KcLsVZryo4/USmCfOHD0KEjUKvrHm3n/NdpQzZrXsdcOY+Evg1fx472k7PYjI+Pt2r5dhNsXkjVAVer1Ta9SdraplqtxmAwNMnmR6v28frq44w724X2lGccgZ7+dtPaHmyG9BwNK6GLcoKVSUe44pJzdQib4yt7aG2uzZxDmwDI0nQkzDPAJjabgq1t2ttPiR078LDxUrxEOS+4LmGbqSvfGy9lascOTdpeSx7TsmPbAMhziybkgntFc+w2hbZ2PTmTXeknG9i86F6y/nyPMH0aS3zu547+E51XK7bzU+qeDfRQjOQLX+ITe3M4OblVnfuFrmGg201lfppNj62z+ak++/XZPZW6C4ATmij6urrYxGZTaDGbF91L1u9vEWbK5o+4mYwbcI/VthzpJ2eyaQ0yQVAbJT23kDdWHwdg4NmSJysKY2SWrebiHUKuOhSVIsg50LYLemty9gBQFCCzFzeGqGBfnhndiQPCPB4oRDnD06M7OX0B8Q9+30Noqbmm6p8lsTIFvETSACUdzPdGdVmug5W0HKcPm597R127oLLDS7a9qfAMB0BVeMLBSpwTU475GXDGM9bBSlqOU+7mfS0/k+dgJW0XGWy2UQ6fPG3phT7gbLC53dSF5KwzDtPUVjjdoS8A2py2m5G2XFdJRIX5vPGJl+NaGsuDl/VkYPfOAESo8nlwdHcHK6qf9NxC5qzNYJDKXCswxRTOm6uPy49TEkk9uHcyF7yP0KdiNJoaWLpt4JFrbvkqDOjdwJLOieJnHsrgVd46soO3NL4lR83/CW5eF+PWRKV/HAAexWmOFdKGkcFmG6VLuD8K4EEF3ZR0AP4xdaFzmJ9DdbUFvBKGARBTcZByfaWD1diHzYcz6K2YHzoh3Yc7WE3rJDomHoNQ4YIBSnIcLadeDp88ze3qVfRU0gCY5bKEG9V/yY9TEkk9hCaaP8QlKukcyT7jWDEtgRBEVZjLQnjGD3awmKbhEWxuxQowOPc92RGYTCYiKs3vix1i+jpYTcvhGmYOrIP0xx2spO0ig802SlSwL3f0DWSUaicaxUSW8Gfi6L5O35WvNRDWaxQAvZSjbD/cNrviHN6/Ex+lDD2uqEKdv1i3MxIdGkCW6GD+cca5H2LdfCp4WfO5JQ28WhG8pvmURO/y+leUSNoxmvBeGFEIVs5wKLnt1+bLO36IQM6gF2q69B/paDlNIqhTFwD8KQJdiYPVOBdZGccIUIowCYW4Xpc4Wk6LERxnDqyjRCalFXoHq2mbODzYXLBgAdHR0bi5uXHxxRezbdu2epd/99136dKlC+7u7kRGRjJ9+nQqKipaSG3rYpzpL95z+QCAUOU0D3bY5WBFbQNVcFeKFW/cFT3H9m5ytBy7UJlhPlfyvLqA2rokAZLqxIX4kSGCACjJOeJgNfUToeSiUqpXwdIoJiIUOYZFIqkTVw9yNOZyRiXpbf/5mr5zFQApqhgCA6xLGudsxHaK5LQwl707nXHYwWqci/QDWwDIUELx9PZzrJgWpEN0TwAClSKOpDr3s7q14tBg87vvvuPxxx9n9uzZ7Ny5k969ezN27Fhyc2sfbP/111/zzDPPMHv2bJKSkvj000/57rvvmDlzZgsrbwUUZjLo8JuozrZUKAA/PwaFcpxCs1EUcn3MNyeRsd3BYmxPpcFIUPFBANSdBjpYTevF38uNHJU52DyTmeJgNQ0QEEeNEWeKGgLaT5IIiaQpFPqYW8pc8pMcrMT+GI6bn3cnPZ17DHp9eLm7chJzDcXc9IMOVuNclJ4wJ4XLdYt2rJAWRuXmTbZiflbnp+51sJq2iUODzblz53L//fczadIkunXrxr///W88PDxYtGhRrctv2rSJIUOGcPvttxMdHc3ll1/Obbfd1mBraLuk4CiqC18fhREKUh2jp42hiTaP1elYdqDNJYbYfiSbnmfHawYlDnOwmtZNoWsIAPr8Yw5W0gC+HdlDl3O/FTVc9S74dnSYJImkNaAON2ekDSpv+y0iQYXmYMQU3t/BSprHKRfzfbk4q+37rDFoT5vL5JX7JjhYScuT5xoJQEVW2/9o5AgcVmdTr9fzzz//8Oyzz1qmqVQqxowZw+bNm2tdZ/DgwSxZsoRt27Zx0UUXkZqayooVK7jrrrvq3I5Op0On01l+FxUVWaZXVlai0+lsWuTU1jartJ+/D1bhFYkGBTXnusYJRY3eKwJ0OrtobU82g7qPgD1z6Mthtidn0Dc2pOm+srPWxtrcfPAYjyrmMYaGkN4YrNgfZ/VTbbSkn3TuoVAJSuGJRm+vJY+pwWiizKQBFZzq/RBeQx8Cn3CwUrNT3fvqoTWdp61Fa3v3k39sf9gPnU3HyMw/Q6C3e7NtOqOfDOXFRBvTQIGgroPR2ek9Auzv/xK3MCgBU0Fas85bZ/RTXVijNajc/FFUG97dqu23luvUGpslXjGg24nrmaNW7bsj/eRsNq1BEUKIhhezPSdPnqRjx45s2rSJQYPOlVaYMWMG69atY+vWrbWu99577/Hkk08ihMBgMPDQQw+xcOHCOrfz4osv8tJLL9WY/swzz+Dm5tb8HXFShIAgQypTNT8CYELhF8awS+npWGFtBLUwMIOFuFLJLI8X0ZQXOVqSzShx8eBt/SucUXyZLyZjyRojaTQaNzXPlb/NSSWEj7nD0XLqpNLFi/sqPiJalcOn3EyGEuFoSRJJq8BNlPM05neQ593/hUvFGccKshNhmtM8UPkZJ0UH/qO6BwWHvDrahCjXXO7RLeEfVW9+EaMdLccpMKHiGdN7uCt63tU8TKGx+R9NWhOdXU9ym+5btik9+Y3LHC2n1VBRUcEbb7xBYWEhPj4+dS7XqoLNtWvXcuutt/LKK69w8cUXc+TIER599FHuv/9+Zs2aVet2amvZjIyMJCsri6ysLLp06WLTSP/w4cM2tanT6Zg3bx7Tp09Hq9Vavd7R7DN8/NE85rkuxBjUHcPNX5lbKuyotb3ZzJ4/iqiy/Xzs83/cPXVWk33VElqttWk0mljwxhM8pfqK/PBReE/81il1NoeW9NPSP9Zy186bqUSD6ZkMUKwfudCSx3TdgeOM/OliXBUjuqm7wKdx3Wed6d5XH63pPG0tWqWfjBS/04sQUx7fJszluhvvtIlNZ/PT7iUzufjEJ/ytGczFT/1oN532snu+zXW/fsO4A4+Troog9Omm18t2Rj/VRUNad/+zhYtXXk25cEX1TDqoGt6f1nKdWmPz6I4/6LbqLrJEB/yeOYBKVf+z2lF+cjabYWFhhIWFNRhsOqwbbWBgIGq1mpyc6rWOcnJyCA0NrXWdWbNmcdddd3HfffcB0LNnT0pLS3nggQd47rnnaj05tFptrSeCVqvFxcUFrVZr04Nva5tV1LUfdZGaX0qsKgsAdaeLUAfF2F1re7Np6jgQUvYTWLQfF5dzGVsb66v6aOn9X7HzKN2EOZmNX5chaKzcD2f2U120hJ9CI+Iw/KPCRTGA/gz4hDXbpj10ns7NxFUxYkCNtkOUVS8a9tZaRWu+npzNrvSTffx01C2OkLI8lNwDNjkGzugn97w9ABQG9Las31rP/YBOXeEAhJhy0bq6Nrn3jjP6qS4a0no6zZwYJ1MTSby7h01s2kOnvWxGdbsYVkGYcorswsI645ALaWk/OaNNa3BYgiBXV1f69+/P6tWrLdNMJhOrV6+u1tJ5PmVlZTUCyqoD56AGWqclJbuQOOWk+UdgZ8eKaaOE9hoDQC/TIY62gYLeH63ax5Slh+ijMicHWpEX5GBFrZ/Y8A6WWpuGgjTHiqmHslxz4rDT6qBGB5oSSXun3M+cUMW/ONnBSuyEyURUhTlxikfcxQ4W03wiY7tiFApu6Kk4fdLRcpwCU67Zv2c822cGck//EPIx16E/cfgfB6tpezg0G+3jjz/Oxx9/zOLFi0lKSuLhhx+mtLSUSZMmAXD33XdXSyB01VVXsXDhQr799luOHTvGqlWrmDVrFldddZVdWj5aM6n5pTLYtDPucYMBiFNlsX1v606hnp5byBurj9ODVDoqpzAKmLnDi/TcQkdLa9VEBfmSiTloLzjhvDXdxJkTAObEGRKJpFFogrsCEG04RoXe4GA1tqco4wC+lFAuXOnad4Sj5TSb0A7+ZGP+CJiVut/BapwDn+KzmXmDEx0rxIFkacwZaYszDjhYSdvDYd1oAW655Rby8vJ44YUXyM7Opk+fPvz++++EhJjTUh8/frxaS+bzzz+Poig8//zzZGZmEhQUxFVXXcWrr77qqF1wWk6cKiVayTb/CIx3rJi2ikcA2a6dCNUfp/DIZri09dakPHzyNDep/+INzSeA+SvUePVmkrMGEBXs61hxrRiNWkW+OggEFGUdOVvdzflwLTV3ua/0lqVOJJLG4hbWDXZDnJLJnmMn6NclpsF1WhPpu1bTEzioxNE/OMDRcpqNSqUiRxVCR5HP6YxkYgaMdbQkh2I0muhoSAcFAmL7OlqOwzjjEQVF+1HynbwudiukWcFmVddVpRnZKh955BEeeeSRWuetXbu22m+NRsPs2bOZPXt2k7fXXjAVnUSrGDCqtKh9Ix0tp81SFtwfMo7jmb+b9LzWm5G2m08FozWfoFKqrml4TfMp2d73OVhZ66dYGwoVYChId7SUOvHRm4NNTUCUg5VIJK0Pk0cQpxVf/CkkM2l7mws2DelbAMjybDutXoWuoaA7gC5f1h5PPp5JF8z5UyK7XeJgNY7D6B8PReBV6uR1sVshTepG+8UXX9CzZ0/c3d1xd3enV69efPnll7bWJmkiRWU6OugzARABcXIMlh05rDF3n+puOszYD7aR00rHO0QouaiV6uOeNYqJCCXPQYraDpWe5izQmuIMByupHYPRRKDR7GfvUNkLQiJpNIpCtlscAPrMPQ4WY3sCC81dTQ1h/R2sxHZUeJl7caiLTjhYieM5nrQDlSI4gzcuvu13KIV7x+4AhOrlOWFrGh1szp07l4cffpjx48ezdOlSli5dyhVXXMFDDz3EvHnz7KFR0kiSMk4Re3a8piZYjte0F+m5hbx+2NwxspdylEiy+T0/oHW2cAbEIS68HShqCGidwbMzofbvBIBXRbaDldRO5qliOmIONv0juzhYjUTSOikP6AaAZ6Hzjs1uCqay00QazS/fod2HO1iN7VD5mXtxeJXJBEGlJ/YBkK2Nadd1tUPj+gAQLnLQlZc4Vkwbo9HB5vvvv8/ChQt58803ufrqq7n66qt56623+PDDD3nvvffsoVHSSJJlJtoW4fDJ01yiOogQ4KKYWKt9ghvVf3E0pxUm1fHtyIqQB879VtRw1bvgK8fwNRfvUHPAHmDMBZPJwWpqkppTQLhyCgBNQLRjxUgkrRTPqH4AdNSlYnLC67ypnNy3FoA0EULv7m2nG63n2ftyB4NzfgRsSTzydwNQ7h5e/4JtnIioeIqEB2pFkJHc9nooOJJGB5tZWVkMHjy4xvTBgweTlZVlE1GS5nEkp5i4szU2ZbBpP7r5VPC65hPLh0CVInhN8yldvMocK6yJ/FNhDixLNAHw2D7od7eDFbUNQiJiMQgVrhigJKfhFVqYvMw0XBQjlWjA27raYhKJpDoR3c3vRZ05TkZugYPV2I6ifb8BkK6Owd3VpYGlWw9BUeaW6CBRgKmywsFqHMfaJa8zVm8uQdj79B+sXfK6gxU5DrVGzXFVBACnjslg05Y0OkFQfHw8S5cuZebMmdWmf/fddyQkJNhMmL0xGo3V/nVmmxqNBqPRaLXdY6fKLN1ojQGxUMt6rWn/ndVmmMiudZxjuMi1md6W3H/PInN9zdIOPXD3Cq31vGmszeZgD5tV9hp7TVlj8/x/zyc2NIAs0YFIJY+ik8l4elqXk7aljqmlxqYmiA6CRvm9PrvNpaX95Ew27WVX+sl+fnIJjKEUdzyVclL2b6dj0OXNtuloPym7viQx4zsAhpm2YtrxOaLvXXbVaS+7F9qMiIimTGjxUHScTEsiJLaX0+i09fVUZff8fwEyjh1iWMqb1T6YD015i/QjVxMR07VJNu2hsyVtntJGQkUyldlJ9S7fkn5qCzYVUZVS1kp++OEHbrnlFsaMGcOQIUMA2LhxI6tXr2bp0qVcd911jZTcMixYsIAFCxZgNBpJTk5m8+bNeHl5OVqWXXj6pyR+rTRnET147SpMGg8HK2qbaMpy6bLiBhTOdZkSiorD437A4OGsRS5qp6LSSMoPs7lN8xfpcXdS3PdhR0tqU5T9934uUg6yI/FZ3Lpf6Wg51di08lseKHqfFLde6K5c6Gg5EkmrRf3j/SQaDrI06FG6jbjZ0XKahaYsl84rrkfFuVdEEyqSx7e+51tduH9/C3Fk8Fe31wjq1vrrhzaWzAMbGJv0dI3pK7u9RXi3IQ5Q5HiOrf6Yq05/zjbtYDyuetvRcpyekpISBg0aRGFhIT4+PnUu1+iWzRtuuIGtW7cyb948fvzxRwASExPZtm0bffs6b32eqVOnMnXqVIqKivD19SU+Pp7s7Gw6d+6MWm2bbK1Vgawtbep0OubNm8f06dPRarVWaDDhUf4naEDvEUKXnrVnj7OH1vZnMxGhmge/TkcRJoSAnzRXcnmPS6zyVctqrd/m9iPZdFGZk0B07DkcJbFxY3Oc20/Vaew1ZQ0NaV2lCQbjQbSVZ0i08ti21DHd87M5OZDJr5PV2lpCqyP85Cw27WVX+sm+ftrj1xXyD+JTerTJ15K9tDbWTzm706sFmgAqTPioKwg5u2+t/dz/RxNCnCEDV31Bk/xVm82MY4fISd1PSGwPq1oHL8Qe11NdWr3dFEwHFUvJMwCDUNGl31CrWzZbw3XaGJtZh7rDaQiuzCCynnOiJf3kzDbj463LYN+kOpv9+/dnyZIlTVnVaag64Gq1utaDbzQaqaysbJRNo9GIyWSisrLSZgkCDAYDWq0Wg8GAStXwENuM/CJ6eBZT4RKJqeNATHXsgz20tkubPW6FjoMxfXU9KuCwdgijrPRVi2utx2Zq1inGekOFEglBPcAJzn172ITGX1N14erqWmP9uu4npW5hUArizIlG3+zrstkczrfprTcnyHAJiG72dmypVa1WYzAY7L7/zmzTXnaln+zjJ5eI3pC/jJCyozbZhiP9lKIPJFTUDESOVHYg/IL1W+u5X+IWDiX/IM4cb9Z2qmyuXfI6w1LeJEoRGDcqrE94mpF3PttoW/a6ns7XChAV3501bqMYpTOP2TQIFRsSZjAyvnuTbdpDZ0vaDIjuDfsh3HgSNQLUtYdJLeknZ7dpDVYFm0VFRZbm0aKi+ss61NeM2hoQQpCdnc2ZM2eatK4QgvT0dBQbpY8WQjBkyBAyMjKsslmuNzBu5EiOcRFoveFY7cVp7aW1vdo0DnkXNUZGKD5W+8oaWmr/Yz0ryR36GgIFpVCBosYVNW4tfqqy25hrqi5UKhUxMTG4uro2uKzJOxxKwbU0s8nbswcGo4kgYy6owCssztFyJJJWTXi3wbAb4kQaxWXleHu4O1pSk4mO68pKY3+u0OwAzIHI84Z7eTi28a11zorRJxJKwK2k+TWQM1LN4x+r8jioz45/zEi9jggnPmalroGggz1uA+lw8weMdGKtLUFsl56U/+yKu6KnMPMQvp16OFpSm8CqYNPf35+srCyCg4Px8/Or9QVNCIGiKDYfKN7SVAWawcHBeHh4NOplVAiBTqdDq9Xa7OXYZDKRn59PYGCgVa0w+cXlaMs0eCvl4BkKngEtprU929TlgVaUU4AffoGhNmvZbKn9z83LJVgoVCquuAQ1vrZma/ETNP6aqsvGyZMnycrKolOnTg0u79IhCrLBR+dcafbPr7EZECFrbEokzSE4ri864YKPUs72A7sYOLBm5v7WQlSwL+keHqCHrw2X8oHheiaO7ktUsK+jpdkMl8AYOAk++ubfl7OO7iWiloSB2an7nTrY9C43B9rGjhc7tc6Wwt/bgyTCSSSNk8k7ZbBpI6wKNtesWUNAgDlo+euvv+wqyJEYjUZLoNmhQ4dGr1+Va8nNzc2mwaZGo8HNzc2qF2NTiQFvjRE3RQFPL3BzazGt7dqmmztulRW4nbVry2AT7L//bmoTbiioXT1wqeOccQadtqCx11RdBAUFcfLkSau64/qFx8EBCDTlmWtt2uj8aC7Hsk8xRDGXaZA1NiWSZqJ24bimEwnGo5xK2Q6tONgEiDSZA5HS8KF8c+M1bSrQBPCP6Ax7IcTY/JJUYXG9MG5QqmWoNwgVobHOHawEVZ4EBTzCZJm8KrJdIkg0pFGWedDRUtoMVgWbI0acy9IVExNDZGRkjZc/IQQnTpywrboWpmqMpodH683eWmk04srZ8XaaxgcNkqahuLhBJbiIxo11dAYMRhOuQg8KqFxb77nf0lR1nzUajQ0Gm+FRnc21NhUDxqIs1H4dW0Jig+SdPFdj08U7zNFyJJJWT4FXZyg8imfaKjJSx7be1iKTkXCD+Z2uc8+BbS7QBOgYax6b6KuUUnw6F2//pmfZjYjtyhZNfy4xmrsdCwF/RU3jMif2f3FZBZGYW3WbUvqlrVLkGQOFG1AXpDhaSpuh0Z/XY2JiyMvLqzG9oKCAmJgYm4hyNLZsRWlphFGPSgGBCtRtpwCzs6NxNY/NcaESo6lR1YQcTrnegBt6ANSurXeMUUvTmPtEVEgA2Zh7h+QdP2wvSY2mLNc8NrdAE+Q0ra0SSaumshyAYfr1hC2+hLVLXnewoKZRkZeKlkp0woWYzm0zEAkMDOKUMOcZOXn0QLNs6SoNBBmyLL8VBdTBzj00ITX1KD5KOSah4B/Z9OzJbQ0RYM6w6lOa5lghbYhGv11Ujc28kJKSEtya0P1OYjuMJhMak7llTWi00IqD5tZGVbCppRJdpcHBahpHhb7yvNZwGWzaA62LhhwlCIBTGc4TbJrOmFsuSt1kq6ZE0lwyUg8xsHSd5fe5JDGHHKiqaWQcMrfQHSOciKC216pZRY4qBICCZt6XN61fTZySiV5oOOBmLjlXkbq52frsyanj5m6ieaoO4CLf36vwijS3eIcZTpibqCXNxurSJ48//jhg/po/a9asal1NjUYjW7dupU+fPjYXKLGeCr0RrWIOGhTZhbZlUbtgwpwmvlJXDu62q7tkb4z6ClSKuWC3SraG240zLiFQmUR5buMy/doT19KTAFR6RThYiUTS+mmtSWJqo+j4PgCyNBF0bcO9Hs5ow6AiBX1+8+7LxbuWAXDYsz8VHQdDyj90OLPPFhLtRnl2MgD5LuGEOFiLMxEe14PKdWrcFR3G08dRB0Q5WlKrx+o7yK5du9i1axdCCPbt22f5vWvXLg4dOkTv3r35/PPP7ShV0hAVlQa0Z1uoFJfWE+zYgpEjR/LYY481ap0XX3zRdh9IFHMmVwBTpc7q1YYPH87XX39tGw1NxVABgFHVulvD165di6IolrJFv//+O3369LFpjc7mUO5+tvWw0HnGtvvozYkxNB0azqgrkUjqJyyuF0ZR/R7aGpLE1Eq+ORAp8mobw6PqQucZDoCq8HiTbZTrKulWtB4ATY9riOg9GoCuxsMUlZY3X6SdUJ9JA6DUU97/zyehYzBpIhSA3KO7HSumjWB1y2ZVFtpJkyYxf/78Vl9Ps6pEy/mlWoxGo6W2n2hC03nVOk1Ztz6biqI0qGnSpEksXryYyXfezKdvPoNQay3N/1OnTmXhwoVMnDiRzz77zK5aHWnTWr9duExj9KpUKpYtW8a1115bY55J5QpGHYpRZ5XN5cuXk5OTwy233FLn8i1xTNUmHSggXNyavJ2m6ly7di2jRo2ioKAAPz+/Ztk8f3khBGPHjmXWrFksWbKEu+66q9py1lxT1mxPCFEtQVB9pZ+ETwQUgVvZSatKRNV2j2ou59s0GE0EVtXYDIlr1nbspVWj0WA0Gm1m197H1Ja0Fq3ST+fshkUlsD5+BiOOvImigEkobIh/imFRCY3arjP4ybdqvFqHurW3iXPfLwpOgWd5VqO3V7X8unWrmaBkokdD3NBbUbt7UYYWX6WMv7etZ8jw0Y2yaevr6Xyt59v0Kjd/+DT5RTdpW63lOm2sTZUCGeqOJIhMCo7tJrjflbXabCk/tQWbVgebVVQFK62NBQsWsGDBAsuBOXLkCF5eXiQnJ1uWMZlMltp+zaFqfb1eT0VFBW5ublYVfq8LPz8/9Hp9vcsYjUbCOkbww/IVfPDiYyjeCqKigoqKCr755hsiIyMxGo1UVFTUqtWWNNWmXq+v8zg1ZNNkMtW6f/VhMBgwmUyNWqdKZ23rmFQuYAS1qfb5FzJ//nzuvPPOBn0L9vOTwSTQnk0OJFSuteo2Go0oilIj42pt/mqszqp9rzh7rtals6m2br/9dubPn89NN91UbVlrrqmG0Ol0VFZWcvToUcuxOf9+UkOf1pwgyE+fQ1JSktXbqc9mU0lOTianWEfnszU2iwyu5DVCU312bck111xDamqqTW2C/Y6pPWgNWqWfztkN7Hs1WzM3c0nF3/zhfgWRfa9u1PVem01bYbWfhCDmbCZag2d4g/pb87lf6Wq+L3cwZDfZT+V7/gfAYbe+KMfNSYIUlwS6V+4nd/8akoLCG2XPXtcTVN//oErzMIoKbVCT9/1Cm7bC0TbzXSNBt42KzP11HpuW8pMz2zxy5Ih1C4omsH37dvHUU0+JW265RVx33XXV/pydwsJCAYi8vDyxb98+odPphMFgEAaDQZSUlIgDBw6IsrIyYTKZhMlkEkajUVRWVlr1p9frRXFxsdDr9SIjI0OsXbvW8peRkWG1nao/o9EoDAaDOHnypDAYDBZNtf1NnDhRjBk7TvToGi+WvP+KMBnNyy9ZskT06tVLXHPNNWLixImW5SsrK8VLL70koqOjhZubm+jVq5dYunRptfmTJk2yzO/cubOYN29etW2uWbNGDBw4UHh4eAhfX18xePBgcejQIWE0GsXEiRPFNddcU235adOmiREjRlh+jxgxQkyZMkVMmzZNdOjQQYwcOVKYTCaxd+9eccUVVwhPT08RHBwsbrvtNpGTk2NZr7i4WNx1113C09NThIaGirfffluMGDFCTJs2rd5j9Nprr4ng4GDh5eUl7r77bjFjxgzRu3dvy/ytW7eKMWPGiA4dOggfHx8xfPhwsWPHDsv8qKgoAVj+oqKihMlkEikpKeLqq68WwUGBwtPDXfTr3V388ccf9WrJyckRiqKIffv2WaalpqYKQOzcudMy7dSpUwIQq1evthxzQKxatUr0799fuLu7i0GDBomkpKRq9n/66ScxYMAAodVqRYcOHcS1115rmZefny9uv/124efnJ9zd3cXlI4eI5PU/ClNFkTCZTGLRokXC19dX/PjjjyIxMVGo1WqRmpoqoqKixEsvvSTuuusu4e3tbTmf/v77bzF06FDh5uYmIiIixCOPPCKKi4st2ysvLxdPPfWUiIiIEK6uriIuLk58/PHHlv09/6/KpsFgEK+++qqIioqq9fw0mUzil19+EQkJCcLNzU2MHDlSLFq0SACioKDAskxaWpoAREpKimWatddUQ39lZWXiwIEDoqSkROh0uhr3kwv/tm3fLMRsH1HxQgdhqNTXuVzVnzU2G/t3vs3Vu1JE5Qt+Qsz2EYaC4zazayutpaWl4pVXXhGlpaV22X9nttmatEo/1bS7bfFMIWb7iLUvX+E0Whvjp9Lso0LM9hH6F/zF0Yxspzim9rKZmrRbiNk+QveCnzh6aE+jbW7buVukvJAoxGwfkbziQ8u8rf/5PyFm+4jVr1xlNz81Z/9LSstFyQtBQsz2EZmHtzu9n1ra5lcfviLEbB+R8UovYShId5ifnN1mXl6eAERhYWG9sVejWza//fZb7r77bsaOHcvKlSu5/PLLzV/Jc3K47rrrGmvOYajVasu/5/9fURTLH5hbzDZu3Njs7R05csT6LwBnGTp0qKW73/ma6sbE5Fuu5rOlP3PHI88B5pboSZMmsXbtWuBcuYY33niDr7/+moULF9K5c2f+/vtv7rrrLoKDgxkxYgRCCCIjI/nvf/9Lhw4d2LRpEw888ADh4eHcfPPNGAwGrrvuOu6//36++eYb9Ho9W7duraGztv+fP+2LL77g4YcfthzjwsJCRo8ezX333ce8efMoKytjxowZ3HrrraxZswaAGTNmsG7dOn766SeCg4OZOXMmO3fupE+fPnUeo6VLl/LSSy+xYMEChgwZwmeffcbChQuJjY21rFNSUsLEiRN5//33EUIwZ84cJkyYQEpKCt7e3mzfvp3g4GA+++wzrrjiCsv5Ulpayvjx4/nXC8/hUZ7F59//yjXXXMPhw4fp1Kn2sRAbN27Ew8ODbt261Tgu5x/DC6dV/X7++eeZM2cOQUFBPPTQQ9x7772WY/jrr79y/fXX89xzz/HFF1+g1+tZsWKFZd3JkyeTnJzMTz/9hFHR8OYLTzL+rv/j4KHDuJzdRllZGW+99RaffPIJHTp0ICTEnD5gzpw5vPDCC8yePRuA1NRUxo0bx8svv8yHH35IUVER//d//8f//d//WXpBTJw4kc2bN/Pee+/Ru3dvjh07Rn5+Pp06deKHH37ghhtu4PDhw/j4+ODu7o6iKLzxxhssWbKE9957j+7du7N+/fpq5+eJEye44YYbmDp1Kg888AA7duzgiSeeqHH8oqKiCAkJYcOGDcTHx1vmW39N1U3V+hfeQ6r+fyFRcYkYhAqtUknp6Sw8g6wbJ1OfzaaiVqs5lZ2ORjGZa2z6drRJ6RNbalWr1RgMBrvtf2uwaS+70k/29ZNvRFdIhaDKrGZty1F+yk7ZRSyQTiixYUEN1hFuzed++rblRAtwVUxEfT2c9QlPM/LOZ622d+jQfu6q6kI74lZUZ20HdhsBmYuJ1yeZexC5WPe6bc/rqcq+Wq0m88RhOis6jEIhNKaHRXdzbNoSR9vs7GLu9dOxMg3xXm+Uq+ZDv7ur2WoJP7UGm9bQ6GDztddeY968eUydOhVvb2/mz59PTEwMDz74IGFhMn2+ozCaBCoEd94wgWff+ID09HTAHNR8++23lmATzN3/Xn/9dX799VdGjBiBoijExsayYcMGPvroI0aMGIGLiwsvvfSSZZ2YmBg2b97M0qVLufnmmykqKqKwsJArr7ySuLg4ALp27droLqkJCQm89dZblt+vvPIKffv25bXXXgPM4+KqAuLk5GTCw8P59NNPWbJkCaNHm8dBLF68mIiI+rNpvvvuu9x7773ce++9CCF48cUXWbduXTW9o0aNqrbOf/7zH/z8/Fi3bh1XXnklQUHm0hV+fn6EhoZaluvduze9e/fGZDSgytnHqzMe5n8rN7B8+XIeeeSRWvWkp6cTEhLS4EO8Ll599VVGjBgBwDPPPMOECRMsXbZfffVVbr311mr+6927NwApKSksX76cNWvWMGzYMHLy8vnq/VeJHDieH5f/YuluWllZyYcffmhZ7/xjVBXUAdx3333ccccdPPbYY5btv/fee4wYMYKFCxdy/Phxli5dyqpVqxgzZgwAsbGxlvUDAsxdmIKDgy1jNnU6Ha+99hqrVq2ib9++uLm5ERcXV+38XLhwIXFxccyZMweALl26sG/fPt58880axyo8PNxyPTiSQF9vsgigI/mcPJZEgpXBpr04v8ZmSBvONimRtDTh8b3hb4ggm9wzJQT7eTlaUqM4nb4XgJOaSOLb8L0hI/UQw47OseTFO1eq5jqrswdr08wfwY96DSDRw98yParPKEwrFTopuWzbv5+L+vaxtfxmkXtsP52BHCWIcFn2pDqFmfTL+NzyUxEmTMsfRRU3Gnw7Ok5XK6bRwebRo0eZMGECAK6urpSWlqIoCtOnT2fUqFHVXnDbAiqViqFDh1q1rBCCiooKFEVhx44dNeYPHDgQrdb6LLEqlcrqBCZGkwkVJoI6+DPh8tF8/vnnCCGYMGECgYGB1ZY9cuQIZWVlXHll9UHPer2evn37Wn4vWLCARYsWcfz4ccrLy9Hr9ZbsrQEBAdxzzz2MHTuWyy67jDFjxnDTTTfh7+9PY+jfv3+133v27OGvv/7Cy6vmw/no0aMWHRdffLFlekBAAF261F88OSkpiYceeqjatEsuuaRaEJ6Tk8Pzzz/P2rVryc3NxWg0UlZWxvHj9WepKykp4cUXX+TXX38l62QmBoOB8gpdveuVl5c3qy5tr17nimxXfeTJzc2lU6dO7N69m/vvv7/W9ZKSktBoNAwcOBAAxVhBhwA/OsfHVBuX4OrqWm0bVQwYMKDa7z179rB3716++uoryzQhBCaTiWPHjrFv3z7UarUlMLaGqvPz8ssvrzb9/PMzKSmp2jkAMGjQoFrtubu7U1ZWZvX27YVKpSJPFUxHkc+ZkynAWIfqEWcyAChxk2nvJRJb4hXWGRMKPko5+44eIbh/H0dLahx55pqTRZ7RjtVhZ5pbqqaguJx+5ZtBBR59rq82T+3pT7omiihjGll7/wInCzbLc1IAyHMJp3EjSts+2al7CaX6eaHCRHbqPkL7ymCzKTQ62PT396e4uBiAjh07sn//fnr27MmZM2ec4oXO1lR1k7MGIQRqtRo3NzdLS1wVnTt3rlab1FqsDTZNJoH67MUxeeKdPPKkuRvtggULaixbUlICwLJly4iJianWlbAqGP7222958sknmTNnDoMGDcLb25u3336brVu3Wpb97LPPmDZtGr///jvfffcdzz//PL/88gvDhw+vNVCurKysocXT07OGtquuusrSQiXOJmzSarWEh4c3uityY5g4cSKnTp1i/vz5REVFodVqGTRoUIOJZJ588klWrVrFW2+9RaSfhgA3uO7BmfWuFxgYyOnTp6tNq2rlPP+41XbMAFxcztXDPL/LN5iDK2sQQqAx6UEBlOpfr6u6s15Ibf568MEH+b//+z+Ln6rW69SpU5P8VXV+/vLLLwQGBlaz2ZiPNVUUFBRYWqUdTaE2BCoOostLc7QUS41Ng7essSmR2BQXN/KVDgSLfPLSDkArCza9S829HkwBCQ5WYl/C4nph3KCgPi/gbEypmnXrVnOjytyFNmrorTXmn/LvRVR+Gprsf2ym2VaoTpt9XOIR6WAlzsfhigCCRM3zIlnnT2g960nqptH9I4YPH86qVasAuOmmm3j00Ue5//77ue222yzdGiXm1qZLLrmE3r17c8kll9i9i7FRCFSYg40rxo1Hr9dTWVnJ2LE1W0+6deuGVqvlxIkTxMfHV/uLjDTfeDZu3MjgwYOZMmUKffv2JT4+nqNHj9aw1bdvX5599lk2bdpEjx49+O677wAICgoiKyur2rK7d+9ucD/69evHgQMHiI6OtmiKi4sjPj4eT09P4uLicHFxqRb0nj59usEsW4mJidXWAWr83rhxI9OmTWP8+PF0794drVZLfn5+tWVcXFxqpHreuHEj99xzD9dddx1du3UjNDiQ4yfqr6XYt29fsrOzqwWcVQHR+cfNmmN2Ib169WL16tW1zktMTMRgMLB9+3YqjSa06DlVcIbkI6l069at0dvq168fBw8erOanqj9XV1d69uyJyWRi3bp1ta5flc32/GNadX4eP368hs2q8zMxMZFt27ZVs7Vly5Ya9isqKjh69Gi1FntHovcwf0NWF2c4WAn46LMB0ATIGmsSia0542b+iKPLrfncdGqEIKzS/PzyjuzuYDH2JSK2K+sTnrbURhUCNiTMsLoLrTHpFwCOeQ8AN98a8z3ihgAQWXbQaeo9V+FZavax8G/bdVSbQnRcV2Ya7sN0NtY0CYXnDfcSZeV5IalJo4PNDz74gFtvNX/Bee6553j88cfJycnhhhtu4NNPP7W5wNaMVqvFz8+vSa0xjaVqzCaAWutBUlISBw8erLVV1tvbmyeeeIKnn36axYsXc/ToUXbu3Mn777/P4sWLAfNYyh07dvDHH3+QnJzMrFmz2L59u8XGsWPHePbZZ9m8eTPp6emsXLmSlJQUS3fWUaNGsWPHDr744gtSUlKYPXs2+/fvb3A/pk6dSkFBAbfddhvbt2/n6NGjrFq1ismTJ2M0GvHy8uLee+/lqaeeYs2aNezfv5977rmnwbGPjz76KIsWLeKzzz4jOTmZl19+mQMHDlRbJiEhgS+//JKkpCS2bt3KHXfcUaOVMDo6mtWrV1cLFBMSEli2bBm7d+9mz4EUbp86E9HAg6Vv374EBgZWSz7l7u7OJZdcwhtvvEFSUhLr1q1j1qxZDR6zC5k9ezbffPMNs2fPJikpqdpYxoSEBK655hqmTp3KmrXrOHxwH3dOe56O4eFcc801jd7W008/zaZNm3jkkUfYs2cPKSkp/PTTT5axqtHR0UycOJHJkyfz448/cuzYMdauXcvSpUsBcwIfRVH45ZdfyMvLo6SkBG9vb5588kkef/xxlixZUuv5+dBDD5GSksJTTz3F4cOH+frrr/n8889r6NuyZYulhdoZUPzMgZ1HeVYDS9oXc41NcwIE79B4h2qRSNoiOu8oANSFjh8v3hh0pzPxphSjUIjq2s/RcuzOyDufZf+YJQBUomLgtbXnWbiQf45m06/M/Pz27HtDrcvE9DcPBUkkjaQ0x39gPJ/As2VP3ELbdut1U4gK9iV25EQ+NowH4BfjxcSMnEhUcM0PChLraHSwGRAQQHi4+eu8SqXimWeeYfny5cyZM6dZY9AkTSevqMwS3BiFirwSAz4+Pvj4+NS5zssvv8wzzzzDG2+8QWJiIldccQW//vorMTHmr1wPPvgg119/PbfccgsXX3wxp06dYsqUKZb1PTw8OHToEDfccAOdO3fmgQceYMqUKdx3330AjB07llmzZjFjxgwGDhxIcXExd999d61azic8PJyNGzdiNBq5/PLL6dWrFzNmzMDX19cSUL799tsMGzaMq666ijFjxjB06NAaYz8v5JZbbrHoGTBgACdOnKgxhvPTTz/l9OnT9OvXj7vuuotp06YRHBxcbZk5c+awatUqIiMjLa1lc+fOxd/fn6FDh3LbxPsZO3IQfXok1tsFWq1WM2nSpGpjHQEWLVqEwWCgf//+PPbYY7z88ssNHrMLGTlyJP/9739Zvnw5ffr0YdSoUdVaARctWkTfvn259cbrGXb1RIQQrFjxa7WuudbSq1cv1q1bR0pKCpdddhn9+vXjhRdesNwjABYuXMiNN97IlClT6Nq1K/fffz+lpaWAuSv+Sy+9xDPPPENISIglSH355Zd5/vnneeedd+jWrVuN87Mqk+2PP/5I7969+fe//21JKnU+33zzDXfccUeTurDbA88Qc3Ik/8och+rILCim49kamwGR9Y93lkgkjUcbbP6I46tzriCjITIPmfNNHCeU6DDnGH5gb3oOHkeh8MRVMZG8Z1ODy3+0ah9zPllMgioTnVCzsqL2brfaoBhOKQG4KEaO7FxrY9VNx2Aw0FGYe7YER1vXZbi98eBlPXELM/f26uxRyoOX9XSwolaOLWpXVlRUiDlz5oiQkBBbmLMrVXU2CwoKxL59+4TBYLDMKy8vFwcPHhTl5eVNsl1Ve89kMtlKrjAajSIzM1MYjcZa51foK8WeE6dF2olMITJ3itKMA2LvidOiQl/Z4lrbs00hzL7KyjwuROZOYczYKUordPUun5WVJQICAkRaWlqLaq2ymZ2dJUTmTqE/ud9mNp3JT3l5eSIgIECkpqZWm97QNWUt598vDAZDjftJbSQf2GWptWk01H+NWmuzMVTZPL/Gpig8aTO7ttRaUVEhXnzxRVFRUWEzm/Y8pra0aS+70k8t56f8bd8LMdtH7JvVS5RV6G1iszlY66d/vn5JiNk+Yv2/Rjdosy2d+7tfHmqui/n5v+q1k5ZzRsyY+YQwvuAjxGwfYXrBRzw98wmRlnOm1uX3zblSiNk+4oc5U6zSaY/rSYjq+38s5YAQs31E5Qt+olLX9O20luu0qTaXff+1ELN9RO5LsTXmtYSfWoPNgoICq+psWt2yqdPpePbZZxkwYACDBw/mxx9/BMxJYmJiYpg3bx7Tp0+3T0QsqZOKSvNYN61iTkajwwUB6CqN9awlsRcm1JhQUClQqau/DExoaCiffvppg9lu7YXKqANAqO3fzdsRpKWl8eGHH1paQ52ByNiullqbuSfTHKbjVNYxS41NvGQuWonE1vh3MreKRCk5JGXkN7C082A6m4m20CPasUJamELfRABccvfVu1za0UO8pvkE1dn8eYoCr2g+JT31UK3LK5HmrOnBRQdqne8I8o6ZhzRlKcFoXNvm898W+Iabuxh3MJ0CQ/2JIiX1Y3U22hdeeIGPPvqIMWPGsGnTJm666SYmTZrEli1bmDt3LjfddJNdCpvai6qEJOcnJjEajQghLH+NpWqdpqxbn82qIvS12XVzMR9zLeaspTpcUACti7peHfbS2l5tVtlTFIVK4YIWPUZ9OUJ417tO1TjJurTYa/+FEGiEOROt4urebPvO6Kf+/fvTv3//Gus3dE01Rp8QAqPRaOnifWHyqAtxcXEhRwkgnHxOph4kKLzuQLi2e1RzqbJVmpMGQIEmmEAhoJnbsJdWjUaD0Wi0mV17HlNb2rSXXemnFvSTXxQmFLyVctKOHaV3dHAtazfSZjN1WuMnr2JzllKjf3yD229L5766Y2849R3BZcn1bjfBNb9allIwl0uJdzlV63ode42CA2/TUxzmWNYpOgX7NajT1tdTld2qf0uzzB8U8lzCCW/GNlrLddpUmxGRUZQJLR6KDn1+Kuqgc+NbW8JPbcmmIqx824qNjeXdd9/l6quvZv/+/fTq1Yt77rmHTz/9tNYSCc7GggULWLBgAUajkeTkZDZv3lyjlqPJZEIIYSl70VrIKqokzHACd0VPpikQlYcffu6NrmojsRGiMAMPUwkFqg64+zrnmBe9wYRrURruih6dZ0dMrvUHxZKa6HQ60tPTURSlwQRV52Nadj+9TAf5w+dGPPvehF9Qy5ce2bLya+4rWkCyW2/0V37Y4tuXSNoDIcuuJciUx4dhrzF8iPW1hh1JxPdX4EcxP3R7ny7d2n6CoCrOZKcxdMMd6IWa/VevxFVbew4STVkunVdcb0nICGBCRfL4HzB41PJBwWQgbtnluKNjSZcP6dOzt712wWpy/niL0cU/scZzAsHjZjpajtNiMJpw/+F2uqhOsLPfm7jGDnW0JKejpKSEQYMGUVhYWG+eGKsjkoyMDEsSlh49eqDVapk+fXqrCDTBnOV06tSpFBUV4evrS3x8PNnZ2XTu3NnSIltRUUF6ejparbZJyY7EeTUhbXVcTCYTubm5BAcH1/lC61dcgBvmJv5wVT5o3cHNq9Zl7am1PduEc77yddGCvgS1Sd/spFn22v8SXSneZ1vDXT18QNO8jyut0U/1XVPW4uLiQlRUFC4uLiQnJ1e7n9TFIQwAjC36HuPaH1gfP4Nhtz9dY7mqD2PW2LSWKpueOnNyIOEXRWJios3s2lKrTqezDM+w1cc/ex5TW9q0l13pp5b103GPSIJK8nAty27UdeYoP1UW5eBGMSah0POiS4mLDK91OXvqtJfdhmyaOnemcL0nvkopSkU+iX0uq8NSIkm/xdNdpAAgUGEaP4eE/nV/TDi6ogudK/aiyT9IYmLNepznY4/rCarvf8Xys9nQO8Q16/7fWq7T5tjc+L9gunACN8MZupx3rFrCT86w/w3ZjI+3Lpu91cGm0Wi01MQD0Gg0NVoGWxNVB1ytVlf7v6Iolr+m0tz1L7RV1e2vVpsGPf6GXKpmKQCFJ0DrAxrXmsvbUWt7t1nlK5WLO+hBIypteh7YUquxUodKEebxpRotOKlOe9hs8JpqpK4L7yH13cwzUg+RaEw+e6GCWhEMPfI2WenX11nbrSGbTcG7qsZmh2ib2ralVrVajcFgsMv+txab9rIr/dQyfjL5x0LJTtxKTjRpmy3tp+PJu4gBMgkkLrIjarV1H+PawrmvVqs55hJHH8NeCo/uQD3kilrXP11cRoDpFChw6pLnyAsYQEL/EfXq1IcNgGN78Tu9t8H9sef1VGW/w3llT2yxjdZynTbFZqE2FHRQmZ9Wbd2W8JMz7L81Nq3B6mBTCME999xjieArKip46KGH8PT0rLbcsmXLGiFT0myMOmp9XTbqrAo2JbZHo3WHUnClkkqDEReNE45lPpscyKBocW0lvRPaAllH9xJxweHWKCayU/dbXUi8uVhqbKrAO0zW2JRI7IVnWGc4AQGVJzEaTVYHb46iIG2POdhURxLp5FrtQaFPVyjYi7qeJEH/7NjMGKUAPRr8Rk4h60hag3ZDeoyEY4voajjM6ZJy/L3cG1zHXpiMBsJNOaBAYCdZ9qQhdJ4RoAN1kWMSObYVrL6bTJw4keDgYHx9ffH19eXOO+8kPDzc8rvqT9KyCLWWWkfdttEMo60BlYu566yLYkSnd74MZnlF5WiM5ky5ZSY1eUVlDlbUfgiL64VJVI82DUJFaGzLPfRPlVUSocgamxKJvQk8W8OwEzmk5xU5WE3DmHLNGVVPe0Q5WIlj0ET0ASC4NLnOZQqT/gIgTdsVXKwLGoO6j8SEQrQqhy9WrCc9t7DZWptK7okUtEoleqEmMr75QyjaOoq/+VrwqshysJLWjdUtm5999pk9dUiaiAE1ZXjgy3kBg2+kbNV0JIqKStS4YMSgKwcPx33FvBBdpQF9ySk6Yn7x8aWUzOJT6Nxd0brIpFL2JiK2K39HPsjwjH8DYBQqNiTMYGQLtWoC5BSWM5JTAGgColtsuxJJe0NzNntltJLN2oxTxIb6OVZQA3haMtEmNLBk2yS82xDYC9Gm41SUl+Hm7lFjmYBT/wBQGnqJ9YbdfElXIokRxzHs+ZZbd2Zzz+i+PHhZT1tJt5q8YwfoCJxUQoh2c553E2fFKzQOjkKgIQuEsNmQo/ZG++sn0cbQGYwYq9zo0QGCu4NnoGNFSTAq5mDfVFl/rc2WRq+roCP558b4KtCRfPQN1ASV2I7hk19HL8xdq7cOW8TIO59t0e3n5WSiVgR6XMDT+nIMEomkkfhHYUKFl1JBRsYxR6tpkBC9uaugZ0Q3BytxDFEJPSkUnmgVAyl7NtaYf6aknJ4Gc43KkD5jrbabnltItsE85OwJlx/YoJ3GsbWLHdLCWVX2JFcT1uLbbo0ER5l7/3hRDuWnHaym9SKDzVaO3mDC9Wx2S1y92m2L5siRI3nssccatc6LL75Inz597KLHdLYbs8pYfzfa4cOH8/XXX1t+K4rCjz/+WOfyaWlpKIrC7t27m6TLTWWs8WFOUczT2zvR0dG8++67AOj1eqKjo9mxY4ftN6RSka0KMW+nomW7MH+8+gDJh83jkU6a/PlotfMUGpdI2hwaLWdczOWvyrOPOFhM/RhKCujAGQA6dm4/JU/OR3U2SRBAQcq2GvN379hAoFJEBa6E97S+lE3a0UNcrDpk+a1WBK9oPiU99VA9a9mJ0+aPHsXuLV9yqzUSFxFOjvAD4EyGA/zVRnB4sLlgwQKio6Nxc3Pj4osvZtu2mhf4+Zw5c4apU6cSFhaGVqulc+fOrFixooXUOh86gxFXKrnnsdkonh146KGHaiwzdepUFEXhnnvuaXmBbYyGgkHLcmdLiaiFrs5lli9fTk5ODrfeWn8q9POJjIwkKyuLHj2aNsbPxdWdC4f4irPTWyPnB4i2xNXVlSeffJKnn65ZksQWnHYxf1WuyE21i/3aSM8t5Ni6L3jf5QMAopRch31dl0jaC+Ve5jFfypl0Byupn8xk84e1k6IDsZ0iHazGcZzxMQ9pUOfsrTGv+OAaAI5quzWqXFgXtwJUSvUnr0Yx0Vnb8i1lHqUnADD4Rrf4tlsjPh5aTmL+OJybnuRgNa0Xhwab3333HY8//jizZ89m586d9O7dm7Fjx5Kbm1vr8nq9nssuu4y0tDS+//57Dh8+zMcff0zHjh1bWLl1ZBWWs+loPlmF5XbbRqXBiAvmVqnIyEi+/fZbysvPba+iooKvv/6aTp062U2DrdA7YTKdpqLWmoM3F1GJqDWDE7z33ntMmjSpUbUe1Wo1oaGhaDRNHF+pcaVUda7wrgAUO4/xraysrDGtNfj6jjvuYMOGDRw4YPvWv3JP8z1LKWy5DHdpRw/xmuYTy0uPouC4r+sSSTtBHWhuKfMqz3SwkvrJO7obgAx1BJp2mIm2CnV4HwCCymomCQo4ZQ7Iy8IubpTN0NhemC6oG2BCRWhsy4/Z7KA/V/ZEYh2nXMzBZqmT905wZhr9xlpaWlqj3ElTmTt3Lvfffz+TJk0C4N///je//vorixYt4plnnqmx/KJFiygoKGDTpk24uLgA5paN+tDpdOh051qXioqKLNMrKyvR6XSWOjF6vR4hBCaTCZPJBJhLvpRXWtfF0FyE3oABPct2neSlnw9iEqBSYPZV3bihX+OCYneXcyUzqnTV2KZBj6KYg4a+ffuSmprK999/zx133AHA999/T6dOnYiOjq5mw2g08tZbb/H555+TnZ1N586dee6557jxxhst8x988EH++usvsrOz6dSpEw8//DDTpk2zbHvt2rU888wzHDhwABcXF7p168aiRYtISEhg8uTJnDlzhv/973+W5adPn86ePXtYs8b8dXDUqFF0794djUbDV199Rc+ePVm9ejX79+9nxowZbNiwAU9PT0aNGsW7775LUJC5O1JpaSlTpkzhf//7H97e3jzxxBP1HqMq3nzzTd59913Kysq4/vrrCQkx30Cq1tm+fTvPPfccu3fvprKykj59+jBnzhz69TN3KYqNjQXguuuuAyAqKorU1FSOHj3KE088wdatWykpKaFbt2688q+XGNs7DFcqqdBX1ki+k5eXx5o1a5g3b14NzZmZmVxxxRWsW7eOsLAwXn75ZW699VYURSEtLY24uDj++ecf+vTp0yQ/dUmIZ+mClwiN6oxLQCeE2gXO05CRkcGMGTNYuXIlOp2OxMRE3n//fS6+2PyAXbhwIXPnzuXEiRPExMQwc+ZM7rzzTsvx12g0fPDBB/z++++sWbOGJ598EoCffvqJKVOm8Prrr5Oeno7BYODMmTM89dRTLF++HJ1Ox4ABA5gzZw69e/dGCIEQgp9++olXX32Vffv24eXlxdChQ1m2bBmjRo0iPT2d6dOnM336dMt5C7Bhwwaee+45duzYQWBgINdeey2vvfYanp6eCCHIz8/ngQceYPXq1YSGhvKvf/2rxjnk6+vLkCFD+Oabbyzzz8dkMiGEQK/XYzKZatxP6sPkEwGnwaMss9r96XyMRmOjbDZErDoHdS1f12NUuXVqsBZbawUsmpqr7XzsodMeNu1lV/qp5f3kFZoAKRBmyuJk/hk6eDfci8QRfjJkmz86FbhHWe3LtnjuB3e5CPZDjPE4RYWn0bqZkwSVlFfQ3XAAFOjQbTQ6nc56nW6BqMbPRVkxHQVzoGkcP4dKt0C44Fjb43qCs/uvryD0bNkT77DOTnnfd0abJW7hUALidFoN/9jFT062/w3ZtIZGB5shISHcfPPNTJ48maFDhzZaYBV6vZ5//vmHZ589lxxDpVIxZswYNm/eXOs6y5cvZ9CgQUydOpWffvqJoKAgbr/9dp5++uk6D+Drr7/OSy+9VGP6/PnzcXNzqzbN09OTIUOGkJ+fb2k5Kq80Mnphze4UjcEkYPbyg8xefrBR661+uJcl4MzJyal1GeXs1zKBgk6n44YbbuA///kPo0ePBuCjjz7i+uuvZ/PmzZSXl5OdbS7mPn/+fJYtW8arr75KTEwMW7Zs4e6770atVjNo0CAqKyvx8/Pjww8/xN/fnx07djBjxgzc3Ny4+uqrMRgMXHfdddx+++28++67VFZWsmvXLoqLi8nJyaG8vBydTmfZHpiDRL1eb5mm1+tZvHgxd999t6U+6+HDhxk1ahS33XYbM2fOpKKigldffZXrr7+e//73vwA8++yz/PXXX3z66acEBgbyxhtv8M8//xAfH19te+ezfPlyXnrpJV599VUGDhzIDz/8wMKFC+nUqZNlnfT0dK655hpeeOEFhBB89NFHjB8/ng0bNuDl5cXPP/9Mr169mDt3LpdeeilqtZrs7GzS0tIYMmQIjz32GK6urnz//fdce/0NJP29jOiOoZwpOFWjdfO3337D3d0df3//GppnzZrFzJkzee655/jhhx+YOHEiERERJCQkkJdnLlmRn59PdnZ2k/yUtGsziqJQblJzKu9UtW2XlpZy2WWXERoayqJFiwgKCmLfvn3k5eWRnZ3Nb7/9xvTp03nxxRcZNmwYf/75J/feey8eHh4MGTLEYufFF19k5syZzJw5E41Gw7fffktKSgrffvstH330ESqViuzsbG699Vbc3Nz44osv8Pb2ZsmSJYwePZr169fj7+/Pn3/+yeTJk5k2bRrvvPMOer2eNWvWkJ2dzYIFC7jsssu44447LB9Xqvwxbtw4ZsyYweuvv86pU6d4/vnnue+++5g3bx5g/vCRnZ3N0qVLcXFxYdasWeTk5FBUVFTNH926dWPNmjVMmTKlxjllMBgoLCxkxYoVlJaW1nre1UW0SzGDAf/KbN54441GrdtUvEUxj6GgOq8jtREV/12xjuLfdraIhqZQ5TOJcyP9VDsJIpXbgWglh3c//QbPitp7brUUdfnpKsXckpdl8G2xe5IzIoTgMeGJn1LKvz98i9N683uit9bE40opJcKdJSs2IX7b0mjbd9ORGDL5Uns7ab+dgN/qPs72uJ68VeX0VQzohAs//bKKn3/53ebbaIuEuZo/OGjOpNW4Ntr7fa+iwrrkko0ONpcsWcLnn3/OqFGjiI6OZvLkydx9992Eh4c3yk5+fj5Go9HSulRFSEgIhw7V3q0rNTWVNWvWcMcdd7BixQqOHDnClClTqKysZPbs2bWu8+yzz/L4449bfhcVFREZGcmjjz5KVlYWXbp0sQSqOp2OjIwMAgMDLYFomd7QqP2yJSEhIbi7qMnJySEkJATlgswuQkB2dgYooKjUaLVaHn74Yd544w3L14YdO3bwww8/sHPnTtzd3QkNDUWn0/HBBx/wyy+/MHz4cBRF4eKLL2b//v18//33lpa7d955x7Ktiy66iKSkJFatWsUDDzxAQUEBRUVF3HzzzVxyiTkF+NChQ9HpdGi1Wtzd3dHpdISGhlpseHp64urqapnm6upK586d+eCDDyzLvPrqq/Tr14/58+ef3UdBTEwMnTt3pqioiPDwcL799lu++OILbrrpJgB69+5Np06d8PT0rLa98/niiy+YPHky06dPRwjBgAED2LJlSzWNVa26VQwZMoSAgAAOHTrElVdeaVkuKiqKXr16WZYLDQ1l9OjRCCHIyclhzpw5/Pnnn/y4cgOPTboRtVrBxy8QV825DyJFRUWEhobWet3cfPPNlnN28ODBbNq0iW+//ZYFCxZYLuzAwECLnsb4yWgycXF8B9yUSip9AvBx96m27f/85z8UFBSwY8cOAgICABg0aJBl/qJFi5g4caKl58GQIUM4ePAgn332GRMmTECrNY9jueOOO3j00Uct6/3yyy9UVlbyzTffWFqoN2zYwJ49e8jOzrasN3jwYP7880/Wr1/P/fffz4cffsgtt9xSbR+rPqSEhobi6upKeHh4NX/MmjWLO+64g1mzZlmm+fv7c+mll/LZZ5+Rnp7OmjVr2Lx5MxdddBEAnTp1onv37vj4+FQ7hxISEvj1119rPa8qKiooKSnhgQceQKPRcPjw4Wr3k/rITd4GP3xJmMjlsSefxK2WsjNGo7FRNq0hb0MMwX/PNPeGUFSYxs1hau87mm3XHlp1Oh3z5s1j+vTplvOjudhDpz1s2suu9FPL+0k5dQT+8yPRSjaxfQYxaWTDtQ0d4aczry8GILLnMG6/7FqrbLbVcz/prT/pY9xH746eXHLLUwCs/vR5qICjbt14+vGZTdKZNH8LlGUS4OvNrffW7L0H9rmeqrRu+2URHIQMgpn5zFM2sdkartPm2lz5y39h3yKCRb7l3ceefnK2/a/PZlhYmFUfpxodbF577bVce+215OXl8eWXX/L5558za9Ysxo4dy+TJk7n66qubPp6sAUwmE8HBwfznP/9BrVbTv39/MjMzefvtt+sMNrVaba0nglarxcXFBa1Wazn4QggURUGlUlnG0XlqXTj4L+tSXAshqKjQcUYnuGze35jOa8xSKfDn4yMI9XWr28AFuLuoLS1iVbrOR1dpsGSiVVRqFMVESEgIEyZM4IsvvkAIwYQJEwgODkZRFIuN1NRUysrKuOqqq6rZ0+v19O3b17KdBQsWsGjRIo4fP055eTl6vZ4+ffqgUqkIDAzknnvuYdy4cVx22WWMGTOGm266CX9/f1QqVbXtVVEVLJ8/rX///tV+7927l7Vr1+LjUz0IAjh27Bg6nQ69Xs+gQYMs6wUGBtKlS5daj1EVSUlJPPTQQ6hUKoufBw0axNq1ay3r5OTk8Pzzz7N27Vpyc3MxGo2UlZWRkZFRze755wdASUkJL774Ir/++isnT57EaDRSXl5OWqY5CFdVlnAsV0OgjwdBPuYvZBUVFbi5udWqd/DgwZbpQgjLh4Dzt3v+/xvjp2EjRnLnqJ6Ehwah0XqgXLD9vXv30rdvXwIDay+fk5SUxAMPPFBN99ChQ5k/f3614z9w4MAavo+Kiqr2cWnfvn2UlJRYgs8qysvLOXbsGCqVir1799bY3oVc6Pe9e/eyd+/eall+q7rHpqenc+jQITQaDQMGDLCs161bN/z8/GrY8vDwoKysrNbtV53nrq6uuLi41Lif1EfHePNYnQ5KMYdycukaF1NjGaPR2Cib1pAceBkhivlFSXloAy4h3W1i1x5aq6jrHt4U7KHTXvvemrSC9FOddoMTMKHCU9FxKjcTrbaPw7RC7X4ylJ0h5Gzt3ejISKv92FbP/dM+iXB6H+q8/ZZjEXjeeM2qaY3VqffqCGXgWnqywWNsy+sJzg4xKcwAIEcTTpwNbDvaTy1ls0NUIuyDQJGPRqMCtYtlnj385Gz735BNa2hyVBgUFMTjjz/O448/zvvvv89TTz3FihUrCAwM5KGHHuKZZ57Bw6NmQdwqAgMDUavVNbqH5uTk1Nk6FRYWhouLS7WDlZiYSHZ2Nnq9HldX2yc5URQFD1frDpMQApXJQICPG69f35OZy/ZjFAK1ovDa9T2IDfJq9PbrSi4D5zLRmoWq4ez/J0+ezCOPPAKYA5ELKSkpAWDZsmXExMRUazGtOnG+/fZbnnzySebMmcOgQYPw9vbm7bffZuvWrZZlP/vsM6ZNm8bvv//Od999x/PPP29pLa0K6s6ntkQxF47/LSkp4aqrruLNN9+07H9Va2l4eDhHjthvgPbEiRM5deoU8+fPJyoqCq1Wy6BBgxpMZvPkk0+yatUq3nrrLfz8/IiMjOSmm25Crzfvb6BSTAeKySwOROfuitZFQ2BgIKdPNz8TXWP99P3S73j5xRf4/Zt/M/jKPjXsubvbJittbeO6a/N1WFgYa9eurbGsn59fk/WUlJTw4IMPVhu3WkWnTp3q7DlRGwUFBTWCYVugcvfjDN74UUxO+qFag017UJBpvn5K8cAzuH3W0pNIWhSNK8XaEHx1WVTmt1z26cZQuPItOpz9f9yPE8A4H/rd7VBNjkTVsTecXkpQqblrcWlZGd0MSaBASG/r62teiOIXBbngVZFlK6mNQlNszkRbJMueNIqo6HgqhAtuSiW6/HS0IfGOltTqaHLKsZycHN566y26devGM888w4033sjq1auZM2cOy5Yt49prr613fVdXV/r378/q1ast00wmE6tXr67Wbe98hgwZwpEjR6olVElOTiYsLMwugWZzuGVgJzY8cynf3H8JG565lFsG2j4brL7yvBqb57W8XHHFFej1eiorKxk7tuaNsVu3bmi1Wk6cOEF8fHy1v8hIc8rzjRs3MnjwYKZMmULfvn2Jj4/n6NGjNWz17duXZ599lk2bNtGjRw++++47wPwxIiur+g3VmtqQ/fr148CBA0RHR1s0xcXFER8fj6enJ3Fxcbi4uFQLpk6fPk1ycs3MceeTmJhYbR2gxu+NGzcybdo0xo8fT/fu3dFqteTn51dbxsXFxZKE5vz17rnnHq677joSExMJDQ0lPT0dF84F14oCHclHrzN3g+3bty/Z2dm1BpxbtlQfC7Jt2za6du1a63411k+//7qcHl3i+OrHP2q116tXL3bv3k1BQUGt8xMTE9m4sXqx640bN9KtW+MDl379+pGdnY1Go6lxHla1rPbo0cOSUKo2XF1da/ijX79+HDx4sIbN+Ph4XF1d6dq1KwaDgX/++ceyzuHDhzlz5kwN+/v376dv376N3jdryNeYW3mLT6bYxX5t6PJSz237woKrEonELlSeLTPhUpzhWCG1UZhJwO4PLT8VYcK0/FEodO7sufYkPHEwADHGdCp15ezfuhovpZwi4Ul0zyENrF03nqHmzMQdjI4Zt+tdYc5Ea/CNcsj2WythAd5kYv7onJ0ma1M3hUYHm8uWLeOqq64iMjKSr7/+milTppCZmcmSJUu49NJLueuuu/jpp59qba24kMcff5yPP/6YxYsXk5SUxMMPP0xpaaklO+3dd99dLYHQww8/TEFBAY8++ijJycn8+uuvvPbaa0ydOrWxu9EihPm6MyiuA2G+9qlhqDcYzwWbyrnWXrVaTVJSEgcPHqy1ybwqg+vTTz/N4sWLOXr0KDt37uT9999n8WLzuI2EhAR27NjBH3/8QXJyMrNmzWL79u0WG8eOHePZZ59l8+bNpKens3LlSlJSUujSpQtgzjS7Y8cOvvjiC1JSUpg9ezb79+9vcJ+mTp1KQUEBt912G9u3b+fo0aOsWrWKyZMnYzQa8fLy4t577+Wpp55izZo17N+/n3vuuafB8iGPPvooixYt4rPPPiM5OZmXX365RkmLhIQEvvzyS5KSkti6dSt33HFHjZa16OhoVq9eXS1QTEhIYNmyZezevZsDBw5wxx13YDJdmOjc/G7vpjIHRlVdVS8M3AD++9//smjRIpKTk5k9ezY7duywtFRfSGP99NfqVaQcO0HnzrWnPb/tttsIDQ3l2muvZePGjaSmpvLDDz9YknY99dRTfP755yxcuJCUlBTmzp3LsmXLLBmBG8OYMWMYNGgQ1157LStXriQtLY1NmzZZssgCzJw5k2+++YbZs2eTlJTEvn37LK3eYPbH33//TWZmpuXDwNNPP82mTZt45JFH2L17NykpKfz000+WY9ilSxcuvfRSHn74YbZu3co///zDfffdV2sr6vr167n88ssbvW/WUOJmzk5tPN1y5U+qSq0UuzdujL1EImk62mDz/TZAf5Jyfc0ePo4kO3UvygXVl1WYyE7d5yBFjie2S2/OCE9cFQNH9m6m+NBfAKS49UClcWlg7boJijR/NA4XuVQ4IB9Ih0pzA0DV+SixDpVKRa7K/HH4TEb9DRuS2ml0sDlp0iTCw8PZuHEju3fv5pFHHrF0easiPDyc5557rkFbVYk/XnjhBfr06cPu3bv5/fffLeO6jh8/Xq11LDIykj/++IPt27fTq1cvpk2bxqOPPlprmZT2gMFoQK2cbeVVqrvSx8en1nGPVbz88ss888wzvPHGGyQmJnLFFVfw66+/EhNj7s734IMPcv3113PLLbdw8cUXc+rUqWoZOT08PDh06BA33HADnTt35oEHHmDKlCncd999AIwdO5ZZs2YxY8YMBg4cSHFxMXff3XC3nKpzy2g0cvnll9OrVy9mzJiBr6+vJaB8++23GTZsGFdddRVjxoxh6NCh9O/fv167t9xyi0XPgAEDOHHiBA899FC1ZT799FNOnz5Nv379uOuuu5g2bRrBwcHVlpkzZw6rVq0iMjLS0uI1d+5c/P39GTp0KPfccw9jx46lX9++XNgBWgAuruaARq1WM2nSJL766qsaWl966SW+/fZbevXqxZdffsnixYvrbDlsrJ8ef+pZpt5zE5PvuadWe66urqxcuZLg4GDGjx9Pz549eeONNywfLa699lrmz5/PO++8Q/fu3fnoo4/47LPPGDlyZB1Hvm4URWHFihUMHz6cSZMm0blzZ2699VbS09Mt94Dhw4ezdOlSli9fTp8+fRg1ahTbtm2z2PjXv/5lKQdT1d21V69erFu3juTkZIYNG0bfvn154YUXqiVjmjt3LmFhYYwYMYLrr7+eBx54oIavN2/eTGFhYY3EUbbC4GPuRaAtabnWDvdSc2uFwav9Fm2XSFoaz3BzkBGlZJN8svnDJ2zJ4YoALhytYxAqknX+jhHkBKg1atI05lJn+Ye30CG/arzmJc2y26FTZwB8lDJOZLTcR0YATAZCTOYWVf/IhpNUSapTqA0DoDL/mIOVtFJEIyktLW3sKk5FYWGhAERBQYHYt2+fMBgMlnnl5eXi4MGDory8vEm2TSaTKCsrEyaTyVZyhdFoFJmZmcJoNNaYl56VI0TmTmE4ubfRdu2htT3bFKIWX5XkCVPmTiEyd5r/LcmrtnxWVpYICAgQaWlpLaLVZDKJ8oz9QmTuFCUFOU5/TFvMT3Vw8803i1dffbXO+effLwwGQ437SUPsWfa2ELN9xKYXh9c6vyk2G+LvF0cJMdtH7P3v6zazKYR9tFZUVIgXX3xRVFRU2MymPXTaw6a97Eo/OchPh38XYraPODCrp/h24yHb2GwkdfkpLeeMODkrSojZPkLM9hGVL/iJp2c+IdJyzjhEp73sNtbm6nmThZjtIza+db0ofSFIiNk+4vDOv5utM392pNnuXytqnW+P60kIIYqOHxBito+oeCFA7Nvzj01sOoOfWsrmj+8/KcRsH7Hr7SuFEPbzk7Puf102CwoKBCAKCwvrXb7RLZsGg4GioqIaf8XFxQ0mUpHYFpXJ3B1HqJxrvKrkLJ6BVGLuclPsGgqe1TO8hoaG8umnn3L8eMt84dSfl1BK5WK77GltEb1eT8+ePZk+fbrdtuEfYe5yHmzKwWg0NbB08zGZTASbzAnZvMNlNyqJpMUIMI/Vi1KySckqdLCY6kQFeuOvmJMGTtNPZbhuPjEjJxIV7OtgZY7l/9m77/CmyvaB49+TpE33opPuUjayRBCVIYIIgoqIqCBTccCLgKDiAhfgQFF/OF5FQF8Vt+JEREBBQFH2ptCyOqB7t0nO74/QSOigI2kSen+ui0uTnHOf+5wnSfOcZ2madwKgc8HveCmlZKl+JHasej6RusjUmnvQFKbZb7LDquz+yjz8RK8YaPtFP9b9b36jHt/VaQPN41wrxr2KuqnzbLQVywNUJyoqinHjxjFnzpwLjqMT9WcyqWhVg3mNTZ1UHJyVQeOOu6kco8lY5esXmkjLlspLitErKiYU0NR/3ElT4O7uzuOPP27XY4THmZcdieI0x07nEB8eZNfjnckrIhpzN6qQGOlGJUSjCYixLH+SlXEC6O7ojCxyUpMIUMopV7X0v3EMD8ZHNPmKJkB42ytgL3gp5jXLD3lcQg9tw5f1y/OIgMJDGBpxrP6JI/vpkfkNFRNJaBWVqw69wIkjw4hKqHryQWHNJyIRDkGwIc3RqbikOtcGly1bRvPmzXn00Uf5+uuv+frrr3n00UeJjIzkzTffZNKkSbz22mu1WuRT1F+ptFK5hIpWZ8Xo+EkhjGXFAJThLjOROgG3oBgMaNArBo4n2/8u9/GUI3gppZhUBY/QxllqRQgB6Nwp8jSP+TI1YiWjNk4c2mH+L2Hc0KONVDTPSmzbhRz13+W6CgJb2yRuqbd5Yjj3Rhyrn5q0E41iPTBXp5hIO3LhSRuFWVic+QatPwWYipxr3LUrqPNtmuXLl7Nw4UJuvfVWy3NDhw7lkksu4e2332bNmjXExMTw3HPP8eijj9o0WVuqWC7h3GUTjEYjqqpa/tVVxT712bemmIqiVMqptNzw70y0Wvc6H9NeuTbVmBXxzi8rRecOBtCq5Q5/T6kG87IrRo07GhvFtMR28XKqbxxVVTEajZZeHOcvw1IzhTOaEMJN6WSdOIDRaD3JVVXfUQ2RfeIAAKeVZgSiBRvFBdvnWhFLp9NhNBptFtdeedo6pr3iSjk5rpxMgQlQfBLPohMcPpVJfFhAg2PWNc+qyinv5D4ATrtFEFPH4zn6mtozpqJAtuJPAIUA9E1dyroPQuh1x8MNylMTaF5r07ckrcr97PF5Co1vj7rB+h6zQdUQEte2QcdwhnJqrJgxEeGcUf0IVvJIT96Df1xXm5fTuTlebDEVtY6/tjw9Pdm5cyctW1qP+Tl06BCdOnWiqKiIo0eP0r59e4qKiuoS2q4WL17M4sWLMRqNHDx4kE2bNuHj42O1jclkQlVVYmNj0eudu7Uwp9hAUHEyesVAqU8MJjcvR6ckqmAsycOn+BTFqjsEJTg0F0POCXzVAvJ0zXDzDXFoLheD0tJSUlJSUBSl3kMGNCvvo13ZTv4X9B8697vNxhlaO7DhC4anvcweXXvUm/5r12MJIayVrJlHt+zvedMwlBcMt3PvpX5c39bxM76eWfU8ffNXssprKJGDm+bM/lXJOX2CK9aNRHNeBW1z348JCImqd9zT+37j6j2zOUIkRbd8aoNMa6fZ5zcSgXl5MIOqYXXkFKKvGNlox78YmD4fR0cO8Xvrxwm8ZJCj03EKBQUF9OzZk9zc3BpXwKhzy2Z0dDRLliyp1E12yZIlREebp9PPzMwkMNDxX6Lnmjx5MpMnTyYvLw9/f38SExNJS0ujVatWlmUdSkpKSElJQa/X4+HhUedjqKpKaWkper2+xnGtdWEymcjIyCA0NNTqB62pqMDSsunu5QPauk0SZI9cm3JMqLqsTBoVisEdAyY3d3TaulVKbJlriWqewEt7dvkVZ7+mjVlO9eXm5kZsbCxubm4cPHjQ6vukNnavi4YzO/EqyaBtW+txlBU3xuoaszrH1pjHa+bpw7nURjEr2DpXMFfmX3nlFaZPn26zm3/2yNMeMe0VV8rJMeWUcjqXLzKC6OYGcUoaKvD233mM6NWe2JDK3VYbs5x2rjRPeKI0a1HpO+hCLub3/t+n9lhVNMHc9dTdkGe5TvXJM1BvgD0QoZ5GbZGI3t16/gR7fJ4A8jGPPf0+6kE69B3OtfENH6vpDOXUmDF/14WD4RD6siwSEhLsUk7OfP5VxUxMTKzV9nWubL700kuMGDGCH3/8kcsuuwyArVu3sn//fj7//HMA/vrrL0aOdO47JhUXXKvVWv2/oiiWf/XV0P3Pj1XR7e/cmCZjOYoCKgqKtv5j8GyZa1OPWVVZac+Op9UqJkrKy3DTeTokV5PJhLtaDgq46b0w2iBmVVwhZnWfqfrmdf53SF2+zLVB8XAGfEpSq92vrjGr41Vk/lFZ4hlhs5jns2VcrVaLwWCwS66uEtNecaWcGrecDqflcVQNByBOMc8IbQKS0vNJqGFisMYop+By89q7nhGt632si/G93zyxE8aNCtpzxjoaVA3NW3SstH9d8gyPbYNJVfBUyjh66hjxLVpVimXrz5OpJJ8A8gGI7zOK2MQWNolbwVU+pw2NWejVHPJAyTlm1+89cM7zry5mbdT5tv4NN9zAgQMHGDx4MFlZWWRlZTFo0CD279/PkCFDALjvvvt4+eWX6xpa1IXR3Epl0rjJZC/OTKPFgPnDaCgrcVgaZaUlaBUVkwo6ff0qvML2fCLMwxGaGdMwmey7/ElgWSoAJt9Iux5HCGGtdfNAjp2tbMYq6YCKBmgVEeDItDCWlRJuMvd4CEu4xKG5OJuohDb83vJhDGdXCDSoGja0fKjBs7dq3T04rZhvMJw+tr/BedZG9inzBHS5qhcxkfL9X1+qfwwAHkUnHZyJ66lTZbO8vJxrrrmG8vJy5s+fz5dffsmXX37J/PnziYuLs1OKoiraijU269h9VlRPURS+/vprAJKTk1EUhe3bt9c7XkWMv/eYv+hN5Y5bh9ZQah4/Xa6427zlUdRfeHw7AKLIICPXvmPcw86usakLrP94IyFE3cWG+nN730sxqApeSimXcISHr4lx+Myvacn7cFOMFKvuxCbYZrbVi0nf0bNJG7uJrVctIW3sJvqOnm2TuGfOrrVZkJZkk3gXcjrFXKlNIwRPvSx7Vl+eIeZ5NwLLZPmTuqpTZdPNzY2dO3faKxdRS0ajCV3Fsidn19gcN26cVRfgin+HDx+2vF7Tmo7FxcXMmTOHVq1aodfrCQ4OZsSIEezZs8dqu7lz51p1H4yOjmbSpElkZWVZbRcXF8eiRYssj3fs2MENN9xAaGgoHh4exMXFMXLkSDIyMmxwRWwvOjqa1NRUOnToUKvtx40bx7Bhw6qM0batuUKBsdTWadaaqdzcqlquyM0JZ+IRah7vEKbkkHQi1W7HycnNIRTzdO3ewTF2O44Qomp3h+yydMn8Rv8k9zTb5uCMIPWI+ffcCSUcvbv8bahKVEIbuvW/xabrUeZ7NO4yOAXpRwA4o5WJARsiMNrc5TnMlAHVrJ0uqlbnbrSjR49myZIl9shF1JJ5jU3z5EDnrrF53XXXkZqaavUvPv7C6+mVlpbSv39/3nvvPZ599lkOHjzIDz/8gMFgoEePHmzevNlq+/bt25OamsqxY8dYunQpP/30E/fdd1+18U+fPs0111xDUFAQq1atYt++fSxdupTmzZtTWFhYz6tQtfJy26xnqdVqCQ8PR6er/yLOFTG0evNMwRqT49ba1Jyt6Jq0zj3LcpPjGUgh5vdHxrGDdjtM6tF9aBSVQtUDD99mdjuOEKIKuSfh2weo6FOiUVTUb6eZn3eg4lTzckiZ7s0dmkdTU1ax1mZh45S/8WylNt89tFGOd7GKS2hDmarFTTFScDrF0em4lDpXNg0GA2+++SbdunXjnnvuYcaMGVb/xDlyT8LR32z+B6W03HjOGpv/Vh70ej3h4eFW/2ozeHfRokVs2rSJ7777jltvvZXY2Fi6d+/OF198Qdu2bZk4caLVeoQ6nY7w8HAiIyPp378/I0aMYPXq1dXG37hxI7m5ubz77rt06dKF+Ph4rr76al555ZUaK8NxcXE888wzjB07Fh8fHyIjI1m8eLHVNoqi8Oabb3LDDTfg7e3Nc889B8A333xD165d8fDwICEhgaeeegqDwWDZ7/Dhw/Tp0wcPDw/atWtXKf+qutHu2bOHIUOG4Ofnh6+vL7169SIpKYm5c+eyfPlyVq5cSWRkJFqtlnXr1lli7Np/CDCvtbl+/Xq6d++OXq8nIiKCRx55xCqvvn37MnXqVB566CGCgoKIiIjg2WefraH0akdrMlc2FZ1UNp2KopDpZh7LVZRxxG6HyT5hrsimasJkjLcQjS0rCVTrMdmKaoQs+33ma0PJSQag0DvWoXk0Ndog8/X2LbFfb5ZzuRWYJ4cr9ghrlONdrIL8fTiFuXU4/eheB2fjWurcbLN79266du0KwMGD1nfiL8qxYKoK5bUcS6WqUFYCGiPs+Bh+fMj8B0bRwKAXoPMddTt2NWtnlhpM+J7tRlvXJU+q8tFHHzFgwAA6depk9bxGo2H69OmMGjWKHTt20Llz50r7Jicns2rVKtxr6IITHh6OwWDgq6++4pZbbqnT++Sll15i1qxZPPPMM/z888888MADtGrVigEDBli2mTt3LgsWLGDRokXodDp+//13xowZw2uvvWapEE6aNAmAOXPmYDKZuP322wkPD2fLli3k5uYybdq0GvM4efIkvXv3pm/fvvz666/4+fmxceNGDAYDM2fOZN++feTm5rJgwQJCQ0MJDg7m1CnzF7xWZ742GadOMnjwDYwbN47333+f/fv3c/fdd+Ph4cHcuXMtx1q+fDkzZsxgy5Yt/PHHH4wfP54+ffpw7bXX1vq6WVFVy0y0WndZj9XZFHlFQu4RsGOXqrLT5h+1WW7hyDtAiEYW1ML8O+CcCqcJDRoHr73sW2j+zlGCLtwDStiOV3gL2A/BxvRGOZ5P6dnJ4XzCG+V4F7MMbRhxpjTyG2m87cWizpXNtWvX2iMP51VeBPNq18VEAaqc51M1wQ8zzf/q4tFTUMVSGQaDAZ1y9o+W7t9K3nfffYePj4/l8aBBg/jss88ueJiDBw9y9dVXV/laxXpSBw8etFQ2d+3ahY+PD0ajkZIS81jAmmYfvvzyy3n00Ue54447uPfee+nevTv9+vVjzJgxhIXVfKftyiuvZObMmXh4eNC6dWs2btzIK6+8YlXZvOOOOxg/frzl8YQJE3jkkUcYO3YsAAkJCTzzzDM89NBDzJkzh19++YUDBw6watUqIs/OzDZv3jwGDap+kd7Fixfj7+/PihUrcHMzD7Bv1erfKcs9PT0pKSkhNDSU8PBwq/UbtWdbE999/2OioqL5v//7PxRFoU2bNpw6dYqHH36YJ5980rJPx44dmTNnDgCJiYm8/vrrrFmzpt6VTYOhDJ1iQlXB3UNmonU2akAs5P6Opx1nuNPmmX9UFno2l8qmEI3NPxKGvgrfPgCq+bt4RcgU7vB37MygIQbzDVGfyLqtrykaJjTGPP4zTD1DeVkZbnYeL9vMcBoAnb90l26ofH1zKN6BMSsZkJbi2qr3iuaHDx9m1apVFBcXA1h1sxT2pVYse4IWNP/eL7j66qvZvn275d9rr71W+5h1KL/WrVuzfft2/vrrLx5++GEGDhzIf/7znxr3ee6550hLS+Ott96iffv2vPXWW7Rp04Zdu3bVuN/ll19u9bhnz57s27fP6rlu3bpZPd6xYwdPP/00Pj4+ln933303qampFBUVsW/fPqKiomjevLlV3Jps376dXr16WSqadaHRuaEC+w8f4bLLulm17F555ZUUFBRw4sQJy3MdO3a02j88PLxBEymVl5jHxZbh1qAxqMI+PELMrQqB5fab4a6iImv0i7bbMYQQNeg6Bu7fAph7sn9f0tmh6ZQUFRCuZgIQmdjxAlsLWwqPbkm5qsVdMXLq2GG7HstUVkQzcgDwaibLnjRUma95Nnf3/BMX2FKcq86/PDMzM7n11ltZu3YtiqJw6NAhEhISmDhxIoGBgSxcuNAeedqc0Wi0+m/F/6uqavkHmFsWZ9euxUFVVUpLS9GXZqK8eTnKOV1mVEUL928GvzrcWdJ5WhafPzcnpaKyqXVDOaeS6O3tTYsW1ov1nl+JrHh87n9btWrFvn37qqxw7t1r7pfesmVLSw7u7u6W48yfP58hQ4Ywd+5cnn766Uqxz40ZFBTELbfcwi233MJzzz1H165deemll1i2bNkFL0VVeVfw8vKyelxQUMDcuXO5+eabK8XR6/8ds3juPufnfP5jT0/PSvtU5dyyssQAyjFXUlWTqcbjgnlM7LnPK4qC6bz96sJYVjETrRvu552brbhKzIp453+m6htHVVWMRqOlVfrc75PaCopuA1shQs0gM6+QAG8Pq1j1iXm+ioqsLijOZjHPZctcz42p0+kwGo02i2uvPG0d015xpZwcXE5BLSj3jsKj8AQe+Uer3acxyil5/3baKCp5qheh4VH1OpZTXFNXjKnRkK40My95lbKPqPh/l52x9ecp89hBQoEC1YNmgcHOcf4uHFMTGAsZ4Ft6Cp2uh02/98D5z7++Metc2Zw+fTpubm4cO3bM0sUSYOTIkcyYMcNpK5uLFy9m8eLFlgtz+PBhfHx8rMadVvygLy09f4mKC0+yY+Guo9TdG+3Al3BbNQtFNaIqWsoHvojRJxrqsm772TwCAgIoKzNXMFVVRXt2/J1JcaP8bDfWijd8RbfW81X3emlpKcOHD2fu3Ln8+eefVq1qJpOJl19+mbZt29K6dWtKSkowGAyYTCarOLNmzWLQoEGMHz+e5s2bU1paiqqqGAyGavMB8wRAeXl51W6jqip//PGHJU+AP/74g1atWlntU1ZWZvW4c+fO7N27l6lTp1aKWVZWRosWLThx4gTJyclERJinIP/tt9+sYlUcr7S0lJKSEtq2bcuHH35Ifn5+la2bGo2G8vJyq7I6N4ZR8aRtYjyf/rie4uJiS+vmunXr8PX1JTg4mJKSEkwmU5XldP41r5Nyc+8Dg8bdKkbl93nDuUrMc8upvkpLSykvLycpKclS2Tx/HHttuBdp8AeilQxWbd1Nq1Bvq9frE9OKqhJvTAcFStwCbBOzGraOe+ONN3LkiO0nUbHH+bvKNbVHTCmn2seN9InBo/AEkYZjbPpnJwGe1feWsWc5ndi9iTbAKU04hgYex9HX1BVjGrRhRBkzOHN0F/vCrNc4teXnKXv/H4QCaQTj7qZ1mvN31Zjl+iAAQozp3DjMPt974Lznf76K5RUvpM6VzZ9//plVq1YRFWW9MHjLli1JSXHeqYAnT57M5MmTycvLw9/fn8TERNLS0mjVqpVlxtaSkhJSUlLQ6/V4eHjU+RiWlk29HqXHBGg7EDXrCAQl4OYXSX2W0jWZTGRkZBAaGmqu0BhNuJ2diVan98TtbJ5arRatVltt3lqtloKCAvbv32/JtaysjIiICGbNmsUPP/zAiBEjeOmll+jRowfp6enMnz+fAwcOsHr1akvLnk6nQ6PRWB2nT58+dOzYkZdffpmXXnrJfP6Kgk6nw8PDg++++45PPvmEkSNH0qpVK1RV5dtvv2XVqlW899571easKAqbN2/m5ZdfZvjw4fzyyy98+eWXfPfdd1b7uLu7Wz2eM2cOQ4cOJT4+nltuuQWNRsOOHTvYvXs3zz77LIMGDaJly5bcc889vPjii+Tl5VlaZStiVbSAVrwXpk2bxltvvcX48eN55JFH8Pf3Z/PmzXTv3p3WrVvTokUL1qxZw9atW2nVqhWBgYFWMVSNO/ePvZVF737MrFmzmDJlCgcOHOC5555j+vTpeHmdXR5Fo7Eqx4qWt/OveV2U5JytVOk88PDwsH6f2mhSL1eJCZU/Uw3h5uZGbGwsbm5uHDx40Or7pNYMCZhWKXgrpaiqwXITz2g01j/mOYqzU/FWSjCpCh0uvYKczMwGxzyfrXI9V2lpKa+88grTp0+36pHQEPbI0x4x7RVXysnx5aSkXg7pf9BaOUGpPpC2bSt3bW+Mcjq9cSkAWfooerSt35hNZ7mmrhjzjx8joHAXnmWZVg03tv48bd39LQCZuhC8wWnO31Vjevv4wE4IIo//fvUZUx982Gbfe+D8539+zMTExFptX+fKZmFhoeWH8bmysrJsesHtreKCV1TSKv5fURTLv/qy7O8fZf7XABXd/SpilpUbcT87E61Gp6+0jEFNea9bt84yk3CFCRMmsGTJEn799VfmzZvHY489RkpKCr6+vlx99dVs3ryZDh06VIp//nGmT5/OuHHjmDZtmuXNV5Fz+/bt8fLyYubMmRw/fhy9Xk/Lli159913GTNmTI3nP2PGDP755x/mzZuHn58fL7/8Mtddd12lcz43n+uuu47vvvuOp59+mhdeeAE3NzfatGnDXXfdhaIoaLVaVqxYweTJk+nRowdxcXG89tprXHfddZXKv+L/g4OD+fXXX5k1axZ9+/ZFq9XSuXNnrrrqKhRFYdKkSaxbt45BgwZRWFjI2rVriYuL+/daad2JjAjlsw/eYe7zr9K5c2eCgoKYOHEiTzzxhFX+Vb3/GvKedFPPvl/cPC94nIZyhZjnf6Yamtf53yF1/jLXepGpbUYz4xkK0o6g1V5p/XJ9Yp7jdMp+fIB0AgkLDiYnM7PBMatjy7harRaDwWCXXF0lpr3iSjk5sJzC2wPQSnOcv9LyuKZj9fvYs5zczk4aVuob2+BjOPyaumDMct8oKAT3ghNW+9r682TMMY8tLNCH413PXC+kKcWMjo4hW/UiUCkiSJsn7/1axqtzZbNXr168//77PPPMMwCW8WQvvPBCtTOaCtspNRjxtqyx+e8MZhca97hs2TKrbVRVpaSkxNJa5uXlxbPPPnvBNR3nzp1rtUxHhdtuu42RI0daumkmJydbXktISOC///1vjXGr4+fnx//+9z88PDyqrBhUN+Zu4MCBDBw4sNq4LVu25LfffrOKeW6suLi4SrE7duzIqlWrqowXEhLCqlWrSEtLs5qNtiJGSe5pKIO+Pbvy559/VpvXunXrKj336aef1rtVUzWWo8PcddxNLzPROqt8fQTNis5gyEq2eezcU+auM2maMEIb2JIrhGigUHMrVmvlBB+n5TssjYCS4wDoQlpcYEthD9qAGEgDv1L7TQwH4HZ2Ipsyb5mJ1hY2fPw8fTAvh3hX6VLWfxLM1WMec3BWzq/Olc0XXniBa665hq1bt1JWVsZDDz3Enj17yMrKYuPGjfbIUZyjzGAisKKyqXOdluSmTufuAYXmVkaTydTg7pu1ZSgtwg0oU3Xo7Ty9uqi/ct8YKNqFe4Htlz+pWGMz203WWBPC4Zq1xKRo8aOI3DOOm9EyzJAKCgREt3dYDk2ZdyOttelTao6vDYix63GaghNH9tPr0POWDoUaRaVX0kucODKcqIQ2jk3OydX5F2+HDh04ePAgV111FTfeeCOFhYXcfPPNbNu2rdJMqML2jIYyNIqKCqCtzyhQ4Qhad3PLpJtipLTc0GjHNZSaJwcqU9zQaGzbvVXYji7YvPyJ79nFt22pYo3NIi+Z9l4Ih9O5U+wbB4BnnmMWhs/OzCBEyQEgqlVnh+TQ1IXGmisnIWoWhrJ6Tv5XC0EG87JpXqFxdjtGU5GatBOtYt3jTaeYSDuy20EZuY56Lbrn7+/PY49Js7FDGM3j70yKG1rl4u4Sl5ycbOnu6+oUjQ4jClpUyktL8NQ3TiujWm6+dgZFWjWdWUBkK9gDYcYMisvK8XS33Y0k7yLzwu2yxqYQzsEtoj3kJRFRfpzsgmICfRp3iMOxAzsIBDJVP5o1C23UYwuz5tEtKFHd8FDKOZlygMiWnWx+DFN5CcFqFigQHNMKQ3ldlkMQ54to0RHjBsWqwmlQNYQndKhhLwH1rGzm5OTw559/kpGRgclk/ea90IQvomE0prNLoGil8uBSFAUDbmgpw1Ru+6U8qqMxmo9l0kqXa2fmH2me+j5ak0FSWg4dYkJsFjuo3Nxa6h6SYLOYQoj6c29+CRxYSRvNcXalnKF3+8a9EZRz3Lx+dpquOc0a9ciigk6n5bgSQjynOH1sv10qm1mnjhCsqJSobkRFJ5B8pHbLVIiqRSW0YV3Lh+l16Hm0ioqqwu8tZnK1dKG9oDpXNr/99ltGjRpFQUEBfn5+lWa3vBgqm7ZeRN5WzGtsGkABRSeVTVdj1LiBqQzV2HiVTZ1qPpaiq98EQ6J6tvye0ASZu9E2J5Otp87YrrJpKCNYzQTAv3lL28QUQjTM2UmCWinH+eNUTqNXNg1nzJWOXI+GzZYvGiZTF0a84RRF6fZZqzEj5QDBQKoSQoxehl3ZQt/Rs/nzt650//VWVKDHTfc7OiWXUOd+mA8++CATJkygoKCAnJwcsrOzLf+ysrLskWOjcXMzfxiLioocnEnVygznLHviJpUHV6NqzDcIlLNdoe3OZPh3Jlo3+UNja2Vl5l4GNplK3CeUUtzRKCpZJ21397k8KxkNKkWqnqjoeJvFFUI0QGg7AFoqJzmcntPoh/fIN4/jNvjHNfqxxb/y9REAmHKO2Sd+mnlMcJbWdj1lBHS6vB/5qicaBU4f2+/odFxCnVs2T548ydSpU6tca9PVabVaAgICyMg4O6Day6tO6/BVLEIPNa93WRcmkwmDwUBJSQlFpQYUQxklikp5mYpbA8Yy2iPXphwTrMuqqtlmy0watAYVIyW1HofakFxN+RloDObWN232YUrKI8Ar0GWuqaPKqbYxTp8+jZeXFzqdrtJwgjpTFLLdIwgvS6HkdHLDYp3jdMpemgPH1VASm/kBztlrQ4gmJTAOg8YdD1MZRRnJQK/GPXypedZr9zDp7eBI/661aftZyAEMWeZKbJ5eZiK3Jb27G0eUCNpyhDMpe4jt0NPRKTm9Olc2Bw4cyNatW0lIcO3xP0aj0eq/FUJCQjCZTKSn12866vLycpu2IqmqSn5+Pvn5+WQXlhFgzESLkQy1HE+PDHw86n8sW+fa1GOeW1ZVVY6MpUXoSjIpR4euoPY/+uuVq2pEyTtl/RRnwC8CFK3LXFNHlFNtaTQaIiMjMZlM1X6f1EWJdxSUpVCWmcyRtCyignwaHDP3xEGaA+maUBJRbZJnVewR12g0otPpMBqNNotrrzxtHdNecaWcnKecivxa4JezD8+8I5X2tWc5lZeVEWk6CQoERbdt0DGc7Zq6WkxNQPTZtTZTrWLZ6vOkq1hj0yvCKc/flWNmukVA+RFK0g85fa7OEFNR6zjwaMmSJTz99NOMHz+eSy65pNIPwRtuuKEu4RrN4sWLWbx4MUajkYMHD7Jp0yZ8fHyq3V5VVacau5lZWM7Tv57iR/1sAG4unUsePjzTL4Rm3tJF0iXkHKPVn49QoHpysO/b+OjrNT9XrZSf3EH7Pc9Xen5Ph0dwa97RbsdtKioqqbZscT390zyuLvietwxDeN5wB/de6sf1bQMbFLN43UIuO/MlX7sNJvFGmUFcCGcRuulpQk+uYmH5LVw1fCre7jbojl8L2Zmn6bX2JgC2D12NTn/x9VJzFSkHtnH9rilk4k/qLT/YPL761SQuMe5hZeSDJPS82ebxm7KUn17h+oLP2eh9Lf6D5jg6HYcpKCigZ8+e5Obm4ufnV+12df61e/fddwPw9NNPV3pNURSb3+GylcmTJzN58mTy8vLw9/cnMTGRtLQ0WrVqZZsxV2CpyNoyZmlpKa+88gqdr70VpeAfPMqPU6y6kVVaSBqelHkE0r59rFPk2pRjwr9lNX36dPT6KmZ/LY9H++txPIAjeg/at29tt1x/KyhEn3+cc+tCBlVDvl8LrmjTxiWuqcPKqR4ammvK6Vx+yw7iajforBwmjEze/hu6RXnR69JL6n3+B382Dwko9o6ibdu2drum9vzuc6ZyaqyY9oor5eQ85aRk94STq2itOUG5ZxBtWzW3a64V5dTnMvPfnXSCuKTzpQ2K6WzX1NVienh6wC5oRi6+8dFoPXxs+nlK/9z8/R8S14FWrVo53fm7csxjf7WAAggoO0Wbtm1tEhNc5/wrYiYmJtZq+zpXNhs8NslJVFxwrVZr0y9JW8fUarUYDAZaRgQwWvsLAJ5KORv1D/CY4S7aRF7VoGM5+/m7UsyKsqo2rtaXLMWfIDWXrBOH0HZsZ7dcExLbkbPKm0AKAXNF83HDRO5LbOcy7317xbxgOTUwdn1iHk7LI0Yxd92/XLufjZqpzDbcxam8WxqUp3exuSu1yT/GKoY9zt3WcZ2xnBo7pr3iSjk5QTmFm9fma6Uc5/fUPK5qW3lGWnuUU3HqIQAydJGE2TC2U1xTF4sZGxNHvuqJr1LMmROHad76Upt9nkyGMkLOrrEZEvNvJcOZzt+VY3qFt4QUCClPbfLv/dqo3+wYotHF6gsYr1tleaxVVOa5LSFWX+DArERdZenMA/WLM5LsepxYPwhUzBXNCWUP0rv0VeL7jiU21N+uxxX1086vhDHa1ZbHWkVlnm4J8e459Q+qqjQrTwNAHywz0QrhVM4ufxKvpHEkNbPxjpt9FIB878ZdbkVU5uamI1UxzxSbedy2s5qeOXkUN8VImaolMrZ2rU+i9oJj2wMQSiamUvkdfiG1rmwOHjyY3Nxcy+MFCxaQk5NjeZyZmUm7drVvqRF1o2QfQTlvJkkNJsiyz/pMwj4KPc92lco9btfjGNL3AZCuBtD72uF8PONG7hlwiV2PKeovSslAq1h/vnWKiUg1o/5Bi7PxxryMU2CkzDophFPxi6RU64ObYqQkw3bLHV2Id5H5b48aIDegnEGmLgyAogzb/pZLTzFXXtOVZnh6yFJ5thYTHUOuah7vnH50j4OzcX61rmyuWrXKsgwBwLx586zW1TQYDBw4cMC22QkLNTABlPOKS9FCkGvPCtzUGP3Nd5M9Cu0z1XmFjEN/A3BYjWJ0r7bSounsglpU+nyripZSn/ovum7MNP94SVMDiQmXddaEcCqKQnGA+SaQZ17j3TRuVmbuWu8V0abRjimqV3B2rU01O8W2cdPNLdhntKE2jSvM9O5unFDMPdUyju52cDbOr9aVzfNnZnWmmVqbBL/mMPRVcwUTzP8dugj8Ix2alqgbfbD55kDg2e6N9lJ4fBcAqW4x6LTSW97p+UfC0FctfRdMKKjXv4zBq/4/FHJPmG/+HVdDiQmpfpY4IYRjuEeYu+JFlB8jv7j0Als3nElViFJTAQhLkJ4uzqDc13xDUW/jG9DlWckAFLiH2TSu+Fe65uywqLRDDs7E+cmvUFfSdQxM2wVjvzP/t+sYR2ck6iggqhUAocYMDEb7TbalzTwIQKGvdJVyGV3HkNn6dgC+MvbC0HFUg8LlnTK/B9I0Yejd7LfMjhCifryiOwHmSYL2Hrf/uE0PvYK3UopB1RAeb7sZNEX96YJiAPArte0NaF2+uQW7zFsaJOwlS2vuMaTJOergTJxfrSubiqJUWlPOVmvMLV68mLi4ODw8POjRowd//vlnrfZbsWIFiqJw00032SQPl+AfCfG9pEXTRYXFme9kRyqnOX46x27HCSwyf/kpIRdeXkU4D/+WPQEIJ5OUM3kNiqVm7AWgzE1aNYVwSmcnCWqtHGffqRy7H85XKQYgXROCxk3G8TkD7zDz5D1hxlOcOGK7SYK8S8wt2JpAmQjKXnI0QQD4FNl3Do6LQa1vd6uqyrhx4yzr/pSUlHDvvffi7e0NYDWesy4++eQTZsyYwVtvvUWPHj1YtGgRAwcO5MCBA4SGVt+FLDk5mZkzZ9KrV696HVcIR9AFRGFAi14xcCL5EPHhPWx/kOIcAk3m8dRBcR1tH1/YjVu4+WZEouYkf57MJt6znoH+eZ+49J8BGFb+PfzzvvSEEMLZnK1sxmhOk3wqHbDvJIteJvMNrAxdJHK72jnkH/gNAB9K8Fx+Ob+1mGWTuEGG0wB4hUrvJnspUMw3ckPKTzk4E+dX68rm2LFjrR6PHj260jZjxtT9x8zLL7/M3Xffzfjx4wF46623+P7773nvvfd45JFHqtzHaDQyatQonnrqKX7//XerWXHPV1paalURzsvLszxfXl5OaWmpTRc5tXXMitzrW5mvjj1ybcoxofZllaUJJsKUTtaJA5SWdq5x2/rkajy2HS8gVQ0iPqp5pXxc5Zo6upzqwma5+saiB8KUHJJSjhPVIqDuMfNO4f7tA1T0O9Ggon47jbLoXhi9w+xyTV3lu8+V3qeukquUUwPi6nwx6ALxNmRTln6A0tIr7JbrO7/uIaDoJOjgYLEvm1ftYELfhk0S5JTX1IVinkw+wICTr1PxZa1VVHolvcRf7v9p0OfJZDQQpp4GBfzD4yktLXXK83flmKWlpeSZzHeDQ8imOPcMGg/fBsd1lfM/N2ZtKKoDZ/opKyvDy8uLzz//3Kor7NixY8nJyeGbb76pcr85c+awc+dOvvrqK8aNG0dOTg5ff/11ldvOnTuXp556qtLzjzzyCB4yHbRwgGHKT3Q07eW/+vGklgXaPH5H7WGGGVbyu6kjazT9sVFvd9FI/sNSgtRsHtM/jntZUZ33j1OPMZbPKz2/jBGkKNKlSghncruyklamwzzDJEyKj12OUe7mTXlhGgt076Ao5omCHjXchc47HLfyQrscU1xYqHsh95W+Xen5N/X3klHmVe+4HhoDDxtfw6gqPKtMQ34E2IeKwlTT2wQpBfyf7m4yjQ2vbLqakpISFixYQG5uLn5+1Q/ZceisEWfOnMFoNBIWZj1bVlhYGPv3V913fcOGDSxZsoTt27fX6hizZ89mxowZlsd5eXlER0fzwAMPkJqaSuvWrW1a0z9w4IBNY5aWlvLKK68wffp0SxdmW7BHrk05JtS+rPa8vR+y9hLqVszYGc/bPNejH0yGE5DmHsPsWZV7B7jKNXV0OdWFLXM98+ZvkLOVYDLpe8PIusfMO4W6+AurdXlNaLh98mMYvcPsck1d5bvPld6nrpKrlFPD4pZ8mwW7DxNhOM4tD7+Lp7ubzXPd+OdW+vxyvaXOoVFUntUt4ffe33HFZd3qHddZr6mrxDyZfADjR/+1WmPZoGrIN+kb9Hnau3kVrIUMpRmPzJ5tk1yr0pRjVnzvnVQiCOIQV3ZuSbsB4xoc11XOvyJmREQECxYsuOD2LjVFYX5+PnfeeSfvvPMOwcHBtdpHr9dX+YHV6/W4ubmh1+ttevFtHbNCdedRX/bItSnHPNeFykoTGAtZ5gH8FyrT+uTqnm1eILzQN6HK+K5yTR1dTnVhy1y1YW0hZyuBxcfqFTNFDeJ3Qz9G69YA5h8vjxsmcp8aRJQdvvfAdb77XOl96kq5gpRTfeO6xXWB3ctopZwgKaOAS1uE2zzXdj55VhUaAJ1ioq13foPKzFmvqavETGjdkXUtH6b3oefRKCqqCr+3mEnxUUODPk+FGckAnNGEEHE2hjOevyvHrJDp3hzKDlF2Oskm33+ucv7nxqwNhy59EhwcjFarJT093er59PR0wsPDK22flJREcnIyQ4cORafTodPpeP/991m5ciU6nY6kpKTGSl2IevMMbQFAUHkaKRm5No8fVJwMgDZUZqJ1RQGx5kmdYtSTZBeV13n/A6eySVfN3bPXGztyVemrrDBezcHUHFumKYSwAU2YeVKw1prjfL01xS5/E8ITOmLCuiulCQ3hstamw/UdPZtv4h4FIE0TyhUjZzY4piErBYB8feXf0cK2Cr3M66Rqs2X5k5o4tLLp7u7OpZdeypo1ayzPmUwm1qxZQ8+ePStt36ZNG3bt2sX27dst/2644Qauvvpqtm/fTnS0jEcSzu/vbPMMznGc4raXv+Ht1btsF7woiwBTNgDN4mUmWlfk0dz847OlcpLk7LpPEtG6eSAtNObZ8Tab2pFGMzRAq4gAG2YphLCJs8tThSk5fLvtKH1f3sA7a/bY9hj+kez0vtLyUFW0aG54VZZQcxIh7fsBEKqeBkNJg+NpCyrW2IxocCxRMzXQPNuvT7Esf1ITh1Y2AWbMmME777zD8uXL2bdvH/fddx+FhYWW2WnHjBnD7LN9zj08POjQoYPVv4CAAHx9fenQoQPu7u6OPBUhLiglI5fDu/4AoJmSzwb9VI6uW26zu9mlp8w/Uk6owbSNj7FJTNHIzv74jFTOkJpV97U2Y0P96aw3LxB+WG2OBnj4mhhiQ/1tmaUQwgZS8lROqOZhQa2UE6jAC78eJz3ftjPQ55aaANgXNgRl2i5ZCsmJtGzZhlzVCy0qRacavtZmxRqbBMhvAHvzCmsJyPInF+LwMZsjR47k9OnTPPnkk6SlpdG5c2d++ukny6RBx44dQ6NxeJ1YCJtITtrPY7r/WR5rz07U8MeREcSGNnzNzYzDfxMNHFEjuSqk+pnBhBPzDqZA64+PMRdDVnLd9zeZaG48CUBQ80TW3nqVVDSFcFIHTmWjNUUTpT3DQO1fHDOEkkYzTuXVvQt9dQxGE3HlSaCAtv1N0qLpZMICffiHaLpygPTD/zQ4XmB5BgDeIXENjiVqFhLXDjZDM3IxFeeg8QxwdEpOyeGVTYApU6YwZcqUKl9bt25djfsuW7bM9gkJYSetPbKqnKihlT7bJvGLTpi75KbrY+QmjQvL947DJ28H7vnH6r5z7nHc1VJKVR3t2l0iFU0hnFjr5oEcwdzqOFH3E+O0q3jMcBfN/W632TH2H06ig2KeGyOy/ZUX2Fo4QoZ7NJQfoPjUPqBZveOYjEbCz66x2Sy6le0SFFWKjYrkjOpHsJJH+tE9RLSTz1dV5NeoEI3IPFGD9cfOlhM1uGUdAqDIt4VN4gnHUIPNPxICSuoxDuTMQQCS1XDaRNVu1m4hhGPE6gvoq91peaxVVOa5LSFSa7uJgo7tNg/dOEUoOp/6V2SE/eT7mMf+uWUfalCc06kn8FTKAGge16bBeYma6d10nFTMEzGdSd7t4Gycl1Q2hWhM/pFobngV9ezMgKoKDF1ks25Nzc7ORKsLkz8yrswvxnzzIcp4kpyCuk0YkXfM3Lp9RG1O+2ipbArh1LKSrNbEBdBgQl9wwmaHMJzcBsAxrYzhc1bK2bH6gcUpDYqTlrIPgAwC8fDybnBe4sIy3c2/34rTDjo4E+cllU0hGlvXMZTe/gUAxbiT0nyIbeIWnsFfNd8ND46XKe1dmU+0ufxaKifYezKrTvvmHTffXT2ljcTHUyZNE8KpBbUAxfqnmKpoKfWJstkhAnLNFZA0bXObxRS2FRhr/s4PM6aiVQ31jpObal4C8IwmxCZ5iQsr8javhKHNadiNgouZU4zZdASj0Wj1X2eOqdPpMBqNLpFrU41ZEa+2ZeWW0IsiPPBSSji4cwsxoQMbnGvZ8R14AsdNIbSOjax2H1e5ps5QTnWJee5/GywoES0Qq6Sz7ngGV7Su/Y9ETab57mq+d2ylfOx5TW0d1yXKyU4x7RVXyskJy8knHOX6V1C+ewAFMKFgGvQSBq9Qm+RaWm4g3mCeHCiLZk5fTvaK6+wx4xNakqd64acUEaIrqHc5lZ8y92wp1Ppb7e/s5++KMSu+95TAeMg2L3/S0GO40vnXJaaiqqp64c1c3+LFi1m8eDFGo5GDBw+yadMmfHx8HJ2WaMK030yibfkePmk2hfZXN3wyCMP2FXQ+/Drr1C4EDX9NJghyZapKiy8H4KkWMy/8VW64qlutd4378jp8TPk8H7aQ63tdbsckhRC20mzzs0Sc+JFPDH2IuelpfD1s0xaQfCqNIX8MB2DXkB9QPGTCMGdkMpkwfjGBLsoh/m73GPp2g+sc4/gfnzDw5GtoFDCpsCpyKtFXjLRDtuJcB/f+w817/0M2fpy85UdHp9OoCgoK6NmzJ7m5ufj5Vb8CQpNp2Zw8eTKTJ08mLy8Pf39/EhMTSUtLo1WrVmi1Wpsco6Iia8uYpaWlvPLKK0yfPh29Xm+TmGCfXJtyTKh7WW3b0BHS9hBYcJC2bds2ONdDf5jH+Jx2j6FX+/bVbucq19RZyqk27JFrxk+xeBbuR19wnLZt76zdTkWZaE35AIQkdKr0vrLXNXWV7z5Xep+6Sq5STraJq5TfCCd+pIUmldOKL74U2yTXo3s3A3BSE8HKVeucvpzsFdcVYq76JpouxkOcObyVXkOm1KmcThzdz7UnX0djng4CjQIDTv4fJz1uIyq+jUucvyvFPPd7T+vpBXshkDx8YiPQeAU4Va72jJmYmFir7ZtMZfN8FRdcq9Xa9EvS1jG1Wi0Gg8EueVbEd+bzd6WYdS0rz/jukPYx0aUHL7h9bWLqsw8DUOSXUKvju8I1tUdMe36mbBnTGNQKCvfjW5hS+5hZ5vfACTWY1rHNq93PFb5PXKWc7BnTXnGlnJywnKIuBaC9kszrxzOJTvSySa7qqR0AnPZuhaHAdcrJXnGdOWa+TzzkQpDxdJ1jZhzdQ2wVy6qdTt5HbOK/N5+d+fxdKea533sJMTFkqAGEKjlkHttLePteNonvzOd/bszakH52QjhIbOe+ALRUj3EyPaNhwVSVZiXJAOjC2zUslnAK3lHmHwjhhhMUl9Zugff84+bxOodNkbSPliUOhHAZzRIp1XjiqZSRc3yvzcIG5u0HwBgqk8Y5O7WZecmrcFNanfeNaNERo6pYPWdQNYQndLBJbqJ65y5/clqWP6mSVDaFcBCf0HjOEIBOMZG0/feGBSs8jZ+aj0lVCJOZaC8KvtHmHwktlZPsO5lZq31yU8yVzRPaSAK8PeyWmxDCxjQaCgLNNwq9svbZJGRxaTktjObZSZu1kvHbzs4vxvydH6Gmg7GsTvtGJbThN/+hlscGVcOGlg8RlSDLoDWGiuVPStIatk7qxUoqm0I4iqJw3MN8J7M4+a8GhSo+Ya5kHFNDaZ8Q3eDUhOMpIeYfCQlKKvtPnKnVPurpAwDkecXaLS8hhH14xponAosuTyK/pP7LX1TYvW8fzZVM803I1j0aHE/YV1xCa/JVT3QYMZ45XOf9je7myZ+26zqRNnYTfUfPtnWKohrFPmeXP8lNdmwiTkoqm0I4UEGQuRXSJ6thXS/SDv0NwFElioggmWX5ohAQQxnu6JVyTh+r3d1S74JkAEyBCXZMTAhhD17x3QHoqDnKoTMlDY6Xum8TAKe0zdF4yiy0zq5l8yCSVHMLWeaRHXXePzDb/DuiIKaftGg2tsB4APyKjjs4EecklU0hHKjix0Vs6YEGxSk9tQeADH1Mg3MSTkKjtXTNMZ6pRWWzrJAgg3nsr29k1bMbCyGcWPMuALRTUkg6U9jgcGqqucKS6du6wbGE/bnptJzUmVvIKsbf15bBYKSFwbzGckjbq2yem6iZT3PzZyzEcMrBmTgnqWwK4UAJnfsAEEUGp9OO1TuOPsdcGSn1b2GTvIRzKPQxd4f1yj964Y3PVkgzVV/iYuPsmJUQwi4C4ynReKNXyik9ndTgcM0Kzt7EjOjc4FiiceScHQKhZB6s034Hdv9JgFJIiepGi05S2WxsFRMx+VOAqbB2cyw0JVLZFMKBAkMiSCECgOTt6+sXRFUJLkkBwD1cWrQuJurZrjkhZScoNxhr3LbwhLkL1WFVZqIVwiVpNBQEmWeh9str2EQj+cWlJJ6dHCi87ZUNTk00DlOged1C/4Ja3GA8R/oe8ySDR3QJ6NxlcrjGFh8ZQaoaBEDGEZmR9nxS2RTCwY55mLtflNR3kqCCdHzVAoyqQlhCRxtmJhxN18w89rKFcoJDqdk1bpuVbO52dVyJJNjPy+65CSFszzPWvN5mTHkS2QX1H7e5a89uwpVs89+FNjITravwijTPSBxqOAXG2i15BaBJ3Q5Apl/7mjcUduHhruPU2eVPMlOksnk+naMTcBSj0Wj1X2eOqdPpMBqNLpFrU41ZEa8+ZVXc7BI4uQ7frN2V9qtNrkUp2/EFUtQw2sRGXPDYrnJNna2cLhTz3P/aKqYhwNyy2UJJZdXxM7RuHljt9uXp5vX0cjyiq83DntfU1nFdqZxsHdNecaWcnL+cPGO7wd9wieYoW5PSGNAprl5x0ismB9JF0VznibG01CXKyV5xXSVmZEwiBZs88FFKKM84iCa0dhP9RBSa12bVRV9aZT6ucv6uFPP8z1Ome3Mo24sueT3G7KHgF+k0uTo6pqKqqmqzozuxxYsXs3jxYoxGIwcPHmTTpk34+MisncLxjuzdyg17HyAbX04O/xEU5cI7nUPd8haXHP+AjWpH/Ee8aacshSMopnJaf3kNOow8H7eE67tV/8Oj2de3EWE4zmtBj9Ov36BGzFIIYStuBSdp/dOtlKla5id+yvAu4fWKc+D7RQwv/oytPlfjcd2zNs5S2EuZwYTmy3F01iSxp/Nc1MQBF9ynuKiQzt8Pwk0xsrnvJ/gERzVCpuJ8pd8+yKWlmwFQ0XDq0ofIjh96gb1cW0FBAT179iQ3Nxc/P79qt2syLZuTJ09m8uTJ5OXl4e/vT2JiImlpabRq1QqtVmuTY1RUZG0Zs7S0lFdeeYXp06ej1+ttEhPsk2tTjgn1L6tmoWGU7ZlBoJIP/jr8IlvVOldl2wcoxz8A4AplJ2rJVtQud9Z4PFe5ps5WTjWx5/ln65sTUnoct/wTtG07rOqNTQZMhlQA/GIuoW3bqsfu2uuausp3nyu9T10lVyknG8dV21C82hdPYz6mnKO0bXt1vcLkfmEer+kecylt27Z1mXKyV1xXiVlaWsoaTSSdScK9OI2Ear7Lz/XX2q9xU4ycIYDLrupf5Q1rVzl/V4lZ6fOUdxKldIvldQUTEX+/QPhVo+rUwukq518RMzExsVbbN5nK5vkqLrhWq7Xpl6StY2q1WgwGg13yrIjvzOfvSjHrW1YRoSHsUeJoTxIndv/GJTGV/7hUGTP3JKbvplPxZ0UB1O+mo205APwv/OXmCtfUHjHt+ZmyR8xivxZw+jj6vKPVx84+ihYDRaqeqPjWF8zBFb5PXK2cXOGa2iOmlJPt4xYEtsPzzBY8sw/UK2Z2fjGtTEmgQGSHXpbcXKmc7BXX2WNqtVoydM3BAKbTB2sVt+CIuZKTrG9NsK7mn/XOfv6uEvP8z1Na8h7Cse4oqsFEWvJewrvUfUk6Zz//c2PWhkwQJIQTOOFhbs0sTdla633SjuxEg8nqOQ0m0o7UbX0u4dzczs4w3KzkGEfTq54kqPiUeUKCI2oEHWJCGi03IYTtecZ2AyCm7DDZBcV13n/n7p2EKLkY0NAssbut0xN2lq0LA8A3v3bL3/ic2QlAYXAnu+UkanagJAijat2ibFA1HCytfp6FpkQqm0I4geLgSwDwy679LGYHSoIwyZfbRW9XsXkZk3jlFP1e+YO3V1e+mXA6ybx4e4oSSXigjEUXwpV5xl8GQEfNUbYmZdR5/8yD5nFjJ3Ux4C4zU7uaXI35b3hI+UkwGmrc1mQyEVdqXk/VP1FmHXaUuBZteNRwl+U3marC44aJxCZceIKnE0f289fqTzlxZL+903QYqWwK4QR8Esx3n2PKDl/wj0uFuBZt2KG2sDw2qJpaf7kJ15CeX8qiPeYfi22VZMLI5Pk1x0jJyLXarixtHwCZ+uhGz1EIYWMRXQBorRxn19HUOu9uSjW3dGV4t7rAlsIZlZvcKFL1uGGArCM1bnv8yEHClSyMqkLLS+s3vlc0XGyoPwl9xzKqbDYAZWhpddUIYkP9a9zvl+XPELH8ci7beDcRyy/n94+eb4x0G51UNoVwAq0vuYw81RMPyig8saNW+8QG+5KgMd/1nlt2J71LXyW+79gLfrkJ13Eyt5wumkOoKvgpJWzUT+UW7VoOpuZYbafPNXe3KvNPcECWQgib8o+iQOOHm2Ik//jOOu369updRBbuAWDdmYAqe0II5+Zensch1TzvQt6xmn8PHNu5DoBkTQzefkH2Tk3U4J4Bl/DQXWM4YopArxi51qvmlsoTR/bT78hCtIp5rKdWUbnq8IvknD7RGOk2KqlsCuEEokMC2Iu5lfLkzt9qtY/x5D/4k0ee6ol399F8PONG7hlwiT3TFI0sQZ/D07pllskFtYrKPN0S2vqeM45LVWlWehwA9/DWDshSCGFTikK2r7lV0jO79l3rUjJyObJuOZdrzD0dHtR9xtF1yyv1hBDOTWsq55jGvHxJ5pGaK5uG438BkOpz4Vlrhf11aRHBds8eAORs+6rGbU8d3olGsZ5USKeYKDqdYrf8HEUqm0I4iVOe5h8XZcf+qtX2J7d8DcBmtQP/GdxNWjQvQpFquuWuZwWdYiJCPWccV34qXmoxBlVDWFy7Rs5QCGEPaoi58hBblkR2QUmt9klO2s883buWm1MaReVZ3RJSLuKxYBerTI9YAIzpe2vcrlmu+XU1oqvdcxK1o7S+DoC4nM1gKKt2u8zCyp9rg6rBKyTWbrk5ilQ2hXASJWcnCfLP2VO7HZLWAHDM/zI83JvsKkYXtVKfaFTF+mvapMLXSf9WQEtTzT82UtQw2sXWbwF4IYRzUUPMN446ao7w95H0Wu3T2iOryptTrfRVz2ItnFfFkAivvOrHbJaWlpBoNA+hiGjfu1HyEhd2Zb8hZKgB+FBMyl/fVbudsv8HwDyZEJgrmhsSZxEQEtUYaTYqqWwK4SR8W5i7XjQvT4aDP0Puyeo3LsoistjcVcqnbf9GyE44gsErFPX6V0Axr2WlAhoFdm/6geKycgAyDm8DIFmJJDJIZqIV4mJQHGie6K2lcoJdR2v4W3CuoATLD9cKJjSEJ8jwClejPzskIrjsRLWTBh7YvhEvpZR81ZP49pc1ZnqiBqEBvmzTmyd9PPPXF1Vuczg5hatK1gKwu/vzbL1qCWljN9HrjocbLc/G1GSbQ4xGo9V/nTmmTqfDaDS6RK5NNWZFvIaUVZvWbchZ502AUggfjUBVNKiDFoJX90rxzvzzHWGoHDRF0rvHZXU6nqtcU2ctp+pinvtfW8Y0dLwDbYt+kHWUsr0/4Pn3WzxsWsKnX3Vl1C23UHTK3LJ52j0KVVVrzMGe19TWcV2tnFzlmtojppST7XM1eIZQoAvEx5DNqYP/cCStDbEhNQ+VWL3pH+5UzDelFEBVtHD9yxh9wjGWlrpEOdkrrivF1Ol0hEW3pHi7O55KGcadn0BcL/CLtNr2zL6NACS5teQSRWn07/6mHrOmz1N5i2th38/EZW3AaCiH83oo7fruDRKVEo5romg38C4q+r670vnXJaaiquffB7s4LV68mMWLF2M0Gjl48CCbNm3Cx0daAYTz0Bak0ebH4ZbxNgCqouHAoC8weIVabWtcPYdOub/wmW4IbW+a3ciZCodRTXismk5iwVaOqyGcGriUZr/NJrFkF+/6/YfLr73N0RkKIWxE/XYql5T+zZPlY/nAOJB7L/Xj+rbVr6N88KtnuNn4E/u9u6O/9E5KfaIq/e0QriG7qJz474YTpckEQEXDqUsfIjt+qGWbrG8fp3fpWlb7jyBiwDQHZSqqkltYRMcfbsBXKeaPbq/jF/fvmNqi0nJCV95GnJLGhtjJBFx2hwMzbZiCggJ69uxJbm4ufn5+1W7XZFo2J0+ezOTJk8nLy8Pf35/ExETS0tJo1aoVWq3WJseoqMjaMmZpaSmvvPIK06dPR6/X2yQm2CfXphwTbFBWyWesKpoAimpCX3CChM5X/ZurqpL7xd8AlEX3om3bus1C5yrX1GnLqQqNef7l0R9zatGVRCsZFG58ipBS88x1QSHhF3wv2Ouausp3nyu9T10lVykn++T6+9+72FUYyyW6v7lGs42fjd14+28Y0at9lS2c+1NS6WvYAAoE95tMYOehVq+7SjnZK66rxKwopxl3jcRDybQ8r2Ci+T8vEn7VKEsL5/HPDwDgk3iFQ777m3LM2nyeNv5yKb3LN0DyBtoOGmV5/vvP3qW7kkYBnnQfMQut57+VNFc5/4qYiYmJtdq+yVQ2z1dxwbVarU2/JG0dU6vVYjAY7JJnRXxnPn9XitnQsjqhhBOhKlYTPBhUDSeVUGLOiVmU8g9BajZFqp4OVw6u9zm4wjW1R0x7fqYaI6Y2IJR9V7xM0MbxtCn8d+biYUlPoOzwg65jHJKnreO6ejk5c1wpJ+cvp5O55fhTAEAf7U42aqYy23AXSekdSAivvJ7izrWf0V4pIFsJILjzEDgvF1crJ3vFdfaYlnLKTani5rMRbU4KBMaQmZFKHKcASOzWv9bHd/bzd5WYtfk8lSYMgAMbiM78Da1GA4qCyWQi5MDHABwMHUxXn6p7Kjj7+Z8bszZkgiAhnMTePA9mG+7CpJr/wqgqzDWM4WhZgNV2SRvNA87/VtpxScLFN2uZuLCr+1/PBx7WXW8UTPDttJonlhJCuIQEfQ5jtKstj6tcY/csk8lE+PHvATgVNRi0TbYd4aJxQg3BqFrXNlUVTpabW8EO/f2reTvCCImIafT8xIV17n87paqOaDWVw7s2A7Dxjw30MG0HoMXQBx2YXeOSyqYQTqJ180A+M15Nn9JXSDMFoCgQQBHN/dystnNPWQ9AWrPL0WjkI9wUaTQael7Rt/ILqhGyqp8qXwjhGqpbYzdKOV1p2y0793Kl+g8AcQMmNUp+wr7253sx23AXBtX8N15VzXPIePz4HyjKovjoFgCOe7ZxZJqiBiEhYexy6wjA8T8+BSB/43/RKCp7PLvhH93ekek1KvmlKoSTiA3155FrYjhJKPMMowG43+1bmrv/eyfbWJRDQol59tHgToMckqdwDgHR7Srd+TaoGk6oIQ7KSAhhK1WtsWtUFZKNzSpte+L3D3BXjKS4JeAd06WxUhR2lBjuz2fGq7mq9FVuK3uc+8oeIE/1oln2DnIW9yP6jPmmc4lvnGMTFTUqjDUvTReRvp4jx09wVdEvAHhdea8j02p0UtkUwoncM+AS1s64itBLb2C3KQ4vinHf9p7l9cObVuKmGElWw7j88iscmKlwtIpu1xV3vg2qhkcNE9mX7+ngzIQQDXX+GrsAqWoQ835Ns9qutNxA2zM/A5Df8uZGzVHYT2yIH49cE0MGzdhsascqtQfjmUOqGkRA4VESTckA9El/n3X/m+/YZEW12l0zCpOq0EZNYufHc/BTijmpiSD+iuGOTq1RSWVTCCcTG+rP48N78HXAWABiTnyDKSsZgLzdPwFwwOtSPN3dqgshmoCKbtcVd76vKn2Vz41X0yoiwNGpCSFsQO1yJ0zbBbd+gEHnQ5Qmk9iUz/nnyL8Vzg2//UoH5QjlqpbWA6UL7cWk4ubzO7d3YN2Mq/jfYxP5PPoJzl2wUKOoXHXoBU4c2e+4REW1QprHcUDXGoAbCj8H4EzrO6CJDYFyirNdvHgxcXFxeHh40KNHD/78889qt33nnXfo1asXgYGBBAYG0r9//xq3F8JV3Xb7eDaaOuCGgZTPHwVVJTr77Hs94WrHJiccrqLbdcWd7wya8fA1McSG1rzwuxDChfhHQrsb0A02t17N0H3GkpVrLC8X/2Oe2XK/92W4+Yc5JEVhP7Gh/gzoFEtsqD+e7m5cHutTaZZanWIi7chuxyQoLijfzdz1XaOYx97mlRgdnFHjc3hl85NPPmHGjBnMmTOHf/75h06dOjFw4EAyMjKq3H7dunXcfvvtrF27lk2bNhEdHc21117LyZMyA6O4uCRGBLKv1f0AJKStImXdMsI5TanqRqfeNzk2OeEUzr3zvXbGVdwz4BJHpySEsIcud1IYcTmeShkjM99k7a4UcvILuaxgLQBuXW5zcIKiMUS06FjlWP3whA4OykjU5MSR/Vxa/IflsaJAzyOvNbmWaIdXNl9++WXuvvtuxo8fT7t27Xjrrbfw8vLivffeq3L7Dz/8kPvvv5/OnTvTpk0b3n33XUwmE2vWrKlyeyFc2W3Db2G10hONohK6fhYA+zQtCA8LdXBmwlmce+dbCHGRUhS8b3mDctzord3F1m//y+ZVnxCmZJOHN636SGWzKYhKaMPvLR+2Gqu/oeVDRCXIrLTOKDVpZ5WzSje1lmiHLsZUVlbG33//zezZsy3PaTQa+vfvz6ZNm2oVo6ioiPLycoKCKi9yDFBaWkppaanlcV5enuX58vJySktLbbbIqdFotHnMitzPPQdbsEeuTTkm2KesNKqRnA4TMezcjKdSDkBH037Wvv8cV4ycWa+YrnJNXamcXOX87XVNXeW7r6lfUyknFy8nnygKuk+j2Z8vcnfpexzbFQEK7A/sRydVAzWUgauUk73iukrM2pRTzxEzOJZ8PaeT9xIS146eca0vWK6ucv6uErO2n6dmMW0wblCsKpwGVUNQdKsq93WV8z83Zm0oqqqqF97MPk6dOkVkZCR//PEHPXv2tDz/0EMPsX79erZs2XLBGPfffz+rVq1iz549eHh4VHp97ty5PPXUU5Wef+SRR6rcXghn46kzMrPsVTTn9JwxqBpedv8PxQbb/YEXQgjh3DSqkXv4gFCyLM99qxvEP8a2DsxKCFGdFu5p3FayAp1iwqBqWOFxG0ll4Y5OyyZKSkpYsGABubm5+Pn5VbudQ1s2G2rBggWsWLGCdevWVVtxnD17NjNmzLA8zsvLIzo6mgceeIDU1FRat25t05r+gQMHbBqztLSUV155henTp6PX620SE+yTa1OOCfYpK6PRyPpvlqE5YP28TjHRp1s7OvcdVq+YrnBNXa2cXOH87XVNXeW7r6lfUykn1y+nk8kHCP7oNTjn5uOg8lW0v+NRIuNaVxvTVcrJXnFdJab85nONmHUtp2PJ91laokfU8Dl1lfOviBkREcGCBQsuuL1DK5vBwcFotVrS09Otnk9PTyc8vOZa/0svvcSCBQv45Zdf6NixY7Xb6fX6Kt8Ier0eNzc39Hq9TS++rWNWqO486sseuTblmOeyZVkZjUZ8wxMw7q/cDSOyZed6HcdVrqmrlZMrnL+9rqmrfPc19Wsq5eT65ZR5bD8JVYwByzp+kITW1f8WquDs5WSvuK4Ss4L85nPumBVqW04JrTvW6vPpKud/bszacOgEQe7u7lx66aVWk/tUTPZzbrfa873wwgs888wz/PTTT3Tr1q0xUhXCYQJCovg98SGZEEAIIZo4mY1UCOFqHN6NdsaMGYwdO5Zu3brRvXt3Fi1aRGFhIePHjwdgzJgxREZGMn++eY2p559/nieffJKPPvqIuLg40tLMixv7+Pjg4+PjsPMQwp563fEwqSk3k3ZkN+EJHegrFU0hhGhyohLasK7lw1x16AXLGLANLR+SvwlCCKfl8MrmyJEjOX36NE8++SRpaWl07tyZn376ibAw8+LEx44dQ6P5twH2zTffpKysjFtuucUqzpw5c5g7d25jpi5Eo4pKaCOtmUII0cT1HT2bE0eGyc1HIYRLcHhlE2DKlClMmTKlytfWrVtn9Tg5Odn+CQkhhBBCOCm5+SiEcBUOHbMphBBCCCGEEOLiJJVNIYQQQgghhBA2J5VNIYQQQgghhBA25xRjNh3BaDRa/deZY+p0OoxGo0vk2lRjVsSzdVm5yvm7SsyKeFJOtr+mto7blMvJXnGlnKScnL2c7BXXlWLKbz7XiCnlVPuYiqqq6oU3c32LFy9m8eLFGAwGDh06xC+//IK3t7ej0xJCCCGEEEIIl1JYWEj//v3JycnB39+/2u2aTGWzwokTJ4iOjnZ0GkIIIYQQQgjh0o4fP05UVFS1rze5yqbJZOLUqVP4+vrSvXt3/vrrL5vGv+yyy2waMy8vj+joaI4fP46fn5/N4oLtc23qMe1VVq5y/q4SU8rJ9jHtEbepl5O94ko5STk5eznZK64rxJTffK4RU8rJHPPPP/8kPz+f5s2bo9FUPw1QkxuzqdFoLLVvrVZr8zeJPWIC+Pn5uUSuTTlmBVuXlaucv6vErCDlZFuu8t3X1K+plJOUk7OXk73iukpMkN98rhATpJz8/f1r7D5boUnPRjt58mSXiGkvrnL+rhLTXlzl/F0lpr24yvnb65q6Slk19Wsq5WR7rpSrrTX1a+oq5QSuc/6uEtNeXOX86xKzyXWjdTV5eXn4+/uTm5trtxYeYRtSVq5Bysk1SDm5Bikn1yDl5BqknFyDlFPdNOmWTVeg1+uZM2cOer3e0amIC5Cycg1STq5Bysk1SDm5Bikn1yDl5BqknOpGWjaFEEIIIYQQQtictGwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYQQQgghhLA5qWwKIYRwSuvWrUNRFD7//HNHp1Ir6enp3HLLLTRr1gxFUVi0aFGjHHfZsmUoikJycnKjHO9iM3fuXBRFcXQaQghxUZLKphBCNGEVFRUPDw9OnjxZ6fW+ffvSoUMHB2TmeqZPn86qVauYPXs2H3zwAdddd1212yqKYvmn0Who3rw51157LevWrWu8hIG9e/cyd+7ci66iGhcXZ3WNPTw8aNmyJbNmzSIrK8vR6QkhRJMhlU0hhBCUlpayYMECR6fh0n799VduvPFGZs6cyejRo2nTpk2N2w8YMIAPPviA5cuXc++997Jz50769evHjz/+WKfj3nnnnRQXFxMbG1vnnPfu3ctTTz110VU2ATp37swHH3zABx98wP/93//Rv39/Fi1aVOkmwOOPP05xcbGDshRCiIubztEJCCGEcLzOnTvzzjvvMHv2bJo3b+7odBpVYWEh3t7eDY6TkZFBQEBArbdv1aoVo0ePtjweNmwYHTt2ZNGiRQwaNKjWcbRaLVqtti6pujyDwYDJZMLd3b3abSIjI62u71133YWPjw8vvfQShw4domXLlgDodDp0Ovk5JIQQ9iAtm0IIIXj00UcxGo0XbN1MTk5GURSWLVtW6TVFUZg7d67lccVYuIMHDzJ69Gj8/f0JCQnhiSeeQFVVjh8/zo033oifnx/h4eEsXLiwymMajUYeffRRwsPD8fb25oYbbuD48eOVttuyZQvXXXcd/v7+eHl50adPHzZu3Gi1TUVOe/fu5Y477iAwMJCrrrqqxnM+cuQII0aMICgoCC8vLy6//HK+//57y+sVXZFVVWXx4sWWrpt1dckllxAcHMzRo0ctz/3666/06tULb29vAgICuPHGG9m3b5/VflWN2YyLi2PIkCFs2LCB7t274+HhQUJCAu+//77VfiNGjADg6quvtuRd0ZV369atDBw4kODgYDw9PYmPj2fChAkXPI+KY//888907twZDw8P2rVrx5dffllp25ycHKZNm0Z0dDR6vZ7ExESef/55TCaTZZuK99xLL73EokWLaNGiBXq9nr1799bqup4rPDwcwKpyWdWYTUVRmDJlCl9//TUdOnRAr9fTvn17fvrppzofUwghmjKpbAohhCA+Pp4xY8bwzjvvcOrUKZvGHjlyJCaTiQULFtCjRw+effZZFi1axIABA4iMjOT5558nMTGRmTNn8ttvv1Xa/7nnnuP777/n4YcfZurUqaxevZr+/ftbdX389ddf6d27N3l5ecyZM4d58+aRk5NDv379+PPPPyvFHDFiBEVFRcybN4+777672tzT09O54oorWLVqFffffz/PPfccJSUl3HDDDXz11VcA9O7dmw8++AD4t2tsxeO6yM7OJjs7m2bNmgHwyy+/MHDgQDIyMpg7dy4zZszgjz/+4Morr6xVt9fDhw9zyy23MGDAABYuXEhgYCDjxo1jz549lrynTp0KmG82VOTdtm1bMjIyuPbaa0lOTuaRRx7h9ddfZ9SoUWzevLlW53Lo0CFGjhzJoEGDmD9/PjqdjhEjRrB69WrLNkVFRfTp04f//e9/jBkzhtdee40rr7yS2bNnM2PGjEoxly5dyuuvv86kSZNYuHAhQUFBNeZQXl7OmTNnOHPmDCdOnODbb7/l5Zdfpnfv3sTHx1/wHDZs2MD999/PbbfdxgsvvEBJSQnDhw8nMzOzVtdACCEEoAohhGiyli5dqgLqX3/9pSYlJak6nU6dOnWq5fU+ffqo7du3tzw+evSoCqhLly6tFAtQ58yZY3k8Z84cFVAnTZpkec5gMKhRUVGqoijqggULLM9nZ2ernp6e6tixYy3PrV27VgXUyMhINS8vz/L8p59+qgLqq6++qqqqqppMJrVly5bqwIEDVZPJZNmuqKhIjY+PVwcMGFApp9tvv71W12fatGkqoP7++++W5/Lz89X4+Hg1Li5ONRqNVuc/efLkWsUF1IkTJ6qnT59WMzIy1C1btqjXXHONCqgLFy5UVVVVO3furIaGhqqZmZmW/Xbs2KFqNBp1zJgxlucqyvDo0aOW52JjY1VA/e233yzPZWRkqHq9Xn3wwQctz3322WcqoK5du9Yqv6+++sryvqirimN/8cUXludyc3PViIgItUuXLpbnnnnmGdXb21s9ePCg1f6PPPKIqtVq1WPHjqmq+u97zs/PT83IyKhTDuf/u/LKK9UzZ85YbVvxnjgXoLq7u6uHDx+2PLdjxw4VUF9//fXaXQghhBCqtGwKIYQAICEhgTvvvJP//ve/pKam2izuXXfdZfl/rVZLt27dUFWViRMnWp4PCAigdevWHDlypNL+Y8aMwdfX1/L4lltuISIigh9++AGA7du3c+jQIe644w4yMzMtrVmFhYVcc801/Pbbb1bdMgHuvffeWuX+ww8/0L17d6uutj4+PkyaNInk5OR6deWssGTJEkJCQggNDaVHjx5s3LiRGTNmMG3aNFJTU9m+fTvjxo2zasHr2LEjAwYMsJx7Tdq1a0evXr0sj0NCQqq9xuerGHv63XffUV5eXudza968OcOGDbM89vPzY8yYMWzbto20tDQAPvvsM3r16kVgYKClzM6cOUP//v0xGo2VWrmHDx9OSEhIrXPo0aMHq1evZvXq1Xz33Xc899xz7NmzhxtuuKFWEwL179+fFi1aWB537NgRPz+/Wl0/IYQQZjIiXgghhMXjjz/OBx98wIIFC3j11VdtEjMmJsbqsb+/Px4eHgQHB1d6vqouihUTuVRQFIXExERLV9JDhw4BMHbs2GpzyM3NJTAw0PK4Nt0oAVJSUujRo0el59u2bWt5vb5Lw9x4441MmTIFRVHw9fWlffv2lomKUlJSAGjdunWVx161atUFJzY6/7oDBAYGkp2dfcHc+vTpw/Dhw3nqqad45ZVX6Nu3LzfddBN33HEHer3+gvsnJiZWGgfZqlUrwDwGMzw8nEOHDrFz585qK5AZGRlWj2tbZhWCg4Pp37+/5fH1119P69atueWWW3j33Xf5z3/+U+P+Dbl+QgghzKSyKYQQwiIhIYHRo0fz3//+l0ceeaTS69VNfGM0GquNWdVMqdXNnqqqai0z/VdFq+WLL75I586dq9zGx8fH6rGnp2edj2NrUVFRVpUhW2vINVYUhc8//5zNmzfz7bffsmrVKiZMmMDChQvZvHlzpetZHyaTiQEDBvDQQw9V+XpF5bSCLcrsmmuuAeC33367YGXTlu9RIYRoqqSyKYQQwsrjjz/O//73P55//vlKr1W0Dubk5Fg9X9ESZw8VLZcVVFXl8OHDdOzYEcDS1dHPz8/mlbfY2FgOHDhQ6fn9+/dbXreHirjVHTs4ONgmy7VcaNbcyy+/nMsvv5znnnuOjz76iFGjRrFixQqrrtFVOXz4MKqqWsU/ePAgYJ6tFszlVlBQYNcK9/kMBgMABQUFjXZMIYRoymTMphBCCCstWrRg9OjRvP3225bxdRX8/PwIDg6uNJ7ujTfesFs+77//Pvn5+ZbHn3/+OampqZa1KC+99FJatGjBSy+9VGUl4vTp0/U+9uDBg/nzzz/ZtGmT5bnCwkL++9//EhcXR7t27eoduyYRERF07tyZ5cuXW1Xsd+/ezc8//8zgwYNtcpyKCuv5Nw+ys7MrteBVtBqXlpZeMO6pU6css/UC5OXl8f7779O5c2fL8iO33normzZtYtWqVZX2z8nJsVQMbenbb78FoFOnTjaPLYQQojJp2RRCCFHJY489xgcffMCBAwdo37691Wt33XUXCxYs4K677qJbt2789ttvllYrewgKCuKqq65i/PjxpKens2jRIhITEy1Llmg0Gt59910GDRpE+/btGT9+PJGRkZw8eZK1a9fi5+dnqWTU1SOPPMLHH3/MoEGDmDp1KkFBQSxfvpyjR4/yxRdfoNHY757tiy++yKBBg+jZsycTJ06kuLiY119/HX9/f6v1TBuic+fOaLVann/+eXJzc9Hr9fTr14+PPvqIN954g2HDhtGiRQvy8/N555138PPzq1VFt1WrVkycOJG//vqLsLAw3nvvPdLT01m6dKllm1mzZrFy5UqGDBnCuHHjuPTSSyksLGTXrl18/vnnJCcnVxrXWxcnT57kf//7HwBlZWXs2LGDt99+m+Dg4At2oRVCCGEbUtkUQghRSWJiIqNHj2b58uWVXnvyySc5ffo0n3/+OZ9++imDBg3ixx9/JDQ01C65PProo+zcuZP58+eTn5/PNddcwxtvvIGXl5dlm759+7Jp0yaeeeYZ/u///o+CggLCw8Pp0aMH99xzT72PHRYWxh9//MHDDz/M66+/TklJCR07duTbb7/l+uuvt8XpVat///789NNPzJkzhyeffBI3Nzf69OnD888/X+fJcqoTHh7OW2+9xfz585k4cSJGo5G1a9fSp08f/vzzT1asWEF6ejr+/v50796dDz/8sFbHbtmyJa+//jqzZs3iwIEDxMfH88knnzBw4EDLNl5eXqxfv5558+bx2Wef8f777+Pn50erVq146qmn8Pf3b9C5bd++nTvvvBMw35AIDg7m5ptv5plnniEyMrJBsYUQQtSOospIdyGEEELYSFxcHB06dOC7775zdCpCCCEcTMZsCiGEEEIIIYSwOalsCiGEEEIIIYSwOalsCiGEEEIIIYSwORmzKYQQQgghhBDC5qRlUwghhBBCCCGEzUllUwghhBBCCCGEzTW5dTZNJhOnTp3C19cXRVEcnY4QQgghhBBCuBRVVcnPz6d58+ZoNNW3Xza5yuapU6eIjo52dBpCCCGEEEII4dKOHz9OVFRUta83ucqmr68vYL4wfn5+Ds7mwsrLy/n555+59tprcXNzc3Q6ogZSVq5Bysk1SDm5Bikn1yDl5BqknFyDlJNZXl4e0dHRlrpVdZpcZbOi66yfn5/LVDa9vLzw8/Nr0m9oVyBl5RqknFyDlJNrkHJyDVJOrkHKyTVIOVm70LBEmSBICCGEEEIIIYTNSWVTCCGEEEIIIYTNSWVTCCGEEEIIIYTNOdWYzfnz5/Pll1+yf/9+PD09ueKKK3j++edp3bq1ZZuSkhIefPBBVqxYQWlpKQMHDuSNN94gLCzMZnmoqorBYMBoNNosZn2Vl5ej0+koKSlxinxE9aSsXIOtysnNzQ2tVmvDzIQQQgghLi5OVdlcv349kydP5rLLLsNgMPDoo49y7bXXsnfvXry9vQGYPn0633//PZ999hn+/v5MmTKFm2++mY0bN9okh7KyMlJTUykqKrJJvIZSVZXw8HCOHz8u64I6OSkr12CrclIUhaioKHx8fGyYnRBCCCHExcOpKps//fST1eNly5YRGhrK33//Te/evcnNzWXJkiV89NFH9OvXD4ClS5fStm1bNm/ezOWXX96g45tMJo4ePYpWq6V58+a4u7s7vNJgMpkoKCjAx8enxgVTheNJWbkGW5STqqqcPn2aEydO0LJlS2nhFEIIIYSoglNVNs+Xm5sLQFBQEAB///035eXl9O/f37JNmzZtiImJYdOmTVVWNktLSyktLbU8zsvLA8xd6crLyyttazQaiYyMxMvLy+bnUx+qqlJWVoZer3d4xVfUTMrKNdiqnJo1a0ZBQQHFxcXo9XobZigAy/fz+d/TwrlIObkGKSfXIOXkGqSczGp7/k5b2TSZTEybNo0rr7ySDh06AJCWloa7uzsBAQFW24aFhZGWllZlnPnz5/PUU09Vev7nn3+uVKHU6XSEh4dTVFSEwWCwzYnYSH5+vqNTELUkZeUaGlpOZWVlFBcXs379eqf7vriYrF692tEpiFqQcnINUk6uQcrJNTT1cqrtkEOnrWxOnjyZ3bt3s2HDhgbFmT17NjNmzLA8zsvLIzo6mmuvvRY/Pz+rbUtKSjh+/Dg+Pj54eHg06Li2oqoq+fn5+Pr6SmuZk2vSZZW2s/Jz4R0dd/wajm2rciopKcHT05PevXs7zffFxaS8vJzVq1czYMAAWTTbiUk5uQYpJ9cg5eQapJzMKnqLXohTVjanTJnCd999x2+//UZUVJTl+fDwcMrKysjJybFq3UxPTyc8PLzKWHq9vsoubm5ubpXeIEajEUVR0Gg0TjPmzmQyAVjyEs6raZeVWvmpRr0G5x2/hmPbqpw0Gg2KolT5XSJsR66va5Bycg1STq5Bysk1NPVyqu25O9UvYlVVmTJlCl999RW//vor8fHxVq9feumluLm5sWbNGstzBw4c4NixY/Ts2bOx0xVOpG/fvkybNq1O+8ydO5fOnTvbJZ/a6t27Nx999JFDc7hYrPtjK0pkV3Jyzd1jf/rpJzp37mypXAohhBBCiMblVC2bkydP5qOPPuKbb77B19fXMg7T