From a00a2be458b38db4cb46670868eb31604d08cd87 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 16 Feb 2024 13:53:05 +0000 Subject: [PATCH 01/20] Update subplot widths, test_plotting dataset access, remove square brackets from plots as per SI standard, quick plot for varying length signals, add pybop.is_numeric and restore design cost exception for optimisation --- pybop/__init__.py | 7 +++- pybop/_utils.py | 8 ++++ pybop/costs/design_costs.py | 24 +++++++++-- pybop/plotting/plot_cost2d.py | 4 +- pybop/plotting/plot_dataset.py | 13 +++--- pybop/plotting/plot_problem.py | 73 +++++++++++++++------------------- pybop/plotting/quick_plot.py | 24 +++++++++-- tests/unit/test_plotting.py | 28 ++++++------- 8 files changed, 108 insertions(+), 73 deletions(-) create mode 100644 pybop/_utils.py diff --git a/pybop/__init__.py b/pybop/__init__.py index 1fe3774f8..0aacb79f3 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -24,7 +24,12 @@ script_path = path.dirname(__file__) # -# Cost function class +# Utilities +# +from ._utils import is_numeric + +# +# Cost class # from .costs.base_cost import BaseCost from .costs.fitting_costs import ( diff --git a/pybop/_utils.py b/pybop/_utils.py new file mode 100644 index 000000000..6fbfeaab5 --- /dev/null +++ b/pybop/_utils.py @@ -0,0 +1,8 @@ +import numpy as np + + +def is_numeric(x): + """ + Check if a variable is numeric. + """ + return isinstance(x, (int, float, np.number)) diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 297697e39..710e63c2d 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,3 +1,4 @@ +import pybop import numpy as np import warnings @@ -29,7 +30,7 @@ def __init__(self, problem, update_capacity=False): problem : object The problem instance containing the model and data. """ - super().__init__(problem) + super(DesignCost, self).__init__(problem) self.problem = problem if update_capacity is True: nominal_capacity_warning = ( @@ -92,7 +93,7 @@ class GravimetricEnergyDensity(DesignCost): """ def __init__(self, problem, update_capacity=False): - super().__init__(problem, update_capacity) + super(GravimetricEnergyDensity, self).__init__(problem, update_capacity) def _evaluate(self, x, grad=None): """ @@ -110,6 +111,9 @@ def _evaluate(self, x, grad=None): float The negative gravimetric energy density or infinity in case of infeasible parameters. """ + if not all(pybop.is_numeric(i) for i in x): + raise ValueError("Input must be a numeric array.") + try: with warnings.catch_warnings(): # Convert UserWarning to an exception @@ -126,10 +130,16 @@ def _evaluate(self, x, grad=None): return negative_energy_density + # Catch infeasible solutions and return infinity except UserWarning as e: print(f"Ignoring this sample due to: {e}") return np.inf + # Catch any other exception and return infinity + except Exception as e: + print(f"An error occurred during the evaluation: {e}") + return np.inf + class VolumetricEnergyDensity(DesignCost): """ @@ -142,7 +152,7 @@ class VolumetricEnergyDensity(DesignCost): """ def __init__(self, problem, update_capacity=False): - super().__init__(problem, update_capacity) + super(VolumetricEnergyDensity, self).__init__(problem, update_capacity) def _evaluate(self, x, grad=None): """ @@ -160,6 +170,8 @@ def _evaluate(self, x, grad=None): float The negative volumetric energy density or infinity in case of infeasible parameters. """ + if not all(pybop.is_numeric(i) for i in x): + raise ValueError("Input must be a numeric array.") try: with warnings.catch_warnings(): # Convert UserWarning to an exception @@ -176,6 +188,12 @@ def _evaluate(self, x, grad=None): return negative_energy_density + # Catch infeasible solutions and return infinity except UserWarning as e: print(f"Ignoring this sample due to: {e}") return np.inf + + # Catch any other exception and return infinity + except Exception as e: + print(f"An error occurred during the evaluation: {e}") + return np.inf diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 54f0e86aa..c6cefe66a 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -98,5 +98,5 @@ def get_param_bounds(cost): for i, param in enumerate(cost.parameters): bounds[i] = param.bounds return bounds - - return None + else: + return None diff --git a/pybop/plotting/plot_dataset.py b/pybop/plotting/plot_dataset.py index edbe73d0e..459c31cc2 100644 --- a/pybop/plotting/plot_dataset.py +++ b/pybop/plotting/plot_dataset.py @@ -19,8 +19,8 @@ def plot_dataset( If True, the figure is shown upon creation (default: True). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- @@ -30,10 +30,9 @@ def plot_dataset( # Get data dictionary dataset.check(signal) - data = dataset.data # Compile ydata and labels or legend - y = [data[s] for s in signal] + y = [dataset[s] for s in signal] if len(signal) == 1: yaxis_title = signal[0] if trace_names is None: @@ -41,15 +40,15 @@ def plot_dataset( else: yaxis_title = "Output" if trace_names is None: - trace_names = signal + trace_names = pybop.StandardPlot.remove_brackets(signal) # Create the figure fig = pybop.plot_trajectories( - x=data["Time [s]"], + x=dataset["Time [s]"], y=y, trace_names=trace_names, show=False, - xaxis_title="Time [s]", + xaxis_title="Time / s", yaxis_title=yaxis_title, ) fig.update_layout(**layout_kwargs) diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index e0ab1a89b..032571387 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -19,8 +19,8 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): If True, the figure is shown upon creation (default: True). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- @@ -31,73 +31,62 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): parameter_values = problem.x0 # Extract the time data and evaluate the model's output and target values - time_data = problem.time_data() + reference_time_data = problem.time_data() model_output = problem.evaluate(parameter_values) target_output = problem.target() - # Ensure outputs have the same length - len_diff = len(target_output) - len(model_output) - if len_diff > 0: - model_output = np.concatenate( - (model_output, np.full([len_diff, np.shape(model_output)[1]], np.nan)), - axis=0, - ) - elif len_diff < 0: - target_output = np.concatenate( - (target_output, np.full([-len_diff, np.shape(target_output)[1]], np.nan)), - axis=0, - ) - # Create a plot for each output figure_list = [] for i in range(0, problem.n_outputs): default_layout_options = dict( - title="Scatter Plot", xaxis_title="Time [s]", yaxis_title=problem.signal[i] + title="Scatter Plot", + xaxis_title="Time / s", + yaxis_title=pybop.StandardPlot.remove_brackets(problem.signal[i]), ) # Create a plotting dictionary if isinstance(problem, pybop.DesignProblem): trace_name = "Optimised" + opt_time_data = model_output[:, -1] else: trace_name = "Model" + opt_time_data = reference_time_data + plot_dict = pybop.StandardPlot( - x=time_data, + x=opt_time_data, y=model_output[:, i], layout_options=default_layout_options, trace_names=trace_name, ) - # Add the data as markers - if isinstance(problem, pybop.DesignProblem): - name = "Initial" - else: - name = "Target" target_trace = plot_dict.create_trace( - x=time_data, + x=reference_time_data, y=target_output[:, i], - name=name, + name="Reference", mode="markers", showlegend=True, ) plot_dict.traces.append(target_trace) - # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) - - # Convert x and upper and lower limits into lists to create a filled trace - x = time_data.tolist() - y_upper = (model_output[:, i] + plot_dict.sigma).tolist() - y_lower = (model_output[:, i] - plot_dict.sigma).tolist() - fill_trace = plot_dict.create_trace( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - fillcolor="rgba(255,229,204,0.8)", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - ) - plot_dict.traces.append(fill_trace) + if isinstance(problem, pybop.FittingProblem): + # Compute the standard deviation as proxy for uncertainty + plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) + + # Convert x and upper and lower limits into lists to create a filled trace + x = reference_time_data.tolist() + y_upper = (model_output[:, i] + plot_dict.sigma).tolist() + y_lower = (model_output[:, i] - plot_dict.sigma).tolist() + + fill_trace = plot_dict.create_trace( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + fillcolor="rgba(255,229,204,0.8)", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + ) + plot_dict.traces.append(fill_trace) # Reverse the order of the traces to put the model on top plot_dict.traces = plot_dict.traces[::-1] diff --git a/pybop/plotting/quick_plot.py b/pybop/plotting/quick_plot.py index 1861041a0..d482c10ae 100644 --- a/pybop/plotting/quick_plot.py +++ b/pybop/plotting/quick_plot.py @@ -20,6 +20,7 @@ start_cell="bottom-left", ) DEFAULT_TRACE_OPTIONS = dict(line=dict(width=4), mode="lines") +DEFAULT_SUBPLOT_TRACE_OPTIONS = dict(line=dict(width=2), mode="lines") class StandardPlot: @@ -184,6 +185,23 @@ def wrap_text(text, width): wrapped_text = textwrap.fill(text, width=width, break_long_words=False) return wrapped_text.replace("\n", "
") + @staticmethod + def remove_brackets(s): + """ + Remove square brackets from a string and replace with forward slashes + as per section 7.1 of the SI Handbook + """ + # If s is an iterable (but not a string), apply the function recursively to each element + if hasattr(s, "__iter__") and not isinstance(s, str): + return type(s)(StandardPlot.remove_brackets(i) for i in s) + elif isinstance(s, str): + start = s.find("[") + end = s.find("]") + if start != -1 and end != -1: + char_in_brackets = s[start + 1 : end] + return s[:start] + " / " + char_in_brackets + s[end + 1 :] + return s + class StandardSubplot(StandardPlot): """ @@ -226,7 +244,7 @@ def __init__( layout=None, layout_options=DEFAULT_LAYOUT_OPTIONS, subplot_options=DEFAULT_SUBPLOT_OPTIONS, - trace_options=DEFAULT_TRACE_OPTIONS, + trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, trace_names=None, trace_name_width=40, ): @@ -297,8 +315,8 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): Name(s) for the trace(s) (default: None). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- diff --git a/tests/unit/test_plotting.py b/tests/unit/test_plotting.py index 474f44f6c..08cc8e256 100644 --- a/tests/unit/test_plotting.py +++ b/tests/unit/test_plotting.py @@ -134,41 +134,39 @@ def dataset(plotly_installed): @pytest.mark.unit def test_standard_plot(dataset, plotly_installed): # Check the StandardPlot class - pybop.StandardPlot(dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"]) + pybop.StandardPlot(dataset["Time [s]"], dataset["Terminal voltage [V]"]) # Check the StandardSubplot class pybop.StandardSubplot( - dataset.data["Time [s]"], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + dataset["Time [s]"], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_rows=1, ) pybop.StandardSubplot( - dataset.data["Time [s]"], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + dataset["Time [s]"], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_cols=1, ) # Check plotting numpy arrays, lists, and lists of numpy arrays + pybop.plot_trajectories(dataset["Time [s]"], dataset["Terminal voltage [V]"]) pybop.plot_trajectories( - dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"] + dataset["Time [s]"].tolist(), dataset["Terminal voltage [V]"].tolist() ) pybop.plot_trajectories( - dataset.data["Time [s]"].tolist(), dataset.data["Terminal voltage [V]"].tolist() + [dataset["Time [s]"]], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) pybop.plot_trajectories( - [dataset.data["Time [s]"]], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], - ) - pybop.plot_trajectories( - [dataset.data["Time [s]"], dataset.data["Time [s]"]], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + [dataset["Time [s]"], dataset["Time [s]"]], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) # Test incorrect dimensions with pytest.raises(ValueError): pybop.plot_trajectories( - [dataset.data["Time [s]"], dataset.data["Current [A]"]], - dataset.data["Terminal voltage [V]"], + [dataset["Time [s]"], dataset["Current [A]"]], + dataset["Terminal voltage [V]"], ) From 89e2932b58aaacc9de8648fe00703952cd39dc3d Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Feb 2024 09:12:12 +0000 Subject: [PATCH 02/20] Add plotting support for notebook rendering, adds kaleido as dependancy --- .../equivalent_circuit_identification.ipynb | 9183 +--------- examples/notebooks/spm_Adam.ipynb | 9281 +--------- examples/notebooks/spm_CMAES.ipynb | 10913 +----------- examples/notebooks/spm_electrode_design.ipynb | 14155 +--------------- .../spm_scipy_DifferentialEvolution.ipynb | 9187 +--------- pybop/plotting/plot_convergence.py | 9 +- pybop/plotting/plot_cost2d.py | 5 +- pybop/plotting/plot_dataset.py | 5 +- pybop/plotting/plot_parameters.py | 5 +- pybop/plotting/plot_problem.py | 5 +- pybop/plotting/plotly_manager.py | 4 +- pybop/plotting/quick_plot.py | 13 +- setup.py | 2 +- 13 files changed, 382 insertions(+), 52385 deletions(-) diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index ffde7e5c8..3362de471 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "dd0e1a20-1ba3-4ff5-8f6a-f9c6f25c2a4a", "metadata": {}, "outputs": [ @@ -24,27 +24,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", - "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", - "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", - "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", - "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", + "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", + "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", + "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", + "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (8.20.0)\n", + "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (5.14.1)\n", + "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (4.0.10)\n", + "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (3.0.10)\n", + "Requirement already satisfied: decorator in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", + "Requirement already satisfied: jedi>=0.16 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", + "Requirement already satisfied: matplotlib-inline in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", + "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n", + "Requirement already satisfied: pygments>=2.4.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", + "Requirement already satisfied: stack-data in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", + "Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", + "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", + "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "id": "d6afb8f9-3872-4a7e-a76d-0b50855fe089", "metadata": {}, "outputs": [], @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "id": "734d6d86-61e3-4125-bcea-e83b3235814b", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "id": "8d4a0635-51da-4998-8b48-deda13a49e39", "metadata": {}, "outputs": [], @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "id": "e84b6dd0-8f9e-4b68-b7cb-f3bcb9988802", "metadata": {}, "outputs": [], @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "id": "e75da7e3-8815-4159-a5ad-600a235b028c", "metadata": {}, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "id": "c346b106-99a9-46bc-8b5d-d330ed911660", "metadata": {}, "outputs": [], @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "id": "62369a4d-96e5-49d2-8951-4468b3fc5831", "metadata": {}, "outputs": [], @@ -268,17 +268,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "f69b34f5-0b46-4646-acbe-991046997b98", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.02481396482148223" + "0.024944621550803514" ] }, - "execution_count": 28, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "id": "6244882e-11ad-4bfe-a512-f1c687a06a08", "metadata": {}, "outputs": [ @@ -305,10 +305,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial parameters: [2.82225575e-04 1.72658962e-04 9.33921482e-05 1.06071260e+04\n", - " 4.57347160e+03]\n", - "Estimated parameters: [1.12930671e-03 6.47383206e-05 2.94920796e-04 1.06071280e+04\n", - " 4.57347320e+03]\n" + "Initial parameters: [2.29696565e-04 3.53341865e-05 1.63145688e-05 1.07259649e+04\n", + " 9.73352990e+03]\n", + "Estimated parameters: [8.44919651e-04 4.36094658e-04 1.99997447e-04 1.07259693e+04\n", + " 9.73353443e+03]\n" ] } ], @@ -332,4557 +332,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 15, "id": "2cec5659-31fa-4164-82f0-4467a4894729", "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898, - 898, - 896, - 894, - 892, - 890, - 888, - 886, - 884, - 882, - 880, - 878, - 876, - 874, - 872, - 870, - 868, - 866, - 864, - 862, - 860, - 858, - 856, - 854, - 852, - 850, - 848, - 846, - 844, - 842, - 840, - 838, - 836, - 834, - 832, - 830, - 828, - 826, - 824, - 822, - 820, - 818, - 816, - 814, - 812, - 810, - 808, - 806, - 804, - 802, - 800, - 798, - 796, - 794, - 792, - 790, - 788, - 786, - 784, - 782, - 780, - 778, - 776, - 774, - 772, - 770, - 768, - 766, - 764, - 762, - 760, - 758, - 756, - 754, - 752, - 750, - 748, - 746, - 744, - 742, - 740, - 738, - 736, - 734, - 732, - 730, - 728, - 726, - 724, - 722, - 720, - 718, - 716, - 714, - 712, - 710, - 708, - 706, - 704, - 702, - 700, - 698, - 696, - 694, - 692, - 690, - 688, - 686, - 684, - 682, - 680, - 678, - 676, - 674, - 672, - 670, - 668, - 666, - 664, - 662, - 660, - 658, - 656, - 654, - 652, - 650, - 648, - 646, - 644, - 642, - 640, - 638, - 636, - 634, - 632, - 630, - 628, - 626, - 624, - 622, - 620, - 618, - 616, - 614, - 612, - 610, - 608, - 606, - 604, - 602, - 600, - 598, - 596, - 594, - 592, - 590, - 588, - 586, - 584, - 582, - 580, - 578, - 576, - 574, - 572, - 570, - 568, - 566, - 564, - 562, - 560, - 558, - 556, - 554, - 552, - 550, - 548, - 546, - 544, - 542, - 540, - 538, - 536, - 534, - 532, - 530, - 528, - 526, - 524, - 522, - 520, - 518, - 516, - 514, - 512, - 510, - 508, - 506, - 504, - 502, - 500, - 498, - 496, - 494, - 492, - 490, - 488, - 486, - 484, - 482, - 480, - 478, - 476, - 474, - 472, - 470, - 468, - 466, - 464, - 462, - 460, - 458, - 456, - 454, - 452, - 450, - 448, - 446, - 444, - 442, - 440, - 438, - 436, - 434, - 432, - 430, - 428, - 426, - 424, - 422, - 420, - 418, - 416, - 414, - 412, - 410, - 408, - 406, - 404, - 402, - 400, - 398, - 396, - 394, - 392, - 390, - 388, - 386, - 384, - 382, - 380, - 378, - 376, - 374, - 372, - 370, - 368, - 366, - 364, - 362, - 360, - 358, - 356, - 354, - 352, - 350, - 348, - 346, - 344, - 342, - 340, - 338, - 336, - 334, - 332, - 330, - 328, - 326, - 324, - 322, - 320, - 318, - 316, - 314, - 312, - 310, - 308, - 306, - 304, - 302, - 300, - 298, - 296, - 294, - 292, - 290, - 288, - 286, - 284, - 282, - 280, - 278, - 276, - 274, - 272, - 270, - 268, - 266, - 264, - 262, - 260, - 258, - 256, - 254, - 252, - 250, - 248, - 246, - 244, - 242, - 240, - 238, - 236, - 234, - 232, - 230, - 228, - 226, - 224, - 222, - 220, - 218, - 216, - 214, - 212, - 210, - 208, - 206, - 204, - 202, - 200, - 198, - 196, - 194, - 192, - 190, - 188, - 186, - 184, - 182, - 180, - 178, - 176, - 174, - 172, - 170, - 168, - 166, - 164, - 162, - 160, - 158, - 156, - 154, - 152, - 150, - 148, - 146, - 144, - 142, - 140, - 138, - 136, - 134, - 132, - 130, - 128, - 126, - 124, - 122, - 120, - 118, - 116, - 114, - 112, - 110, - 108, - 106, - 104, - 102, - 100, - 98, - 96, - 94, - 92, - 90, - 88, - 86, - 84, - 82, - 80, - 78, - 76, - 74, - 72, - 70, - 68, - 66, - 64, - 62, - 60, - 58, - 56, - 54, - 52, - 50, - 48, - 46, - 44, - 42, - 40, - 38, - 36, - 34, - 32, - 30, - 28, - 26, - 24, - 22, - 20, - 18, - 16, - 14, - 12, - 10, - 8, - 6, - 4, - 2, - 0 - ], - "y": [ - 3.691877767098035, - 3.690982195694465, - 3.690450017858201, - 3.6899541033903858, - 3.689486536950672, - 3.6890422318257263, - 3.688618144754777, - 3.6882113470882616, - 3.687818212233097, - 3.687436200185412, - 3.6870637130473383, - 3.68669918915146, - 3.686341219722998, - 3.685988807409595, - 3.6856409085908823, - 3.685296906556755, - 3.684956049756571, - 3.68461788718519, - 3.6842819303526007, - 3.683954905717804, - 3.683629413859806, - 3.683305190892518, - 3.682982022606289, - 3.68265968244516, - 3.6823380367261462, - 3.682017018205116, - 3.681696482188532, - 3.68137629806354, - 3.681056510069224, - 3.6807370874546494, - 3.680417828287784, - 3.680098812894353, - 3.6797800519916386, - 3.679461391392775, - 3.679142865852717, - 3.6788244658367697, - 3.678506152496277, - 3.67819942283852, - 3.677892747776779, - 3.677586113456092, - 3.6772795061743873, - 3.676972921567925, - 3.676666356090868, - 3.6763598093524426, - 3.6760532583197354, - 3.6757466524685825, - 3.6754400296138896, - 3.6751333942476383, - 3.67482675442544, - 3.674520121766536, - 3.674213511453794, - 3.6739069422337134, - 3.6736004364164225, - 3.673294019875678, - 3.672987450718933, - 3.672700417528277, - 3.672413385659752, - 3.672126367495173, - 3.671839377505088, - 3.6715524322487822, - 3.67126555037427, - 3.670978752618302, - 3.670692061806362, - 3.670405502852667, - 3.670118656066053, - 3.6698318076880376, - 3.669544977823141, - 3.669258170484076, - 3.6689713900505567, - 3.668684641269291, - 3.66839792925399, - 3.668111259485358, - 3.667824637811104, - 3.6675674943712258, - 3.6673102726712377, - 3.667053056275163, - 3.6667958428245297, - 3.6665386297344535, - 3.666281414193631, - 3.6660241931643465, - 3.665766963382464, - 3.665509721357436, - 3.665252463372297, - 3.66499521288205, - 3.664737972634743, - 3.664480724580827, - 3.6642234672088914, - 3.663966198968389, - 3.663708918269638, - 3.6634516234838177, - 3.6631943129429714, - 3.662973134638159, - 3.662751937125, - 3.662530758055586, - 3.662309576807321, - 3.6620883900231753, - 3.6618671986085913, - 3.6616460035958185, - 3.6614248061439088, - 3.661203607538724, - 3.6609824091929295, - 3.660761212645996, - 3.6605400164284942, - 3.660318809052985, - 3.6600976018675304, - 3.659876395831076, - 3.659655191962363, - 3.6594339913399287, - 3.65921279510211, - 3.6590272669019126, - 3.658841745542392, - 3.658656232341276, - 3.65847071657728, - 3.658285193184237, - 3.658099671863124, - 3.657914152595753, - 3.6577286353285174, - 3.657543119972398, - 3.657357606402959, - 3.657172094460353, - 3.6569865839493145, - 3.6568010746391657, - 3.6566155620289993, - 3.6564300487311607, - 3.656244535551894, - 3.65605902241059, - 3.655873509226642, - 3.655716122219563, - 3.655558735008622, - 3.655401347513212, - 3.655243959652724, - 3.6550865713465495, - 3.654929182514081, - 3.6547717930747097, - 3.6546144029478262, - 3.654457012052825, - 3.6542996203090943, - 3.65414222763603, - 3.6539848368430774, - 3.6538274505238166, - 3.6536700641664783, - 3.653512677722884, - 3.6533552911448592, - 3.653197904384224, - 3.653040517392805, - 3.652900673933016, - 3.652760830146088, - 3.652620985983842, - 3.652481141398103, - 3.652341296340693, - 3.6522014507634353, - 3.6520616046181527, - 3.651921757856668, - 3.6517819104308047, - 3.6516420622923857, - 3.651502213393233, - 3.65136236368517, - 3.651222513120022, - 3.651082661649609, - 3.650942809225755, - 3.650802955800283, - 3.6506631013250166, - 3.650523245751778, - 3.6503916704524513, - 3.650260101089512, - 3.6501285316178738, - 3.6499969620323465, - 3.64986539232774, - 3.649733822498865, - 3.649602252540528, - 3.6494706824475425, - 3.649339112214717, - 3.64920754183686, - 3.6490759713087817, - 3.648944400625292, - 3.6488128297812015, - 3.648681258771319, - 3.6485496875904535, - 3.648418116233416, - 3.648286544695016, - 3.648154972970062, - 3.648025725590325, - 3.647896478013654, - 3.6477672302348583, - 3.647637982248748, - 3.6475087340501338, - 3.6473794856338255, - 3.6472502369946302, - 3.647120988589778, - 3.64699174169072, - 3.64686249480706, - 3.646733247943259, - 3.646604001103781, - 3.646474754293086, - 3.646345507515637, - 3.6462162607758937, - 3.64608701407832, - 3.645957767427376, - 3.645828520827525, - 3.645698544100115, - 3.6455685674327216, - 3.6454385908298046, - 3.645308614295827, - 3.645178637835251, - 3.6450486614525377, - 3.644918685152148, - 3.644788708938544, - 3.644658732816189, - 3.644528756789543, - 3.644398780863068, - 3.644268805041226, - 3.644138829328479, - 3.644008853729289, - 3.6438788781314138, - 3.643748901943241, - 3.643618925785819, - 3.6434889496599423, - 3.6433565455739023, - 3.643224141520996, - 3.643091737502015, - 3.642959333517755, - 3.6428269295690097, - 3.6426945256565713, - 3.642562121781236, - 3.642429717943796, - 3.6422973141450457, - 3.64216491038578, - 3.642032506666791, - 3.6419001029888745, - 3.641767699352823, - 3.6416352957594302, - 3.641502892209491, - 3.6413704887038, - 3.6412380852431494, - 3.641105681828334, - 3.640968839806274, - 3.640831997831637, - 3.6406951559052154, - 3.6405583140003177, - 3.640421471784328, - 3.6402846295736406, - 3.640147787367318, - 3.64001094516442, - 3.639874102964009, - 3.639737260765148, - 3.639600418566897, - 3.639463576368319, - 3.639326734168475, - 3.639189891966428, - 3.639053049761238, - 3.638916207551968, - 3.638779365337679, - 3.6386425231174337, - 3.6384978158791657, - 3.638353108633065, - 3.638208401378192, - 3.63806369411361, - 3.637918986838379, - 3.637774279551562, - 3.6376295722522207, - 3.637484864939417, - 3.637340157612212, - 3.637195450269668, - 3.6370507429108794, - 3.636906035656464, - 3.6367613283981615, - 3.636616621135579, - 3.6364719138683266, - 3.6363272065960137, - 3.636182499318247, - 3.6360377920346383, - 3.635880583095189, - 3.635723374149112, - 3.6355661651960194, - 3.6354089562355183, - 3.6352517472672186, - 3.635094538290729, - 3.634937329305657, - 3.634780120311613, - 3.634622911308205, - 3.634465702295043, - 3.6343084932717344, - 3.63415128423789, - 3.633994075193116, - 3.633836866137023, - 3.6336796570692207, - 3.633522447989317, - 3.633365238896919, - 3.6332080297916383, - 3.6330342193418454, - 3.632860408896045, - 3.6326865984515706, - 3.632512788009171, - 3.6323389775695936, - 3.632165167133584, - 3.6319913567018913, - 3.631817546275262, - 3.6316437358544422, - 3.6314699254401814, - 3.631296115033224, - 3.6311223046327714, - 3.6309484942342407, - 3.630774683836966, - 3.630600873440794, - 3.6304270630452384, - 3.630253252650741, - 3.630079442257303, - 3.629887688150684, - 3.629695934045124, - 3.629504179940624, - 3.62931242583516, - 3.6291206717289417, - 3.628928917623267, - 3.6287371635181342, - 3.6285454094135456, - 3.6283536553095, - 3.628161901205998, - 3.627970147103037, - 3.627778393000621, - 3.627586638898748, - 3.627394884797418, - 3.6272031306966306, - 3.6270113765963865, - 3.6268196224910056, - 3.626627868385305, - 3.626420470344012, - 3.626213072302932, - 3.6260056742620654, - 3.625798276221411, - 3.625590878180971, - 3.6253834801407425, - 3.6251760821007286, - 3.624968684060927, - 3.6247612860213385, - 3.624553887981964, - 3.6243464899428015, - 3.624139091903852, - 3.623931693865116, - 3.623724295826593, - 3.623516897788284, - 3.6233094997501865, - 3.623102101712303, - 3.622894703674632, - 3.622676402198626, - 3.622458100722834, - 3.6222397992472546, - 3.622021497771888, - 3.6218031962967343, - 3.621584894821795, - 3.621366593347068, - 3.6211482918725544, - 3.620929990398253, - 3.6207116889241657, - 3.6204933874502903, - 3.620275085976629, - 3.62005678450318, - 3.6198384830299446, - 3.619620181556922, - 3.619401880084113, - 3.619183578611216, - 3.618965277135141, - 3.618741042330089, - 3.618516807525056, - 3.6182925727200423, - 3.618068337915048, - 3.617844103110071, - 3.617619868305113, - 3.617395633500174, - 3.617171398695254, - 3.616947163890352, - 3.6167229290854697, - 3.6164986942806054, - 3.616274459475759, - 3.616050224670933, - 3.615825989866125, - 3.615601755061336, - 3.615377520256565, - 3.6151532854518127, - 3.614929050647079, - 3.614702407512002, - 3.6144757643769423, - 3.6142491212419015, - 3.61402247810688, - 3.6137958349718766, - 3.613569191836892, - 3.6133425487019264, - 3.613115905566979, - 3.612889262432051, - 3.612662619297141, - 3.6124359761622498, - 3.6122093330273777, - 3.611982689892524, - 3.611756046757689, - 3.611529403622873, - 3.611302760488076, - 3.6110761173532966, - 3.610849474218409, - 3.610621768305372, - 3.610394062392328, - 3.6101663564792745, - 3.6099386505662134, - 3.609710944653143, - 3.609483238740065, - 3.609255532826978, - 3.609027826913884, - 3.608800121000781, - 3.608572415087669, - 3.608344709174549, - 3.60811700326142, - 3.607889297348284, - 3.607661591435139, - 3.6074338855219854, - 3.607206179608824, - 3.6069784736956536, - 3.6067507677824753, - 3.6065206438480018, - 3.6062905199135193, - 3.6060603959790294, - 3.6058302720445305, - 3.605600148110024, - 3.605370024175509, - 3.6051399002409847, - 3.6049097763064526, - 3.604679652371912, - 3.604449528437363, - 3.6042194045028064, - 3.603989280568241, - 3.603759156633667, - 3.6035290326990848, - 3.6032989087644935, - 3.603068784829895, - 3.6028386608952876, - 3.602608536960672, - 3.6023710257866486, - 3.602133514612651, - 3.6018960034386502, - 3.601658492264648, - 3.601420981090643, - 3.6011834699166365, - 3.600945958742627, - 3.600708447568615, - 3.6004709363946015, - 3.6002334252205848, - 3.5999959140465663, - 3.599758402872545, - 3.599520891698522, - 3.5992833805244966, - 3.5990458693504688, - 3.5988083581764387, - 3.598570847002406, - 3.596550409511153, - 3.596787920685186, - 3.597025431859216, - 3.5972629430332437, - 3.5975004542072697, - 3.597737965381292, - 3.597975476555314, - 3.598212987729332, - 3.598450498903349, - 3.598688010077362, - 3.598925521251374, - 3.5991630324253836, - 3.59940054359939, - 3.5996380547733953, - 3.5998755659473973, - 3.600113077121398, - 3.6003505882953957, - 3.600588099469419, - 3.6008182234040347, - 3.601048347338642, - 3.6012784712732406, - 3.601508595207832, - 3.601738719142414, - 3.6019688430769876, - 3.6021989670115535, - 3.6024290909461105, - 3.602659214880659, - 3.6028893388152, - 3.603119462749732, - 3.603349586684255, - 3.603579710618771, - 3.6038098345532776, - 3.604039958487777, - 3.6042700824222664, - 3.604500206356749, - 3.6047303302912224, - 3.6049580362044007, - 3.605185742117571, - 3.605413448030733, - 3.605641153943886, - 3.605868859857031, - 3.606096565770167, - 3.606324271683296, - 3.606551977596416, - 3.606779683509528, - 3.607007389422631, - 3.6072350953357257, - 3.607462801248812, - 3.60769050716189, - 3.607918213074961, - 3.6081459189880216, - 3.608373624901075, - 3.6086013308141194, - 3.608829036727155, - 3.609055679862044, - 3.609282322996823, - 3.60950896613162, - 3.609735609266436, - 3.6099622524012713, - 3.610188895536124, - 3.610415538670997, - 3.610642181805888, - 3.610868824940798, - 3.611095468075726, - 3.6113221112106735, - 3.6115487543456393, - 3.611775397480624, - 3.612002040615627, - 3.612228683750649, - 3.61245532688569, - 3.612681970020749, - 3.6129086131558266, - 3.61313284796056, - 3.613357082765312, - 3.613581317570083, - 3.613805552374872, - 3.61402978717968, - 3.6142540219845065, - 3.614478256789353, - 3.6147024915942167, - 3.614926726399099, - 3.615150961204001, - 3.615375196008921, - 3.61559943081386, - 3.615823665618818, - 3.616047900423794, - 3.6162721352287894, - 3.6164963700338033, - 3.616720604838836, - 3.616944839643887, - 3.617163141119963, - 3.61738144259286, - 3.617599744065669, - 3.617818045538692, - 3.618036347011927, - 3.618254648485375, - 3.618472949959038, - 3.618691251432912, - 3.618909552907, - 3.6191278543813015, - 3.619346155855815, - 3.619564457330542, - 3.619782758805482, - 3.6200010602806354, - 3.6202193617560017, - 3.620437663231581, - 3.6206559647073737, - 3.620874266183379, - 3.62108166422105, - 3.6212890622589335, - 3.6214964602970303, - 3.62170385833534, - 3.621911256373863, - 3.622118654412599, - 3.6223260524515486, - 3.6225334504907103, - 3.6227408485300856, - 3.6229482465696745, - 3.623155644609476, - 3.6233630426494896, - 3.623570440689718, - 3.6237778387301582, - 3.623985236770813, - 3.624192634811679, - 3.624400032852759, - 3.624607430894052, - 3.6247991849997527, - 3.6249909391051336, - 3.6251826932053777, - 3.625374447306165, - 3.6255662014074943, - 3.625757955509368, - 3.625949709611784, - 3.626141463714745, - 3.6263332178182472, - 3.6265249719222927, - 3.6267167260268818, - 3.626908480132014, - 3.627100234237688, - 3.627291988343907, - 3.62748374244937, - 3.627675496553871, - 3.627867250659431, - 3.6280590047660506, - 3.628232815159488, - 3.6284066255539855, - 3.6285804359495417, - 3.628754246345713, - 3.628928056742988, - 3.6291018671415185, - 3.629275677541971, - 3.629449487948929, - 3.6296232983631898, - 3.6297971087840097, - 3.6299709192106384, - 3.630144729642331, - 3.6303185400783407, - 3.6304923505179185, - 3.6306661609603177, - 3.630839971404792, - 3.631013781850593, - 3.631187592300386, - 3.631344801405666, - 3.631502010498063, - 3.631659219577968, - 3.6318164286457706, - 3.631973637701863, - 3.632130846746637, - 3.6322880557804815, - 3.6324452648037906, - 3.632602473816952, - 3.63275968282036, - 3.632916891814404, - 3.633074100799475, - 3.6332313097759656, - 3.633388518744266, - 3.6335457277047665, - 3.6337029366578593, - 3.633860145603935, - 3.634017354543386, - 3.6341620618269945, - 3.634306769104761, - 3.6344514763770737, - 3.6345961836443266, - 3.634740890906909, - 3.6348855981652113, - 3.6350303054196265, - 3.635175012778415, - 3.6353197201209593, - 3.635464427448164, - 3.635609134760968, - 3.6357538420603097, - 3.635898549347126, - 3.636043256622357, - 3.636187963886939, - 3.636332671141812, - 3.6364773783879127, - 3.636622085626181, - 3.6367589278464263, - 3.636895770060715, - 3.637032612269985, - 3.637169454475175, - 3.637306296677222, - 3.637443138877066, - 3.637579981075644, - 3.637716823273895, - 3.637853665472756, - 3.637990507673167, - 3.638127349876065, - 3.638264192082388, - 3.638401034293075, - 3.638537876509064, - 3.6386747184139625, - 3.638811560340383, - 3.638948402315021, - 3.639085244337081, - 3.639217647751897, - 3.6393500512125474, - 3.6394824547182383, - 3.6396148582681778, - 3.63974726186157, - 3.6398796654976215, - 3.640012069175538, - 3.640144472894527, - 3.6402768766537927, - 3.640409280452543, - 3.640541684289983, - 3.6406740881653183, - 3.6408064920777568, - 3.6409388960265026, - 3.641071300010762, - 3.641203704029743, - 3.64133610808265, - 3.64146851216869, - 3.6415984882945662, - 3.641728464451988, - 3.641858440640161, - 3.641988416238036, - 3.642118391837226, - 3.642248367549973, - 3.6423783433718153, - 3.64250831929829, - 3.642638295324936, - 3.642768271447291, - 3.642898247660895, - 3.643028223961284, - 3.643158200343998, - 3.6432881768045746, - 3.643418153338552, - 3.6435481299414687, - 3.643678106608862, - 3.643808083336272, - 3.643937329936123, - 3.644066576587067, - 3.6441958232846408, - 3.644325070024383, - 3.6444543168018337, - 3.644583563612528, - 3.6447128104520066, - 3.6448420573158073, - 3.644971304199467, - 3.6451005510985257, - 3.6452297995033778, - 3.6453590481425726, - 3.645488296558881, - 3.645617544757495, - 3.645746792743606, - 3.6458760405224, - 3.646005288099072, - 3.6461345354788097, - 3.646266107203763, - 3.6463976787421633, - 3.6465292500992006, - 3.646660821280066, - 3.646792392289949, - 3.646923963134039, - 3.647055533817529, - 3.647187104345607, - 3.647318674723464, - 3.6474502449562896, - 3.6475818150492754, - 3.647713385007611, - 3.6478449548364873, - 3.6479765245410936, - 3.648108094126621, - 3.6482396635982592, - 3.6483712329611984, - 3.648502808260525, - 3.648642663833764, - 3.6487825183090306, - 3.6489223717345025, - 3.649062224158356, - 3.6492020756287697, - 3.6493419261939177, - 3.64948177590198, - 3.649621624801133, - 3.649761472939552, - 3.649901320365415, - 3.6500411671269, - 3.6501810132721824, - 3.65032085884944, - 3.65046070390685, - 3.650600548492589, - 3.6507403926548343, - 3.6508802364417634, - 3.651020079901552, - 3.651177466892971, - 3.6513348536536063, - 3.651492240231631, - 3.6516496266752254, - 3.651807013032564, - 3.651964399351825, - 3.652121790144777, - 3.652279182817842, - 3.652436574561572, - 3.6525939654565738, - 3.6527513555834568, - 3.652908745022828, - 3.653066133855297, - 3.653223522161471, - 3.6533809100219594, - 3.653538297517369, - 3.65369568472831, - 3.653853071735389, - 3.6540385849193378, - 3.654224098060641, - 3.654409611239908, - 3.6545951245377464, - 3.654780637147912, - 3.6549661464580616, - 3.655151656969099, - 3.6553371689117062, - 3.655522682481144, - 3.655708197837265, - 3.6558937151045, - 3.6560792343718713, - 3.656264755692984, - 3.656450279086027, - 3.656635794850023, - 3.6568213080511383, - 3.65700682941066, - 3.657192357610857, - 3.657413553848676, - 3.65763475447111, - 3.657855958339823, - 3.6580771643762775, - 3.658298371561732, - 3.658519578937242, - 3.658740775154743, - 3.6589619717016766, - 3.6591831700474713, - 3.659404368652656, - 3.6596255661045656, - 3.6598467611173384, - 3.6600679525319224, - 3.660289139316068, - 3.660510320564333, - 3.660731499633747, - 3.660952697146906, - 3.6611738754517185, - 3.661431185992564, - 3.661688480778385, - 3.661945761477136, - 3.6622030297176384, - 3.662460287089574, - 3.66271753514349, - 3.662974775390797, - 3.663232025881044, - 3.663489283866183, - 3.663746525891211, - 3.6640037556730936, - 3.6642609767023786, - 3.6645181922432006, - 3.664775405333277, - 3.66503261878391, - 3.6652898351799847, - 3.665547056879973, - 3.665804200319851, - 3.6660908219941057, - 3.666377491762737, - 3.666664203778038, - 3.666950952559304, - 3.6672377329928234, - 3.667524540331888, - 3.6678113701967847, - 3.6680982185748, - 3.668385065361414, - 3.668671624315109, - 3.6689583151270497, - 3.6692451128830177, - 3.66953199475753, - 3.669818940013835, - 3.67010593000392, - 3.670392948168499, - 3.670679980037024, - 3.67096701322768, - 3.671273582384425, - 3.6715799989251696, - 3.671886504742461, - 3.672193073962541, - 3.672499684275283, - 3.6728063169341874, - 3.6731129567563854, - 3.6734195921226367, - 3.6737262149773295, - 3.6740328208284825, - 3.6743393718611896, - 3.674645918599615, - 3.674952484076672, - 3.6752590686831343, - 3.675565675964839, - 3.675872310285526, - 3.676178985347267, - 3.676485715005024, - 3.676804028345516, - 3.677122428361464, - 3.677440953901522, - 3.6777596145003857, - 3.6780783754031, - 3.678397390796531, - 3.678716649963397, - 3.6790360725779703, - 3.6793558605722874, - 3.6796760446972794, - 3.6799965807138633, - 3.6803175992348938, - 3.680639244953907, - 3.680961585115036, - 3.681284753401265, - 3.681608976368553, - 3.6819344682265513, - 3.682261492861348, - 3.682597449693937, - 3.682935612265318, - 3.683276469065502, - 3.6836204710996294, - 3.683968369918342, - 3.684320782231745, - 3.684678751660207, - 3.685043275556086, - 3.685415762694159, - 3.685797774741844, - 3.6861909095970087, - 3.686597707263523, - 3.687021794334474, - 3.687466099459419, - 3.687933665899133, - 3.688429580366947, - 3.688961758203212, - 3.6898573296067823 - ] - }, - { - "mode": "markers", - "name": "Target", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 3.6908279824380976, - 3.690788790743272, - 3.6891115305760143, - 3.69010930651636, - 3.6876304640946462, - 3.689240220946332, - 3.6869905425355327, - 3.6861441181397465, - 3.6858523467856505, - 3.686019799529597, - 3.6851573367995454, - 3.6866270977686337, - 3.6843274165167545, - 3.685954428452293, - 3.684011368718368, - 3.6841226900358097, - 3.684814640509044, - 3.6825752625485126, - 3.6849811758051487, - 3.682726994927077, - 3.682055733145114, - 3.6815725160310495, - 3.6830921248664503, - 3.681607913914868, - 3.680721591283853, - 3.681653176625066, - 3.6787194957710834, - 3.681494728802438, - 3.681104541058693, - 3.6806912335952298, - 3.6798686797773903, - 3.679752355170941, - 3.675898294218387, - 3.677669959519552, - 3.6790124816648966, - 3.678080487689376, - 3.676811985563561, - 3.676126744841999, - 3.678692717922791, - 3.6755285626698257, - 3.676849055062851, - 3.6780074936131375, - 3.675774375933216, - 3.675491580434464, - 3.675179125518452, - 3.6736183684009194, - 3.6725512511214786, - 3.672769947873789, - 3.674136266520881, - 3.671359405395984, - 3.673867895268325, - 3.6728791779470935, - 3.674280190338578, - 3.6733380196238214, - 3.6724974108309953, - 3.671606193541511, - 3.673345584875538, - 3.670513195784604, - 3.6717193560656622, - 3.6718578164379303, - 3.671455896186951, - 3.6714157674376184, - 3.668693846870512, - 3.6701037938294623, - 3.66777207036676, - 3.668155402718257, - 3.6697528729821314, - 3.669512261660989, - 3.667805264829029, - 3.66770515296521, - 3.667263467225871, - 3.6667354634143337, - 3.665743417170219, - 3.666044981231405, - 3.66612902254643, - 3.665153536084542, - 3.6658096642332847, - 3.6660335339633368, - 3.664994432948803, - 3.666626350378762, - 3.66635050091046, - 3.6646670586882433, - 3.6652063515319258, - 3.6649525464169455, - 3.661484117307103, - 3.6638374008499817, - 3.6626825660112567, - 3.6613589943774967, - 3.661623722492179, - 3.662330017822946, - 3.6624166248147936, - 3.660857346857191, - 3.662758476716261, - 3.661784395016517, - 3.658716407290336, - 3.661240764863128, - 3.660212120654664, - 3.660647169234575, - 3.66066545927065, - 3.659845095408192, - 3.659002403927248, - 3.6585976344130193, - 3.6598680100140584, - 3.6614987299502575, - 3.660424205796129, - 3.65838918143716, - 3.661141234994858, - 3.658992216941288, - 3.658422699959778, - 3.658494718979784, - 3.6589516347077584, - 3.657499738380255, - 3.655852586121794, - 3.657440485464376, - 3.656965970572126, - 3.655839146945757, - 3.65770430908224, - 3.6569355511214727, - 3.653658437491412, - 3.6560902741461474, - 3.656901098593372, - 3.6554122102794153, - 3.65471826046814, - 3.656066181393089, - 3.655827088020665, - 3.6531684332439984, - 3.654511837824132, - 3.656715370667973, - 3.65539387440579, - 3.654696121571472, - 3.654974329305589, - 3.6533350827434834, - 3.655711236552487, - 3.6540230831372416, - 3.6536453738216177, - 3.654629004461088, - 3.651474894806266, - 3.6534951243021663, - 3.652683479577333, - 3.651375017753518, - 3.6534433312064207, - 3.652977700211502, - 3.652180219537176, - 3.65440743486929, - 3.651449574678473, - 3.652050586035735, - 3.653052043153222, - 3.652443838648291, - 3.651635200533006, - 3.651629135849556, - 3.651691250213099, - 3.65064061545273, - 3.652252247282003, - 3.650275548302443, - 3.65020059126672, - 3.6501727423225088, - 3.64988916376585, - 3.649440579093307, - 3.650287604540572, - 3.650516896361814, - 3.64939231361492, - 3.649538565035285, - 3.650057742746345, - 3.648596265695657, - 3.649515388059114, - 3.6479308621101327, - 3.649302260986484, - 3.6494317817666864, - 3.6484687042759534, - 3.6497999122548688, - 3.6496465966140854, - 3.648873315666544, - 3.648068355123064, - 3.6476998199741817, - 3.64565620960889, - 3.647806993516533, - 3.647126285110482, - 3.6465450507140353, - 3.647138950183273, - 3.6483227202854502, - 3.645589502749089, - 3.648181903426341, - 3.647090779341468, - 3.64654386180702, - 3.646784004226195, - 3.6460701058775777, - 3.647541093714545, - 3.6461881543964494, - 3.645478584752323, - 3.645952651409413, - 3.645108008708492, - 3.6451688213711337, - 3.6446732745823223, - 3.646015068545296, - 3.645644697248775, - 3.643271430176075, - 3.646082650300933, - 3.643155803710725, - 3.64461543105231, - 3.643455418134634, - 3.6448245508549975, - 3.646034455807901, - 3.644632316093568, - 3.645108244295416, - 3.6438991372684257, - 3.644555963523084, - 3.645219799705867, - 3.6433440064891025, - 3.643675134182943, - 3.6424817845475026, - 3.6427852084817096, - 3.644324792112166, - 3.6409258236576783, - 3.64198692762414, - 3.642284414018858, - 3.641777782726124, - 3.6419957501029665, - 3.642088444031256, - 3.643931875434436, - 3.642608538178706, - 3.6407355692685313, - 3.6395164380598057, - 3.642835213271414, - 3.641048613679834, - 3.6410037633474457, - 3.6417664599644346, - 3.640315496659654, - 3.6391751369721272, - 3.6386848173771207, - 3.641030976338825, - 3.641006793753908, - 3.6401837064457663, - 3.6406092330773903, - 3.6392277134491415, - 3.6407393301103785, - 3.6400710503148, - 3.6379833269621384, - 3.640876212505298, - 3.640355455274317, - 3.639217067797079, - 3.640530068636796, - 3.641040219668276, - 3.638428491900769, - 3.640446385823444, - 3.6379245845228287, - 3.6380679958996383, - 3.6382942526483766, - 3.640960030930286, - 3.638798321030541, - 3.63891993916387, - 3.638177020102962, - 3.63883480320775, - 3.6374796036665424, - 3.637543445434949, - 3.6378113035730784, - 3.6379328464886953, - 3.636730057838897, - 3.636630801008133, - 3.6355662915372, - 3.63568965556303, - 3.6372662845506225, - 3.63760289623245, - 3.636579983700168, - 3.635998558338697, - 3.636085629137811, - 3.63418056399829, - 3.633335457503687, - 3.635690307501139, - 3.634471830087058, - 3.634274956449934, - 3.634104501475765, - 3.635539500632689, - 3.63614061412238, - 3.633424422893499, - 3.6334570664693207, - 3.634358951678619, - 3.634016804289935, - 3.634913611906659, - 3.6337892641677656, - 3.633872055780349, - 3.633240468138249, - 3.633179199275817, - 3.633360747938992, - 3.6340729045965063, - 3.634128202356316, - 3.6317403861488256, - 3.633465355663108, - 3.632739969430114, - 3.6315857341949767, - 3.6326331317698792, - 3.6340180273528184, - 3.633801476437701, - 3.632638372443723, - 3.6303609675268502, - 3.629827685968304, - 3.631962721165088, - 3.630326582985027, - 3.6304516359261503, - 3.6299172758880394, - 3.629579394992047, - 3.629129427282058, - 3.630512112393793, - 3.629700248047332, - 3.629235170011301, - 3.6287533025600665, - 3.6292975085003167, - 3.628794399858944, - 3.629271185953682, - 3.628854573999875, - 3.628221743092607, - 3.629203237302338, - 3.6274827749924015, - 3.627204309178241, - 3.627841242443936, - 3.6274785296905416, - 3.629332522490925, - 3.627232741430688, - 3.625660636322247, - 3.626049176197475, - 3.626611964513049, - 3.626216426225284, - 3.626279567529499, - 3.626173158969942, - 3.6259825524085807, - 3.6267954434022616, - 3.626210254453084, - 3.624863698994826, - 3.6245156694774177, - 3.6245735364671714, - 3.624219298275247, - 3.626175246722312, - 3.62471771227812, - 3.6235463906910694, - 3.62395312686613, - 3.623194970546028, - 3.624551071599905, - 3.6234917534854687, - 3.621356325557499, - 3.6225228405410377, - 3.621280227460429, - 3.623124636749218, - 3.621833247407671, - 3.623159181286752, - 3.623321145083691, - 3.6209571481715983, - 3.6224571177800544, - 3.6199994020841544, - 3.621831497302588, - 3.6211002813052526, - 3.619451016014723, - 3.6189713376350703, - 3.619688995894754, - 3.6217845263722865, - 3.6188570376118174, - 3.618554434635609, - 3.618552227581634, - 3.618584444649701, - 3.61918826943514, - 3.6198123410953102, - 3.618945605045708, - 3.618378392183926, - 3.6184142145544698, - 3.619105123616427, - 3.615671147854808, - 3.616419398437768, - 3.617506222110838, - 3.616217057431959, - 3.617264908311276, - 3.614962208419183, - 3.616567996865805, - 3.6134931416515745, - 3.6134030583238816, - 3.6130319269009634, - 3.6133475735020633, - 3.6142225126391665, - 3.6138854246510377, - 3.61349651606974, - 3.6149387020628048, - 3.613938379275765, - 3.613592846256834, - 3.611514378209153, - 3.6132147819182943, - 3.61419862241443, - 3.6136082618294063, - 3.611331724788681, - 3.6124816487974822, - 3.611453776847774, - 3.6113140250203246, - 3.609671160401911, - 3.6115116443523942, - 3.6107550024902784, - 3.61207377143684, - 3.6107082249660896, - 3.6090770132250047, - 3.609575020050634, - 3.6107121057173592, - 3.608520097373439, - 3.6086268857677863, - 3.60880910958809, - 3.6082790469680153, - 3.609003180977038, - 3.608955031367059, - 3.610160685596163, - 3.607531542114754, - 3.605905647482606, - 3.6067558053853417, - 3.607483994782863, - 3.606528008217724, - 3.606364957034237, - 3.606545310354692, - 3.606316303364708, - 3.60728131441218, - 3.6075283624166743, - 3.605841774938592, - 3.607424301331495, - 3.604466340163044, - 3.605975707359408, - 3.60601239538064, - 3.605021408339018, - 3.6060262068722504, - 3.60603079388491, - 3.604552876812898, - 3.6038216868724295, - 3.6029216132691415, - 3.6057656246625887, - 3.602936217888154, - 3.6045355099960936, - 3.6042266131167553, - 3.603165585465751, - 3.6013570076713353, - 3.6018590760392004, - 3.601898983411038, - 3.6012565448219815, - 3.5997955293533987, - 3.6003673073259628, - 3.600126062339338, - 3.6003841965361465, - 3.601785856022796, - 3.600610450320514, - 3.6009763242554778, - 3.596949256006608, - 3.597668641634311, - 3.5981977563405905, - 3.5998196699751266, - 3.598195385771701, - 3.5965471227891586, - 3.598432908492177, - 3.5981937460719604, - 3.5975628087593696, - 3.5966597090557237 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Model", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 3.6908675483524087, - 3.6899719769488386, - 3.689439799112574, - 3.688943884644759, - 3.688476318205046, - 3.6880320130801, - 3.68760792600915, - 3.687201128342635, - 3.6868079934874705, - 3.6864259814397857, - 3.686053494301712, - 3.6856889704058338, - 3.685331000977372, - 3.684978588663968, - 3.684630689845256, - 3.6842866878111287, - 3.683945831010945, - 3.683607668439563, - 3.683271711606974, - 3.682944686972178, - 3.6826191951141793, - 3.682294972146891, - 3.6819718038606624, - 3.6816494636995336, - 3.68132781798052, - 3.6810067994594897, - 3.680686263442906, - 3.680366079317914, - 3.680046291323597, - 3.679726868709023, - 3.6794076095421575, - 3.6790885941487264, - 3.678769833246012, - 3.678451172647149, - 3.678132647107091, - 3.677814247091143, - 3.6774959337506505, - 3.6771892040928935, - 3.676882529031153, - 3.6765758947104654, - 3.676269287428761, - 3.675962702822299, - 3.6756561373452414, - 3.675349590606816, - 3.675043039574109, - 3.674736433722956, - 3.674429810868263, - 3.674123175502012, - 3.673816535679814, - 3.6735099030209097, - 3.673203292708167, - 3.672896723488087, - 3.672590217670796, - 3.6722838011300514, - 3.6719772319733064, - 3.6716901987826502, - 3.6714031669141254, - 3.671116148749546, - 3.670829158759462, - 3.670542213503156, - 3.670255331628644, - 3.669968533872676, - 3.669681843060736, - 3.66939528410704, - 3.6691084373204266, - 3.668821588942411, - 3.6685347590775144, - 3.66824795173845, - 3.6679611713049303, - 3.667674422523664, - 3.667387710508363, - 3.667101040739732, - 3.666814419065478, - 3.666557275625599, - 3.666300053925611, - 3.666042837529536, - 3.6657856240789033, - 3.665528410988827, - 3.665271195448005, - 3.66501397441872, - 3.6647567446368376, - 3.6644995026118097, - 3.6642422446266703, - 3.663984994136423, - 3.6637277538891166, - 3.6634705058352006, - 3.663213248463265, - 3.6629559802227623, - 3.6626986995240114, - 3.662441404738191, - 3.662184094197345, - 3.661962915892533, - 3.6617417183793735, - 3.66152053930996, - 3.6612993580616946, - 3.661078171277549, - 3.660856979862965, - 3.660635784850192, - 3.6604145873982823, - 3.6601933887930977, - 3.659972190447303, - 3.6597509939003694, - 3.659529797682868, - 3.6593085903073583, - 3.659087383121904, - 3.6588661770854496, - 3.658644973216736, - 3.6584237725943023, - 3.658202576356483, - 3.658017048156286, - 3.657831526796765, - 3.6576460135956497, - 3.6574604978316536, - 3.6572749744386104, - 3.657089453117498, - 3.6569039338501264, - 3.656718416582891, - 3.656532901226771, - 3.6563473876573327, - 3.656161875714726, - 3.655976365203688, - 3.655790855893539, - 3.655605343283373, - 3.6554198299855343, - 3.655234316806267, - 3.655048803664964, - 3.654863290481015, - 3.654705903473936, - 3.654548516262995, - 3.654391128767586, - 3.6542337409070975, - 3.654076352600923, - 3.6539189637684544, - 3.653761574329083, - 3.6536041842022, - 3.6534467933071983, - 3.653289401563468, - 3.6531320088904033, - 3.652974618097451, - 3.65281723177819, - 3.652659845420852, - 3.6525024589772577, - 3.652345072399233, - 3.652187685638598, - 3.6520302986471784, - 3.65189045518739, - 3.651750611400461, - 3.651610767238215, - 3.651470922652477, - 3.6513310775950663, - 3.651191232017809, - 3.651051385872526, - 3.6509115391110414, - 3.6507716916851782, - 3.650631843546759, - 3.650491994647606, - 3.650352144939544, - 3.650212294374396, - 3.6500724429039826, - 3.649932590480129, - 3.649792737054657, - 3.64965288257939, - 3.6495130270061513, - 3.649381451706825, - 3.6492498823438857, - 3.6491183128722473, - 3.64898674328672, - 3.6488551735821138, - 3.648723603753238, - 3.648592033794902, - 3.648460463701916, - 3.64832889346909, - 3.6481973230912335, - 3.6480657525631552, - 3.647934181879666, - 3.647802611035575, - 3.647671040025693, - 3.647539468844827, - 3.6474078974877897, - 3.6472763259493894, - 3.647144754224436, - 3.6470155068446983, - 3.646886259268027, - 3.646757011489232, - 3.6466277635031217, - 3.6464985153045073, - 3.646369266888199, - 3.646240018249004, - 3.646110769844152, - 3.645981522945094, - 3.645852276061434, - 3.645723029197633, - 3.6455937823581546, - 3.64546453554746, - 3.64533528877001, - 3.645206042030267, - 3.6450767953326935, - 3.6449475486817495, - 3.644818302081898, - 3.6446883253544886, - 3.644558348687095, - 3.644428372084178, - 3.644298395550201, - 3.644168419089624, - 3.644038442706911, - 3.6439084664065216, - 3.6437784901929176, - 3.6436485140705623, - 3.643518538043916, - 3.6433885621174418, - 3.643258586295599, - 3.6431286105828526, - 3.6429986349836625, - 3.6428686593857873, - 3.6427386831976145, - 3.642608707040193, - 3.642478730914316, - 3.642346326828276, - 3.64221392277537, - 3.642081518756389, - 3.641949114772129, - 3.641816710823383, - 3.641684306910945, - 3.6415519030356096, - 3.6414194991981694, - 3.641287095399419, - 3.641154691640154, - 3.6410222879211647, - 3.640889884243248, - 3.640757480607197, - 3.640625077013804, - 3.640492673463865, - 3.640360269958174, - 3.640227866497523, - 3.6400954630827074, - 3.6399586210606474, - 3.63982177908601, - 3.639684937159589, - 3.639548095254691, - 3.639411253038702, - 3.639274410828014, - 3.639137568621691, - 3.639000726418794, - 3.6388638842183822, - 3.6387270420195215, - 3.6385901998212704, - 3.638453357622693, - 3.638316515422849, - 3.6381796732208014, - 3.638042831015611, - 3.6379059888063416, - 3.6377691465920527, - 3.6376323043718073, - 3.637487597133539, - 3.6373428898874383, - 3.6371981826325657, - 3.637053475367983, - 3.636908768092753, - 3.636764060805936, - 3.6366193535065943, - 3.6364746461937902, - 3.6363299388665857, - 3.6361852315240415, - 3.636040524165253, - 3.6358958169108377, - 3.635751109652535, - 3.635606402389953, - 3.6354616951227, - 3.6353169878503873, - 3.635172280572621, - 3.635027573289012, - 3.634870364349562, - 3.6347131554034857, - 3.634555946450393, - 3.634398737489892, - 3.634241528521592, - 3.634084319545102, - 3.6339271105600304, - 3.6337699015659863, - 3.6336126925625782, - 3.633455483549417, - 3.633298274526108, - 3.633141065492263, - 3.6329838564474897, - 3.632826647391397, - 3.6326694383235942, - 3.63251222924369, - 3.6323550201512926, - 3.632197811046012, - 3.632024000596219, - 3.631850190150418, - 3.631676379705944, - 3.631502569263545, - 3.631328758823967, - 3.631154948387958, - 3.630981137956265, - 3.630807327529636, - 3.630633517108816, - 3.630459706694555, - 3.630285896287597, - 3.630112085887145, - 3.6299382754886143, - 3.629764465091339, - 3.629590654695168, - 3.629416844299612, - 3.6292430339051145, - 3.629069223511677, - 3.6288774694050576, - 3.628685715299498, - 3.628493961194997, - 3.628302207089533, - 3.628110452983315, - 3.62791869887764, - 3.627726944772508, - 3.627535190667919, - 3.6273434365638737, - 3.6271516824603713, - 3.626959928357411, - 3.626768174254994, - 3.626576420153121, - 3.626384666051791, - 3.626192911951004, - 3.62600115785076, - 3.625809403745379, - 3.6256176496396786, - 3.6254102515983857, - 3.6252028535573055, - 3.624995455516439, - 3.624788057475785, - 3.624580659435345, - 3.624373261395116, - 3.624165863355102, - 3.623958465315301, - 3.623751067275712, - 3.623543669236337, - 3.623336271197175, - 3.6231288731582256, - 3.62292147511949, - 3.6227140770809663, - 3.622506679042657, - 3.62229928100456, - 3.622091882966677, - 3.6218844849290055, - 3.621666183453, - 3.621447881977208, - 3.621229580501628, - 3.621011279026262, - 3.620792977551108, - 3.620574676076168, - 3.6203563746014416, - 3.620138073126928, - 3.6199197716526266, - 3.619701470178539, - 3.619483168704664, - 3.619264867231002, - 3.6190465657575537, - 3.618828264284318, - 3.6186099628112953, - 3.618391661338486, - 3.6181733598655894, - 3.617955058389514, - 3.6177308235844623, - 3.6175065887794298, - 3.617282353974416, - 3.617058119169421, - 3.616833884364445, - 3.6166096495594866, - 3.6163854147545473, - 3.616161179949627, - 3.6159369451447256, - 3.615712710339843, - 3.615488475534979, - 3.615264240730133, - 3.6150400059253065, - 3.6148157711204982, - 3.6145915363157095, - 3.6143673015109385, - 3.6141430667061862, - 3.613918831901453, - 3.6136921887663753, - 3.613465545631316, - 3.613238902496275, - 3.6130122593612537, - 3.61278561622625, - 3.6125589730912657, - 3.6123323299563, - 3.612105686821352, - 3.611879043686424, - 3.6116524005515145, - 3.6114257574166233, - 3.6111991142817503, - 3.610972471146898, - 3.6107458280120626, - 3.6105191848772464, - 3.6102925417424494, - 3.61006589860767, - 3.609839255472782, - 3.609611549559746, - 3.6093838436467016, - 3.609156137733648, - 3.608928431820587, - 3.608700725907517, - 3.6084730199944386, - 3.608245314081352, - 3.608017608168258, - 3.607789902255154, - 3.607562196342043, - 3.607334490428922, - 3.6071067845157936, - 3.6068790786026574, - 3.606651372689512, - 3.606423666776359, - 3.6061959608631975, - 3.605968254950027, - 3.605740549036849, - 3.6055104251023753, - 3.605280301167893, - 3.605050177233403, - 3.604820053298904, - 3.6045899293643977, - 3.604359805429882, - 3.6041296814953583, - 3.603899557560826, - 3.6036694336262856, - 3.603439309691737, - 3.60320918575718, - 3.602979061822614, - 3.6027489378880406, - 3.6025188139534583, - 3.602288690018867, - 3.6020585660842683, - 3.601828442149661, - 3.6015983182150455, - 3.601360807041022, - 3.6011232958670245, - 3.600885784693024, - 3.6006482735190217, - 3.600410762345017, - 3.60017325117101, - 3.5999357399970005, - 3.5996982288229886, - 3.599460717648975, - 3.5992232064749583, - 3.59898569530094, - 3.5987481841269187, - 3.598510672952896, - 3.59827316177887, - 3.5980356506048423, - 3.5977981394308123, - 3.5975606282567796 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - -54.313231642426395, - 952.3132316424264 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 3.590304167535209, - 3.6972237460223942 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Voltage [V]" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "02004006008003.63.623.643.663.68ReferenceModelOptimised ComparisonTime / sVoltage / V" ] }, "metadata": {}, @@ -4905,996 +362,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 16, "id": "f66e0b0f-4861-42dd-bb7f-8734fcca3328", "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Covariance Matrix Adaptation Evolution
Strategy (CMA-ES)", - "type": "scatter", - "x": [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31 - ], - "y": [ - 0.00047358645764378974, - 0.00047174334628764743, - 0.0008275704423698347, - 0.000631698748769824, - 0.0007632473501339241, - 0.00046129488141684425, - 0.0008555078656202401, - 0.00045940993811405023, - 0.00046972414086689446, - 0.0004920849498925588, - 0.0005132760012242371, - 0.0005099132761267845, - 0.0005138628488773953, - 0.00054518426693242, - 0.000661224012009725, - 0.0004887710733952704, - 0.0004785925229307194, - 0.000499730800368749, - 0.0004649193708961848, - 0.0004774182783781329, - 0.0004613145908995495, - 0.00046337662170261933, - 0.0004619744320051879, - 0.0004605529418892647, - 0.00046120346875993656, - 0.00046189587366794645, - 0.0004596582357791574, - 0.00045955184177525615, - 0.0004596437882143896, - 0.0004598884023147322, - 0.00045961640764853866 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Convergence", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - 1, - 31 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Iteration" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 0.0004374044976970397, - 0.0008775133060372507 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Cost" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "510152025300.00050.0010.00150.0020.00250.003ConvergenceIterationCost" ] }, "metadata": {}, @@ -5902,3574 +377,8 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "name": "R0 [Ohm]", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247 - ], - "xaxis": "x", - "y": [ - 0.0005088957784053409, - 0.00017537076254087834, - 0.00011584108727178174, - 0.0004073316990460465, - 0.0005397940987619291, - 0.0014693339617402395, - 0.00010006664122030438, - 0.00010970562812482206, - 0.0005844203203067386, - 0.0049499373637117445, - 0.0008605787722814727, - 0.002128453196251973, - 0.001376772252711185, - 0.0011915692037239823, - 0.00045072210493824584, - 0.00084139312995156, - 0.00139003297004744, - 0.00025711136326786287, - 0.001335596667151841, - 0.000977189391882321, - 0.0017133257317750378, - 0.0021083233312993084, - 0.00015626194728828898, - 0.00010108084604749042, - 0.000812539764372022, - 0.00043101334264735087, - 0.0004426331316177653, - 0.001287639833862678, - 0.0015529614258932697, - 0.0030821581635479644, - 0.0003164426525505378, - 0.0013420181537203983, - 0.0008793789519210143, - 0.0007280657462613054, - 0.0026113019268085387, - 0.0007292627458404714, - 0.0011447701178040272, - 0.002940138611378593, - 0.0011813007069618705, - 0.0005000392870790351, - 0.0008002395506057398, - 0.001208170116540957, - 0.001217294598883013, - 0.0006913166146193815, - 0.000924956448715328, - 0.0008591631344526342, - 0.001160091215211093, - 0.0006265000445039438, - 0.00042703298879554966, - 0.0007199608917044153, - 0.0005796811297904549, - 0.0009613076978875582, - 0.0015617930175205765, - 0.001639410156015497, - 0.0005374665321442863, - 0.0010301690839613236, - 0.0006044495759071424, - 0.00039556741737505394, - 0.0005023168148330172, - 0.0007105768842187676, - 0.0009760962601792236, - 0.0012869617568972871, - 0.0011293067108854693, - 0.0009290811294738146, - 0.000955120189291834, - 0.0007012684558563505, - 0.0011414706209190444, - 0.001033560248745729, - 0.001015116119675381, - 0.0012252591104383369, - 0.001112320280415058, - 0.001594595317276204, - 0.0008561162743540165, - 0.00166095629070267, - 0.0005705578794840406, - 0.0009301432331474084, - 0.0012960660491154222, - 0.0012688235081400503, - 0.0013716704642505102, - 0.0008740151432433196, - 0.0010445741068285028, - 0.0007523399978607342, - 0.000794767653646583, - 0.0009019511054401922, - 0.0011977935240328104, - 0.0009631588336047002, - 0.0014798324186319457, - 0.0007804361940140866, - 0.0007653951324004862, - 0.0008493074079121823, - 0.000529938236873479, - 0.0008024766475883254, - 0.0008135944663273363, - 0.0007619280281271242, - 0.0012017244861994949, - 0.0012426981584751375, - 0.0007649149020800326, - 0.000568621306395677, - 0.0006322019162644207, - 0.0007459244343341375, - 0.0010922469711837567, - 0.0007452554208880269, - 0.0009395155635571512, - 0.000720976484467373, - 0.0006104257434821502, - 0.000627801285083803, - 0.0007919873435728414, - 0.0007249011093949521, - 0.000643070234166071, - 0.0008898289700917612, - 0.0006977048391315049, - 0.0010043794987000523, - 0.0004548436781313744, - 0.0007254500860549795, - 0.0008617039432107826, - 0.00062343796207779, - 0.0009348147320630424, - 0.0005897706808427211, - 0.000747155159436132, - 0.0009907243346915164, - 0.0006713986910488096, - 0.0003585115194163927, - 0.0004316518550914552, - 0.0008542986499601355, - 0.0008472238447146217, - 0.0006816644217992326, - 0.0005459112009930393, - 0.0008093426888522213, - 0.000788357650624001, - 0.0007601044258967543, - 0.0007388053565749865, - 0.0003751545039446213, - 0.000816885379170918, - 0.000810445775778932, - 0.0005397864656649965, - 0.0007924228525805385, - 0.0006732437198194075, - 0.0010408819932275035, - 0.0006025943352283983, - 0.0005070470506574166, - 0.0006024576771585667, - 0.0008902976779174395, - 0.0006750852120273074, - 0.0006138984597991124, - 0.0008377722086906976, - 0.0005546639861738407, - 0.0007851252101918085, - 0.0007031735711484294, - 0.0008635404065908449, - 0.0007932952984785334, - 0.0006193640594809607, - 0.0007680113922331671, - 0.0007370602285718295, - 0.0007541616101653777, - 0.0007615477920326663, - 0.0007805719388419821, - 0.0008826988983372183, - 0.0008591812568453191, - 0.0008403779323901058, - 0.0007296992770937109, - 0.000826804780351234, - 0.0007828489494485247, - 0.0008592398813409506, - 0.0009372219759246056, - 0.0008979825065219534, - 0.0007660984700616465, - 0.0007435748167048157, - 0.0007245223319272964, - 0.000678578182018273, - 0.0008303414666757975, - 0.0007731059461138672, - 0.000808221097995958, - 0.0008862687753901589, - 0.0009229287296654018, - 0.0008683599459123456, - 0.0009472505273225244, - 0.0008659253205120162, - 0.0008317581313710168, - 0.0007873698699850748, - 0.0008806912106967546, - 0.000749934480548026, - 0.0008434058045105537, - 0.0008459844170729255, - 0.0007557522722427039, - 0.0008197035580311813, - 0.000871773502731166, - 0.0008165567540380987, - 0.0008981243904719921, - 0.0009572153784669984, - 0.0009326488467953928, - 0.0008810108168796702, - 0.0008463581949340142, - 0.0008891960944682667, - 0.000883581637677164, - 0.0008889382275585819, - 0.0008894520265078078, - 0.0008884957689875216, - 0.0008272981792449747, - 0.0009075504474080212, - 0.0009454431235089972, - 0.0008826056063237772, - 0.0008945793672936737, - 0.0009289382641963828, - 0.0008559494668330946, - 0.000850980598221355, - 0.0008295783181172883, - 0.0009232551603509992, - 0.0008424500917387286, - 0.0009708468898483226, - 0.0009111285408944904, - 0.0008596793817687599, - 0.0009049142138744232, - 0.0009503723725255136, - 0.0007855364003831732, - 0.0009395772353096736, - 0.0008789406423626815, - 0.000862301136935378, - 0.0009613946265418432, - 0.0008972653450110608, - 0.0008847013920603779, - 0.0008961523584156776, - 0.0010183614464079308, - 0.0010103899252326266, - 0.0009086565319650656, - 0.001049998847924034, - 0.0010956059805245358, - 0.001073721596469628, - 0.000839238455793923, - 0.0010492913348692393, - 0.0009157987174526408, - 0.0009767146162252942, - 0.0010336851630840598, - 0.0010290472640072277, - 0.0009684810478616088, - 0.0011862058007710882, - 0.001044946575533356, - 0.0011108245434966418, - 0.0010653891039870945, - 0.0009540893676329748, - 0.0009704038286755828, - 0.0010399994114060993, - 0.0009571564768642788, - 0.000881491602364509, - 0.0009832898321341032, - 0.0010254851971152057, - 0.0011025167443238816, - 0.0010118268225140805, - 0.0010240872960649563 - ], - "yaxis": "y" - }, - { - "name": "R1 [Ohm]", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247 - ], - "xaxis": "x2", - "y": [ - 0.00001018688516260966, - 0.00019269423527682575, - 0.0001150514769149076, - 0.000010959817574809127, - 0.0006580689154289276, - 0.00003152474941564034, - 0.000241709614498688, - 0.0004291277031369154, - 0.00024807933608708764, - 0.00009512921053968949, - 0.00025101866956427944, - 0.00022544541745420449, - 0.00007272647777619742, - 0.0005086970326426135, - 0.000054456816132497366, - 0.0007343226531176305, - 0.00031556015967623246, - 0.00018236107357750425, - 0.00008213869065227903, - 0.0013455162343228653, - 0.0007253684846419581, - 0.000013769910962176162, - 0.0002852071229469999, - 0.0004606466996593276, - 0.0007742627360356791, - 0.0005533819669727222, - 0.0001211202763199557, - 0.000019378313395582976, - 0.000045098978654500566, - 0.00015012698234470644, - 0.00003635299749100361, - 0.00013693979373467988, - 0.000010117144857636726, - 0.00010607765721976584, - 0.00003667525426748099, - 0.0009980079232229382, - 0.00006692280899031007, - 0.00026176343200967807, - 0.00010527047643349324, - 0.00008584108357684603, - 0.0005221768166995741, - 0.0003825670517738024, - 0.0004135978423027707, - 0.001258621126044516, - 0.00022337008510017856, - 0.00035265809831205126, - 0.000019137040313400916, - 0.0003502103627414309, - 0.0013825842031151643, - 0.0004704063301673706, - 0.00120938073981227, - 0.000282104422093892, - 0.0001930478435674429, - 0.00010955107070241453, - 0.0001038512899278768, - 0.0004599763402699593, - 0.0007665248274024493, - 0.00016183196333537474, - 0.0001690708525779781, - 0.0006310464300485539, - 0.0004390270806665533, - 0.00008631352119210992, - 0.0000647383205770532, - 0.001119269143969774, - 0.00019704934885059772, - 0.00012915837431390302, - 0.00011519181305044464, - 0.00034774485058971215, - 0.0001146310197174262, - 0.00011152968551935153, - 0.00009189720891057748, - 0.00038116926195436325, - 0.00020479915320439172, - 0.00026908437238856345, - 0.00021957969718970808, - 0.0003297023167994534, - 0.00005717141182550611, - 0.00030377996761292734, - 0.000059033824238366614, - 0.00001056358265685804, - 0.0003236576587833574, - 0.00019719020660297903, - 0.0004853723384647998, - 0.0003386354270899009, - 0.00014942615574609452, - 0.00019035103166935424, - 0.00007123659611724325, - 0.000023106805713498675, - 0.0003757842062673541, - 0.00002605265239976025, - 0.00019671985356389763, - 0.0001617993254601168, - 0.00015242715489095432, - 0.00004299337238874596, - 0.000013180478665686397, - 0.00013951102527672382, - 0.000021954904198008463, - 0.00010217546611075872, - 0.0004141298426935341, - 0.00004057103703873551, - 0.000060524572269612664, - 0.00007061364634695475, - 0.00002705390698815388, - 0.00004055347394797973, - 0.00014067216233916042, - 0.000010263666246505876, - 0.0003119286394671391, - 0.000033625135746369275, - 0.00003359417197342875, - 0.00008597878950920509, - 0.00019854622390171224, - 0.000025175732725138697, - 0.00009578884492989796, - 0.0001859676453876176, - 0.0003978188104438618, - 0.00010253851358084507, - 0.00012314805558492938, - 0.000012021992713733874, - 0.00014507690345691272, - 0.00007648088087776011, - 0.00027821122913818564, - 0.00005605587923330534, - 0.00006615381291560963, - 0.00011836812862160453, - 0.0002595110559286947, - 0.0002611862795775672, - 0.0006004573710266713, - 0.00002161042413849105, - 0.00002430120832427961, - 0.0004171482767114256, - 0.0003760136097887762, - 0.0007862171018276881, - 0.00012800553826550247, - 0.0002988214934130748, - 0.00008490932085452768, - 0.00007080171187544501, - 0.000046208440298420365, - 0.00008284485066702783, - 0.00023362346414904515, - 0.0005151888309156734, - 0.0007603867445432039, - 0.00020875156067064115, - 0.0003290835022242107, - 0.0001373675615774308, - 0.00023213092731857825, - 0.0002286765905216848, - 0.00030818592351901597, - 0.0001452532156759366, - 0.00013591970874954893, - 0.0002988888185035423, - 0.00020685675056811067, - 0.00012390824638738176, - 0.00033764749406399203, - 0.00016947185842148174, - 0.0002565010680991809, - 0.00027248670537575954, - 0.0003571799281842449, - 0.0006574038801132001, - 0.0004188317256057462, - 0.0001663269345813331, - 0.00024223875812659623, - 0.0005083884367370684, - 0.00030436410017277866, - 0.00030415174530158115, - 0.00036480224354180343, - 0.0004017317209268584, - 0.00012887047728284235, - 0.0003120145038625729, - 0.0004436130912037552, - 0.00020778241774114368, - 0.00031575469237684413, - 0.0002794607050476917, - 0.0003739931954805064, - 0.000301649942028396, - 0.00042979517129111385, - 0.0003138509767298508, - 0.00028355313301352226, - 0.00029376121834569875, - 0.0003817999343585214, - 0.00026785850396751865, - 0.0003397706911748848, - 0.00025739925185167917, - 0.00025773893870809376, - 0.0003070091431834656, - 0.00026634528226306646, - 0.0002690855049519976, - 0.00019359620375554776, - 0.00042004107443667624, - 0.0002980841398921729, - 0.00017303980896602117, - 0.00031234927564726403, - 0.000167873223050087, - 0.00030075594353273663, - 0.00026595099906732446, - 0.0001949139614183073, - 0.00021256169134286303, - 0.00015405065644341547, - 0.00043196986870661406, - 0.0002046101296291423, - 0.00016476736345016413, - 0.00023698267159114695, - 0.00035102535867511254, - 0.0003316916616703129, - 0.00024378948914067415, - 0.0002547648677470326, - 0.0002372833496332031, - 0.0002580231265889644, - 0.0002683103218286906, - 0.0002319657459692169, - 0.00034633603039985585, - 0.00026513700424138527, - 0.00026636470966297736, - 0.00022574172181706695, - 0.0002511124576553697, - 0.0002530493530074255, - 0.00018369524458345076, - 0.00020785506780079708, - 0.0002439856483837882, - 0.00024292745895398228, - 0.00025101340265375015, - 0.00019528433365804165, - 0.0002172732890342621, - 0.0002044316785529008, - 0.00023556291552141956, - 0.00019834761637338788, - 0.00018178040255267556, - 0.00013940610227256283, - 0.0002198137708726777, - 0.00017582118187593925, - 0.00023362988230395937, - 0.0002110520119003554, - 0.00020777281668805583, - 0.00024178495739595983, - 0.00029827994717724254, - 0.00016208266792850983, - 0.0001884011945637605, - 0.00019023028621218405, - 0.00020974433137175476, - 0.00023199483393458357, - 0.0002632445370530659, - 0.0002616166284304343, - 0.00029111317670391427, - 0.00035811258368502274, - 0.0002899173409193361, - 0.00021229397361694055, - 0.00018988271733696844, - 0.00023268265691632912, - 0.0002075713115177541 - ], - "yaxis": "y2" - }, - { - "name": "R2 [Ohm]", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247 - ], - "xaxis": "x3", - "y": [ - 0.0005471790823943137, - 0.0004029998768939217, - 0.00007337192211332336, - 0.0006192510661634289, - 0.0003545358586175526, - 0.000010869665379422942, - 0.000018358911820074418, - 0.0002716178723379991, - 0.000130322176237424, - 0.0005308713052288336, - 0.00041138538714980186, - 0.000056366877880577554, - 0.0004176722109207048, - 0.00052771047260388, - 0.0003786190594971212, - 0.000010120045263049092, - 0.00047994759723430334, - 0.000011797030011467977, - 0.0002550890834176079, - 0.00005020897554025079, - 0.000499697501792654, - 0.001215368817562437, - 0.00003233761471526754, - 0.0005622665497896642, - 0.00021776213420476528, - 0.00002540673144846383, - 0.00008504553834081085, - 0.0003635643734611389, - 0.00011177072635246978, - 0.0004975519388609423, - 0.000011595661973826646, - 0.00013366052111486577, - 0.000033172895099563954, - 0.00007737301609921622, - 0.0008409926996312357, - 0.000013161689214125854, - 0.0004489528945049182, - 0.0008779545148993014, - 0.000796833486891147, - 0.00014355210504812411, - 0.00007269059267261118, - 0.001326907275850003, - 0.00007561408029473179, - 0.0007676202055157156, - 0.0003371370805565016, - 0.00006352400345855778, - 0.0001728324255454404, - 0.000051646189024927384, - 0.0004692462060104517, - 0.000014247403329001629, - 0.00003337190948416772, - 0.00005031646007203378, - 0.00013293406894466267, - 0.00012378637617535996, - 0.0005647627246616396, - 0.0001892715875040161, - 0.00005469452343972356, - 0.00010464114309788055, - 0.00001779850676565433, - 0.00012443169602434806, - 0.00007201376175731954, - 0.0002515491896364341, - 0.0002949207964595838, - 0.000012118577927303934, - 0.00015063886484524184, - 0.0001942683563659152, - 0.0005866818015279056, - 0.00039331805062561696, - 0.00017540294836614642, - 0.000294994743943588, - 0.00031962048987727796, - 0.00026053610780794114, - 0.000505026490022963, - 0.00016296437657995177, - 0.00034635142219947916, - 0.00017845972161549344, - 0.00043737027633921407, - 0.0002553960998447483, - 0.00027705002058542885, - 0.0006585340774085288, - 0.00019735763316818355, - 0.0001605974476577919, - 0.0003137670900691175, - 0.00046079579420570246, - 0.0006842199992710083, - 0.0004563947083080674, - 0.00035258661295211534, - 0.0007246781546956993, - 0.00021948340865416717, - 0.0005083076012602332, - 0.0003714536923414205, - 0.0004648948387448051, - 0.00024885349694876756, - 0.000717299096479815, - 0.0006654803962137459, - 0.0005325417523583211, - 0.0014735515737852123, - 0.0010606590554561488, - 0.00028365925017538666, - 0.0007342268533201572, - 0.0009842154409410344, - 0.0005465121826009177, - 0.0006389260058741198, - 0.0009116135895261372, - 0.00035777624913774854, - 0.0007269104760255553, - 0.00024914025733051537, - 0.0011245957967824212, - 0.0008231363478606566, - 0.001217737549852052, - 0.0003638979042179437, - 0.000985931490503649, - 0.0008522516722166647, - 0.0003616335190567379, - 0.0003863807671883294, - 0.0008868214742896299, - 0.000699036016279369, - 0.0007758367930548573, - 0.0003503922364446675, - 0.0006024178011467982, - 0.000575550343508962, - 0.0013980340251437712, - 0.0007017850451232463, - 0.00036279260626619623, - 0.0005585771658739172, - 0.0003269527653851223, - 0.0005670340264600336, - 0.0008993681741709235, - 0.0006067244950206544, - 0.0003543917367954305, - 0.0006274153388265147, - 0.0007579382123263393, - 0.0005022119667026318, - 0.0006431026150399397, - 0.0008005614970496072, - 0.0010050477655823122, - 0.00103800839452357, - 0.0004897941378149059, - 0.0006626629680981277, - 0.0006341423178161231, - 0.0003369472527372468, - 0.0003136994330320755, - 0.0005403654209337002, - 0.0008711786385587058, - 0.0004280178432949282, - 0.0007008279006055054, - 0.00035091402200028237, - 0.0005065948707360129, - 0.0003764210473624451, - 0.00034448275665045384, - 0.0004391943767091711, - 0.0005845891749584507, - 0.0005654410879211766, - 0.00047233295128986345, - 0.0007096700328636281, - 0.0005120145176586298, - 0.0003613588465067786, - 0.0003175820180675526, - 0.000272280166875667, - 0.0005833994279142269, - 0.0003010649523275951, - 0.0002944724064979628, - 0.0003343012811421649, - 0.00029351659476461854, - 0.0002575940282833958, - 0.00030856139700698567, - 0.0005764560757377217, - 0.0004535839631576949, - 0.0003307351175762731, - 0.0004296931906700013, - 0.0004082911411419295, - 0.00035352241589092635, - 0.00033392266154075474, - 0.0002857929885353151, - 0.0003277758155075042, - 0.00019104305966800863, - 0.00035182922841281157, - 0.0003572259455043924, - 0.0003771157387653997, - 0.0003274318546870743, - 0.00036535341718553146, - 0.0003815706702374552, - 0.0003974720870393622, - 0.0003655740990738528, - 0.0004739732606980898, - 0.0003994345837831134, - 0.00045628572182424665, - 0.000297420030504211, - 0.0002799160248717147, - 0.00039261022477300137, - 0.00029583963739654044, - 0.00043887478158840263, - 0.00031153959665188935, - 0.00033989289088877763, - 0.0003996138646306728, - 0.000371636217891745, - 0.000402597117994405, - 0.00027835233966463945, - 0.00031706801641886805, - 0.0003622997435260968, - 0.00037486321075088215, - 0.00026753760662732653, - 0.00028102015308789765, - 0.0003862771804122869, - 0.0003922059453970126, - 0.00044784849345615846, - 0.0003256074483107377, - 0.0003485500129291507, - 0.0002901499570786009, - 0.00028012328798728036, - 0.0003544944191561955, - 0.0003562911134713794, - 0.0003408422198185663, - 0.0004708002800333871, - 0.0003454178273865174, - 0.0004700426238698213, - 0.00047035490454771253, - 0.0002807679730114965, - 0.00033103158395605985, - 0.0003395731914464831, - 0.00038047184353845286, - 0.00025260363017569375, - 0.00028083926520936723, - 0.0003565836381543382, - 0.0002416803869655051, - 0.0002339918302036676, - 0.00026249791542078463, - 0.0004176350903950725, - 0.00030336971960153136, - 0.00031470128818178076, - 0.00030221326241473096, - 0.00024343727958220723, - 0.0002416078808990339, - 0.00026853422172935424, - 0.00017124497255683883, - 0.00023315658546204027, - 0.00020726449482630653, - 0.0002507679269619539, - 0.00030873027204830197, - 0.0002673312864072966, - 0.00022238432565494185, - 0.0002590680296915747, - 0.00028675963398935243, - 0.00024072558063953128, - 0.00024818614451743406, - 0.0001848128200002866, - 0.0002464738607659225, - 0.0002561523310944868 - ], - "yaxis": "y3" - }, - { - "name": "C1 [F]", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247 - ], - "xaxis": "x4", - "y": [ - 10607.127760561129, - 10607.12415463132, - 10607.124325790915, - 10607.126371039696, - 10607.128166802948, - 10607.128153961909, - 10607.127443664716, - 10607.1258472133, - 10607.12786454036, - 10607.124391383126, - 10607.12821888517, - 10607.129652033522, - 10607.129666430554, - 10607.12770334478, - 10607.12795319528, - 10607.13260010326, - 10607.128536808295, - 10607.12727848981, - 10607.12925186971, - 10607.130992580518, - 10607.131150366404, - 10607.129667492694, - 10607.129729913244, - 10607.12537170115, - 10607.126084301432, - 10607.128456208857, - 10607.13096178796, - 10607.129529774693, - 10607.129088694188, - 10607.128587463463, - 10607.129300953153, - 10607.126372616853, - 10607.128734676531, - 10607.123408955891, - 10607.127080042414, - 10607.12841678632, - 10607.127254827345, - 10607.129964119124, - 10607.125703082966, - 10607.127848678265, - 10607.126280771898, - 10607.127563519043, - 10607.128480073436, - 10607.126789150989, - 10607.128866780102, - 10607.125822315926, - 10607.129917143964, - 10607.12722405211, - 10607.126135964209, - 10607.126314907002, - 10607.12783286178, - 10607.128075448867, - 10607.127876286591, - 10607.126980767163, - 10607.129015575456, - 10607.127992725294, - 10607.126777909389, - 10607.126704195593, - 10607.12793050158, - 10607.12522918776, - 10607.126348047595, - 10607.129123375962, - 10607.127992846265, - 10607.12828807096, - 10607.126012540908, - 10607.126750224412, - 10607.128260493202, - 10607.126713695554, - 10607.126591289903, - 10607.128358051386, - 10607.126783249973, - 10607.127259476149, - 10607.12730443808, - 10607.127680571788, - 10607.128057451006, - 10607.12749370873, - 10607.126666299258, - 10607.1288183178, - 10607.12661283895, - 10607.125792518991, - 10607.127713908758, - 10607.125848107047, - 10607.126823337168, - 10607.128552679897, - 10607.127749901392, - 10607.126193254426, - 10607.125914383205, - 10607.127719208616, - 10607.126854433338, - 10607.126907339523, - 10607.126477190235, - 10607.126678516435, - 10607.127106305355, - 10607.12893285362, - 10607.12960625232, - 10607.128388027151, - 10607.128606531893, - 10607.128976832517, - 10607.129388476082, - 10607.127116783857, - 10607.126671427235, - 10607.126991131185, - 10607.126611026944, - 10607.128083705573, - 10607.12814546676, - 10607.128285461677, - 10607.128345221055, - 10607.127164200618, - 10607.126561062603, - 10607.127867327734, - 10607.126084592408, - 10607.127443412694, - 10607.126417136677, - 10607.127417945963, - 10607.1279333034, - 10607.127802152994, - 10607.126742476234, - 10607.127128725886, - 10607.128162357852, - 10607.12712199402, - 10607.1259899395, - 10607.1275496854, - 10607.127723863225, - 10607.12637239651, - 10607.125830965424, - 10607.127385869007, - 10607.127768117443, - 10607.12770097796, - 10607.127126580415, - 10607.127470011066, - 10607.12406994653, - 10607.126836490996, - 10607.126661035069, - 10607.126136964946, - 10607.126812670229, - 10607.126538385011, - 10607.1267507867, - 10607.126750607747, - 10607.126065354802, - 10607.12705704709, - 10607.128155012764, - 10607.128629423329, - 10607.127473919183, - 10607.128777022755, - 10607.127114038029, - 10607.127140597106, - 10607.125662149912, - 10607.127185123369, - 10607.127198011338, - 10607.12646235816, - 10607.127024846131, - 10607.12859032906, - 10607.127129249497, - 10607.127817248782, - 10607.126395779022, - 10607.126859391818, - 10607.128135845229, - 10607.12772480076, - 10607.126604051542, - 10607.127055560572, - 10607.12706932261, - 10607.12507263878, - 10607.126446343224, - 10607.127992662516, - 10607.12756936384, - 10607.125951714434, - 10607.127070225815, - 10607.126961876287, - 10607.12649680614, - 10607.126702684907, - 10607.126749665613, - 10607.127100925994, - 10607.125242214792, - 10607.126956309556, - 10607.126387529785, - 10607.1250383383, - 10607.12687702978, - 10607.12713116878, - 10607.126748028091, - 10607.127097020471, - 10607.12734490388, - 10607.12707407564, - 10607.12746036209, - 10607.126725785662, - 10607.127132288517, - 10607.127406428985, - 10607.126978812092, - 10607.127185576635, - 10607.127005467377, - 10607.127320560983, - 10607.126346420248, - 10607.127463488703, - 10607.126748343297, - 10607.126983184551, - 10607.127522303575, - 10607.127160070751, - 10607.127147786374, - 10607.126532321456, - 10607.12623666238, - 10607.127536063672, - 10607.126987123882, - 10607.126224008509, - 10607.126985763784, - 10607.126651553746, - 10607.127681998903, - 10607.127208027548, - 10607.126669780086, - 10607.126458089397, - 10607.126786813324, - 10607.126598148629, - 10607.126479911463, - 10607.12675609806, - 10607.127106523494, - 10607.127011120358, - 10607.127677108176, - 10607.12715369399, - 10607.12731455172, - 10607.12653727517, - 10607.127213069343, - 10607.126807567149, - 10607.127513209938, - 10607.126761760548, - 10607.126467620625, - 10607.126746308662, - 10607.126422705136, - 10607.126390275387, - 10607.126080848166, - 10607.126889929978, - 10607.12691642553, - 10607.12668216474, - 10607.126625360132, - 10607.126208767872, - 10607.12619648397, - 10607.127055321856, - 10607.126610510626, - 10607.1262215563, - 10607.126290653508, - 10607.126324641666, - 10607.126198250347, - 10607.125940506294, - 10607.125990232702, - 10607.126630695762, - 10607.125755856445, - 10607.12564952706, - 10607.126032422731, - 10607.125878451458, - 10607.12520185459, - 10607.125854092905 - ], - "yaxis": "y4" - }, - { - "name": "C1 [F]", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247 - ], - "xaxis": "x5", - "y": [ - 4573.468290370735, - 4573.473085555318, - 4573.471500395359, - 4573.471834018761, - 4573.473632630433, - 4573.470703153292, - 4573.47223748432, - 4573.470791719329, - 4573.471616685585, - 4573.473448680049, - 4573.470556547952, - 4573.47072741519, - 4573.4708284998715, - 4573.469496088671, - 4573.46857546466, - 4573.472893448083, - 4573.471381746779, - 4573.470121254313, - 4573.470527362205, - 4573.47253362722, - 4573.470634770797, - 4573.474626067902, - 4573.471759028684, - 4573.470518837104, - 4573.471327844771, - 4573.46892295488, - 4573.471748701546, - 4573.471316407592, - 4573.47229617546, - 4573.469060263153, - 4573.470537298399, - 4573.470500041221, - 4573.472124427146, - 4573.470965077007, - 4573.470701301776, - 4573.474465266036, - 4573.470724672914, - 4573.471402497249, - 4573.471587346265, - 4573.472968099683, - 4573.473177011573, - 4573.471563499045, - 4573.473004807021, - 4573.471103354873, - 4573.472256188521, - 4573.474279748037, - 4573.472045872859, - 4573.475174792936, - 4573.473963525378, - 4573.474202163322, - 4573.474915691477, - 4573.4714083199615, - 4573.471641147991, - 4573.472249143853, - 4573.4707831413, - 4573.473624770846, - 4573.473945698847, - 4573.470223115569, - 4573.473215670227, - 4573.4724924857, - 4573.473007529497, - 4573.471712067358, - 4573.473198126364, - 4573.475735655069, - 4573.471432215784, - 4573.473182933827, - 4573.473299255041, - 4573.473159438099, - 4573.473606435567, - 4573.472566009593, - 4573.474354312474, - 4573.4731504611, - 4573.474290727423, - 4573.474464355466, - 4573.472888951668, - 4573.473757540506, - 4573.474651221938, - 4573.474479436878, - 4573.474115182914, - 4573.47366073948, - 4573.474004802187, - 4573.47382744202, - 4573.47400482515, - 4573.47646066061, - 4573.473162862109, - 4573.473174053662, - 4573.474370628491, - 4573.475083621218, - 4573.472642244115, - 4573.473156993568, - 4573.4746310721, - 4573.475962752797, - 4573.472305790665, - 4573.475182522625, - 4573.475160120211, - 4573.4756799957995, - 4573.474879561061, - 4573.477021057567, - 4573.474463155318, - 4573.476004652806, - 4573.473886540494, - 4573.475510288044, - 4573.475371678425, - 4573.476414675809, - 4573.475419840927, - 4573.474919182961, - 4573.475156529453, - 4573.47808084963, - 4573.476489676727, - 4573.474624409868, - 4573.476093299096, - 4573.475617803957, - 4573.475627806229, - 4573.476967267821, - 4573.473274560463, - 4573.476137037174, - 4573.474206422755, - 4573.475694861014, - 4573.47448535099, - 4573.475145413088, - 4573.474960881819, - 4573.477638560115, - 4573.4746158377575, - 4573.476717636549, - 4573.4740571548555, - 4573.4753178417795, - 4573.4739617053965, - 4573.475746780499, - 4573.476841041696, - 4573.475274542993, - 4573.476147907342, - 4573.473246727146, - 4573.475030608735, - 4573.474836968004, - 4573.4765505714295, - 4573.476089537446, - 4573.476584044215, - 4573.4756092019325, - 4573.476611808051, - 4573.4751746096135, - 4573.474903511333, - 4573.474322447646, - 4573.47613939881, - 4573.475832427081, - 4573.47572224069, - 4573.476810809612, - 4573.475170995926, - 4573.475603937176, - 4573.475739557817, - 4573.475058612032, - 4573.475843172356, - 4573.475776900988, - 4573.476482539219, - 4573.475649176484, - 4573.475434878786, - 4573.475736679733, - 4573.475993713716, - 4573.474997081729, - 4573.474740115264, - 4573.476318370737, - 4573.474636280986, - 4573.475174688127, - 4573.475536531166, - 4573.474468963842, - 4573.474785685583, - 4573.4743045589685, - 4573.475933188332, - 4573.47536772332, - 4573.474574300367, - 4573.4756399267, - 4573.4750002421915, - 4573.47557243754, - 4573.474632452419, - 4573.474876461177, - 4573.474675677546, - 4573.475399672868, - 4573.4748909835735, - 4573.475331630863, - 4573.474670935213, - 4573.475418449587, - 4573.474510165108, - 4573.475065211818, - 4573.475384837548, - 4573.475124295947, - 4573.47533049705, - 4573.475293145175, - 4573.475642042269, - 4573.474812104203, - 4573.4743125622745, - 4573.475107760057, - 4573.474394820945, - 4573.475242860905, - 4573.47442193519, - 4573.475509900366, - 4573.474401713796, - 4573.474539408393, - 4573.474811747639, - 4573.474673418105, - 4573.474675131414, - 4573.475247755298, - 4573.474789821547, - 4573.474783690782, - 4573.474463464162, - 4573.475372991618, - 4573.4744984606605, - 4573.475377319547, - 4573.474539233324, - 4573.474528227876, - 4573.474137973893, - 4573.474754328908, - 4573.475041213995, - 4573.474711636371, - 4573.4747918702515, - 4573.4753430777455, - 4573.474702483403, - 4573.475131156003, - 4573.474750815668, - 4573.474695162964, - 4573.475221949969, - 4573.474857986597, - 4573.474900248409, - 4573.474177894787, - 4573.473910161099, - 4573.474449790298, - 4573.4743021748845, - 4573.474116159141, - 4573.473595519335, - 4573.474551288438, - 4573.473937377235, - 4573.474284552671, - 4573.474495725551, - 4573.4742294614525, - 4573.474142212901, - 4573.47490993069, - 4573.473615046084, - 4573.474460089526, - 4573.473541748187, - 4573.474483033463, - 4573.474696131507, - 4573.47416890132, - 4573.474126772743, - 4573.473838541021, - 4573.474585762205, - 4573.4745167965175, - 4573.474086057211, - 4573.473989661999, - 4573.473970241986, - 4573.474069415822 - ], - "yaxis": "y5" - } - ], - "layout": { - "height": 576, - "legend": { - "orientation": "h", - "x": 1, - "xanchor": "right", - "y": 1.02, - "yanchor": "bottom" - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Parameter Convergence" - }, - "width": 1024, - "xaxis": { - "anchor": "y", - "autorange": true, - "domain": [ - 0, - 0.2888888888888889 - ], - "range": [ - 0, - 247 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis2": { - "anchor": "y2", - "autorange": true, - "domain": [ - 0.35555555555555557, - 0.6444444444444445 - ], - "range": [ - 0, - 247 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis3": { - "anchor": "y3", - "autorange": true, - "domain": [ - 0.7111111111111111, - 1 - ], - "range": [ - 0, - 247 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis4": { - "anchor": "y4", - "autorange": true, - "domain": [ - 0, - 0.2888888888888889 - ], - "range": [ - 0, - 247 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis5": { - "anchor": "y5", - "autorange": true, - "domain": [ - 0.35555555555555557, - 0.6444444444444445 - ], - "range": [ - 0, - 247 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis6": { - "anchor": "y6", - "domain": [ - 0.7111111111111111, - 1 - ] - }, - "yaxis": { - "anchor": "x", - "autorange": true, - "domain": [ - 0, - 0.425 - ], - "range": [ - -0.00016937062114033113, - 0.00521937462607238 - ], - "title": { - "text": "R0 [Ohm]" - }, - "type": "linear" - }, - "yaxis2": { - "anchor": "x2", - "autorange": true, - "domain": [ - 0, - 0.425 - ], - "range": [ - -0.00006613102504555924, - 0.0014588323730183603 - ], - "title": { - "text": "R1 [Ohm]" - }, - "type": "linear" - }, - "yaxis3": { - "anchor": "x3", - "autorange": true, - "domain": [ - 0, - 0.425 - ], - "range": [ - -0.00007118170632151553, - 0.0015548533253697768 - ], - "title": { - "text": "R2 [Ohm]" - }, - "type": "linear" - }, - "yaxis4": { - "anchor": "x4", - "autorange": true, - "domain": [ - 0.575, - 1 - ], - "range": [ - 10607.122898336596, - 10607.133110722558 - ], - "title": { - "text": "C1 [F]" - }, - "type": "linear" - }, - "yaxis5": { - "anchor": "x5", - "autorange": true, - "domain": [ - 0.575, - 1 - ], - "range": [ - 4573.4677464552415, - 4573.478624765124 - ], - "title": { - "text": "C1 [F]" - }, - "type": "linear" - }, - "yaxis6": { - "anchor": "x6", - "domain": [ - 0.575, - 1 - ] - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "010020000.00050.0010.00150.002010020000.00050.001010020000.0010.002010020010.72596k10.725965k10.72597k01002009,733.539,733.535R0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C1 [F]Parameter ConvergenceFunction CallFunction CallFunction CallFunction CallFunction CallR0 [Ohm]R1 [Ohm]R2 [Ohm]C1 [F]C1 [F]" ] }, "metadata": {}, @@ -9508,7 +417,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/examples/notebooks/spm_Adam.ipynb b/examples/notebooks/spm_Adam.ipynb index 35be8b828..d3860b53d 100644 --- a/examples/notebooks/spm_Adam.ipynb +++ b/examples/notebooks/spm_Adam.