From 4b28fab549e43766b770d0e7177c3f80a5a056f5 Mon Sep 17 00:00:00 2001 From: Paul Date: Mon, 25 Nov 2024 21:41:26 -0700 Subject: [PATCH] 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)