398fT09P/P39mThxIjNmzCAoKAg/Pz/+85//0LNnzwbPRHsh69evt2v88/Xp06dO248bN47ly5dzzz338NZbb1m9NnnyZN544w3Gjh3LsmXLbJhl06MoCl999RU33XRTg2OtXLmS9PR0brvttoYn5qLWrVvH1VdfTXZ2dqWx2A113XXX8cQTT/Dhhx9y55132jS2EEIIIYS4MKdq2XzzzTfJzc2lb9++REREWP598sknlm1eeeUVhgwZwvDhw+nduzfh4eF8+eWXDszaeURHR7NixQqKi4stz5WUlPDRRx8RExPjwMxqp6yszNEpNKrXXnuN8ePHO32XW6PRWGXroCuU17hx43jttdccnYYQQgghRJPkVL9yVVWt8t+4ceMs23h4eLB48WKysrIoLCzkyy+/rHa8ZlPTtWtXoqOjrSrfX375JTExMXTp0sVqW5PJxPz584mPj8fT05NOnTrx+eefW143Go1MnDjR8nrr1q159dVXrWKsW7eO7t274+3tTUBAAFdeeSUpKSmA+Uf++a1/06ZNo2/fvpbHffv2ZcqUKUybNo3g4GAGDhwIwO7duxk0aBA+Pj6EhYVx5513cubMGct+hYWFjBkzBh8fHyIiIli4cGGtrs+CBQsICwvD19eXiRMnUlJSYvX6X3/9xYABAwgODsbf358+ffrwzz//WF6Pi4sDYNiwYSiKYnmclJTEjTfeSEREBFFRUfTo0YNffvmlxlxOnz7Nr7/+ytChQy3PJScnoygK27dvtzyXk5ODoiisW7cOMF9zRVFYs2YN3bp1w8vLiyuuuIIDBw5Yxf/222+57LLL8PDwIDg4mGHDhlley87OZsyYMQQGBuLl5cWgQYM4dOiQ5fVly5YREBDAypUradeuHXq9nmPHjhEXF8czzzzDmDFj8PPzY9KkSQBs2LCBXsMm4NmiJ9HdBjH1iRcoLPr3hkdpaSkPP/ww0dHR6PV6EhMTWbJkCcnJyVx99dUABAYGoiiK5bN+ofcnwA8//ECrVq3w9PTk6lsmkXz8VKXrPHToULZu3UpSUlKN5SGEEEIIIWzPqSqbouEmTJjA0qVLLY/fe+89xo8fX2m7+fPn8/777/PWW2+xZ88epk+fzujRoy3dhU0mE1FRUXz22Wfs3buXJ598kkcffZRPP/0UAIPBwE033USfPn3YuXMnmzZtYtKkSXWe3XP58uW4u7uzceNG3nrrLXJycujXrx9dunRh69at/PTTT6Snp3Prrbda9pk1axbr16/nm2++4eeff2bdunVWlcKqfPrpp8ydO5d58+axdetWIiIieOONN6y2yc/PZ+zYsWzYsIHNmzfTsmVLBg8ebFki46+//gJg6dKlpKamWh4XFBQwePBgVq9ezfr16xk4cCBDhw7l2LFj1eazYcMGvLy8aNu2bZ2uV4XHHnuMhQsXsnXrVnQ6HRMmTLC89v333zNs2DAGDx7Mtm3bWLNmDd27d7e8Pm7cOLZu3crKlSvZtGkTqqoyePBgq/WSioqKeP7553n33XfZs2cPoaGhALz00kt06tSJbdu28cQTT5CUlMR1113H8MHXsHP1J3zy5gI2/LmdKY8tsMQaM2YMH3/8Ma+99hr79u3j7bffxsfHh+joaL744gvAPPY6NTXVckPjQu/P48ePc/PNNzN06FC2b9/OXXfcxCPzX690nWJiYggLC+P333+v13UWQgghhBD151RjNkXDjR49mtmzZ1taGDdu3MiKFSssLWNgbmmaN28ev/zyi2VipYSEBDZs2MDbb79Nnz59cHNzs1qfND4+nk2bNvHpp59y6623kpeXR25uLkOGDKFFixYA9ao4tWzZkhdeeMHy+Nlnn6VLly7MmzfP8tx7771HdHQ0Bw8epHnz5ixZsoT//e9/XHPNNYC5wnrurMVVWbRoERMnTmTixImW4/zyyy9WrZv9+vWz2ue///0vAQEBrF+/niFDhhASEgJAQECAVWt6p06d6NSpEyaTiby8PJ5++mm+/vprVq5cyZQpU6rMJyUlhbCwsHp3oX3uuecs43ofeeQRrr/+ekpKSvDw8OC5557jtttusyq/Tp06AXDo0CFWrlzJxo0bueKKKwD48MMPiY6O5uuvv2bEiBGAeVrvN954w7LfudfowQcftDy+6667GDVqFNPuHgVAy4QYXntmFn2G382bJSUcO3aMTz/9lNWrV9O/f3/A/F6rEBQUBEBoaKhlzGZt3p9vvvkmLVq0sLRqt/YdzK79h3l+8bJK16p58+aWz4MQQgghhGg8Utm8yISEhHD99dezbNkyVFXl+uuvJzg42Gqbw4cPU1RUxIABA6yeLysrs+puu3jxYt577z2OHTtGcXExZWVlltlbg4KCGDduHAMHDmTAgAH079+fW2+9lYiIiDrle+mll1o93rFjB2vXrsXHx6fStklJSZY8evToYXk+KCiI1q1b13icffv2ce+991o917NnT9auXWt5nJ6ezuOPP866devIyMjAaDRSVFRUYwslmFs2586dy/fff8+pU6cwGo0UFxfXuF9xcXGD1mbs2PHfdSQrrnlGRgYxMTFs376du+++u8r99u3bh06ns7p+zZo1o3Xr1uzbt8/ynLu7u9UxKnTr1s3q8Y4dO9i5cycf/u8Dy3OqqmIymTh69Ci7du1Cq9XWacKr2rw/9+3bZ3UOAD0vrXptTU9Pz1ovPCyEEEIIIWxHKpsXoQkTJlha1BYvXlzp9YKCAsDc3TIyMtLqtYo1SVesWMHMmTNZuHAhPXv2xNfXlxdffJEtW7ZYtl26dClTp07lp59+4pNPPuHxxx9n9erVXH755Wg0GlTVeu3Dc7tpVvD29q6U29ChQ3n++ecrbRsREcHhw4drcwnqZezYsWRmZvLqq68SGxuLXq+nZ8+eF5wIZ+bMmaxevZoXXniB8PBwQkJCuPXWW2vcLzg4mOzsbKvnKlo5z71uVV0zsF7bqKLrcsUkPp6enjXmWxuenp5Vdomuqrzuuecepo68ptK2MS1a1Ku8avP+rIusrCxLq7QQNZrrX8VzuY2fhxC1Ie9XcbE4/70s7+OLilQ2L0LXXXcdZWVlKIpimXTnXOdO+lJdi1NFN8v777/f8lxVk6x06dKFLl26MHv2bHr27MlHH33E5ZdfTkhICLt377badvv27RdcALZr16588cUXxMXFodNVfnu2aNECNzc3tmzZYplhNzs7m4MHD9bYeta2bVu2bNnCmDFjLM9t3ry50jm/8cYbDB48GDCPCzx3YiIwV/KMRmOl/caNG8ewYcPIy8tDo9GQnJxc43l26dKFtLQ0srOzCQwMBLBUiFJTUy0teOdOFlRbHTt2ZM2aNVWO1W3bti0Gg4EtW7ZYutFmZmZy4MAB2rVrV+djde3alb1795IYX/lYuLtzySWXYDKZWL9+vaUbrfUm7gBW17Q278+2bduycuVKq+c2/7Or0nYlJSUkJSVVmiBLCCGEEELYn0wQdBHSarXs27ePvXv3otVqK73u6+vLzJkzmT59OsuXLycpKYl//vmH119/neXLlwPmsZRbt25l1apVHDx4kCeeeMIyIQ7A0aNHmT17Nps2bSIlJYWff/6ZQ4cOWcZt9uvXj61bt/L+++9z6NAh5syZU6nyWZXJkyeTlZXF7bffzl9//UVSUhKrVq1i/PjxGI1GfHx8mDhxIrNmzeLXX39l9+7djBs37oJjHx944AHee+89li5dysGDB5kzZw579uyx2qZly5Z88MEH7Nu3jy1btjBq1KhKrYRxcXGsWbPGUlGs2O/LL79k+/bt7Nq1i1GjRlW5VMi5unTpQnBwMBs3brQ85+npyeWXX86CBQvYt28f69ev5/HHH7/gNTvfnDlz+Pjjj5kzZw779u1j165dlpbili1bcuONN3L33XezYcMGduzYwejRo4mMjOTGG2+s87Eefvhh/vjjD6Y8toDtuw9w6Mgxvlm1zjJBUFxcHGPHjmXChAl8/fXXHD16lHXr1lkmmoqNjUVRFL777jtOnz5NQUFBrd6f9957L4cOHWLWrFkcOHCAj776kWWfflspv82bN1taqIUQQgghROOSls1aqsuYM2fg5+dX4+vPPPMMISEhzJ8/nyNHjhAQEEDXrl159NFHAbjnnnvYtm0bI0eORFEUbr/9du6//35+/PFHALy8vNi/fz/Lly8nMzOTiIgIJk+ezD333APAwIEDeeKJJ3jooYcoKSlhwoQJjBkzhl27Krc+nat58+Zs3LiRhx9+mGuvvZbS0lJiY2O57rrrLBXKF1980dLd1tfXlwcffJDc3Jq7XIwcOZKkpCRLPsOHD+e+++5j1apVlm2WLFnCpEmTLEvIzJs3j5kzZ1rFWbhwITNmzOCdd94hMjKS5ORkXn75ZSZMmMBVV11FUFAQjzzyiGUG2+potVrGjx/Phx9+yJAhQyzPv/fee0ycOJFLL72U1q1b88ILL3DttdfWGOt8ffv25bPPPuOZZ55hwYIF+Pn50bt3b8vrS5cu5YEHHmDIkCGUlZXRu3dvfvjhhwu2OlelY8eOrF+/nsdmTqXXzRNRVZUWsVGMvOHfnN98800effRR7r//fjIzM4mJibG8zyIjI3nqqad45JFHGD9+PGPGjGHZsmUXfH/GxMTwxRdfMH36dF5//XW6d27HvEcmM2HGU1b5ffzxx4waNQovL686n5sQQgghhGgYRT1/YN1FLi8vD39/f3JzcytVyEpKSjh69Cjx8fENmrzFlipmOPXz86v3zKWicdS1rNLS0mjfvj3//PMPsbGxjZChHZ3aVvm55o3YdfX84zfvwpkzZ2jdujVbt24lPj7e8pKtPlPO+H1xMSkvL+eHH35g8ODB9boRUi8yBq7OHFJOwqwO71cpJ9fQZMvJxcZsNtlyOk9NdapzSe1FCAcJDw9nyZIlF5ztVtRPcnIyb7zx+7FvcwAATThJREFUhlVFUwghhBBCNB7pRiuEA910002OTuGi1a1bt0pLtQghhBBCiMYjLZtCCCGEEEIIIWxOKptCCCGEEEIIIWxOKptCCCGEEEIIIWxOxmwK1+LoWU+F/UkZ258rzrrqYrMVOrWmfi2b+vnXVX2ul1xjIcRZ0rIphBBCCCGEEMLmpLIphBBCCCGEEMLmpLIphBBCCCGEEMLmZMxmLcU98n2jHi95wfWNejxX17dvXzp37syiRYtqvc/cuXP5+uuv2b59u93yupDevXtz7733cscddwCgKApfffVVtetvJicnEx8fz7Zt2+jcuXPjJdoExMXFMW3aNKZNm0ZZWRmtWrXi888/l7U6hRBCCCHqSVo2LxLjxo1DURTuvffeSq9NnjwZRVEYN25c4yd2kVEUha+//tomsVauXEl6ejq33XZbrfeJjo4mNTWVDh062CQHVxcXF1enGwy15e7uzsyZM3n44YdtHlsIIYQQoqmQyuZFJDo6mhUrVlBcXGx5rqSkhI8++oiYmBgHZlY7ZWVljk6hUb322muMHz8ejab2H0OtVkt4eDg6net0SigvL6/0nCuU9ahRo9iwYQN79uxxdCpCCCGEEC5JKpsXka5duxIdHc2XX35pee7LL78kJiaGLl2sl44wmUzMnz+f+Ph4PD096dSpE59//rnldaPRyMSJEy2vt27dmldffdUqxrp16+jevTve3t4EBARw5ZVXkpKSAphbWs/vCjpt2jT69u1redy3b1+mTJnCtGnTCA4OZuDAgQDs3r2bQYMG4ePjQ1hYGHfeeSdnzpyx7FdYVMyYqU/g0/JKIrpcy8KFC2t1fRYsWEBYWBi+vr5MnDiRkpISq9f/+usvBgwYQHBwMP7+/vTp04d//vnH8npcXBwAw4YNQ1EUy+OkpCRuvPFGIiIiiIqKokePHvzyyy815nL69Gl+/fVXhg4dWum11NRUBg0ahKenJwkJCVblkpycjKIolq6/DS2nqpw4cYLbb7+doKAgvL296datG1u2bLG8/uabb9KiRQvc3d1p3bo1H3zwgdX+SmRX3lz+GTfccAPe3t4899xzzJ07l86dO/Puu+8SHx+Ph4cHADk5Odx1112EhITg5+dHv3792LHnoFW8b39ez2WXXYaHhwfBwcEMGzYMML9/UlJSmD59OkpkV5TIrpZ9NmzYQK9evfD09CQ6OpqpU6dSWFhodf1vuOEGPD09iY+P58MPP6x0HQIDA7nyyitZsWJFtddKCCGEEEJUTyqbF5kJEyawdOlSy+P33nuP8ePHV9pu/vz5vP/++7z11lvs2bOH6dOnM3r0aNavXw+YK6NRUVF89tln7N27lyeffJJHH32UTz/9FACDwcBNN91Enz592LlzJ5s2bWLSpEkoilKnfJcvX467uzsbN27krbfeIicnh379+tGlSxe2bt3KTz/9RHp6Orfeeqtln1nPLGL95r/55r2X+fmjxaxbt86qUliVTz/9lLlz5zJv3jy2bt1KREQEb7zxhtU2+fn5jB07lg0bNrB582ZatmzJ4MGDyc/PB8yVUYClS5eSmppqeVxQUMDgwYNZvXo169evZ+DAgQwdOpRjx45Vm8+GDRvw8vKibdu2lV574oknGD58ODt27GDUqFHcdttt7Nu3r8o4ti6ngoIC+vTpw8mTJ1m5ciU7duzgoYcewmQyAfDVV1/xwAMP8OCDD7J7927uuecexo8fz9qNf1nFmfvy2wwbNoxdu3YxYcIEAA4fPswXX3zBl19+aaksjxgxgoyMDH788Uf+/vtvunbtyjUj7yUr27wm2/e//M6wu2YyePBgtm3bxpo1a+jevTtgvpESFRXF008/Teq2n0nd9jMAScnHue666xg+fDg7d+7kk08+YcOGDUyZMsWS3/3338+JEydYu3Ytn3/+OW+88QYZGRmVrkf37t35/fffqy5EIYQQQghRI9fpiydqZfTo0cyePdvScrVx40ZWrFjBunXrLNuUlpYyb948fvnlF3r27AlAQkICGzZs4O2336ZPnz64ubnx1FNPWfaJj49n06ZNfPrpp9x6663k5eWRm5vLkCFDaNGiBUCVFacLadmyJS+88ILl8bPPPkuXLl2YN2+e5bn33nuP6OhoDh48SHOliCUrvuZ/rz3LNb16AOYKa1RUVI3HWbRoERMnTmTixImW4/zyyy9WrZv9+vWz2ue///0vAQEBrF+/niFDhhASEgJAQEAA4eHhlu06depEp06dMJlM5OXl8fTTT/P111+zcuVKqwrOuVJSUggLC6uyC+2IESO46667AHjmmWdYvXo1r7/+eqXKMWDzcvroo484ffo0f/31F0FBQQAkJiZaXn/ppZcYN24c999/PwAzZsxg8+bNvPTWB1x95WWW7e646bpKNznKysp4//33Lddxw4YN/Pnnn2RkZKDX6y3xv/7iEz7//hcmjR7Oc68t4bYbr7U6x06dOgEQFBSEVqvF19eX8NBgy+vz/28po0aNYtq0aYD5Pfbaa6/Rp08f3nzzTZKTk/nll1/YvHkzPXqY30NLliyp8ro0b968xlZgIYQQQghRPalsuqJT26wfN/+3i2xISAjXX389y5YtQ1VVrr/+eoKDg602P3z4MEVFRQwYMMDq+bKyMqvutosXL+a9997j2LFjFBcXU1ZWRuf2reDUNoKAcbcOZeDAaxkw4Fr69+/PrbfeSkRERJ1O5dJLL7V6vGPHDtauXYuPj0+lbZOSkijW5VFWVk6Prv9OkBMUFETr1q2rP8ipbezbu5t7Rw4yX7uz16tnz56sXbvWsll6ejqPP/4469atIyMjA6PRSFFRUY0tlGBuDZw7dy7ff/89p06dwmg0UlxcXON+xcXFlq6k56u4AXDu45pmzK2ynM7OVBsUFMS4ceMYOHAgAwYMuGA5bd++nS5dulgqmufbt28fkyZNsnruyiuv5NWXX7R6rlundpX2jY2NtVQ0wVzWBQUFNGvWzGq74uJiklJOmPPZc5C7Rw2r9tyrsmPvQXbu+8Gqa6yqqphMJo4ePcr+/fvR6XRW7702bdoQEBBQKZanpydFRUV1Or5wInP9z3uc27SOL8TF7PzPF/z7GZPPXtWawnWx5TnW9B5zZCwXI5XNi9CECRMsLWqLFy+u9HpBQQEA33//PZGRkVavVbQwrVixgpkzZ7Jw4UJ69uyJr68vL774Ils2rLNsu/SVp5g68XZ++juZTz75hMcff5zVq1dz+eWXo9FoUFXVKnZVE8V4e3tXym3o0KE8//zzlbaNiIjg8B/f1eIK1M/YsWPJzMzk1VdfJTY2Fr1eT8+ePS84mc3MmTNZvXo1L7zwAuHh4YSEhHDrrbfWuF9wcDDZ2dkNzrnacjpnjOXSpUuZOnUqP/30U6VyOp+np2eDcwLw9qocp6qyjoiIsGp1ByB9DwH+vuZ8PPR1PnZBYRH33HMPU6dOrfRaTEwM+/fvr3WsrKwsqwqyEEIIIYSoPRmzeRG67rrrKCsro7y83DLpzrnatWuHXq/n2LFjJCYmWv2Ljo4GzN1vr7jiCu6//366dOlCYmIiSUlJlWJ16dCG2bNn88cff9ChQwc++ugjwNzCmpqaarVtbdaz7Nq1K3v27CEuLq5Sbt7e3rSIi8LNTceWf3Zb9snOzubgwYM1RIW2ifFs2bbL6rnNmzdbPd64cSNTp05l8ODBtG/fHr1ebzUxEZi7rRqNxkr7jRs3jmHDhtG+fXvCw8NJTk6uMZ8uXbqQlpZWZYXz/Lw2b95cbdfXWpdTly5VltP5OnbsyPbt28nKyqry9bZt27Jx48ZKObRrGV/l9jXp2rUraWlp6HQ667KOjyE4KNCcT9uWrNnwZ7Ux3N3dK5VH10vasnfv3krvn8TERNzd3WnTpg0Gg4G///7bss+BAwfIycmpFH/37t2VJtcSQgghhBC1I5XNi5BWq2Xfvn3s3bsXrVZb6XVfX19mzpzJ9OnTWb58OUlJSfzzzz+8/vrrLF++HDCPc9u6dSurVq3i4MGDPPHEE5YJcQCOHjvJ7Pmvs2nrDlJSUvj55585dOiQpVLUr18/tm7dyvvvv8+hQ4eYM2cOu3fvrpTL+SZPnkxWVha33347f/31F0lJSaxatYrx48djNBrx8fZi4m03MevZRfy64U927z/MuHHjLrh8yAMTb+e9T1ay9JNvOHjwIHPmzKm0pEXLli354IMP2LdvH1u2bGHUqFGVWvri4uJYs2aNVUWxZcuWlklvdu3axahRoywT6lSnS5cuBAcHV6q4AXz22We89957ljz//PPPasd+XrCcjh5l9uzZbNq0qcpyOt/tt99OeHg4N910Exs3buTIkSN88cUXbNq0CYBZs2axbNky3nzzTQ4dOsTLL7/Ml19+ycx7x9R4vlXp378/PXv25KabbuLnn38mOTmZP/74g8cW/B9bd+wFYM6MSXz89SrmzJnDvn372LVrl1Wrd1xcHL/99hsnUzM4k2Uuj4fvH8sff/zBlClT2L59O4cOHeKbb76xXMPWrVtzzTXXcN9997Flyxb+/vtv7rrrripbdX///XeuvfbaOp+bEEIIIYSQbrS1lrzgekenUCd+fn41vv7MM88QEhLC/PnzOXLkCAEBAXTt2pVHH30UgHvuuYdt27YxcuRIFEXh9ttv5/777+fHleZlVbw8Pdh/OJnln31LZnYeERERTJ48mXvuuQeAgQMH8sQTT/DQQw9RUlLChAkTGDNmDLt27ao2JzBPyLJx40Yefvhhrr32WkpLS4mNjeW6666zVChffGIaBYVFDB03DV8fbx6c9TC5uTX3ex9540CSUk7w0LOvUvLkQoYPH859993HqlWrLNssWbKESZMmWZaQmTdvHjNnzrSKs3DhQmbMmME777xDZGQkycnJvPzyy0yYMIGrrrqKoKAgHnnkEcsMttXRarWMHz+eDz/8kCFDhli99tRTT7FixQruv/9+IiIi+Pjjj2nXrvIYSKihnH78EQAvLy/279/P8uXLyczMrFRO53N3d+fnn3/mwQcfZPDgwRgMBtq1a2fpjn3TTTfx6quv8tJLL/HAAw8QHx/P0qVL6XtFhyrj1URRFH744Qcee+wxxo8fz+nTpwkPD6f3ZR0ICzaPGe17RTc+e/t5nln8IQsWLMDPz4/evXtbYjz99NPcc889tLjyBkpLy1BP/kPHdq1Yv349jz32GL169UJVVVq0aMHIkSMt+y1evJgZM2bQp08fwsLCePbZZ3niiSes8tu0aRO5ubnccsstdT43IYQQQggBinr+wLqLXF5eHv7+/uTm5laqkJWUlHD06FGrdQAdrWKGUz8/v39b72qYIMjuzj+2Kxy/ka5XlWVVg7S0NNq3b88///xDbGysXXJqNLZ8X9i5jGtbTiNHjqRTp06WGzDnc8bvi1pzgYkKysvL+eGHHxg8eDBubm71m/ShrvvY+ro462QcNsyrUjm5Amctl7qqw/u1QeXUGJ+9+rgIJwiy++fJWa+LPSf1scMEQfUqJxf4u1tXNdWpziXdaIVwkPDwcJYsWXLB2W5F4ysrK+OSSy5h+vTpjk5FCCGEEMJlNbiyuXz5cr7//nvL44ceeoiAgACuuOIKWZ9OiAu46aab6NWrl6PTEOdxd3fn8ccft9nsvEIIIYQQTVGDK5vz5s2z/CDbtGkTixcv5oUXXiA4OFhaBYQQQgghhBCiiWrwBEHHjx8nMTERgK+//prhw4czadIkrrzySvr27dvQ8EIIIYQQQgghXFCDK5s+Pj5kZmYSExPDzz//zIwZMwDw8PCguLi4wQmKOnDkxEH1Of7FMNlQffZpyDlWF8vRebmitJ3AOfOj2eP8bTkhQH1iOfr4TUFTv8bzo6DTf83/NZXIRDAVnHVSHWfVWO99R09EU90+Fc9rPP79PD2ZXv/cbMUVv5Maw/nfeyDXpQYNrmwOGDCAu+66iy5dunDw4EEGDx4MwJ49e4iLi2toeCGEEEIIIYQQLqjBYzYXL15Mz549OX36NF988QXNmjUD4O+//+b2229vcIJCCCGEEEIIIVxPg1s28/LyeO211yqtVzd37lyOHz/e0PBCCCGEEEIIIVxQg1s24+PjOXPmTKXns7KyiI+Pb2h4IYQQQgghhBAuqMEtm6qqVvl8QUEBHh4eDQ3vPKoaJG3X48lA48amRHblq6++4qabbiI5OZn4+Hi2bdtG586d6xXPEmPVx3Tu0Nq2yQohhBBCCOHk6t2yOWPGDGbMmIGiKDz55JOWxzNmzOCBBx5g5MiRdf6R/ttvvzF06FCaN2+Ooih8/fXXVq+PGzcORVGs/l133XX1PYWLyrhx41Aiu1r/UxQOHz5sef2mm26qdv/i4mLmzJlDq1at0Ov1BAcHM2LECPbs2WO13dyFb1nia6O7Ed1tEJMmTSIrK8tqu7ge17No0SLL4x07dnDDDTcQGhqKh4cHcXFxjLz3YTLOWO/nLKKjo0lNTaVDhw612n7cuHEMGzas6hhtWtgjRSGEEEIIIZxavVs2t20zL5ugqiq7du3C3d3d8pq7uzudOnVi5syZdYpZWFhIp06dmDBhAjfffHOV21x33XUsXbrU8liv19cj+4vTdVdfwdKX5/77RPglhISEXHC/0tJS+vfvz7Fjx1i4cCE9evQgPT2d+fPn06NHD3755Rcuv/xyy/btW7fglxVvYjSa2HfoKBMemkdubi6fvPJIlfFPnz7NNddcw5AhQ1i1ahUBAQEkJyez8qN3KSyy7fI45eXluLm5NTiOVqslPDzcNjFOpTY4HyGEEEIIIVxNvSuba9euBWD8+PG8+uqr+Pn5NTiZQYMGMWjQoBq30ev1Da4EXKz07u6Ehwb/+0Qtr9OiRYvYtGkT27Zto1OnTgDExsbyxRdf0KNHDyZOnMju3btRFAUAnVZrOU5kRCgjRow4ewOg6srmxo0byc3N5d1330WnM7/l4uPjubp1QI15xcXFMXHiRPbu3cvKlSsJCAjg0cl3MnncSMs2SmRX3njjDX788UfWrFnDrFmzmDt3Lt988w1PPfUUe/fupXlYMGNHDOGxqRMtxz906BAT75zIn9v3kBATyatPz7I6dlXdaPfs2cPDD0zlty3bUFWVzu1bseyVp/jgv9+wfPlyAAIDAwHz5yMuLq5SN9r1m/5m1vP3sGPHDoKCghg7dizPPvusJa++ffvSsWNHPDw8ePfdd3F3d+fee+9l7ty5FypGIYQQQgghnEqDx2ye28rYGNatW0doaCiBgYH069ePZ5991rLcSlVKS0spLS21PM7LywPMLWDl5eVW25aXl6OqKiaTCZPJZPVag2dSqqOK41eMia3Iy0w5f2NUVUUFTOe+ds45qKp6Xox/ffTRR/Tv359LLrmk0usPPPAAd955p6XSpZ6NX3Gc5OOnWLVqFe7u7tbHPifn0NBQDAYDX3zxBbfccoul0lrpPM7LGeDFF19k9uzZzJkzh59//pkHZswgMSGOAb3/bWmdO3cu8+bN4+WXX0an07F+/XrGjBnDokWL6NWrF0lbf+Heh55BReHJGfdgMhi4+eabCQvwYtO375ObX8CMOS9Zrvu55V/x/ydPnqR37970ubwzv3z6Nn4+Pmzcup0yg4kZM2awd+9e8vLyePXVV/Hx8aFZs2acOnXKcq1MKJxMzWDwnf9h7LjxLFu2jP3793PPPfeg1+uZM2eO5XyWL1/O9OnT2bRpE5s2bWLChAn07NmTAQMGVFn2tb2WlVUXqz7qc3xbxqr9uVg+U2gwcc52dczXdPZzV15ejlarrXojTRXj1s/73qm1+sSqbh9bxrKTiu9ny/f0+cevzbHruo+tr0tjHL8+6nMtq1F+NlbFfxsl33OP0xjvi/pwsvdrpc9TXTjZudRqn+qOb8uyt+V1Oe9zVK7xcI73pQt+J9k11tl4lb73anOcRv4b2hhq+32iqNXN8FNLhYWFLFiwgDVr1pCRkVGpsnLkyJF6xVUUxTJZS4UVK1bg5eVFfHw8SUlJPProo/j4+LBp06Zqf+zNnTuXp556qtLzH330EV5eXlbP6XQ6wsPDiY6OtuoWDBCwKLZe51FfOdNS6rT9/fffz6effmo1KVP//v1ZtmyZ5fXc3Fw+/PDDSvtGREQwbtw45s+fX+m1nTt30qdPH9577z2GDRvGggULePHFF/H09MRoNFJSUgLAc889x/3332/Zr2PHjtx3333cd999APx/e3ceH1V1/3/8PYGskEUgkER2AihCWIVvRFkECVQp1H5bwOWLSlEQvyCgIFr2tiwqVfyhtHUB+3W3ghULlC0gCAGUFAQEElFQCMiWEEJIYM7vD5yRycZk5k4mE17PxyMPM+fc8znn3DP3mg/3zp2ZM2dq/vz5ioyMVIcOHdStWzcNHjxYdevWLXVOSUlJatGihT788ENn2UMPPaSzZ8/qgw8+kHT5SuLIkSP1pz/9ybnNwIED1a1bN40bN85Z9t5772natGnau3ev1q5dq0GDBmnnzp2Kj4+XJK1evVq/+c1v9H//93+68847dejQIbVt21YbNmxQmzZtNGPGDH300Ufatm1bibfplrR/i8aYOXOmPvnkE6WlpTkT7ldffVXTp0/Xd999p6CgIN111126dOmSli9f7ozTq1cv3XbbbVzdrGQKCgp0+PBhZWVl6eLFi/4eDgAAQIXJy8vTPffco+zs7DLvcPX6yubvfvc7rV+/Xvfff7/i4+OvuGplvcGDBzt/b9OmjZKSktSsWTOlpqaqV69eJbaZNGmSS9KRk5OjBg0aqE+fPsV2TH5+vg4fPqyaNWv6/Um6jrEZY3T27FlFRkb+vG+zdrpuHJek4OBg9bilk16e9bSzuEbjjs44wcHBql69evE3Q9ZOSUbBhdmKyvtWiktyqa5Ro4YkKTw8XFFRUQotPKOWzRpp6RsvKP9Cgd766FOlHziiJ554QtVP7HG2CzIXFRYW5uzv2Wef1VNPPaW1a9dq69atWrx4sf487zml/uM1tbmxuctcnDGCgnTbbbe5jLlbu0S9+Opbl8f6k+TkZJdtdu/erbS0NM2bN+9ygbHrkt2u/PwLqn5yrw4dOqQGDRqoZfQF6ac4vVrXc5lnzZo1nfOPiorS3r171a1bN9Uu/EEq+g85P+1/x62wjrVyxjh/RFF5kfpmb7puaX+joqOjnfu+V9sGejI3VzmZaWp4fbyqV6+upKQkl/ldXyfy5wO5hLX/eR2Lj6tMpcXyhCf9WxmrHHNxHlN5h2S78spmOfvIv2gUHh6ubt26lX6+mFW/eNmk70uuc5SXxpNYpbUpK5Yv+7/auK5QWFioVatW6Y477rj8Dzzl3V9l9e/u9t62qYj+PWHhviyck6hVbebrjl2jFWzPd2+NrXy/Wvm+qKyx3N2+jDbFjqfy9F/J5uJWGyvn4m7/FrxfCoPCfj6eJmZ41k9l/f9Oefqp6HVxN9ZP8Yqd99zpp6LO7xXIcbfo1XidbC5fvlyffvqpunbt6m2ocmvatKnq1KmjjIyMUpPN0NDQEh8iFBwcXOyEe+nSJdlsNgUFBSkoqKJvnHXl6N9xpdgxrstM0Y0vJzcR4WrRpMHP5QnXO391PL23+LyMWjRpqK8PHFSQjFSkft++fZKkG264QUFBQbLJKCQ42NlP0tOjdefw32vmzJmaOdL1oU5F+4uNjdWgQYM0aNAgzZo1S+3b3Kh5C9/U4hdnuMylrBi2n+YedMU+iIyMdNkmNzdX06dP//khU8d+fqJuRGiIM2m/Mobjd8faO+I5fo+IiLg8lqL7/qcxX/mPLEXfQ0EyCpKR7aefK9fR8ZtjG+nyA7ZcxmazyRhHu+Jr74hV0rjKVlosT3jSv5Wx3J+L85iS3XU9y9lHkIxsNluJ55KfO8svXubYtmjd1R5s5Ums0tqUFcuX/V9tXCVw7t/y7q+y+nd3e2/bVET/nrByX/5UHmzPv/xHlztrbOX71QdzqXSx3N3ejTblOl9V8rlYek7yhA/fL8H2/J/XqSL+X2FlLCvWsqLXxd1YjnhFz3vu9FNR5/cK5O4DOb3OqK677jrVqlXL2zAe+f7773Xy5EnnrZDwzOABKVr9WZr+s3u/S7ndbtef//xntWrVyvngoJL8/ve/13PPPacjWT+63WdISIiaNap/1afRbtmyxfX1l7t0Y/MmZbbp0KGD9u3bp8TExMs/TRo6f4KCgnTjjTfq8OHDOnrs5/Fu+XJXmTGTkpL02WeflXp/ekhIiC5dulRmjBsTm2jzF7tcvpt207Z0Rdasofrx9cpsCwAAAAQar5PNmTNnasqUKcrLy/N6MLm5uUpPT1d6erok6eDBg0pPT9ehQ4eUm5urJ598Ulu2bNG3336rNWvWaMCAAUpMTFRKSorXfV8LsrOznfvX8XP4hyyNHX6vOre7Sf0feFwffPCBDh06pG3btunXv/619u7dq9dee63M26OTk5OVlJSkP730Won1y5Yt03333adly5Zp//792rdvn5577jn9a+0mDUjpXuaYN23apLlz52r//v1asGCBPli2WmOGDSmzzZQpU/Tmm29q+vTp2r17t/Ye+EbvfrxSv5+zQNLlz7K2aNFCQx+fqv/s3q/P0r7UMz/Vleaxxx5TTk6OBj86Sdv/s0cHvjmkv3+4TPsyvpV0+cm5u3bt0oEDB3TixIkSk9JHh/5Wh49k6X//93/19ddf6+OVqZr6/EKNe/hev19JBwAAAKzm9W20zz//vDIzM1WvXj01bty42CXVL7/80u1Y27dvV8+ePZ2vHZ+1HDp0qF555RXt3LlTixcv1pkzZ5SQkKA+ffpo5syZFfNdm9Oyfd+Hj6Wmpqp9+/YuZcOGDNSrz03R2g/+oj/Nf11PP/20vvvuO0VGRqpnz57asmWLWrdufdXYY8eO1QMPDNXERx9Qg+tdv3KlVatWioiI0Pjx43X48GGFhoaqefPmevXZybr/v+8qM+748eO1fft2TZ8+XVFRUZo3dZxSetxSZpuUlBQtW7ZMM2bM0Jw5cxRcvZpuSGys3w0ZKOnyrbFLlizRsPsHqfNd96tx/QTNn/mk+t77WKkxa9eurbVr1+rJ0Y+o+69/p2rVqqndTS3U9eZ2kqThw4dr3bp1uv3225Wbm+v86pMrXR9fV//6+0t6cs5f1bZtW9WKidKwIQP1+zG/K3M+AAAAQCDyOtm88mmx3urRo4fKejjuypUrLeurqlm0aJF0ZEeZ9Y4n07r4qU1EeLj+MHGU/vDiq2X2M238CE0bP6JY+eDBgzW4W0vn62/TPpUSLie2TZs21V//+tdS+y5LVFSU3n///VLbmB++dPZzpZSUlJ+veJfQT4sWLfTZktdLjdW4ceNi78WkpCStfPvlEscZGxurlStXKicnR1FRUc4rlcYYl/67J3fU1q1bSx1Xampqsbqlr88rcY4AAABAZeZ1snnl9wMCAAAAACBZ8JlNAAAAAACK8ujKZq1atbR//37VqVNH1113XZkPjzl16pTHg8O17dtvv/X3EAAAAAB4yKNk889//rMiIyMlSS+88IKV40FVVNJnM6vaZxCP7JBkkyIaS1k7JZmqN0ep+FpeC3OUrj7PadFFXgf+A8VQyZT2Hita7qgrrRzl5+996Un/Rds8c8K68VRFZe3j8p7f/f1+qUqs3JdWrnF5+mHtPUs2hw4dWuLvVUVZDykCAEniNAEAAFA2rx8QJEmXLl3S0qVLtXfvXknSTTfdpF/+8peqVq2aFeErjONrW/Ly8hQeHu7n0QCozArsl/8baOc5AACAiuJ1spmRkaFf/OIX+uGHH9Sy5eWvvpg1a5YaNGigTz/9VM2aNfN6kBWlWrVqiomJ0fHjxyVJERERZX4etSLY7XYVFBQoPz/f+XUauljkkkp+ftnlZSlvm6Lbu9N/aW3KiuVJ/+62udq4POn/opFdurxWF42CZDzfL+VpY+VcvHm/eNK/u324E6scc3EeU451KtrGjfer3Ug/Zucr4rraql7dkn+zAwAAqHK8/itp9OjRatasmbZs2aJatWpJkk6ePKn77rtPo0eP1qeffur1ICtSXFycJDkTTn8zxuj8+fMKDw//OfE986PrRucOll1elvK2Kbq9O/2X1qasWJ70726bq43Lk/7P/Cgjm86H2BVecFI2Gc/3S3naWDkXb94vnvTvbh/uxCrHXJzHlGOdirZx6/1qFHT+lBq26uT3f5ACAACorLxONtevX++SaEpS7dq1NXv2bHXt2tXb8BXOZrMpPj5edevWVWFhob+Ho8LCQm3YsEHdunVz3uar//cb140e2152eVnK26bo9u70X1qbsmJ50r+7ba42Lk/6/3+/UaEtVBtumKFuX09RsLng+X4pTxsr5+LN+8WT/t3tw51Y5ZiL85hyrFPRNu68X+2XFHL+uIJuv6fscQEAAFzDvE42Q0NDdfbs2WLlubm5CgkJ8Ta831SrVq1SfBarWrVqunjxosLCwn5ONnMPu24UFlZ2eVnK26bo9u70X1qbsmJ50r+7ba42Lk/6zz2sakFhl9fq3PcKtud7vl/K08bKuXjzfvGkf3f7cCdWOebiPKYc61S0TXnerwAAAChVkLcB7rrrLj388MNKS0uTMUbGGG3ZskUjRozQL3/5SyvGCAAAAAAIMF4nm/Pnz1ezZs2UnJyssLAwhYWFqWvXrkpMTNSLL75oxRgBAAAAAAHG69toY2Ji9PHHHysjI8P51Sc33nijEhMTvR4cAAAAACAweZxs2u12Pfvss/rnP/+pgoIC9erVS1OnTuX7KauKadFFXmfTf6Dy91xK69/f46qsrvX94ph/UJjU9q/ub+9SVsn3WWVd40Dcl4HG3/t4Vn3JXuSroSqyf3/PvyqprOeR0li99oE2/2uYx7fR/vGPf9TTTz+tmjVr6vrrr9eLL76oUaNGWTk2AAAAAECA8jjZfPPNN/Xyyy9r5cqVWrp0qT755BO99dZbstvtVo4PAAAAABCAPE42Dx06pF/84hfO171795bNZtORI0csGRgAAAAAIHB5nGw6vvvxSsHBwSosLPR6UAAAAACAwObxA4KMMXrggQcUGhrqLMvPz9eIESNUo0YNZ9lHH33k3QgBAAAAAAHH42Rz6NChxcruu+8+rwYDAAAAAKgaPE4233jjDSvHAQAAAACoQjz+zCYAAAAAAKUh2QQAAAAAWM7j22gRQKZFl1CWXfHj8LWi86yKc6wIlfn9whr73rWwjyvzexwAKtq1cN6vzKr4/ufKJgAAAADAch4lmx06dNDp06clSTNmzFBeXp6lgwIAAAAABDaPks29e/fq3LlzkqTp06crNzfX0kEBAAAAAAKbR5/ZbNeunR588EHdeuutMsboueeeU82aNUvcdsqUKV4NEAAAAAAQeDxKNhctWqSpU6dq2bJlstlsWr58uapXLx7KZrORbAIAAADANcijZLNly5Z69913JUlBQUFas2aN6tata+nAAAAAAACBy+uvPrHb7VaMAwAAAABQhVjyPZuZmZl64YUXtHfvXklSq1atNGbMGDVr1syK8AAAAACAAOP192yuXLlSrVq10tatW5WUlKSkpCSlpaXppptu0qpVq6wYIwAAAAAgwHh9ZfOpp57S2LFjNXv27GLlEydO1B133OFtFwDgmWnRxcueOVHx40DgK/pempbtn3HAPSUd+6yZtSpqH3PsAQHN6yube/fu1bBhw4qVP/TQQ9qzZ4+34QEAAAAAAcjrZDM2Nlbp6enFytPT03lCLQAAAABco7y+jXb48OF6+OGH9c033+iWW26RJG3atElz5szRuHHjvB4gAAAAACDweJ1sTp48WZGRkXr++ec1adIkSVJCQoKmTZum0aNHez1AAAAAAEDg8TrZtNlsGjt2rMaOHauzZ89KkiIjI70eGAAAAAAgcHn9mc0rRUZGepVobtiwQf3791dCQoJsNpuWLl3qUm+M0ZQpUxQfH6/w8HD17t1bBw4c8HLUAAAAAACrWZpseuvcuXNq27atFixYUGL93LlzNX/+fC1cuFBpaWmqUaOGUlJSlJ+fX8EjBQAAAACUxevbaK3Ur18/9evXr8Q6Y4xeeOEF/f73v9eAAQMkSW+++abq1aunpUuXavDgwRU5VAAAAABAGSpVslmWgwcPKisrS71793aWRUdHq0uXLtq8eXOpyeaFCxd04cIF5+ucnBxJUmFhoQoLC307aAs4xugy1qCwohuVr9yTNlbGctRZGas8bXw0l8Kf6hz/Dbi5+GK/+Kv/MmI5j6lr/P1qWazytClHLOfxxH7xrE0FzcXt8543/VeFdfHzfin1vOdJ/6yxz2K5HE8BPpdidVVhjX+qK3be81X/lZy7eZTNGGO86aRv375auHChmjdv7mmYEtlsNi1ZskQDBw6UJH3++efq2rWrjhw5ovj4eOd2v/3tb2Wz2fTee++VGGfatGmaPn16sfK3335bERERlo4ZAAAAAKq6vLw83XPPPcrOzlZUVFSp23l1ZTM4OFg7d+70JoTPTZo0yeX7PnNyctSgQQP16dOnzB1TWRQWFmrVqlW64447FBwcfLlwVn3XjSZ9X75yT9pYGctRZ2Ws8rTx0VwKg8K0qs183bFrtILt+YE3F1/sF3/1X0aswicOXj6mHOvkbv9VYb846gJgLs7jyXHuY7+Ur00FzaVwTqJ7570AmEvAxHLUlSNWqec9T/pnjX0Wy+XviIkZAT2XYnVVYY1/qit23vNV/5Wc427Rq/H6Ntr77rtPr732mmbPnu1tqDLFxcVJko4dO+ZyZfPYsWNq165dqe1CQ0MVGhparDw4OPjn5C0AuIy36P8oylvuSRsrYznqrIxVnjY+nkuwPf/yySfQ5uKL/eKv/t2I5Vwnd9tUhf3iqAuguTjPfeyX8rWp4Llc9bznTf9VYV0qyX4pdt7zpP9KMpdKty4Wxgq25/M3n1X9+3AuLseTL/qv5NzNo7xONi9evKjXX39dq1evVseOHVWjRg2X+nnz5nnbhSSpSZMmiouL05o1a5zJZU5OjtLS0jRy5EhL+gAAAAAAWMPrZPOrr75Shw4dJEn79+93qbPZbOWKlZubq4yMDOfrgwcPKj09XbVq1VLDhg31+OOP6w9/+IOaN2+uJk2aaPLkyUpISHB+rhMAAAAAUDl4nWyuW7fOinFIkrZv366ePXs6Xzs+azl06FAtWrRIEyZM0Llz5/Twww/rzJkzuvXWW7VixQqFhYVZNgYAAAAAgPcs++qTjIwMZWZmqlu3bgoPD5cxptxXNnv06KGyHo5rs9k0Y8YMzZgxw9vhAgAAAAB8KMjbACdPnlSvXr3UokUL/eIXv9DRo0clScOGDdP48eO9HiAAAAAAIPB4nWyOHTtWwcHBOnTokMv3Vg4aNEgrVqzwNjwAAAAAIAB5fRvtv//9b61cuVL167t+R0zz5s313XffeRseAAAAABCAvL6yee7cOZcrmg6nTp0q8fstAQAAAABVn9fJ5m233aY333zT+dpms8lut2vu3LkuT5YFAAAAAFw7vL6Ndu7cuerVq5e2b9+ugoICTZgwQbt379apU6e0adMmK8YIAAAAAAgwXl/ZbN26tfbv369bb71VAwYM0Llz53T33Xdrx44datasmRVjBAAAAAAEGEu+ZzM6OlrPPPOMFaEAAAAAAFWAJcnm6dOn9dprr2nv3r2SpFatWunBBx9UrVq1rAgPAAAAAAgwXt9Gu2HDBjVu3Fjz58/X6dOndfr0ac2fP19NmjTRhg0brBgjAAAAACDAeH1lc9SoURo0aJBeeeUVVatWTZJ06dIlPfrooxo1apR27drl9SABAAAAAIHF6yubGRkZGj9+vDPRlKRq1app3LhxysjI8DY8AAAAACAAeZ1sdujQwflZzSvt3btXbdu29TY8AAAAACAAeXQb7c6dO52/jx49WmPGjFFGRob+67/+S5K0ZcsWLViwQLNnz7ZmlAAAAACAgOJRstmuXTvZbDYZY5xlEyZMKLbdPffco0GDBnk+OgAAAABAQPIo2Tx48KDV4wAAAAAAVCEeJZuNGjWyehwAAAAAgCrE668+kaQjR45o48aNOn78uOx2u0vd6NGjregCAAAAABBAvE42Fy1apEceeUQhISGqXbu2bDabs85ms5FsAgAAAMA1yOtkc/LkyZoyZYomTZqkoCCvv0kFAAAAAFAFeJ0d5uXlafDgwSSaAAAAAAAnrzPEYcOG6YMPPrBiLAAAAACAKsLr22hnzZqlu+66SytWrFCbNm0UHBzsUj9v3jxvuwAAAAAABBhLks2VK1eqZcuWklTsAUEAAAAAgGuP18nm888/r9dff10PPPCABcMBAAAAAFQFXn9mMzQ0VF27drViLAAAAACAKsLrZHPMmDF66aWXrBgLAAAAAKCK8Po22q1bt2rt2rVatmyZbrrppmIPCProo4+87QIAAAAAEGC8TjZjYmJ09913WzEWAAAAAEAV4XWy+cYbb1gxDgAAAABAFeL1ZzYBAAAAACjK6yubTZo0KfP7NL/55htvuwAAAAAABBivk83HH3/c5XVhYaF27NihFStW6Mknn/Q2PAAAAAAgAHmdbI4ZM6bE8gULFmj79u3ehgcAAAAABCCffWazX79++sc//uGr8AAAAACASsxnyeaHH36oWrVq+So8AAAAAKAS8/o22vbt27s8IMgYo6ysLP344496+eWXvQ0PAAAAAAhAXiebAwcOdHkdFBSk2NhY9ejRQzfccIO34QEAAAAAAcjrZHPq1KlWjAMAAAAAUIX47DObvjJt2jTZbDaXH66gAgAAAEDl4vGVzaCgIJfPapbEZrPp4sWLnnZRqptuukmrV692vq5e3esLtAAAAAAAC3mcpS1ZsqTUus2bN2v+/Pmy2+2ehi9T9erVFRcX55PYAAAAAADveZxsDhgwoFjZvn379NRTT+mTTz7RvffeqxkzZng1uNIcOHBACQkJCgsLU3JysmbNmqWGDRuWuO2FCxd04cIF5+ucnBxJUmFhoQoLC30yPis5xugy1qCwohuVr9yTNlbGctRZGas8bXw0l8Kf6hz/Dbi5+GK/+Kv/MmI5j6lr/P1qWazytClHLOfxxH7xrE0FzcXt8543/VeFdfHzfin1vOdJ/6yxz2K5HE8BPpdidVVhjX+qK3be81X/lZy7eZTNGGO87ezIkSOaOnWqFi9erJSUFM2aNUutW7f2NmyJli9frtzcXLVs2VJHjx7V9OnT9cMPP+irr75SZGRkse2nTZum6dOnFyt/++23FRER4ZMxAgAAAEBVlZeXp3vuuUfZ2dmKiooqdTuvks3s7Gz96U9/0ksvvaR27dppzpw5uu222zwN55EzZ86oUaNGmjdvnoYNG1asvqQrmw0aNNCJEyfK3DGVRWFhoVatWqU77rhDwcHBlwtn1XfdaNL35Sv3pI2VsRx1VsYqTxsfzaUwKEyr2szXHbtGK9ieH3hz8cV+8Vf/ZcQqfOLg5WPKsU7u9l8V9oujLgDm4jyeHOc+9kv52lTQXArnJLp33guAuQRMLEddOWKVet7zpH/W2GexXP6OmJgR0HMpVlcV1vinumLnPV/1X8nl5OSoTp06V002Pb6Ndu7cuZozZ47i4uL0zjvvlHhbbUWIiYlRixYtlJGRUWJ9aGioQkNDi5UHBwf/nLwFAJfxFv0fRXnLPWljZSxHnZWxytPGx3MJtudfPvkE2lx8sV/81b8bsZzr5G6bqrBfHHUBNBfnuY/9Ur42FTyXq573vOm/KqxLJdkvxc57nvRfSeZS6dbFwljB9nz+5rOqfx/OxeV48kX/lZy7eZTHyeZTTz2l8PBwJSYmavHixVq8eHGJ23300UeeduGW3NxcZWZm6v777/dpPwAAAAAA93mcbP7P//zPVb/6xBeeeOIJ9e/fX40aNXJ+VrRatWoaMmRIhY8FAAAAAFAyj5PNRYsWWTgM933//fcaMmSITp48qdjYWN16663asmWLYmNj/TIeAAAAAEBxHieb/vLuu+/6ewgAAAAAgKsI8vcAAAAAAABVD8kmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMsFbLK5YMECNW7cWGFhYerSpYu2bt3q7yEBAAAAAH4SkMnme++9p3Hjxmnq1Kn68ssv1bZtW6WkpOj48eP+HhoAAAAAQAGabM6bN0/Dhw/Xgw8+qFatWmnhwoWKiIjQ66+/7u+hAQAAAAAkVff3AMqroKBAX3zxhSZNmuQsCwoKUu/evbV58+Zi21+4cEEXLlxwvs7OzpYknTp1SoWFhb4fsJcKCwuVl5enkydPKjg4+HJhQYjrRidPlq/ckzZWxnLUWRmrPG18NJfCoJDLa1UQomC7PfDm4ov94q/+y4hVePKk6zq5239V2C+OugCYi/N4cpz72C/la1NBcykscPO8FwBzCZhYjrpyxCr1vOdJ/6yxz2K5/B0R4HMpVlcV1vinumLnPV/1X8mdPXtWkmSMKXM7m7naFpXMkSNHdP311+vzzz9XcnKys3zChAlav3690tLSXLafNm2apk+fXtHDBAAAAIAq7fDhw6pfv36p9QF3ZbO8Jk2apHHjxjlf2+12nTp1SrVr11bnzp21bds2S/u7+eabLY2Zk5OjBg0a6PDhw4qKirIsrmT9WK/1mL5aq0CZf6DEZJ2sj+mLuNf6OvkqLuvEOlX2dfJV3ECIyd98gRGTdbocc+vWrTp79qwSEhLK3Dbgks06deqoWrVqOnbsmEv5sWPHFBcXV2z70NBQhYaGupTFxMRIkqpVq2b5m8QXMSUpKioqIMZ6Lcd0sHqtAmX+gRLTgXWyVqCc+671fco6sU6VfZ18FTdQYkr8zRcIMSXWKTo6WtHR0VfdNuAeEBQSEqKOHTtqzZo1zjK73a41a9a43FbrjlGjRlk9PJ/E9JVAmX+gxPSVQJl/oMT0lUCZv6/2aaCs1bW+T1kn6wXSWK12re/TQFknKXDmHygxfSVQ5l+emAH3mU3p8lefDB06VH/5y1/UuXNnvfDCC3r//ff19ddfq169ev4enqVycnIUHR2t7Oxsn13hgTVYq8DAOgUG1ikwsE6BgXUKDKxTYGCdyifgbqOVpEGDBunHH3/UlClTlJWVpXbt2mnFihVVLtGULt8GPHXq1GK3AqPyYa0CA+sUGFinwMA6BQbWKTCwToGBdSqfgLyyCQAAAACo3ALuM5sAAAAAgMqPZBMAAAAAYDmSTQAAAACA5Ug2AQAAAACWI9ms5BYsWKDGjRsrLCxMXbp00datW/09pGvatGnTZLPZXH5uuOEGZ31+fr5GjRql2rVrq2bNmvr1r3+tY8eO+XHE14YNGzaof//+SkhIkM1m09KlS13qjTGaMmWK4uPjFR4ert69e+vAgQMu25w6dUr33nuvoqKiFBMTo2HDhik3N7cCZ1H1XW2dHnjggWLHV9++fV22YZ18b9asWbr55psVGRmpunXrauDAgdq3b5/LNu6c6w4dOqQ777xTERERqlu3rp588kldvHixIqdSpbmzTj169Ch2TI0YMcJlG9bJt1555RUlJSUpKipKUVFRSk5O1vLly531HEuVw9XWiWPJcySbldh7772ncePGaerUqfryyy/Vtm1bpaSk6Pjx4/4e2jXtpptu0tGjR50/GzdudNaNHTtWn3zyiT744AOtX79eR44c0d133+3H0V4bzp07p7Zt22rBggUl1s+dO1fz58/XwoULlZaWpho1aiglJUX5+fnObe69917t3r1bq1at0rJly7RhwwY9/PDDFTWFa8LV1kmS+vbt63J8vfPOOy71rJPvrV+/XqNGjdKWLVu0atUqFRYWqk+fPjp37pxzm6ud6y5duqQ777xTBQUF+vzzz7V48WItWrRIU6ZM8ceUqiR31kmShg8f7nJMzZ0711nHOvle/fr1NXv2bH3xxRfavn27br/9dg0YMEC7d++WxLFUWVxtnSSOJY8ZVFqdO3c2o0aNcr6+dOmSSUhIMLNmzfLjqK5tU6dONW3bti2x7syZMyY4ONh88MEHzrK9e/caSWbz5s0VNEJIMkuWLHG+ttvtJi4uzjz77LPOsjNnzpjQ0FDzzjvvGGOM2bNnj5Fktm3b5txm+fLlxmazmR9++KHCxn4tKbpOxhgzdOhQM2DAgFLbsE7+cfz4cSPJrF+/3hjj3rnuX//6lwkKCjJZWVnObV555RUTFRVlLly4ULETuEYUXSdjjOnevbsZM2ZMqW1YJ/+47rrrzKuvvsqxVMk51skYjiVvcGWzkiooKNAXX3yh3r17O8uCgoLUu3dvbd682Y8jw4EDB5SQkKCmTZvq3nvv1aFDhyRJX3zxhQoLC13W7IYbblDDhg1ZMz86ePCgsrKyXNYlOjpaXbp0ca7L5s2bFRMTo06dOjm36d27t4KCgpSWllbhY76Wpaamqm7dumrZsqVGjhypkydPOutYJ//Izs6WJNWqVUuSe+e6zZs3q02bNqpXr55zm5SUFOXk5LhcKYB1iq6Tw1tvvaU6deqodevWmjRpkvLy8px1rFPFunTpkt59912dO3dOycnJHEuVVNF1cuBY8kx1fw8AJTtx4oQuXbrk8qaVpHr16unrr7/206jQpUsXLVq0SC1bttTRo0c1ffp03Xbbbfrqq6+UlZWlkJAQxcTEuLSpV6+esrKy/DNgOPd9SceSoy4rK0t169Z1qa9evbpq1arF2lWgvn376u6771aTJk2UmZmpp59+Wv369dPmzZtVrVo11skP7Ha7Hn/8cXXt2lWtW7eWJLfOdVlZWSUec446WKukdZKke+65R40aNVJCQoJ27typiRMnat++ffroo48ksU4VZdeuXUpOTlZ+fr5q1qypJUuWqFWrVkpPT+dYqkRKWyeJY8kbJJtAOfTr18/5e1JSkrp06aJGjRrp/fffV3h4uB9HBgS+wYMHO39v06aNkpKS1KxZM6WmpqpXr15+HNm1a9SoUfrqq69cPpuOyqe0dbry88xt2rRRfHy8evXqpczMTDVr1qyih3nNatmypdLT05Wdna0PP/xQQ4cO1fr16/09LBRR2jq1atWKY8kL3EZbSdWpU0fVqlUr9kSyY8eOKS4uzk+jQlExMTFq0aKFMjIyFBcXp4KCAp05c8ZlG9bMvxz7vqxjKS4urtiDty5evKhTp06xdn7UtGlT1alTRxkZGZJYp4r22GOPadmyZVq3bp3q16/vLHfnXBcXF1fiMeeog3VKW6eSdOnSRZJcjinWyfdCQkKUmJiojh