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -32,27 +32,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", - "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", - "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", - "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", - "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", + "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", + "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", + "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", + "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (8.20.0)\n", + "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (5.14.1)\n", + "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (4.0.10)\n", + "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (3.0.10)\n", + "Requirement already satisfied: decorator in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", + "Requirement already satisfied: jedi>=0.16 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", + "Requirement already satisfied: matplotlib-inline in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", + "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n", + "Requirement already satisfied: pygments>=2.4.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", + "Requirement already satisfied: stack-data in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", + "Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", + "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", + "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": { "id": "SQdt4brD04p1" }, @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "id": "sBasxv8U04p3" }, @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": { "id": "zuvGHWID04p_" }, @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": { "id": "WPCybXIJ04qA" }, @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": { "id": "etMzRtx404qA" }, @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -308,10 +308,10 @@ { "data": { "text/plain": [ - "array([0.77574635, 0.66084088])" + "array([0.71969102, 0.67039216])" ] }, - "execution_count": 24, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -356,4551 +356,8 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898, - 898, - 896, - 894, - 892, - 890, - 888, - 886, - 884, - 882, - 880, - 878, - 876, - 874, - 872, - 870, - 868, - 866, - 864, - 862, - 860, - 858, - 856, - 854, - 852, - 850, - 848, - 846, - 844, - 842, - 840, - 838, - 836, - 834, - 832, - 830, - 828, - 826, - 824, - 822, - 820, - 818, - 816, - 814, - 812, - 810, - 808, - 806, - 804, - 802, - 800, - 798, - 796, - 794, - 792, - 790, - 788, - 786, - 784, - 782, - 780, - 778, - 776, - 774, - 772, - 770, - 768, - 766, - 764, - 762, - 760, - 758, - 756, - 754, - 752, - 750, - 748, - 746, - 744, - 742, - 740, - 738, - 736, - 734, - 732, - 730, - 728, - 726, - 724, - 722, - 720, - 718, - 716, - 714, - 712, - 710, - 708, - 706, - 704, - 702, - 700, - 698, - 696, - 694, - 692, - 690, - 688, - 686, - 684, - 682, - 680, - 678, - 676, - 674, - 672, - 670, - 668, - 666, - 664, - 662, - 660, - 658, - 656, - 654, - 652, - 650, - 648, - 646, - 644, - 642, - 640, - 638, - 636, - 634, - 632, - 630, - 628, - 626, - 624, - 622, - 620, - 618, - 616, - 614, - 612, - 610, - 608, - 606, - 604, - 602, - 600, - 598, - 596, - 594, - 592, - 590, - 588, - 586, - 584, - 582, - 580, - 578, - 576, - 574, - 572, - 570, - 568, - 566, - 564, - 562, - 560, - 558, - 556, - 554, - 552, - 550, - 548, - 546, - 544, - 542, - 540, - 538, - 536, - 534, - 532, - 530, - 528, - 526, - 524, - 522, - 520, - 518, - 516, - 514, - 512, - 510, - 508, - 506, - 504, - 502, - 500, - 498, - 496, - 494, - 492, - 490, - 488, - 486, - 484, - 482, - 480, - 478, - 476, - 474, - 472, - 470, - 468, - 466, - 464, - 462, - 460, - 458, - 456, - 454, - 452, - 450, - 448, - 446, - 444, - 442, - 440, - 438, - 436, - 434, - 432, - 430, - 428, - 426, - 424, - 422, - 420, - 418, - 416, - 414, - 412, - 410, - 408, - 406, - 404, - 402, - 400, - 398, - 396, - 394, - 392, - 390, - 388, - 386, - 384, - 382, - 380, - 378, - 376, - 374, - 372, - 370, - 368, - 366, - 364, - 362, - 360, - 358, - 356, - 354, - 352, - 350, - 348, - 346, - 344, - 342, - 340, - 338, - 336, - 334, - 332, - 330, - 328, - 326, - 324, - 322, - 320, - 318, - 316, - 314, - 312, - 310, - 308, - 306, - 304, - 302, - 300, - 298, - 296, - 294, - 292, - 290, - 288, - 286, - 284, - 282, - 280, - 278, - 276, - 274, - 272, - 270, - 268, - 266, - 264, - 262, - 260, - 258, - 256, - 254, - 252, - 250, - 248, - 246, - 244, - 242, - 240, - 238, - 236, - 234, - 232, - 230, - 228, - 226, - 224, - 222, - 220, - 218, - 216, - 214, - 212, - 210, - 208, - 206, - 204, - 202, - 200, - 198, - 196, - 194, - 192, - 190, - 188, - 186, - 184, - 182, - 180, - 178, - 176, - 174, - 172, - 170, - 168, - 166, - 164, - 162, - 160, - 158, - 156, - 154, - 152, - 150, - 148, - 146, - 144, - 142, - 140, - 138, - 136, - 134, - 132, - 130, - 128, - 126, - 124, - 122, - 120, - 118, - 116, - 114, - 112, - 110, - 108, - 106, - 104, - 102, - 100, - 98, - 96, - 94, - 92, - 90, - 88, - 86, - 84, - 82, - 80, - 78, - 76, - 74, - 72, - 70, - 68, - 66, - 64, - 62, - 60, - 58, - 56, - 54, - 52, - 50, - 48, - 46, - 44, - 42, - 40, - 38, - 36, - 34, - 32, - 30, - 28, - 26, - 24, - 22, - 20, - 18, - 16, - 14, - 12, - 10, - 8, - 6, - 4, - 2, - 0 - ], - "y": [ - 4.066105347613955, - 4.05761967457949, - 4.050305266153682, - 4.044049520593223, - 4.038660144671588, - 4.033974852750363, - 4.0298650397764995, - 4.026230238476654, - 4.022992962411548, - 4.020091969822013, - 4.0174794473235504, - 4.015116460194554, - 4.012970759322049, - 4.011015832840792, - 4.009229698412319, - 4.007593767737496, - 4.006092462136447, - 4.004712597988969, - 4.003442589466103, - 4.002272790978113, - 4.001193996752235, - 4.000198706561292, - 3.9992795559025303, - 3.998430419594829, - 3.997645454726415, - 3.996919580792493, - 3.9962480902354462, - 3.995626731286716, - 3.9950515369625506, - 3.9945190373928936, - 3.9940260309512303, - 3.993569166565743, - 3.993145841954449, - 3.992753515365953, - 3.992389597984289, - 3.992051764044457, - 3.9917379852991544, - 3.991446255707619, - 3.991174581762894, - 3.9909212723330696, - 3.990684682343621, - 3.9904632157889433, - 3.9902554257856866, - 3.99005994229812, - 3.989875469094177, - 3.989700786329245, - 3.9895347536225754, - 3.989376293490431, - 3.989224380171567, - 3.989078063511411, - 3.9889364449138274, - 3.988798651489605, - 3.988663887230116, - 3.988531405260823, - 3.988400501062998, - 3.988270514637151, - 3.9881407986791584, - 3.988010735746256, - 3.987879799374508, - 3.9877474741120578, - 3.987613279394087, - 3.987476763020187, - 3.987337436056881, - 3.987194936487739, - 3.987048893205018, - 3.986898961863952, - 3.9867448239566152, - 3.9865861531117206, - 3.9864226706003936, - 3.98625412702422, - 3.9860802848485863, - 3.985900926219853, - 3.9857158465879814, - 3.9855248523844296, - 3.985327782774646, - 3.985124486212421, - 3.9849148265913135, - 3.9846986826436295, - 3.984475936734279, - 3.9842464983176913, - 3.984010284723341, - 3.983767225574437, - 3.983517262355448, - 3.983260332035069, - 3.9829963922896376, - 3.9827254207536953, - 3.982447398156598, - 3.982162314833694, - 3.9818701704124657, - 3.981570973520072, - 3.981264741510914, - 3.980951474382314, - 3.980631158635882, - 3.980303864008748, - 3.979969630148205, - 3.979628503170707, - 3.9792805354370975, - 3.978925785341539, - 3.978564317113191, - 3.978196194375262, - 3.9778213975418666, - 3.97744005771284, - 3.9770522494329055, - 3.976658051019026, - 3.9762575443800303, - 3.9758508148462104, - 3.9754379510083906, - 3.975019044565837, - 3.9745941165176033, - 3.974163281934169, - 3.973726648379272, - 3.97328431036887, - 3.9728363644341855, - 3.9723829090540046, - 3.971924044596348, - 3.9714598732693753, - 3.9709904990807097, - 3.9705160278047456, - 3.970036566957552, - 3.969552225778609, - 3.969063115219157, - 3.968569260399729, - 3.968070724554464, - 3.967567688524227, - 3.9670602575722, - 3.966548537561305, - 3.9660326349329926, - 3.965512656690508, - 3.964988710386434, - 3.964460904114091, - 3.963929346502655, - 3.96339414671555, - 3.962855414451929, - 3.962313259951051, - 3.961767754539925, - 3.9612188325991777, - 3.960666739596147, - 3.960111574311669, - 3.95955343487421, - 3.9589924187280854, - 3.958428622603694, - 3.957862142489763, - 3.9572930736074023, - 3.956721510386125, - 3.956147546441324, - 3.9555712745534977, - 3.9549927866489503, - 3.954412173781957, - 3.9538294426936225, - 3.953244725630241, - 3.95265811552135, - 3.952069694286443, - 3.951479542438605, - 3.950887739057584, - 3.95029436176411, - 3.9496994866954247, - 3.9491031884820553, - 3.948505540225705, - 3.947906613478227, - 3.9473064782218095, - 3.946705202850093, - 3.946102892048, - 3.9454996180221342, - 3.944895423936502, - 3.9442903754186016, - 3.9436845367381466, - 3.9430779708041577, - 3.9424707391627423, - 3.941862901995463, - 3.941254518118402, - 3.940645644981682, - 3.940036338669627, - 3.939426653901507, - 3.9388166440326, - 3.938206379518894, - 3.937595956597897, - 3.936985385702924, - 3.9363747186177966, - 3.93576400608877, - 3.9351532978379886, - 3.9345426425771257, - 3.933932088021091, - 3.933321680901902, - 3.932711466982571, - 3.9321014910711583, - 3.931491797034768, - 3.930882427813606, - 3.9302734254351126, - 3.929664824849201, - 3.929056670274603, - 3.928449000964205, - 3.927841855099619, - 3.9272352699941626, - 3.926629282106008, - 3.926023927051314, - 3.925419239617293, - 3.92481525377518, - 3.924212002693152, - 3.923609518749162, - 3.92300783354367, - 3.922406977912265, - 3.92180695019909, - 3.9212077844753184, - 3.9206095222519672, - 3.920012189982373, - 3.919415813373683, - 3.918820417400911, - 3.918226026320851, - 3.917632663685814, - 3.917040352357309, - 3.9164491145195335, - 3.915858971692733, - 3.915269944746411, - 3.914682053912429, - 3.9140953187978726, - 3.9135097583978498, - 3.9129253911080784, - 3.912342234737331, - 3.9117603065197137, - 3.9111796231268063, - 3.9106002006795832, - 3.910022019737663, - 3.9094451054935297, - 3.908869485467374, - 3.9082951725291912, - 3.9077221790022545, - 3.9071505166736142, - 3.9065801968044926, - 3.906011230140434, - 3.90544362692142, - 3.904877396891752, - 3.904312549309818, - 3.9037490929576895, - 3.9031870361505994, - 3.902626386746231, - 3.902067152153907, - 3.9015093393435833, - 3.900952954854715, - 3.900398004804979, - 3.899844494898842, - 3.899292430436001, - 3.8987418094655824, - 3.898192639012096, - 3.8976449254008663, - 3.897098672186823, - 3.896553882550091, - 3.896010559303415, - 3.8954687048994296, - 3.894928321437872, - 3.894389410672585, - 3.8938519740184288, - 3.8933160125580617, - 3.8927815270485673, - 3.8922485179279818, - 3.891716985321699, - 3.8911869290487178, - 3.8906583486277815, - 3.8901312432834434, - 3.889605611951904, - 3.8890814532868583, - 3.888558765665103, - 3.888037559103256, - 3.88751782631511, - 3.8869995616573862, - 3.886482762793849, - 3.885967427154553, - 3.885453551941091, - 3.884941134131734, - 3.8844301704864552, - 3.883920657551824, - 3.88341259166586, - 3.882905968962703, - 3.882400785377208, - 3.8818970366494554, - 3.8813947183291577, - 3.880893825779903, - 3.8803943541834, - 3.879896298543549, - 3.8793996536904345, - 3.878904414284276, - 3.8784105748191697, - 3.8779181371921094, - 3.8774270919614007, - 3.876937431475285, - 3.876449149831608, - 3.875962240992266, - 3.87547669878667, - 3.8749925169152046, - 3.8745096889525774, - 3.8740282083510698, - 3.873548068443722, - 3.873069262447435, - 3.872591783465985, - 3.8721156244929698, - 3.8716407784146742, - 3.871167238012852, - 3.870694995967437, - 3.870224044859179, - 3.869754377172228, - 3.869285985296619, - 3.868819102685783, - 3.868353482170397, - 3.867889003119859, - 3.8674256881526654, - 3.866963641121012, - 3.8665027864318864, - 3.86604311816485, - 3.865584659800968, - 3.86512739904927, - 3.8646713236155215, - 3.864216426887926, - 3.8637627020923313, - 3.8633101427269785, - 3.862858741996774, - 3.862408493006559, - 3.861959388763258, - 3.861511347883145, - 3.861064424772097, - 3.8606186139827567, - 3.8601739082132447, - 3.8597303000729055, - 3.859287782084124, - 3.858846346684172, - 3.858405986226859, - 3.857966692984307, - 3.857528459148522, - 3.857091276833026, - 3.856655138074356, - 3.856219985419585, - 3.8557857795661254, - 3.8553525753644617, - 3.8549203646501136, - 3.8544891391868448, - 3.85405889066789, - 3.8536296107171264, - 3.8532012908902433, - 3.852773922675762, - 3.8523474974961487, - 3.851922006708752, - 3.8514974416068126, - 3.8510737934203263, - 3.8506509441732937, - 3.850228960148355, - 3.849807848115201, - 3.849387599143988, - 3.8489682042434974, - 3.848549654361756, - 3.848131940386609, - 3.847715053146278, - 3.847298983409854, - 3.846883721887722, - 3.846469259232008, - 3.846055586036934, - 3.845642657452216, - 3.845230409860326, - 3.844818905938204, - 3.844408136121077, - 3.843998090785414, - 3.8435887602490455, - 3.8431801347711914, - 3.842772204552508, - 3.8423649597350455, - 3.841958390402163, - 3.8415524865784496, - 3.84114723822953, - 3.840742635261905, - 3.8403385864908457, - 3.839935122884998, - 3.8395322582840574, - 3.839129982406645, - 3.8387282849085897, - 3.8383271553824856, - 3.8379265833571465, - 3.837526558297056, - 3.8371270696017783, - 3.83672810660528, - 3.8363296585752593, - 3.835931714712391, - 3.8355342451832577, - 3.8351371733633206, - 3.834740555795552, - 3.83434438094538, - 3.8339486372064977, - 3.8335533128997303, - 3.833158396271798, - 3.8327638754941216, - 3.832369738661495, - 3.831975973790735, - 3.831582568819311, - 3.8311895116038768, - 3.830796789918755, - 3.830404391454408, - 3.830012303815807, - 3.82962051452076, - 3.829229010998215, - 3.828837780586468, - 3.828446810531314, - 3.828056087984184, - 3.827665600000171, - 3.827275333536027, - 3.826885247009613, - 3.826495321536007, - 3.826105569818117, - 3.825715978057677, - 3.8253265323466574, - 3.82493721866482, - 3.82454802287719, - 3.824158930731434, - 3.8237699278552433, - 3.823380999753572, - 3.8229921318058535, - 3.822603309263136, - 3.822214517245152, - 3.821825740737311, - 3.8214369645876287, - 3.8210481735035886, - 3.820659352048901, - 3.8202704846402455, - 3.819881555543871, - 3.819492548872207, - 3.8191034485802913, - 3.8187142384622264, - 3.8183248961634098, - 3.817935408719801, - 3.8175457607697503, - 3.817155935380459, - 3.816765915438909, - 3.816375683647847, - 3.8159852225216344, - 3.815594514382027, - 3.8152035413539056, - 3.8148122853608775, - 3.814420728120873, - 3.8140288511415776, - 3.813636635715867, - 3.813244062917128, - 3.812851113594492, - 3.8124577683680343, - 3.812064007623869, - 3.8116698115091854, - 3.811275159927219, - 3.810880032532121, - 3.8104844087238186, - 3.8100882678568344, - 3.8096915912828138, - 3.809294356375235, - 3.808896541618072, - 3.8084981252148062, - 3.808099085082997, - 3.807699398848718, - 3.805461403432769, - 3.805861089667048, - 3.8062601297988574, - 3.8066585462021223, - 3.8070563609592862, - 3.807453595866865, - 3.8078502724408856, - 3.8082464133078697, - 3.808642037116172, - 3.8090371645112704, - 3.809431816093237, - 3.80982601220792, - 3.810219772952086, - 3.810613118178543, - 3.811006067501179, - 3.811398640299918, - 3.811790855725629, - 3.812182732704924, - 3.8125742899449286, - 3.812965545937957, - 3.8133565189660783, - 3.8137472271056856, - 3.8141376882318982, - 3.81452792002296, - 3.81491793996451, - 3.8153077653538015, - 3.815697413303852, - 3.816086900747461, - 3.8164762430462775, - 3.8168654531643424, - 3.8172545534562583, - 3.817643560127922, - 3.8180324892242967, - 3.818421356632952, - 3.81881017808764, - 3.81919896917168, - 3.8195877453213622, - 3.819976521829203, - 3.820365313847187, - 3.8207541363899047, - 3.8211430043376233, - 3.8215319324392945, - 3.821920935315485, - 3.822310027461241, - 3.822699223248871, - 3.8230885369307086, - 3.823477982641728, - 3.823867574402168, - 3.8242573261200583, - 3.824647251593664, - 3.825037338120078, - 3.825427604584222, - 3.8258180925682352, - 3.8262088151153657, - 3.826599785170519, - 3.8269910155822666, - 3.827382519104811, - 3.8277743083998583, - 3.8281663960384593, - 3.8285587945028063, - 3.828951516187928, - 3.829344573403362, - 3.8297379783747862, - 3.830131743245546, - 3.8305258800781727, - 3.8309204008558497, - 3.8313153174837815, - 3.831710641790549, - 3.832106385529431, - 3.832502560379603, - 3.832899177947372, - 3.833296249767309, - 3.833693719296442, - 3.8340916631593105, - 3.8344901111893313, - 3.8348890741858295, - 3.835288562881107, - 3.8356885879411977, - 3.8360891599665368, - 3.836490289492641, - 3.836891986990696, - 3.8372942628681086, - 3.837697127469049, - 3.838100591074897, - 3.838504639845956, - 3.838909242813582, - 3.839314491162501, - 3.839720394986214, - 3.8401269643190967, - 3.840534209136559, - 3.8409421393552425, - 3.8413507648330967, - 3.841760095369465, - 3.842170140705128, - 3.8425809105222553, - 3.8429924144443777, - 3.8434046620362663, - 3.843817590620985, - 3.8442312638160594, - 3.8446457264717737, - 3.845060987993905, - 3.845477057730329, - 3.84589394497066, - 3.846311658945807, - 3.846730208827549, - 3.847149603728039, - 3.847569852699252, - 3.847990964732406, - 3.848412948757345, - 3.848835798004378, - 3.849259446190864, - 3.849684011292803, - 3.8501095020802, - 3.850535927259813, - 3.8509632954742945, - 3.8513916153011776, - 3.851820895251941, - 3.852251143770896, - 3.8526823692341647, - 3.853114579948513, - 3.8535477841501766, - 3.853981990003636, - 3.854417142658407, - 3.854853281417077, - 3.855290463732574, - 3.8557286975683582, - 3.8561679908109103, - 3.856608351268223, - 3.857049786668175, - 3.8574923046569567, - 3.857935912797296, - 3.858380618566808, - 3.858826429356148, - 3.859273352467196, - 3.85972139334731, - 3.86017049759061, - 3.860620746580825, - 3.8610721473110297, - 3.8615247066763825, - 3.8619784314719774, - 3.8624333281995726, - 3.862889403633321, - 3.8633466643850194, - 3.863805122748901, - 3.8642647910159376, - 3.864725645705063, - 3.8651876927367166, - 3.86565100770391, - 3.866115486754448, - 3.8665811072698344, - 3.8670479898806702, - 3.867516381756279, - 3.86798604944323, - 3.868457000551488, - 3.868929242596903, - 3.8694027829987254, - 3.869877629077021, - 3.870353788050036, - 3.8708312670314866, - 3.871310073027773, - 3.871790212935121, - 3.8722716935366286, - 3.872754521499256, - 3.873238703370721, - 3.873724245576317, - 3.874211154415659, - 3.874699436059336, - 3.875189096545452, - 3.8756801417761606, - 3.876172579403221, - 3.8766664188683273, - 3.8771616582744857, - 3.8776583031276, - 3.878156358767451, - 3.8786558303639542, - 3.879156722913209, - 3.8796590412335066, - 3.8801627899612594, - 3.880667973546754, - 3.8811745962499113, - 3.881682662135875, - 3.8821921750705064, - 3.8827031387157858, - 3.8832155565251423, - 3.883729431738604, - 3.8842447673779, - 3.8847615662414374, - 3.885279830899161, - 3.885799563687307, - 3.8863207702491542, - 3.8868434578709095, - 3.8873676165359554, - 3.8878932478674946, - 3.8884203532118327, - 3.888948933632769, - 3.8894789899057503, - 3.890010522512033, - 3.8905435316326185, - 3.891078017142112, - 3.89161397860248, - 3.892151415256636, - 3.892690326021923, - 3.893230709483481, - 3.8937725638874663, - 3.894315887134143, - 3.894860676770874, - 3.8954069299849174, - 3.895954643596147, - 3.8965038140496335, - 3.897054435020052, - 3.8976064994828934, - 3.8981600093890303, - 3.8987149594387662, - 3.8992713439276345, - 3.899829156737958, - 3.9003883913302824, - 3.9009490407346505, - 3.9015110975417406, - 3.9020745538938697, - 3.902639401475803, - 3.9032056315054713, - 3.903773234724486, - 3.904342201388544, - 3.9049125212576654, - 3.9054841835863057, - 3.9060571771132424, - 3.906631490051425, - 3.907207110077581, - 3.9077840243217143, - 3.9083622052636344, - 3.9089416277108575, - 3.909522311103765, - 3.9101042393213823, - 3.9106873956921295, - 3.911271762981901, - 3.911857323381924, - 3.91244405849648, - 3.913031949330462, - 3.913620976276784, - 3.9142111191035847, - 3.91480235694136, - 3.9153946682698657, - 3.915988030904902, - 3.916582421984962, - 3.917177817957734, - 3.917774194566424, - 3.9183715268360184, - 3.9189697890593695, - 3.9195689547831414, - 3.920168982496316, - 3.9207698381277214, - 3.921371523333214, - 3.9219740072772034, - 3.9225772583592313, - 3.923181244201344, - 3.923785931635366, - 3.924391286690059, - 3.924997274578214, - 3.92560385968367, - 3.926211005548255, - 3.926818674858654, - 3.927426829433253, - 3.928035430019164, - 3.928644432397657, - 3.929253801618819, - 3.92986349565521, - 3.9304734715666223, - 3.931083685485953, - 3.931694092605142, - 3.932304647161176, - 3.93291530242204, - 3.933526010672822, - 3.934136723201848, - 3.9347473902869754, - 3.935357961181948, - 3.935968384102946, - 3.936578648616651, - 3.937188658485558, - 3.9377983432536783, - 3.938407649565733, - 3.939016522702454, - 3.9396249065795144, - 3.940232743746794, - 3.940839975388209, - 3.9414465413221977, - 3.942052380002653, - 3.942657428520554, - 3.9432616226061854, - 3.9438648966320513, - 3.944467207434144, - 3.9450684828058606, - 3.945668618062278, - 3.946267544809756, - 3.9468651930661065, - 3.947461491279476, - 3.948056366348162, - 3.948649743641635, - 3.949241547022656, - 3.949831698870494, - 3.950420120105401, - 3.951006730214292, - 3.9515914472776736, - 3.952174178366008, - 3.952754791233002, - 3.953333279137549, - 3.953909551025375, - 3.954483514970176, - 3.9550550781914535, - 3.9556241470738143, - 3.956190627187745, - 3.956754423312137, - 3.9573154394582617, - 3.95787357889572, - 3.958428744180198, - 3.958980837183229, - 3.959529759123976, - 3.960075264535102, - 3.96061741903598, - 3.961156151299601, - 3.961691351086706, - 3.962222908698142, - 3.9627507149704857, - 3.963274661274559, - 3.963794639517044, - 3.964310542145357, - 3.964822262156251, - 3.965329693108278, - 3.965832729138515, - 3.966331264983779, - 3.966825119803208, - 3.96731423036266, - 3.967798571541603, - 3.9682780323887967, - 3.968752503664761, - 3.9692218778534265, - 3.969686049180399, - 3.970144913638056, - 3.9705983690182367, - 3.971046314952922, - 3.9714886529633233, - 3.97192528651822, - 3.9723561211016545, - 3.972781049149888, - 3.9731999555924418, - 3.9736128194302616, - 3.9740195489640815, - 3.974420055603077, - 3.9748142540169566, - 3.975202062296891, - 3.9755834021259178, - 3.975958198959314, - 3.976326321697242, - 3.9766877899255904, - 3.9770425400211487, - 3.977390507754758, - 3.977731634732256, - 3.978065868592799, - 3.978393163219933, - 3.978713478966365, - 3.979026746094965, - 3.979332978104123, - 3.979632174996517, - 3.979924319417745, - 3.980209402740649, - 3.9804874253377465, - 3.980758396873689, - 3.981022336619119, - 3.9812792669394983, - 3.981529230158488, - 3.981772289307392, - 3.9820085029017425, - 3.98223794131833, - 3.9824606872276807, - 3.9826768311753646, - 3.982886490796472, - 3.983089787358697, - 3.9832868569684807, - 3.983477851172033, - 3.983662930803904, - 3.9838422894326375, - 3.9840161316082714, - 3.9841846751844447, - 3.984348157695772, - 3.9845068285406664, - 3.984660966448003, - 3.9848108977890697, - 3.98495694107179, - 3.985099440640932, - 3.9852387676042382, - 3.9853752839781382, - 3.985509478696109, - 3.985641803958559, - 3.985772740330307, - 3.9859028032632096, - 3.9860325192212023, - 3.9861625056470498, - 3.9862934098448743, - 3.986425891814167, - 3.986560656073656, - 3.9866984494978785, - 3.986840068095462, - 3.9869863847556184, - 3.9871382980744823, - 3.9872967582066265, - 3.987462790913296, - 3.987637473678228, - 3.987821946882171, - 3.9880174303697378, - 3.9882252203729944, - 3.988446686927672, - 3.9886832769171208, - 3.988936586346945, - 3.98920826029167, - 3.9894999898832055, - 3.989813768628508, - 3.99015160256834, - 3.990515519950004, - 3.9909078465385, - 3.991331171149794, - 3.991788035535282, - 3.9922810419769448, - 3.9928135415466017, - 3.993388735870767, - 3.9940100948194974, - 3.994681585376544, - 3.9954074593104663, - 3.99619242417888, - 3.9970415604865814, - 3.997960711145343, - 3.998956001336286, - 4.000034795562163, - 4.0012045940501535, - 4.002474602573019, - 4.003854466720497, - 4.005355772321546, - 4.006991702996369, - 4.008777837424843, - 4.010732763906099, - 4.012878464778605, - 4.015241451907601, - 4.017853974406063, - 4.020754966995598, - 4.023992243060705, - 4.02762704436055, - 4.031736857334414, - 4.036422149255638, - 4.041811525177273, - 4.048067270737732, - 4.05538167916354, - 4.063867352198005 - ] - }, - { - "mode": "markers", - "name": "Target", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.06471977983984, - 4.056677922810598, - 4.049512665172218, - 4.041068497084215, - 4.036598660233501, - 4.032776763904335, - 4.028326864358035, - 4.024243174890612, - 4.0216426679822375, - 4.016844471022015, - 4.0135313685128615, - 4.013141432819571, - 4.009891988112922, - 4.007899123024522, - 4.007868323326693, - 4.005657944431246, - 4.002345487711473, - 4.002758729939496, - 4.000157726928405, - 3.9991874898580826, - 3.997374111743748, - 3.9988713261186937, - 3.997425959156761, - 3.9950371425391857, - 3.995997001080973, - 3.9947535788371034, - 3.992532343873753, - 3.9935598421822673, - 3.99153015044207, - 3.993217112088624, - 3.99218417042831, - 3.989914613123058, - 3.990238626368865, - 3.9900450290576552, - 3.9902637650483985, - 3.991826831781326, - 3.988737597431456, - 3.9879845422283062, - 3.98897597312648, - 3.989024621890654, - 3.989902409976938, - 3.988510778343043, - 3.987687766152248, - 3.990055461780533, - 3.9867050736708944, - 3.9885362910836544, - 3.986542761164608, - 3.9875387464297143, - 3.989083006335263, - 3.986789910312208, - 3.988474712279941, - 3.9854252234139538, - 3.9859400860254577, - 3.9862474592002552, - 3.9859537253669655, - 3.9862526301468586, - 3.9849160338535854, - 3.984739644267952, - 3.985458965307637, - 3.986524295379048, - 3.985609738872625, - 3.9867779840571873, - 3.9847930020058393, - 3.9839311410395273, - 3.985395340527292, - 3.983192375780093, - 3.983712660926793, - 3.9834472860448313, - 3.984822089202456, - 3.984445749378283, - 3.982303797676741, - 3.983015644099083, - 3.983997133372148, - 3.9826080315069383, - 3.982803508651738, - 3.984442792095237, - 3.9813931406328047, - 3.984590064731354, - 3.9809965598703454, - 3.982257385539171, - 3.9820317260557014, - 3.9808220430696, - 3.981616256932131, - 3.9805860010343417, - 3.981743435806523, - 3.980188592708464, - 3.9809803419804672, - 3.9798904172071152, - 3.9792639100754186, - 3.978878157647422, - 3.978543559567428, - 3.97952178349528, - 3.979074659484582, - 3.9796825031238177, - 3.976573851271448, - 3.977424800394471, - 3.977308797014684, - 3.976255091912582, - 3.975421684862331, - 3.975915984010137, - 3.975796747642474, - 3.9755554324606455, - 3.974805341936603, - 3.9759866046275096, - 3.97416545075614, - 3.973353537140665, - 3.973326102790661, - 3.9744453073580273, - 3.973156396512306, - 3.973238119755639, - 3.971367064987546, - 3.970471887978706, - 3.971788398109534, - 3.972486789916292, - 3.9691270813525, - 3.9694969287928576, - 3.9695940940909025, - 3.969147363785802, - 3.968737687656958, - 3.9667576674120055, - 3.96708677397722, - 3.965866228038769, - 3.967444500269796, - 3.963923122717476, - 3.965810514918352, - 3.966489551497605, - 3.966321269415025, - 3.9624705465134578, - 3.963448030006705, - 3.962042978065472, - 3.9624469165617855, - 3.962268801492209, - 3.959332185733911, - 3.959773177530882, - 3.95988449267411, - 3.958998212257139, - 3.9605856892251983, - 3.958460449547634, - 3.958167320433053, - 3.9574605043958258, - 3.9575856535294935, - 3.956848276404944, - 3.958581453095677, - 3.9550415314813736, - 3.9537157240232386, - 3.9520912945319497, - 3.952290082900416, - 3.953986649014828, - 3.954801056158604, - 3.950232184994539, - 3.951962347701682, - 3.949797164492721, - 3.949795291108174, - 3.9498026875669434, - 3.949891480691493, - 3.946476310004648, - 3.947591052826918, - 3.9478458094727222, - 3.9484949949803863, - 3.945114639575653, - 3.946660046602848, - 3.945666714749035, - 3.9434785468443665, - 3.9441031114091887, - 3.942440372555646, - 3.942983505039562, - 3.941618252201682, - 3.9401657542837616, - 3.941181722083384, - 3.9418572811956087, - 3.938746531115573, - 3.9383077205734183, - 3.9385546716591873, - 3.936008865993196, - 3.9350744425392583, - 3.936160888112522, - 3.9364538272521488, - 3.933789767866229, - 3.9345754110165423, - 3.9344755172359127, - 3.933692774984267, - 3.932844238011166, - 3.9328422926231936, - 3.9293389346231886, - 3.9303742473549814, - 3.9296440230437617, - 3.930953431861552, - 3.9285052924572863, - 3.929520327948837, - 3.928093580275596, - 3.926995975056252, - 3.92800703294, - 3.9265360460862695, - 3.925403311536124, - 3.923121740478926, - 3.923388551929652, - 3.9250310773912864, - 3.9232542012497182, - 3.9237372557049826, - 3.921569109625307, - 3.922832071378313, - 3.9206360322317697, - 3.922023728102832, - 3.9206512907914575, - 3.9201744033248778, - 3.918346432542424, - 3.9175400245976, - 3.917378635838934, - 3.9161074803112146, - 3.915375687407393, - 3.9149006756098457, - 3.9160127346621465, - 3.913654969127839, - 3.91288856258806, - 3.914421571853833, - 3.912055483831779, - 3.911834402510157, - 3.91141701283952, - 3.9119382686759825, - 3.9097776478014863, - 3.91038711775684, - 3.90831842316204, - 3.9074725013590186, - 3.908014665261903, - 3.905999131680834, - 3.9073285921329903, - 3.904951094082055, - 3.904103815151253, - 3.904142205562916, - 3.906050108170741, - 3.9040749206900447, - 3.903206872948836, - 3.9008653017143247, - 3.9022310568350767, - 3.903755597789173, - 3.8989665427117326, - 3.901624514840595, - 3.8992419705610417, - 3.897969149569015, - 3.898681190882484, - 3.898741515737, - 3.896910969937301, - 3.896587971345469, - 3.8966196914196383, - 3.89634206169477, - 3.894249196371343, - 3.893363115697437, - 3.892841235957728, - 3.893752939847443, - 3.8930272462663185, - 3.894220172345729, - 3.8927989358216424, - 3.8902283832554887, - 3.8913831382699304, - 3.8919544760788343, - 3.8922411085000497, - 3.8903993150213423, - 3.8886217738201094, - 3.8891141026768463, - 3.889719559587516, - 3.888363070619392, - 3.886279230567264, - 3.886218192293992, - 3.8871186934211863, - 3.885061368963615, - 3.884986977390622, - 3.8855213189590905, - 3.8830926708453983, - 3.883146914286322, - 3.881705539531771, - 3.882579381124136, - 3.883014485009865, - 3.8822999585127698, - 3.880055382292792, - 3.8805911126906345, - 3.8797636063730274, - 3.8790218044128273, - 3.881341101215294, - 3.877874748717924, - 3.8789364202529937, - 3.8786820048385824, - 3.8779462704889993, - 3.876347263656867, - 3.8754087572400664, - 3.875352247726027, - 3.874907585427533, - 3.873872605694672, - 3.874748334995816, - 3.8730953926598497, - 3.872796102426632, - 3.8722385030161823, - 3.87204980189333, - 3.8716019742033487, - 3.8702424725417983, - 3.870072783587898, - 3.871332909693517, - 3.867984898158914, - 3.870037140498438, - 3.86911540469453, - 3.868646819356559, - 3.867993304638917, - 3.86791638415228, - 3.86743577097611, - 3.866890887525142, - 3.8664321773888424, - 3.8640246029975778, - 3.865058694237642, - 3.8655463512558783, - 3.864475934084604, - 3.862509020792936, - 3.8650476451842546, - 3.862218817880733, - 3.861739945256161, - 3.8623106381884367, - 3.861101428045842, - 3.859673180367587, - 3.8616925646065514, - 3.860680263907884, - 3.858617840391033, - 3.858503208285899, - 3.857136033407929, - 3.858641880718284, - 3.85871642245004, - 3.857365167019121, - 3.858120965533814, - 3.857888538019182, - 3.8567377332010406, - 3.856792004693668, - 3.855734831810397, - 3.8523546820187384, - 3.853171994170852, - 3.852661603230736, - 3.8524805652117102, - 3.852510115237965, - 3.852846394106592, - 3.8525172877326206, - 3.8532254252420257, - 3.851770725276331, - 3.850338929369217, - 3.849400279132487, - 3.8479984643438434, - 3.8500257518289938, - 3.849476220800347, - 3.850190412706914, - 3.8464142078176486, - 3.847983064170394, - 3.848675575347392, - 3.8469352616409216, - 3.846844354031986, - 3.8462264845921377, - 3.845120357264588, - 3.843659883998657, - 3.8442610261414702, - 3.84428929146349, - 3.8428199811979695, - 3.8431188436786594, - 3.841732653308266, - 3.8442684949715367, - 3.844278428024952, - 3.842222498784957, - 3.842113714590965, - 3.843128417537069, - 3.8410904549289087, - 3.8398375697887617, - 3.840754508263455, - 3.839724417770237, - 3.8375400103576953, - 3.838778798627743, - 3.8383708513850543, - 3.838162795001563, - 3.837011858881156, - 3.836673300692936, - 3.8349128211843047, - 3.8381333552660055, - 3.836456896021954, - 3.83463201394255, - 3.8361552409506534, - 3.833778786592612, - 3.8322901056906087, - 3.8329203591620855, - 3.8342007410275687, - 3.834488380358761, - 3.834072457265687, - 3.835031138576912, - 3.831372949235014, - 3.831921903649646, - 3.829994785594772, - 3.830573080128059, - 3.83089523284986, - 3.830810888251571, - 3.827830364033204, - 3.8286600125545127, - 3.8281790739491353, - 3.829373423391408, - 3.828288999799333, - 3.828311760797029, - 3.829894636070927, - 3.827730513202857, - 3.827054643058136, - 3.8249540880078534, - 3.8252647491194094, - 3.825684940836542, - 3.825091354146144, - 3.8250541691906137, - 3.825151058405714, - 3.823056484347142, - 3.8224706249937417, - 3.821979383895115, - 3.823730110607342, - 3.821309168643596, - 3.8232062294042257, - 3.8196581874029745, - 3.822611009695059, - 3.8199669535076377, - 3.822753854170101, - 3.8203362529570737, - 3.819710350471649, - 3.818113410746103, - 3.817963217544269, - 3.818362975405113, - 3.81668422989477, - 3.817205425810662, - 3.816372828615999, - 3.8149197941643735, - 3.8174152989101793, - 3.8147389404262304, - 3.8164347169274113, - 3.816846735645065, - 3.815524270886293, - 3.8161172712822062, - 3.814479619079187, - 3.81190421956377, - 3.81403883914785, - 3.8133495972845126, - 3.811167831293824, - 3.812006566236078, - 3.810675232840358, - 3.8121940034999895, - 3.809834869391101, - 3.8089541696239455, - 3.810372705757173, - 3.8102357537345393, - 3.80837679118511, - 3.8092578094856138, - 3.807744799370103, - 3.8082103635862334, - 3.808625010340719, - 3.807107704695925, - 3.805360563322404, - 3.805758309088908 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Model", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.06498634990598, - 4.056500676871515, - 4.049186268445707, - 4.042930522885248, - 4.037541146963613, - 4.0328558550423885, - 4.028746042068525, - 4.0251112407686795, - 4.021873964703573, - 4.018972972114038, - 4.016360449615576, - 4.0139974624865795, - 4.011851761614074, - 4.0098968351328175, - 4.008110700704344, - 4.006474770029521, - 4.004973464428472, - 4.003593600280994, - 4.002323591758128, - 4.001153793270138, - 4.00007499904426, - 3.999079708853318, - 3.998160558194556, - 3.9973114218868546, - 3.9965264570184407, - 3.995800583084518, - 3.995129092527472, - 3.994507733578742, - 3.993932539254576, - 3.993400039684919, - 3.992907033243256, - 3.992450168857769, - 3.992026844246474, - 3.991634517657978, - 3.9912706002763145, - 3.990932766336482, - 3.99061898759118, - 3.990327257999645, - 3.990055584054919, - 3.989802274625095, - 3.989565684635646, - 3.989344218080969, - 3.989136428077712, - 3.9889409445901456, - 3.9887564713862025, - 3.98858178862127, - 3.988415755914601, - 3.9882572957824567, - 3.988105382463593, - 3.987959065803437, - 3.987817447205853, - 3.98767965378163, - 3.987544889522142, - 3.9874124075528488, - 3.987281503355024, - 3.987151516929177, - 3.987021800971184, - 3.986891738038282, - 3.9867608016665335, - 3.986628476404083, - 3.986494281686113, - 3.986357765312213, - 3.986218438348906, - 3.986075938779765, - 3.985929895497044, - 3.9857799641559777, - 3.985625826248641, - 3.985467155403746, - 3.985303672892419, - 3.985135129316246, - 3.984961287140612, - 3.9847819285118784, - 3.984596848880007, - 3.984405854676455, - 3.9842087850666714, - 3.9840054885044465, - 3.983795828883339, - 3.983579684935655, - 3.983356939026304, - 3.983127500609717, - 3.9828912870153665, - 3.9826482278664623, - 3.982398264647473, - 3.982141334327094, - 3.9818773945816632, - 3.981606423045721, - 3.981328400448624, - 3.98104331712572, - 3.980751172704491, - 3.9804519758120978, - 3.980145743802939, - 3.979832476674339, - 3.979512160927907, - 3.979184866300774, - 3.9788506324402304, - 3.978509505462733, - 3.978161537729123, - 3.9778067876335648, - 3.977445319405216, - 3.977077196667288, - 3.976702399833892, - 3.9763210600048655, - 3.975933251724931, - 3.975539053311051, - 3.975138546672056, - 3.974731817138236, - 3.974318953300416, - 3.973900046857862, - 3.973475118809629, - 3.9730442842261944, - 3.9726076506712977, - 3.972165312660896, - 3.971717366726211, - 3.97126391134603, - 3.9708050468883735, - 3.970340875561401, - 3.9698715013727353, - 3.969397030096771, - 3.9689175692495775, - 3.9684332280706345, - 3.9679441175111823, - 3.967450262691754, - 3.9669517268464896, - 3.966448690816253, - 3.965941259864225, - 3.965429539853331, - 3.964913637225018, - 3.9643936589825337, - 3.96386971267846, - 3.963341906406117, - 3.96281034879468, - 3.962275149007576, - 3.9617364167439546, - 3.961194262243077, - 3.9606487568319504, - 3.9600998348912033, - 3.959547741888173, - 3.958992576603694, - 3.958434437166236, - 3.957873421020111, - 3.95730962489572, - 3.956743144781789, - 3.956174075899428, - 3.95560251267815, - 3.95502854873335, - 3.9544522768455233, - 3.953873788940976, - 3.953293176073982, - 3.952710444985648, - 3.952125727922266, - 3.9515391178133754, - 3.950950696578469, - 3.9503605447306303, - 3.9497687413496094, - 3.949175364056136, - 3.9485804889874503, - 3.947984190774081, - 3.94738654251773, - 3.946787615770253, - 3.946187480513835, - 3.945586205142118, - 3.9449838943400257, - 3.94438062031416, - 3.943776426228528, - 3.943171377710627, - 3.942565539030172, - 3.9419589730961833, - 3.941351741454768, - 3.9407439042874888, - 3.940135520410428, - 3.9395266472737074, - 3.9389173409616527, - 3.938307656193533, - 3.937697646324626, - 3.93708738181092, - 3.9364769588899224, - 3.93586638799495, - 3.9352557209098222, - 3.934645008380796, - 3.934034300130014, - 3.933423644869151, - 3.932813090313117, - 3.932202683193927, - 3.9315924692745967, - 3.930982493363184, - 3.9303727993267934, - 3.929763430105631, - 3.929154427727138, - 3.928545827141227, - 3.927937672566629, - 3.92733000325623, - 3.9267228573916446, - 3.926116272286188, - 3.925510284398033, - 3.92490492934334, - 3.924300241909318, - 3.9236962560672057, - 3.9230930049851778, - 3.922490521041188, - 3.921888835835696, - 3.9212879802042906, - 3.920687952491116, - 3.920088786767344, - 3.919490524543993, - 3.918893192274399, - 3.918296815665709, - 3.917701419692937, - 3.9171070286128766, - 3.91651366597784, - 3.9159213546493343, - 3.915330116811559, - 3.914739973984758, - 3.914150947038437, - 3.9135630562044543, - 3.9129763210898982, - 3.9123907606898753, - 3.911806393400104, - 3.9112232370293567, - 3.9106413088117393, - 3.910060625418832, - 3.909481202971609, - 3.9089030220296888, - 3.908326107785555, - 3.9077504877594, - 3.907176174821217, - 3.90660318129428, - 3.90603151896564, - 3.9054611990965182, - 3.90489223243246, - 3.9043246292134457, - 3.903758399183778, - 3.903193551601844, - 3.902630095249715, - 3.902068038442625, - 3.901507389038257, - 3.900948154445933, - 3.900390341635609, - 3.899833957146741, - 3.899279007097005, - 3.898725497190868, - 3.898173432728026, - 3.897622811757608, - 3.897073641304122, - 3.896525927692892, - 3.895979674478848, - 3.895434884842117, - 3.8948915615954407, - 3.8943497071914552, - 3.8938093237298976, - 3.8932704129646103, - 3.8927329763104543, - 3.892197014850087, - 3.891662529340593, - 3.891129520220007, - 3.890597987613725, - 3.8900679313407434, - 3.889539350919807, - 3.889012245575469, - 3.88848661424393, - 3.887962455578884, - 3.887439767957129, - 3.886918561395282, - 3.886398828607136, - 3.885880563949412, - 3.8853637650858746, - 3.884848429446578, - 3.884334554233117, - 3.88382213642376, - 3.883311172778481, - 3.8828016598438495, - 3.8822935939578858, - 3.881786971254729, - 3.881281787669234, - 3.880778038941481, - 3.8802757206211833, - 3.8797748280719286, - 3.8792753564754254, - 3.8787773008355746, - 3.87828065598246, - 3.8777854165763017, - 3.877291577111195, - 3.876799139484135, - 3.8763080942534263, - 3.8758184337673103, - 3.8753301521236336, - 3.874843243284291, - 3.874357701078695, - 3.87387351920723, - 3.873390691244603, - 3.8729092106430953, - 3.8724290707357474, - 3.871950264739461, - 3.8714727857580105, - 3.8709966267849953, - 3.8705217807067, - 3.8700482403048775, - 3.8695759982594624, - 3.869105047151205, - 3.8686353794642536, - 3.868166987588645, - 3.867700104977809, - 3.867234484462422, - 3.866770005411885, - 3.866306690444691, - 3.8658446434130376, - 3.865383788723912, - 3.8649241204568754, - 3.864465662092994, - 3.8640084013412954, - 3.863552325907547, - 3.863097429179952, - 3.862643704384357, - 3.862191145019004, - 3.861739744288799, - 3.861289495298585, - 3.860840391055284, - 3.8603923501751702, - 3.8599454270641225, - 3.8594996162747823, - 3.8590549105052703, - 3.858611302364931, - 3.85816878437615, - 3.8577273489761974, - 3.8572869885188847, - 3.856847695276333, - 3.856409461440548, - 3.855972279125051, - 3.855536140366382, - 3.8551009877116105, - 3.854666781858151, - 3.8542335776564873, - 3.853801366942139, - 3.8533701414788704, - 3.852939892959915, - 3.852510613009152, - 3.852082293182269, - 3.851654924967787, - 3.8512284997881743, - 3.8508030090007774, - 3.850378443898838, - 3.849954795712352, - 3.8495319464653193, - 3.8491099624403806, - 3.848688850407226, - 3.848268601436014, - 3.847849206535523, - 3.8474306566537817, - 3.8470129426786346, - 3.846596055438304, - 3.846179985701879, - 3.845764724179748, - 3.845350261524034, - 3.844936588328959, - 3.844523659744241, - 3.844111412152352, - 3.8436999082302297, - 3.8432891384131023, - 3.842879093077439, - 3.842469762541071, - 3.842061137063217, - 3.841653206844534, - 3.841245962027071, - 3.840839392694189, - 3.840433488870475, - 3.840028240521556, - 3.83962363755393, - 3.8392195887828713, - 3.8388161251770234, - 3.838413260576083, - 3.8380109846986703, - 3.837609287200615, - 3.837208157674511, - 3.836807585649172, - 3.8364075605890817, - 3.836008071893804, - 3.8356091088973057, - 3.835210660867285, - 3.8348127170044166, - 3.834415247475283, - 3.834018175655346, - 3.8336215580875774, - 3.8332253832374055, - 3.8328296394985233, - 3.832434315191756, - 3.832039398563824, - 3.831644877786147, - 3.8312507409535206, - 3.830856976082761, - 3.830463571111336, - 3.8300705138959024, - 3.8296777922107808, - 3.8292853937464337, - 3.8288933061078327, - 3.828501516812785, - 3.828110013290241, - 3.8277187828784935, - 3.82732781282334, - 3.8269370902762097, - 3.826546602292197, - 3.826156335828053, - 3.8257662493016382, - 3.8253763238280327, - 3.8249865721101424, - 3.8245969803497024, - 3.824207534638683, - 3.823818220956846, - 3.823429025169216, - 3.823039933023459, - 3.822650930147269, - 3.8222620020455977, - 3.821873134097879, - 3.821484311555162, - 3.8210955195371774, - 3.8207067430293367, - 3.8203179668796543, - 3.819929175795614, - 3.8195403543409263, - 3.819151486932271, - 3.818762557835897, - 3.8183735511642327, - 3.817984450872317, - 3.817595240754252, - 3.817205898455435, - 3.816816411011826, - 3.816426763061776, - 3.816036937672485, - 3.815646917730934, - 3.815256685939873, - 3.81486622481366, - 3.814475516674053, - 3.814084543645931, - 3.813693287652903, - 3.813301730412898, - 3.8129098534336032, - 3.812517638007893, - 3.8121250652091536, - 3.811732115886518, - 3.81133877066006, - 3.810945009915894, - 3.810550813801211, - 3.810156162219245, - 3.809761034824146, - 3.809365411015844, - 3.80896927014886, - 3.808572593574839, - 3.8081753586672606, - 3.807777543910097, - 3.807379127506832, - 3.8069800873750226, - 3.806580401140744 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - -54.31794507575758, - 952.3179450757576 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 3.788214475300354, - 4.0818658678618895 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Voltage [V]" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "02004006008003.83.853.93.9544.05ReferenceModelOptimised ComparisonTime / sVoltage / V" ] }, "metadata": {}, @@ -4922,1013 +379,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": { "id": "N5XYkevi04qD" }, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Adam", - "type": "scatter", - "x": [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39 - ], - "y": [ - 3.941288396171724, - 0.06364156926607942, - 0.2813819172268491, - 0.9202563427715488, - 1.4272995127552168, - 1.7137803458809504, - 1.7931964237248592, - 1.6984304444111813, - 1.4652384310413316, - 1.13332180873225, - 0.751664281564622, - 0.38379705577664885, - 0.1072957467108006, - 0.0006045120596826991, - 0.10637472220988156, - 0.36788437831418297, - 0.5917166071111266, - 0.5858934742108703, - 0.3801330018594796, - 0.15208776185006112, - 0.02193171677180485, - 0.006019036752275078, - 0.06499136077582217, - 0.14852832373876432, - 0.2172199651451913, - 0.2487550925406304, - 0.23692952357875133, - 0.1885486628548571, - 0.11997814088145774, - 0.053100160191200355, - 0.009619938830684698, - 0.0034034801467778294, - 0.032321620056373984, - 0.07540567861713895, - 0.10257391975216956, - 0.09573648242760192, - 0.06221114322311953, - 0.025109533874789217, - 0.0036563079223506326 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Convergence", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - 1, - 39 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Iteration" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - -0.21832237039098631, - 4.160215278622393 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Cost" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "510152025303500.511.522.533.54ConvergenceIterationCost" ] }, "metadata": {}, @@ -5936,1110 +395,8 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "name": "Negative electrode active material volume fraction", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38 - ], - "xaxis": "x", - "y": [ - 0.5854606983552765, - 0.7423651689898498, - 0.8534295929321716, - 0.929641049309509, - 0.97780539714648, - 1.00319194213445, - 1.0099773594523638, - 1.0015309777501544, - 0.9806647002030584, - 0.9498503015869976, - 0.9114165964729012, - 0.8677450294701005, - 0.8214793378491715, - 0.7757463500014091, - 0.7343301909504216, - 0.7016152836183076, - 0.6818702366758884, - 0.6773793580126345, - 0.6866524363240337, - 0.7053104783538886, - 0.7283672883752498, - 0.7515380920732153, - 0.771597195464243, - 0.7863729014298443, - 0.7946257570699398, - 0.7958920222704288, - 0.7903364351247087, - 0.778637204705584, - 0.7619095097721903, - 0.7416605917559185, - 0.7197604503711271, - 0.6983996939110517, - 0.6799909584812125, - 0.6669228472371665, - 0.6610453647308699, - 0.6629717504072878, - 0.6717320846673271, - 0.6851769130209538, - 0.7007531193703675 - ], - "yaxis": "y" - }, - { - "name": "Positive electrode active material volume fraction", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38 - ], - "xaxis": "x2", - "y": [ - 0.47071341235858943, - 0.627617883115226, - 0.7401486418133868, - 0.8163569109209738, - 0.8638557811679324, - 0.8887212026453712, - 0.8954759535080488, - 0.8874771958293967, - 0.8673184555865409, - 0.8371651816089553, - 0.799041218553892, - 0.7551012720205535, - 0.7079231719213461, - 0.6608408774719994, - 0.6182756239957282, - 0.5857695837890812, - 0.5688340000925769, - 0.5699956355437192, - 0.5868697231746707, - 0.6139850025289205, - 0.6454996222512103, - 0.6765687585760977, - 0.7036991761074721, - 0.7246811357504079, - 0.7383681177820396, - 0.744430276159587, - 0.7431497316148101, - 0.7352822751177693, - 0.7219829153135375, - 0.7047762150107635, - 0.6855414999087588, - 0.6664650302254246, - 0.6498961973765655, - 0.6380375580122912, - 0.6324671472800969, - 0.633662656994411, - 0.6408302013997966, - 0.6521817962940387, - 0.6654721854392738 - ], - "yaxis": "y2" - } - ], - "layout": { - "height": 576, - "legend": { - "orientation": "h", - "x": 1, - "xanchor": "right", - "y": 1.02, - "yanchor": "bottom" - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Parameter Convergence" - }, - "width": 1024, - "xaxis": { - "anchor": "y", - "autorange": true, - "domain": [ - 0, - 0.45 - ], - "range": [ - 0, - 38 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis2": { - "anchor": "y2", - "autorange": true, - "domain": [ - 0.55, - 1 - ], - "range": [ - 0, - 38 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "yaxis": { - "anchor": "x", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 0.5618764394054383, - 1.033561618402202 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis2": { - "anchor": "x2", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 0.44711549340584167, - 0.9190738724607964 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "01020300.60.650.70.750.80.850.90.95101020300.450.50.550.60.650.70.750.80.850.9Negative electrode active material volume fractionPositive electrode active material volume fractionParameter ConvergenceFunction CallFunction CallNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -7062,1190 +419,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "autocontour": true, - "contours": { - "end": 11, - "size": 1, - "start": 0 - }, - "type": "contour", - "x": [ - 0.5, - 0.5214285714285715, - 0.5428571428571428, - 0.5642857142857143, - 0.5857142857142857, - 0.6071428571428572, - 0.6285714285714286, - 0.65, - 0.6714285714285715, - 0.6928571428571428, - 0.7142857142857143, - 0.7357142857142858, - 0.7571428571428571, - 0.7785714285714287, - 0.8 - ], - "y": [ - 0.4, - 0.42142857142857143, - 0.4428571428571429, - 0.4642857142857143, - 0.4857142857142857, - 0.5071428571428571, - 0.5285714285714286, - 0.55, - 0.5714285714285714, - 0.5928571428571429, - 0.6142857142857143, - 0.6357142857142857, - 0.6571428571428571, - 0.6785714285714286, - 0.7 - ], - "z": [ - [ - 11.317325195331104, - 10.799385589187947, - 10.31666638756394, - 9.883057716727594, - 9.510116511629889, - 9.200199172426604, - 8.945227580131789, - 8.732159801291358, - 8.5487584517189, - 8.385972204084299, - 8.237818780848327, - 8.100511440365228, - 7.971655956553089, - 7.849703814709127, - 7.733614278537722 - ], - [ - 9.003563141996004, - 8.542762359322026, - 8.113166815275902, - 7.727433188586863, - 7.396130084380547, - 7.121464817390969, - 6.896192920874701, - 6.708531930582358, - 6.547467460936703, - 6.404871338261234, - 6.2753826330588325, - 6.155610574872357, - 6.043413192616348, - 5.937404036473897, - 5.836650769127074 - ], - [ - 7.089519726306956, - 6.682566903838721, - 6.3035698583252575, - 5.963696129899983, - 5.672333592097081, - 5.4313776288575, - 5.234301822943923, - 5.070572679685353, - 4.930396287111636, - 4.80657382172268, - 4.694368191503139, - 4.590787658128111, - 4.493941787014133, - 4.4026059240796815, - 4.315954845085618 - ], - [ - 5.532888857818209, - 5.1753595613150045, - 4.843341486893465, - 4.546407809605274, - 4.292589507354568, - 4.083304584145494, - 3.9126091028488656, - 3.7711402860291, - 3.6502819976291634, - 3.543740089321247, - 3.44738816997106, - 3.3586241009953772, - 3.275803220363898, - 3.19785802702386, - 3.1240687555719626 - ], - [ - 4.288220747098139, - 3.975047310078134, - 3.6855807272047274, - 3.4278212596166404, - 3.208433263611018, - 3.028241868814694, - 2.8817504346112637, - 2.760648216317538, - 2.6574094626752727, - 2.5665862979823992, - 2.484626264527868, - 2.409293521212306, - 2.3391744195760396, - 2.273350504155583, - 2.2111967711888423 - ], - [ - 3.304924577773111, - 3.0310506831562822, - 2.7794509824983735, - 2.5567535374783046, - 2.3683043253546288, - 2.2143275910959797, - 2.089670353680372, - 1.9869434187417188, - 1.8995960336258009, - 1.822944282146426, - 1.7539547303876946, - 1.690723941645771, - 1.6320485191905, - 1.577143283312914, - 1.5254665502115454 - ], - [ - 2.532087304358739, - 2.292960395972122, - 2.074864134031268, - 1.8832524607988408, - 1.7223097605948567, - 1.591706037318632, - 1.4865674721786684, - 1.4003046009156404, - 1.3272219438941435, - 1.263308656021115, - 1.2059898037419594, - 1.153655845092357, - 1.105290661161252, - 1.0602214188147316, - 1.0179917323865184 - ], - [ - 1.9245215799798332, - 1.716313531060015, - 1.5279664488974425, - 1.3639464248559798, - 1.2274483707651234, - 1.1176779603683902, - 1.0299728786129223, - 0.9584740443657136, - 0.8982220624540715, - 0.845794026018493, - 0.7990161969562718, - 0.756537916512251, - 0.7175013737643661, - 0.68134138654197, - 0.6476696525053999 - ], - [ - 1.445904274845284, - 1.265505191792988, - 1.1038429223997674, - 0.9645150927743168, - 0.8498939177080013, - 0.7587910250720685, - 0.6867763740048892, - 0.6286031189001857, - 0.5799700964253598, - 0.5379703090679798, - 0.500782286383818, - 0.46728101163527913, - 0.43675011696847166, - 0.40871969013808274, - 0.3828595242161933 - ], - [ - 1.068788698615006, - 0.9137570383149892, - 0.7762796768081804, - 0.6593009948925758, - 0.5644670486255817, - 0.4902684703040405, - 0.4324991264436703, - 0.3864623843155822, - 0.3484432556177435, - 0.31599270423181713, - 0.28760219876733684, - 0.26234414363989667, - 0.2396332903994348, - 0.21907917547379924, - 0.2004062464751083 - ], - [ - 0.7727944671740535, - 0.6411441794790536, - 0.5259070806788964, - 0.42940908149585, - 0.3526879492798334, - 0.2939719388175035, - 0.24927395554618748, - 0.21440048023297892, - 0.18616682160130457, - 0.16253677239670242, - 0.1422829211842736, - 0.12465771196109296, - 0.10919103813635551, - 0.09556610838979004, - 0.08355629625908645 - ], - [ - 0.5429954129071154, - 0.4331331616075782, - 0.33855042026584115, - 0.2610239634113037, - 0.2010593981815332, - 0.15666735278148777, - 0.12407640835659188, - 0.09955950340767086, - 0.08041888827321525, - 0.06499637802044066, - 0.05231962628350627, - 0.04180838106462222, - 0.03309599208213115, - 0.025933451315075136, - 0.020142933346956098 - ], - [ - 0.3681424038781088, - 0.2787551581045782, - 0.2035271991808568, - 0.14373818029437513, - 0.09941867145177974, - 0.06839554452710131, - 0.047109826539373505, - 0.03227199982908735, - 0.02163746670353349, - 0.01389809182492236, - 0.008319271805119658, - 0.004473768140703515, - 0.0020918814879576464, - 0.000987827331901914, - 0.001019704651353527 - ], - [ - 0.2395678499685267, - 0.16955525851715195, - 0.11259382294251127, - 0.06950829068314826, - 0.0399059442338293, - 0.021450033210687676, - 0.010791258416710863, - 0.0050524551722287165, - 0.002417380817428643, - 0.0019040298761643856, - 0.003003074014954813, - 0.005432040255257906, - 0.009007646402358249, - 0.013600685562441725, - 0.01911642823007783 - ], - [ - 0.15039893292644935, - 0.09879925051482592, - 0.05917597350538621, - 0.031926162096480235, - 0.016249646998909892, - 0.009674444151677857, - 0.009053610656418527, - 0.011911829329879777, - 0.01682587836576159, - 0.023137210509581452, - 0.03054139369444544, - 0.03889679792237, - 0.04808926771317956, - 0.05806658026682753, - 0.06875860544456033 - ] - ] - } - ], - "layout": { - "height": 600, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.5, - 0.8 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis": { - "range": [ - 0.4, - 0.7 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "0.50.550.60.650.70.750.80.40.450.50.550.60.650.70246810Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -8253,1375 +433,8 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "autocontour": true, - "contours": { - "end": 2.4000000000000004, - "size": 0.2, - "start": 0.2 - }, - "type": "contour", - "x": [ - 0.6, - 0.6214285714285714, - 0.6428571428571428, - 0.6642857142857143, - 0.6857142857142857, - 0.7071428571428572, - 0.7285714285714285, - 0.75, - 0.7714285714285715, - 0.7928571428571429, - 0.8142857142857143, - 0.8357142857142857, - 0.8571428571428572, - 0.8785714285714286, - 0.9 - ], - "y": [ - 0.5, - 0.5214285714285715, - 0.5428571428571428, - 0.5642857142857143, - 0.5857142857142857, - 0.6071428571428572, - 0.6285714285714286, - 0.65, - 0.6714285714285715, - 0.6928571428571428, - 0.7142857142857143, - 0.7357142857142858, - 0.7571428571428571, - 0.7785714285714287, - 0.8 - ], - "z": [ - [ - 2.512387532707103, - 2.371298813085517, - 2.2558321975617517, - 2.158630246032394, - 2.0741022900040287, - 1.998529830043667, - 1.9295639391341424, - 1.865727237318272, - 1.806070016754138, - 1.7499510347149512, - 1.6969182822430204, - 1.6466420920680112, - 1.5988634613863824, - 1.5533742166640172, - 1.5100006243691226 - ], - [ - 1.82357007063377, - 1.7040037722169434, - 1.6065779445659214, - 1.524839555358202, - 1.4539765997853684, - 1.390819997038458, - 1.3333779972526478, - 1.280400038127518, - 1.2310767688590611, - 1.184859360377806, - 1.1413635773941082, - 1.1002998747366666, - 1.0614427055575446, - 1.024629766954292, - 0.9896440179987216 - ], - [ - 1.2973902183702086, - 1.1970362940827597, - 1.1157551452027332, - 1.0478953052032811, - 0.9893255622025444, - 0.9373581297360792, - 0.8903143839801327, - 0.8471428320951687, - 0.8071557015664116, - 0.7698919043973902, - 0.7350191943562332, - 0.7022879123385447, - 0.6715188536256755, - 0.6425149398540935, - 0.615142653413343 - ], - [ - 0.8971894205780748, - 0.8140495530205606, - 0.7472849709488225, - 0.6919473241303125, - 0.6445012678060944, - 0.6026804105884203, - 0.5650817829045172, - 0.5308227535661557, - 0.4993318705618834, - 0.47021738927327217, - 0.4431966107246157, - 0.4180543837102393, - 0.39463355929050353, - 0.3727515270893397, - 0.3523276400633306 - ], - [ - 0.5953667443753835, - 0.5277866076308557, - 0.47418616656261287, - 0.43024151746787787, - 0.392944070248512, - 0.3603998098718084, - 0.3314476461442359, - 0.3053581497190951, - 0.2816586698394383, - 0.2600214447988958, - 0.24020729608549085, - 0.2220431491130888, - 0.2053575444825166, - 0.1900304869916861, - 0.17597227291494225 - ], - [ - 0.3716484274698599, - 0.3182858556996577, - 0.2767448813715213, - 0.2432649241330761, - 0.21531018816710276, - 0.19132074969446755, - 0.17035002887570982, - 0.15180970038039465, - 0.13531324737252673, - 0.12059108293607568, - 0.10745161258086793, - 0.09572135380931732, - 0.08528155318207993, - 0.07601994242902527, - 0.0678529261961639 - ], - [ - 0.21130104089725577, - 0.171060023569674, - 0.14066883291803392, - 0.11688278087161628, - 0.0975968734035832, - 0.08155439193482193, - 0.06800575909996628, - 0.05648838304744738, - 0.04669583409995397, - 0.03841643567820744, - 0.031475134422409415, - 0.025747053258806933, - 0.02112157946895544, - 0.017510851850942786, - 0.014830947841062106 - ], - [ - 0.1033316981411799, - 0.0753118501423817, - 0.05530978458387847, - 0.04056986238589816, - 0.029381963059514512, - 0.02076800803579361, - 0.01416190758396456, - 0.00921573993637305, - 0.00569724648267642, - 0.0034397168732752943, - 0.002308786826042704, - 0.002199754773989806, - 0.003022342694691581, - 0.004697242140656195, - 0.007154636194086497 - ], - [ - 0.039323755785290695, - 0.02276177589068047, - 0.01251265601669638, - 0.006264782564127162, - 0.0026825312880173267, - 0.0010476006543783277, - 0.0009656900885448692, - 0.0021956823802934513, - 0.004572773499663965, - 0.007977213626313791, - 0.012308325737615, - 0.01748151091980506, - 0.023425367785082973, - 0.030069184413234418, - 0.03735590490772675 - ], - [ - 0.012625818594881724, - 0.0068734625909305696, - 0.005823566258107555, - 0.00758178738606211, - 0.01117107045751473, - 0.016118858707746212, - 0.02218426343942576, - 0.02924946436830988, - 0.03718418679871344, - 0.04593035404612179, - 0.055412461703686064, - 0.06557126423060901, - 0.0763401046611927, - 0.08766947099463915, - 0.0995101040479805 - ], - [ - 0.01781882868165533, - 0.022310878521320715, - 0.02997327408702367, - 0.03930835528128833, - 0.04968417030931828, - 0.06085854994771867, - 0.07273880976258898, - 0.08531120322465058, - 0.09850966080911933, - 0.11230578227028136, - 0.1266571971617331, - 0.14152622440387644, - 0.15685793233581324, - 0.17261604460503727, - 0.18876078730843815 - ], - [ - 0.05036782610566616, - 0.06460236170512208, - 0.08053159777561539, - 0.09706770573038943, - 0.11388035717521051, - 0.13095736202330166, - 0.14836047035221037, - 0.1661428198289788, - 0.1843220764047806, - 0.20290085340844236, - 0.22186335196751877, - 0.24119224553950053, - 0.2608442413477098, - 0.28079541568480715, - 0.3010150226987527 - ], - [ - 0.10639274034152972, - 0.12991662993905695, - 0.15372316448357456, - 0.17710212663502636, - 0.2000308103874602, - 0.22271228420996347, - 0.24534834499911395, - 0.26806236516945575, - 0.29098508053972594, - 0.31409909942883674, - 0.3374336205756997, - 0.36099082292981893, - 0.3847381323547639, - 0.4086640220691059, - 0.4327460882743081 - ], - [ - 0.18251545369095343, - 0.2149136508995438, - 0.2462336241424864, - 0.27613019076807765, - 0.30487741302521115, - 0.3328863822608173, - 0.36049373767814186, - 0.3879070372485962, - 0.4153166994456581, - 0.44273525652518225, - 0.4702187149288315, - 0.4977876342538176, - 0.52542071794533, - 0.5531174713327137, - 0.5808636802299922 - ], - [ - 0.2757559638195207, - 0.316643561351556, - 0.35513860744586945, - 0.3912495306049447, - 0.4255371894499693, - 0.45861417382864017, - 0.4909475299913653, - 0.5228265555067318, - 0.5544979706808109, - 0.5860044176884744, - 0.6174273239338263, - 0.6488047668541456, - 0.6801266053728969, - 0.711402743898814, - 0.7426268079208163 - ] - ] - }, - { - "marker": { - "color": "red", - "line": { - "color": "midnightblue", - "width": 1 - }, - "showscale": false, - "size": 12, - "symbol": "x" - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.5854606983552765 - ], - "y": [ - 0.47071341235858943 - ] - }, - { - "marker": { - "color": [ - 0, - 0.02564102564102564, - 0.05128205128205128, - 0.07692307692307693, - 0.10256410256410256, - 0.1282051282051282, - 0.15384615384615383, - 0.1794871794871795, - 0.20512820512820512, - 0.2307692307692308, - 0.2564102564102564, - 0.28205128205128205, - 0.3076923076923077, - 0.3333333333333333, - 0.358974358974359, - 0.38461538461538464, - 0.41025641025641024, - 0.4358974358974359, - 0.4615384615384616, - 0.4871794871794872, - 0.5128205128205128, - 0.5384615384615384, - 0.5641025641025641, - 0.5897435897435898, - 0.6153846153846154, - 0.6410256410256411, - 0.6666666666666666, - 0.6923076923076923, - 0.717948717948718, - 0.7435897435897436, - 0.7692307692307693, - 0.7948717948717948, - 0.8205128205128205, - 0.8461538461538461, - 0.8717948717948718, - 0.8974358974358975, - 0.9230769230769232, - 0.9487179487179488, - 0.9743589743589745 - ], - "colorscale": [ - [ - 0, - "rgb(255,255,229)" - ], - [ - 0.125, - "rgb(255,247,188)" - ], - [ - 0.25, - "rgb(254,227,145)" - ], - [ - 0.375, - "rgb(254,196,79)" - ], - [ - 0.5, - "rgb(254,153,41)" - ], - [ - 0.625, - "rgb(236,112,20)" - ], - [ - 0.75, - "rgb(204,76,2)" - ], - [ - 0.875, - "rgb(153,52,4)" - ], - [ - 1, - "rgb(102,37,6)" - ] - ], - "showscale": false - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.5854606983552765, - 0.7423651689898498, - 0.8534295929321716, - 0.929641049309509, - 0.97780539714648, - 1.00319194213445, - 1.0099773594523638, - 1.0015309777501544, - 0.9806647002030584, - 0.9498503015869976, - 0.9114165964729012, - 0.8677450294701005, - 0.8214793378491715, - 0.7757463500014091, - 0.7343301909504216, - 0.7016152836183076, - 0.6818702366758884, - 0.6773793580126345, - 0.6866524363240337, - 0.7053104783538886, - 0.7283672883752498, - 0.7515380920732153, - 0.771597195464243, - 0.7863729014298443, - 0.7946257570699398, - 0.7958920222704288, - 0.7903364351247087, - 0.778637204705584, - 0.7619095097721903, - 0.7416605917559185, - 0.7197604503711271, - 0.6983996939110517, - 0.6799909584812125, - 0.6669228472371665, - 0.6610453647308699, - 0.6629717504072878, - 0.6717320846673271, - 0.6851769130209538, - 0.7007531193703675 - ], - "y": [ - 0.47071341235858943, - 0.627617883115226, - 0.7401486418133868, - 0.8163569109209738, - 0.8638557811679324, - 0.8887212026453712, - 0.8954759535080488, - 0.8874771958293967, - 0.8673184555865409, - 0.8371651816089553, - 0.799041218553892, - 0.7551012720205535, - 0.7079231719213461, - 0.6608408774719994, - 0.6182756239957282, - 0.5857695837890812, - 0.5688340000925769, - 0.5699956355437192, - 0.5868697231746707, - 0.6139850025289205, - 0.6454996222512103, - 0.6765687585760977, - 0.7036991761074721, - 0.7246811357504079, - 0.7383681177820396, - 0.744430276159587, - 0.7431497316148101, - 0.7352822751177693, - 0.7219829153135375, - 0.7047762150107635, - 0.6855414999087588, - 0.6664650302254246, - 0.6498961973765655, - 0.6380375580122912, - 0.6324671472800969, - 0.633662656994411, - 0.6408302013997966, - 0.6521817962940387, - 0.6654721854392738 - ] - } - ], - "layout": { - "height": 600, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.6, - 0.9 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis": { - "range": [ - 0.5, - 0.8 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "0.60.650.70.750.80.850.90.50.550.60.650.70.750.80.40.81.21.622.4Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -9665,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_CMAES.ipynb b/examples/notebooks/spm_CMAES.ipynb index 82813081c..73b783ce6 100644 --- a/examples/notebooks/spm_CMAES.ipynb +++ b/examples/notebooks/spm_CMAES.ipynb @@ -30,53 +30,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", - "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", - "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", - "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", - "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", + "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", + "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", + "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", + "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (8.20.0)\n", + "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (5.14.1)\n", + "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (4.0.10)\n", + "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (3.0.10)\n", + "Requirement already satisfied: decorator in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", + "Requirement already satisfied: jedi>=0.16 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", + "Requirement already satisfied: matplotlib-inline in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", + "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n", + "Requirement already satisfied: pygments>=2.4.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", + "Requirement already satisfied: stack-data in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", + "Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", + "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", + "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: pybop in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.11)\n", - "Requirement already satisfied: pybamm>=23.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (23.9)\n", - "Requirement already satisfied: numpy>=1.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (1.26.2)\n", - "Requirement already satisfied: scipy>=1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (1.11.4)\n", - "Requirement already satisfied: pandas>=1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (2.1.3)\n", - "Requirement already satisfied: nlopt>=2.6 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (2.7.1)\n", - "Requirement already satisfied: pints>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybop) (0.5.0)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pandas>=1.0->pybop) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pandas>=1.0->pybop) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pandas>=1.0->pybop) (2023.3)\n", - "Requirement already satisfied: cma>=2 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pints>=0.5->pybop) (3.3.0)\n", - "Requirement already satisfied: matplotlib>=1.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pints>=0.5->pybop) (3.8.2)\n", - "Requirement already satisfied: tabulate in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pints>=0.5->pybop) (0.9.0)\n", - "Requirement already satisfied: threadpoolctl in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pints>=0.5->pybop) (3.2.0)\n", - "Requirement already satisfied: casadi>=3.6.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybamm>=23.1->pybop) (3.6.4)\n", - "Requirement already satisfied: xarray in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybamm>=23.1->pybop) (2023.11.0)\n", - "Requirement already satisfied: anytree>=2.4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pybamm>=23.1->pybop) (2.12.1)\n", - "Requirement already satisfied: six in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from anytree>=2.4.3->pybamm>=23.1->pybop) (1.16.0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (1.2.0)\n", - "Requirement already satisfied: cycler>=0.10 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (4.45.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (23.2)\n", - "Requirement already satisfied: pillow>=8 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (10.1.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from matplotlib>=1.5->pints>=0.5->pybop) (3.1.1)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -323,7 +298,7 @@ { "data": { "text/plain": [ - "array([0.74839253, 0.66530766])" + "array([0.75174245, 0.66487169])" ] }, "execution_count": 10, @@ -359,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -371,4551 +346,8 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898, - 898, - 896, - 894, - 892, - 890, - 888, - 886, - 884, - 882, - 880, - 878, - 876, - 874, - 872, - 870, - 868, - 866, - 864, - 862, - 860, - 858, - 856, - 854, - 852, - 850, - 848, - 846, - 844, - 842, - 840, - 838, - 836, - 834, - 832, - 830, - 828, - 826, - 824, - 822, - 820, - 818, - 816, - 814, - 812, - 810, - 808, - 806, - 804, - 802, - 800, - 798, - 796, - 794, - 792, - 790, - 788, - 786, - 784, - 782, - 780, - 778, - 776, - 774, - 772, - 770, - 768, - 766, - 764, - 762, - 760, - 758, - 756, - 754, - 752, - 750, - 748, - 746, - 744, - 742, - 740, - 738, - 736, - 734, - 732, - 730, - 728, - 726, - 724, - 722, - 720, - 718, - 716, - 714, - 712, - 710, - 708, - 706, - 704, - 702, - 700, - 698, - 696, - 694, - 692, - 690, - 688, - 686, - 684, - 682, - 680, - 678, - 676, - 674, - 672, - 670, - 668, - 666, - 664, - 662, - 660, - 658, - 656, - 654, - 652, - 650, - 648, - 646, - 644, - 642, - 640, - 638, - 636, - 634, - 632, - 630, - 628, - 626, - 624, - 622, - 620, - 618, - 616, - 614, - 612, - 610, - 608, - 606, - 604, - 602, - 600, - 598, - 596, - 594, - 592, - 590, - 588, - 586, - 584, - 582, - 580, - 578, - 576, - 574, - 572, - 570, - 568, - 566, - 564, - 562, - 560, - 558, - 556, - 554, - 552, - 550, - 548, - 546, - 544, - 542, - 540, - 538, - 536, - 534, - 532, - 530, - 528, - 526, - 524, - 522, - 520, - 518, - 516, - 514, - 512, - 510, - 508, - 506, - 504, - 502, - 500, - 498, - 496, - 494, - 492, - 490, - 488, - 486, - 484, - 482, - 480, - 478, - 476, - 474, - 472, - 470, - 468, - 466, - 464, - 462, - 460, - 458, - 456, - 454, - 452, - 450, - 448, - 446, - 444, - 442, - 440, - 438, - 436, - 434, - 432, - 430, - 428, - 426, - 424, - 422, - 420, - 418, - 416, - 414, - 412, - 410, - 408, - 406, - 404, - 402, - 400, - 398, - 396, - 394, - 392, - 390, - 388, - 386, - 384, - 382, - 380, - 378, - 376, - 374, - 372, - 370, - 368, - 366, - 364, - 362, - 360, - 358, - 356, - 354, - 352, - 350, - 348, - 346, - 344, - 342, - 340, - 338, - 336, - 334, - 332, - 330, - 328, - 326, - 324, - 322, - 320, - 318, - 316, - 314, - 312, - 310, - 308, - 306, - 304, - 302, - 300, - 298, - 296, - 294, - 292, - 290, - 288, - 286, - 284, - 282, - 280, - 278, - 276, - 274, - 272, - 270, - 268, - 266, - 264, - 262, - 260, - 258, - 256, - 254, - 252, - 250, - 248, - 246, - 244, - 242, - 240, - 238, - 236, - 234, - 232, - 230, - 228, - 226, - 224, - 222, - 220, - 218, - 216, - 214, - 212, - 210, - 208, - 206, - 204, - 202, - 200, - 198, - 196, - 194, - 192, - 190, - 188, - 186, - 184, - 182, - 180, - 178, - 176, - 174, - 172, - 170, - 168, - 166, - 164, - 162, - 160, - 158, - 156, - 154, - 152, - 150, - 148, - 146, - 144, - 142, - 140, - 138, - 136, - 134, - 132, - 130, - 128, - 126, - 124, - 122, - 120, - 118, - 116, - 114, - 112, - 110, - 108, - 106, - 104, - 102, - 100, - 98, - 96, - 94, - 92, - 90, - 88, - 86, - 84, - 82, - 80, - 78, - 76, - 74, - 72, - 70, - 68, - 66, - 64, - 62, - 60, - 58, - 56, - 54, - 52, - 50, - 48, - 46, - 44, - 42, - 40, - 38, - 36, - 34, - 32, - 30, - 28, - 26, - 24, - 22, - 20, - 18, - 16, - 14, - 12, - 10, - 8, - 6, - 4, - 2, - 0 - ], - "y": [ - 4.0642603329504245, - 4.055863092292122, - 4.048612851870128, - 4.042407351224265, - 4.0370583058385545, - 4.032405792913264, - 4.0283227589520285, - 4.024709880858944, - 4.021490602544497, - 4.018604645438421, - 4.016004009841746, - 4.013650603590584, - 4.011512469808443, - 4.009563357951767, - 4.007781511269488, - 4.006148442492618, - 4.0046491393564825, - 4.0032700122141325, - 4.002000346134365, - 4.000829802364609, - 3.9997500020837737, - 3.998752922340895, - 3.997831710184658, - 3.9969800391600674, - 3.996192301504481, - 3.995463396385829, - 3.9947887367344825, - 3.994164028738822, - 3.993585476692611, - 3.993049655476875, - 3.9925532220583992, - 3.9920930799086904, - 3.991666622120928, - 3.9912712752409902, - 3.9909043747768855, - 3.990563831588685, - 3.990247573020385, - 3.9899534627827142, - 3.98967970488907, - 3.9894245706346, - 3.98918639854457, - 3.988963634828096, - 3.988754844198416, - 3.988558662983392, - 3.9883738012049665, - 3.9881990570724817, - 3.988033295652556, - 3.9878754325814127, - 3.9877244644001086, - 3.987579441029778, - 3.987439444699574, - 3.987303625755749, - 3.987171194088555, - 3.987041401394652, - 3.986913545373309, - 3.98678693761861, - 3.9866609231955774, - 3.9865349363239946, - 3.986408424588794, - 3.986280871524292, - 3.9861517821718895, - 3.986020647309517, - 3.9858870664819377, - 3.9857506367965616, - 3.985610983021168, - 3.9854677566247174, - 3.985320590006914, - 3.985169195395021, - 3.985013287784805, - 3.9848526028639255, - 3.9846868967686353, - 3.9845159331978297, - 3.98433950355377, - 3.984157419245383, - 3.983969505494649, - 3.983775603650553, - 3.983575566434607, - 3.983369260508688, - 3.9831565725575095, - 3.982937399828176, - 3.982711652521756, - 3.9824792480100295, - 3.9822401015314854, - 3.9819941672662265, - 3.98174139892015, - 3.9814817607496873, - 3.981215227224275, - 3.9809417827124727, - 3.980661421190323, - 3.98037414243882, - 3.980079881070882, - 3.9797786973520064, - 3.979470611195062, - 3.979155649764969, - 3.97883384723859, - 3.9785052445794418, - 3.978169889326644, - 3.9778278353964702, - 3.977479057639233, - 3.977123647605675, - 3.976761677273462, - 3.976393211772367, - 3.97601832044294, - 3.975637076652312, - 3.975249557620128, - 3.9748558442540167, - 3.9744559617367967, - 3.97404999224912, - 3.973638043320246, - 3.973220200644793, - 3.972796552415349, - 3.972367189245891, - 3.971932204105704, - 3.971491692263274, - 3.971045751239477, - 3.9705944807697455, - 3.970137982774424, - 3.9696763613368815, - 3.96920972268892, - 3.968738087756728, - 3.9682615127572274, - 3.967780176039227, - 3.967294179266155, - 3.966803625044902, - 3.9663086168998, - 3.965809259251419, - 3.965305657400189, - 3.964797917514283, - 3.9642861466216353, - 3.9637704526056097, - 3.963250944204188, - 3.962727731012275, - 3.962200854642682, - 3.961670292254488, - 3.9611362713027702, - 3.9605988902367666, - 3.9600582470555694, - 3.9595144392696224, - 3.958967563864495, - 3.958417717266784, - 3.9578649953121343, - 3.9573094932151935, - 3.956751305541341, - 3.9561905261802632, - 3.9556272483211616, - 3.955061552511883, - 3.954493450395264, - 3.953923091310643, - 3.953350559928647, - 3.952775939625373, - 3.9521993124473025, - 3.951620759077598, - 3.9510403588038616, - 3.950458189487189, - 3.949874327532555, - 3.9492888478605224, - 3.948701823880107, - 3.948113327462963, - 3.947523428918706, - 3.946932266209808, - 3.9463398628629793, - 3.945746285881837, - 3.9451516032146663, - 3.944555881480996, - 3.943959185964786, - 3.943361580608374, - 3.94276312800721, - 3.94216388940522, - 3.941563924691005, - 3.940963292394495, - 3.940362049684427, - 3.939760252366288, - 3.9391580151449177, - 3.93855536915916, - 3.937952356305185, - 3.937349031208676, - 3.936745447475926, - 3.9361416577057824, - 3.935537713501723, - 3.934933665484292, - 3.934329563303536, - 3.9337254556516807, - 3.9331213902759137, - 3.9325174139912624, - 3.9319135726935577, - 3.9313099071731448, - 3.930706461303545, - 3.9301032801979177, - 3.929500405828336, - 3.9288978792832543, - 3.9282957407797383, - 3.927694029675719, - 3.9270927844822183, - 3.926492042875555, - 3.925891841709535, - 3.925292217027572, - 3.924693204074768, - 3.9240948373099696, - 3.923497136931064, - 3.9229001250509232, - 3.922303846882851, - 3.921708332174864, - 3.921113609815551, - 3.920519707841808, - 3.919926653446568, - 3.9193344729864585, - 3.9187431919895466, - 3.9181528351629216, - 3.917563426400381, - 3.916974988789988, - 3.9163875446216183, - 3.9158011169173874, - 3.915215730241989, - 3.9146314012558334, - 3.9140481494449095, - 3.9134659935587033, - 3.9128849516181847, - 3.91230504092376, - 3.911726278063107, - 3.911148678919006, - 3.9105722586770306, - 3.9099970318332247, - 3.9094230122016818, - 3.90885021292206, - 3.9082786513186294, - 3.907708389649964, - 3.9071393989748393, - 3.9065716913019775, - 3.9060052780272496, - 3.905440169937097, - 3.9048763772118815, - 3.904313909429255, - 3.9037527755673977, - 3.9031929840083017, - 3.9026345425409303, - 3.902077458364356, - 3.901521738090898, - 3.90096738774914, - 3.900414412786956, - 3.899862818074487, - 3.8993126079070777, - 3.8987637860081104, - 3.898216355531922, - 3.897670319066524, - 3.8971256786364017, - 3.896582435705221, - 3.896040591178489, - 3.895500172109463, - 3.8949613211660137, - 3.894423904100843, - 3.893887922756165, - 3.893353378548444, - 3.892820272471128, - 3.892288605097305, - 3.8917583765822896, - 3.891229586666195, - 3.8907022346764095, - 3.8901763195299703, - 3.8896518397359983, - 3.889128793397956, - 3.888607178215897, - 3.888086991488685, - 3.887568230116084, - 3.887050890600864, - 3.8865349690507847, - 3.8860204611805687, - 3.885507362313789, - 3.8849956673847057, - 3.88448537094005, - 3.883976467140712, - 3.883468993497351, - 3.882963087687846, - 3.882458595276626, - 3.8819555123504794, - 3.8814538347640126, - 3.8809535581428216, - 3.88045467788659, - 3.8799571891721096, - 3.879461086956247, - 3.878966365978752, - 3.8784730207650946, - 3.8779810456291273, - 3.877490434675727, - 3.8770011818033216, - 3.87651328070639, - 3.8760267248778257, - 3.8755415076112665, - 3.875057622003341, - 3.874575060955824, - 3.8740938171777346, - 3.8736138831873608, - 3.873135251314209, - 3.872657913700873, - 3.8721818649762496, - 3.8717071134819094, - 3.8712336534781024, - 3.870761480715694, - 3.8702905911172176, - 3.869820980787656, - 3.869352646024981, - 3.868885819625957, - 3.8684202482406183, - 3.867955810485114, - 3.867492529490604, - 3.8670305100660927, - 3.8665696765703608, - 3.8661100234133294, - 3.8656515741166926, - 3.8651943167599145, - 3.8647382394088816, - 3.864283335378581, - 3.863829597655831, - 3.863377020557521, - 3.8629255975562784, - 3.862475322014845, - 3.862026187188146, - 3.861578109298481, - 3.86113114424512, - 3.8606852876721462, - 3.86024053248819, - 3.859796871502569, - 3.859354297426992, - 3.858912802877166, - 3.858472380374436, - 3.8580330223472417, - 3.857594721132625, - 3.8571574689776367, - 3.856721258040668, - 3.856286034284662, - 3.8558517483988424, - 3.855418461589669, - 3.8549861657701503, - 3.8545548527688367, - 3.8541245143308713, - 3.853695142118875, - 3.853266727713848, - 3.8528392626160017, - 3.8524127382455218, - 3.8519871459432946, - 3.8515624769715657, - 3.8511387225145386, - 3.850715771369961, - 3.850293674166752, - 3.849872445959047, - 3.849452077712753, - 3.8490325603159783, - 3.848613884579271, - 3.8481960412358127, - 3.847779020941565, - 3.8473628142753538, - 3.846947411738913, - 3.846532803756833, - 3.8461189806764824, - 3.845705908203699, - 3.845293500634357, - 3.844881831099628, - 3.8444708897094015, - 3.84406066649306, - 3.843651151399018, - 3.84324233429422, - 3.8428342049635895, - 3.842426753109349, - 3.8420199683503897, - 3.8416138402214703, - 3.841208358172432, - 3.840803511567296, - 3.840399222095699, - 3.839995494128893, - 3.839592353327904, - 3.839189788758411, - 3.838787789392948, - 3.838386344109659, - 3.8379854416909103, - 3.837585070821916, - 3.837185220089205, - 3.836785877979083, - 3.8363870328759857, - 3.835988673060802, - 3.8355907822397266, - 3.8351932560616135, - 3.834796162322323, - 3.8343994884132417, - 3.8340032216124422, - 3.833607349082428, - 3.8332118578677616, - 3.832816734892624, - 3.8324219669583215, - 3.8320275407406625, - 3.8316334427872567, - 3.831239659514776, - 3.830846177206081, - 3.830452982007267, - 3.8300600599246377, - 3.829667396821596, - 3.829274978415392, - 3.8288827902738465, - 3.828490817811939, - 3.8280990462883144, - 3.827707460801693, - 3.8273160462871703, - 3.8269247689593993, - 3.826533589643843, - 3.826142527176108, - 3.8257515654619665, - 3.8253606882233666, - 3.824969878994088, - 3.82457912111522, - 3.824188397730648, - 3.8237976917823207, - 3.8234069860055144, - 3.8230162629239097, - 3.822625504844608, - 3.8222346938530394, - 3.821843811807743, - 3.821452840335054, - 3.821061760823705, - 3.820670554419285, - 3.820279202018585, - 3.8198876842639233, - 3.8194959815372465, - 3.819104073954214, - 3.818711941358152, - 3.818319558930346, - 3.817926906827638, - 3.8175339665369887, - 3.817140716706404, - 3.816747135679724, - 3.816353201490004, - 3.815958891852786, - 3.815564184159311, - 3.81516905546964, - 3.81477348250568, - 3.8143774416441905, - 3.813980908909683, - 3.813583859967245, - 3.8131862701153953, - 3.812788114278781, - 3.812389367000911, - 3.811990002436846, - 3.81158999434583, - 3.81118931608396, - 3.8107879405968097, - 3.810385840412081, - 3.80998298763225, - 3.809579356284624, - 3.8091749168706586, - 3.8087696401369286, - 3.8083634965685578, - 3.807956456190015, - 3.8075484885582287, - 3.8056305565431967, - 3.806038524174983, - 3.806445564553526, - 3.8068517081218967, - 3.8072569848556266, - 3.807661424269592, - 3.808065055617218, - 3.808467908397049, - 3.8088700085817777, - 3.809271384068928, - 3.809672062330798, - 3.810072070421814, - 3.810471434985879, - 3.810870182263749, - 3.8112683381003634, - 3.811665927952213, - 3.812062976894651, - 3.8124595096291585, - 3.812855550490648, - 3.813251123454608, - 3.813646252144279, - 3.814040959837754, - 3.814435269474972, - 3.814829203664692, - 3.815222784691372, - 3.8156160345219567, - 3.816008974812606, - 3.816401626915314, - 3.81679400934312, - 3.817186141939182, - 3.8175780495222145, - 3.8179697522488913, - 3.818361270003553, - 3.818752622404253, - 3.819143828808673, - 3.819534908320022, - 3.819925879792711, - 3.8203167618380074, - 3.820707572829576, - 3.8210983309088777, - 3.8214890539904824, - 3.8218797597672887, - 3.822270465715616, - 3.822661189100188, - 3.823051946979056, - 3.823442756208334, - 3.8238336334469345, - 3.824224595161076, - 3.824615657628811, - 3.8250068369443673, - 3.8253981142721383, - 3.825789528786661, - 3.8261811142732824, - 3.826572885796907, - 3.8269648582588145, - 3.82735704640036, - 3.827749464806564, - 3.8281421279096057, - 3.828535049992235, - 3.828928245191049, - 3.829321727499744, - 3.8297155107722247, - 3.8301096087256306, - 3.8305040349432895, - 3.830898802877592, - 3.8312939258527297, - 3.831689417067396, - 3.8320852895974102, - 3.8324815563982098, - 3.832878230307291, - 3.8332753240465816, - 3.8336728502246946, - 3.83407074104577, - 3.8344691008609537, - 3.834867945964051, - 3.835267288074173, - 3.835667138806884, - 3.8360675096758783, - 3.836468412094627, - 3.836869857377916, - 3.8372718567433792, - 3.837674421312872, - 3.838077562113861, - 3.838481290080667, - 3.838885579552264, - 3.8392904261574, - 3.8396959082064384, - 3.8401020363353577, - 3.840508821094317, - 3.8409162729485575, - 3.841324402279188, - 3.841733219383986, - 3.842142734478028, - 3.8425529576943696, - 3.842963899084596, - 3.843375568619325, - 3.843787976188667, - 3.8442010486614504, - 3.844614871741801, - 3.845029479723881, - 3.8454448822603218, - 3.845861088926533, - 3.8462781092207807, - 3.846695952564239, - 3.8471146283009463, - 3.847534145697721, - 3.847954513944015, - 3.84837574215172, - 3.848797839354929, - 3.8492207904995066, - 3.8496445449565337, - 3.850069213928262, - 3.85049480623049, - 3.8509213306009697, - 3.851348795698816, - 