07atasWWrbtq1efPFFjqVKprR1KgnHkvtINiupkJAQdezYUWvWrHGW2e12rVmzxuX+cfhXbm6uMjMzFR8fr44dOyo4ONhlzfbt26dDhw6xZn7UpEkTxcXFuaxLTk6O0tLSnOuSnJysM2fO6IsvvnBus3btWtntduf/UFDxvv/+e508eVLx8fGSWKeKYozRY489piVLlmjt2rVq0qSJS70757rk5GTt2rXL5R8HVq1apaioKOdtafDO1dapJOnp6ZLkckyxThXPbrfrwoULHEuVnGOdSsKxVA7+fkIRSvfuu++a0NBQs2jRIrNnzx7z8MMPm5iYGJcnXaFijR8/3qSmppqDBw+aTZs2md69e5s6deqY48ePG2OMGTFihGnYsKFZu3at2b59u0lOTjbJycl+HnXVd/bsWbNjxw6zY8cOI8nMmzfP7Nixw3z33XfGGGNmz55tYmJizMcff2x27txpBgwYYJo0aWLOnz/vjNG3b1/Tvn17k5aWZjZu3GiaN29uhgwZ4q8pVUllrdPZs2fNE088YTZv3mwOHjxoVq9ebTp06GCaN29u8vPznTFYJ98bOXKkiY6ONqmpqebo0aPOn7y8POc2VzvXXbx40bRu3dr06dPHpKenmxUrVpjY2FgzadIkf0ypSrraOmVkZJgZM2aY7du3m4MHD5qPP/7YNG3a1HTr1s0Zg3XyvaeeesqsX7/eHDx40OzcudM89dRTxmazmX//+9/GGI6lyqKsdeJY8g7JZiX30ksvmYYNG5qQkBDTuXNns2XLFn8P6Zo2aNAgEx8fb0JCQsz1119vBg0aZDIyMpz158+fN48++qi57rrrTEREhPnVr35ljh496scRXxvWrVtnJBX7GTp0qDHm8tefTJ482dSrV8+EhoaaXr16mX379rnEOHnypBkyZIipWbOmiYqKMg8++KA5e/asH2ZTdZW1Tnl5eaZPnz4mNjbWBAcHm0aNGpnhw4cX+8c11sn3SlojSeaNN95wbuPOue7bb781/fr1M+Hh4aZOnTpm/PjxprCwsIJnU3VdbZ0OHTpkunXrZmrVqmVCQ0NNYmKiefLJJ012drZLHNbJtx566CHTqFEjExISYmJjY02vXr2ciaYxHEuVRVnrxLHkHZsxxlTcdVQAAAAAwLWAz2wCAAAAACxHsgkAAAAAsBzJJgAAAADAciSbAAAAAADLkWwCAAAAACxHsgkAAAAAsBzJJgAAAADAciSbAAAAAADLkWwCAAJKamqqbDabzpw541WcBx54QAMHDrRkTFbGqsx9v/baa+rTp0+Fj2fFihVq166d7Ha7pXEBAL5FsgkA8IuFCxcqMjJSFy9edJbl5uYqODhYPXr0cNnWkWBmZmbqlltu0dGjRxUdHe3T8Tn6tNlsCgoKUnR0tNq3b68JEybo6NGjLtu++OKLWrRokU/H8+2338pmsyk9Pb3C+5ak/Px8TZ48WVOnTvV5X0X17dtXwcHBeuuttyq8bwCA50g2AQB+0bNnT+Xm5mr79u3Oss8++0xxcXFKS0tTfn6+s3zdunVq2LChmjVrppCQEMXFxclms1XIOPft26cjR45o27ZtmjhxolavXq3WrVtr165dzm2io6MVExNTaoyCggKfje9qfVvlww8/VFRUlLp27erzvkrywAMPaP78+X7pGwDgGZJNAIBftGzZUvHx8UpNTXWWpaamasCAAWrSpIm2bNniUt6zZ0/n71feRrto0SLFxMRo5cqVuvHGG1WzZk317dvX5erjpUuXNG7cOMXExKh27dqaMGGCjDFujbNu3bqKi4tTixYtNHjwYG3atEmxsbEaOXKkc5uit4726NFDjz32mB5//HHVqVNHKSkpkqSvvvpK/fr1U82aNVWvXj3df//9OnHihLOd3W7X3LlzlZiYqNDQUDVs2FB//OMfJUlNmjSRJLVv3142m8159bdo3xcuXNDo0aNVt25dhYWF6dZbb9W2bdtc9qXNZtOaNWvUqVMnRURE6JZbbtG+ffvK3A/vvvuu+vfv71Lmzn612+2aNWuWmjRpovDwcLVt21Yffvihyzb//Oc/1bx5c4WFhalnz55avHhxsVul+/fvr+3btyszM7PMcQIAKg+STQCA3/Ts2VPr1q1zvl63bp169Oih7t27O8vPnz+vtLQ0Z7JZkry8PD333HP6+9//rg0bNujQoUN64oknnPXPP/+8Fi1apNdff10bN27UqVOntGTJEo/GHB4erhEjRmjTpk06fvx4qdstXrxYISEh2rRpkxYuXKgzZ87o9ttvV/v27bV9+3atWLFCx44d029/+1tnm0mTJmn27NmaPHmy9uzZo7ffflv16tWTJG3dulWStHr1ah09elQfffRRif1OmDBB//jHP7R48WJ9+eWXSkxMVEpKik6dOuWy3TPPPKPnn39e27dvV/Xq1fXQQw+VOe+NGzeqU6dOLmXu7NdZs2bpzTff1MKFC7V7926NHTtW9913n9avXy9JOnjwoP77v/9bAwcO1H/+8x898sgjeuaZZ4r137BhQ9WrV0+fffZZmeMEAFQiBgAAP/nb3/5matSoYQoLC01OTo6pXr26OX78uHn77bdNt27djDHGrFmzxkgy3333nTHGmHXr1hlJ5vTp08YYY9544w0jyWRkZDjjLliwwNSrV8/5Oj4+3sydO9f5urCw0NSvX98MGDCg1LEV7edKy5cvN5JMWlqaMcaYoUOHusTq3r27ad++vUubmTNnmj59+riUHT582Egy+/btMzk5OSY0NNT87W9/K3E8Bw8eNJLMjh07XMqv7Ds3N9cEBwebt956y1lfUFBgEhISnPN3zGv16tXObT799FMjyZw/f77Evk+fPm0kmQ0bNriUX22/5ufnm4iICPP555+7tBs2bJgZMmSIMcaYiRMnmtatW7vUP/PMMyXu+/bt25tp06aVOEYAQOVT3U85LgAA6tGjh86dO6dt27bp9OnTatGihWJjY9W9e3c9+OCDys/PV2pqqpo2baqGDRuWGiciIkLNmjVzvo6Pj3dedczOztbRo0fVpUsXZ3316tXVqVMnt2+lLcrRrqzPjXbs2NHl9X/+8x+tW7dONWvWLLZtZmamzpw5owsXLqhXr14ejckRp7Cw0OVzlcHBwercubP27t3rsm1SUpLz9/j4eEnS8ePHS9zP58+flySFhYU5y9zZrxkZGcrLy9Mdd9zhEq+goEDt27eXdPkzsTfffLNLfefOnUucX3h4uPLy8kqZPQCgsiHZBAD4TWJiourXr69169bp9OnT6t69uyQpISFBDRo00Oeff65169bp9ttvLzNOcHCwy2ubzeZxIukOR+LWuHHjUrepUaOGy+vc3Fz1799fc+bMKbZtfHy8vvnmG0vHeDVX7jNH0lzaV4vUrl1bNptNp0+fLlcfubm5kqRPP/1U119/vUtdaGhouWJJ0qlTpxQbG1vudgAA/+AzmwAAv+rZs6dSU1OVmprq8pUn3bp10/Lly7V169YyP695NdHR0YqPj1daWpqz7OLFi/riiy88inf+/Hn99a9/Vbdu3cqV+HTo0EG7d+9W48aNlZiY6PJTo0YNNW/eXOHh4VqzZk2J7UNCQiRdfihPaRxP6920aZOzrLCwUNu2bVOrVq3cHmtJfbdq1Up79uxxlrmzX1u1aqXQ0FAdOnSo2JwbNGgg6fKDoq58IrEklwcaOeTn5yszM9N5RRQAUPmRbAIA/Kpnz57auHGj0tPTnVc2Jal79+76y1/+ooKCAq+STUkaM2aMZs+eraVLl+rrr7/Wo48+6vKk07IcP35cWVlZOnDggN5991117dpVJ06c0CuvvFKuMYwaNUqnTp3SkCFDtG3bNmVmZmrlypV68MEHdenSJYWFhWnixImaMGGC3nzzTWVmZmrLli167bXXJF1+Km54eLjzwULZ2dnF+qhRo4ZGjhypJ598UitWrNCePXs0fPhw5eXladiwYeUab1EpKSnauHGjS9nV9mtkZKSeeOIJjR07VosXL1ZmZqa+/PJLvfTSS1q8eLEk6ZFHHtHXX3+tiRMnav/+/Xr//fed3xt65W3KW7ZsUWhoqJKTk72aBwCg4nAbLQDAr3r27Knz58/rhhtucD55VbqcbJ49e9b5FSneGD9+vI4ePaqhQ4cqKChIDz30kH71q1+VmLAV1bJlS9lsNtWsWVNNmzZVnz59NG7cOMXFxZVrDAkJCdq0aZMmTpyoPn366MKFC2rUqJH69u2roKDL//Y7efJkVa9eXVOmTNGRI0cUHx+vESNGSLr8ecj58+drxowZmjJlim677TaXr41xmD17tux2u+6//36dPXtWnTp10sqVK3XdddeVa7xFDRs2TJ06dVJ2draio6MlubdfZ86cqdjYWM2aNUvffPONYmJi1KFDBz399NOSLn+ly4cffqjx48frxRdfVHJysp555hmNHDnS5Vbbd955R/fee68iIiK8mgcAoOLYjC8/1AIAAKqM3/zmN+rQoYMmTZrk037++Mc/auHChTp8+LAk6cSJE87bbR3fNwoAqPy4jRYAALjl2WefLfFput56+eWXtW3bNn3zzTf6+9//rmeffVZDhw511n/77bd6+eWXSTQBIMBwZRMAAPjV2LFj9d577+nUqVNq2LCh7r//fk2aNEnVq/NpHwAIZCSbAAAAAADLcRstAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACwHMkmAAAAAMByJJsAAAAAAMuRbAIAAAAALEeyCQAAAACw3P8H6XbzleWnBCAAAAAASUVORK5CYII=", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -3315,17 +3652,18 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "wd_bias_list: [ 0. 30. 44.9625 0. 0. 0. 0. ]\n", + "wd_bias_list: [ 2.50000e-04 1.49625e+01 -4.49625e+01 0.00000e+00 0.00000e+00\n", + " 0.00000e+00 2.50000e-04]\n", "Removing 0.00 deg bias for ti = 000.\n", - "Removing 30.00 deg bias for ti = 001.\n", - "Removing 44.96 deg bias for ti = 002.\n", + "Removing 14.96 deg bias for ti = 001.\n", + "Removing -44.96 deg bias for ti = 002.\n", "Removing 0.00 deg bias for ti = 003.\n", "Removing 0.00 deg bias for ti = 004.\n", "Removing 0.00 deg bias for ti = 005.\n", @@ -3368,7 +3706,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -3395,13 +3733,13 @@ "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mDeleting stored wind_data information.\u001b[0m\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", - "\u001b[32m2024-11-25 21:40:11\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 00 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 01 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 02 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 03 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 04 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 05 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n", + "\u001b[32m2024-12-02 11:23:51\u001b[0m Faulty measurements for WTG 06 increased from 0.000 % to 0.000 %. Reason: 'Turbine is impacted by faulty upstream turbine'.\n" ] }, { @@ -3474,22 +3812,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Wind direction')" + "Text(0, 0.5, 'Wind direction (deg)')" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -3499,30 +3837,59 @@ } ], "source": [ + "# Show the effects of homogenization\n", "# Show the wd channels for the turbines\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_000\"], label=\"wd_000\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_003\"], label=\"wd_003\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_004\"], label=\"wd_004\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_005\"], label=\"wd_005\", color=\"k\")\n", - "ax.plot(df_scada_homogenized[\"time\"], df_scada_homogenized[\"wd_006\"], label=\"wd_006\", color=\"k\")\n", "ax.plot(\n", " df_scada_homogenized[\"time\"],\n", " df_scada_homogenized[\"wd_001\"],\n", - " label=\"wd_001\",\n", + " label=\"wd_001 (Fixed Bias)\",\n", " color=\"blue\",\n", - " ls=\"--\",\n", ")\n", "ax.plot(\n", " df_scada_homogenized[\"time\"],\n", " df_scada_homogenized[\"wd_002\"],\n", - " label=\"wd_002\",\n", + " label=\"wd_002 (Bias Changes)\",\n", " color=\"red\",\n", - " ls=\"--\",\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_000\"],\n", + " label=\"wd_000\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_003\"],\n", + " label=\"wd_003\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_004\"],\n", + " label=\"wd_004\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_005\"],\n", + " label=\"wd_005\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", + ")\n", + "ax.plot(\n", + " df_scada_homogenized[\"time\"],\n", + " df_scada_homogenized[\"wd_006\"],\n", + " label=\"wd_006\",\n", + " color=\"k\",\n", + " alpha=0.5,\n", ")\n", "ax.legend()\n", "ax.set_xlabel(\"Time\")\n", - "ax.set_ylabel(\"Wind direction\")" + "ax.set_ylabel(\"Wind direction (deg)\")" ] } ], From c52e4bbcbe9ad84822ba1f449466c12c825b721f Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 2 Dec 2024 11:48:59 -0700 Subject: [PATCH 31/31] add ref --- .../04_northing_calibration.ipynb | 86 ++++++++++--------- 1 file changed, 44 insertions(+), 42 deletions(-) diff --git a/examples_smarteole/04_northing_calibration.ipynb b/examples_smarteole/04_northing_calibration.ipynb index 52dcd63f..5f54acc1 100644 --- a/examples_smarteole/04_northing_calibration.ipynb +++ b/examples_smarteole/04_northing_calibration.ipynb @@ -12,18 +12,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" - ] - } - ], + "outputs": [], "source": [ "import warnings as wn\n", "from pathlib import Path\n", @@ -49,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -115,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -148,41 +139,41 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-10-16 09:54:11\u001b[0m Matching curves for turbine 000...\n", - "\u001b[32m2024-10-16 09:54:15\u001b[0m T001 T002 T003 T004 T005\n", + "\u001b[32m2024-12-02 11:44:16\u001b[0m Matching curves for turbine 000...\n", + "\u001b[32m2024-12-02 11:44:18\u001b[0m T001 T002 T003 T004 T005\n", "0 0.0 0.0 0.0 0.0 0.0\n", - "\u001b[32m2024-10-16 09:54:15\u001b[0m Matching curves for turbine 001...\n", - "\u001b[32m2024-10-16 09:54:18\u001b[0m T002 T000 T003 T004 T005\n", + "\u001b[32m2024-12-02 11:44:18\u001b[0m Matching curves for turbine 001...\n", + "\u001b[32m2024-12-02 11:44:18\u001b[0m T002 T000 T003 T004 T005\n", "0 0.0 -0.0 0.0 0.0 0.0\n", - "\u001b[32m2024-10-16 09:54:18\u001b[0m Matching curves for turbine 002...\n", - "\u001b[32m2024-10-16 09:54:20\u001b[0m T001 T003 T000 T004 T005\n", + "\u001b[32m2024-12-02 11:44:18\u001b[0m Matching curves for turbine 002...\n", + "\u001b[32m2024-12-02 11:44:19\u001b[0m T001 T003 T000 T004 T005\n", "0 -0.0 0.0 -0.0 0.0 -2.0\n", - "\u001b[32m2024-10-16 09:54:20\u001b[0m Matching curves for turbine 003...\n", - "\u001b[32m2024-10-16 09:54:22\u001b[0m T004 T002 T005 T001 T006\n", + "\u001b[32m2024-12-02 11:44:19\u001b[0m Matching curves for turbine 003...\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m T004 T002 T005 T001 T006\n", "0 0.0 -0.0 0.0 -0.0 0.0\n", - "\u001b[32m2024-10-16 09:54:22\u001b[0m Matching curves for turbine 004...\n", - "\u001b[32m2024-10-16 09:54:24\u001b[0m T005 T003 T006 T002 T001\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m Matching curves for turbine 004...\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m T005 T003 T006 T002 T001\n", "0 -2.0 -0.0 -2.0 -0.0 -0.0\n", - "\u001b[32m2024-10-16 09:54:24\u001b[0m Matching curves for turbine 005...\n", - "\u001b[32m2024-10-16 09:54:25\u001b[0m T004 T006 T003 T002 T001\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m Matching curves for turbine 005...\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m T004 T006 T003 T002 T001\n", "0 2.0 0.0 -0.0 2.0 -0.0\n", - "\u001b[32m2024-10-16 09:54:25\u001b[0m Matching curves for turbine 006...\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m T005 T004 T003 T002 T001\n", + "\u001b[32m2024-12-02 11:44:20\u001b[0m Matching curves for turbine 006...\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m T005 T004 T003 T002 T001\n", "0 -0.0 2.0 -0.0 2.0 0.0\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", - "\u001b[32m2024-10-16 09:54:26\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 000 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 001 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 002 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 003 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 004 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 005 seems to have no jumps in its WD measurement calibration. [CLEAN]\n", + "\u001b[32m2024-12-02 11:44:21\u001b[0m Turbine 006 seems to have no jumps in its WD measurement calibration. [CLEAN]\n" ] }, { @@ -216,6 +207,19 @@ "Fortunately, the relative wind direction offsets between all turbines are zero. Therefore, we only need to identify a single offset for each turbine to complete the northing calibration." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the event of a detection of a jump in the relative wind direction, FLASC now includes a homeginization function that can be used to correct for this. See the example:\n", + "\n", + "```\n", + "examples_artificial_data/01_raw_data_processing/03_northing_calibration_hoger.ipynb\n", + "```\n", + "\n", + "for example usage. " + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -2199,11 +2203,9 @@ } ], "metadata": { - "interpreter": { - "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" - }, "kernelspec": { - "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", + "display_name": ".venv", + "language": "python", "name": "python3" }, "language_info": { @@ -2216,7 +2218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.13.0" }, "toc": { "base_numbering": 1,