3.851777210103843, - 3.8522065823158393, - 3.8526369207538047, - 3.8530682337551183, - 3.853500529574637, - 3.8539338163838104, - 3.85436810226963, - 3.854803326025636, - 3.8552395369626047, - 3.855676789117593, - 3.8561150903322097, - 3.856554448359404, - 3.856994870862134, - 3.85743636541196, - 3.857878939487537, - 3.858322600473158, - 3.8587673556571143, - 3.859213212230088, - 3.859660177283449, - 3.860108255173114, - 3.860557389999813, - 3.8610076655412464, - 3.861459088542489, - 3.861911665640799, - 3.862365403363549, - 3.8628203073938496, - 3.8632763847448826, - 3.8637336421016606, - 3.8641920913982974, - 3.864651744555329, - 3.8651125780510607, - 3.865574597475572, - 3.866037878470082, - 3.8665023162255863, - 3.8669678876109246, - 3.867434714009949, - 3.867903048772624, - 3.8683726591021856, - 3.8688435487006614, - 3.8693157214630705, - 3.8697891814668774, - 3.8702639329612176, - 3.870739981685841, - 3.871217319299177, - 3.871695951172329, - 3.8721758851627026, - 3.872657128940792, - 3.873139689988309, - 3.8736235755962345, - 3.8741087928627937, - 3.874595348691358, - 3.8750832497882897, - 3.875572502660695, - 3.8760631136140953, - 3.8765550887500626, - 3.87704843396372, - 3.877543154941215, - 3.8780392571570776, - 3.878536745871558, - 3.8790356261277896, - 3.8795359027489806, - 3.880037580335447, - 3.880540663261594, - 3.881045155672814, - 3.881551061482319, - 3.88205853512568, - 3.882567438925018, - 3.8830777353696737, - 3.883589430298757, - 3.8841025291655367, - 3.8846170370357527, - 3.885132958585832, - 3.885650298101052, - 3.886169059473653, - 3.886689246200865, - 3.887210861382924, - 3.8877339077209663, - 3.8882583875149384, - 3.8887843026613775, - 3.889311654651163, - 3.8898404445672576, - 3.890370673082273, - 3.890902340456096, - 3.891435446533412, - 3.891969990741133, - 3.892505972085811, - 3.8930433891509817, - 3.893582240094431, - 3.894122659163457, - 3.894664503690189, - 3.8952077466213697, - 3.895752387051492, - 3.89629842351689, - 3.8968458539930784, - 3.8973946758920457, - 3.897944886059455, - 3.898496480771924, - 3.899049455734108, - 3.899603806075866, - 3.900159526349324, - 3.9007166105258984, - 3.9012750519932697, - 3.9018348435523658, - 3.902395977414223, - 3.9029584451968495, - 3.903522237922065, - 3.9040873460122176, - 3.9046537592869455, - 3.9052214669598073, - 3.905790457634932, - 3.9063607193035974, - 3.906932280907028, - 3.9075050801866498, - 3.9080790998181927, - 3.908654326661998, - 3.909230746903974, - 3.909808346048075, - 3.910387108908728, - 3.9109670196031527, - 3.9115480615436713, - 3.9121302174298775, - 3.9127134692408014, - 3.913297798226957, - 3.913883184902355, - 3.9144696126065863, - 3.915057056774956, - 3.915645494385349, - 3.9162349031478896, - 3.9168252599745146, - 3.9174165409714266, - 3.918008721431536, - 3.918601775826776, - 3.919195677800519, - 3.919790400159832, - 3.920385914867819, - 3.9209821930358912, - 3.921579204916032, - 3.9221769052949376, - 3.922775272059736, - 3.92337428501254, - 3.923973909694503, - 3.924574110860524, - 3.9251748524671863, - 3.925776097660687, - 3.9263778087647063, - 3.9269799472682223, - 3.927582473813304, - 3.9281853481828857, - 3.928788529288513, - 3.929391975158113, - 3.9299956406785257, - 3.9305994819762304, - 3.9312034582608817, - 3.9318075236366488, - 3.932411631288504, - 3.93301573346926, - 3.933619781486691, - 3.9342237256907504, - 3.934827515460894, - 3.935431099193644, - 3.936034424290153, - 3.936637437144128, - 3.9372400831298857, - 3.937842320351256, - 3.938444117669395, - 3.939045360379463, - 3.939645992675973, - 3.940245957390188, - 3.940845195992178, - 3.941443648593342, - 3.942041253949754, - 3.942637949465964, - 3.9432336711996343, - 3.943828353866805, - 3.9444219308479473, - 3.945014334194776, - 3.945605496903674, - 3.946195395447931, - 3.946783891865075, - 3.9473709158454904, - 3.947956395517523, - 3.948540257472157, - 3.9491224267888296, - 3.949702827062566, - 3.9502813804322705, - 3.950858007610341, - 3.951432627913615, - 3.952005159295611, - 3.952575518380232, - 3.953143620496851, - 3.9537093163061297, - 3.9542725941652312, - 3.954833373526309, - 3.9553915612001616, - 3.9559470632971023, - 3.956499785251752, - 3.957049631849463, - 3.9575965072545904, - 3.9581403150405374, - 3.9586809582217346, - 3.9592183392877383, - 3.959752360239456, - 3.96028292262765, - 3.960809798997243, - 3.961333012189156, - 3.9618525205905777, - 3.9623682146066033, - 3.962879985499251, - 3.963387725385157, - 3.963891327236387, - 3.964390684884768, - 3.96488569302987, - 3.965376247251123, - 3.965862244024195, - 3.9663435807421954, - 3.966820155741696, - 3.967291790673888, - 3.9677584293218495, - 3.968220050759392, - 3.9686765487547135, - 3.969127819224445, - 3.969573760248242, - 3.970014272090672, - 3.970449257230859, - 3.970878620400317, - 3.971302268629761, - 3.971720111305214, - 3.972132060234088, - 3.9725380297217647, - 3.9729379122389847, - 3.973331625605096, - 3.97371914463728, - 3.974100388427908, - 3.974475279757335, - 3.97484374525843, - 3.975205715590643, - 3.975561125624201, - 3.9759099033814382, - 3.976251957311612, - 3.97658731256441, - 3.976915915223558, - 3.977237717749937, - 3.97755267918003, - 3.9778607653369744, - 3.97816194905585, - 3.978456210423788, - 3.978743489175291, - 3.9790238506974407, - 3.979297295209243, - 3.9795638287346553, - 3.9798234669051182, - 3.9800762352511945, - 3.9803221695164535, - 3.9805613159949975, - 3.980793720506724, - 3.981019467813144, - 3.9812386405424776, - 3.981451328493656, - 3.981657634419575, - 3.981857671635521, - 3.982051573479617, - 3.982239487230351, - 3.982421571538738, - 3.9825980011827977, - 3.9827689647536033, - 3.9829346708488935, - 3.983095355769773, - 3.9832512633799886, - 3.983402657991882, - 3.9835498246096854, - 3.983693051006136, - 3.9838327047815296, - 3.9839691344669057, - 3.984102715294485, - 3.9842338501568575, - 3.98436293950926, - 3.984490492573762, - 3.9846170043089626, - 3.9847429911805454, - 3.984869005603578, - 3.984995613358277, - 3.98512346937962, - 3.985253262073523, - 3.985385693740717, - 3.985521512684542, - 3.985661509014746, - 3.9858065323850767, - 3.9859575005663808, - 3.986115363637524, - 3.9862811250574497, - 3.9864558691899346, - 3.98664073096836, - 3.986836912183384, - 3.987045702813064, - 3.987268466529538, - 3.987506638619568, - 3.987761772874038, - 3.9880355307676822, - 3.988329641005353, - 3.988645899573653, - 3.9889864427618535, - 3.9893533432259582, - 3.989748690105896, - 3.9901751478936585, - 3.9906352900433673, - 3.991131723461843, - 3.991667544677579, - 3.99224609672379, - 3.9928708047194506, - 3.993545464370797, - 3.994274369489449, - 3.9950621071450354, - 3.995913778169626, - 3.996834990325863, - 3.9978320700687417, - 3.998911870349577, - 4.0000824141193325, - 4.0013520801991, - 4.00273120734145, - 4.004230510477585, - 4.005863579254455, - 4.007645425936734, - 4.00959453779341, - 4.011732671575551, - 4.014086077826713, - 4.016686713423388, - 4.019572670529464, - 4.022791948843911, - 4.026404826936996, - 4.030487860898231, - 4.035140373823522, - 4.040489419209232, - 4.046694919855095, - 4.053945160277089, - 4.062342400935392 - ] - }, - { - "mode": "markers", - "name": "Target", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.063316852435602, - 4.056208765354541, - 4.048557080260912, - 4.040914948309032, - 4.035107388323031, - 4.0316209578534234, - 4.028598675935667, - 4.023426004893352, - 4.021292727383525, - 4.017021890677268, - 4.012975155188826, - 4.013301075295993, - 4.0104527964329355, - 4.007647290414742, - 4.005574283191637, - 4.005823768361199, - 4.002737956818605, - 4.0028403387419695, - 4.00220414577378, - 4.0015474141530865, - 3.999469179697504, - 3.997797663828473, - 3.997819294598826, - 3.995456734629008, - 3.994664606911642, - 3.9957660732748175, - 3.9947113559543856, - 3.993192681295931, - 3.992719076609304, - 3.992234577538066, - 3.9914657903014352, - 3.990101672961132, - 3.990335501690317, - 3.990108955465178, - 3.988621274585786, - 3.987537189896979, - 3.989622051194655, - 3.988084628384094, - 3.987120541943893, - 3.9880760726337585, - 3.98907606907527, - 3.987058302431552, - 3.9873856484698105, - 3.987148251524266, - 3.985625521632212, - 3.988065774275503, - 3.9873120968128712, - 3.986256167493079, - 3.987088163267585, - 3.988143983384717, - 3.987509249758301, - 3.986029868019604, - 3.986516902851974, - 3.987216650884984, - 3.9863352625243382, - 3.98475010776851, - 3.985944818955333, - 3.9847722855268737, - 3.98655578459638, - 3.9847630717138367, - 3.984032247749398, - 3.9847351125970727, - 3.984545807595497, - 3.986305578706914, - 3.984869007394037, - 3.9846871006680336, - 3.984066711922765, - 3.9833857755729194, - 3.984215340737964, - 3.983506281053155, - 3.9840512346249617, - 3.9833509445601702, - 3.9829639280678233, - 3.9821259086210383, - 3.98322317459241, - 3.9822006293547543, - 3.9829014587010954, - 3.98292038508256, - 3.9801308463474063, - 3.9820737212754014, - 3.981622863936407, - 3.981310295248388, - 3.983294153342889, - 3.981729896358351, - 3.9800981951403185, - 3.980758165849979, - 3.981329262548772, - 3.9799687469126215, - 3.9796909458004497, - 3.981154160085456, - 3.980361829857391, - 3.977948716738303, - 3.9780754604261177, - 3.977321935157691, - 3.975295171316911, - 3.977320728682469, - 3.9793409239157, - 3.976557828538108, - 3.9759662428476226, - 3.974849066967581, - 3.975636703646064, - 3.974786097402641, - 3.976339256942752, - 3.9752561041037753, - 3.9749103159949706, - 3.972550980142258, - 3.9733359660614136, - 3.973303043753626, - 3.97187489050498, - 3.971820470419705, - 3.9710552332586526, - 3.973009571175853, - 3.9732316742876663, - 3.970586858266038, - 3.969770800727748, - 3.9677765141023538, - 3.9708681968417414, - 3.969229790531219, - 3.9673233557382086, - 3.968763165619063, - 3.968194772463423, - 3.966432518934401, - 3.965277352780451, - 3.966710533112004, - 3.96587486304797, - 3.96556711785954, - 3.9657138224865265, - 3.962937838111006, - 3.96444913407295, - 3.9647762150072583, - 3.960728392763308, - 3.9626037010658366, - 3.961942581199413, - 3.9609346715791096, - 3.9603395675245703, - 3.961040286657915, - 3.957666286284489, - 3.959227711959158, - 3.959674637367426, - 3.9571526626263127, - 3.954873215106217, - 3.957224944390881, - 3.955451236378557, - 3.9540500562184375, - 3.954325675835695, - 3.9545612144246767, - 3.954144122005261, - 3.954259345045105, - 3.95481772859789, - 3.95217758434784, - 3.952148240571275, - 3.951040132871348, - 3.949251237228026, - 3.947587291207306, - 3.948617653340526, - 3.948720200425972, - 3.9483555562290342, - 3.9481016128549618, - 3.946732264280564, - 3.9463465518272183, - 3.944850738286223, - 3.9453486599045817, - 3.943222932543005, - 3.9440346720085215, - 3.9425997620769135, - 3.942830665862968, - 3.941777016799422, - 3.9401951649425424, - 3.94035211453462, - 3.940439078497019, - 3.939276031848431, - 3.9388451747676014, - 3.93924162780022, - 3.936811333152371, - 3.9374519584733103, - 3.9354946185143658, - 3.936790088253856, - 3.933840984425747, - 3.9334059525219742, - 3.933499334564671, - 3.933864242691521, - 3.9341518275965774, - 3.932378843224009, - 3.932094447992212, - 3.931849721572781, - 3.930385219542485, - 3.9295786888054063, - 3.929173522326594, - 3.9294038731434, - 3.927794257036669, - 3.9265373706741222, - 3.925220272263997, - 3.925614353098005, - 3.926583769470803, - 3.92596942382635, - 3.923766498360429, - 3.924600309648354, - 3.9240317170877086, - 3.9206545914879247, - 3.9229624604818345, - 3.920904121305461, - 3.922035535408469, - 3.9213014512368023, - 3.9205320192025503, - 3.919052040802787, - 3.920169552474122, - 3.916865341554688, - 3.917335034052436, - 3.916643429136777, - 3.9149415862576102, - 3.916914005059162, - 3.914815056894573, - 3.9152704744845375, - 3.914673048086096, - 3.911504863125051, - 3.913125369245392, - 3.9121022887340455, - 3.9103925424866066, - 3.911406704217715, - 3.9115711428878424, - 3.9080019236563097, - 3.911419736486131, - 3.907877700281851, - 3.907092853870112, - 3.907150472472923, - 3.906236856668966, - 3.906245569460833, - 3.9037012581788657, - 3.9041303271732257, - 3.9054199314530735, - 3.902796294680427, - 3.903718987479453, - 3.9012718037518335, - 3.903455092115934, - 3.9022312139424185, - 3.902170069563942, - 3.901185944415845, - 3.899224612519012, - 3.898267636487287, - 3.899599656578459, - 3.8998520236394953, - 3.897320673831119, - 3.8965849582820344, - 3.8958941955153943, - 3.8962808586607127, - 3.897221707107678, - 3.8951304705695247, - 3.893989607429907, - 3.895310051028976, - 3.891888840298102, - 3.892498936239659, - 3.8932090117114977, - 3.8931360205480607, - 3.891064375798883, - 3.8915821593582263, - 3.8910625218347663, - 3.890274230904227, - 3.889818300477958, - 3.891154778976592, - 3.889081389783803, - 3.886207069450396, - 3.887387024424036, - 3.886552949715929, - 3.886489254965051, - 3.8852712497127846, - 3.8854024424051143, - 3.884327416604259, - 3.8856998235675846, - 3.8842950188205663, - 3.883970786118674, - 3.880989948941136, - 3.882533753444134, - 3.8814018389670766, - 3.881524891492765, - 3.8804234013688865, - 3.8797085013292847, - 3.879755180853216, - 3.879880119078155, - 3.878221695366005, - 3.8780914647807103, - 3.87812781015874, - 3.8780175179050262, - 3.8763701352749136, - 3.8767942794105585, - 3.8770929693329688, - 3.8774552628770698, - 3.8740516284315074, - 3.8741071106365994, - 3.872758345663138, - 3.874872152701664, - 3.8715435214328098, - 3.872670828394842, - 3.8738776945293334, - 3.8706907676934312, - 3.871523258403328, - 3.870076264346411, - 3.868840455157122, - 3.869191595647318, - 3.8677892224579153, - 3.870489368645821, - 3.867286246770909, - 3.867014397552421, - 3.8659520734074295, - 3.866850710434623, - 3.8658015479575982, - 3.8658320574019776, - 3.864216331837168, - 3.8653966485360782, - 3.86379292212878, - 3.86485258954837, - 3.8637873784053025, - 3.862188677437094, - 3.86366101171771, - 3.861894213929925, - 3.861238119044843, - 3.8609310774043752, - 3.8589415015113646, - 3.8581945695217774, - 3.85848740421776, - 3.859001695172195, - 3.860436975139527, - 3.8582597878518032, - 3.857156938491188, - 3.857179017333942, - 3.856700338961284, - 3.856045528820108, - 3.85567906581045, - 3.855639350508539, - 3.855276722077614, - 3.853598430308031, - 3.856471075412357, - 3.853260893358633, - 3.8530468334048424, - 3.850908533357765, - 3.85309299796771, - 3.851283553089066, - 3.853077202813849, - 3.850132176656393, - 3.8524779652981698, - 3.848330040949235, - 3.851461512673494, - 3.848313573364299, - 3.8498076476101297, - 3.8479919782159895, - 3.847778408006768, - 3.84732520261816, - 3.848034198883768, - 3.8468991449515264, - 3.8476575297509887, - 3.8453182722648593, - 3.846779481822976, - 3.846815793813399, - 3.8449539926213943, - 3.8443960426241666, - 3.8447417313219945, - 3.8437310615044353, - 3.8432128292439054, - 3.842375048019766, - 3.841669440142653, - 3.843156223893458, - 3.841286540408986, - 3.84111232115921, - 3.8406879962489926, - 3.8410217779528137, - 3.839830218645939, - 3.839503397707325, - 3.838606422949163, - 3.837134084853471, - 3.83881186302182, - 3.8390096969376457, - 3.83865760103825, - 3.837196815371407, - 3.8366499289134266, - 3.836978243865555, - 3.836072135463952, - 3.835651253425086, - 3.833446732104823, - 3.8348665134475266, - 3.83483398555556, - 3.8339071230050674, - 3.8337260558118342, - 3.8329298418836304, - 3.834172684549629, - 3.832627067330249, - 3.832161523241389, - 3.8323695697570246, - 3.8315053730516855, - 3.829593574487552, - 3.830894733110041, - 3.8291618419671702, - 3.8302569365890857, - 3.829411560012668, - 3.827639049769717, - 3.827102247855191, - 3.829171695996437, - 3.82737739859058, - 3.827537182317615, - 3.8275081784855014, - 3.8259617689331327, - 3.8258838495261385, - 3.824568573927784, - 3.824668081085507, - 3.823323160308574, - 3.825766756492329, - 3.8270240336862704, - 3.823096218965891, - 3.823126120787766, - 3.8227041933411146, - 3.8228347236500353, - 3.823287422335571, - 3.82431379131002, - 3.822393996487309, - 3.821284527917933, - 3.8205012103670017, - 3.820638042354416, - 3.8184375033547657, - 3.8197733534982383, - 3.8187249782871984, - 3.8175826359535927, - 3.820346638428277, - 3.819251364581688, - 3.816647021290933, - 3.817168966122999, - 3.8171783252969727, - 3.8185304288189825, - 3.81564026552631, - 3.8140732779520223, - 3.814441079983572, - 3.8160802865196426, - 3.814488813210758, - 3.8138396251810542, - 3.8146101071631784, - 3.81440830279387, - 3.8131744131224488, - 3.811855917436637, - 3.812582776078989, - 3.8122125320384166, - 3.811514303199616, - 3.810689081976909, - 3.810787030225234, - 3.8091354151982486, - 3.810968890189926, - 3.8085824967348154, - 3.8081333668611657, - 3.807778625866613, - 3.807325171649645, - 3.807951193334397, - 3.807509273079961, - 3.806799140231, - 3.806135801917686 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Model", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.063301366942908, - 4.0549041262846055, - 4.047653885862611, - 4.041448385216748, - 4.036099339831038, - 4.031446826905747, - 4.027363792944512, - 4.023750914851427, - 4.0205316365369805, - 4.017645679430904, - 4.015045043834229, - 4.0126916375830675, - 4.010553503800926, - 4.0086043919442504, - 4.006822545261971, - 4.005189476485102, - 4.003690173348966, - 4.002311046206616, - 4.001041380126849, - 3.999870836357093, - 3.9987910360762577, - 3.997793956333379, - 3.996872744177142, - 3.9960210731525514, - 3.995233335496965, - 3.994504430378313, - 3.9938297707269665, - 3.993205062731306, - 3.992626510685096, - 3.992090689469359, - 3.9915942560508832, - 3.9911341139011745, - 3.990707656113411, - 3.9903123092334742, - 3.9899454087693695, - 3.989604865581169, - 3.989288607012869, - 3.9889944967751982, - 3.988720738881554, - 3.988465604627084, - 3.988227432537054, - 3.988004668820581, - 3.9877958781909, - 3.987599696975875, - 3.9874148351974505, - 3.987240091064965, - 3.98707432964504, - 3.9869164665738968, - 3.9867654983925926, - 3.986620475022262, - 3.986480478692058, - 3.986344659748233, - 3.986212228081039, - 3.986082435387136, - 3.985954579365793, - 3.985827971611094, - 3.9857019571880614, - 3.9855759703164786, - 3.985449458581278, - 3.985321905516776, - 3.9851928161643735, - 3.985061681302001, - 3.9849281004744217, - 3.9847916707890456, - 3.984652017013652, - 3.9845087906172014, - 3.984361623999398, - 3.984210229387505, - 3.9840543217772897, - 3.9838936368564095, - 3.9837279307611193, - 3.9835569671903137, - 3.983380537546254, - 3.983198453237867, - 3.983010539487133, - 3.982816637643037, - 3.982616600427091, - 3.982410294501172, - 3.9821976065499936, - 3.981978433820659, - 3.98175268651424, - 3.9815202820025135, - 3.9812811355239694, - 3.9810352012587105, - 3.9807824329126342, - 3.9805227947421713, - 3.98025626121676, - 3.9799828167049567, - 3.979702455182807, - 3.979415176431304, - 3.979120915063366, - 3.9788197313444904, - 3.978511645187546, - 3.978196683757453, - 3.977874881231074, - 3.9775462785719258, - 3.977210923319128, - 3.9768688693889542, - 3.976520091631717, - 3.976164681598159, - 3.975802711265946, - 3.975434245764851, - 3.975059354435424, - 3.974678110644796, - 3.974290591612612, - 3.9738968782465007, - 3.9734969957292807, - 3.973091026241604, - 3.97267907731273, - 3.972261234637277, - 3.9718375864078337, - 3.971408223238376, - 3.970973238098188, - 3.970532726255758, - 3.970086785231961, - 3.9696355147622295, - 3.969179016766908, - 3.9687173953293655, - 3.968250756681403, - 3.967779121749212, - 3.9673025467497114, - 3.966821210031711, - 3.966335213258639, - 3.965844659037386, - 3.965349650892284, - 3.964850293243904, - 3.964346691392672, - 3.963838951506767, - 3.9633271806141193, - 3.9628114865980937, - 3.962291978196672, - 3.961768765004759, - 3.961241888635166, - 3.960711326246973, - 3.9601773052952542, - 3.9596399242292506, - 3.9590992810480534, - 3.9585554732621064, - 3.958008597856979, - 3.957458751259268, - 3.9569060293046183, - 3.9563505272076775, - 3.955792339533825, - 3.9552315601727472, - 3.9546682823136456, - 3.954102586504367, - 3.953534484387748, - 3.952964125303128, - 3.952391593921131, - 3.951816973617857, - 3.9512403464397865, - 3.950661793070082, - 3.9500813927963456, - 3.949499223479672, - 3.948915361525039, - 3.9483298818530064, - 3.947742857872591, - 3.947154361455448, - 3.94656446291119, - 3.945973300202292, - 3.9453808968554633, - 3.944787319874321, - 3.9441926372071503, - 3.94359691547348, - 3.94300021995727, - 3.942402614600858, - 3.941804161999694, - 3.941204923397704, - 3.940604958683489, - 3.940004326386979, - 3.939403083676912, - 3.938801286358772, - 3.9381990491374017, - 3.937596403151644, - 3.936993390297669, - 3.93639006520116, - 3.93578648146841, - 3.9351826916982664, - 3.934578747494208, - 3.933974699476776, - 3.93337059729602, - 3.9327664896441648, - 3.9321624242683977, - 3.9315584479837464, - 3.9309546066860417, - 3.9303509411656288, - 3.929747495296029, - 3.9291443141904017, - 3.92854143982082, - 3.9279389132757383, - 3.9273367747722223, - 3.926735063668203, - 3.9261338184747023, - 3.92553307686804, - 3.924932875702019, - 3.924333251020056, - 3.923734238067252, - 3.9231358713024536, - 3.922538170923548, - 3.9219411590434072, - 3.921344880875335, - 3.920749366167348, - 3.920154643808035, - 3.919560741834293, - 3.918967687439052, - 3.9183755069789425, - 3.9177842259820306, - 3.9171938691554056, - 3.916604460392865, - 3.916016022782472, - 3.9154285786141023, - 3.9148421509098714, - 3.914256764234473, - 3.9136724352483174, - 3.9130891834373935, - 3.9125070275511873, - 3.9119259856106687, - 3.911346074916244, - 3.910767312055591, - 3.91018971291149, - 3.909613292669514, - 3.9090380658257087, - 3.9084640461941658, - 3.907891246914544, - 3.9073196853111134, - 3.906749423642448, - 3.9061804329673233, - 3.9056127252944615, - 3.9050463120197336, - 3.904481203929581, - 3.9039174112043655, - 3.903354943421739, - 3.9027938095598818, - 3.9022340180007857, - 3.9016755765334143, - 3.90111849235684, - 3.900562772083382, - 3.900008421741624, - 3.89945544677944, - 3.898903852066971, - 3.8983536418995617, - 3.8978048200005944, - 3.897257389524406, - 3.896711353059008, - 3.8961667126288857, - 3.895623469697705, - 3.895081625170973, - 3.894541206101947, - 3.8940023551584977, - 3.893464938093327, - 3.892928956748649, - 3.892394412540928, - 3.891861306463612, - 3.891329639089789, - 3.8907994105747736, - 3.890270620658679, - 3.8897432686688935, - 3.8892173535224543, - 3.8886928737284823, - 3.88816982739044, - 3.887648212208381, - 3.887128025481168, - 3.886609264108568, - 3.886091924593348, - 3.8855760030432687, - 3.8850614951730527, - 3.884548396306273, - 3.8840367013771897, - 3.883526404932534, - 3.883017501133196, - 3.882510027489835, - 3.88200412168033, - 3.88149962926911, - 3.880996546342963, - 3.8804948687564966, - 3.8799945921353056, - 3.879495711879074, - 3.8789982231645936, - 3.878502120948731, - 3.878007399971236, - 3.8775140547575786, - 3.8770220796216113, - 3.876531468668211, - 3.8760422157958057, - 3.875554314698874, - 3.8750677588703097, - 3.8745825416037505, - 3.874098655995825, - 3.873616094948307, - 3.8731348511702186, - 3.872654917179845, - 3.872176285306693, - 3.871698947693357, - 3.8712228989687336, - 3.8707481474743934, - 3.8702746874705864, - 3.869802514708178, - 3.8693316251097016, - 3.86886201478014, - 3.868393680017465, - 3.867926853618441, - 3.8674612822331023, - 3.866996844477598, - 3.866533563483088, - 3.8660715440585767, - 3.8656107105628448, - 3.8651510574058134, - 3.8646926081091766, - 3.8642353507523985, - 3.8637792734013656, - 3.863324369371065, - 3.862870631648315, - 3.862418054550005, - 3.8619666315487624, - 3.861516356007329, - 3.86106722118063, - 3.860619143290965, - 3.860172178237604, - 3.8597263216646303, - 3.859281566480674, - 3.858837905495053, - 3.858395331419476, - 3.85795383686965, - 3.85751341436692, - 3.8570740563397257, - 3.856635755125109, - 3.8561985029701207, - 3.855762292033152, - 3.855327068277146, - 3.8548927823913264, - 3.854459495582153, - 3.8540271997626343, - 3.8535958867613207, - 3.8531655483233553, - 3.852736176111359, - 3.852307761706332, - 3.8518802966084857, - 3.851453772238006, - 3.8510281799357786, - 3.8506035109640497, - 3.8501797565070226, - 3.849756805362445, - 3.849334708159236, - 3.848913479951531, - 3.848493111705237, - 3.8480735943084623, - 3.847654918571755, - 3.8472370752282967, - 3.846820054934049, - 3.8464038482678378, - 3.845988445731397, - 3.845573837749317, - 3.8451600146689664, - 3.844746942196183, - 3.844334534626841, - 3.843922865092112, - 3.8435119237018855, - 3.843101700485544, - 3.842692185391502, - 3.842283368286704, - 3.8418752389560735, - 3.8414677871018337, - 3.8410610023428737, - 3.8406548742139544, - 3.840249392164916, - 3.83984454555978, - 3.839440256088183, - 3.839036528121377, - 3.838633387320388, - 3.838230822750895, - 3.837828823385432, - 3.837427378102143, - 3.8370264756833943, - 3.8366261048144, - 3.836226254081689, - 3.835826911971567, - 3.8354280668684697, - 3.835029707053286, - 3.8346318162322106, - 3.8342342900540975, - 3.833837196314807, - 3.8334405224057257, - 3.8330442556049262, - 3.832648383074912, - 3.8322528918602456, - 3.831857768885108, - 3.8314630009508055, - 3.8310685747331465, - 3.8306744767797407, - 3.83028069350726, - 3.829887211198565, - 3.829494015999751, - 3.8291010939171217, - 3.82870843081408, - 3.828316012407876, - 3.8279238242663305, - 3.827531851804423, - 3.8271400802807984, - 3.826748494794177, - 3.8263570802796543, - 3.8259658029518833, - 3.825574623636327, - 3.825183561168592, - 3.8247925994544505, - 3.8244017222158506, - 3.824010912986572, - 3.823620155107704, - 3.823229431723132, - 3.8228387257748047, - 3.8224480199979984, - 3.8220572969163937, - 3.821666538837092, - 3.8212757278455234, - 3.820884845800227, - 3.820493874327538, - 3.820102794816189, - 3.819711588411769, - 3.819320236011069, - 3.8189287182564073, - 3.8185370155297305, - 3.818145107946698, - 3.817752975350636, - 3.81736059292283, - 3.816967940820122, - 3.8165750005294727, - 3.816181750698888, - 3.815788169672208, - 3.815394235482488, - 3.81499992584527, - 3.814605218151795, - 3.814210089462124, - 3.813814516498163, - 3.8134184756366745, - 3.813021942902167, - 3.812624893959729, - 3.8122273041078794, - 3.811829148271265, - 3.811430400993395, - 3.81103103642933, - 3.810631028338314, - 3.810230350076444, - 3.8098289745892937, - 3.809426874404565, - 3.809024021624734, - 3.808620390277108, - 3.8082159508631426, - 3.8078106741294127, - 3.8074045305610418, - 3.806997490182499, - 3.8065895225507127 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - -54.313231642426395, - 952.3132316424264 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 3.7891337111919574, - 4.0803189431613305 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Voltage [V]" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "02004006008003.83.853.93.9544.05ReferenceModelOptimised ComparisonTime / sVoltage / V" ] }, "metadata": {}, @@ -4937,1025 +369,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": { "id": "N5XYkevi04qD" }, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Covariance Matrix Adaptation Evolution
Strategy (CMA-ES)", - "type": "scatter", - "x": [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45 - ], - "y": [ - 0.004411711089363526, - 0.005830879807423535, - 0.005375404335221186, - 0.002881125238847097, - 0.0020386582628807845, - 0.001452723526972283, - 0.001138954926950212, - 0.0014071587189440449, - 0.006486420598601779, - 0.004256235561667734, - 0.00399803111422376, - 0.0009155271534778232, - 0.0006933920250352247, - 0.0006533572518697612, - 0.0006097645748991324, - 0.0005135016900704044, - 0.0005422527696330972, - 0.0006072159685855464, - 0.00044604288440474086, - 0.00042035522246848056, - 0.0004245854175423076, - 0.00041545425658610625, - 0.00041661078412882934, - 0.0004140071530269469, - 0.00041475959826343977, - 0.0004143493288122635, - 0.0004139678081364512, - 0.0004139346505102296, - 0.0004139426374679559, - 0.0004138617188765794, - 0.0004138630986312129, - 0.0004138832547943191, - 0.0004138316896185448, - 0.0004138500441090939, - 0.000413833161521332, - 0.00041385187527160435, - 0.0004138406489930524, - 0.00041383205173274807, - 0.0004138362814898684, - 0.00041383327184654425, - 0.0004138320579210566, - 0.0004138317785459333, - 0.0004138314759850062, - 0.0004138314747321133, - 0.0004138314830112939 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Convergence", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - 1, - 45 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Iteration" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 0.00007646541229490959, - 0.006823786661038982 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Cost" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "1020304000.050.10.15ConvergenceIterationCost" ] }, "metadata": {}, @@ -5963,2034 +385,8 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "name": "Negative electrode active material volume fraction", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247, - 248, - 249, - 250, - 251, - 252, - 253, - 254, - 255, - 256, - 257, - 258, - 259, - 260, - 261, - 262, - 263, - 264, - 265, - 266, - 267, - 268, - 269 - ], - "xaxis": "x", - "y": [ - 0.6398015275435182, - 0.7834095929218652, - 0.6889740628400505, - 0.6234041886887529, - 0.7472987116338267, - 0.7542734972117308, - 0.6419412905618748, - 0.6783026187078912, - 0.657028445816533, - 0.6967436629809198, - 0.6298077120656006, - 0.6285843263077633, - 0.6000082474714872, - 0.6955322817007007, - 0.6273696391708145, - 0.6307723252620865, - 0.62684229429316, - 0.720746888362131, - 0.6081882553336139, - 0.6115269780187048, - 0.6284493312020082, - 0.6000173662755234, - 0.6633457814593835, - 0.6632885397594831, - 0.6604271664950124, - 0.6297456058567239, - 0.6324599880964914, - 0.6541468730138755, - 0.6850966649271291, - 0.6545813391925616, - 0.7140024010556135, - 0.7002798068515736, - 0.72475831743897, - 0.6562000954444471, - 0.6617569374762292, - 0.6588597524568218, - 0.6599370948477885, - 0.7327244109948049, - 0.6543755422656028, - 0.7368598036834967, - 0.7118995283656964, - 0.7035529116587953, - 0.6856404670642229, - 0.7874619507069341, - 0.8347447119266668, - 0.7952661687473195, - 0.6948326989106055, - 0.8025459058164388, - 0.8023909087739224, - 0.8413035783556975, - 0.8429791769481227, - 0.8215793054323863, - 0.7941168566652124, - 0.817385311535264, - 0.7534210439836811, - 0.7320027540875633, - 0.7622205415933236, - 0.886417585811722, - 0.897026581135707, - 0.6955344176974225, - 0.8123340502097736, - 0.7661303864763724, - 0.8412559369392237, - 0.8364931415088712, - 0.777957172464202, - 0.8541148070899864, - 0.7897906812886395, - 0.6963974015685278, - 0.7771182959883609, - 0.8019974308187632, - 0.702078244365795, - 0.8014441837617875, - 0.7079450888854993, - 0.7962642768613842, - 0.7920638353800669, - 0.7151501223496658, - 0.7358391924764545, - 0.8278533733855753, - 0.7124869250307805, - 0.7348553088810768, - 0.8334840060589617, - 0.7764289301575326, - 0.7950577181630605, - 0.7112830158837191, - 0.7073896226812689, - 0.7076645262323324, - 0.7718085894819768, - 0.705975703768142, - 0.7354747372075696, - 0.6985759536543132, - 0.7460830864135181, - 0.7247593868054502, - 0.7735310256261346, - 0.7831005245852658, - 0.7812987523660673, - 0.7223483312511143, - 0.7335383532528784, - 0.7163899486916715, - 0.7666854163887278, - 0.7385206791820437, - 0.7488601755959889, - 0.688862834317936, - 0.7864848324486302, - 0.7461617845028201, - 0.765284356969859, - 0.7387961110973175, - 0.7335658164518817, - 0.7537842940548691, - 0.7237328418295452, - 0.7277991422014717, - 0.7387787764987062, - 0.760473896200636, - 0.7503729941476718, - 0.7521471891571413, - 0.7355964234728628, - 0.7383871844071798, - 0.7531484988934661, - 0.740356135309568, - 0.7468489281830667, - 0.7345480428291439, - 0.7487886779669092, - 0.7542891622629306, - 0.7610346667506344, - 0.7612010243545771, - 0.7544357417241132, - 0.7504001610339999, - 0.7605611505635547, - 0.7462336979565392, - 0.7535933763121919, - 0.7530969385922034, - 0.7379769125400184, - 0.7563100179542935, - 0.7445989047200426, - 0.7543521697854405, - 0.762457507449366, - 0.7512750946862986, - 0.741423365873329, - 0.7504272616178943, - 0.7522380364202639, - 0.75091364504222, - 0.7486358306117241, - 0.7548234842165206, - 0.7491469720681629, - 0.7511279374636878, - 0.749617610198614, - 0.7476003179328695, - 0.750369931117366, - 0.7505338022181203, - 0.7557962768784904, - 0.7505013731989336, - 0.7510182106587123, - 0.7471308643958997, - 0.7488343565008274, - 0.7456332985972772, - 0.7488412049790049, - 0.7457285567260475, - 0.74644808903075, - 0.7491314652326265, - 0.747010573156756, - 0.7474520922932261, - 0.7479037960827083, - 0.7500150908726078, - 0.7488982363745749, - 0.747762947592885, - 0.7467598497113074, - 0.7472612506053942, - 0.7478758656395419, - 0.7470499147889761, - 0.7471624119426061, - 0.7470474410663344, - 0.7478240506600677, - 0.7465744939982786, - 0.7459454081731658, - 0.7468805778707786, - 0.7480630920647846, - 0.7477703696516877, - 0.7475196775453345, - 0.7475242762120593, - 0.7482347575908042, - 0.7472334260505487, - 0.7479783710356227, - 0.7476575108918704, - 0.7472096081229995, - 0.7476071873145033, - 0.7482627819086766, - 0.7468358374676501, - 0.7476295273469726, - 0.7490225577596015, - 0.7477172724799762, - 0.747616043551513, - 0.7481017866390718, - 0.7471080442096341, - 0.7493568325560195, - 0.7485598082286591, - 0.7483784427338819, - 0.7484127359500952, - 0.7486014010276004, - 0.7479954563835443, - 0.7488198011011512, - 0.7491198277272945, - 0.748693537617128, - 0.7487474786682461, - 0.7485752495166857, - 0.7481632082523264, - 0.7477266089741006, - 0.7483368402303322, - 0.7484268685148276, - 0.7481922079026848, - 0.7493528562192566, - 0.7483799110710669, - 0.7487127986092711, - 0.7483236735870027, - 0.7477307374624874, - 0.7486634891082754, - 0.7482956807831075, - 0.7478639505717516, - 0.7482378234432122, - 0.7487357950590608, - 0.7486533009128502, - 0.7481760029573876, - 0.7486264880466273, - 0.7483535704545061, - 0.7482922086763294, - 0.7484060675803813, - 0.7481509890902289, - 0.7484112159780258, - 0.7483750819870393, - 0.7480772713357768, - 0.7483113624023876, - 0.7482820913661309, - 0.7485009616053125, - 0.7485332052828106, - 0.7484031692818295, - 0.7484242230474862, - 0.7483692816489709, - 0.7483280482902646, - 0.7484428906915788, - 0.7487108739170031, - 0.7481733095129082, - 0.7483352377665545, - 0.7484229556050997, - 0.7484036943462062, - 0.7483616558227326, - 0.7482549728604215, - 0.7483062330471475, - 0.7484845018869662, - 0.7484487561250316, - 0.7483313571673764, - 0.7485076603749453, - 0.7483740195947701, - 0.748401446465961, - 0.7483269710153728, - 0.7484050342172691, - 0.7484242559277622, - 0.7483524836989649, - 0.7483750599216641, - 0.7484270226816604, - 0.7483871326274112, - 0.7483925316918323, - 0.7484135477756078, - 0.7483685303108918, - 0.748501183840965, - 0.7483650572896052, - 0.7483830039428074, - 0.7483992351657923, - 0.7484111602741179, - 0.7483917677990026, - 0.7483812339565569, - 0.7483626840080347, - 0.7483911546398662 - ], - "yaxis": "y" - }, - { - "name": "Positive electrode active material volume fraction", - "type": "scatter", - "x": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 66, - 67, - 68, - 69, - 70, - 71, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 83, - 84, - 85, - 86, - 87, - 88, - 89, - 90, - 91, - 92, - 93, - 94, - 95, - 96, - 97, - 98, - 99, - 100, - 101, - 102, - 103, - 104, - 105, - 106, - 107, - 108, - 109, - 110, - 111, - 112, - 113, - 114, - 115, - 116, - 117, - 118, - 119, - 120, - 121, - 122, - 123, - 124, - 125, - 126, - 127, - 128, - 129, - 130, - 131, - 132, - 133, - 134, - 135, - 136, - 137, - 138, - 139, - 140, - 141, - 142, - 143, - 144, - 145, - 146, - 147, - 148, - 149, - 150, - 151, - 152, - 153, - 154, - 155, - 156, - 157, - 158, - 159, - 160, - 161, - 162, - 163, - 164, - 165, - 166, - 167, - 168, - 169, - 170, - 171, - 172, - 173, - 174, - 175, - 176, - 177, - 178, - 179, - 180, - 181, - 182, - 183, - 184, - 185, - 186, - 187, - 188, - 189, - 190, - 191, - 192, - 193, - 194, - 195, - 196, - 197, - 198, - 199, - 200, - 201, - 202, - 203, - 204, - 205, - 206, - 207, - 208, - 209, - 210, - 211, - 212, - 213, - 214, - 215, - 216, - 217, - 218, - 219, - 220, - 221, - 222, - 223, - 224, - 225, - 226, - 227, - 228, - 229, - 230, - 231, - 232, - 233, - 234, - 235, - 236, - 237, - 238, - 239, - 240, - 241, - 242, - 243, - 244, - 245, - 246, - 247, - 248, - 249, - 250, - 251, - 252, - 253, - 254, - 255, - 256, - 257, - 258, - 259, - 260, - 261, - 262, - 263, - 264, - 265, - 266, - 267, - 268, - 269 - ], - "xaxis": "x2", - "y": [ - 0.6866247125275846, - 0.6364681188248305, - 0.5965886968516367, - 0.6660258705915133, - 0.6275061144028632, - 0.6251617498111561, - 0.7224532880184631, - 0.7289160363994348, - 0.6921773962251418, - 0.7247545300658802, - 0.7513856087780815, - 0.7457382219315379, - 0.7551685099493607, - 0.7291443806578158, - 0.7141548194405872, - 0.6911229225038843, - 0.7379516621622741, - 0.777837703980055, - 0.7244504679650009, - 0.7405112034190808, - 0.7358836789335765, - 0.7618219511988441, - 0.6789817528741565, - 0.6062001211285537, - 0.6812043266013955, - 0.7171132005802143, - 0.6912206493419717, - 0.704799341642163, - 0.673393157300267, - 0.7271980744258015, - 0.6267825752981799, - 0.65064092344774, - 0.6645867985746339, - 0.6576987664651354, - 0.6451723623049832, - 0.6590833200623419, - 0.6557180749586652, - 0.6560449254838345, - 0.7086741013446526, - 0.6583204160621425, - 0.655458336737195, - 0.6754402827422513, - 0.6627891780499229, - 0.6122181344142449, - 0.6190393862793186, - 0.6327593476579223, - 0.6876139439226782, - 0.6541649558877185, - 0.6827332407704083, - 0.6695668123835893, - 0.6850739771093443, - 0.6789816929663839, - 0.6464750422700254, - 0.6720816961016086, - 0.684156303444881, - 0.6930852293129227, - 0.6729842890286399, - 0.6416090211669931, - 0.6054191915997689, - 0.7108972355420703, - 0.6634112094769272, - 0.6965684039931663, - 0.6705679216656908, - 0.6647212704943212, - 0.6743705450429236, - 0.6796222826430791, - 0.6648926425657091, - 0.6843896794096097, - 0.663393745371838, - 0.6719560998632164, - 0.6704958017029286, - 0.6713995372611415, - 0.6865789272728248, - 0.6613975280534948, - 0.6487240007030157, - 0.6572931584836466, - 0.6649620068994143, - 0.6438688820893349, - 0.6431741769524745, - 0.669850019657571, - 0.6510573948035685, - 0.6587605572070525, - 0.6486461016541625, - 0.675347235111066, - 0.6665524995451028, - 0.6676514809150907, - 0.6627022880306225, - 0.673111201247168, - 0.6725887016058728, - 0.6813413519664382, - 0.664105160941047, - 0.6704285631714935, - 0.6688202571784456, - 0.6633635281486342, - 0.6596094305535337, - 0.671138842517764, - 0.6622915956065181, - 0.6735121042943643, - 0.6610407345486717, - 0.6648508765352975, - 0.6634148928140992, - 0.6712742574261868, - 0.6570256845566627, - 0.655982173199172, - 0.6644327312955974, - 0.6648430371466058, - 0.6649466158716633, - 0.6619684165487295, - 0.6701474383426015, - 0.6680603990093963, - 0.663447440652594, - 0.661192268845853, - 0.6625732936964915, - 0.6638259137461971, - 0.6651175061786415, - 0.6650030296750225, - 0.6643850470998328, - 0.6671390370924041, - 0.6669547812689007, - 0.6665640967095965, - 0.6670199908935588, - 0.6641144723053162, - 0.6616517648553679, - 0.6619798163188976, - 0.6652469537054858, - 0.665559230383553, - 0.6636038597672236, - 0.6657726366056322, - 0.6656908689848722, - 0.6649733441888416, - 0.666677149058746, - 0.6642536625484553, - 0.6658443295180828, - 0.6647784419019307, - 0.6638785235127931, - 0.664951668500953, - 0.6671028090189277, - 0.666462202880858, - 0.6647186789448336, - 0.6650992718608973, - 0.6653319061675408, - 0.6647323965249505, - 0.6651551078313285, - 0.6651359109079447, - 0.6649806996007741, - 0.6655800530882241, - 0.6650007852076433, - 0.6648729582161904, - 0.6640339073691309, - 0.664672015602371, - 0.6649434347783275, - 0.6655710089879313, - 0.6653605701103825, - 0.6659282565219087, - 0.66504257049908, - 0.6657906447125375, - 0.665833633713905, - 0.6652979035939126, - 0.6655029692593243, - 0.665499891041223, - 0.6653441858383358, - 0.6648585438657062, - 0.6650941474079926, - 0.6654039515308926, - 0.6654785379461289, - 0.6654551644037424, - 0.6651415185563612, - 0.6655312723276966, - 0.6654249030854259, - 0.6655020713129259, - 0.6653743390492347, - 0.6656016009088621, - 0.6657180017816687, - 0.665405097520238, - 0.6653710793901598, - 0.6654091789027219, - 0.6653870490445738, - 0.6654036972679006, - 0.66528133178443, - 0.6655620122986592, - 0.6654323213327458, - 0.6653827612241039, - 0.6655412482977183, - 0.6654376175394979, - 0.6653550965428916, - 0.6654583400678383, - 0.6654056986341513, - 0.6652311851087725, - 0.6654622272063525, - 0.6654564489996346, - 0.6653835515309753, - 0.6654821989523526, - 0.6652229912957907, - 0.6652764289062365, - 0.6654017645039588, - 0.6653045979399675, - 0.665242483389746, - 0.6653987728744556, - 0.665165960429941, - 0.6651956556742828, - 0.66519189292382, - 0.6652412952558492, - 0.6652914481091496, - 0.6652919685255246, - 0.6653646478766847, - 0.6652504140171038, - 0.665295111186783, - 0.6653282212163226, - 0.6652176823014453, - 0.6653463973182714, - 0.6652751221814872, - 0.665291188620783, - 0.6654153860961469, - 0.6652640830162271, - 0.6652783081891461, - 0.6653750149169088, - 0.6653186330948742, - 0.6652642754668254, - 0.6652795334642339, - 0.6653256117377516, - 0.665277889323846, - 0.6653283422013764, - 0.6653483767376702, - 0.6653080069734126, - 0.6653507557805092, - 0.6653190165995263, - 0.6652955586006951, - 0.6653872185139995, - 0.6652951711597194, - 0.6653177348777891, - 0.6652715218218141, - 0.6652898470208235, - 0.6653248057191449, - 0.6652830190388519, - 0.6653002347245491, - 0.6653123105541945, - 0.6652892680387847, - 0.6652438219377682, - 0.6653507444170728, - 0.6653069663336174, - 0.6653054732960361, - 0.6653110395250771, - 0.6653082035178534, - 0.66531554077811, - 0.6653140342751883, - 0.6652885269663483, - 0.6653011382384398, - 0.6653224538116091, - 0.6652855278194739, - 0.6653043695107316, - 0.6653078611733345, - 0.665317028873498, - 0.6653013957364677, - 0.6653034696571328, - 0.6653040571988419, - 0.6653187178674982, - 0.6652995202187427, - 0.6653087345226069, - 0.6653076618229143, - 0.6652966224697726, - 0.665306351273456, - 0.6652898404082866, - 0.6653087998780384, - 0.6653117191814542, - 0.6653049245675259, - 0.6653030637117724, - 0.6653081968224175, - 0.665312646971721, - 0.6653104757573955, - 0.6653054543409095 - ], - "yaxis": "y2" - } - ], - "layout": { - "height": 576, - "legend": { - "orientation": "h", - "x": 1, - "xanchor": "right", - "y": 1.02, - "yanchor": "bottom" - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Parameter Convergence" - }, - "width": 1024, - "xaxis": { - "anchor": "y", - "autorange": true, - "domain": [ - 0, - 0.45 - ], - "range": [ - 0, - 269 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "xaxis2": { - "anchor": "y2", - "autorange": true, - "domain": [ - 0.55, - 1 - ], - "range": [ - 0, - 269 - ], - "title": { - "text": "Function Call" - }, - "type": "linear" - }, - "yaxis": { - "anchor": "x", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 0.5835072289345861, - 0.913527599672608 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis2": { - "anchor": "x2", - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - 0.5865193075667247, - 0.7879070932649671 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "0501001502002500.650.70.750.80.850501001502002500.50.550.60.650.70.75Negative electrode active material volume fractionPositive electrode active material volume fractionParameter ConvergenceFunction CallFunction CallNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -8013,1190 +409,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "autocontour": true, - "contours": { - "end": 2.4000000000000004, - "size": 0.2, - "start": 0.2 - }, - "type": "contour", - "x": [ - 0.6, - 0.6214285714285714, - 0.6428571428571428, - 0.6642857142857143, - 0.6857142857142857, - 0.7071428571428572, - 0.7285714285714285, - 0.75, - 0.7714285714285715, - 0.7928571428571429, - 0.8142857142857143, - 0.8357142857142857, - 0.8571428571428572, - 0.8785714285714286, - 0.9 - ], - "y": [ - 0.5, - 0.5214285714285715, - 0.5428571428571428, - 0.5642857142857143, - 0.5857142857142857, - 0.6071428571428572, - 0.6285714285714286, - 0.65, - 0.6714285714285715, - 0.6928571428571428, - 0.7142857142857143, - 0.7357142857142858, - 0.7571428571428571, - 0.7785714285714287, - 0.8 - ], - "z": [ - [ - 2.518483144840509, - 2.377262683817391, - 2.2616905831213607, - 2.164403212564914, - 2.079803742522767, - 2.0041691075835706, - 1.9351473883638337, - 1.8712593119892753, - 1.8115540016362952, - 1.7553894717506613, - 1.7023132237717462, - 1.651995269709941, - 1.6041763730045082, - 1.5586481956033906, - 1.5152368819434148 - ], - [ - 1.8287473605061615, - 1.7090492814774738, - 1.6115179690455983, - 1.5296941605106102, - 1.4587596877969635, - 1.3955409128475922, - 1.3380430821667986, - 1.2850137507962007, - 1.2356423934195848, - 1.1893794358124832, - 1.1458401590243896, - 1.1047346907717182, - 1.0658372551800062, - 1.0289854573949235, - 0.9939619191786376 - ], - [ - 1.3017199818605016, - 1.201234276564807, - 1.1198476425162438, - 1.0519023826422074, - 0.9932611201556444, - 0.9412315167752294, - 0.8941319398513997, - 0.85090901618777, - 0.8108737987161752, - 0.7735644520613016, - 0.7386482491471413, - 0.7058752003325777, - 0.6750659517183902, - 0.6460231047667887, - 0.618613031522361 - ], - [ - 0.900735579154189, - 0.8174639300842939, - 0.750593861441417, - 0.6951707929426605, - 0.6476532189068392, - 0.6057701888475753, - 0.5681157309554523, - 0.5338053309351255, - 0.5022663600789157, - 0.4731063307276756, - 0.44604205762882265, - 0.420858067281644, - 0.39739705247841983, - 0.3754760236259347, - 0.35501441578524845 - ], - [ - 0.598185412517215, - 0.5304734930980259, - 0.476767564696257, - 0.4327374904253301, - 0.39536852734341166, - 0.36276209388587566, - 0.33375409987923566, - 0.30761323246247696, - 0.2838656662438443, - 0.262182891946944, - 0.24232525122167767, - 0.22411941996630155, - 0.2073935473661337, - 0.19202749540066755, - 0.17793155989035703 - ], - [ - 0.3737884208665301, - 0.3202940651907955, - 0.27864760247712417, - 0.24508221747853431, - 0.21705596599670912, - 0.19300435462487284, - 0.17197780373819185, - 0.15338610451509388, - 0.13684156432913852, - 0.1220738525078768, - 0.10889097723011368, - 0.09711894920257726, - 0.08663888198245101, - 0.07733827781517877, - 0.06913353936053125 - ], - [ - 0.21280537973200825, - 0.17243257626818517, - 0.14193589574205373, - 0.11806441700302275, - 0.0987069926666208, - 0.08260233752866344, - 0.06899787412139419, - 0.05742912890290986, - 0.04758849148885914, - 0.03926364103222851, - 0.03227884429484433, - 0.02650899362754497, - 0.021843191729575336, - 0.01819353398192628, - 0.015475906878293757 - ], - [ - 0.10423884595834632, - 0.07608726832339154, - 0.05597965225254225, - 0.04115430358362984, - 0.02989488734582022, - 0.021218758381584032, - 0.014556826836498102, - 0.009559288787587937, - 0.005992709324731413, - 0.003689731061462786, - 0.0025153044924100844, - 0.0023645041054539944, - 0.003146765509954847, - 0.004782734113435378, - 0.007202404081658567 - ], - [ - 0.039668691473452214, - 0.02297498156208481, - 0.012620306597562664, - 0.006287007026677996, - 0.0026333371526606588, - 0.0009362334470703204, - 0.0007983928920754467, - 0.001977012433501772, - 0.00430612398421098, - 0.007665015484473067, - 0.011952630158676885, - 0.01708404781092645, - 0.022987579523243807, - 0.029592464512403268, - 0.03684146038442936 - ], - [ - 0.012440831382263488, - 0.006556745052816221, - 0.005401289844358328, - 0.007074186652664861, - 0.010591953905527418, - 0.015477569656963256, - 0.02148722157484142, - 0.02850086737314284, - 0.03638761419034056, - 0.04508823243883624, - 0.05452684241142537, - 0.0646438192910587, - 0.07537239455830853, - 0.086662828419682, - 0.09846573637946736 - ], - [ - 0.017134081755175574, - 0.02149440080845006, - 0.02905123556433376, - 0.03830099534622837, - 0.048605294478661354, - 0.05971750224933562, - 0.07154200661991442, - 0.08406295587024805, - 0.0972133280632493, - 0.11096389976975082, - 0.12527181738565207, - 0.14009902103820435, - 0.15539046317799632, - 0.17110964229864264, - 0.18721665967535703 - ], - [ - 0.04921174375001043, - 0.06331454804954591, - 0.07913832282878049, - 0.09558901088862652, - 0.1123301463883606, - 0.12934497959748534, - 0.14669225775385128, - 0.16442323687439345, - 0.18255440762372, - 0.20108763433865628, - 0.22000663611713503, - 0.23929370790939075, - 0.25890543720089365, - 0.278817678054451, - 0.29899955966049496 - ], - [ - 0.1047922689868204, - 0.1281844268816947, - 0.15188550121562108, - 0.17517904314248509, - 0.19803621126646975, - 0.22065551313262968, - 0.24323574337720943, - 0.2658984475367472, - 0.28877302217024425, - 0.31184149042764076, - 0.3351325150873826, - 0.3586478975285074, - 0.3823549396537118, - 0.40624189554002593, - 0.43028623643746144 - ], - [ - 0.18049623396177655, - 0.21276269907514728, - 0.24397721290100916, - 0.2737883590239779, - 0.3024640659335579, - 0.3304108628784123, - 0.35796238744071907, - 0.38532436598600583, - 0.4126858919449912, - 0.4400588982985767, - 0.4674988602016171, - 0.495025961475323, - 0.5226187771478782, - 0.5502765965143669, - 0.5779850802582551 - ], - [ - 0.2733424453843406, - 0.3140983105000469, - 0.35248789769545097, - 0.388513400195036, - 0.42272954411349783, - 0.4557443558611662, - 0.4880218808073551, - 0.5198495806582729, - 0.5514728637248935, - 0.5829337598662178, - 0.6143131695305581, - 0.645648796132726, - 0.6769303662243025, - 0.7081675706235988, - 0.739353909675269 - ] - ] - } - ], - "layout": { - "height": 600, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.6, - 0.9 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis": { - "range": [ - 0.5, - 0.8 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "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", - "text/html": [ - "
" + "image/svg+xml": [ + "0.60.650.70.750.80.850.90.50.550.60.650.70.750.80.40.81.21.622.4Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -9204,2068 +423,8 @@ }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "autocontour": true, - "contours": { - "end": 2.4000000000000004, - "size": 0.2, - "start": 0.2 - }, - "type": "contour", - "x": [ - 0.6, - 0.6214285714285714, - 0.6428571428571428, - 0.6642857142857143, - 0.6857142857142857, - 0.7071428571428572, - 0.7285714285714285, - 0.75, - 0.7714285714285715, - 0.7928571428571429, - 0.8142857142857143, - 0.8357142857142857, - 0.8571428571428572, - 0.8785714285714286, - 0.9 - ], - "y": [ - 0.5, - 0.5214285714285715, - 0.5428571428571428, - 0.5642857142857143, - 0.5857142857142857, - 0.6071428571428572, - 0.6285714285714286, - 0.65, - 0.6714285714285715, - 0.6928571428571428, - 0.7142857142857143, - 0.7357142857142858, - 0.7571428571428571, - 0.7785714285714287, - 0.8 - ], - "z": [ - [ - 2.518483144840509, - 2.377262683817391, - 2.2616905831213607, - 2.164403212564914, - 2.079803742522767, - 2.0041691075835706, - 1.9351473883638337, - 1.8712593119892753, - 1.8115540016362952, - 1.7553894717506613, - 1.7023132237717462, - 1.651995269709941, - 1.6041763730045082, - 1.5586481956033906, - 1.5152368819434148 - ], - [ - 1.8287473605061615, - 1.7090492814774738, - 1.6115179690455983, - 1.5296941605106102, - 1.4587596877969635, - 1.3955409128475922, - 1.3380430821667986, - 1.2850137507962007, - 1.2356423934195848, - 1.1893794358124832, - 1.1458401590243896, - 1.1047346907717182, - 1.0658372551800062, - 1.0289854573949235, - 0.9939619191786376 - ], - [ - 1.3017199818605016, - 1.201234276564807, - 1.1198476425162438, - 1.0519023826422074, - 0.9932611201556444, - 0.9412315167752294, - 0.8941319398513997, - 0.85090901618777, - 0.8108737987161752, - 0.7735644520613016, - 0.7386482491471413, - 0.7058752003325777, - 0.6750659517183902, - 0.6460231047667887, - 0.618613031522361 - ], - [ - 0.900735579154189, - 0.8174639300842939, - 0.750593861441417, - 0.6951707929426605, - 0.6476532189068392, - 0.6057701888475753, - 0.5681157309554523, - 0.5338053309351255, - 0.5022663600789157, - 0.4731063307276756, - 0.44604205762882265, - 0.420858067281644, - 0.39739705247841983, - 0.3754760236259347, - 0.35501441578524845 - ], - [ - 0.598185412517215, - 0.5304734930980259, - 0.476767564696257, - 0.4327374904253301, - 0.39536852734341166, - 0.36276209388587566, - 0.33375409987923566, - 0.30761323246247696, - 0.2838656662438443, - 0.262182891946944, - 0.24232525122167767, - 0.22411941996630155, - 0.2073935473661337, - 0.19202749540066755, - 0.17793155989035703 - ], - [ - 0.3737884208665301, - 0.3202940651907955, - 0.27864760247712417, - 0.24508221747853431, - 0.21705596599670912, - 0.19300435462487284, - 0.17197780373819185, - 0.15338610451509388, - 0.13684156432913852, - 0.1220738525078768, - 0.10889097723011368, - 0.09711894920257726, - 0.08663888198245101, - 0.07733827781517877, - 0.06913353936053125 - ], - [ - 0.21280537973200825, - 0.17243257626818517, - 0.14193589574205373, - 0.11806441700302275, - 0.0987069926666208, - 0.08260233752866344, - 0.06899787412139419, - 0.05742912890290986, - 0.04758849148885914, - 0.03926364103222851, - 0.03227884429484433, - 0.02650899362754497, - 0.021843191729575336, - 0.01819353398192628, - 0.015475906878293757 - ], - [ - 0.10423884595834632, - 0.07608726832339154, - 0.05597965225254225, - 0.04115430358362984, - 0.02989488734582022, - 0.021218758381584032, - 0.014556826836498102, - 0.009559288787587937, - 0.005992709324731413, - 0.003689731061462786, - 0.0025153044924100844, - 0.0023645041054539944, - 0.003146765509954847, - 0.004782734113435378, - 0.007202404081658567 - ], - [ - 0.039668691473452214, - 0.02297498156208481, - 0.012620306597562664, - 0.006287007026677996, - 0.0026333371526606588, - 0.0009362334470703204, - 0.0007983928920754467, - 0.001977012433501772, - 0.00430612398421098, - 0.007665015484473067, - 0.011952630158676885, - 0.01708404781092645, - 0.022987579523243807, - 0.029592464512403268, - 0.03684146038442936 - ], - [ - 0.012440831382263488, - 0.006556745052816221, - 0.005401289844358328, - 0.007074186652664861, - 0.010591953905527418, - 0.015477569656963256, - 0.02148722157484142, - 0.02850086737314284, - 0.03638761419034056, - 0.04508823243883624, - 0.05452684241142537, - 0.0646438192910587, - 0.07537239455830853, - 0.086662828419682, - 0.09846573637946736 - ], - [ - 0.017134081755175574, - 0.02149440080845006, - 0.02905123556433376, - 0.03830099534622837, - 0.048605294478661354, - 0.05971750224933562, - 0.07154200661991442, - 0.08406295587024805, - 0.0972133280632493, - 0.11096389976975082, - 0.12527181738565207, - 0.14009902103820435, - 0.15539046317799632, - 0.17110964229864264, - 0.18721665967535703 - ], - [ - 0.04921174375001043, - 0.06331454804954591, - 0.07913832282878049, - 0.09558901088862652, - 0.1123301463883606, - 0.12934497959748534, - 0.14669225775385128, - 0.16442323687439345, - 0.18255440762372, - 0.20108763433865628, - 0.22000663611713503, - 0.23929370790939075, - 0.25890543720089365, - 0.278817678054451, - 0.29899955966049496 - ], - [ - 0.1047922689868204, - 0.1281844268816947, - 0.15188550121562108, - 0.17517904314248509, - 0.19803621126646975, - 0.22065551313262968, - 0.24323574337720943, - 0.2658984475367472, - 0.28877302217024425, - 0.31184149042764076, - 0.3351325150873826, - 0.3586478975285074, - 0.3823549396537118, - 0.40624189554002593, - 0.43028623643746144 - ], - [ - 0.18049623396177655, - 0.21276269907514728, - 0.24397721290100916, - 0.2737883590239779, - 0.3024640659335579, - 0.3304108628784123, - 0.35796238744071907, - 0.38532436598600583, - 0.4126858919449912, - 0.4400588982985767, - 0.4674988602016171, - 0.495025961475323, - 0.5226187771478782, - 0.5502765965143669, - 0.5779850802582551 - ], - [ - 0.2733424453843406, - 0.3140983105000469, - 0.35248789769545097, - 0.388513400195036, - 0.42272954411349783, - 0.4557443558611662, - 0.4880218808073551, - 0.5198495806582729, - 0.5514728637248935, - 0.5829337598662178, - 0.6143131695305581, - 0.645648796132726, - 0.6769303662243025, - 0.7081675706235988, - 0.739353909675269 - ] - ] - }, - { - "marker": { - "color": "red", - "line": { - "color": "midnightblue", - "width": 1 - }, - "showscale": false, - "size": 12, - "symbol": "x" - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.7002826287477735 - ], - "y": [ - 0.6289336137120225 - ] - }, - { - "marker": { - "color": [ - 0, - 0.003703703703703704, - 0.007407407407407408, - 0.011111111111111112, - 0.014814814814814815, - 0.018518518518518517, - 0.022222222222222223, - 0.025925925925925925, - 0.02962962962962963, - 0.03333333333333333, - 0.037037037037037035, - 0.040740740740740744, - 0.044444444444444446, - 0.04814814814814815, - 0.05185185185185185, - 0.05555555555555555, - 0.05925925925925926, - 0.06296296296296296, - 0.06666666666666667, - 0.07037037037037037, - 0.07407407407407407, - 0.07777777777777778, - 0.08148148148148149, - 0.08518518518518518, - 0.08888888888888889, - 0.0925925925925926, - 0.0962962962962963, - 0.1, - 0.1037037037037037, - 0.1074074074074074, - 0.1111111111111111, - 0.1148148148148148, - 0.11851851851851852, - 0.12222222222222222, - 0.1259259259259259, - 0.12962962962962962, - 0.13333333333333333, - 0.13703703703703704, - 0.14074074074074075, - 0.14444444444444443, - 0.14814814814814814, - 0.15185185185185185, - 0.15555555555555556, - 0.15925925925925927, - 0.16296296296296298, - 0.16666666666666666, - 0.17037037037037037, - 0.17407407407407408, - 0.17777777777777778, - 0.1814814814814815, - 0.18518518518518515, - 0.18888888888888888, - 0.1925925925925926, - 0.1962962962962963, - 0.2, - 0.2037037037037037, - 0.2074074074074074, - 0.2111111111111111, - 0.2148148148148148, - 0.21851851851851853, - 0.2222222222222222, - 0.22592592592592592, - 0.22962962962962963, - 0.23333333333333336, - 0.23703703703703705, - 0.24074074074074073, - 0.24444444444444444, - 0.24814814814814817, - 0.2518518518518518, - 0.25555555555555554, - 0.25925925925925924, - 0.26296296296296295, - 0.26666666666666666, - 0.27037037037037037, - 0.2740740740740741, - 0.2777777777777778, - 0.2814814814814815, - 0.2851851851851852, - 0.28888888888888886, - 0.29259259259259257, - 0.2962962962962963, - 0.3, - 0.3037037037037037, - 0.3074074074074074, - 0.3111111111111111, - 0.3148148148148148, - 0.31851851851851853, - 0.32222222222222224, - 0.32592592592592595, - 0.3296296296296296, - 0.3333333333333333, - 0.337037037037037, - 0.34074074074074073, - 0.34444444444444444, - 0.34814814814814815, - 0.35185185185185186, - 0.35555555555555557, - 0.3592592592592593, - 0.362962962962963, - 0.36666666666666664, - 0.3703703703703703, - 0.37407407407407406, - 0.37777777777777777, - 0.3814814814814815, - 0.3851851851851852, - 0.3888888888888889, - 0.3925925925925926, - 0.3962962962962963, - 0.4, - 0.4037037037037038, - 0.4074074074074074, - 0.4111111111111111, - 0.4148148148148148, - 0.4185185185185185, - 0.4222222222222222, - 0.42592592592592593, - 0.4296296296296296, - 0.43333333333333335, - 0.43703703703703706, - 0.4407407407407408, - 0.4444444444444444, - 0.4481481481481482, - 0.45185185185185184, - 0.45555555555555555, - 0.45925925925925926, - 0.46296296296296297, - 0.4666666666666667, - 0.4703703703703704, - 0.4740740740740741, - 0.4777777777777778, - 0.48148148148148145, - 0.4851851851851851, - 0.4888888888888889, - 0.4925925925925926, - 0.4962962962962963, - 0.5, - 0.5037037037037037, - 0.5074074074074074, - 0.5111111111111111, - 0.5148148148148148, - 0.5185185185185185, - 0.5222222222222223, - 0.5259259259259259, - 0.5296296296296297, - 0.5333333333333333, - 0.5370370370370371, - 0.5407407407407407, - 0.5444444444444444, - 0.5481481481481482, - 0.5518518518518518, - 0.5555555555555556, - 0.5592592592592592, - 0.562962962962963, - 0.5666666666666667, - 0.5703703703703704, - 0.5740740740740741, - 0.5777777777777777, - 0.5814814814814815, - 0.5851851851851851, - 0.5888888888888889, - 0.5925925925925926, - 0.5962962962962963, - 0.6, - 0.6037037037037037, - 0.6074074074074074, - 0.6111111111111112, - 0.6148148148148148, - 0.6185185185185185, - 0.6222222222222222, - 0.6259259259259259, - 0.6296296296296297, - 0.6333333333333333, - 0.6370370370370371, - 0.6407407407407407, - 0.6444444444444445, - 0.6481481481481481, - 0.6518518518518519, - 0.6555555555555556, - 0.6592592592592592, - 0.662962962962963, - 0.6666666666666666, - 0.6703703703703704, - 0.674074074074074, - 0.6777777777777778, - 0.6814814814814815, - 0.6851851851851852, - 0.6888888888888889, - 0.6925925925925925, - 0.6962962962962963, - 0.7, - 0.7037037037037037, - 0.7074074074074074, - 0.7111111111111111, - 0.7148148148148148, - 0.7185185185185186, - 0.7222222222222222, - 0.725925925925926, - 0.7296296296296296, - 0.7333333333333333, - 0.737037037037037, - 0.7407407407407407, - 0.7444444444444445, - 0.7481481481481481, - 0.7518518518518519, - 0.7555555555555555, - 0.7592592592592593, - 0.762962962962963, - 0.7666666666666667, - 0.7703703703703704, - 0.774074074074074, - 0.7777777777777778, - 0.7814814814814814, - 0.7851851851851852, - 0.7888888888888889, - 0.7925925925925926, - 0.7962962962962963, - 0.8, - 0.8037037037037037, - 0.8074074074074075, - 0.8111111111111111, - 0.8148148148148148, - 0.8185185185185185, - 0.8222222222222222, - 0.825925925925926, - 0.8296296296296296, - 0.8333333333333334, - 0.837037037037037, - 0.8407407407407408, - 0.8444444444444444, - 0.8481481481481481, - 0.8518518518518519, - 0.8555555555555555, - 0.8592592592592593, - 0.8629629629629629, - 0.8666666666666667, - 0.8703703703703703, - 0.8740740740740741, - 0.8777777777777778, - 0.8814814814814815, - 0.8851851851851852, - 0.8888888888888888, - 0.8925925925925926, - 0.8962962962962963, - 0.9, - 0.9037037037037036, - 0.9074074074074074, - 0.9111111111111112, - 0.9148148148148147, - 0.9185185185185184, - 0.9222222222222224, - 0.925925925925926, - 0.9296296296296296, - 0.9333333333333332, - 0.937037037037037, - 0.9407407407407408, - 0.9444444444444444, - 0.9481481481481482, - 0.9518518518518518, - 0.9555555555555556, - 0.9592592592592591, - 0.9629629629629628, - 0.9666666666666668, - 0.9703703703703704, - 0.974074074074074, - 0.9777777777777776, - 0.9814814814814816, - 0.9851851851851852, - 0.9888888888888888, - 0.9925925925925926, - 0.9962962962962963 - ], - "colorscale": [ - [ - 0, - "rgb(255,255,229)" - ], - [ - 0.125, - "rgb(255,247,188)" - ], - [ - 0.25, - "rgb(254,227,145)" - ], - [ - 0.375, - "rgb(254,196,79)" - ], - [ - 0.5, - "rgb(254,153,41)" - ], - [ - 0.625, - "rgb(236,112,20)" - ], - [ - 0.75, - "rgb(204,76,2)" - ], - [ - 0.875, - "rgb(153,52,4)" - ], - [ - 1, - "rgb(102,37,6)" - ] - ], - "showscale": false - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.6398015275435182, - 0.7834095929218652, - 0.6889740628400505, - 0.6234041886887529, - 0.7472987116338267, - 0.7542734972117308, - 0.6419412905618748, - 0.6783026187078912, - 0.657028445816533, - 0.6967436629809198, - 0.6298077120656006, - 0.6285843263077633, - 0.6000082474714872, - 0.6955322817007007, - 0.6273696391708145, - 0.6307723252620865, - 0.62684229429316, - 0.720746888362131, - 0.6081882553336139, - 0.6115269780187048, - 0.6284493312020082, - 0.6000173662755234, - 0.6633457814593835, - 0.6632885397594831, - 0.6604271664950124, - 0.6297456058567239, - 0.6324599880964914, - 0.6541468730138755, - 0.6850966649271291, - 0.6545813391925616, - 0.7140024010556135, - 0.7002798068515736, - 0.72475831743897, - 0.6562000954444471, - 0.6617569374762292, - 0.6588597524568218, - 0.6599370948477885, - 0.7327244109948049, - 0.6543755422656028, - 0.7368598036834967, - 0.7118995283656964, - 0.7035529116587953, - 0.6856404670642229, - 0.7874619507069341, - 0.8347447119266668, - 0.7952661687473195, - 0.6948326989106055, - 0.8025459058164388, - 0.8023909087739224, - 0.8413035783556975, - 0.8429791769481227, - 0.8215793054323863, - 0.7941168566652124, - 0.817385311535264, - 0.7534210439836811, - 0.7320027540875633, - 0.7622205415933236, - 0.886417585811722, - 0.897026581135707, - 0.6955344176974225, - 0.8123340502097736, - 0.7661303864763724, - 0.8412559369392237, - 0.8364931415088712, - 0.777957172464202, - 0.8541148070899864, - 0.7897906812886395, - 0.6963974015685278, - 0.7771182959883609, - 0.8019974308187632, - 0.702078244365795, - 0.8014441837617875, - 0.7079450888854993, - 0.7962642768613842, - 0.7920638353800669, - 0.7151501223496658, - 0.7358391924764545, - 0.8278533733855753, - 0.7124869250307805, - 0.7348553088810768, - 0.8334840060589617, - 0.7764289301575326, - 0.7950577181630605, - 0.7112830158837191, - 0.7073896226812689, - 0.7076645262323324, - 0.7718085894819768, - 0.705975703768142, - 0.7354747372075696, - 0.6985759536543132, - 0.7460830864135181, - 0.7247593868054502, - 0.7735310256261346, - 0.7831005245852658, - 0.7812987523660673, - 0.7223483312511143, - 0.7335383532528784, - 0.7163899486916715, - 0.7666854163887278, - 0.7385206791820437, - 0.7488601755959889, - 0.688862834317936, - 0.7864848324486302, - 0.7461617845028201, - 0.765284356969859, - 0.7387961110973175, - 0.7335658164518817, - 0.7537842940548691, - 0.7237328418295452, - 0.7277991422014717, - 0.7387787764987062, - 0.760473896200636, - 0.7503729941476718, - 0.7521471891571413, - 0.7355964234728628, - 0.7383871844071798, - 0.7531484988934661, - 0.740356135309568, - 0.7468489281830667, - 0.7345480428291439, - 0.7487886779669092, - 0.7542891622629306, - 0.7610346667506344, - 0.7612010243545771, - 0.7544357417241132, - 0.7504001610339999, - 0.7605611505635547, - 0.7462336979565392, - 0.7535933763121919, - 0.7530969385922034, - 0.7379769125400184, - 0.7563100179542935, - 0.7445989047200426, - 0.7543521697854405, - 0.762457507449366, - 0.7512750946862986, - 0.741423365873329, - 0.7504272616178943, - 0.7522380364202639, - 0.75091364504222, - 0.7486358306117241, - 0.7548234842165206, - 0.7491469720681629, - 0.7511279374636878, - 0.749617610198614, - 0.7476003179328695, - 0.750369931117366, - 0.7505338022181203, - 0.7557962768784904, - 0.7505013731989336, - 0.7510182106587123, - 0.7471308643958997, - 0.7488343565008274, - 0.7456332985972772, - 0.7488412049790049, - 0.7457285567260475, - 0.74644808903075, - 0.7491314652326265, - 0.747010573156756, - 0.7474520922932261, - 0.7479037960827083, - 0.7500150908726078, - 0.7488982363745749, - 0.747762947592885, - 0.7467598497113074, - 0.7472612506053942, - 0.7478758656395419, - 0.7470499147889761, - 0.7471624119426061, - 0.7470474410663344, - 0.7478240506600677, - 0.7465744939982786, - 0.7459454081731658, - 0.7468805778707786, - 0.7480630920647846, - 0.7477703696516877, - 0.7475196775453345, - 0.7475242762120593, - 0.7482347575908042, - 0.7472334260505487, - 0.7479783710356227, - 0.7476575108918704, - 0.7472096081229995, - 0.7476071873145033, - 0.7482627819086766, - 0.7468358374676501, - 0.7476295273469726, - 0.7490225577596015, - 0.7477172724799762, - 0.747616043551513, - 0.7481017866390718, - 0.7471080442096341, - 0.7493568325560195, - 0.7485598082286591, - 0.7483784427338819, - 0.7484127359500952, - 0.7486014010276004, - 0.7479954563835443, - 0.7488198011011512, - 0.7491198277272945, - 0.748693537617128, - 0.7487474786682461, - 0.7485752495166857, - 0.7481632082523264, - 0.7477266089741006, - 0.7483368402303322, - 0.7484268685148276, - 0.7481922079026848, - 0.7493528562192566, - 0.7483799110710669, - 0.7487127986092711, - 0.7483236735870027, - 0.7477307374624874, - 0.7486634891082754, - 0.7482956807831075, - 0.7478639505717516, - 0.7482378234432122, - 0.7487357950590608, - 0.7486533009128502, - 0.7481760029573876, - 0.7486264880466273, - 0.7483535704545061, - 0.7482922086763294, - 0.7484060675803813, - 0.7481509890902289, - 0.7484112159780258, - 0.7483750819870393, - 0.7480772713357768, - 0.7483113624023876, - 0.7482820913661309, - 0.7485009616053125, - 0.7485332052828106, - 0.7484031692818295, - 0.7484242230474862, - 0.7483692816489709, - 0.7483280482902646, - 0.7484428906915788, - 0.7487108739170031, - 0.7481733095129082, - 0.7483352377665545, - 0.7484229556050997, - 0.7484036943462062, - 0.7483616558227326, - 0.7482549728604215, - 0.7483062330471475, - 0.7484845018869662, - 0.7484487561250316, - 0.7483313571673764, - 0.7485076603749453, - 0.7483740195947701, - 0.748401446465961, - 0.7483269710153728, - 0.7484050342172691, - 0.7484242559277622, - 0.7483524836989649, - 0.7483750599216641, - 0.7484270226816604, - 0.7483871326274112, - 0.7483925316918323, - 0.7484135477756078, - 0.7483685303108918, - 0.748501183840965, - 0.7483650572896052, - 0.7483830039428074, - 0.7483992351657923, - 0.7484111602741179, - 0.7483917677990026, - 0.7483812339565569, - 0.7483626840080347, - 0.7483911546398662 - ], - "y": [ - 0.6866247125275846, - 0.6364681188248305, - 0.5965886968516367, - 0.6660258705915133, - 0.6275061144028632, - 0.6251617498111561, - 0.7224532880184631, - 0.7289160363994348, - 0.6921773962251418, - 0.7247545300658802, - 0.7513856087780815, - 0.7457382219315379, - 0.7551685099493607, - 0.7291443806578158, - 0.7141548194405872, - 0.6911229225038843, - 0.7379516621622741, - 0.777837703980055, - 0.7244504679650009, - 0.7405112034190808, - 0.7358836789335765, - 0.7618219511988441, - 0.6789817528741565, - 0.6062001211285537, - 0.6812043266013955, - 0.7171132005802143, - 0.6912206493419717, - 0.704799341642163, - 0.673393157300267, - 0.7271980744258015, - 0.6267825752981799, - 0.65064092344774, - 0.6645867985746339, - 0.6576987664651354, - 0.6451723623049832, - 0.6590833200623419, - 0.6557180749586652, - 0.6560449254838345, - 0.7086741013446526, - 0.6583204160621425, - 0.655458336737195, - 0.6754402827422513, - 0.6627891780499229, - 0.6122181344142449, - 0.6190393862793186, - 0.6327593476579223, - 0.6876139439226782, - 0.6541649558877185, - 0.6827332407704083, - 0.6695668123835893, - 0.6850739771093443, - 0.6789816929663839, - 0.6464750422700254, - 0.6720816961016086, - 0.684156303444881, - 0.6930852293129227, - 0.6729842890286399, - 0.6416090211669931, - 0.6054191915997689, - 0.7108972355420703, - 0.6634112094769272, - 0.6965684039931663, - 0.6705679216656908, - 0.6647212704943212, - 0.6743705450429236, - 0.6796222826430791, - 0.6648926425657091, - 0.6843896794096097, - 0.663393745371838, - 0.6719560998632164, - 0.6704958017029286, - 0.6713995372611415, - 0.6865789272728248, - 0.6613975280534948, - 0.6487240007030157, - 0.6572931584836466, - 0.6649620068994143, - 0.6438688820893349, - 0.6431741769524745, - 0.669850019657571, - 0.6510573948035685, - 0.6587605572070525, - 0.6486461016541625, - 0.675347235111066, - 0.6665524995451028, - 0.6676514809150907, - 0.6627022880306225, - 0.673111201247168, - 0.6725887016058728, - 0.6813413519664382, - 0.664105160941047, - 0.6704285631714935, - 0.6688202571784456, - 0.6633635281486342, - 0.6596094305535337, - 0.671138842517764, - 0.6622915956065181, - 0.6735121042943643, - 0.6610407345486717, - 0.6648508765352975, - 0.6634148928140992, - 0.6712742574261868, - 0.6570256845566627, - 0.655982173199172, - 0.6644327312955974, - 0.6648430371466058, - 0.6649466158716633, - 0.6619684165487295, - 0.6701474383426015, - 0.6680603990093963, - 0.663447440652594, - 0.661192268845853, - 0.6625732936964915, - 0.6638259137461971, - 0.6651175061786415, - 0.6650030296750225, - 0.6643850470998328, - 0.6671390370924041, - 0.6669547812689007, - 0.6665640967095965, - 0.6670199908935588, - 0.6641144723053162, - 0.6616517648553679, - 0.6619798163188976, - 0.6652469537054858, - 0.665559230383553, - 0.6636038597672236, - 0.6657726366056322, - 0.6656908689848722, - 0.6649733441888416, - 0.666677149058746, - 0.6642536625484553, - 0.6658443295180828, - 0.6647784419019307, - 0.6638785235127931, - 0.664951668500953, - 0.6671028090189277, - 0.666462202880858, - 0.6647186789448336, - 0.6650992718608973, - 0.6653319061675408, - 0.6647323965249505, - 0.6651551078313285, - 0.6651359109079447, - 0.6649806996007741, - 0.6655800530882241, - 0.6650007852076433, - 0.6648729582161904, - 0.6640339073691309, - 0.664672015602371, - 0.6649434347783275, - 0.6655710089879313, - 0.6653605701103825, - 0.6659282565219087, - 0.66504257049908, - 0.6657906447125375, - 0.665833633713905, - 0.6652979035939126, - 0.6655029692593243, - 0.665499891041223, - 0.6653441858383358, - 0.6648585438657062, - 0.6650941474079926, - 0.6654039515308926, - 0.6654785379461289, - 0.6654551644037424, - 0.6651415185563612, - 0.6655312723276966, - 0.6654249030854259, - 0.6655020713129259, - 0.6653743390492347, - 0.6656016009088621, - 0.6657180017816687, - 0.665405097520238, - 0.6653710793901598, - 0.6654091789027219, - 0.6653870490445738, - 0.6654036972679006, - 0.66528133178443, - 0.6655620122986592, - 0.6654323213327458, - 0.6653827612241039, - 0.6655412482977183, - 0.6654376175394979, - 0.6653550965428916, - 0.6654583400678383, - 0.6654056986341513, - 0.6652311851087725, - 0.6654622272063525, - 0.6654564489996346, - 0.6653835515309753, - 0.6654821989523526, - 0.6652229912957907, - 0.6652764289062365, - 0.6654017645039588, - 0.6653045979399675, - 0.665242483389746, - 0.6653987728744556, - 0.665165960429941, - 0.6651956556742828, - 0.66519189292382, - 0.6652412952558492, - 0.6652914481091496, - 0.6652919685255246, - 0.6653646478766847, - 0.6652504140171038, - 0.665295111186783, - 0.6653282212163226, - 0.6652176823014453, - 0.6653463973182714, - 0.6652751221814872, - 0.665291188620783, - 0.6654153860961469, - 0.6652640830162271, - 0.6652783081891461, - 0.6653750149169088, - 0.6653186330948742, - 0.6652642754668254, - 0.6652795334642339, - 0.6653256117377516, - 0.665277889323846, - 0.6653283422013764, - 0.6653483767376702, - 0.6653080069734126, - 0.6653507557805092, - 0.6653190165995263, - 0.6652955586006951, - 0.6653872185139995, - 0.6652951711597194, - 0.6653177348777891, - 0.6652715218218141, - 0.6652898470208235, - 0.6653248057191449, - 0.6652830190388519, - 0.6653002347245491, - 0.6653123105541945, - 0.6652892680387847, - 0.6652438219377682, - 0.6653507444170728, - 0.6653069663336174, - 0.6653054732960361, - 0.6653110395250771, - 0.6653082035178534, - 0.66531554077811, - 0.6653140342751883, - 0.6652885269663483, - 0.6653011382384398, - 0.6653224538116091, - 0.6652855278194739, - 0.6653043695107316, - 0.6653078611733345, - 0.665317028873498, - 0.6653013957364677, - 0.6653034696571328, - 0.6653040571988419, - 0.6653187178674982, - 0.6652995202187427, - 0.6653087345226069, - 0.6653076618229143, - 0.6652966224697726, - 0.665306351273456, - 0.6652898404082866, - 0.6653087998780384, - 0.6653117191814542, - 0.6653049245675259, - 0.6653030637117724, - 0.6653081968224175, - 0.665312646971721, - 0.6653104757573955, - 0.6653054543409095 - ] - } - ], - "layout": { - "height": 600, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.6, - 0.9 - ], - "title": { - "text": "Negative electrode active material volume fraction" - }, - "type": "linear" - }, - "yaxis": { - "range": [ - 0.5, - 0.8 - ], - "title": { - "text": "Positive electrode active material volume fraction" - }, - "type": "linear" - } - } - }, - "image/png": "iVBORw0KGgoAAAANSUhEUgAABF4AAAJYCAYAAABbzSLYAAAgAElEQVR4XuydB3xUZdbGz0wKCS2IiiIKay8f2HatYMVVKfZGERsqgiu7dsDeKIIVBbGw6opgbxR17WB3baCoKCp2RSVIT5nvnPfOncwQQmIyk7kz87/7m28mk1ve+3+vXy7Pfc5zQhFdhAUCEIAABCAAAQhAAAIQgAAEIAABCEAg6QRCCC9JZ8oOIQABCEAAAhCAAAQgAAEIQAACEICAI4DwwoUAAQhAAAIQgAAEIAABCEAAAhCAAARSRADhJUVg2S0EIAABCEAAAhCAAAQgAAEIQAACEEB44RqAAAQgAAEIQAACEKgDgUpdJ1yH9VgFAhCAAAQgAIF4AggvXA8QgAAEIAABCEAAAhCAAAQgAAEIQCBFBBBeUgSW3UIAAhCAAAQgAAEIQAACEIAABCAAAYQXrgEIQAACEIAABCAAAQhAAAIQgAAEIJAiAggvKQLLbiEAAQhAAAIQgAAEIAABCEAAAhCAAMIL1wAEIAABCEAAAhCAAAQgAAEIQAACEEgRAYSXFIFltxCAAAQgAAEIQAACEIAABCAAAQhAAOGFawACEIAABCAAAQhAAAIQgAAEIAABCKSIAMJLisCyWwhAAAIQgAAEIAABCEAAAhCAAAQggPDCNQABCEAAAhCAAAQgAAEIQAACEIAABFJEAOElRWDZLQQgAAEIQAACEIAABCAAAQhAAAIQQHjhGoAABCAAAQhAAAIQgAAEIAABCEAAAikigPCSIrDsFgIQgAAEIAABCEAAAhCAAAQgAAEIILxwDUAAAhCAAAQgAAEIQAACEIAABCAAgRQRQHhJEVh2CwEIQAACEIAABCAAAQhAAAIQgAAEEF64BiAAAQhAAAIQgAAEIAABCEAAAhCAQIoIILykCCy7hQAEIAABCEAAAhCAAAQgAAEIQAACCC9cAxCAAAQgAAEIQAACEIAABCAAAQhAIEUEEF5SBJbdQgACEIAABCAAAQhAAAIQgAAEIAABhBeuAQhAAAIQgAAEIAABCEAAAhCAAAQgkCICCC8pAstuIQABCEAAAhCAAAQgAAEIQAACEIAAwgvXAAQgAAEIQAACEIAABCAAAQhAAAIQSBEBhJcUgWW3EIAABCAAAQhAAAIQgAAEIAABCEAA4YVrAAIQgAAEIAABCEAAAhCAAAQgAAEIpIgAwkuKwLJbCEAAAhCAAAQgAAEIQAACEIAABCCA8MI1AAEIQAACEIAABCAAAQhAAAIQgAAEUkQA4SVFYNktBCAAAQhAAAIQgAAEMoJApY4ynBEjZZAQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAAQgAAEIQCATCCC8ZMIsMUYIQAACEIAABCAAAQhAAAIQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAAQgAAEIQCATCCC8ZMIsMUYIQAACEIAABCAAAQhAAAIQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAAQgAAEIQCATCCC8ZMIsMUYIQAACEIAABCAAAQhAAAIQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAAQgAAEIQCATCCC8ZMIsMUYIQAACEIAABCAAAQhAAAIQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAAQgAAEIQCATCCC8ZMIsMUYIQAACEIAABCAAAQhAAAIQgAAEMpIAwktGThuDhgAEIAABCEAAAhCAAARqIhDRX4TAAwEIQCAgBBBeAjIRDAMCEIAABCAAAQhAAAIQgAAE0kugUg8fTu8QOHoWEkB4ycJJ5ZQgAAEIQAACEIAABCAAAQhAAAIQCAYBhJdgzAOjgAAEIAABCEAAAhCAAAQgAAEIQCALCSC8ZOGkckoQgAAEIAABCEAAAhCAAAQgAAEIBIMAwksw5oFRQAACEIAABCAAAQhAAAIQgAAEIJCFBBBesnBSOSUIQAACEIAABCAAAQhAAAIQgAAEgkEA4SUY88AoIAABCEAAAhCAAAQgAAEIQAACEMhCAggvWTipnBIEIAABCEAAAhCAAAQgAAEIQAACwSCA8BKMeWAUEIAABCCQcgIRPUIo5UfhABCAAAQgAAEIQAACEIgngPDC9QCBLCVQqecVztJz47QgAAEIQAACEIAABCAAAQhkCgGEl0yZKcYJAQhAAAIQgAAEIAABCEAAAhCAQMYRQHjJuCljwBCAAAQgAAEIQAACEIAABCAAAQhkCgGEl0yZKcYJAQhAAAIQgAAEIAABCEAAAhCAQMYRQHjJuCljwBCAAAQgAAEIQAACEIAABCAAAQhkCgGEl0yZKcYJAQhAAAIQgAAEIAABCEAAAhCAQMYRQHjJuCljwBCAAAQgAAEIQAACEIAABCAAAQhkCgGEl0yZKcYJAQhAAAIQWAOBhb+Vym+L/pAN1ltHSlo2g9FaCCxbvkJuu/dJ2W6rv8jB++0KKwhAAAIQgAAEINAoBBBeGgUzB4EABCAAAQgkj8DPCxfJpaPvktfe+UgqKipjOy4sLJCje+wt/Y4+SNq3a5O8A+qevvjqO/n3A0/LQfvuInvttn2d9n3X5Oly/YQHZdjg46XvkQfUaZtUrvTTL7/L/secrePvJLeNOjeVh2LfEIAABCAAAQhAIEYA4YWLAQIQgAAEIJBBBN754FM55ZxRTnDpsPEGsu8eO0rz5k1lzifz5fX/fSyrVpXJMT33lcvPOympZ/Xy6x/IoKE3yIB+h8jg/kfVad93TJoqN97xsAovfVV4+XudtknlSggvqaTLviEAAQhAAAIQqIkAwgvXBgQgAAEIQCBDCFRWRuTAXufKDz//Jsceup9c8q8TJBwOxUb/e+kfct6V42WTtm3WKLxEIhEJharW/zOnncnCi3/eCC9/ZsZZFwIQgAAEIACBZBFAeEkWSfYDAQhAAAIQSDGBx5+eJReNvFPatmkt/33guhpFFBNg1ilp4UZjuSbDb54kL7z6rpQuXiobrL+OHNltbxl00uEJos0HH3/hyoI+/uwrWamumfVal8ief+soZ5xwqHz/468ybMTtTvBp3qxYy5g2cPvedcdt5PxBvWo867o6XoYOv0Pefn+u/KpZNebYsWN07bKz/Ou0Y6TNeq1i+7/w6gny4y+/ydCz+sqIsZPkvTnz3O+67NpJRgw9vVrGzQNPvCA2Bht30+Imsv12m8sb6gqKLzWyc53wnyflyWdelZ9/XSRNtFxrs/YbOWHrKC3b8pfSP5bK6HFTtLxrjliujvGxkqvT+vaUjduuL5Me/a++npNfdB/Llq9059Bx6031HI6WTttuFtvPvQ89I0/993U5d8Cxcv/jz8nrWi5mY7B1r7tskLTdYN0EntOef0PH95R89c0P7nvLp7nqglNky003TvHVxu4hAAEIQAACEEgWAYSXZJFkPxCAAAQgAIEUEzDhYepzr8s1Q06Vww/uUuvRysrKpVvfC5zwYELBVpttIh/O/UKWLF2eID58+sU3cmT/S9z+7B/0LbR06ZPPv3YCwkX/7CebbLS+c9LYdpYjs05Jc7euCTNXX9i/xnHUVXjZ6cDTtHSqwgkeFhD8yecL3LFMYJpx/2gpyM9zxzjg2HPcufiL/d7EEiu72mePHWTciLNjvxs9forcrZk0/jlVqtvHcmpsiRde/jHsJnnxtfc8YWbbzeX7nxbKgu9+dj+/PWOCW98ydXr0u9DxaN2qhbRTocWYmUhkwouJKwMuuE5mvTXbZeu00aDjH376Vb77caHk5YVl6r0jY5k7l1w7UR6d/kpsnDYvi5csc/uyY8564hYn/thiZVrG0JadO22l+/tFzLVjy3+njJGNNlyv1muAFSAAAQhAAAIQSD8BhJf0zwEjgAAEIAABCNSJwFGnXupEiUfuvFK22aJ9rdv8e8oMGXPbA9J1r53lxivOcg4Xc8AcO+AK+XLBDzLxhgtlt522lSuuv0cefPJFOX9gLznpuIPdfq2s6YlnZjkRofMuHSWVpUbmXOm4zWYxgcWO3efMq2T23Pny0O2XO5eHLb7wYm6U8wcep0JFkRNFDjjuHCe+fPj8RCd0mHBigpOJRI8qq03bt3Xb2zn3PGFoTHgxcWe3HgOrCR7fq2By/+PPy3lnHOe288WZE485SC44s3eMj7lc8vLypM8RXd28mBDSUkUrfxk78VHXRcmEGRNobPGFF3PpmGi1/rqtnOjS76zhMufTL53QZfvzx2oOpQcnXO6EM1vMMTPq1slyyIF7yshhp9d6DbACBCAAgVwiYHHz4Vw6Yc41YwggvGTMVDFQCEAAAhDIdQImEphY8NqTt9apdbS5WMyZ8cJDN7gSI38xh4eJCUd028v943/4zfe5MpljD9nXdSAqKMivhjqVwosdzMSWT79YoCU1P7pynadffEus/OnaS86QHl13d+Mx4cUcLiawxC8mWrw7+zN57sHrnUvGSnNuvusR6XnAHjLq4gGxVVfPePGFFxNoHr7jCtm8w0bVzrtcnTg7dO3vSodeffIWyVehZW2LiT6ff/mtlkT97sq2HpsxUw47qLMMH3qa28wXXh6YcJkrL/KXt977RE4+e6QTyW6+arDcdOcjcvt9T8mlZ58gR2tYsr8sXrJUuhx2lgtWnn7fqFz/T4LzhwAEIAABCGQEAYSXjJgmBgkBCEAAAhAQOfSki1y5zGMTr9ayodozPnbpNkDK1Qny3rN3JODzBYgdNPPk/nGXOKfFceqCscUcIzt13NLlplgLaHOV2JJK4cWya8x1Y86P1ZdRFw2Qnn/fw31dk/Ay+JKb5fmZ78qMSaNc/oyVRc144U0Zc+lA6bb/brFdrilct/+517rcF1vaqWNl5+23kuPUUWMMbDEhqEe/IbL7ztvJXddfUONlOHfe13LmsBtjpUDxK8a7U2oSXn7TfJu9Dj/LOZnM0eSfU00HjC+F4r8NCEAAAhCAAASCTQDhJdjzw+ggAAEIQAACMQLnXD5OnnnprQQXyNrwbN/1FJcX4meV+Ouu/o98+37Bdz8558trGvZqZTu2mBPkgdsucyJPqoQXE0xMZDDB59Q+PTSwd1uXh2IBtOZaqYvwcvZlt8izL78TE158MeXJu6+Rzf/SLoZoTcKLOVomTp4uUzSI189PsQ18l4oJKkefdlm1DJl47ha2u8+R/3Rfmchy4N5/c+VNv5cu0RKiaxLKgmoTXswFY26Y/ueoIPTux9Lv6ANlAy33Wn1ppg4ccyixQAACEIAABCAQfAIIL8GfI0YIAQhAAAIQcAQsLNZCY+0f9VPvHVEjlbLyCpeXcmCv81zA6ztP3y7FRYWx9S07pdfAK+WgfXeV6y8flLAfa71s5UlW6vLKGx+4f9xfdu5JMeGlf+/uco525KnLUpdwXT8w+PrLz9Tx7BLbrZ9lUh/hxRc3bh99nsun8Zfa2klb6ZGVOF2jApS5b2Y9MdYJV7t0O8OJQTMmXbvG037q2ddkyPDbtSRoH7nivJNj63z+5Xdy2MkX1Ul4+d+Hn8kJg4fH1r3mpv/I/Y89L2OvHiz7a4cnFghAAAIQgAAEMpcAwkvmzh0jhwAEIACBHCNgbYf3PmKwy3mxoNdzNfzVnCL+snTZCrnqxntVZGkil51zolw86i6XMWIhsSf36hZb79wrxjmBwQ9ytc9/22HrWICrrfj+R59L3zOvdq2Qp4y/1OWt9Bl01RrFmpqmoS7Ci+WaWL7JLcP/KfvtuZPblblQTJCxcdVHeJmswbhX3/ifWF6KPz4rP7IyJL+rkWXJvDt7XoLgY+vaedr53nfLRa7kyNws5mq5c8z5ssff/i92uhbsa6VI1inqhtsfcqG4xtRfHpr6klw+5u5ahRfLtzEO73zwaczN9N9X3pF/XXqLE3yevHt4Qu6Ozf9b781FkMmx//45XQhAAAIQyFwCCC+ZO3eMHAIQgAAEcpCAX/Jjp25BsuaGsOBXC3J9UwUMc2oco2Gsl593kuv4s9/R/3KUTBTYevP28sqbH7g8FGuL/OIjN7qwWPtHvzkuenTdQ7bfbjMn7Dz01EvOLeM7Luy7PQ8905Uh9T3y79JK2z7nq6vm9OMPqXEWfOHFslP8zkLxK3fd669SuniJa5ts52BhuKFQSEx0MKHDlvoIL8tXrHJjNRa77rSN64r0zvufuiwbW3zhxS8jsnKkAzTUdqMN1pPZn8yXh6e+7MJrrQ20dYIy58/AITe4bXsf3tWdy0e6L2vtfUqv7tJV58AcRCaCHbDX31xWzGvvzHGdjmxZU8aLhR0fuM8uUtSkUF549T2X3bO6k8n2ae4kmyvr5NSiWVMVeea79tfbahaM5fOwQAACEIAABCAQfAIIL8GfI0YIAQhAAAIQSCBgnXMuGnmn6+QTv1gmi4kXA/odIhu3Xd/9ysSGM4feGBMy7DsLcL11xL9kw/Vbu3WspOWmOx92gou/mIhwRr9DZdBJh8e+m/78mzJ24iOuXbMttQXO3nn/NOcEqWmxrkomEP1DQ2lnvjk7tpoJF9ts2d4JRNaVyM7JlprCdf3sGysFMoeILSZYnHLOKG2fvdL9bOfT76gD5e4Hn5a9d99Bxo882zE5+7Jbq3HcctON5forzpTNom2obXsTgy4b828VipbGxmnC19UXniq7/3U714HIyrP8xcJv7fysW9ShB3aWEcMSuxqZmGJZO/5iYtCoi8+QkhbNYt/Z2E2UmvLE87HcHftliYpeZ+q8mADGAgEIQAACEIBA8AkgvAR/jhghBCAAAQhAYI0ELI/l+59+ld9+X+zKhNpusG6NpH785Tf5QdfddJO20qqk+RrX+730D+dyad602Lk21tRW2ja0/VTqsdu2Wdc5QpKxfK/HtTG20SBZXzRq6H6thOeLr78THaprFR1flhW/b8vE+e6HX2TJsuXO9WKiSE2LMTInkQXers5xkYbpzl/wg3PvbKEumjWxiQ/XtQ5Mvyz8XTbeqI3LkqlpsXm29tS/L1qs87J+nVqJN5Qd20MAAhCAAAQgkDwCCC/JY8meIAABCEAAAhCAwFoJ1NTVCGwQgAAEIAABCGQvAYSX7J1bzgwCEIAABCAAgYARQHgJ2IQwHAhAAAIQgEAjEEB4aQTIHAICEIAABCAAAQgYAetcZLk7hx3UWdYpqbmkCVoQgAAEIAABCGQPAYSX7JlLzgQCEIAABCAAAQhAAAIQgAAEIACBgBFAeAnYhDAcCEAAAhCAAAQgAAEIQAACEIAABLKHAMJL9swlZwIBCEAAAhCAAAQgAAEIQAACEIBAwAggvARkQip1HOGAjIVhQAACEIAABCAAAQhAAAIQgAAEIJAcAggvyeHIXiAAAQhAAAIQgAAEIAABCEAAAhCAQDUCCC9cFBCAAAQgAAEIQAACEIAABCAAAQhAIEUEEF5SBJbdQgACEIAABCAAAQhAAAIQgEDQCUR0gKGgD5LxZTgBhJcMn0CGDwEIQAACEIAABCAAAQhAAAIQgEBwCSC8BHduGBkEIAABCEAAAhCAAAQgAAEIQAACGU4A4SXDJ5DhQwACEIAABCAAAQhAAAIQgAAEIBBcAggvwZ0bRgYBCEAAAhCAAAQgAAEIQAACEIBAhhNAeMnwCWT4EIAABCAAAQhAAAIQgAAEIAABCASXAMJLcOeGkUEAAhCAAAQgAAEIQAACEIAABCCQ4QQQXjJ8Ahk+BCAAAQhAAAIQgAAEIAABCEAAAsElgPAS3LlhZBCAAAQgAAEIQAACEIAABCAAAQhkOAGElwyfQIYPAQhAAAIQgAAEIAABCEAAAhCAQHAJILwEd24YGQQgAAEIQAACEIAABCAAAQhAAAIZTgDhJcMnkOFDAAIQgAAEIAABCEAAAhCAAAQgEFwCOSu8lC5eKmXl5bJe65I6zc6Spctl5aoyWXedlnVan5UgAAEIQAACEIAABCAAAQhAAAIQgEDOCS8moJx63miZPXe+m/12G64n944dJhuu33qNV8N3Py6Uc68YJx9/9pX7/cZt15fhQ0+THf9vC64eCEAAAhCAAAQgAAEIQAACEIAABCCwVgI5J7yMue0Beeipl+Sxu66SZk2LpdfAK2TT9m1l3Iiz1wiq/znXyu+lf8iU8ZdKOC8sgy++WX765Xd55M4rubQgAAEIQAACEIAABCAAAQhAAAIQgADCSzyB/Y85W7rtv5ucP7CX+/qRaa/IpaMnypwX/y2hUKgarG59L5AOG28gt4061/3u3oeekbETH5W3Z0zg0oIABCAAAQhAAAIQgAAEIAABCEAAAggv8QS273qKXH7uSXJk973d1+/Onif9zrpGZj0xVtYpaVEN1lPPviZDht8uO2y3ufQ58gAZfvN9clqfnnJyr25cWhCAAAQgAAEIQAACEIAABCAAAQhAAOHFJxCJRKTjfifL6EsGSveuu7mv5877Wo4+7TKZMelaad+uTTVYX33zo/Q58yrZrP1G8uHcLyQvL08m3XKRbLfVX7i0IAABCEAAAhCAAAQgAAEIQAACEIAAwks8AXO8XHHeyXJEt73c17U5Xg449hzZv8vOMmzw8WKdkAZfcrO8N2eevPvsHZKvIsweBbdziUEAAmkgsDgyVySyTCKyXCplmeRHCqRYmklTaSmF+qkw1FTfm+nLe2eBAAQgAAEINJTAT/KJVEaWSnlopVREVkhYd7gytFj/zhRJUahYivVVKE2kSahIPxdJYaRICkJN3O+b6CsUsi1YIAABCKSOwJUreqdu5+y53gRyLlzXMl66d91dzjvjOAft4akvy2Vj/r3GjJfFS5bJHj0Hychhp8shB+4ZFWo+09Kk4fLAhMuk49abIrzU+9JjQwgkl0ClrJJyFWAqVIypVDEmEhVlKiIqzISWSZNIM5VjSvRdb39DzeNEmaZ6U1yc3MGwNwhAAAIQyCECESmTFfo3aIW+r9T35d67CjPe36alUqE/V+jvTXzJD+U7kUblGWkWaqYiTaETawpUqGmiQk2hvufrdywQgAAE6kMA4aU+1FK/Tc4JL6PHT3Fiy+MTr5amTYuk1xmJXY1uuvMReealt2T6faMc/d16DHQtpO8Yc7401/UvG3O3vPzG+/LKYzfjeEn99ckRIJA0AhUqythNcLks0fflul8TZ8wts1zyJU//F9bbYRVnIs31hticMvqKqFtGnTP5+rSSBQIQgAAEINBQAuWVKsSETKDxhJrySJmU6cOBSv1cqeJMJLLK/SyhCmkhrSSsfR/MK9NU/y7lhwqcc8a9oi4a+xwO5TV0WGwPAQhkEQGEl2BOZs4JL+ZisRbRH3/2lZuRtm1ay3/GXiRtN1jX/Tx0+B0y7fnX5cPnJ7qfraxo9Lgp8sHHlu8Slq03by8XDOolu+y4jfs9pUbBvLAZFQT+DIHKykqp0BtdJ84418wK3VxvfPUGuFKFmpD+X30uqY6Z5tI80lLyzD7uSpi8Vx5PJv8MbtaFAAQgAIFaCFRGKqTMHhZoSVO5uWUiK9W9uVz/Vum7c9Lo9yrgmIumSB8QhCMq0OjfJnPSxIsyvovGlTvp71kgAIHsJ4DwEsw5zjnhxZ+G3xb9IavKymTD9VvXaWZMsCkrK5d112mZsD7CS53wsRIEMppAZaQ8QZSxTBmJumUq1DFToE8cC9QzY44Z+19BzC1T7D5jGc/o6WfwEIAABAJNoMK5aFbGypwiJtSoKGPijHuQELKCp8Uq4Kxy5U2WP2MlT/ZuIo36afRvVWEsi8ZcNAU8UAj0nDM4CKyNAMJLMK+PnBVekjUdCC/JIsl+IJC5BCory/Wm13fMLJeQ3eja00lzz+grrJ+aRVq7kqZitYsXRJ0yTqBRHw0ZM5k794wcAhCAQCYRcC4al0PjvUdUoIlouVN5aKk6abwyKPtdpf5fT5xx/1dLnZqru9PkGS8o2A8Mts9WAsUCAQgEhwDCS3DmIn4kCC8NnBeElwYCZHMI5AABs4xb6G+F2sStlCnkRBkTZ7y6fns6WRxp4W5oLV/GE2bMLWNPIe1VrDe8+TlAilOEAAQgAIEgEIhEKp2DxkqZ3HtcqZN9dmHBUZFG9CeTZ4o1laZA82as5Mm5aeLyaAqigg1/y4Iwu4wh2wkgvARzhhFeGjgvCC8NBMjmEICAPm3UZ4uu+5IJMxb4a6KMvrs6fu+VH7H6fCtqaqpdL5rGnDMxkYbOTFxJEIAABCCQBgKV9jfMBQVruVNUjPFcM55wYyKN+6y/y4vkqTijf8uck6bYiTReJo12dXJlu14plL0j0qRhMjlkVhBAeAnmNCK8NHBeEF4aCJDNIQCBOhGwGn5zzFimjL28zkxmETfHjAozWtAU1hhgq9U3ccbyZswpU+WcKSZrpk6kWQkCEIAABFJFwDLTvI5OXrmTuWnEhJlYaLBX9mS/D+n/LBDYRJn4bBoLEM7Xb02w8Zw0Tfj7lqoJY78ZSQDhJZjThvDSwHlBeGkgQDaHAASSQiASicREGXvCaM4ZE2UkKsyYaybk7ODqmDFhRl0zXlmTX9Kk7xEtaQpT0pSUCWEnEIAABCDQIAKeSON1dfIzaey9wgLvXa5a1EljLlENEFYpxgk1JtJofyeXT2OfnKPGiTSeiGNiTSikfbpZIJClBBBegjmxCC8NnBeElwYCZHMIKAG7/dFOmCwpJuCyZqKOGe/dE2bs5YQZm4SQdb3wxZlivXH1MmZ8gSbfiTN5KR4pu4cABCAAAQjUnYBX7uSVNtl7lWDjizNetprXhnulE2lMlvG6OnnlTSbZmHDjXDRRpw0lT3WfA9YMDoF0CC+lqzZLC4CSwvlpOW59DorwUh9qcdsgvDQQIJtDAAKBIlBp3S1i4oxXyuSXNHniTESfFEadMyrCeF2aqlwzhfqddWkKhcKBOi8GAwEIQAACEPAJWB6N56RJdNR4uTRa2utn1KhIE9ZHQxYU7AUGm0jTxNJpotk0XocnPzyYNtxcY0EggPAShFmoPgaElwbOC8JLAwGyOQQgkHEEKrTlqO+YsXevnEnbkup7yKzhocXRm1DLmLEwYKCMpVgAACAASURBVAtQjHPOROxGtVjCdGrKuLlnwBCAAARyjUBiyVNVpycTaSybxnV88oOEQ2Wek8YJNPa3L5pQE+3yVCXSeG25eUiRa1dT45wvwkvjcP6zR0F4+bPEVlsf4aWBANkcAhDISgJeu1HrZuGVNFVoGHBMnNFP5bLYhQFb+K8TZ+xpohNozEFjVm9z0XgCDQsEIAABCEAgEwhY3trqLhqvBKqq1Mlz1HhdnvJdTLDnonHJNPbulzu5v4F+xycLEC7IBASMMQAEEF4CMAlrGALCSwPnBeGlgQDZHAIQyFkCVtbkbj59ccY+u25N5p7RV/RG1RNnzOJt5m7PQVOogkxMnNFv8/SpIgsEIAABCEAgkwiYg9QXavxcmkSRxm/Fbd2fLEDYF2k8R439zwQZK3WqEmkK3YOLMCW/mXQpJHWsCC9JxZm0nSG8NBAlwksDAbI5BCAAgVoIVGgJk+eeib78Tk0qzrg2pPoKuxtSr322Vd8XWtcm97TQSpzMQeO5aLB1c7lBAAIQgEAmErCA/NVzaSrsAUZouZY8rZJK5zS18icTc1ZInv4vMUDYK4Iq8IODo9k0fktu/j5m4lWx5jEjvARzLhFeGjgvCC8NBMjmEIAABJJAwG5ILfx3TQKNuWc8gcZs3QVVpU3qnrEw4CJ9apjvlza5p4dNkjAidgEBCEAAAhBIHwETZTyhxnPVeB2fVKCJZdN4JU8un0azalYXaqyzk9fpycKDTbDxWnLbZ3PeINSkb25rOzLCS22E0vN7hJcGckd4aSBANocABCDQiAS8YGAveyYWhuhaanulTRH9XUQbkZprxhwyrneFumfcz6uVNxEO3IgTx6EgAAEIQCClBKqEGk+s8Vtzey6aMueo8R5w6O+cUJMfddC4lDaXTWNZNSbK+KVPvpvG3kOhUErHz86rCCC8BPNqQHhp4LwgvDQQIJtDAAIQCBiBSKQy0TnjwoGtrbbedMYJNHla4OSVNln+TDMVaKy9qN+9KVreRDhwwGaX4UAAAhCAQDIIVOXTVOXUeLltXrcnr/TJDxJe6YKEnUSjmWwut01FmipHTXxGjSfcINTUf5YQXurPLpVbIrw0kC7CSwMBsjkEIACBDCXghQOvXt60Ultqe+21zVFjDhoTZ8ye3cR1bzIHjRcO7MqbXGttwoEz9BJg2BCAAAQgUEcC8UHC8SVQnvvUSqBW6CteqDH5xQSaqpbc5qcxwcaEG6/0yYKErRyqqI6jyI3VEF6COc8ILw2cF4SXBgJkcwhAAAJZTmBN4cB2gxky90w0GDEU7VZh5U1+ByfXXludNPl602nCTb69yJ/J8quF04MABCAAASNQHu34ZA6axFDhVe7hhn1v764sSh029lAjP6TtuZ2jRiUb16I7Wvpkf0djYcKeWBMO5WUtaISXYE4twksD5wXhpYEA2RwCEIAABFSAqYi21vYcNO4JoDpnKvWWMiLLou21Vympsqgw4zloCtUxUyzNtZ22tRP1xBn/HZs2FxYEIAABCOQKgfJKFWG0o1OVUBMNFbaHHNHvPUeNFzDcREuDXYvuqFBjYo3Xrlsff6iDxsup8UQae1nb7kxZEF6COVMILw2cF4SXBgJkcwhAAAIQqDMBL38m2oXClTlZHb0JMlre5IIPvSeA9u5ZsC1/RnNo9EmgZ9m2EqcqccZ+n81P/eoMlhUhAAEIQCCnCFRUljunTFXXJ99Zo4KN/T21nBp92OGVQml3xFCF+7ta1aJbP5urJq7kqUqo0b+/EXXVhNPjqkF4CealjPDSwHnpkH+SPnNsqTeyzfXVTJVRffKoN7ksEIAABCAAgXQSqHDBhtEuFPY5pA4a11pbhZpYSPBKjQgOq0W7uf79Uou2CTQxcUaf8MVcNPa0jxr6dM4nx4YABCAAgfQRsAcfXrenKrHG/9mcNF6gsLlpPFdNOBR2oo0VPpkg4wqfYkJNXD6Nc9NEnTX6nowl14WXZctXyMLfSmXjtm1U/ApONy2ElwZe3c2LLtSnjEslHFkueWoLz9P3kP7HVhRZx9nBi1SUMTHGRBkTZzKu/WhYAVU2EBKbQwACEIBAYAm4kODo0z0vLFjFGb15dK213btny7YuTiH9yRw0LofGuWjMih110WjZk2XQmFiTpyIOCwQgAAEIQCCXCVRE1FXj59DEsmo8ccZ7mVhjzpvlzn1T4cqJXYSwy6Txuj75mTUayK/lTtYbyjlao06bsP51Xn3JZeGl18ArZfbc+Q5JYWGB9Oi6u1x9Yf9aL8OVq8rkiFMulhUrV8kLD91Q6/r1WQHhpT7U4rZpWTxyDXuolMrKpV5dvr4KKlc4YSYU0c/O+N3K1eTnqyBT4Jwy9hmXTAOngs0hAAEIQCDFBLygYLs5jObQWGmTE2esm5P+7ZOl7kbSni95nZys1MlEGsuiaaaCTGG01KnKTUMWTYonjd1DAAIQgEBGEIhEInGOmmhGTVS48TLf9G+w5tW496h4Ew6FoiVQXqtuk20eLLu+0c+3dNVmjX5MO2BJoSey+MtVN9wrR3TfSzZrv5G8+Np7csFVt8ndNw6RXXbcpsbxGfeBQ66XmW/Olg3WXwfhJS0zWYeDrll4qXlDV4OvDhlRQcY5ZCrVKaOfwxqs2EQFmCJpocpmi2jpkifMhNSqxgIBCEAAAhDIFAKV+jfNOjcliDTqrPGzaMxFI9EsGi1wigYG6+1itNTJBRn6nZxiwYaUOmXK/DNOCEAAAhBoHAImyCSWQK2SqRVDGufgcUcJivCy+onv0m2AHNNzX7ngzN41Mhk9bopMfe51OfTAzjLt+dcRXhr96qnjAf+s8FLTbq2jhde5YqkrVzJRxhwyJsgUWP29CjJWtmQumfyIZcm00CeHTeo4SlaDAAQgAAEIBJNAhauJ95/eeW4aK3WyLBp7WGEijTlq7N2yaCwM2Fw0Flzo2m+78iZfqNF3/d7yaHDSBHO+GRUEIAABCKSWwH8q+qb2AGvYexCFl3lffiuHn3yxjL5koHTvutsamTw2Y6ZcqS6Zp+4ZLjNeeFMmP/48wkujXz11PGCyhJe1HS6iQkxERZiIWrjzrWzJXiH9HBEnyDSV1irCWI19tHQp1KyOo2c1CEAAAhBYnUDzkjz517UdZO9DW8sPX62Uu4Z/K6889TugAkCgQv/+mc3a7zLhtd220iZ100SDg7WGWluKLtG/iVbWZOKM39nJauW1Rt5EGnXWeHk0llGDkyYAU8sQIAABCEAgSQTSIbzM+bx9kkb/53bTcYsFa9xg8ZJlcsgJQ6VZ0yIVVUZIXl71CpJ3PvhUTjlnlEy8/kL52w5byx2TpiK8/Dn8jbt2YwgvNZ1RRG3bnktmmd5EmjtmmbpklkfLliz0sKWKMuvozaUX7GshvywQgAAEILB2Ave+0VG23CFRwD7zoI/l3Vf+AF0GEfCcNH5np+i7E2ksj8bq6JdEu09Y622rio8KNJZNE2kaDS7UwGDXfttEmmJCgzNo/hkqBCAAgVwlkA7hZeLIDdOC+5QhP1Y7rnU1Ov4f18gvvy6SxyZeLeu1Llnj2M6+7BZ58725svfuO7jfz/nkS1nw3U/qjtldhv6jr5S0TK6ZgXDdBl4i6RReahZkKp07xsqWzC2Tb12X9OdQZZnePjZTMaZEikOt1KatWTIhK18i2LeBlwGbQwACWUJgyx2ayr1vdKp2NtP/84tcdXpigFuWnDKnoQSqWm9Hg4OjgcGWQ+OVPXltuW0xB01RxP52asmTOmdclyd1zdj3Js4UaJBwXrgArhCAAAQgAIG0EEiH8FI2d6u0nGvBtp8lHHdR6RI5/qxrZMWKlTLltstqFF1so2deeks++OiL2Pb/+/Az+UzLk3oftr8MOulwad4suf9GRnhp4CUSROGlZkEmooLMkmj76yXabcme+mmmjFq0i7Vcqdi1vlYxRm8oLeA3rC3LWCAAAQjkEgGEl1ya7T9/rrHQYBVjXPtPy6bRTDbLoPFacGvbbXXTVGpbUBNi/DKnolhosGXTeGVO9jvr8sQCAQhAAAIQSCaBXBVeli5bIQf3OV8qKitlwqhzpUXzpg5rOByW9u3aSOnipXJE/4tl4ImHucDd1RdKjZJ5FaZgX5kkvNQsyJRHOy39ofkx1m1ppZYrlbrWZEWyrmsBWqjOmAIVZKxciS5LKbiQ2CUEIBAYApQaBWYqMnYgq3d1cu23XQaN/X21tttW5qSfVapxQcHRMidzyxSpL9XC8z0HTbTMiRyajL0WGDgEIACBxiaQq8LLdz8ulAN7nVcNd2Fhgbz37B3y6++LZe8jBsvg/kfJgH6HILw09oXZ0ONlg/BSsyBjN4rW+nqJlilZmG+ZOmRKXblSsbRyXZZMiPFDfUNqu2aBAAQgkOkE2nYolH+N1nDdQ1rLj1+vlDuv+Vam/Wdhpp8W4w8gAeto6LfcduKMCws254y6aKJCjdfRqSwqzmj+jJU2qUDTxAUFe+KMJ9B4Lx6OBHCiGRIEIACBRiSQq8JLIyKu16EoNaoXtqqNsll4qVmQWeoEGQvzDbswXwv1XaU3fnl6Q9jChfoWa6lSgX42UYa21w28yNgcAhCAAARymkDEypeiQcExgcYXZ6IlTibQhLTboYQqnADTxLlo9BUVaXyBxtptWycnK3digQAEIACB7COA8BLMOUV4aeC85KLwUrMgY0/oNNDXBftap6XlrstSfkSLlvQGzxwyxZHmrmwpX10zJsqEQqEGzgCbQwACEIAABCDgE6hUd6q5aCwM2HPTmItmhXZzUieNiTT2ClkujblovG5NTfyuTpo/s6ZSp3AoH8AQgAAEIJAhBBBegjlRCC8NnBeEl9oBVlZqRwgNIfS6LHkumTy9IQxpnkwTFWCaaMvrIrvxcy4ZT5DJo569drCsAQEIQAACEKgngTW7aLSsyQk39jfby6SpVMEmrJ8KrGNT1EXTJNJMXTPWz8m+M/EmWu7E3+56zgabQQACEEgeAYSX5LFM5p5SIrx8Nv9b+XLBD9XGuX+XnaUgP7tyQBBeGnY5WrtrL0dmmXaE0GDfyhV6g2fCTIVapFtoy057lUhhuKkTZMwpw5O3hjFnawhAAAIQgMCfIVAZKXcdnPxyJ89Vs8y5aUSFGVGRxgQaK3cqVCHGRBpzuOarU8ZKnazddiwo2M+loaPTn5kC1oUABCBQZwIIL3VG1agrJlV4sd7Xg4beIEuWLl/jScx8fKy0btWiUU8w1QdDeEkN4Yje5EX0ps4cMibO5DmXjF5X+nOBPV9TQcbLk2mhN3R6c2dP38LNUzMY9goBCEAAAhCAQJ0IVKgI40qd7F1LmkyQse/MPePKnFxYsL3K9W95M3W4qjgTl0dTpKHBnoNGBRwVbSyLhrbbdULPShCAAAQcAYSXYF4ISRVeeg280jldRl00QDbRXtkF+Yk1wRu3XV/7aGdXpgfCS+Nf2GZ/rhJl/NKlCs2TWaySTFO9kWuut2wtXMCv55JRt0yoWeMPlCNCAAIQgAAEILBGAhFtq22CTGVIhZmYm2aVCjaeWGMuGrtjrJA/3P+1kiZz0niOmiax4GDfTeOEGit7otyJKw4CEMhxAggvwbwAkiq87NJtgBzRbW8ZNrhvMM82BaNCeEkB1Abs0twxziXjAn69cN88vYkL69O1JtoCu1C7OVjIb5Frg215MvoKNW3AEdkUAhCAAAQgAIFUEjCRxjlnXOZM1FETbb1tP3vlTiraOCeNdVk0YcaEGOvsZJ+tBbe+R0ue7Pe+qyYcsgQbFghAAALZQwDhJZhzmVThpf+510p5eYXcc9PQYJ5tCkaF8JICqCnYpYUImiBjWTIRFxq4XApcnkxUlFG7swky1go739mcTZTBKZOCqWCXEIAABCAAgZQSqHAijHV18sSYWOmTdnKykidz05hAYyVPYf1r7ztovHcN+7eW2xYcrGVO8SKNlUWxQAACEAg6AYSXYM5QUoWXGS+8KeddOV5GXTxArKxo9aXTNptJXl52PVlAeAnmhf1nRmWijBNkTJhRd4w5ZOwVcu03l+tNmHVesjwZCwrULJlIU60394SZEE/K/gxq1oUABCAAAQgEikBV++04kca5aTwnTVUuTZneE0RciZNX9lTkteF24cH2wCZRpMknPDhQ88xgIJBLBBBegjnbSRVeLONl9tz5NZ4p4brBvAgY1doJ+AG/liuTrzdiYXXKmCAjJspE7IlYM31Xx4zLlFGXjH4210yYmy4uLQhAAAIQgEDWEIhEKuLKnRLdNM5Bo92eRJ02npum3IkznovGz6SJZtS4kqeqcif7HAplVwZi1kw6JwKBDCSA8BLMSUuq8DLvy29lUemSGs90p05bSn4e7aSDeSkwqvoQiKgrxi9dMoEmz8IAtSNTWJboZ3Fhv4UqzFjnhiZRl0yeiTNqYQ6HCupzSLaBAAQgAAEIQCDgBMxNW5VH4wcI+x2e4p00JtJYLo31bPSFGnXRqJPGUukKoiKNdXjKc6VPhXr/QMlTwKef4UEgrQQQXtKKv8aDJ1V4CeYppnZUlBqllm8m791riW0tsD1xxmrK/aBfK2PKj4T1JsvKmLxXgYb8+qJMnv4cDmWXSJnJc8nYIQABCEAAAqkk4OXSVIUHu3wad/9Q5sqdwvqpQh/qmEgT0v+5Dk4qwnghwl54cJNYJo224I6VPjXhfiKVE8e+IRBAAggvAZwUHVLShZdPPl8g1932oHzy+deyYuUqabfh+tLniK5ydM99s66VtE0pwkswL+xMGJVZkiNmSdagXytbCjthRm+u9EbLrMr5Eb2lUjHG3DKWM2Pdl6yUyRdnQggzmTDNjBECEIAABCCQVAKVWvLkiTR+p6do2ZPeQ1Tl0nhBwpUq3Fi6ouemKXSOW5NrzEFjGTVVAcIm1lgJlDlqePCT1AljZxBoZAIIL40MvI6HS6rw8s4Hn8qJ/xzhDr3lphvLuuu0lHfnzJNVq8pUeNlHrjjv5DoOK3NWQ3jJnLnKtJF6gX6+MKPdl6wLk5UyWRcGCwB2t1Amyqg4o8G/5phxGTNOnCkm+DfTJpzxQgACEIAABFJAoEqosQ5PJtJUCTXmoLFW3PZu35twE3a9niyXpkqocZl29k0sn6YwKtSYoya7GmekYArYJQQalQDCS6PirvPBkiq8HDfgCvnq2x/lxYdvkKbFRW4QVuN6ybUT5bEZM+XlR2+S9VqX1HlwmbAiwksmzFJ2jtETZrzW2CbQeMKMWZCXO3Em33VasBsnL/zX3DP2pMtEGXuqZbXiLBCAAAQgAIEgEbB/wlcGaUA5OJZKLZWuctT4IcJW+uS14Bbn2PXEGq8ltz0K8gKEvY5Pfrcnz0FT1fHJK4FCqMnBi4pTblQCCC+NirvOB0uq8LLTgae5sqLzB/ZKGMBX3/woPfoNkdtHnyedd+lY58FlwooIL5kwS7k5xoi6YzzHjFe6ZBkz5pgJ29Ms55zRjgvaGtsrZ9J3V87kCzPeO3bj3Lx2OGsIQAACEIBAXQn4Qo3npvFLoCxA2Fpyexk15qQxR42JNXlRocYXayyfxpw08bk08YJNCEdNXaeC9SDgCCC8BPNCSKrw0q3vBVJc1EQeveuqhLOd9OhzMvzm++SRO6+UbbZoH0wS9RzViCsflEn3/yALvracDhYIZA6BSMSeKXrCjBNo9JVXaaKMuWY8kcZujgpVgPFCgK11tlfG5L9MqGGBAAQgAAEIQAACdSVQqY4ZL58mXqixrpCek8YrfbLPnoiT53wzXltuL5tG70W0s5Mn1PiOGr/0iYyaus4D62UvAYSXYM5tUoWXuyZPl+snPCi777yd7L3HDq6saNabs+Xpl96SdVu1kP8+cJ3mToSCSaK+oyp7RxYtKpM+fT+UWTN/r+9e2A4CgSTgBwB7nZnspsgCgM01430OWQtMrfsuVAHGlTOpKGMhwDFhRn8XDtM2O5CTy6AgAAEIQAACGUCgSqiJumnsAVHIBJpEocbcNibYVIUJm1DjlT7lxzo+mUCjP6uAkx8qcMJNXqgwAygwRAjUnQDCS91ZNeaaSRVeLM9lzG0PyN0PPJ1wDp223Uyuu2yQdjharzHPrXGOpcKLLV+r46VTp1cb55gcBQIBIhBzzERLmsKVZXpTEw0AjpjQuty1uizQJ1See0YdM66kyWzF5M0EaCoZCgQgAAEIQCDjCay565OVPlmnJ+89pF6aClkWzampiGbTaLcnP5/GOj/Fyp/MVRMVbKIuG8qfMv4yyeoTQHgJ5vQmVXjxT3HZ8hViuS7LV6ySTTZqI23WaxXMs0/GqKLCi+2qZcvnkrFH9gGBrCIQ0baX1tLSHDMm0phLxpU0OXHGbn70Ow3yM0HGE2aiIcDuhseC+dQ140SaouxzzGXVTHMyEIAABCAAgcwjYKXXXqcn7+V3fvLLnrycmqpAYb/8qTjSUrPwRB2/fltuX5xJFGlw1WTeNZHpI0Z4CeYMNlh4Ka+okBUqsDRrmqP/KIoKL7Nn/yGdO78ZzFlmVBAIOAEvb8YXZ6ycycuaydMboJCKNKGQhQNrWZM9gTKBRkuYXBBwVJwJ6xOomIMGy3DAZ5vhQQACEIAABDKbgCt/0vuSCi158gWbkAo0XvmTvXsdn+yzFypcHsujcZ2fInY/o90nXZfJgri8mirxJqw5NiwQqA8BhJf6UEv9Ng0WXqY9/4ZccNVt8vi/r5Yrr79X3p39WY2jnvXEWFmnpEXqz6oxj6DCS2lpuXTv/o7Mnr2kMY/MsSCQcwS8FtrWytLyZTzHjAsDjgo1ErLsGc89Yzc0BSrQFPgCjeXNaI23J9BYO0uyZ4J6AdFONqgzw7ggAAEIQKA+BCyOId5V4zlrNLNG71nsfsaEGbu3EX33QoX9rBqv/Ck/mlVT4LJqqsSZfBVw8ty9jVcORTfK+sxO9m2D8BLMOW2w8PLJ5wvkgSdekDNPPkLeePdj+WrBjzWeaf8+PbTrUXYFWA089R6ZOvUXJ76wQAAC6SfguWeiT5rcTYw5aDR3Rr8PRZY5kSakAk04InqTouKMK3HySpp8B40TZqLlTTxxSv+cMgIIQAACEIBArhGwUm2/7CletKlq0W0lUCboWFaN56wJq4xTYO5gFWosUNj/XOjEGRNt1F0TDRb2u0Ih1mTflYXwEsw5bbDwEn9aPy9cJIUF+dKqpHnC2S5bvlJ+Xvi7dNh4g6zLaGhZPDKYM8uoIAABj4Dl+6rIsvpSlT1jQXsm0Ghpk5U3We6Mc9HodyrQ5GlAcJVAoyVOEe3aFPadM172jJc/Yz4NFghAAAIQgAAEIJAeAl6wcFX5U/xnL1zY7nF8scbWs6w98QQaFWaaRJqraya0Wm6NHy4cl2GDazg9E1zHoyK81BFUI6+WVOGl18ArZfttN5dhg/smnMbHn30lx5x+ucyYdK20b9emkU8xtYdDeEktX/YOgXQTiKgNOMFBE82eMYHGbMHhmEDj12s39UqdYp2bimLuGVpWpns2OT4EIAABCEAAAvEEEpw1ll0Tl1vjZdVYGZT3boHDLnRYvTj5llXjukA1U3dN2Mut0ew956qx0id13dh9j5VA2brcAzXedYfw0nis/8yRGkV4WfDdz9Kt7wUyZfylYq2ls2npuM3NsuDrZdl0SpwLBCBQDwLx+TNWn23dm8Kue5OXQWMOmrC7UbEwvZbu6ZJ1cXIhwX5bbReyV1SPo7MJBCAAAQhAAAIQaBwCCZ2g9AFVpTqEzT1jpVEh56wxsaZCRZrlrgzK5fPpb4siLdQhbKXeFjDsCTdNrHulBgn7pU9OqHGijT64wk1crwlFeKkXtpRvlBTh5Y5JU2XpshXy4FMvSpt115F999wxNvBVq8rk2Zffll8X/SHvzJggeXlZZsevmCsffrBI+vR5GwEm5ZcrB4BAZhPwuzdVutIm7dTkAoJVpDGBxuXO2FOkMi8U2GXPNHUvzz1jr2hpE+JMZl8IjB4CEIAABCCQYwRcHo3e5/iOGhcubA4aJ9DYPZHXtrvSiTT2qGqx3veEXQ5fkZZAhUJ50XKoIifWeG4ay62JvjTHhsUjkA7h5Yvn02Ou2Lzr/IyZ9qQIL7v1GChLli5f40mb0LLLDtvI8Uf/Xfbbc6eMAVPngarwYsu0qT9I715v1XkzVoQABCCwJgJ2Y2KijGtDGfHEGeeWsc5N7rM+TUKc4eKBAAQgAAEIQCDLCVjXJxNiKpxgo+9OrPG7Qem9kmvfbV2hzGmjzRVcyZO5idVTrCVQeSrWNLGgYRNnrCOUlkO596hYk635fOkQXv59boe0XI0nX/d1Wo5bn4MmRXjxDzxk+O2y5aYbS//e3eszlszcJiq82OBbNn8iM8+BUUMAAhlFYE3ijOXNWDiwtaX02muXuU5NVs5U4Jwz+jmkwcBm6XU3JZ6NlwUCEIAABCAAAQhkAwFz1PgijfvsSqBUtKnU8qeQZfN5GTXea6UKMVbi7TtpmnnpNOoy9sqdNK9GQ4RdPk20pXc4nJcRmNIhvFRMT4/BIq/7exkxJzbIpAov73/0uTw89WU5uVc32bzDRjEI1946WTZs01pOOOagjAFT54FGhZcFC5ZJx+3+W+fNWBECEIBAKgmsWZxZ4QSZPL0NkcgS/VzuxJkC11a7qbPuFjhxxkqa7MbDa7HNAgEIQAACEIAABLKNQIUKMhENE3Ztu12w8Ar32e6Vwq6d9wqvHMpKw/XOyH62jD4TYwq1/MlChc1R44UKe2JNVQmUdoFS5006FoSXdFCv/ZhJFV7OuPA6mTtvgbzw0A0JWS633fukjJ34qLw94zZpWtw4N/GVlRH5/qeFssH6raUgP4XqZFR4GXLhbBl3a+bUmNV+abAGBCCQ7QQ8ccYsumbXjebOxMqavEBgc8/4woxlzRRqO217GlSVOeNlz4QsLY8FAhCAAAQgAIE4ApZtqQ87WLKCQEK7bhVnKlSoMWHGXMcWKGzlThWy4hfCLgAAIABJREFULNbS274zgcZz1VhTBW2woEKNl1Fj/hore4p2fbKSKBVqknE/hfASzMstqcLLXoefJYcd3EXOO+O4hLP94eff5IBjz2m0rkYzXnhTLrxmglRUeP+P7uzTj5FT+/RY4wzc89AzYo6c1Ze/7bC13HPTUHnimVdl2Ig7qv3+nadvl+KiQpn2xAty330LNOPlx2DOcBaNauhF20qPHm3dGU3VTJ2Rw718HRYIQCC1BCxrJj5zxmujbU+EvO5NJs7YjYO5ZVxpk5YyeR2bfPeM56DJ1lrq1NJn7xCAAAQgAAEIZCIB1/3JlTypo8ZcNdFAYcuusQdf4kQbCxT2MmoqQsu8+yn3MleNBQl7WTWeSOO/vN/nadl4SL04qy8IL8G8WpIqvJi4st3Wf5GbrxqccLYz35wt5oZ55M4rZZst2qeUxLLlK2T3noOc0DLwxMNl2nOvy0Uj75Sp946QTdt7/2iPX0r/WCo/L/w94bszh94oHbfZTK6/fJA8/vQsuXT0RDf2+GWLv7RzimTL4pEpPR927hEw0WXosG0TcIxQ4WXENYgvXCMQCAIBc8zEizP2s8uccW20vXdXK+1EmRbupqKJc85ou0hXP+13bKIrQRDmkzFAAAIQgAAEIND4BJxQ40Sa6MsChDWrxgQaE2q8zphaCqWSS3noD30I5mXU2MuEGZVr5KXKGxp94GS81I48qcLLhVdPkKkqdNx1/QWuk5F1NPr+x4Vy1sU3y7wvv3XtpAsLC2ofVQPWmP78m3L+VePl3WfvkCbRY+156Jly/JF/l0EnHV7rnt967xM5+eyR8uQ9w11OjQkvV1x/j7yn+1vTgvBSK9KkrDBn7kHSvn2zhH19/fVS6bTdM0nZPzuBAARSTyDiQu2sW5PdNHhCTdhaaZtAo0+D3LveTJg4Y+20vZbamj/jnDNx4oyJNSrisEAAAhCAAAQgAIFcJhDRciYTabycGhNryuSD8isaHQnCS+3Ikyq8/Pr7Yjm4z/mybLk+6VTRZZ2SFrLwt1I3iov+2U/6HNG19hE1cI07758mE6dMl9eevDW2p14DrxRzqFx9Yf9a997zhKHamamd3HDFP9y6JryYY6bLrp2kSZMC6fy3jnJkj31iuTEIL7UiTcoK33x/iJSUJP5Dq7S0TDbZ6Kmk7J+dQAACwSBQlTtTJdB4pU0WdGdPeKIumlDEhdhZoF1hVKjJjzpoTKQJRx004VB+ME6MUUAAAhCAAAQgAIFGIPB62emNcJTEQyC81I48qcKLHW7J0uVi4sd7c+Y5AWYzLe857rD9ZOdOW9U+miSsMea2B2T682+4gF9/MQdL82ZNZezViSVQqx/umZfelnMuv1WenTJG2m24nvv1Ox98Ko9Of0XWadVCvvn+Z3l+5rty0L67ujIkWxBekjBpddjFyGu3l0FnbpGw5rhbP5chF3xYh61ZBQIQyDYCEe02YJbbeAeNZc14LbVNpNG6aXXU5EVCLhy4qrW216kpT8Uaq432RZqw1k+zQAACEIAABCAAgUwngPASzBlMuvCS7tOsr+PFgnj3P+Zs2Wu37dfqjLn7wadl9Lgp8v5zdznXC8JL48y4uV0s58UXX0x0sXwXc72wQAACEKiJQCQaYBcxkSZa4pRnLbVVuAlFljvxxgKCTaDJd221vTbaVuJkwoznnPHLnPTnNLWGZIYhAAEIQAACEIBAXQggvNSFUuOvk3Th5adffpf3P/pcli6zG9rE5ZC/7ykFBam1ffsZL5bJ4ufJ7NZjoJx4zEFrzXh5QLsTXXnDvfLyozfJeq1LapwJ3xXztubVNC1ugvDS+NcsR4QABCCQdAKeQOM7aHy3TJmKM355kyfQhFSwcQKNljUVWKmT697kCTNVIcHWaaAo6WNkhxCAAAQgAAEIQKA2AggvtRFKz++TKrz4wbQ1ncrMx8dKay3ZSeWydNkK2bX7GXLGCYfq67BqXY1eeeMDufy6u2XCtedqlsvGbigrV5VJl8P+IUd020uGDT4+YXjj731COm69mfx1+y3lt0V/yOnnj5H8/Hx58u5r3Ho4XlI5m+wbAhCAQLAIWGvIxBInr512VYmTiTUm3FiLbeswoBk0LijYK3fyypsSRRrabAdrjhkNBCAAAQhAIJMJILwEc/aSKrz0GXSV/PjLbzJi2Olyytmj5IEJl8lGG6wn/xh2o6YsR2TK+EsbhcLU/74uF14zIXaswf2PkgH9DnE/W9cl675kY+m07Wbuu9vve0pu+fdjMvOxsVLSMrFzzsWj7pLHZsyM7cuyX0y08VtTI7ykbkoHnbm5DBy0hXTo0FTuu2+BDL3wQ0qLUoebPUMAAkkm4LXYVhEm+m6OmXCl56CxoGD7PqT9B/J0LSfSqEvGXDQm1FhJkyfQ2LuVPHl5NCGyaJI8S+wOAhCAAAQgkF0EEF6COZ9JFV6sbfMJRx8kp/btITt07S+Tbr1Ydvy/LeSN/30s/c+9Vp574Dppu8G6jULCMlssDHcjPV5DW1hbSLAJSi2bN61WhoTwkprp7Ht8exl/218Tdj5z5kLp0a1KBEvNkdkrBCAAgcYlUJVDUxbNoTEXTbkLCA7HgoLVSeMEm3C01EmFmrhSJ1+Y8d4LXTYNgcGNO48cDQIQgAAEIBAEAggvQZiF6mNIqvBiWSonHXewDNQSH/s8uP+R0vfIv8u8L7+Vw0++WG4ffZ503qVjMEnUc1QIL/UEV8tm02d0kS57rV9trU3aTcX1khrk7BUCEMgAAlVZNBYW7JU0uS5OlZ6Lxj6bQGPfh/3AYC1vKtAuTuaqsfbbiSKN56ah7XYGTD5DhAAEIAABCNSBAMJLHSClYZWkCi+HnnSRa8M8fuTZMviSm+XNd+fKxf/s58p7Zr01W1576lYpaZFYypOGc07qIRFekooztjOEl9RwZa8QgEDuEKhque13dLIucNFyJxcaHBVpnLNGYsKM56SxTBq/s9NqZU+hgtyByJlCAAIQgAAEMowAwkswJyypwsuDT70k8+Z/Ixep2PLDT79K935DZJUG19pyWt+e8q/Tjg4mhQaMCuGlAfDWsmmPnm1l8pTdE9aYNvUH6d3rjdQckL1CAAIQyGEC8SKNqBDjsmmcSKMlT/buOjqVu+Bgy6UxUca6OXkijTpposHB1dw0iDQ5fFVx6hCAAAQgkA4CCC/poF77MZMqvLww613X+efonvu4I5eVV8jsufNl8w4bVQutrX1ombEGwkvq5snCdfv27SDtNVx3kobrjhg+lzKj1OFmzxCAAATqRKCqs5MfHGwPWFa6UqewiTPmpDGBxl4hFWki1t3JxBnPRWPvXncn7xUTazRQmAUCEIAABCAAgYYRSLrwEtHxhNY+porpOzVs0PXcOq/7e/XcsvE3S6rw0u+s4bJ02XJ59K6rGv9M0nREhJc0geewEIAABCAQeAKJIo3npLFXXjQ8uEqkUfEmVK5ZNJ4YY24a++w7azyBxkKD/fBg/YybJvDzzwAhAAEIQKDxCSRdeKnDKSC81A4pqcLL8JvvkyeeeVXenDa+9iNnyRpBEV667NVa+vTZyFG9//7vZdbM37KEMKcBAQhAAAK5QCASsUdq0TIndc/45U7inDTmolE3Tch30+h3oYgKOJ6bxlpxm7PGlT25AGETaeJbcRfSijsXLiLOEQIQgAAEBOElmBdBUoWXn375Xf7e61y59uIz5OD9dg3mGSd5VEEQXvr2bSfjxyd2i+rd5z2ZNvXnJJ8tu4MABCAAAQgEg0CimyZeqIm24xYre7LPfrenkFf2ZNk0JtBoPo0TbaLCjRNrIirW6O/tcyhkkcMsEIAABCAAgcwigPASzPlKqvBy8tkj5a33PqnxTGc9MVbWKWkRTBL1HFUQhJc5c/aW9u2LE87gww8XS5cur9fzrNgMAhCAAAQgkF0EvFbcUYHGBQhXffayabQEyorYI8vcenn2vzihZnVHTVX5kzlrcNRk19XC2UAAAhDIXAIIL8GcuwYLLxaga0tBfp4rM/pywQ81numAfodKcVF2hecFQXhZvPigasxLS8tkk01eCOZVx6ggAAEIQAACASfgCTWeQON1eqoSasxFEzZHjWXVuA5Q+h4JRfNpCqOBwl7HJy+nJr70ycuqCYfyAk6A4UEAAhCAQCYSQHgJ5qw1WHg5bsAVstGG68oNV/xDJj36nLRt01r277JzMM82BaMKgvAy+f6dpEfPNglnN+n+72TgGXNScMbJ32Wn7VvK+HGdZPsdStzOhw+fJyNHzEv+gdgjBCAAAQhAIEUEEh01VaVPEVfqZMJMtCW3dX3SV1gjbaysyZU6+c4ay6mxEqiwJ9RUZdVYmHB2PbhK0TSwWwhAAAI5TwDhJZiXQIOFl6NOvVQ2bd9Wxlw6UHoNvFK233ZzGTa4bzDPNgWjCoLw0r5DsZj40qmTV8Y1a9Zv0rv3e9p62Z7WBX+Z89G+WirVNGGgvXv/TzNqfgr+4BkhBCAAAQhAoB4EIhFzDPsumnihxnPaVAUKm2hjLbut/MlEGi+Hxr27Nt1+To3vqokTbej8VI+ZYRMIQAACmU0A4SWY89dg4eXfU2bIzRMflX+cfIQ8PPUlad9uA+lzxAFrPNs9d+noSpKyaQmC8OLz9IWX2bP/yBjE5nZ59dUu1cZroouJL3ZOCxYszxgRKWPAM1AIQAACEMg4ApFoyZMTbPSzJ9xYKVQ0QNiyamJhwvaddn5S54wv1hRYp6e47k/2u3hnjftMCVTGXRcMGAIQgEA8AYSXYF4PDRZefi/9Qy64aoK89k7tZS0zHx8rrVsRrhvMSyE9ozK3zpw5+1U7+PTpP8n/bddUOujvbbn11q9l6NBP0zNIjgoBCEAAAhDIQAJe56eoOLMWsca5a0zAcSVQ2v1Jmqkgo+9a3uQyakysUZeNuWu8Nt3RnJqocBMKaSgxCwQgAAEIBIJArgsvy5avkIW/lcrGbdtIOBycv08NFl78q2v5ilVy3BlXyJabtpMzTjh0jRfd5h3aBerkk/FfRpAcL8k4n3TsY/Lkv2pGzQYJh/7x+6WyYdsmCd8NGfKJjBu3IB1D5JgQgAAEIACBnCBgJVCWSyMhz0kTceHBGi4cLXeyvBrnqolm17huUJECLX/ySp/yzWETbdftyqCi7bmrBBtz1RTkBEtOEgIQgEA6COSy8GLRJ7PnznfYCwsLpEfX3eXqC/unYxqqHTNpwovt2Toc2UOP/LzsKida20whvDT8Oi4pyZehw7aS7bf33FBTpnwvt4zdttqOLbume/d3Gn5A9gABCEAAAhCAQNII+KJMfAeoKrHGy6hxLbtVzHEdodSJ40qgosJMQbT8KSbS6M8WJuw5a4qcqMMCAQhAAAJ1I5DLwstVN9wrR3TfSzZrv5G8+Np7Wplzm9x94xDZZcdt6gYvhWslVXhJ4TgDu2uEl+RPjQkx33yzf7Ude52aPkr+AdkjBCAAAQhAAAKNRqDmEihzz1RIXmVEC51W6Ms6QXklUE6kcWHCxbE23RYy7DlpiqLv9nMTFXiCYy1vNKgcCAIQgECUQC4LL6tfBLt0GyDH9NxXLjizd9qvD4SXBk4BwksDAdaw+ciR28igQe0Tftu58+uSScHBqSHDXiEAAQhAAAK5RyASWemVPbmAYa/8KS8WJux3gbISKfs+3k1TpNKMCjMq0lgplAkzVe+FKtLkjks7964azhgCuUkgHcLL/Lv/Ly2wNzup5ofy8778Vg4/+WIZfclA6d51t7SML/6gCC8NnAKElwYCXMvmJrz07NlGvtauRuNuXYDokjrU7BkCEIAABCCQNQSs9CkiK/V8/O5PK6MlTlbmFO0A5cQbbdEdyXO5NHkhddK4kqciDRL2HTT6Hm3fbYJNOJSfNYw4EQhAIHsJpEN4uevE6jERjUG4/z1z13iYxUuWySEnDJVmTYvkqXtGSF5euDGGs9ZjILw0cAoQXhoIkM0hAAEIQAACEIBAmghE1DXj8mhcgHA0SNjcNJXRPBoNEw6riGNdn0La8ckCg02YyY9EnTTmoFlTyRMBwmmaUQ4LAQikQ3hZOa56l9rGmIkmg16sdhjranT8P66RX35dJI9NvFrWa13SGEOp9RgpE17shAvy86WgILufDiC81HqNsQIEIAABCEAAAhDIeALW8ckXafySJy84WJ0zrtuTZtI4gUZLn6TSteI2B42JNCbOuLBgFW3sPaHsifDgjL82OAEIBIlALgsvi0qXyPFnXSMrVqyUKbddFhjRxa6PpAov5RUVcu2tk+WhqS/LqlVlMmzw8dL3yAPkqFMvlSbazun+cZcE6ZpMylgQXpKCsf47MddYZf03Z0sIQAACEIAABCCQbAJegHA0j8YEGRNoomHB1pI7bO4ae2kmTUhdN14rbmu/7ZU8mWhjXZ5iwcEq3linJwsTJjw42bPF/iCQXQRyVXhZumyFHNznfKmorJQJo86VFs2buokNh8PSvl2btE9yUoWXac+/4Vo27bPHDvK/Dz+Twf2PcsLLYzNmysWj7pJZT4yVdUq8lsHZsiC8ZMtMch65TqDHIRtJ+02ayaxZv8jsDxdlBY4ePTeUvn03kZJWBTLpvgVy/6Rvs+K8OAkIQAAC2UQgEokkiDReeLCGCUfdNJ5gY52aljpHTV6kwBNqVJjJj4o0BfrZhQjHuWncZ9w02XSpcC4QqBOBXBVevvtxoRzY67xqjArVAPLes3fUiV0qV0qq8GLOlg4bbyjXXz5Ijux/iRzVYx8nvHz7wy9yUO/z5b5bLpKdOm6ZyvNp9H0jvDQ6cg4IgaQSKCkpkGkz9pHtd1gntt8hF7yvgc7zknqcxt6ZiS6Tp+yacNgRwz8Ve7FAAAIQgEDmEvDCg6N5NC6XJvqzK3WKljw5h41+dkKNl0PjXDQq2lhGjTlpCk20iQYJxwQbsmky98Jg5BCIEshV4SXoF0BShRfrk33GCYdJ/97d1yi8PHLnlbLNFoktgoMOqLbxIbzURojfQyDYBAaduaWMvHbHaoPcZKPHpbRUW5Nm6PLqa/tIp+0Tw8QWLVol7Td+OkPPiGFDAAIQgEB9CFhocFUrbvu7VhUkbKJNONaW2zo+VTgXjSfUaFvuqDhTaA4by6qJhgm7vBqXWVNQnyGxDQQgkEICCC8phNuAXSdVeLH04EWLl8iTdw+Xo0+7NOZ4ueK6u+XBp16Sd56+XYqLChsw3OBtivASvDlhRFUEBg1sK0Mu3ERaaanJzFmlMmTol9qWeymI4ghMntJZrMxo9aV7t5dk1iu/ZCyrOR8fIO3be7Wt8UvL5k9m7DkxcAhAAAIQSC0BL5vGd9Cs7qwxwcYPEvbacluIsHPUuNKnQq8dtyt/su5P2oI7VvpkQo0JOtn174DUzgZ7h0D9CCC81I9bqrdKqvAye+586TXwSilp2UyWr1glW2++iVRUVMrHn30lJx13sJw/sFeqz6fR94/w0ujIOWAdCfTo0VomT9o2Ye2vF6xQF8T/6riH3FgtWx0vI0d1lEFnbpYwidOm/ii9e72VGxPLWUIAAhCAQMoJrFmoMcFGnTXW3UndNNb1KRSyEiivRbe5ZbwgYRVq1EVjLbqtDMrCg2NOmjjBJhSyTgosEIBAXQkgvNSVVOOul1ThxYZu4svVN/5H5n7+tRNdrG/2ScceLCfqKxy2YLDsWhBesms+s+lsxo/bUvr2qZ7g3b3nHA2QLc2mU23QuVjGy/Sn91VBqlVsP9mQ8WLndduEncSyXmyZNXOhii5vZ3T5VIMmmo0hAAEIQCAQBKoyalSgicuoyXMijZdL48QaJ9jY5zyv7Ckq0BRGmur3ec4948qd4l5ey25cNYGYaAaRNgIIL2tDb2Hm6dEkki68xJ+mpbRne8u7xhBeuuzVWv/R9Fva/uPlwJlJYOSITWXQwOolNJ33ep9yozVMad/j/yLtOzSVqU99nzVdjew0TYCxrkYLvl6mJVVtpUuX9VV8KdcOR1/rd5SdZeZ/3YwaAhCAQO4QiKgYEx8gHNHQ4DwTZVxbbhNo9F3FGvvZRB0XKBxtz10QzamJuWlWE2vIqsmd6yiXzhThJZiznRLhpbIyIn8sWVbtjK0EKduWVAovfY9vJyNGbOPyOWwZcuFcGTfu62xDyPmkiECnTs3k1ZmJobHmdDHHC0vuERh60bYydNh2sRO3oN0e3V5RkQn3U+5dDZwxBCAAgewm4AUKW5CwlTx57pmwBge70icTaaLOGvve1rFQ4VgJlAsUNidNQTRAuMpVE18KRbBwdl9DmXx2CC/BnL2kCi9fffOjjLxlkrz2zkeuzGj1ZebjY6V1qxbBJFHPUaVKeGnfoVjmzNmn2qg6d35V/6H0Rz1Hy2a5RqBLlxIZNmQTDVltouG6izVcd766HSpyDQPnqwQWLz2qGodp6u7p3et1+EAAAhCAAARymoC59GOuGhNiKtVVo2KME2w0n8Z31pibpkqs0WDhWAmUFyTsvVSwiTprEoQaVwaVn9OcOfnGIYDw0jic/+xRkiq8nPjPEfLenHlywjEHSbsN15O8vLyE8Rx+UGcpLMyutnOpEl7M7TJ+fKdq8zlixOcyYvjnf3aeWT+FBNp3aCZd9lpfFi0qU4fJz2RopJA1u64fASs3+ub7Q6ttPGvmL9L94Ffqt1O2ggAEIAABCOQwgapg4ahrxg8Stve4MGHnuokKNiH14fhijfde5PJrLK8m5AKFq+fWhDXPhgUCf4YAwsufodV46yZVeNnpwNPkqO57y8X/6td4Z5DmI6VKeOnRs41MnrxztbMbOHC2TLrvuzSfNYf3CfQ4RAWyCbtqOZgX5PbhB79Lj4NfRHzhEgkcgTlzD1bnU2K557hbP5chF3wQuLEyIAhAAAIQgEA2EqjeBcoTZawESvzMmnhXjQsYFs208cKFLbvGRBq/ZbfXDSo/0W3jHDfZ9aA7G6+FVJ4Twksq6dZ/30kVXo4bcIW0XqeljB95dv1HlGFbpkp4MQyvvrqndlppGSOyYMEy6bznay4YkyUYBL754QgNL01Mz59035cy8HRa9gZjhhiFT6DT9iXavWkfF7Zri7ldeh/3OiIhlwgEIAABCEAgwAQiESsR91t02/uquJ8tt2aVCxaO5da4z1oGZe25IwWeYKOf86PijQUOV7XuLohz2uhn3DUBvhLqPjSEl7qzasw1kyq8vPr2HDn9/DFy13UXyIZtWlc7j/btNsi6ltKpFF5KSvJl0Jl/kb20q9GHmusyYvg8RJfG/K+jlmO58o0fjqy21iwtN+p+0IsBGilDgYBHwK7ZTjt4bbNnvfILWCAAAQhAAAIQyEICnrNGuzzFBQz7n73OT55Q49p3+2VRLmQ45AQaK3lypVAhDRqOCjZe4HBc6HDc52zvYptplwjCSzBnLKnCy7wvv5WjTr10jcG6dvqE6wbzImBU9SewJsfLtKe+UyfBrPrvlC0hAAEIQAACEIAABCDQyAQS3DWuPbe5a6ICjgo2ea4blIYOx5VHeR2j8qPZNSbWFOh6KtioMFOgwk1Vdo11iYp2inKuG8qhUjW9CC+pItuw/SZVeLFSo89UfBl8ypHSdoN1NVw3nDC6fffcSQrysysgKpWOl4ZNLVs3BoG+/TZ1GS/+Ulq6Sjrv/qws+HppYxyeY0AAAhCAAAQgAAEIQCCtBCIuoya+HMrLrjGXTVhLpZw4E++usfXVaZOvAk2eijFeKZQn1vilUQkiTYJgk13/lkzFxCG8pIJqw/eZVOFll24D5Ihue8mwwcc3fGQZsgeElwyZqBQO07oa9T3+L1oGtkom/ecrMjNSyJpdQwACEIBALhGwconEh3i5dPacKwSynYDnqDGBRoUY/eze/Q5QKtiErSTKlUV55VFeELHXGcpe5qjxgoe9DlFVQcOruWtUuMml/BqEl2D+l5NU4cXaSeero8UyXnJlQXjJlZnmPCEAAQhAAAIQgAAEIACBdBJYvY23ZdY48UaFmryow8YEG9cpygk2VhplAcShqnIoE22kmfvOOWu07KnKYeOXQ0W7RWVgSRTCSzqv0JqPnVThZdrzb8gFV90moy8ZKBttuG61o3baZrNq5UfBxFL3USG81J1VQ9a0oOERI7aQvbq0co6S8bd9J5Mm/diQXbItBCAAAQhAAAIQgAAEIJADBLz8mqrAYfs5bIKMvbvPfuiwrufKolTQiZZQeQ4bLYnSDlGxcqioYBNyZVC+WON/VoeNdpQKh9NTFoXwEswLOqnCS6+BV8rsufNrPFPCdYN5EWTCqCbf31F69FgvYai9+8yWadN+zYThM0YIQAACEIAABCAAAQhAIAMJxFw11iVKxRjLrrGAYRNtqlp5e52iTNzxfpfnnDauO5QJN9GW3gUq3ngum2iHKC2VqnLcRIWbBrpsEF6CeZElVXixrkaLSpfUeKY7ddpS8vPSo/ylCj+Ol1SRrdqvuV2+WdCl2oGmTVsovfvMSf0AOAIEIAABCEAAAhCAAAQgAIE/QaB6lygri/LEGdchyr3i3DZO2DExx8qmzGFjJVGWY1OgeTbFLqfGc9d4L+sYtXqujRVUIbz8iUlqxFWTKrw04rgDcyiEl9RPRfsORTLnw92rHWjWrEXSvcf7qR9A4I8Q0RGGAj9KBggBCEAAAhCAAAQgAAEIrJ1ARAOEPYHG7xRlLhvNrfGza1zgsAo00XIoc9j4naXC+m+CX8unNjrileP2a/Rj2gGbDHoxLcetz0GTKrw8Ov0V+eLr72scxz9OPlKKiwrrM87AboPwkrypad+hqQwZupV00PeZM3+V8bfOj3UImj5tR+mi+S7xS/1KjRApkjdj7AkCEIAABCAAAQhAAAIQCA6BSlm8fFijDwfhpXbkSRVeTjtvjLz53sfVjlpRYe0ARV594hZpVdK89lFl0BoIL8mZrJKSApn90f7SqlWVMPfhB6XSpfMr7gBWbjRy5BbSt8+GsmDBchk37lvQvSNbAAAgAElEQVQZN/675BycvUAAAhCAAAQgAAEIQAACEMgCAouXD2n0s0B4qR15UoWXmg7X/5xrpbyiQu65aWjtI8qwNRBekjNhQ4dtJUOHbV1tZ927vSaz1P3CAgEIQAACEIAABCAAAQhAAAJrJ4DwEswrpFGElzffmyunnD1K/jtljLaZTuxME0wsdR8VwkvdWa1tzZqEl9693pZpU2kbnRzK7AUCEIAABCAAAQhAAAIQyGYCCC/BnN1GEV4+/uwrOeb0y+X20edJ5106BpNEPUeF8FJPcKtt1mWvdWX6jD0Tvi0tLZPOe74iC75elpyDsBcI5CCB9hvnSUlLkdkfV+Tg2XPKEIAABCAAAQhAILcIILwEc76TKry8MOtd+e7HhbEztUTm0j+WysNTX5bFS5bJm1PHSWFhQTBJ1HNUCC/1BLeGzfoev4mMv21H9xsTXc4Y8D5ul+ThZU85RqCkZUgm3V4ie+3u/f/cDz8qk74D/pAF3yLA5NilwOlCAAIQgAAEIJBDBBBegjnZSRVeeg28UmbPnV/tTHfutJX0791d9t3T+0d1Ni0IL8mfzU7bt5TZHy5O/o7ZIwRyiMC4MS2k79FFCWc8840y6dlrUQ5R4FQhAAEIQAACEIBAbhFIh/Ay7+o90gJ5y4tfT8tx63PQpAovZWXlUlae+DS1sDBf8vPy6jO2jNgG4SUjpimnB1lSkqcOIlwOuXYRzJ7VWqzMaPWl5C+/5BoKzhcCEIAABCAAAQjkDIF0CC/jDtklLXwHPfV2Wo5bn4MmVXipzwAyfRuEl0yfwewdf6eORXL/vZtKh/aFsmhRuQy95HuZNPn37D1hziyBwLQpJdJl96r27PbL0sWV0n57uoRxqUAAAhCAAAQgAIFsJZAO4eW3YUenBWfr4Q+n5bj1OWiDhZep/31dvvzmhzod+9Q+PaW4KPEfAnXaMMArIbwEeHJyeGjmcpn9v22kVav8BArdD/tCZr26JIfJ5M6p9ziwUO7XjJf4ZeiVf8i4iStyBwJnCgEIQAACEIAABHKMAMJLMCe8wcLLif8cIe988Gmdzm7WE2NlnZIWdVo3U1ZCeMmUmcqtcXbp3FymP7F5tZMeN2GhDLnou9yCkcNn20WDdftEc16mPbtSpj27KodpcOoQgAAEIAABCEAg+wkgvARzjhssvATztBpvVAgvjceaI9WdQE3Cy4hrf5QR1/5U9x2xJgQgAAEIQAACEIAABCCQMQQQXoI5VSkTXn5euEhWrFwpbTdYTwryCdcN5vQzqmwm8OpLW0mnjsWxU7SA3c77fSYLFuB6yOZ559wgAAEIQAACEIAABHKXAMJLMOc+6cLLvQ89Izfc8bCsWlUWO+Oue+0sl597srRulV1lRnaCOF6CeWEzKpFjjllfLrqwjTRrGpb5Xy6X8y78VmbPId+DawMCEIAABCAAAQhAAALZSgDhJZgzm1ThZfLjz8vVN/5HNm3fVvbZYwdZv3Urmfnmh/LGux/Ldlv9RR6ccJmEQqFgkqjnqBBe6gmOzVJKYNDAjWTkyMSMl+49P5RZM0tTelx2DgEIQAACEIAABCAAAQikjwDCS/rYr+3ISRVeuvW9wB1rxqRrE4551+Tpcv2EB/X7UdK+3QbBJFHPUSG81BMcm6WUwDcL9pCSksSORrNmLZLuPWan9LjsHAIQgAAEIAABCEAAAhBIHwGEl/SxbzThZacDT5N+R/1dzhlwbMIxF3z3s5goM37k2bL37jsEk0Q9R4XwUk9wbJZSAotL96q2/wULVkjHTm+n9LjsHAIQgAAEIAABCEAAAhBIHwGEl/SxbzTh5ahTL5WfF/4uLz96s4TDVSVF1946We7R7JfnHrhOw3bXDQSJ0sVLpay8XNZrXdKg8SC8NAgfG6eIwPRpnaRLl1YJe590/08ycOBnKToiu4UABCAAAQhAAAIQgAAE0k0A4SXdM7Dm4ye11Gjmm7PljAuvk8LCAvnb9lvL+uuWyKtvz5GFv5W6zJdxI85OO4UlS5fLqeeNltlz57uxtNtwPbl37DDZcP3WaxybuXjig4L9lR66/XKXW4PwkvYpZQBrINC+Q5FMnrSddOrUzP3Wyox695krpaXl8IIABCAAAQhAAAIQgAAEspQAwkswJzapwoudogkto8dNkfkLvpeKikrXyei4Q/eX04/v6QSZdC9jbntAHnrqJXnsrqu020ux9Bp4hQsDrkkUmr/gBz2Pitiw3//oc7l8zN3y7JQxTrRBeEn3jHL8tRHwhZfZs5cCCgIQgAAEIAABCEAAAhDIcgIIL8Gc4KQKL5blsslG68c6F0UikcB1Mdr/mLOl2/67yfkDe7kZeWTaK3Lp6Iky58V/12msVk61wfrrxIQahJdgXtiMCgIQgAAEIAABCEAAAhCAQK4RQHgJ5ownVXjpNfBK+e6HX+T4ow6UYw/dV9YpaRG4s96+6yly+bknyZHd93Zje3f2POl31jUy64mxtY73lTc+kIFDbkjozoTwErgpZkAQgAAEIAABCEAAAhCAAARykgDCSzCnPanCi5UZ3XbvkypmeAGe++25k5x47EGyy47bBOLszYHTcb+TZfQlA6V7193cmObO+1qOPu0y1wK7fbs2NY7Ttv37cefKXzW7ZtTFA2LrIbwEYmoZBAQgAAEIQAACEIAABCAAgZwngPASzEsgqcKLf4o/L1wkDz75otz/+HNi3YOsc9BJxx4s/Y45UPLz8tJKwhwvV5x3shzRzWu3W1fHy+NPz5KLRt4pzz90fUIQL8JLWqeTg0MAAhCAAAQgAAEIQAACEIBAlADCSzAvhZQIL/6pVlZG5MY7HpK7Jk93X818fKwL203nYhkv3bvuLuedcZwbxsNTX5bLxvx7rRkvZeUVss+Rg+WgfXeVy845MWH4CC/pnE2ODQEIQAACEAgWgU7b5cnsj6tC+YM1OkYDAQhAAALZTgDhJZgznBLh5adffpcHn3pRJj/+vHO8NC1uIr0O6yqD+x8pBQX5aSUxevwUJ7Y8PvFqadq0SHqdkdjV6KY7H5FnXnpLpt83KjbOux98Wq6f8KC89MhN1YQjhJe0TicHhwAEIAABCASCwJB/FcvQfxa7sSwqrZSeff5AgAnEzDAICEAAArlFAOElmPOdVOFl9YyXnTpu6TJe9u+8s+TlhQNBYPGSZdL/nGvl48++cuNp26a1/GfsRdJ2g3Xdz0OH3yHTnn9dPnx+ovt5+YpVsuehZ0qfI7rGOiHFnwjCSyCmlUFAAAIQgAAE0kagx4EFcv+EREeviS/b712qD6AiceOyz6G0jZMDQwACEIBA9hNAeAnmHCdVeLGuRl989Z12NNpPjj/y7zExI4in/tuiP2RVWVlCXkt9xonwUh9qbAMBCEAAAhDIHgLjxjSTvkc1qXZCPXovlllvlGfPiXImEIAABCAQeAIIL8GcoqQKL5/N/1Y277BRYNwtjYEc4aUxKHMMCEAAAhD4f/bOBLCpalvDfzrSUkiZy5QKAooSVK4DQ3BWlIAiKEIrXhRRW64gji0iDiCtioIgrSg4oC3iiEJQFJwIiMNDaFBUFKTMc1s6t2ne2idN2zStTZqT5CRZ+73eYnvO3mt/+xSaP2v9iwkol0DdMqO6Uer0+VxupNxj48iYABNgAgFJgIUXZR6rrMKLMrfo2ahYePEsX56dCTABJsAEAN2gCORT6YrpN86eUOLzoOkWAqNBDXXr2jIi4w8V0I87rcRwOSYmwASYABMIYAIsvCjzcFl4cfNcWHhxEyDfzgSYABNgAo0S0J4ThjUr2yFWHSpds/H7MiROPlnPN4QBKoGAEF9E5ks8fd5I5UWZr5fyOSnhYDgGJsAEmECQEWDhRZkHzsKLm+fCwoubAPl2nxFQq8OR9tx5uO22M5CXV46sd/Yi9dHtPouHF2YCTMCRgGlzR2i62XcDzFhWhNSn8hkXE2ACTIAJMAEmwAQcCLDwosyHgoUXN8+FhRc3ATbzdk18NBISukl3Z2fvR+7eYmjiWyDp3i6I17TAGsNx+vrRZs4eHLdlvnohEhPPsNts2tzfkPbMb8EBgHfJBBROQJSt5O7o7BClcUsZ9GNPKDx6Do8JMAEmwASYABPwBQEWXnxBvek1WXhpmtG/XsHCi5sAm3G7bmg7ElX+g9jYCOluka1x//QcLHihB30tvGbGd7KOIDn5j2asEBy3FBTd7LDRnO2noBu8ITgA8C6ZgB8QyM/t4hClYV0pEqjciAcTYAJMgAkwASbABOoTYOEFqKqywGKxuNT0p6CwGHn5hejWuQNCQmo92+R6wtwWXkT76PzTRU7Fc945vVzavFOT+vgiFl68fwCbNl0Kbf/WdgsfP5aP9u0cf0D69f+RsmFKvR+kH6zYkPBiysnDkEHr/SB6DpEJBAeBlOmtkEoftpFfUEXZLsfZZDc4jp93yQSYABNgAkzAZQLBLrwIweW+xxZK3F6eO61Jfp999QNmL1hOvmxWTWPlkifQ76weTd7n6gVuCy/jkp6Gaedup9bduGoR2sbW/gLp1E0Kv4iFF+8fUMHpEY6LWooBi2O3j+EjcmDcmOf9IP1gxfTnzkfylF52kaY8sg0Zi//yg+g5RCYQPAT0wyKhHxaF3H1mZH9QLH3mwQSYABNgAkyACTCBhggEs/DyoeE7zHnpbZSXV+CKwRc0KbwYNmzBI7Nfwc0jLkPi6GskrSI6KpI+Wsj+cLktvPy992CNOtRUdOedcyZnvDQFib/fJIEVKy6EfkSc3XXbtx3Def0j7b6Wn1+J7prNTc4XzBekPnYOEm+LR35eBZnr/sOiSzA/DLx3JsAEmAATYAJMgAkwAb8nEMzCS1FxKU7ln8YT895AVGTkvwovIjPm8jH3o0/P7nht3kMeP3e3hRePR6jwBTjjxfsHJMqM1q4dBNGVR4z8/AoMH/49kpPikJhgFWSE6DI+8TfOdvH+8fCKCiCgGxwF/XUtpVa22SsLKEOiQgFRcQhMgAkwASbABJgAE2ACniYQzMKLje39s15GZaX5X4WXYyfyJOFF27cnKioqUVJahiEX9cP0u8dKWS9yD9mFlyPHTmHbr3+hqLjEIdaR1wxGeLh9W0y5N+Tt+Vh48TZx63pCdNENbSv92bjxpCS+iCE6G2moq5Enyos08TGUQdMSmzYe8c2meVUm4ASBxFtbI/OlTjVX5uWboR+9H6Zfy52427uXaM8Gcg+QUHrau+t6fjULLSG/KZvn4+YVmAATYAJMgAkwAX8n4AvhZfuU4T7Bdt7itQ2u64zwIjSLxClzcKVuAK4ccoFkrDv/tfdx2aDzsWjOVNn3I6vw8uMvv+OO6emNBskeL7KfH0/oJQIr3rsC+pHdpdX27i1EwtivYco55aXVeRkm4DyBfX/0JGEy1O4Gw2eFGH/HIecn8fCVQnDJelmF+K7WhRa/ZcGMxv/p8HA0PD0TYAJMgAkwASbABAKHgC+El3lDr/AJwIc2fu228PLNhwvQoV2sNM+b732OF5e8h+3rl0GlkvdNNFmFl4Tk2Th87CTSZtyNO6c/KzkCd+nUHv+bsQBVVEP1buYsnxyIJxfljBdP0lXG3Kkzz0PqY+fZBSPEF+3ZHykjQI6CCdQhUHC4twMP4+ZiDB9NqSUKGSZqnKXpav+PWWqaBRnLFRIgh8EEmAATYAJMgAkwAT8l4AvhZfcdd/uEVs83Xm228CK8YHQ33oc35qfg4gvoXUEar2WtwYLXPoDpqzdkbyktq/Ay+IYpuP3mYbgrUY/zrpqErMUzcf65vbDl/37DpAefw/qVL6Bzp3Y+ORRPLcrCi6fIKmfetV9cS2VN9ma+Irp+JLzkkgDDgwkoicCOn86AprvV/8g2ssjnJWnaEaipC3t+gW+jFdkuxo8d30Ew/mSB/nbfxsarMwEmwASYQHASSH+qLZLvon8kaWzcXIqESUfp38uq4ITBu/Z7AsEsvJjNVTCbzXjgyQxUmiuxcPZUhIWFSSKKaBd906SZSPrvjbhlxOXSOY+5axaEye7yhTNw5Pgp3PPwPMR1bId3Xn5M9udAVuHlEn0SJt56HZJuvxHiz1MnjZbaMu3asx+j7piJV59/SDKsCaTBwksgnWbDe6lbZlT3itZR/PZ84J++/+1Qe24E1n7UrabcyLSjDA+lHMBzj4eh/znWEqS0hRVIX+Qbw111K/J1+dFReMlaZUFyqv/x5oi9R0DdWoWkSa2gGxRBvl5VSJ9/GqbffPMce2/XvBITYAKeJpD6YCxSH7CWGdjGms+KkHDXMU8vzfMzAY8QCGbhxZaxUhfsw8njMHHsdThxqgCX3jSVNIoxuGfCSOmSPbmHpAQR4VMrxplndCXN4kHEdbB6ico5ZBVebpj4GLrGtUdm+nRMfXwhfti6EzOnTcCa9d/D+KMJm1cvhrpVSznj9/lcLLz4/Ag8HoC2fxts+sH6w2kbac9sR9qc7R5fmxdgAs0hoG4dAm0/qxu7cXMJTN+Q6XTXELupklPKkPWhuTnTu31PWqoKyfWyW3Q3WWD63e2peYIAJmB4vz10A2u7DAjj6KHXH6OuXb55jgMYNW+NCQQVgU1fdIF406L+aN31n6DiwJsNHALBLLw09xQPHTlBmTGhNV4vzZ3n3+6TVXh5b/U32LV7Hx4jsUUEP3xCCsrLre9GTU4cgfsn3+yJPfh0ThZefIrfa4sL8SVxQi/EU2ejNatzkfX2315bmxdiAu4Q0PZVwfhplMMUhi/NSEguc2dqt+4Vwov+ajKrJuuZzLfAootbNAP/Zu054TCu6+iw0bT5BUh/MeDaYgX+gSp0h5ruYUh5oC3i6fPefZVIfeI4l5so9KzkDGvtB50ok87x30kWXuSkzHN5kwALL96k7fxasgov9ZetoN7Zpp27cWZ8F/IWCKxMF9teWXhx/mHjK5kAE/A+AU03FUxfO/5CmfVRJZIfVV6Lae8T4hX9gYBuUCQM77V3CDXr/SIkP5DnD1vgGBVOQGQKmn7QILZOV7i9+yqgu2a/jOILt5pX4mOgHxaFFa93sgstY2kBUp44qcRwOSYm0CQBFl6aROSTC9wWXirJvKa0tBwto1vI3nLJJ0RcXJSFFxeB8eV+SUCtDkPC+Dho+7eiNtqnkb3iMHksVPrlXoIx6OyMSOivsW8xrbuBSpB2WqDpXIm0aSeoDLQK+adDkPpSO+QeCgtGTLxnBRMQ/i6m7+PoTRz7krmEu07AsK5UwZFzaP5CIHmyGulPOYp74+88BMPnxf6yDY6zmQSE+DJlcmv6OyYUa9YVI+0FFnSbiZJvUwABFl4UcAgNhOC28GLYsAWPzH4Fq96Yg6dfXI6tpj8b3anxk0VoI5wVA2iw8BJAh8lbaZTAJuNF0Gpjar6fQ+KLbujPTMxPCIhuRkkTwzH0khASzICMtypg/KEK6hgzNi4/iHgSX2wj77QK/Ud3R36hvVDjJ1vlMAOYgCg3yl7WFppuVmGQy4wC+LB9sLXUB9sg9UFHM8Wk6UeRtZLL2XxwJLwkE2ACzSTAwkszwXn4NreFl9//ysXKT77ClDtuwpatv+Gf3MONhjwpQY+oFo7mVR7eo0enZ+HFo3h5cgUQ0OvbY0W21iGSpOSdyMpq/OddAaFzCE0Q0F9ahOxnjzpclbqgLTJWqpkfE1AkAU33UDbUVeTJ+HdQwt9lxw/xdpsQ7YSHXLOPnjfO8PTv0/Wv6DXdQqC/KpQ80CzY9GMllbr5V/wcre8JsPDi+zNoKAK3hZe6k35l3IqTeadx84jLlLlbD0TFwosHoPKUiiKQnNQN6em9HWJKS9+DtLR/7L6uG9oGOl0b5O4tRnY2izKKOsgGgknUn0bGzOOOZ7ssFulL2yg9fI6PCTABJiArgcRbW0nlRqKkTYgu995/JKDLjMQ+E25pCTX52hi3lFIXPC7bk/WBasZkoiw4I70FeQ1Zyyr37q/C0BuKWHxpBstgvoWFF2WevqzCy4T75qKouAQfLZutzN16ICoWXjwAladUFAFRYiRKjeqPIbqfYDIV1nw5Pb0PkpO71/y3KEfS67eyF4yiTtM+GOHvYnzrgOTvUndoqdSIfV4UfHAcGhNgAh4lILJfAj3LRYgua97viP79alu0Zyw9TZ2c2FDWow9XE5Pnbo0h4U9ld1XWRxVIfoRFMV+ei7+tzcKLMk9MVuFl7sJ38Mm6TfjBkKnM3XogKhZePACVp1QcgfpZLykpu5CRub8mTkmc2XSJQ9wpKX8iI2Of4vbDAdUS0A0oQebMY2Sya5bMdZPmtIfhu8DsQsfnzgSYABNgAlYCyXe1QtpTjp422oEHAl50UuozIEqMTN84/vtr/KES+sQSpYbNcSmQAAsvCjwUCklW4eXIsVO4ZtyDeG7mvbjuiouVuWOZo2LhRWagPJ1iCYjORtr+McjNLaVSIvt3XkSJ0VrDAIfYhegixBceTIAJMAEmwASYgHIIZCxoh8Rbak3zbZHpbznCJUc+PCbOePEh/ABamoUXZR6mrMLLHdPT8eMvvze6U+5qpMyHgKNiAu4S0MRHYYdpsMM0nPHiLlm+nwkwASbABJiA/AQ440V+pnLMmDgmHBnPtqiZKr/AAt0NxcglrxceTMBZAiy8OEvKu9fJKryIMqM9uYca3cE9E27grkbePV9ejQk0TUD4t8nw73liYmdkZp5Ts57ReArDh29ten2+wq8JaDQRSLg1VtqDcXMRjJuKZN2P8J8ZMqCCWl6bqQQqErkHuc21rIB5MibABIKWgOGDTtANqn2Rnzz9OLLek/fv8KCF68bGRclRwugw7KOuRmu+rGBjXTdYBuutLLwo8+RlFV6UuUXPRsWlRp7ly7P7FwHh9TKUyo5yyHTXuPGUfwXP0bpMQNuvBQwfn4HY2LCae5Om7kfWijyX52roBiG6bMw6hfgutcpg8lOtkLW69oWCLAvxJEyACTCBICWgG9wC8d1CsfH7MvZ2CdJngLcdeARYeFHmmcouvBw/mY8167+n9mdHMPKaQRig7YOVn3yFjh3a4IrBFyiTghtRsfDiBjy+lQkwAb8msGK5BvrrW9vtIS+vEprejZecurLhtAcLkTze3lAwrwCIv7KDK9PwtUwgaAmkTItE6lRr15qc38zUGaUEpp0ypDgGLVHeOBNgAkxA+QRYeFHmGckqvOQeOIoRt6fAbLb+oz5j6m1IHH01Zj67DIYNW/DTZ68gLDSw0sRZeFHmg81RMQFfE1CrQ5GU1BVDdWqsWXMC2SuOUGtts6/DknX9TV/3gsh6qT9ad9hR8yXRsro7fRQUUreGPyNcWt+w5BR0/6l0uEd7Q1unS470ujwk3Xwc6paV2LgtBs++FYf8wtoMHZcC4ouDjoBo66pWh1AmgP/97EpeEc9F2Z2ZEF+GjuRSkqB7kHnDTIAJBBUBFl6UedyyCi/PvPQ2Nhi34vUXH8UDTy7GGP1lkvCy1fQnJtw3F6uXp6GnprMySTQzKhZemgmOb2MCAUxAiC6G1Vr0py5QtpGTUwjdpdsCateZi7oicVwbuz3l7itHvwHWTlaJI4qQ8fjJmu+/s6Ylpsx2bF/aGJSMJwtojjK7b+efVkFzRXunOArRJXvOP3bXvvN5W0xJ1zh1vz9elKgvJKFLtAZXIdvQkkSmwHqzw5tnkv1aLPTDrMJizq8VSLw7z68EmOxXoqG/xlFk1I0o5KwXbz5IvBYTYAJMwMsEWHjxMnAnl5NVeLno+ntwV8II3DNhJEZPerxGeDlxqgCX3jQVb72UigvPO8vJ0PzjMhZe/OOcOEom4E0CiQkdkZnRx2HJ8Ym/wWCoFSK8GZMn1hKZAGtX9azJehEZPcNH7YFpRylEpotplaPZejIJL1kkwDgzhMeLMfsUzdU8jxfDgl3Qne/47r768vOdWd7vrkm7/ySSx52uiXvvoTAMnSAyfFh8cfUwU6bHIJU+6o6N35djxK3+8/Ob8XwLevPLMctMe1khd0hp4IHQ9lVh/KgQxHdVUZZ2FbI/9q+SLCESJk1qBQ35taxZV4Jn5xeQKavF1Uefr2cCTCAACLDwosxDlFV4uXbcQ/hP/7OQNmOynfDy3ZbtSEqZjw3vv4i4Ds6/26lMZPZRsfDiD6fEMTIB7xJITdFAfNQfKam7kZF5sMFgtGcW4d7RhxEfVwaDsQ0yP/af7EDdEKuQYtpRQuVU1hcruv+UwpBxzGGvGe+2Qup8axckZ4YQX0ZcXg51TBXWfOtaV6PGhBfNiH7NKjfSXVAE3XlFlE0Sgux1bRQlaEhC18cHHJCmLVUjfanzvJ05k2C4xvBeG+gGWr1R6g615rDfbF83kDLvsuxFTsOXlUi4t9hv9uCNQLVnA3NTQ3DpJSqA/h8WEipVKmS8ZUbqXMdSR2/E5OoaukGRMLxn730lzHJHjHX8O9jVufl6JsAE/I8ACy/KPDNZhZd5r6zE8vfX4ckHJ+LN99bhZv2l6Ns7HtNmLUJMdBS+eHeeMim4EZUvhBeZuv+6sWu+lQkwgX8joNG0wI6cCx0u6df/Z+Tmljp8XYguxiUmu6+vIfEl8UllZQiqWwEJN4WgH70zvGOnRXpHOL82wcIufm2fchjfPuKw19T5amS8a2/I66mnKfnmo0j7n73QZdzWEvr7e7u8ZModR5A6sfZFzN5D4Rh6Vy/yjqmQ5sovCkfS2Hzpz6ZdETBstM+WcHlBF2/QDRBClyNvw7dRSHi0o4uz8eWBILyIUxTiS625bhXSF5Zya9o6j7f4Oy1nQwhiycvHflCJFn1Jc2FZo3/HKemnJONF+vfiFsdMQu3gw9ypqIGDSpnWArpLwqkRSBVeebMUJvI+4sEEAokACy/KPE1ZhZeKSjPufvh5/PiLfUeLmJZReHNBiiTCBNrwhfASaAx5P0wgEAmIcqP0tJ5kzBlGWSCVENkuWdlHG9xqxiN/IfHa4w7f0952AXIPO77r7itexo/DINLxbSOHugnPTZoAACAASURBVKMMvanxX1gzZp1Aor723XXTn+HQJ3WkTBEhH3tnpEw8hOSbj0kZMwajGknp3V3OdlHHmJFr2GkfsKUKe3eXIr6DdX+VqkiExZLfjcpa1pP2elukL2vnnU3SKg/cUYKZdx1Fff96kV2UsVLttTgCZSHdIBLPVtpn6GYsK0bqU9RWi0fAEEi8iTJb0hr6+0hkv4RCN6qc/HCUX66TvbQd+RHZGymLQ9INO0KiglUc5mElkL0khryPwmtw5FGW5ohE8j1i8YUfkQAiwMKLMg9TVuHFtsX/y/mT2hb+Te8SFKHXGV1xpe4CREc5dr5QJhLXomLhxTVefDUTCDYCOupqZDRaMyEaG9lP/QH9kFMO39bdo4Xpb+f8UDzNNZEyXTLSHL1CEqZUkh9C4y9MhMFuv94V2HcohLxdYrwqusjFRJQYGRbssZ+uhFJ9KuyNfxFB/87F1IotmmE9vVKOlD2/BPorqCTCTPFU1HracLaLe0+AEF+S74wm8ZSMit8vRdb79q3N3Zvd9bu1vUvRmgTEfYfDkUsZVzzcJ/BvwkvuQRW0V/qHaCH8XbKX2puOm8gQWnedYxac+9T8dwZNN+qu952jEL0sqwwPPM4leP57shx5fQIsvCjzmZBVeMlc/gm6d+mIEVcPstvtrj37Mf/V9zFvVlLACTAsvCjzweaomIA/EUgcdhQZD++2Czn3SAS0iQMUs42U/4Ug9X+OwovwQMhYrvx3hN0BKTJeTCv/kLJmakYhmaxWNWC+2bZrzSX6+7rCuDXaUVDTnkLukSjkHnX/DQlNlyqY1tYxELZQBhJl46z9Ngzjp3unpMsdtnyvcwTSph5F8thacTZ5bhyy1nImk3P0Gr9KQz+uxo9DINqG1w4LDh4Jwdh76efeD7JdbHGnPNAayZNiaC8hMG4pRdIDoguXf3jUuHuOzt6vGxgGQzbVl9Ub+w+rcO5g/zHOdna//nGd+HfUe1mw/sHE/ShZeHGfoSdmkFV4GZf0NPr3PRMzpibaxXro6ElcPfYBrFzyBPqd1cMT+/DZnCy8+Aw9L8wEAopAWvI/SCZzXTGE6JIw6yzFZLuImIQBpXGV47vsulEVMNlXlzZ5LvrLSjD8MuGdoMIKQzRECZLSR+L1p5CRUmteW3QsDy0j672oIUNOtOkibUUY8GquO9NuWzoSXLJSTYglIUeMdzbEYcpLfd3auvYsM4wrHd+pNXwdhoTpjqUHbi3GN/uEgO6CYhgW7XNYW3tLT858keFEdBerkJ5K2S1URvnHXxa88T6Q+ZZ/dTSSAUNQTCFEqdztVBJK4nTdsSs3DBdezkbEQfEQBMkmWXhR5kF7XHipNJux8pOvMHdhFnc1UuYzwFExASagEALqmEpoOpUpSnCpiyb5dhV1rSPTyerRnGyX5PGFSHvA3pFXf29bGP9POV42jT0OmrhyDKWyo72HI6COLEb2Y7/aXxpNGQgtqJyKRJfUhR0oI8E+4yR3xXdkxmvviZP80tnI2uBeByvT2kJohNdMeK3QkjyrBbI+dRS0tN1OonUUlX6dbIncE7UGwOroCjw6ZhcSL9uP/OJw/GyKQoeIfGjaFsL4VxxmrLoQ+SWunZE2Ph/3XvcPYmluw/91QvZ33SVeur7HkTRst2RMbNzZHukfKctEWiF/HdSEkXLncaTeecIhLM56UdpJcTz+QODgn93QMpzKBm3iS1gk1q63YPydXJblD+fHMTpHgIUX5zh5+ypZhJcLrp2M8vJ/r4MdoO2DtxfN8Pb+PL4eZ7w0jlitDseQoZ2kCzZtPEIGo/5RK+3xh4YXYAJ+TEDTVQWRnp9LyR+5B1wrMRKtoXd/cQRhtdqNRMK4lUx377H3J/AHRPqBx5B4lfWX9awNnZBfbvXjEf4b9T04RLaL4ZltDtsybGmPhLnaZm9XiBtv3LcNvTtXi1ntuiJjwzlIfd5RJEkZYUKqfkfNWmmGfkhfY1077fbfkHz9P9bvlZfDctLel2gjiS8jFw9rNE5Nu0JJyOmuLkA3NcUSEY7Pn9hivb7a8Tftw94w/tYWhsc2282z5uc4ZK/vhLmjfkI8CT1irRmrLoLpoPfMiZt9AF64MXnsSaRNdXw3Xn9fdxh/cSxl80JIvISPCIi/cev3X/JRKH67bOKtMcicT623RVkmGSjnF1Rh+JhDMP1a7rd74sCZQH0CLLwo85mQRXgRLaSLSkqR/dF6dGgXi2suq22jGhFOL74v6oeze2mUScDNqFh4aRigtn8bGD6/ErGx1l/+8/LKoL/uK5hyHA1E3TwCvp0JMAE/IZD24Gkkjyt0iDaXjHe1N1hF2kAdmo7UsnTp9w7by/i0G1KXut7e2jaR6aX10LS3b1GevOQ8CDFDZK7Yhsh0MT62zmF93TPDYNrfFvkr1tZ8z3KKRJcyxxch/WePQe5J+zbZaWN+RvIVf0j3Ht1fgRbleSgvqkSV2QILvbApLaxEeYUZ5WVmHMmrwOd/A4UkxFhQhdZ0zcSh0ejQqx3adagjIFgsOJAXjXNn3+oQr/68/UgZ8Ss07YrwxY7OeP/neBSVhWPTH/RCKkCH8BgSpUba3rVmzsZfoqC/LzB/rwrQY+RtKYiAbnALDB1k9djKeo9EY/bCUdDpcChyEGDhRQ6K8s8hi/BiC+vn7X+gdauW6NOzm/yRKnRGFl4aPpgVKy+FfqT9c7DxuyMkvmxQ6ElyWEyACXiagGn1MWg6ky9KvUSZrDVRSH4q1tPLe3x+na41NN0jYNx0Grm59Toe0eppd+1C8g37a+LIL6J2tdMudtlkV5TozL17N67qfxydIxoQs4XXTHgYChGL+5edh/c/b0FihX22iy2IgtDWuPeNS5A99ceauJwVXlKG5yB1uEm6z1xageLD+SjJswo2VZVmFJ4uA31CYVE5DuZV4QsysKywhEEl4hODBJZwSyVGaixQR4egZ582aNkmCiFhoXQNfT9UBeNeDX7Y1xnqqHJc3u8ozu1eR7gT5sa0QBU9T/tOtcQ9bw3G93viPH7ODS2g7UntaHfbi1JyBiLEl8Th+egeV0ldjcKQ8Z59q2s51/L0XPprQpE0MUJaxviDGekLOdPA08wDcX5hlKu7xJo+mf1hOXL3sy9PIJ4z76l5BFh4aR43T98lq/Aigj1y7BS2/foXiood2y6OvGYwwumXwUAaLLw0fJoFxQkNfqN1dHYgHT/vhQkwARcIGLNPQNtHCC+1vyCLF+Y9r6EyHfJF8dehVofC8Glf9O9f2/o7acrfyMo+7rClxKsOQX/JceyljkaZn3Z3WXQRExrSt0OnpawUwTHfMYMIIcSSxAtpqOOQkN4POHEQ2fcaHRHTmyXUrgn/tyMK/zmjWsQpLYUlz96Hx3SgDYbOu8HufsO0L6HrfVT6WtmpYhQfLURFidVwuJTElhL6s9DYDhwrx24SXv4vnzJwbKKLbSYSXwa0qsC5nULQIjIEvc9pj9DIUIRGWH9XUJH4Et6SXqRX36dqEU5fo71V0tyVVagqqzU4tpAC8+n27ujXPQ9x7Sqx/VBHZH59Fgw5Vm+Z5gzhcaPrdRh55G+z6a+ODj43QnBZ81ytYfLij7tgxpKezVkqKO4Rokt2pr3pc8abFUid4yhUBgUQ3mSzCCTeHI6M52qz5PLyqzAisQim31h8aRZQvingCPhCeNl4S8Ov/TwNd+j7/vPaUlbh5cdffscd09Mb5btx1SK0jXVs4+bpA/Hk/Cy8NEx37bqroKv2d7FdIcqMhgz8zJPHIf/c4l1X12ws5I+BZ2QCAUJAfzmVpM6zeYdY3QrSXm2J9Fc9lyngDXTJSZ2QPvcMu6Xy8sgoucf/yb68tudpGBf9UjtvEb3JUVHPP0u8wVFH4DAWXgH9HRH4cZYBZ3UuqL1XiDNqMgAOC8G+gtZY/VVrJF4qzHXD8MeuEFzU+SBlmpAB7l+dkLxC51Bm5CC8HCtCRbE1lpJCEl5KraLIPhJeth214O/ShrtX9WxRiSHkG6Siv2z7XdBJ8rAIb1XrURMeTWJLeLWQREJMSCTNI0SXUvKiqX6dZTFXwVxh/6IrLCYSoSTU6NKuk8qpGhsim+bRG37DlGv+Ql5RGLI2n4EZK88nUekwsiZ9I5kDi7H3REsMfU5fI76IzKOct36u6VJlmz/hqbNh+N53nkXiSBNGhUGIHDk7q/DsyxXkY9G8R1HTuQL33nwC8XFkkGxshezP3MtMM2S1qMlSqBuRulcDAmLzQvbbu0RL60enRqH/uWHI+bUSzy4soXPjX0AaOtDcba3rtQAnQfrLCiTc49jhzW8fCA6cCbhBwBfCy1P9R7oRcfNvfSJndfNv9vKdsgovCcmzcfgYmcDNuBt3Tn9Wah/dpVN7/G/GAkoFtuDdzFle3p7nl2PhpWHGuks7YcXKoVCrrenE+fnlGH/rRhip3IgHEwgmAvrrWyH57rb0S2IINm4uRvq8Y/TzELzvygnxJXm89ZfjbCoxylrt/y2P167pC90Q+w5GYn/DR+6E0djMV7yN/JA4CC/iOvJjOUKJJ9HhZrRqRS/U6maVhITBWDBUEl40HUpgeoF8XkikQChlxbSwehwghKSOFpFQj7jcpR9NXe8jMExbb72Hyn5O556sKTUqLSHhpZiyUiic2owXIQjZZzZZ6IJ+JGAM6BwiJen07d9Rmi6irvDSkoQXWwaPCDeK/l0RQkuxyJKwli2Zy2mt+j9WKgsi27VCxldnIfXD/zS6t4w7f0Li4L1230+jDKbkwSYqgbIXtbJ+6InkrCHStbr++TBQtkv9kfVlRyS/0McllnUvFu3WE/TVPyPUbt3wrWs/I4Z3IqC7uFqooonz8i3of1Wpy+KLEF02LtuNWDLFtg1R4pS6qPl+TI0JL5oBhS7H12zACrxRiC4bDWrEd6s9NyG+DB1hb3KtwNC9HpIQFnO3UQe5esP4QyX044u8Hg8vyASUSMAXwsvP1/mmic6Fn89V4hE0GJOswsvgG6bg9puH4a5EPc67ahKyFs/E+ef2wpb/+w2THnwO61e+gM6dAqtLAQsvjT/rmvgYJN7WQ7og6509yN3L72j5zd8MHKgsBHRDorH24zPs5lqztgAJE2t9PmRZiCfxKYH0ufFITnL0Ful+xs8kstm3j5Yj0F+W/oSene3LeRNmn4PYFmXImFLbtUhaq/0ZSF7QC1mfWF/QmTI3Ssa0doNEjZLKcMSNvdzl8IT4knz57yRQUFbL/vb4b9+fUHa8gLJPKlFcWEGmulUooXKg3BOV+PyAClWqOtk4JLqEksnu9Z2r0KalCp07R6N9XIy1vCjaKtoLnSacMldqSo3CVFBF0PeEt0tZBTUmsWYEmGkNIfLUH5HtY5oUXvKXfuBwX0l5CKIqHV/EGancSL/Q2t1JlBkZMxw7VaW90x3p78S7zFLcIESX7OdP2t2b+mJrZLzrXLaw7pIQGN527GiVtqgC6Ytqy7KcCS7tPjpb6qhUf2jH9nLo2uXMfOKalKkRSKWPukN6wZxobxDt7HyBcl3izZHIeN4x8y/hngIYvuCOkPXP2bQxhrrr1YpU4vtZ5POS/LCjzUGgPCO8DybgCgEWXlyh5b1rZRVeLtEnYeKt1yHp9hsh/jx10mgkjr4Gu/bsx6g7ZuLV5x+SOhwF0mDhJZBOk/fCBOQlsOKt7hAZL/VH996/B3XWi7yUfT+b8HjZtLEfGetWZ5CIF5gz/kFGpvwZfpq4cmx6bSdah5MoYK5+IR0RhYSn+lApiBpjLz2C9Lv/ATUYRElkHBZ83BXpGbXeaiKD4583v0YICR5SsojwgwkNQ0FxKLonXOY2TNNH/0ATVys25X21BSd2HkRZYSFOnCrHh9TVqExlLTmKqKrAcLJf6RAbhvYdoxHbPloSXcKiKMOFsnZqBBiRkSMGfU3KdhExi1FaRpkutBZ5u1SR0FNVT+MKCQ9BuDoa+gVXwbir8SyN3IWfOGS2SPM34FUn/GISll5ew8nwXA5lvtRmNeUXhuL6h7WI71JJJUiVMG5vhdzDjkJIY6CN7xwlHyT7F9p7D4ai/yjnTIPlFF6y5+6DfqjjGyb6qRpqY13rZ+TqQ5M2MxLJE63PgBBdEpJcz8apu6buYvp5S7ZAe7YFxh9VSH1WJbW796eRcn8UUqc5tgZPfboQGW+w/039s9SeQwJjdkxNuZHpNzP0CcGdNeVPzzvH6nkCLLx4nnFzVpBVeLlh4mPoGtcemenTMfXxhfhh607MnDYBa9Z/T/8YmrB59WKohZFfAA0WXgLoMHkrTEBmAmtXxUM32PHvvH4X7qKuN/wupsy4fTqdEF8SE9pTeWUYNhrJh0XmEiPb5nTnF8KwYLfDXtPe7Ij0N517cZ4x9TckXnnYbo6sr+KQvPActxmKlsfZzx6qEV8M37VEQkptXLrz6R38F363W6eyoAihFaWoOk2mvlQ2Fdq6JVTRkVj2WRdMujoXlmoPmzJLOFVHkQgjSqlEuRSZAIMEl8pKC15c3RvXxv+Fvp2sBsEqEmtOWGLx4AcDmzTX/SFtA87uUK87lBB7hLBFJVO2Idpz654b4eB1kzzqAPSDT2DvkRZYvi4OK+fWlufkkWn0iAf6wPSX44vqhmDnbiBfHVEuVm+oLyYTHCeGpht1glpFZWNUulJ3JCSXwbDetRLH5FtOIG2q1Ty5hkFhCDTXn+VEJE1fIkpGmus9Y5tdEls+sl8rZ6cFQ8fIaNYtpnINXdObr3eFplsITBvbONynHXqKu/U0QlM8P6KrUT75gBu3uJbN5fIB8Q1MwM8IsPCizAOTVXh5b/U32LV7Hx4jseXQkRMYPiEF5eXWFxeTE0fg/sk3K5OCG1Gx8OIGPL6VCQQ4gcRxschc2MVul6YdpRhypeML5wBHwduTiYAcwoswhc1ONUHXL0+KyvBDeyQt7Iv8oobNb5sTuhBg8ulFeu4hxzmTRx9C2pR90rQiQ2TnbxYMPMte+Mgnk1vNhCuQeMUB9Is/LRn+rv4xDptfrmNYLExdSIAx7mwHfer50nyDzzgoff71cDvklzadaaLtQSLZi9RK+zS9ehMCj4UECyG6VGfV5BWEIvOzeMoICkfWj2c6dDWqzyZ79t/Q6+x9OXJ2RWHo3X2dwpjxxEkk6u3LJQzftkDCw86XaSeODiWvvfAa8cXZMiNtrxLozi9Gzl8tsGmbVTCum/UizjNhRje3sl2cguDCRWkpFiTf7niDfiIJULUd0l2Y0XeXinKjtMejJT+w/IIqpM4uRtYHnO3iuxPhlZmA/xJg4UWZZyer8FJ/ixVUg23auRtnxnehf0gCK9PFtlcWXpT5YHNU7hFIvrcjmYXGIIdEgsxXjnjEp8K9CP3n7tSHO0B8iGHcXIR7px7kbBf/OT5FRmpY8De9QK71HxEviLXjziYRo7akyJnAhQAjhpyCizPr2q4R2S9G0Z2E4jA8/TO0PaxlLUJ00T/+H5j+cTQsNr2+BZqO9i9GU1/tiQxqzd2coet3CobZWyWDYimDpt5Yb+qAMS/onJ46d/V26s7t6OujvmKAU3OoY6qo89cJ6AZYM22MWyOQ8BCJSHTGzgw1GeH262PGjj9DoT0njH4Hq3IqqyT5luMQni62sZFKiUZMO0P6T2GyK0rc3Ckvcib25lwTSMJLzc/FwHDK4OCMyOY8D3wPE2ACVgIsvCjzSfCo8KLMLcsbFQsv8vLk2XxPYO3qPiS61PqSiLa42gt2sPji+6PhCJiAREBN3iFJNx/HUBJf9h4Ol0qMcg/bG5baUGn7tYB+uLUDiGFtPkTGlVKHrt9JyWvF+GvbRsUgkaGS/fivNeKL4ft2SHim+d5xQvQxLdksmQOjkMQs8ouxDZFlo31oGGXbNMy2IY4NZbzk0hlpx2tdwq4hjxgxcg86L6YlJ5Qi7SFrtkwe2c7MeDEaWZ82nfWj6VwO08pdDvGJ7kUZ7/uuLbYzwIS/i+FN+9Ks3IPk93K1c0KVM2vwNUyACTABfyPAwosyT0xW4aWouBQfrf0O6zf+H/YdtK8LFtv/+PU57PGizOeAowoiAhpNJFIe7Yp4TQTWGE4he8XxGlFFp2uFtZ86tkFNe/Yg0si3gQcT8AcCwiND09UC0+/B/eJLP7w1Viw/w+7Ixt/+Dwkw8ra49sUzIQSY3KMtZMnWSbyS/n67c5dVfKG21AePqvDO193xzMfnurw1ba9iGObTXNVZL6KUKuHxnpTZ41xXIpcXrL5B24eMfN+lcql6QztCTeLNv/8c6C4oguGlfxzuzXi/HbWOds43qLlxy3Gf/ioL0lPpZ74LlRf9REa7adS963d7jxs51uE5mAATYAL+QoCFF2WelKzCy5Pz3sT7a77BWWd2R5dO7RFC3QnqjvQZdyM6qrbrgzKRuBYVZ7y4xouv9i0BIboYvz0XsdRFxDY2kgmofqTV7JKFF9+eD6/uPoG0R81InmB1wtxLnU0Sp4UGrQCz6Zve0PaLsoOaYyIfjyscsxvcJ+/fM4jMF1HqJMqcTHvcE0lERlLisBMSkDWbYl3qatRciin3lCD1HsdsptR5UcjI/vffu4S3i/F1R98pVzNeUm7fV9PhKf1t8oLZbs204sEEmAATYALeJcDCi3d5O7uarMLLBddOxhWDL8CLTyY7u77fX8fCi98fYVBtIDWlK1Ip26X+GHLpDphMxdBQFsymb8+hziyhdpeMv+0vqUyBBxNQMoHEUVXImGPvryHEl/7D5DON9eX+xc/lkMExiKXPxs2F5BVU23GnobgKjvdvMNzW7XM8ug11y0r0O9PqQbPj75aSmMHDswQSbyhDxpPFDoskPNAShm+aLpVKu+8wRBcj2zDtohI18ngRGTvOjLSkfyBMk+sO/UPnsPjiDDy+hgkwASYgMwEWXmQGKtN0sgovw297FOed04vc9CfLFJ7yp2HhRflnxBHWEsjM6IHE8Vaj17pj+Mid1P7WmqaemNAO6c90rxFftucUU1mS9YVr1oqTSH/uEPu98EPldQJCFEy4tY3U8cPwOZmybqo1l7UFk72wEvorHVvx6u8IoxIE/y49EPs3fNJbKhEUQ3gvJfx3D/3cWg1pGxqizEiUG9UdosxIlBt5amg6lWLNc78iPs5qgLv3cCQSnzoLpt0xnlqS5yUCwlTX+G4BGeHWPv+mP0KhG+9oUNwYMFFyJEyb95EnTdZnjq2N/w10/pffO3zbsKkNEp48m8+HCTABJsAEvEyAhRcvA3dyOVmFl+Xvr8P81z7AV++/iDZq91J1nYzf55ex8OLzI+AAXCCg18dixTv2Hi75+fTu9Hnb7cQU8c66VhuN8fRCd0KivblixitHkTJjvwur8qVMwD0CwiDW8HEPuxK5pKn7SQi0b0Fct8yo7oraYWHIPeDfwsuKt3vWmOTa9raXMl60F/zaKFwh1qz9pCc03a1iTe6+cgy/cXeTmTLunFb2E79DP/ik3RSGzW2R8BS/AHeHqzP3CvElKaEMQy+kLlFfRyBrdQTyT3ve50hDIpvpbeoMVW8Yc1pB/2DzjY+d2TNfwwSYABNgAo4EWHhR5lMhq/AizHUHjbSWGUVGOKZ2r1/5YsC1lWbhRZkPNkfVOIG65UZCdBl/266abJf6d+3bc55D2dHe3DJoz2/8xR6zZwJyE1ixPB766+3fuRcZH5reO+2W0p5N7/p/YF9qZPhKhYSp/l/qsunbsx38WsTmW7f7pUncuiEtpWsayhJq8mYXL8hft7nBO9TDBrs4E1/uTwSMr2yH9kz7Uqc08nlJX968Nt++3Hvira2QMNb6903Ga3mUYeeYXefL+HhtJsAEmEBTBFh4aYqQb74vq/Dyvxkv4evNv2DggHPQuVM7hIXa1wY/MmU8mes23drQNyiatyoLL83jxnf5noDIaBG+Lv82dmw/l94tt/+Zzd1XRhkyLLz4/gSDJ4K1n/SAjrxN6o/uvX6lTC2rka5tCPElicx147tQm1kSXTLeds6jQuk0G8p4yc83o3tPz/q1uMrFmLHN4QW46e9o6JLPd3Uqvt6PCGjPLETmw3/XnH3GR52RmnmGH+3AGmrqQ22R+mBbu7jH33GIxRe/O0kOmAkENwEWXpR5/rIKL5fok3DlkAHs8aLMs+aomIDLBFIfpV+e6aPuEG2lubW0yyj5BjcIZC7qhsRx9p4Tomym34A/3JjVv24V3YnWftrbLgNt/ITdijO91p2XDwN5vNQdCeTxYtjczr+Ac7TNIiCMlf3ZTHnf7z0csjxzdpRCdw2X1zbrgeCbmAAT8AkBFl58gr3JRWUVXkbdMRN9enbDc4/f2+TCnr6gqsqCg0eOo1OHtggPc/4dz6PH86TQOraPdSpEznhxChNf5McEhPCSmGB9BzAr+ySLLn58lv4aulodgrWretaU2ohMj+GjdsNEL4iCaQjvpcTxbaUXhsLouqmuRr5io+1ZCP0Qq8+LYVNbNtb11UHwui4TKDjUy+Ge3P0V6HfRXpfn4huYABNgAr4iwMKLr8j/+7qyCi9rN/yAlLlLILxcnBUuPIHls69+wKPPLIHZbE1Bn373LbgrQd/oUkKkeeGVlchetQHl5RUIDQ1BzobXpes/WbcJM9Jec7j3589fRVSLCLDw4okT5DmZABNgAo4EbF4lph0lDiVGzIsJMAEm4C6BFW90hv46qyeSbQifl5RZx92dmu93gYDuwkro/mOmVuihMP3h/x5dLmydL2UCshBg4UUWjLJPIqvwcsf0dPz4y++NBmn8ZJHHux0Vl5Ri4IhkSWhJ+u8oGNZ/j8fSl2LN8jT00NiXTNgCnfnsMhg2bMHkxBG4ZcRlKCPxpVtna8vdVZ8bMev51/Hh0qft9tXrjK5QqVQsvMj+SPKETIAJKIFAako36qLTBvkFZmRkkseBwb6DkBJi5BiYABNgAnISEO3qV7wZB92gaGlaYax777Qj9PegvZeUnGvyXPYEMp4uIxjHlQAAIABJREFURuLIipovvvNpBKY8EcWYmAATcIEACy8uwPLipbIKLyI7ZE/uoUbDv2fCDVKWiCeHyLp5eHYmtn7xWk1npcE3TMFto69B8sRRDksfOnoSV499AML497+3DHP4vhBennrxLfxC8zU0OOPFk6fJczMBJuALAulpZyA5Kc5u6fGJf7D44ovD4DWZABPwOgEhwIjBgot30euvKEf2iyUOi+rGteTMF+8eBa/m5wRYeFHmAcoqvChhi0uzDXj93bXY/OnimnDGJT0NkaEy59FJDiHaSomuvexC/PH3PkSEh+OWkZchkYQaMYTwIjJmdBdrERkZjiEX9sNo/WU1vjEsvCjh1DkGJsAE5CRQkDfQYTqjMR/DR9i3b5ZzTZ6LCTABJiAIqFtZoOkKmH5XMZAgI5BybylS7ylz2HXakkikv9IiyGjwdplA8wmw8NJ8dp68M+CEl3nk1bKWyoa+en9+DTdRAhXTMhqL5kx1YJnx1idY/MbHktCiPbsH/UO/B1kffYlZ02/HrTdeiZ+3/4GP1n6HNrGtsO/gUWzYuBXDLr8YLz6ZLM3FwosnH0+emwkwAV8QYOHFF9R5TSbABNJSqpA8wSKB2HsASJwa4iDAaPtUIuuF09Qyvgp5BUDyUzHkBRLJ8AKAAGe8BMAh8hYUQYCFF0Ucg0MQASe8uJrxIoSXFR+vx8ZVi2rgTH5oHkpKy/DOy485AHvzvc/xfMa72LZ+mZT1wsKLMh9sjir4CIjEcK7Cl+fc167pC51ObTdZWvp+iA8eTIAJMAFPEEgcZUHGM/Z/iwvxpf+1tZ0p1a2qkPPpKcS2to9AOzIWuQed72Dpifh5TnkIZL9YBP0VlTWTZa0OR/Isq+cODybABJwjwMKLc5y8fVXACS82jxfhyRIRES7xvESfJPm3NOTxYrt+25dLER5udU4XGTLFxWVYueQJh/NY981PeODJxfjpsyWIjopk4cXbTyyvxwSYgMcJiHbFK7L61IgvGZmHkZL6j8fX5QWYABMIXgKGN83QXeS4f92Y2qwX/eVlyH6h0OGitFejkL6EX5wHytMjMl+0Z1XB+HOY9MGDCTAB1wiw8OIaL29dHXDCS1FxKS4efi/uvf0G+rjRoavRd1u248kX3sSS5x5E7x7dkJdfiEtHT6VuRpdjxtTb8NP23zHpgedw352jpTkyl3+Cfmf1xH/698bJvNO4++F5CAsLw6dvPiOdEWe8eOtR5XWYABNgAkyACTCBQCUgsl1E1kv9oRkYAo0mAmlPtsHQAaVA3h4WXgL1IeB9MQEmIAsBFl5kwSj7JAEnvAhCa778Ho8+s6QG1tRJY3DPhJHSf6+h9tKPzlmCdzNnQdu3p/Q14dsy/cmXYTZbU1yFh8tzj9+DsNBQiFbTH3+2sWaurnHtJdHG1pqahRfZn0me0I8I6HStYDIVIz/f7EdRc6hMgAkwASagNALasy0wfmhfamTYoELSzFBsXNcZ8d0p88FCJSgnfgfMte2G80+roEtQc6mR0g6U42ECTMBnBFh48Rn6f11YduHlm83byJx2PfbuP4zpd9+C66+8BDPSXkPH9m1w/+SbvUZBiCjCDLdLp3Y1JUf/tnil2Yz9B4+hfVs1GfFG2V1aXFKGw8dOonVMtPT9uoOFF68dKS+kIAJCcMl+uxdiY60pwIszDiP1sX0KipBDYQJMgAkwAX8jIMSXJDLXje9qgRBdMt4OgW5QJAzvd6rdipm63pw+gONHS/DDL0DakiiY/uRyFH87a46XCTABzxFg4YV8H6sssFgsCA0VLpD/PopLSnH8ZD66de6IkBDPddSTVXjZavoTE+6bK3mflJVX4NEpCdQt6Gq8svxTLHr9oxpflKY270/fZ+HFn06LY5WDgPD/MG3rXyO62OZMmkIdwbKPy7EEz8EEmAATYAJMQCLgILxUc0l7MR/p9MFDOQREK/AhF1qzljb9HAKRjcSDCTAB7xMIduFFCC73PbZQAv/y3Gn/egDjkp6Gaedu6RrhD6u/aiDmPDrJI4cmq/CSnDpf8kzJWjwTY+6ahTH6yyTh5Y+/92H0pMfxwWtPoW/veI9sxFeTsvDiK/K8rq8IiGyXtavPdlg+a8VxJCU71t77Kk5eNzAJSL/Y/8da2rbp/0L5F/vAPGbeFROwI2BcFwftuRF2X9MNOwTTr7UlR4zMtwSEGe6aZWU1Haf2HlBhxF2RyKXPPJgAE/AugWAWXj40fIc5L72NckoCuWLwBU0KL7PnL8dNw4eip6YLvt78Cx6Z/QreXJCCi853fK3j7inKKrxccO1kTLtrDCaOvU4SWmzCy7ETebh8zP1Se+YL+vV2N2ZF3c/Ci6KOg4PxAgGtNgqbvuvnsFLasweo3fBBL0TASwQrAe1ZZqxZWmr3i33i9EiY/uA2ssH6TPC+g4OAurUKmfPbS9kvpl/LIbJdjN9TyREPxRAwfV4Cet1iNwxfhSLhfnvBTDEBcyBMIIAJBLPwIhrtnMo/jSfmvYGoyMgmhZf6j8FF198jNd15ZMp42Z8QWYWXUXfMRLu2rbHshUfshJcVqzZgzoK3sfnTxVC3bin7Jnw5IQsvvqTPa/uKwIp3ekGvb1OzfH5+JYZc+htyc/kXYV+dSTCsa3yvRGoxWncYvha/2LcIhu3zHpkAE2ACiiWQn1PiEJvpdzI+Hst/Pyv20DgwlwjoBkVIWXb5BY7d11yayAsX+0J4WX3ZFC/szHGJkd8ubnDd+2e9jMpKs0vCy649+yH0jOcfT8Lwqy6RfT+yCi+rPjfisfSlUlegn7btxGWDzpfMaF/LWoOhl2jxyrMPyr4BX00ofuRE8iQLL746AV7X1wSSkzpiBIkvOfTLVsYrR1h08fWBBMH6+duLHHaZVwDEDw0sQT8IjpK3yASYQIARyN1UAnUr+01xxkuAHXKQbifxlhaY+3grxKqtJq1p84uQvqBQ0TR8Ibw8GC9/hogzkF/Yu0IW4aWgsBgjb09Fy+gWWP1WmlOmvM7EV/caWYUXMfHSbAMWLvuwpjWz+NrAAefg+VlJaBtb729kV6NV4PUsvCjwUDgkJsAEApKA6bMix1R2zngJyLPmTTEBJuBfBPRXVCL7pbptvgH9naIUtOmOIv61U442mAhoulM7e0PbGtHFtveEyadg+KJcsSh8IbysPf8Fn/AYvq3hxA5XMl5EV6Pb/vcMhD3Kx6/PcehiLNfGZBdeRGCio9Ge3EMQNVbx3Tp5LHi5ILgzDwsv7tDje5kAE2ACzhPQXWRG9vzSmndVqYQX+kkt2OPFeYR8JRNgAkzAYwQ01AY84YZKaf7sT8PYWNdjpHlibxEQ5UWGd2tL623rplHGSzplvih1sPACOCu8iMZAt933DEpLy/DuK094VLeQVXiZ/NA8ycPlv7cMg7ZvT6U+i7LGxcKLrDh5MibABJjAvxLQdKlCwo2VKKASozVf0y/2B/ndVH5kmAATYAJMgAkwAfkJsPDiPFOlZLyYzVVUeWPGA09moNJciYWzpyIsLAwhISry5ynCTZNmIum/N0oGuiJJ5LqEh2GuqsISskRpFRMtbTgkJASarh2d37yTV8oqvAgvl0WvfySVGYlgbycB5sZhQxAdFbjGWiy8OPmk8WVMgAkwASbABJgAE2ACTIAJMAE/ImBY2Qa6gbXdufILqqAdclzRJrvBnPEi9IgFr31g94Q9nDxO6rp84lQBLr1pKqZOGoN7JozEgcPHce24hxyexoiIcPzyxWuyP6WyCi8iuoqKSqz79ie88+GXMO3cLRnTjLh6kCTCnN1LI/sGfD0hCy++PgFenwkwASbABJgAE2ACTIAJMAEm4BkCyXdGQT+sBXJ+rZSMdZXe2SiYhRfPPAHyzCq78FI3rENHTmDZirUQ7aTFMH6yCG3qW57Lsw+fzcLCi8/Q88JMgAkwASbABJgAE2ACTIAJMAEmUIcACy/KfBw8JrxsNf1JWS/rse6bH6Wdn3Vmd7zz8mMBV3bEwosyH2yOigkwASbABJgAE3CfQPL/zkbKY/0RGxuJjd8dQdLdm5G7V9mtVN3fNc/ABJgAE/BfAiy8KPPsZBVeRN3Uh4ZvkfXRehw/mS+VGY0Zfiluu/lanBnfRZkE3IyKhRc3AfLtTIAJMAEmwASYgCIJ6Ed2x4r3LreLLWf7SfI7MCgyXg6KCTABJsAEABZelPkUyCq8jEt6WvJ16d2jG/47dhj0Vw2EMKcJ5MHCSyCfLu+NCTABJsAEmEDwEhCiixBf6o9+Z3/MWS/B+1jwzpkAE1A4ARZelHlAsgovqz43ot9ZPdCrR1dl7tYDUbHw4gGoPCUTYAJMgAkwASbgcwIsvPj8CDgAJsAEmIDLBFh4cRmZV26QVXjxSsQKW4SFF4UdCIfDBJgAE2ACTIAJyEJAd2knrF13rd1cxo2HMfzaL2WZnydhAkyACTAB+Qmw8CI/UzlmdFt42fTTDjzx/Ot4fX6K1L1oa86fjca17MVHENMySo64FTMHCy+KOQoOhAkwASbABJgAE5CZgCg1mnLf2dDEx0jmuikP/Yz8/HKZV3FtOt3gKKQ+1E66KWdHGdLnnaD2rlWuTcJXMwEmwAQClAALL8o8WFmEl6deeBNLX3gE7wrhZceuRne6dN7DLLwo8zngqJgAE2ACgKoKHcJP4lh5W6IRYkdEFVKJDqF5OFrRnkkxASbABHxGQIguaz+y951Z89lpJNxxyGcx8cJMgAkwASURYOFFSadRG4vbwosyt+W9qDjjxXuseSUmwAQ8S6BLq32Yc/orzIq5AvsLNXUWs0AdexiPFHyPxRGDcLC0s2cD4dmZABNgAo0QyHypExJvVTt8t3ufvzjrhZ8aJsAEmAARYOFFmY+BrMLLfTMXom8vDZInjrLbreh0dOcDz+KzrOfQvq3jP5bKRONcVCy8OMeJr2ICTEDZBITo8sGp99Gv8hi2hsfhNvVN1eKLVXTZd3ihtIFF0Rex+KLso1RwdKIUxD6TSsHBcmgKJbDijS7QXx/jEN2Qq/bC9GuZQqPmsJgAE2AC3iPAwov3WLuykqzCi2gn3b/vmZgxNdEuhkNHT+LqsQ8ga/FMnH9uL1fiU/y1LLwo/og4QCbABJogUFd0sV1qE19Oh4XXiC6277H4wo8UE2ACviKQeGtrZL4UZ7e8iXxehly911ch8bpMgAkwAUURYOFFUcdRE4wswssv5OtSUWHGE/NeRw9NZ0wce33NAhWVlfjQ8B3WffMjfvrsFURHtVAmiWZG1SZsBCyqljCrolFFn1WqGITQBxDWzBn5NibABJiAFwmQr0u71oex58gih0UPhrRElKUSbSz27yL/TBkxk1vdiL+LRTkSZzB48bR4KSbABIiAMNa1mesK0eXeaYc524WfDCbABJhANQEWXpT5KMgivFxw7WSUl1c0usOIiHASY67DtLvGKJOCG1ENDM9EBQpQYTmNMuSjpPqjioQXs6oFKlTUxUkSZFoClpYICQl1YzW+lQkwASbgGQIxbY7g4KEFTU7+fURX3N36Buy184Bp8ja+gAkwASbABJgAE2ACTMALBFh48QLkZiwhi/Cyc9delFdUInXuq+jVoysmjdfXhBJJokufnt1JcFA1Izzl3zIo/NUGgzRbSkiQOS19lFqsgkyZqgxmWFCFaJhDoihTJpruJTGGPqtU4crfLEfIBJhAQBNoSnxh0SWgj99jm9P2j4VoSZyz/RTWrtnvsXV4YibABJgAE2ACfkNAJAwL6zMPDBZePABVhillEV5scezJPYTtv+3GoP+cg04d2tSEt+6bn9CxfSwu6NdbhpCVNUVjwktjUVZaiiUxphKFKLMUUJZMCf3fKasgExJNX29BJUtCiGlBH/QZUfSZs2SUdeocDRMIVAIWDIzegS9OZjtscGtYJzwZczm+KT1fcZvXaqOh17dDTk4R1q49qbj4gjmgzFcvQeJtPWsQrPl0PxLGbQxmJLx3JsAEmAATYAIeJcDCi0fxNntyWYWXmc8ug2HDFnzzwQKoW1NpTfV4dM4SfPHdz5LHS1hoYIkIrgovjZ2U2VIuiTHio6KqCCUhJMpYSJxBEZUshZAgKkqWIilThgQZEmdUooTJEkWZRIHlmdPsJ5lvZAJMwE0C9t2LGprMvtuRm8vJdHtyUhzS03rUzCbEF/3IX5Gfb5ZpBZ6muQREpsumLbWeb7Z5ku7Zgqy39zR3Wr6PCTABJsAEmAAT+BcCLLwo8/GQVXgRnYt0F/fHkw9NtNvtn7v346Y7Z2LVG3PQu0c3ZZJoZlRyCS//trzZUkqCTLH0YaaPMksRykmQqSTDy3LVacqQiaSPKBJnIlEpZcpEWjNmJIGGhZlmHi3fxgSCiEDToosNhpLEF40mEjtyBjicU0rqHmRkHg6i81PmVhMn9EDmkoEOwaU9Y0LaMzvcDlqtDocmviVMOXluz8UTMAEmwASYABMIFAIsvCjzJGUVXoaOug/XXXExHps2wW63v/+VizF3zUJ2xuM475wzlUmimVF5Q3j5t9AsFoskxlhFmRIIkaaUzH7LVFTSJMqaVCWSIGNBDIkzIZQ9UyvKQIgylhZs+NvMs+fbmEAgEFCpKqGJ2QfTMUe/qt2hasRYKtCxqthuqzvCOmCCerS1q5HFd12NdLrW5BlyrsMxZGUfRVLy34FwPH69B09mvKQ/NwDJ/+sj8cnLK6fyJSOM3x31a14cPBNgAkyACTABOQiw8CIHRfnnkFV4mfTgc9hq2oVNn7xMbaMja6J9ZPYrUgnS92sy0DpGGMoGzvC18OIMSeErI8x+K0mEEeJMKZUwlZNQIz4qSZapVJWTMCOyZiKk7BlrlozwmYmozpqpPUtn1uNrmAAT8C8C3WNysTz/Y/ynojZLxGakeyI80qHb0QstB2Jp+MU4UNrZpxvljBef4ndq8foeL4bV+zH+Vvc8XvQju2LFyqF26wvxRdt3NZWYNd5h0amA+SImwASYABNgAn5OgIUXZR6grMKL6G508+QnpJ1eeN5ZiOvQFt98vw2FRSW46fqhmPPoJGVScCMqfxBemtpeleQvYxVlbMIMFTGRIENdmUiwMatKaQohxlDmDLXFNpPZr5m6MIlSJpE1o6KvC5GGBxNgAv5LoK74Ur97Ud1uR0oRXWyk63u8mExFGD6CPV6U9CSKzJcRI7th48ajsmSlWMWcWl8f216HX/eVLPMriR3HwgSYABNgAkzAVQIsvLhKzDvXyyq8iJC3/foXZs9fjl179sNsrkLb2FYYP+oqTL5tJMLDAstYV+w3EIQXZx61SiHAVJcymamMSZQ0lVVnzVRImTMWEmXCrFkzJMSYhRAjsmckcYYyZiyRVNLELbOdYc3XMAFfERDiy8NFRrzQUoe9hVRGVGcI8eXBgi2KyHSpz0d0NRqhb4scEl0MhlO+wsfreolA6mP9ID7qjyEDP2e/Fy+dAS/DBJgAE2ACyiXAwosyz0Z24aXuNquqLPRiW6XMncsUVbAIL03hslioZEnymSmtyZwpoxKn8mqvmUqVRcqqESVN1rIm0aHJmjUjiTOSETCVNpF4w4MJMAEfEVBVoVPoSRypbEsB2Hu3qELMiAkpxOlKtY+C42WZgJWAMNTdtGUY1OraTEsjZdMMH/YVI2ICTIAJMAEmEPQEWHhR5iPgEeEl/3QRikvKHHYc16ENvbAOLCGGhRfnH+wqMum0CjO14owoZbJ6zVTS53zKlBHzWTNmqiA8Z8Ipk6Y2c4bLmpznHdxXWmj7gfV3TXCfJ++eCdgTEOKLyHqJp89ryDcm4+U/GRETYAJMgAkwASZABFh4UeZjIKvwcuDwcfx36lwcOnqywd1uXLVIKj0KpMHCi7ynaRVnyqqzZqwijejOJMqaKujPImtGmAGL0iWLMAEmgcZSLdJYOzYJkUaYAYs/8wtveU+HZ2MCTIAJMAEmwASYABNgAkxAyQRYeFHm6cgqvEx9fCE2/mDCneOuxyvLP8WjU8ajXVs10hdloUO7WKx85QmEhwdWKQkLL95/sEVZk33mDAk0lDlTZamkNtoFlDlTIok0FpEtI5U1hUvZM+YQyqARXjM1/jNCnGHfGe+fIK/IBJgAE2ACTIAJMAEmwASYgCcIsPDiCaruzymr8HLZ6Gm44doh+N+dN2HAtZPx4dKncXYvDdZu+AEPz87kdtLunxfP4AIBYQBslz1D/10pTIGprbYwBK6QujiZJUNgYQBslkqbKFtG8pkRnjN1s2fs/S5cCIMvZQJMgAkwASbABJgAE2gGAbU6BAlj1dB0D0fuvgpkvsYG6s3AyLcEGQFfCC9vnjPTJ5Qn/jbHJ+s2Z1FZhZdL9EmYnDgCdyXo0f+qOzE3dTJGXD0Ie3IPYcTtqVj24iMYOOCc5sSp2Hs440WxR+NUYBaLkF5svjMic6acBJkia9YMCTRCqLGaAodKXjOirMliiaLsmVApk0Yqa6oWaIRQw4MJMAEmwASYABNgAkzAfQJCdDF8qEF/rWjCYB1r1p5Gwh0H3J+cZ2ACAUzAF8LLJPVdPiG6LH+pT9ZtzqKyCi/XjnsIA/r3QfqMuzHhvrkQJruvk9iyYtUGqfTos6znoOnasTlxKvYeFl4UezSyBlZFgkyNQGMhDxoSZcz0uUwSaaziTIWKyptE1yZVNJU2hdAHlThReZNkCEzZNFaBRnRuYoFG1sPhyZgAE2ACTIAJMIGAI5B8dxukz+7ksK/ho3Nh3FQccPvlDTEBuQj4QnhZFveGXOG7NM+kw3e4dL0vL5ZVeHnqhTfx2597sXLJE/jhl524c/qzNXsboO2DtxfN8OVePbI2Cy8eweq3k1ZKXjNCmLFm0YiMGVHSVE7/Lb4nvGdE+ZOUOSNMgS2ivIk+pNbadQUaq1EwDybABJgAE2ACTIAJBCOB1IfbI/Wh9g5bHz9xPwyfFQYjEt4zE3CKAAsvTmHy+kWyCi/1o9/xxx58s2kbtH174tKB/QOyywwLL15/ZgNiwVr/meoyJxJoROaMrbxJiDWVUgYNZcoI75nq1tpWPxrr12o9aMSf2YMmIB4M3gQTYAJMgAkwASYgEdBfH4MVb3ZzoNHvor+Rm1vBlJgAE2iEAAsvynw0PCq8KHPL8kbFwou8PHk2ewKinMlW4lQltdkW7bVF5kyZ5ENjba9NGTQWmweNtYOT8J8RGTXWttrWcifrnwNHoBE7qeIHhgkwASbABJgAEwhYAqLUSJQc2UbStEPIejc/YPfLG2MCchBg4UUOivLPwcKLm0xZeHETIN8uCwFhCixEGZs4I8qZrKVNVu+ZimqT4CrJJNha5mQRHjSi1XaNQGPr4iQyaEJliYsnYQJMgAkwASbABJiAOwS0/SKhVodKXY0408UdknxvsBBg4UWZJ83Ci5vnwsKLmwD5dq8SqLJU1OniVEZCjc2HxlrmZM2moRInFaqNgq1lTkKoMUtttq3eM5JZsPRn8TUeTIAJMAEmwASYABNgAkyACSiBAAsvSjgFxxhYeHHzXFh4cRMg365IAlaBxirMiM9Wo2DKohEZNNUfleJ7Kku1D01kTfaMWcqgqW61bSt1oswaHkyACTABJsAEmAATYAJMgAl4lgALL57l29zZWXhpLrnq+1h4cRMg3+7XBKos5mpxplagEcbBQpSRDILpu+UooP+tpH2KVtvV3Zyqy5yESGNttV3ddps7Ofn188DBMwEmwASYABNgAkyACfiWAAsvvuXf2OqyCy8WiwW79hzAvgNHcXZvDbrGtcff/xxAy5ZRiOvQVpkU3IiKhRc34PGtQUPAYqmqLnGqk0VTbRwsWm6LdttWoUZ0crL6zlTRZyHMVInSJqmkydbJyVbmxD40QfMA8UaZABPwGwJqdQiy3+iOoUOipZgXLzmJ1FlH/CZ+DpQJMAEm4O8EWHhR5gnKKrwUFBZj3L1PYe9+6z+wM6behsTRVyMheTb2HTyKjasWKZOCG1Gx8OIGPL6VCTRAwNbJqcYomMqeKlAsec9Yy5xE6VOJtaORXQaN1YdGdHCSujhVZ9MITxoeTIAJMAEm4B0Caz/WQDe4pd1iafOOIe35494JgFdhAkyACQQ5ARZelPkAyCq8LM02YPGbq/Bw0ji8vsKAO8YNl4SXb7/fjuTU+Vi/8gV07tROmSSaGRULL80Ex7cxATcJVFlEIZMQYaweNNKfJR+aWoFGardNX4eqJWXSCMNgUepUm1EjujpZhRlryZOKvWjcPBW+nQkwgWAnUHCkrwMC4+YiDL8pN9jR8P6ZABNgAl4hwMKLVzC7vIiswstlo6fh+isvQcr/EjB60uMYo79MEl6OncjD5WPux1svpeLC885yOUgl38DCi5JPh2NjAlYC5ioSZVSizMlepBGijTWLxupLY6b/Ez40QoixlTxJQk112ZPK5kkjiTRc6sTPFxNgAkygPgEWXviZYAJMgAn4lgALL77l39jqsgovQ0fdh5uuH4oH7hlrJ7zs3LUXN09+Aoa303FG9zhlkmhmVCy8NBMc38YEFEjA6kVTt5tTtVhDmTQW6Tuna0QaC+XISGbB1Vk04s+VkkBjLXfiUicFHjCHxASYgMcJpM/uhOS77T39xk/cB8NnhR5fmxdQLoEQCk0qEebBBJiAxwmw8OJxxM1aQFbhZfoTL2Pzz7/iw6VPY+rMhVLGy6jrdLhz+rP4c89+/PzZEoSGir96A2ew8BI4Z8k7YQKuEKhtuW0rdyKRhoyChTxTTlk05dVZNBVkHCwyZixoSUbB1nIna4mTNbNGdHuyZdJwqZMrJ8DXMgEmoFQCqQ+3x4jrWiG/wIzFr55k0UWpB8VxMQEmEJAEWHhR5rHKKrwcP5mP6xMfQXEJeSrQiKFORiWl9KLEXIVnZ96DEVcPUiYFN6Ji4cUNeHwrEwgSAmZLuVWUoXInmx9NBf23tcSpvMY82Ez/JTJnhFAjxBmzKkoqaZJabtfp6mSxRCAkhEuC0WHcAAAgAElEQVSdguTx4W0yASbABJgAE2ACTMBpAiy8OI3KqxfKKryIyItLSiFMdrf/+jdEl6Mzz+iC28Zcg35n9fDqxry1GAsv3iLN6zCBwCdQv9SpigSbclUxykmkqaTMGWEULNpvkxMNiTIk5pA4IzJoKqXyJntxxlrqVG0crKJUGx5MgAkwASbABJgAE2ACAU9AScKL+A1UNLjw1Jh0+A5PTS37vLILL7JHqPAJWXhR+AFxeF4jEKMOxfDb2qBP/yj8mVOC9xZz61BPwq9f6iSyZ8os1UbBkkhDH+RNY5ZKnYQAEypl09S03JY6OLUgcYY+i9KnGuEmsMpBPXkGPDcTYAJMgAkwASbABJRGQEnCi6fZBJXwsumnHTh05IRTTG8cNgTh4WFOXesvF7Hw4i8nxXF6msDyLWehN4kutrH1u9OYct3fnl6W53eCgMicESVOVuNgUdpEHjSi9baqgv5cVC3SlNN/C58aKmESHZuqs2lQXfakoqwa8WebUFPzZxJ0uMOTE4fAlzABJsAEmAATYAJMoJkEdIPCobskAvn5Vcj+sJQ8tBrPI2HhpZmQPXyb2xkv45KehmnnbqfC3LhqEdrGtnLqWn+5iIUXfzkpjtOTBPS3tcXMVzUOS0y57i9s/Y47WXiSvdxzV1kqq9tuUylTtVAjsmusbbeFOCOEGyHkUDYNfd1C4g0sovRJCDMkvKmob4VFeNSEVpdAUQaNVAolPGmEcBNGKaf039LXxD3WDxWXQ8l9lDwfE2ACTIAJMAEmEAAE0mbFIHlSdM1O9u4zY+jwk42KLyy8KPPQ3RZeiopLUV5Bv3jTuH/Wy2T4qMKLT06x2+2U1AX0LqoF72bOUiYFN6Ji4cUNeM24ldsRNgOaF26567E4TKKP+mPBIwew8uVjXoiAl/AlAeFNU0VyjBBthBAj/Zk+LELEEf8tvk7/LQk39L9m6bsl9Jm+R98Xny0QP91R9B3RcJTEGEnIER+iRIrEGpGFQ6bCUIVUZ90IIUeIN6JkSmTdBFY2pS/Pk9dmAkyACTABJsAElEFA3VqFXFMHh2DSFhQhfX5Rg0Gy8KKMs6sfhdvCS90JL9En4b+3DEPyxFF263xPLabveuh5rF/5Ajp3aqdMEs2MioWXZoLj2wKKwIBLY7D4814Oe7p94B/YRX4vPJhAUwSkTBtJuBGZNiLrRogx1s/ivy2UXVNFEo0wGDaLjBsSbCqlr1mFmyqVGSoLteymK4RoI7JqJLGGPldJ4k2olIUDixB1RKco4W1TnW1Tk3XDnaKaOif+PhNgAkyACTABJuA9AqLEyPBuG4cFDV+UIWFyPgsvwWquO3TUfejQLhYfLZtt9xB8+d3PUjbMK88+iKGXaL33pHphpbNDxtI7rcKgMgoh9FmYVYbRn0NV0fSrPn2WTC15MIHAJ1A/62XO3bkwvHMy8DfOO1QEAQtlVdZk2AihplqwqSvgSN+XpBprG2/xIYk49CebeCMJNvR3t4VKpixCsKEPIdjYyqKsmTV1BBvpv6uFHM68UcSzwEEwASbABJgAEwgUAiLjxbSpHdSt7ZsfpD51GhmvN/zmJme8KPP0Zc14mbPgbaxYtQETx16H6664GN27dMS2X//CnAXLcfREHr758CXFeLzkFxShorIS7duqnT6Z8vIKHCQjYZG1Exkh0tuBhNC3JHPKcsn/oBgVlmL63yL6tb6Y3pel/yazyhBLC/p1vLX0LquFxBkhyEjijPQRzd4GTp8AX6h0Ap3jw9E5PhKH9pbRh7UEkQcT8BcComRKZNmYq0um6os3oljKLIyKRXmU8LWhv/dFqZSUeVNdUiUyb4RYYxVnSMARUo+UgRNSnYFDPjfVmTi1/jb1M2+4bMpfnhmOkwkwASbABJiApwkk3tICGfPotWT1MG4ph/7WvEaXZeHF0yfSvPllFV7KSJi4++F5+Hn7Hw7RzE2dDNHVyNejsKhEKnuyGQJ3jWuP5YtmIK5D20ZD+3P3fsrYWYS9+49I10y7awzuvm2k9OcJoVn/uiV6D9YqzJAgUy6EGPpFvdRShBJVkSTMlNH3VJQVEyKZUoqMGZEtYy/OhEhtX3kwASbABJiA0glImTe2Eqkaz5va0ikh3Fh9bkjgEWVVFvqT1E2KvlYt3phVVC5VnW0jBPsqKo8SpVJSBo4k2ogyqTpZNnU8cWoNi4XvDbcGV/rzwvExASbABJgAE3CGgKZ7KIYODKfXo2YYv//3NzdZeHGGqPevkVV4sYW//be/kUMfJ/NOQwgblw06TypBUsKY98pKvL/6G3xM5VAto6MwLukp9NB0Rkba9AbDO3D4OK4d9xAuvuBs3JUwAhf064XikrKaTJmmhBdn9lxhodaukigjMmZKSJqhjBkSZkrpc5l4R5V+3RalTEKYCaFSplAViTMWKmWyCTT03zyYABNgAkwgcAgIIUbytrF53NTxu6kVb6xijeguVfez5JdD4o34l8OafVNtUFwt3Fi/ZhNvHEun7LtNsXgTOE8V74QJMAEmwASCgQALL8o8ZY8IL2KrR4/nobSsjMpy2iM8TDmGhVfeMh3XX3kJHk4aJ53Ih4bvMOv517Hj6zcaLPlJmfsq1n3zE3767BWEhTruQw7hpalHQ/xSLUQZUc4kBJoSFKLMUkbizGmppElk0ahIkLFlzUglTCTG1C1nCpHeJeXBBJgAE2ACwUJAZNPUCjdWIcfmeWP1t6F/W6rLpER5lZSBY/O7EWIOfZ/+cakVbiz07wiVTVVJZVQhUglVbUep+r43te3CraVVPJgAE2ACTIAJMAFvEGDhBTh87CRax0QjOkpUlDQ9jhw7hVbS9Z7zZ5VdeFn+/jrMf+0DCD8U27hq6AA8+eAdivB36X/VnRTLRIwefqkU3lbTLky47xkYP1mENupWDqciDIOFn0tcx3Y4fPQE+vY5A49OGY9una1tvbwhvDT9qKBOKVOxVMpUqrJ6zNjKmcQvyUKYER+SGTAJMyJ7pkackcqceDABJsAEmAATqCVgbRXesFmxJNJIAo1VxBElUuLPIuNG+iyVU4nuVJS3WS3aWLNvKNvGIjxvrCKOhUqibB2orC3Cbe3Ea8uppK+rJBWIRxMELPR9JsWPCRNgAkwgeAkEs/Dy154DuH3aXAg/VzFE5c1LT9+H8PCG/fNEE6DnM97F/7N3HnCu1FX7P8n23VsQG74gShP1BbFgQV+KChZApUtV8UrzFRRUQEREQASkCIjSLYgFBFFB1Bf8q+gLiB0FBRXlBVH6vXf7bpL/ec75nZlJdvdudjNJJskJnzAlk2TmzNzszHee5zmPPL6SCoUCbbbpBvT504+i5UuHUj+AUgUvCNZFwC6sO2IvWnstuuX239Ntv76LXsjA4qqLPt7UEyd47zd77YH06Y8dRju+/pVSzLvv/QftcdDH6cYrz6D1133GjAL/53bvoo2euy69/a2vFQDzuS9dx0qeSfrxNz9DvTydFfAy35ExzbkCppqxMOCJAGigmplk7QxAjFqa+ng4xKqZ3hAADPUMK2p42h9eAa+AV8Ar4BVYSAXKcm/msE+hXbjAG34iAwdhxQp1rF04Woqz7UmCii3rRse1XThDHAY4ZTYpAThxRyqFN66+Wci+82W9Al4Br4BXoPUq0MngZbcVH5M4kYvOOIr+75+P0J4Hf5yOfd9+tO+ur5+xI6emC/Ti7VfQfrvtQB85fF8aGR2nN+zzIdpjp23pqEP2Sn3Hpwpe3rzf0bKCgBjJx2Vf+x6dfdFVPP90hhvPTH0jFvKBULx84kMH0q5v3lreNp/iBeAlGQyMoN1d3308ff3zJ9DmL9iwZcDLfDXCiXEMZsa4SwcnzLCVCfYm69BU5FPgXAmKGZZgIV8mZMyItQmdm5BB40HA85XaX/cKeAW8Al6BRVQAShoNLp6jXXhQ4GircMAbU+TE8KbICh7pMChKG4YwrMQBtLG24ZJ/w9NR+3CZRsB9JcBxTckidqG/xSvgFfAKeAUaUIFOBS/Il4Vb5dIzP0xbbfmfUumjTvwcdyV+VK7dKx8ALa/Y8VBxs7xjzzfKy4ccfRZ1deXnzH+tZfelCl5e8oaD6IDdd5hBiO5/8GEClPn8aUfSNq/aopb1rfm9yHjZ8fWvog8d+nb5rG9e/xP6+JlfmDPjBcu/9Q2voQ8ctIcsbwqZL537Edpyi03bBrxUU1ic9E5y+O9UDq2zxzhnBt2Z1M40yZkzE7lhRjNomgorE6xLfXyqmgQ06NaE6fp556rZDl/GK5DlCmyyxTK693ersryKvm5egZatgLUMT+bdKMjR/BvpLhVUNkhXK1feILRYl4E9CjCmGNmm4q5TgDpJeKPQRsOMIyuVq29a9hjyFfcKeAW8AlmvQKeCF7tOv+mqs+lZz9COxZ+9/Ft07Y0/pR9dfc6su+340y+jb914i3Rf3uKFG9FpF3yNLj/7aG6os0nquzlV8LL7e07gUN0n6CfXnkf5fHw36AzegC9x9stN3ziLw3afmvpGLOQDP/35rwtsue7yU2hwsJ/2PrS8q9G5l17DYbq/oO995XT5WCh1rrjmf6QL0jL2egHS/OwXd9LPORMGYT2tYjVaSI1qWRYBjYAy8mRIg65ME9KhCcHA46F702QIA1bljHZqClAG41DQCLzxO4q17At/b2tVYJu3PpOOv+xFtHQtbR//mQ/eTd84777W2ghfW69Ah1RgtuBigzcaVAyrFCtvoq5TyMFRtQ7AjkAe/k/bhsf2KbVO9YRW4EmAExQ3EmgcK3I03Ng7T3XIYeeb6RXwCngFqqpAM8DLGU8/s6p1S3uhox/5UPSRt/3qLlrxwTPKslsv/eoNdNEV3+FGORfN+tX/739/I6qY522wHv3hz/fRphs9my496+i6ZNOmCl5uuf1OOvSYsyT7ZMsXbcotpJfTz+/4Az3KYTXIfJmrZXPaO2BNn7dqeJRWHHUG3XXP32Ux0LArzv9oBIQ+cuoldMPNt9Lvb75cXp/gkGBs0y9+8yeZRtLxBaceKe2l8XDwsvC9hzuOCmfGZThZ4kBgVtFoEDAAjT5z1MvPPgEzyJ3Jo422qGgUzGDo3ZoWXn9/R/Yq8KznDNCXfvlfEXSxNfzv7W+jX//k8eytsK+RV8ArUHMFZmbfmOpGhwptFN5AZSPTob24KHPYSqVdqtgxFfJsNKwYIAcwJg4qzkkL8bk6TwHezB46WPNG+gd4BbwCXgGvQMMr0AzwskvPYQ3fTnzhdVOfj77XFC83X302rfP0+RUvZk264NQP0HavfjEhmPfAI0+jDZ/zHwR3S9qPVMELVg6gBcnAf7v/n5wMXBRa9Pa3vo4O3n9nATJZeaDQk1NT0U6Zb72eXDlMgDbP/o+nlykxHLzMV7nFvz7FOTPTQSkDQDPOgGZSrE0KaQBnSoxmtJW22psQDoycGQUzZm3yjk2L3wv+zkZUAGqX06952Yyvuuzke+nSk+5txCr4d3gFvAItWoEStw1XBU3SMqWdpQzWTENtI7Am5N9E0wH2iFEYcEaDi6VleIU1SoOKE3k3ZeqbYKNy9U2LHkW+2l4Br0A7VaAZ4OWE/q81pYQnje8Tfa+BlMtYsfKql71Q5h/58c/SQw8/PmvGyw9+fAerXS5gt8659LS1l8vy519+LV1y5fWRCCPNjUoVvMBD9W+2Gp189LtlHXE3p93tIg5e0jwcF/5Z6HoRWZvE3sSGJs6aMeUMhlDWiGqG1TJQ0EA9g5wZAzNidRK7k3dtWvge8HekUYGXbrs2XXDTqxy8pFFM/wyvgFdgwRWI1TeJ4OKgskFYsdqkVIFjShvMEwUOL4cgY1XfwCIchxdbByoocjSw2NQ1leqbOLzY1TcL3n3+Bq+AV8ArUFaBTgUvKMIuBx5Py5cNSbbsAw89yt2LT+Dw3H25c9H20mJ61xXH02HvfBvtufN29Pf/+xftdMCxtM8ur6ej37s3jY5N0D7vPYmWDA3S1RefmPpRlSp4gYXniZWr6VrOQ+mUh4OX1tjTyJtRa5OqaCaQPyPqmXF5oqMTEE5e4AzUMwpp8pI9E0MaABrv3NQa+7zV1vLLbDVCsK49hldO0a4b/Zgw9IdXwCvgFWiFCqj6xiCMdaAK1imBNqFNeDQ0oBPgjUAcjeknUdf0axaOdKJCmHGwTCU6T0UdpyrneevwVjhkfB29Al6BOlSgk8ELOhAfcPgnaXhkTCq79Ss3p/NOPkKcN489sYq22fUIOmLF7nTIAW+R16+54ad06Vevpwf/9ahMv5q7IR3/gXfQes96eup7JlXwcvFXvkuf/cK36Fc/uIR6uvHHsf0fDl7aZx9r9kwMZ9DBScOBdajhwGN8h6+g1iZRz8DmVKGgkdbagDbumW+fo6P+W7JkeQ/t/f7n0ku3fSrd89uVHKz7d3roH/pHwx9eAa+AV6CTKiAqGrFDsQIHGTeipgnhxInOU3H+jVqrtDMVuk9hWaTflHebArgBwNHMG4QYJ7NvvHV4Jx1jvq1egXauQCeDF9uvAClLlwzSMn5W84Adaa1lS2igv34OiFTBy5/+cj/tdciJtPfbXictmysfmz9/Q+mL3U4PBy8Z2ptQOOM8q84PdLOwcOBYPWNwZkxODcdplZz0qXoG/4ChmgGgCaBG7E2qpHEFTZ13mH+8V8Ar4BXwCnRcBXAzRXNvLP8mwBxYpCS0GEOEFjOwCWHF1krc4A2m+Q6L5t/wzRTJvuHbLaa8EfuUKG1mCy92+1THHXS+wV6BjFTAwUtGdkTFaqQKXvY+7CS68+6/zbmlt1x3fl1aMzWztA5emln9bH83WooCzEyxXkZba7OtidUzUM3ECppx8c5DNSOdm6SLkwKaCMyY1Ylf94dXwCvgFfAKeAW8Ao2rwFytw6dDWLFl3Qi0kXnWLjxW6JQY8kjeTci50c5TpsbR7JsY4JQHGGs2Di9f6qJ8vjPU5I3bu/5NXoH2rICDl2zu11TBy733PUDo/jPX4yWbb0LdXe31R8PBSzYP7FZaqyLflTNAg9baEznkzhigUeuTKmwmtIOTAJjeWRU0GhTM+TTeWaKVDgFfV6+AV8Ar4BVo8wqYfSpW4CiYMRvVNDJwAG+gyGEVDm7eSJAxz1frFGfc8E0cQBvrPlUqoX04AA5UOCGwWOxTc3WfYnWO26Db/EjzzfMKEDl4yeZRkCp4efjRJ6m3p5vWWr6kbGuREPwwdzt6znrPbLsuRw5esnlgt+taQTkzHXJoJrnddmX+jGXU4KSri4bkZAxWJ+TRWCcnUdSEZ947ObXroeLb5RXwCngFvAJtVoE12acAdMQ6JfBGlTeRAkcgjnagAszR8GK1SQHoxOqbZHjxTIBj6hu812/wtNnB5ZvTVhVw8JLN3ZkqeIHV6EUv2IiOO2K/sq29656/054Hn0g3XnkGrb/uM7JZiUWulYOXRRbO31bXCkyXJgjPKVbPANQA0kxKSDACglVFgyFOzqR7k3Rz4iya0M1JIU2wPYWhhwXXdZf5h2eiAgiJQliUP7wCXgGvQPtWQDJsEoHFSRUOzh1wbgCztMEahBhb/k0pQByN1FOLFGxQqrzBUG1UCmdUhaOdp1iFE8KMDeC4+qZ9jzHfsuZWwMFLc+s/17c3BLzc/+DD9Ob9jqavf/4E2vwFG2azEotcKwcviyycvy0TFdBOTgxppM22PjE9zsoagBqMG6jRsOAKSMOKGQkINgVNGPqdsEzsXl8Jr4BXwCvgFfAK1KUCc6tvQmcpgTcV6psQdCwdqMRGNR3BGIM2FEEcVuMA5vAzVtrweI4BDi8TQx1X39RlB/uHtnQFHLxkc/elAl4uufJ6Ghkdp6u++//oGU99Cm336hdHWzs5OUU//Mkd9NiTq+mXN17kXY2yeRz4WnkF5q0ATpCSkMbGJwTSqIIGwcFQ08DqlGcFTZ7Ydsh5MyWMM6RRQBODGihr/OEV8Ap4BbwCXgGvQGdWIM6+KVfhRIocKGz47MLyb4qSf5Pj6VGZJy3Ec6y/4bwbUd8gCycKMS5X4iTVNwg6RjtxV98087hzlWm9qu/gpV6Vre1zUwEvr9zpMBoeGStbE+vsi/bRL9/i+bT/HjvQa1/9ktrWNoPvdsVLBneKr1LTK4CQQFHQRHan0NkpABq1OiFAeFIADWxO2tkJrbcBaRTQdJVYYRO6PXV5Hk3T96uvgFfAK+AV8Ap4BbJWgUr1DW4UlUJ3qZIEGENdY3YpmKhYccOIpsDnIQpv+HVeHvAmskqJTaqPX+PZgDkMafCMQI3YrEx9E8KMMe0Pr0AGKuDgJQM7YZZVSAW82Ocee+rFtMkG69GKfXbM5tbWYa0cvNShqP6RHVWBac6fKbc7TRBUNBM59pnzydNYbrWoaKC3UauTwpmc5NEA0qCTk3Z5sq5OeT/56ahjyDfWK+AV8Ap4BbwCtVagsvPUDIDD8AaQBuDGAoyLrL6ZphGZj3kldJUM6htk2xQsByfk3wDekIUbQ3UT2oW7darWvefvT1bAwUs2j4dUwctv//gX+ub1P6ED934zbfSc/4i2+IwLvkbrPGNteseeb8xmFWpYKwcvNRTP3+oVWEAFSiXct7IsGlbQIH8mYXNCcLB2dUJmzRSrZZbwCVBOAI12eYJ6xuxOFhys0zlezh9egVQrYLLPVD/UP8wr4BXwCngFslyBmcHFSQuV5t8gzBgJOEUGOAguLsr5zZjYphBozJMhrBj2KbQR75HzGdijBNywGicnAcZQ2AR4U5aFozYqf3RuBRy8ZHPfpwpeDj3mLLr73vvpR1efU5blcuGXv0PnX34t3XHjhTQ40J/NSixyrRy8LLJw/javQB0rANkxlDTTrJpREAOrEzo7weIUBwbbazh5UbuTdXYyINMt86CoAaDR3BqobRzU1HH3+Ud7BbwCXgGvgFegYysw0zoFJY0CHFHWsJ0buTdT0jJcLVOwSmn7cAY7UObkYKbSjlLI2YvBjc5TeKNdp6TjVMU8bxne2oefg5ds7r9UwcvWuxxOb3vTf9GHDn172dY+9PDjtP1eR7VlV6Ndus7hn6sBeXbJD5g/vAJegVargGbSWHcnBTXTfOIiOTQMbzCN8GDIiwFrcLICUIOTFdif4POWcVHUhGcJNigLGcZrDmta7bjw9fUKeAW8Al4Br0ArVgDwRvNtgspGMm8M3kBvw+c9OPdhUFMosrU7r1YpUd0YwOFPoNwQgxxk4HCjBAkvZiuVnANpy3A9/8H5Dab1Ka+J4oaf0oEK71GLlStxGnM0OXhpTJ0X+i2pghfAlRdu+lw67+QjytbjltvvJKhhrrn0JHr+xusvdB0zvfxO3f/NueqcR8GWBwRwdUsg6IBkTQiQySmU6eF5eK3bu7hken/6ynkFqqkA7jbJSYs8+YRFoA3uQCGfhtU1rKzBXajJ3AifwuhyOBFRUIM7SwAxOsSvRhLQ5CP1DUMblhfn8w50q9knvoxXwCvgFfAKeAW8AulVQJQ3DGFUbQOL1MyhKIwN2PBZEKxTWA46nFKpILClyDeuNAOnwACH55UAcdDxEq/BEp4AMwJr9PVieB3ARx48T4FPcvkwDtDD78MyGCrgAfTpTMuVg5f0/h2k+UmpgpdjTrmIrr/pVrrs7KOlkxE6Gv3zX4/S4cefR/fe94C0k+7tba/E7xP6vxbtD/650VBQ6dYyRiM8Ps7PsdIoZ1EgemsV2x2m+TJLIUw+KGVUMRNADQOanvxAmvvYP8sr4BXIQAXkDhLuJAmgAYyxcb7zBFgjsmEoagzoqO97KjcmsCYfhfAFT7eciMDHrTJhvK7QBuO2fBjKSY0/ml4B7AYQen94BbwCXgGvgFegQyugahwGM2VPzKucr9MIvSmKhcreo/Nx3VUEzAHUARziv6/SnYrPh6b5tjjeg6BjZOlAgQPLFf4IlwTSsP0K3y8ACPNLquaRaSh2oONRqFOKVDsKfvC6wqIwHYEeAJ9snG85eMnmP65UwctjT6yiN+37YRodmxDo8pTlS+nRx1fKln/0/QfQvru+PptVqGGtkuClmo8p8o/NBP8YCJwJUGYMgIb/Y0RDE0U2NPBd8u6gkAGcQVtdszMlh1n5x13NdvsyXgGvwOIqgFBhBTZBAjzLuFqlGODwCYcMI7Cj04C9ODmJw/hUfWMBfbBO4Y6SghsMY5hj094panH7z9/lFfAKeAW8Al4Br0DzKmCgpwgFDituVI0DADPLuLym511qzcIyrPzBOC8vyh92YAHmYK6eW3Xz/0cDyAkAh2EN4I1AHD6rKvH5FZaWsGSZhqoHr8GmpcBHoE4F5NHXkpat6mxbDl6ad7yt6ZtTBS/4ouGRMbr0qzfQb/5wrwCYDdd/Fr39ba+ll27+vGxWoMa1Wih4qfbrJlglAzgzgUBQIBmeHgWo4f+gotEEdDSpUzCj6hkDNDoUZQ1nTvjDK+AV6OwKFIt8Z0j83QA46vFWmGPj6u0GwME8BThhPFp2in9XlsrrFsSnuTYWzgevdzxeqboxqONZN519LPrWewW8Al4Br4BXoF0rAGgzU7lTAXpM6YNlJRRZAZCodzAugCfMEzgE2IPW5Ii1SCzLr+Wk4xWqmYQ5eXpy6hsNL3G9ronn25CTxveZb5HMvJ46eLEtGx0bp57uburpae98gmYdZKjzFNsTYjjDSTMMZABnRD0j4AY5E4AznDtDgwxi+gTUAMiU2Zv4dVfPZObfpK+IVyCzFSgUtWMC7vQAwJSrcOLpKbZViZUqAjhhmudBtttdGpKOCxpSbMBG1TYKb1SRE9unVI1jShyHN5k9RHzFvAJeAa+AV8Ar4BVoUAXmsmf9auroBq1B/DXNuibuWPAyXSjQGRd8ja6+/ic0OTlFxx2xP+232/a0+3tOoD7Odvnq5z7W8IOg3l/YrIOs2u0CORU4I9amcRkC0Ki9SdUzADQI8TT1jEAaWJ1YLWPKGahpPBi42qr7cl4Br3+sLIcAACAASURBVMBcFShwzhWeRc6zUcXN7BCn0j6l2TgKcBTMKLSJ1DdRWHHcJlNtUuV5N5aD4/DGj1GvgFfAK+AV8Ap4BdqxArdOHdzwzWrWNXHHgpcbbr6Njj75Qtp2qy3oV7+/h45YsbuAl2/deAsdf/pl9LNvny+5L+30aNZBlnYNK9UzU2w5GOY4YAAaBAYD0+Audk9pkLq4MxMgDdroqr2JoQxCgXm+25vS3jP+eV4Br0BlBYqAN8EqlVTdJC1Uap0KeTch+0bBjebh5Et9Is+NAY61xURwXjxerrpJZt8w0OnQbgl+RHoFvAJeAa+AV8ArkN0KOHjJ5r5J1WoEZctz1luHzj7xvbTbio/R7jttK+DlgYceoTfu82H6ymc/Si/ZbJNsVmKRa9Uu4KWazUcQFRQyk9wqF8HAHAPM0xoLPMEWhHEGNRjnhAhuqQ17kwIatNbGOKxOanPCUIGNW5yqqbwv4xXwCqRdAagBy0OLk9apOAdHbFOizpmS/0x1o2ods0tZq3DNuLGsGyhzEEpsypvKoUAdUer4wyvgFfAKeAW8Al4Br0A6FXDwkk4d0/6UVMHLy998CB36jrfRin12nBW8XHPpSfT8jddPexua+nmdBF6qLTQADaDMJJuYoJYBkoHNSUxNwdqE0GDMUYsTwoEBZzBUKGNgRocMcDwkuNry+3JeAa9AgyqA37rZw4rj4OLYMgUkbW3ELfOG4Y2E6wHgAMCEblPSJlztVFDf5BMtw2ezT8k8ycbxh1fAK+AV8Ap4BbwCnV4BBy/ZPAJSBS/7v++T9OSqYfrOF0+lPQ46IVK8fOKsL9JV3/0x/fL7F9NAf3t12XHwUtuBDUuTQhqFMxiKtYmDgwXVSIAwhwSjg1OkoolBTVI9I5AmN1DbCvm7vQJeAa9AgyuQtE5Z56nIQsW2KgM2UN5ou3CoczAeulGJ+ka7TVnLcBkKjNF5Cmcs72Zm9k3URtztUw3e+/51XgGvgFfAK+AVSLcCDl7SrWdan5YqeLnz7r/R3oedRMuXDdHY+CRtutGzqVAo0l33/J3e9fY30YcP2zut9c7M5zh4acyuKIqKRu1NkwxjMK6qGahogGg0PLjEoQ2ANFDOaDenPsmiURUNMmnicczzgM3G7D//Fq+AV6D+FSiEduAz4A1wDVumpoJlyuxSCnHi1uKYj0aYMawJIEfsUMi3YesUwopFgaPwpnwYz/Pf1vrvb/8Gr4BXwCvgFfAKzFYBBy/ZPC5SBS/YRMCXUz5zBd39l38IdHna2svpXXu9id7Jz3xeGo231cPBS/Z2J4KCYXMCpNFxqGiQR6PqGYxjDPNzweoEKANYk5N2273B6mS5NAptPI8me/va18gr4BVItwKA3AZukmHFMg54Y7CGA4vjsGJobzQXB+ocqG9K/Ita3nlKwQ1UOKLMCVaq2eCNqW88/ybdfeuf5hXwCngFvAKdUQEHL9ncz6mDl+Rmlti73u53vRy8ZPPArnatklanMkgj+TQKaDAEyMmXuqMcGnR2QlcUqGqSahpMa2hw+0HGamvqy3kFvAJeAbVPxSHF8XgcYoyuUwJyAsSJg4s1IwfTZV2nrIV4UNxAhVOScVXiRHYpUeMkO1AhM8d/k/2o9Ap4BbwCXoHOqICDl2zu55rBy1///iCtXD1S1dZt8cKNqasrX9WyrbKQg5dW2VO1r6dBmimoZRjGyBBJNAA0wfaE+UVW6hdybHcSMAMlTW/C8gQ1jSlp9HVV0/hFQe17yD/BK+AVaLcKFBjgzKnAMbBTloNjrcYV3piVKicKHIUzeKp1CkBGA4xVjaMBxwptklYqAzlYvr3OYdrtePHt8Qp4BbwCXgEiBy/ZPApqBi/IdIG9qJrHLdedT2uvtbSaRVtmGQcvLbOrGrqi6GQClQzgDICNdHYS9YyGCWNoShpR0/AFQReDGs2m6ZUuTzaeBDVig8r1N3Rb/Mu8Al4Br0CrV2BNCpwk2IEKx4CNthE3eKNAh3W8Ad4oxLHsG1XehOkZNioDOQkVjgOcVj+kfP29Al4Br0BmK9AM8PL+vvObUo9zJw5vyvcu5ktrBi9//cc/aeWqahUvG7niZTF7yd/T9hVQOKOgBuHBNq5wRiENOjwpzpmQHJoI1HCr7Tx3fIKyRttx2zOoa9gW5Q+vgFfAK+AVqL0CxVIhKHDiluFJG5Xk3DC8kY5TbKHCf7NZqBBhrKBmtk5UqsDJlXWhmmmfEmWOd6Gqfaf6J3gFvAJegTarQDPAy2vyhzalij8vXtiU713Ml9YMXhbzpe30Hle8tNPebJ1tsQBhVdQYqGEsI224tfOTBAgHWCPhwSW1POUZxEi3J7FCxdYnsz11M8jxh1fAK+AV8ArUrwIaYqyKmrgLVRhnuFPg32+JLI5CjK0DVWylwuslfre1EY8VOGyZCqqbEqxTIdAYoCa2UZVn4GCZVnjA6FVshRX1dfQKeAW8Ak2sQDPAywFdVzZli68o7NeU713Ml6YOXn78v7+lK6+9if7xwL/oyIP3pDe/7pV03KcuoWc87Sn0gYP2WMw6Zvo9Dl4yvXt85UIFNJMmqGpCpydMY77YnqQxt04jE0EADYMZKGvybG0CoAGoiRU1CWDjoMaPM6+AV8Ar0JQKlBjgVCpuZpuGqrKQA6qZDLk3gDsKfqDIwTCZfwNAIzaqkIGj46zCqci+iQONuc14lJvjmWVNORj8S70CXgGvQKiAg5dsHgqpgpdf33kPHXD4qTQ40EcTk1N0zH/vS/vttj1d+OXv0PmXX0t33HiRvNZODwcv7bQ3fVtQAXQji2xPZYoaVdBotydgG1XW4D4sQE0XK2qQTYOOT10snwe4yZcGeLpHrVFmgXJQ4weaV8Ar4BXIXAXQLnxeiCM2Ku1EpcHF5QocQBzivwTaZYoDi6Mhg5wQXizKnGogjufgZO4Y8RXyCngFWqMCDl6yuZ9SBS/v/cg59OTKYbryguNp9/ecQLvvtK2Alz//9f9otxUfo29e8gl6wSbPyWYlFrlWK3pPpv7cEF9uDvJwUC44/eEV6KQK4I6rqGcEx7CyRmCMTheKBRqjYXkF86GsQeYBFDQxrME4lDWAN2yBktcM1KiyplVk8J20331bvQJeAa/AbBUoDzKu7EpVPj0lsAcgJ+5ABaCDvyrIwemmIR6HnQpqG+06heybONQYgAcGpNa3UfnR5BXwCngF0qqAg5e0Kpnu56QKXl7yhoPo/e/Znd6115sEtBh4eeSxJ2m73T9AX/nsR+klm22S7hY0+dM+sNYHabg4RiPyHOVWwjm+TFxCA8Wlcse/h08a+gTKKJzplhMGf3gFOrsCBmfirk9xoLC07RY7FBQ2/P/cBP+7KkVdnlRZo1k1an/ijlCipgGkUVAjGTbeoruzDzLfeq+AV6ClK4AcHKhwigxlyluKz5xG23G1TDHaj3Jx4uXMRpUcUrITFf/V0M5UUOl4O/GWPnB85b0CXoGmtJPOZMaLJtln5pEqeNnlwOPpqWsvo8vOOroMvHztupvplM9cQf/7nQto+bKhzGx8Gity2TpfKPsY3L0ZLY3SmICYERrj8dWFURou8H3/0hj1sfWiWOL7+aVBfg7FYCbAmR63YaSxW/wz2qwC6CSi9ifLqYm7QAHUSJSwKGo0pwZ6GwAZBAr38L81Ysl6F6NPCmqa7gSsiaCNd39qs6PGN8cr4BXwCmgFqrFRFcU2pbDGlDiaixN3psIZfJyFo3YqtU51s0IngBsJKp4nD8e7Ufmh6RXwCtSxAq54qWNxa/joVMHLdd//GX30tEvpjdu9gu747d207VYvpqetvZwuufJ62vqVm9OFp3+whlXN5lsrwct8azlZnGQYMyZAZpSHIwxloJYZ4/krC6vZmlHiS0MGMgxl+kvLqYcvBlUxw08eYtofXgGvwPwVmJYW3fxMtOrWaQM02p4bqhpR1/B/mk1jVicNFs6VEBqpahqDNDpUpY0ra+bfF76EV8Ar4BVohwrE7cRndqNKghsAHLQVR4DxFMMbbSmOoXWyKoR24qquMZUNsm8wrpaqCuUN/y1CwLGGGHuYcTscT74NXoF6VcDBS70qW9vnpgpesCqXfvUGOu+ya6hQiBv+veqlL6RPn3AYrb3W0trWNoPvXih4mW8TplkuC8XMKNuWxvnO/erCiChmRhnOYH6uxH9w+Y8v7uJ3F4eoVyxMbGfKDQiYwdAfXgGvwOIqEMEaCRVWVQ3udk6FQGGoaswCZTk2YnUy+xPGWbUmwcJieVI4I227eRm06nZYs7h94+/yCngFvALtUgGE2M9unyqUzYfyRhQ3bJ/i+3I0mRsVVY6pcLQbFTJukkHGmoNjChwBNTxd3kp8lkwcV+G0y+Hl2+EVcKtRRo+B1MELthMdje67/yEaGR2n56z3TFG9tOsjbfAyX52mi9McVgqFDBQzrJZhC9NoYYpWFVeLcgatgQFlhkpridUCGTMS/CtwRpUz/vAKeAXSq0A5rMFJcrklajyEDcNmOlJaJdoaU9ZASSNPCRbmlt2RFSoZLqwKnJx3+Ehvp/kntV8FcK8H15/+8Ap0WAUQZlyptkl2p8LfKOtCpaobVd8A/FhnKowjxNhCiuOhKm+SihxRgUZqHFXlWFvxuKW4/2PssMPQNzdjFXDFS8Z2SFiduoCXbG5qfdaq0eBlvq1AGJ1amRjISMbMmChmJgoFerK4UuBMH9uYhmgt/ihVzgDK4NnLtgpAmrzf9ZivzP66V6CmCsSwRq1QSVgDeCptu8UGpQobdILKcTYUVDSAqSVJFtAOULPm1QRblNugatpN/mavgFfAK9AxFahWhTPNmWsFqEFDS3GFOQZ0tMW4pllq5o1aqYIiJ1ipBNZIS3FV4lSqcSKQ4w0pOub48w1NtwIOXtKtZ1qf5uClxkpmDbxUszmwMWkAMD8ZzKyaZqVMcYpWsmpmtDjOFqYBsUto+O8gDQDK8P/VyoScmd5qvsaX8Qp4BVKsAPICxPrEQ8CYCNZUZNbADjXTBhVya1hZIwobWKNK/fzvvFttUFELb6hvcILsD6+AV8Ar4BXwCiyuAnEWTrl1qlyJYzaqpHVKwY12qNJh3IlKQQ1gTp7PSfluRLBTabgx4A6U3gZtZgz9b9vidqa/qyUr4OAlm7vNwUuN+6UVwct8m4wA4NHSiIT/As5Yq2wEAK9mFc0U5/cMltaWP3J9paUaAMzKmb58vwQB9/EfRFzQ+cMr4BVobgUM1ljIcFJZUygWaJxGo4DhqZwGDeNOpbXoluwaZNaIJYpPaCW7BhDHWnhrvg1Aratrmruv/du9Al4Br0A7VgCtwtUKFXedKrDqpsh/s5IgR6xTkonDywU1Tgxw8BnFKJi4i288lBjcAOQotFFwg5bicTtxU+Ik83EQcOw2qjSOM1QxTgNN4xP9M5IVaFvwMout+IrCfi2z8x281Lir2hG8zFcSBACLnYltS+M8HIZyhnNmEAQ8xiGkCALGHYn+0jK+q97DoaIa/gso08uQBuqZPtbPIGjUH14Br0C2KoCT3Glp261Ps0VF88QaxZob2KB4HNk1ozQsdxklu8YUNZJLgzBwza6BJUq7QwHaGLwBtOETX394BbwCXgGvgFegjhUosRXfQA1ycQBnDMxUBh1LDk4iG8dycfC3j7jJBf9xk/PcZCaOWqdCW3HJwAFaKFfgiAoHTTL49bx0rnKFaR13eUd/dNuCl1n2qoOXDjrUOxG8VLN7cUE2jjbZDGLQOnukOEGj0xMMaQBseB5Pd/Hdc1y09TGM6WYQ08N3IGBtgu2hh9EMMmd6kWkBS0Te/zhVU3dfxivQrAronUZAGViiFNhg3ICNtO0WuxSgDaCO/of3Acj00lK++4UWq3Z3EYBGn6rAMbBj8+Npt0c1a6/793oFvAJegc6qgAKcCgWOhBXHLcaT6hz924jOVGqdolKeJnIjPA4bFv7mKcBJDkuYF+xTSA3XTByFNbihSay6UcsVYI8CHx3qE+HH/ujsCjh4yeb+T13x8ujjK+n6m26lfzzwb3rLDlvRSzd/Hn3j2z+iZzz9KfTaV78km1WoYa0cvCy+eGp1GJc75xMMaNA+GxdmIwXAmklW0UzwvAlpoTjBNqduCRZF9xcGMqyigZ0J0/IUJQ0/GeZ0I8fCpaCL3zH+Tq9AAyuAQEcBNjg5tbBGHscJKmCNnLIC2PBr1loVvxO4c2nTALisseP/4x5jMqhRgY2BGwzxeuW8Lr4DiRNVV980cMf7V3kFvAJegQ6vgAYaG7Sx7lQAMuXzoDSXGxkMb/AeWIMV/gDfAOgoxLH3YdrajCvQ0afapFiVw/AHNywU1CjEUcVOxbwEzImWS74n+swO35EZ3HwHLxncKThL5X/AOGdN5XH/gw/Tzu84lgqcAYLHcUfsT/vttj0df/pldMPNt9EdN15I3V3tpVxw8JLKoTPvh+CPDi7CFNJweChfeMHSNM5ARp9TCmn40BuG7YH/qADUQEkDZU1XkeFMXpU0PUFRg5BgmQasybvdYd6d4At4BTJaAXRzA7hR6XgIaoSUXOBNsE5xJoAAnhwPRXkTTmT5ddw1ZNOknNTmi3r3MO6yYdP4jQC00QBHAzxd0loVc/U9+gTwCUGPLEn3h1fAK+AV8Ap4BRpZAQ04NhhTYIU5YI5NI/GG/0bK30lbjl/lv4EKb/R1/F/tWbZM+RC2qencOG8WgA6u7wBxFORgHModTJcE+JS/HsMehkAJ+9bc8CcAIny+31yd91By8DJviZqyQKrg5ZPnXkE3/+zXdPnZx9BRJ15Au++0rYCXX995Dx1w+Kn03S9/ijZc/1lN2dB6famDl3pVtrbPBZgxUCMWB7Y2AcxM8B+ZcYwzqIHdARAnxz/4w9zRCUqZboYwoqoRZQ2gzADPh7KGsykM1ARw4wHCte0jf7dXIIsV0BPRWZ5hvnaV0tenJPSReHosAB89kQXWkfuQvCxOOss7bcSdNyQXh8EN7jTG4CapylGQY6+5IieLR4yvk1fAK+AV6NwK4P59KQFvbLx8qAhH5slNEIU5Bb4Rgv+QOwAVD15XEMTKnqDnsXnxd/Dy8jAQE9Q8Mt0jEQam7ikJEEoCm6Sqh8clbyeGRUm7VhkAasEbKA5esvlvMlXw8vI3H0Lv2XdnOuSAt9BuKz4WgZfHnlhF2+x6BH3p3I/Qlltsms1KLHKtHLwssnAZexv+cDCeiQJDNY8CrXunJZ8GVqcJvoiaEIADkAOZZ1FAjQEbwJpuDhLtZlhjNijJqxFgo08PFM7YjvfV8QrUuQJ6kmmtURXY4HfFMgBMQi4KnNCRAyelmo8DkKPvl3F+in8f1ijx9CuUkYBHePoBcSJQEytzInBTqcrxHIA6733/eK+AV8Ar4BVIuwLI2cF/sYonjAMCiWoHZ+iYpwodszSrNQvAJyyDvB5Ryur7Y/WPwh/+MFb0oNsjjMyq2BGoE8EcbRKiqh6DOlC5ltu7ym1as1i6SrHVK63MOgcvaR916XxequDlDXt/iF72ok3pU8cdVAZefnrb7+iwY8+hm68+m9Z5OtoQt8/DwUv77MuFbgnsDQJo8J+AGoU1uAsuqhoBNFDWQGkT8ir4NYE10tVFVTXo8JIXpY1ZoQBzuBtUgDWY77LKhe4dX94r0J4VqFTkCNRhOJNsm4rfoNhmFUMcAToCd3BKifdBkYNfmCGBOsl8HLNbqeIGKhyAHLVR6bzZpqHecWtVex55vlVeAa+AV6AzK6CgJyh2IqjDf0kxn1U7STVOUuljwEdhj+bxmH3LIA/mEYOX6dxYZOmK83litQ5gDq4boAQSsBMClnXZAISkSxaeObpz+tSG76wDuq5s+HfiCzu2q9GZF36Dvnz1D+jED76LvnjVD2iPnbahF2zyHHr/CefTksEB+uHXz2zKDqnnlzp4qWd12++zTVljoGYqAjaqqBkvcJgo/5CjXTdUNggRxbIg7QAxmlvD0IaVNXjmw7SoaSRkOAAbfq0n39d+BfQt8gp4BVKtgIAcaa0asnGCysZAjuXhKMxRLY79Z+odU+NYxw5T3SThjXbd0GechRPbqMqzcRLLeLvVVPe3f5hXwCvgFfAKZLsCADqq5ikHPiXk9jDAidU+Fa+bwoeHPyo2/prbwcv8x1Wqipep6QId/OFP0y9+86eyb14yNEBf/MyxAmHa7XHJOpeJg98fXoF6VsDChaXLi2RMYKgqmwkEC1tmjQz5LjbT89WlVZIfgbvVyK1By16BNwJsVF2jMAd2qFhhg2m/a13Pvemf7RVo3wrAWhUFHItwW9U3lUod/I6ZiUpzcWIbVjRfTjzZpR9Zq1RlA5WOtU+NQ5BjWFMedFyekyPLuyqnfQ9A3zKvgFfAK+AVaIoKxMHL/AdequDFvu5Xv7+Hfn/XX2nl6hHa+Lnr0uv+6yU0OIBWn+332Pupb+WOOb3UmxukPs726KMB6s/1U39ehwMY5ydyPvzhFWh0BSxkWCENIj/DkKdhh9KwYW3TC4CDwGEshwudgdIyJuucSS/qGgAcWKBCVg1f+Ai0YWCjahudn5Y3tdF18u/zCngFslkBSLzjsONEXk4iJwdqnOg/a7sa5qgqJ3S0Agji1zXw2ACOdq0q72Q1E9bE9qpERyvrcOWqnGwePL5WXgGvgFegQyvQDPuNg5f5D7ZUwctnLvkmPffZ69COr3sl9fZ2Rnve7734LLlgHSuO02hhgocTtHpykkbYMjIyNaFDnt9V7OZwJ1y6JuCMABqAmQBreLrLT+DmP2p9ibpXIAY2CmVMYYNjHWDGbFAAN1G+Db9WkssXhTLIrukvLZGEebFFMbQBwDGVjebYGLjRlrz+8Ap4BbwC9a5AeeCxqXJguWLdTbBUqR6HQ9RFjaNhjBaErMtoro5052A4pFYpQBwFMwpyKluR6zzPyqn3HvbP9wp4BbwCnV0BBy/Z3P+pgpcPnPBZ+p+f/lKgy25v3pr2330H2qDu7aNxWde8MD+Al2oeODEbKYwLmBnl4cj0FIMZhTIj09yMlIejDGmklTFfpEaARsBMDGcGWEnTx22P/eEVyGIFEPIJUCPaGlbPqEVqQmANLl8E2PDYRAEwh5cK1ii8DkIDWAMwI7YoUdkgeFjVNmqZAtDhIUMcBTwB8nh3liweDr5OXoGOqACyu9bUglzzcrS9uClzKjNzBPqE3lX8ceLjF0iDdqcJmCNdrfj3DnZSABzpaDUjN8fgTsVrHMboD6+AV8Ar4BVo/wo4eMnmPk4VvGAT//Dn++jKa26iG26+lQqFIr3wec+ld739TfSGbV9OPd3td0e7WvBS7e6fKnL74sKYAhp+Dk8CzDCkmYZ6ZkpUNeM8XJZfTqVinvqJLU7BzhQBmgBrkNvhD69Aq1QgCW2SihsAG7FJIctGMm4Y3EjwMKCOvgawiXBhQBrYoaCsUXiDbJvuEEoc26NUdRPAjf87aZVDxNfTK9AxFcBvWgxzEu3IQ4ZOGegR2B2ydYLtyoKP40BkdN7gvByDNAJ04swcKA7FchXydIDBK4FOeTerAHVcqdgxx6RvqFfAK9A6FXDwks19lTp4sc2cmJyi7918G33lmv+hP/3lflHB3PKt8whBu+30SBu8VFMb3F0TaxNffI6xegaARtQzrJwZZUAD5Qye03ziBuVMb2mQLzNnszipmqbb1QLVlN2XyXAFcJEiOpvQBUo7sQRgg3n8b0VbemMcwwBteBymAahoEDoMhY2obMqmzR6lUMeAjWTbeHZTho8KXzWvgFcgWYHKvByDN/i9RINxnY6tV2qpwu9qEt+o5crmoL+GAJooAFntVKLEEWXOzG5WcSvyyg5XAfb4OYkfuF4Br4BXoKYKOHgh+tcjj9OyJYOZypmtG3gZG58U8HLFN39I9973AHV15emW686n5UuHajqQsvbmZoCXamsABYHlzkBBAzBjgAYqGnmNn1TKcThwvypnAGoQEAwlTZ4tTwk1jQOaaivvy7VSBQAyFdqEjlEB2ohlSsKGg8KG1WhsmmJwEyttoL6R3BpWzUgIMQMb2KLyxaC8CcoaDSDm10KujVmk8i79b6VDxdfVK+AVqKjA3DYrIJlEi3IJOVYzlYIcVfFIK3LL0TH4w69bRg5uHBVyxSj8WK1Vlp2DduTVKHOCssd/b/349Qp4BTqkAp0MXv5y34P0jvefSitXjcje3narLejckw6nnh7Yc+d+QDSy67uPp/GJSfrR1efU5UhJHbzAagSVC6ALrEbrrvM0eudeb6RdOfOlHTsbZRm8VHvEwN40DvVMyJ/BEBeXK6fGWEHD1idW0YyxgqbAvvOB0pDk0HBsKj8HWTHTF8GZvjzG9ekPr0CnVEABjWbamOrG5pklCkMED48UR8OyuPjgXBsR+yehDSxSqr5BrkOlJcqAjaluPJC4U44y306vQGdVwNqPixqH824ia1VQ2+B1xTpxG3KBOAHpyPKSm6MaHkzjJlMS2pR3s4ozc7Qd+dxAB++LgI+rczrrwPSt9Qq0SAU6GbzstuJjNDQ4QBedcRT93z8foT0P/jgd+779aN9dXz/n3sNNhMOOPZtuuf1OeubTn9Ia4OXIj3+WfviTX8pGvem1r6B37fUm2vwFGzblEC0WS/TPfz/KxVu7rtky7QBeqt1BaMkJhQzAjDwlEFiVNBOFgoQHw+I0UZhWBQ3na/Rxx6aeEiANt9TmZ7/AGahrFND0ulWj2vL7cm1YgcpcG9wRNniDCw7tIAWwYyobHmIaNiqGo8hswEWCqG1Cto3apRjghHBigzQSXFymuIECZ830vw1L7pvkFfAKdGgFynNzzFqlipuk1UqDjwPSSYQemzon7milY2qzilU4Np7j31cNR9YQ5DWpc6yduS2Tc3VOhx6lvtlegXQq0Kng5fEnV9PWuxxOl575Ydpqy/+UYh514ueECXz98yfMWdxPf+7rdP1Nt9Jb3/AayaltJXGcAwAAIABJREFUCcXLqed9hZ79H8+g3XbchklTfzpHziI+5cYf3U7HfPIiUdzgceTBe9J79t1pzk/CDsKOSj5AxT76/gPo2z/4OR33qUtmvPeX37+YBvp7qZPAS7W7AtQQCppxATRhyEAG80aRQxMyaBASPMnABiAG9iZV0TCMAahhYJMENIA0HhZc7R7w5TqlApZtI5k28ozBTdRdKrQAN4uUqnN02QJn46g9StU1Ek4s06GbFMMcnWdWKQQVezBxpxxfvp1eAa/A/BUwdc6Mzlamzklk5+D3V5BPAu6oOqc8RwdqyLirlWbhdLPKGJHLSaBjlqw4CHmmUsfVOfPvQ1/CK9BuFehU8HL3vf+gPQ76ON101dn0rGesLbv1s5d/i6698adzwpRv3XgLnXTOl+m7XzqVwBC+dt3NrQFesnDQjo6N06t2fq+AlsPeuQvdwPTqo6ddStd/+VNztrYGeNl5h1czMNo62oSnLF9KT1t7OV33/Z/RCZ++nK659KSyzdv4uevyH7+cg5cadzouHM3mpLBGW2urmmZKLE5jyKPhi8dCsShhwIA0sDv1iZIG46ag4SH+Y1WN59HUuGP87R1RgTjfJoAb3OWNII4paxKKG0AcSPmRcyPLasaNtgEPnaTEOqXjZqPSi4aE4sZycXhZ/I76wyvgFfAKeAXiCsymzimGbJzKIGTLzpHcnABwIowTumCZDQtW1nKgE7pZRW3K+TdZ1Ds6nF2lUwF3XJ3jh65XIHMVaAZ42a3r3KbU4drC+6Pvve1Xd9GKD55BP/v2+YRreTwu/eoNdNEV36E7brxoxvr98nd/pncfdTpdfvYxtOUWm9IlV16fbfCCjkVfv+5H9L5370rY2Pv+76E5i/6efXcWlUg9H9+7+Xb68Mmfp1//8BLq405KeLz6rf9N+++2A733XbvM+tUALysY1MAaVfkAePnE2V+i3/DnzfZwxUs992b5Z+OEQhQ0aKktmTSmqmFQAzjDzwKrbVZOjxAzmqCgCSoaBjYANN3FcpsTlDT93H7bH14Br8DiKmB5NqawSU6L/iaoa5K2KeTdjIa8m1yJzVKwSQVgIzaphOqmmyGN5t0EwMPyfR33duCL22P+Lq+AV6BTKzBTnRPyc8rsVrENK4I6IVNHAQ7/F7Ut1zblCmn4t7qsTTnbrGC3Qpty/j9sV7Bcma1q1vbkod2522A79Qj17U6rAs0ALxt3H5jW6i/oc/4y/YVoeVO83Hz12bQOx43gsSbFC2JSbv/N3bTNq7aQZf/wp/vo/gf/TTu+/lX0Ec6FWb4s3aZANYfrGui47gun0CmfuYJAjuZ6JOnTgiq6gIVBtS7/+vfof79zQfSuvQ87iaBQOeWYFbN+EsDLQH8fbcTLrPesp9Heb3udjOMB8ALFzH+9YnPq6+uh12y5Ge2207ZRboyDlwXsnAYuCkhjCppoGOxOoqLhHBpV0wDgTDGk6eVTBtidEBzMtiZW06iSBk9+NWTSANS4mqaBO9K/qu0rILk2IUdBhomuUvYaxPloCY4OU5Z3g84oBnVwQi/wRtQ3+hSLFKxSRbS1jQOMpQOVQRsDOp510/bHmW+gV8ArUJ8KzNWmPAl5YJFKWrEqgY6AnJx2vtJuWNMCc8xCJbAm5OVAsdPF52eclazLBNijHa9mC0aOP8eBTn2OAf/U7FWgGeBlq56Lm1KIW6cOjr7XMl4uO+toetXLXijzAVceevjxWTNefvDjX9Dv/vjX6P2/+v09dA93Y96HWQAEG0uGBlLdpprBC3JU0HZpcKAvE5LxMy/8hnRUSobiHHjkaVy4QTr/lCNmLR6AUT6f59dKdNMtv6JHH19J1156Mm28wboCkq793k/pKWst5WTkh+nmW35Nb9zuFXT2ie+Vz3Lwkurx2LQPA5xB55kkrME0MmnGxPIUbE88FDUNMmcAZiSXBkoaBAVXqmkU2HjL4KbtVv/iDqmAqWy0Va2qbCyoWK1TIfuGIetEEt6E4GLcw5W24Mi2AYyRUEwFOVGXKYE3QXlTGVKM4EyHNx1ytPlmegW8Ao2oAKxVM3JzQh4OQA5+1cuAjqhwYruVaHP4918/RZU6MdBJQJ0AbqIuVzlYrfgpocmq0rHA5HLrVbnlym2zjTgq/DuqrUCnghfUZ5cDjxelyudPO5IeeOhRznw5gY75731pv922lxbTu644nuNI3kZ77rzdjHJm3mqUXOPPf/nbEq678/ZblW3IvUyOzrn4ajrzhMPq3lJ6MYqX5MpOcg/v17ztfbxzdqAPHLTHjB3yxau+T0g+/u1Nl4nqxcFLtT8B7bPcXGoaVdLwBd90iVYVx2icrU+wQ+HuuqhopLMTK2skmwagRrs6JTs8YdxBTfscK74lrVEBZN2UQZuERSppl9JgYmTchIBiUeKoCqfAlwCwRaniBgBHc20M3mBeLgovVrgjuTd8ct8VQI63B2+N48XX0ivgFWjdCmgXq0qoo/apyvwcCz42AGRKHbVa8XtygPwKdAh2qqDSMWijeTlquVKYE+fnzBaIDLDjHa5a99jK0pp3Mni5528P0AGHf5KGR8Zkl2z9ys3pvJOPoF6OIHnsiVW0za5H0BErdqdDDnhLa4MXWHpe9IKN6Lgj9ivbEMh7tt/rKPrGRR+nzTbdoK7HpVmfkMmCAuPxyp0Oo3fu+cY5M14qVwjr+rr/eilvx/4z1vUHP76D21JdIAE9UPlct8XpfPHsLVnrulNb/MPR/hctgctVNQxlAGagpOHhOF7ncQxzRVy6KZjBsJtBjbXehu3Jxg3cuPWpxQ8QX/22qEDcYarcOjXDRoUW4VDhFAvSXQq5VOOlcYE3RR6HFUpbgYcnK280C0HtUpp3o9OxbSqRe8Pv94dXwCvgFfAKNLYCswYihzwcAzcGfBTgIMDe8nIAgnRaQ5TjluYsxld4E6xUCnMsFJn/FuDvAofU4/U1KXIMCkXAx0ORG3uANPjbOhm8WKkf/NejtHTJIC3jZ1YeNVuNkhsyG3iZ5nbB3/j2j+jU866kZNBNvQowMjpOr9jxUDr0HW/l59tmdDX66W2/oxPP+iJddMYHaZMN1qO//v1B7tt9G+2+0zb0zKc9hb55w08kq+bC0z8ohAwqns023ZBe9qJNpOX0wR8+k7q7u+k7X/ykbMIhz32TWE8G84M0lBvkoNYBGuKw1qGuAVrCz8Gufp5X30DhetXSP7c5FUDHGIE0uEALFihYniZ4PgKExyWrRjNqAGtwl6UHWhoECJfQ14mtT9LfiTs+MRSEqsYgjY778dicPevf6hVYcwWS8KbSKqXKm2Clkq5SUNvE8yz/Bu8Te5SFFEcQR+1TGlyMZzmssWkLLnblnR+tXgGvgFeg+RWYO0MHOku0IleEY2YqVWDykzNzkIVmmTlQ5uh/aseyUGQNP7Yn7FMx1KnMzJlNkTMjV4etWv5ofgUcvDR/H8y2BqmAl5e84SCCRWdNj5du/jy64vzjGlKF6//nVjrmk3HLqKSc6HpuL33MKRdJwM7mL9hQwAuA0ejYRLRugDaHv3s3mT7+9MsI/b3tse46TxNos8H6z5JZv3zTcXzyW6Dh6XEanpqg1TxcxbVYPTlJKyd5moeTTGaW5ofEXsIohmGMApkhfg4ypAGgcW9oQw6NtvwS/DG1fBpT1gDcTHGA8CjsT1DchJyaCZ4HoNPLgKaHAYzYnzDENFQ2YX4lqOnxu+hteez4RrVnBSzjxuxTcWCxhldKo3CxS/HvAUMcDTFmkBO1Ei9wYdCHhAGO5Bxo1ylT4hi8UUgTAI7YpYK9KprnatD2PMJ8q7wCXoFWr4ACnXLLVWTBCvAmslYB19i8HGMdGw/gJ/ocsWvx3eigvjGVDpkFSwCPZeNUtCVPhClbx6syFY+7CxZ0yDl4WVC5GrZwKuDly1f/gEbGxumr195ET3/qWrTDtltGG9Dbw52AXr4ZPX/j9Ru2UfgihP4iDPc/nvnUyHI01wrA3//IYytpeHRMMmqQ3ZJ8AMr865HHRar0tLWXl70G8DLfo8A/bgZlhqcnGMxMCpDBcHiKhwxsWBdDA6yaGchBMTNAAwAzopxhOMNgpi/v8vH56uyvV18BtT2ZoiYxLLGKRixQrLoJ1ifYn6b535MoaqTjk0Iae5qKRnNrVFGDZ5ff9ah+h/iSXoGMVQB3RKW3iMGYaFw7jkRqmyiwGOobtU/hBF1P2DW0OFbZaEAxAI6GGFuHqVh9g5BiAJ24ZTjyERB+7w+vgFfAK+AVyHoFcE01a4ZOgDSiwJFOhhjTIHzNy4n/bqgqJ7Qut3Bk/vui0AZPbl2eyMPBuMAe/tuCvxe4IRBbq9D5iv/m8DnpDHUOZ+7kkb/DVq12ezh4yeYeTQW82KahA9CypUP0vA3Xy+bW1mGtqgEv1XztCAMZQBl5MoxZyZ2ioJwZBqTh6elCicEMAxm2M6lKhiFNV6+MQz2DZ7df6FZTal9mERWABaIM1ogNSoHN6DRDGsmpCbAmABsq5WkJLZN7HxGw4QutSjUNWnlby263Nyxi5/hbvAIZrcDM0OKQfxPyDfAbom3BFeQA2kDBp+3DVTyPk3L0jcWJ9kBpKZ+cF7RVeFDgoFV4DGqC4kbgTcjBSeThZLRMvlpeAa+AV8ArUEUFki3KobKJwpBDByzk44S/HJqTI39rtAeWdLySeZahwy3LWdE5kRvhvzH5YLdSgANoU5Lg+zWrcpK5OVkLRnbwUsUB1YRFagYvDz/6JP3urr+IquWhfz9GK1fzATzHY4sXbkxdXe115yot8DLfvp8WO5OCGUCZ0alpenJCIc0I20kwxA/AAIMYPAdZOTPI9ibky5ilCXCmy+8czldqfz2lCogFio/NOKtGA4RVTZNQ1PA8WKCgrMEfP4AYZNRAQcOaLzlmYYdKqmmwjCtrUtpR/jFegYxXwE6U1UJlQCYoawBp+DdEM280oHgshBWr8kbhDk678ziRlkSsAdli3B0VC1XUhcqUNhpY2Z03JU6swvHOUxk/WHz1vAJeAa/AAisQty6PrVei2gm5akH/qcYsgTwhUyeodHR+3LJcgc8UW3UHpPOVWq5UqaMZOqrcMcgj+Wt8rjsjALnMfgW0w5/DNxa0zfmaHw5e5qtQc16vGbzccPNtdPTJF9J1XziFPnbG5XTn3X+bc0tuue58Wnutpc3Z0jp9a6PASzWrj4vaEQ5hNfUMpp9gSANbE1QzI/zEiaYqZzj8N8e5MwxmBrr6GNJw/oxkzrCFhE82/eEVaEYFLK8maYNCoDDAzBjDRwkWZoUNcikszwawBm15AWoU2HBmDUAN5vDxjRbevXxXXOGNKm48s6YZe9e/0yvQ3ApE2TcWRhlBHAU0osDhE2iobfBbBAWOqXDMQoVASgU4+MUZlIBKyb6BxB2ZN8EqJdk3mA9FjliqkhBHT7L94RXwCngFvALtWwGoPsU2lcjSiXN0TLGjnaw4EVTVOWLD0v8UA6nyE+MANhM0JnOhA5VgZLQol3blCNAPbct5uR8XP9Pwwm7Vc3HDvxNfeOvUwU353sV8ac3g5ZHHnqTf/jEoXrht9MpVa1K8bOSKl8XspRTfAxgD1QzgDNoYP8kBwCOwNE1hvoKbUjFHy7uXUL7Il7EMaPoZxhiYGQCgEUjjGR4p7hb/qBoqIBdIwfakbbnR7YlBzXSRgc2EWKAU1ODOOCtrOMcGgdi47w0oo1k1QWWTBDSYj4sogTWAOfoOv2CqYWf5W70CLV4B2C6TocU6btkEDGrwOxPCitGhzkKLk12nMM4dYsU6hdNps0Wp+kbbhkPiLi3D5Wm2qQByEtPtmE3Q4oeIr75XwCvgFah7BSwaWe1TmpiTBDwXThxd93Wo/AIHL/OXvGbwMv9XtPcSWVK8pFVpnDiOcuDvCEMadMWBUmZ4clrhDD/RPvuxyRGRxPWzeqCfrU39HLwKe1M3X6j2M5Tp44tUqA0QCgy7U6+HA6e1e/xzUqoA7m4LsAltuzEOMAlAM17gP198oI8wuBFYI8vqHXDcXRAYI7BGL5IU3KjSRgEN2nizAocvnrCMzAtdX/xCKaUd6B/jFWjhCkjrcLuzmVTg4M4mbJoMiKf4Nwe/TwJ2+D/QmpHSmPwmQcaOO554TWGNKmvwO1RCXkHIwYECB79PWBYoR0MnNQMnJ3AHUEdl710h08DDjFv4wPJV9wp4BbwCXIGTxvdpeB0cvMxf8lTBy22/vou+84Of01GH7CXdf9CG+ZIrr6ehwQH61EcOoo03WHf+NWqxJdoRvFS7C8T+wWAGcGaUlTJQGQDMjHH+zChem4I1BMoDWEWKAcZodgcuQqGkScIZgzUANQA2riyodk/4co2sgClsNAxU72iLRUHADcYZ0PCwiIuk4rjYpHABBaCJrIk8gxuFMJovoZkTeuEE+wJUNgZpDNjI5ZG16xUVjtsBG7nP/bu8AlmtQHn3KVPexAoc/F6hvSuC0CXrRp64O5rj36QJnQ65BbBGjdAwgxvFNNoBBCinT0LSAZ0F0gQ5O367AGkM6VgnkaijiIAd/RwZkw4i4XPdapXVQ8rXyyvgFWiDCjh4yeZOTBW8HHjkafTvR56g733ldHqIbUfb73WUAJhRbjW9/rrPpGsuPSmbVahhrToZvCykbPA5arAqut/oBeoIq2iQ14GuOGPTBRlOQGWDLjk8xIkaVAN9DGg0r8NUNL0MbeJxABvAGs+mWcge8WWbVQGzHwDYxFYEHReQI/AGQaFFCQ2dwlDsVApw5OKJhwAxuAASIBNCQ3F32wJDgXbkbne4442QYsmZwDICcnRcbAzeEa1Zh4N/r1cgcxWAGkck66FTiIZJhv9snIdoGovfJYAbgzqQvBOjlvEAdaIwSl6e0Y0oeTCvxN+hIZMaOtnLIZTFHM/j3zKDPsgz0PawAEEJZU4Ehfh1ATmm5DHQE0+7wjBzh5evkFfAK9CACjh4aUCRF/EVqYKXrXc5nPZ8y3Z0xIrd6bKvfY/OvugquukbZ9Gq4VHabcXH6I4bL6TBgf5FrGZ23+LgpX77Bid0EawR5QwgDYOZoKQZZXWNdseB8obDq/iW3FpdS/gOXimEqWqGB2xOEqoqQ86twTAxD9P+8Aq0WgVigKN3sHU6jAPUiNIGd7r5soj/bcgdbwE3bHHgobTthYWBxwv8bwYQRi0IwDOcMSFDZE7oHJ3q0zi3YKHCUK0MCnnkvzD0C55WO6J8fb0CjauAhk4mYE4Ij4yBj/5GYTnpYwXAkytpyCR+u/C7Jf9xsgF+zySAUscN9kDX018alNBK/H5ZJxFYqiSUkn+/IhVPANCm3imHOegkokGWkS0roeTxTleNO278m7wCXoHqKuDgpbo6NXqp1MHL/ru/gQ454C10yNFn0Z/+cj/95NpzaWR0nF6x46H0pXM/QltusWmjt7Gu3+fgpa7lXdCHF/ikSzI6IsWAQpkJKGo4s2OCn9IRJ8AaWELGWWmDC1OoAgTOwOrBXZ2kQ45kdSi4EViDZQKwwbQoCbwD1IL2kS+czQrg4iYCMQZkgi0BQEftCUjeLwrkVHiDsGK+0ClT4yjUwVPvUWs3Fw0J1WEMcPhed6S4CcAn2KlwFxs2q0iZg7vibk3I5sHja+UVyHAFigyfCzltCxu3gp2p3lGgo79n0oMk/OZhiN+ycW5Rrrau+DXoglQNpOods2blOGMnHldVT0+pXztgRfarmSodATtBwVMOgNyeleFDzFfNK5DJCjh4yeRuYdiPM+6UHocdew797q6/0Lv33pHOufhq2uutr6WPH/VOuv03d9O7jzydbrzydLEctdPDwUt77E3AGChspPuN2KFCBxyobhjOjLPKRkGNBq9iiLBV3GGL2hWHtsUIGsbJlSlsZh0yuIFawB9egXatgN6VNhVOUNckVDYSViyQJoY1qshRmxXucE+GO9iYR5w7kWzJq+PcljfMTUIcQFGzVclYEuK4vapdDznfLq9A0ypg7V7VnmU2rXiIdrFiyYICESqdoNyRXiQGeeR9AN0wcal6UWAQ3sHLsMla1DsKddRWJSqeYM9SRU+waqHFbFD5xNk7asuSZ0X+Tgx6HHI37SDyL/YKpFgBBy8pFjPFj0oVvPzt/ofo7YecyJkuE2wp6qMffO1MWnutpbT/+z5Jf/7r/XT7DRdSPp9LcfWb/1EfefF2UXtldPcZkC4/vTTInX0GuntpKAzba6ubX/esrEGRT5AAagBlELCqQaqaW6NqG6hwEDLMr4vahscF6qi9A/1wok44UNQgeJhVNKao0aEqbkSVAzUOlDiutMnKIeDr0aAKRHejxR6lKhxt4wtblUEcdJ5ikBNsVmapkuwcTjuGDUFgEF/KwG6AAGTJxVGTVLBaBXWOAJqQhVNmpVJ7VdJWZbk5rspp0MHgX+MV6NAKzAQ8oYVsUN5E4CdAHINA8lsImCMQJwF7ZFztWwXO2DFlo8EaBTsANXH2jkwL1GFlT4A+hNctfDmRuTMb4Ikgj9986tCj2De7ERVw8NKIKi/8O1IFL/j6ickpuv/Bf9NGz1k3giy/+M2f6ClrLaFNNlhv4WuY8Xf8dr93Se7ICOeNaP4Ih8bycHiCO/xweOwIOvzwc2nXAMuL8jTAIAYtmAe6ODSWlREDXT0MaXpk/iCDmn4e90dnVKAS2ojiJihqEDYskEYsUgpsRJUT5kElIAAG9ihuYSwNQ8X6xJBGwEylPSr5mnfE6YwjzLdyTRWQ7IgAbiwbRyGOWqkAaaC8UesUlDsAOmgBrNk5FiZqVixMQ5Uj6hoJOy6HOWK5ioBNSNBJKnNCa19V7sRwx7Ny/Dj2CngFGlmBGfk7syh4KgEPOmeZcicKYxY4HnJ4wm+tvo/1PBiGgGWxV0WdsuIsHlHmJNU8WCYsKx21QpN0ATmhW5Zl8JRZtbwLYCMPH/+ujFTAwUtGdkTFaqQOXvD59973AP3xz38X5csGz16Htnzx86mnuz1tFX878OCq9qwEwzKcAZTRoU4DygDSANCMsJ1lulCSi2kAmQHu5gP1DCDNIKtpYiUNYA1fbOfbs6ZVFbTDF8KJkdiixBoVbFLIrjErVJRnw+GqEbjRPBvYOxTaQGGD9sUKcKC80RDikHcTVDZJ9Q1sU/7wCngFZq8ALiQM5sSqnDjIWOyJIQPHsnCgfMO/S7NZTRvswd1pXlab9WrejeXl2Lh1tpLcnCogjitz/Mj1CngFslSBNVm0Qm+t0F0rtm0B7HBcDk3wf6qEVJCjkEdtWWrhCkoeNEOX3J2QqxNCleNcnqDqCRasHGCPQJ483zCNbV1z27QSwcuu4snS4dXR6+LgJZu7P1XwMslql8OPP49+9os7y7Z2+bIhuvjTH6LNNt0gm1WoYa2qBS8L+YrhqUkBMkklDRQ1Ame4xsOTnPTPf0fQ3aefocwA4EwAM4xqVEUT1DNieRJIk1/IKviybV4BhBCLwiZAG8u0UaWN5tiYusbADaxTldAGF36AMdLvxpQ30jUK8AaWKYAdXBTypSPPR0tjf3gFvALVV8AuKORucmSxUpiDCwu1XOm/W4E2kq0TsnNC1yqdpyoejONOsXWqMouVBiBr+14FO/j3alk5CYVOwmbl4cfV70df0ivgFWheBeIW6cGaZaqbedQ8gDj4T6BOADyRskc+Q3N4ksHNasXS39CokxbPIcvmMXuW5e1gGqochkOq2AmwZ9agZbwWwpv9fKp5B1QLfLODl2zupFTBy6nnfYWuvPYm2m+3Hei1r34x24uW0q2//CNd8tXrZet/cu15bad8qQd4qfZQwYUxlDSmokGuCGCNKGgYzuApihp+4se8ny98+01FE4YANKKuEUCjQ4c01e6BzlyuPIhYVTcGcTCunaPUKgXrxZNT42zR0AyOEk8riAk2KVzshWlpSszwRjtM6R18QB3rHmVdpTqz6r7VXoH0KmB5OUlwY1DHQE7Uolxgjlqu0KIcd5cN7siyoTU5YvoF1PBFhCprKp8B6AjYMduVZuhYEHIEcmSevuYPr4BXwCvQShWYK2C5MnRZcE1ZGHMAOCFkXpdXC5eCHbNoqV0L/wHy9OeGBMKbgkeyd6KQZYU4/OvMv80KhCyPJ1LwRFYtA0Y2dMjTSsdd5bo6eMnm3ksVvGy9y+H0vA2fTZedfXTZ1n7///2CPviJz9E3L/kEvWCT52SzEotcq2aCl4WsMi6CxxIZNGZzgr1phLNoxP6EcYY0LK6kp/QOCX2H3QlKBnTqQf6MZNHwsL9b7VBQNvjDK1BtBdRGAVgDEKPARrvbaBcpjKt9SsGNPS08FdMCasRagVwbAzeac4NLOlimpBV4orMUwogN5Hg3qWr3li/nFai+AsnAzmRmjsKZuCW5hSIDxpoSR+ANfgdCdxeDObjQMJAjrXYDjLG25AJwJE8HFwohWyeZo5PoYCXJOfybYMPqt8yX9Ap4BbwC2atAlMVj8CaAHAAZBToWvBxMWwkrVui9JSpJBfAx4CmDPAH4lPh1qHcA103RY2oegTzSRctCl2HdSrRUl/kxDJqtZbp31Ur/+HLwkn5N0/jEVMHLK3c6jN6yw6vp+A8cULZuf/rL/bT7e04Qu9FrXr5ZGuudmc9oFfCykILhItfUMwAypqoZmWB4wyqa1QxooKQZ4+c0twVB9syS7j61m/A4QI2ECAPQCKzBsFvm9XguzUJ2hS87SwVwUSYqm2CTMnBjORmAOGKVSgQUyzIB5BT4RAO2pyjbBpdiBnGky5RapwBrTHmj4zpPsjRc4uvHplegIRWohDaVFqtkoKeqdGKrlYQl8zQuQqxFOWCPdLNSnY3cGbbQYwtFNrWOwh5cVCjgBbStVOe43aohh4F/iVfAK9DECswWuBzbtwzyGPDRnB1T+ESKHeTuyO+xgh5tqy5RywLhtYU6Y6MQulyWwWNQJ2T1mKLH2qljaOM4P9PXQ/aOBDJbAHNS1dPerdMdvDTYM3tpAAAgAElEQVTxH8wavjpV8HLQh86k239zF1110Ym06UbP5mCqHD36+Eo65pSL6LZf30W3Xv85WrZkMJuVWORatSN4WUgpcBELSCNqGhnqU7o7MaQZM0jDF8HIpOHFqQ8ghuEMQlvN+oRxBTRJWOOKmoXsC1+2ugpYMHG52kZhjtgpQvaNtAiXNuB68RZ3lsKdH1imDN4ouJFpvvsu4CY8zR6l9imez+DRrFTeraa6/eVLeQXSrgB+A0x5o1J+dK2y3BzNztFMhxCMHKT/sTIHFxChs1XIzcEFBYBO6EmVaFMeByNDlROalEt4ZyX4UbATwx0PQ057z/vneQW8Aq1QAQtdLoc7McwxqFNm12J0A2XOZHEqhCsHFQ//nqOLKObKb3rCwsVXIjx/KvTHCmoehC8HFU+s1IEXwJQ91l1Lf61jy5aCnLLOWtKKPcCeBltnHbxk80hPFbzc/+DDtPM7jqUCt9zs7e0RyALwgsf737M7Hbz/W7JZhRrWqtPBy0JLh5NVgzM6DMAGYcLIpQmABmqaUR4vcJcnqGgEyMDyBEWNtEpGS26AmwBs2PqE8T5W1vjDK1DvCuA4rrRKReAGXaQCxIHyZjyAG7STXz09HoEc/EFXSBOgjChvcCmmEKdLOk4pqImsUolxm1/vbfXP9wp4BaqrQNJulVTiRJ2uooBkk/eb3SpYrSQvh+/48g2KidKk5ucA6Mgpv/4+WGcW7WjFU6LQ4wsBAzmhlTnu+FbaqzxDp7r96Et5BbwCnVOBZGet2KalKToxAKqAPolwZu2eBbWPhSzbNHcl5N/sseJYlNNDorwJAAfqHR7PYR6rKnEzzuxa+J2X+RgGcKPTgDsx7DHII0P5+xAHNJ88vm/Dd+JWPRc3/DvxhbdOVddhuCkrV/GlqYIXfPYTK1fTpV+9gf7wp/uidtJ77/I6eunmz8vC9qa+Dg5eUi9p2QdCUTPGF7JJWIMAYQS4angwP0OIMEANLlRFVSNwJlif0GWHqbapaWB7ErUNDzHPc2rquw/902evgGRaRE+obSzvRq0SkfKG4WPSKmV5Nzpk25TAGb340oBiU92EOWKjiuGNvF5ho/LcGz9KvQLZrcBcQCeGO5bRUJ6lIzaroMzB74V0yEoodfB+ATQB6KgaB0AnDj2GMieyXoXwZA1HVquW2a/i6ZC543bM7B5QvmZeAa9AUypQ3l0rzt6JrFkJoBOredTChWkWVYqixzptmY0rDsxnWIRX+bf/xvFvNnwbHbzMX/LUwMu13/sp/fyOP1B3VxftzDkvW79y8/m/vQ2WcPCSrZ0ICTksT2h9DDhjwAbTgDRqfVIVAsbx+hRf2PZz/kwfX6CqDUpbJENVg3kKbBjSCKxRVY3Dmmzt905eG7NMGayxDBydRtZNyLzhC64JViMqsEFGDk/DJsHTuMuOaGIc17HqJg4xtrvqlcBGO1TFUMezbzr5SPRtb8UKRG3KJX+hHNyU5+fEVizN3YHqT9+DrldqvdKW5pLbIDk6seqmrMtVMhBZ7ulqMDLu2FaqdKJp73TVioeXr7NXwCvQpAqs+NeBDf9mBy/zlzwV8HLaZ79KV3zzh2XfdtwR+0lb6XZ/fGPHt9BQTy8NsdVlSU8Pj6Pjj9tdWmm/w/sJACOgJqhrMK6wBiHCBmliaKOwRlU1YnGCHQrKmhxboHgcF6h9eFaoa1qpLr6unVMBAMuou5SEk6r6JglxtI04sm4U3iDzxpQ4NuQ+lUFxE1qGh7vpkSIHOTgAPLBSQakTwopx11whjs7Da/7wCngFWrcC86t0DOSUq3W025W2L8cT0vnRwoTarkI4J9rlKiA261XoaBUCOBXysHIn5OVInk4UjqxS/2QoMn5vvH156x5rvuZeAa/AzAo4eMnmUVEzeBkeGSN0M9p2qy3o3JOPoCnugnPYsefQb/5wL91+w4U00N+bzS1Paa2+tc82tHq8QMPc8We1PFVBsaSb2y7zcwhDVlMMdfXRYAAzgDQANL2sDvJHa1ZgLlgjChq+4zc8yd2gRFmDTBseBnWNKmmCoiZ00sE8ZNfgYlRBjQOb1jwqfK0hdzXljYGcSHkTwRz4n4mhJmdYMMQZlxyLoMRBPk7IuIgsVOECC2BGW4kD2uhFl4QYw2AVAI7Bm+5oGr7nvO8Yr4BXoI0qIJapEHxs4xaEHKl0girHWuUCJuNvM5Q6mp2jCh1V6xgEKgabVTIQ2VqQqwXLWphbxyv9LdKOKeUWLIU7DnXa6MDzTfEKtFAFmgFeXtrzqaZU6NdTH2nK9y7mS2sGL3fd83fa8+AT6VuXn0LP23A9WQdrH33NpSfR8zdefzHr1TLvefy4PWasa4E1tqsnpvjiezo8OZOElROPjzGgYUgDOINnqZSL4MwQwxkAmiU9/Rwa2y1gZtABTcscB9WuKFQDsDmpFQqqAbWBRIqbSWTaANTwBalYpVRtAwuItOrGMLJCBVgTQoUN2KgSR/Nr/OEVaMUKAGzCDhW3BmZ1DV8kmQoHQ83ICbk4osAxG5XmWJilCmF1gDjaecrgTWypMoCDO+QWZmw5OJEKJ9ipWrGWvs5eAa9A9RWYYb2axYKVBD+mzkGmwiTCNUWxo2odgTwSqIx8naJaqgBogtWqUplT3sbccnZUoRNMWPr+oNixXB1XCFa/f31Jr0CnVKAZ4GWgd6+mlHds8qqmfO9ivrRm8IJcl4M/fCb9/NufpbWWL5F1GBkdp1fseChdcOoHaLtXv3gx69Uy75kNvFS78shYGBE4E0MaXGQ/MTYlKpqRoKApFvNiZRrq1k4+g6yeESUNW5oAaMTqxMM+V9BUW/qWW242YCOwBtCGw4VNWaO5NZphA3BjwEaUNnyypvBGrR4SPpxQ2DiwabnDwle4igrInW0AmnAxZAqcpJ3KIA8yb/C7DLuDgBzJwgHg4VaUPFS7gubaKLBJKm9mqm/EQlWhxnErVRU7zRfxCrRhBfCbU6nQSbYtr8zUUcCctF110UhxPCh0NEsHv2cFBtXWhlyHqtgxVQ5+paQvFlrbSjBybLVSi1Ws1qmclvdytxV/eAW8Aq1VgWaAl2UDpzWlSKvGjm3K9y7mS1MDLx869O00ONAn6zDNJ6innncl7bbjNrTZps+N1mvXN28tbabb6VELeKm2DpDjq3pmSjr5KKgp0CpW0ADQQD0Dq1OhkFMIwxfXS1k5AyuT5s8EBQ3DGeTQeMvlaivf+stVAhsLHZYMG9igAGpg90DoMObBHsX/fqGYUVijGTY23lthibKwYVfYtP6x4lswfwWSAcaqvlH7goAbvgiSDJzQPhzgBr/d5Tk4Og1VpMGbfg7xzrEdCne7gW5kGBQ2ln0TZeBI+LHm4STnzb/mvoRXwCvQzhWYS6mDXigCfCLljv5eodsVFDoGdiKIIxYshToyj5/I1FE1jra9NaWNWq/YgiUdrrgdbtQNSy1WasGa3YoVfYZbQdv5sPRta2IFHLw0sfhr+OrUwEs1m3fLdefT2mstrWbRllmmEeCl2mKUAxq2M41P06oJhjYhg0bgDQMaZNCs1dsvd2sHWUEzAJtTIn9msIdBTbA5+Z2OaqvfXsslgQ2UNQhS1cBhtUlJfk3oEIXXxmCN4vloWxx3hgL062NffJ7v+qOlN2BOsEuFzlCmuOnJe95Rex1BvjVrqoCFGc8GbgTuRMHF6EQVrFPSoUpbiKsFSyEOxk15EytxAsJBBk4S0swSaJxU5WDcM3H82PUKeAWSFbBWtXPm6QRIo/AnYbOy7BzL1gmva6aO2rBgK8VvlCl2DEHLMMzPhZbmWIrPJkJkci//VsFIGrc2T+bpeFiyH8OdXgEHL9k8AmoGLxOsvvj3I09UtXXrPevplM+3l2QxS+Clqp3AC+Fu6whnzgwjh4YvnsXuxHBmVbA3CZyZLtFjY5OijlFbEyxOGhaMrk0AMwM8FGDD497Jqdrqt/dyBmzUBoWgaWTWaMeoGN6owgaKG8m54ecUH5MaMqx3+y2AGPO4R5RYo9Q2paHUGMeyvd7au70PKN+6qiqQVOKYlQoXQVGnKsm/UQVO1I3KbFUAOehYFUJHpWNMuOjBv0VT4ERhxpHdKqG8SYQZqxXLu1NVteN8Ia9Ah1cAEHqutuWYPyM0OcwjBLQXJyPblcBoZOyIYieodXgcXbHUSGXKHHTC0t81sWMF1Q6mMR8dsNak1jGgY8HJHb77fPMzXAEHL9ncOTWDl2xuVuPWqhXBy0Kqg3bKmkOjQ0yvYiUNLE8jkyWGNzzOoIb/dsofK8AZ5NDok8clm4ahDQMagBqHNAupfucsi7teBmZkiAtBGYYAYgkkVpUNQI1AmxBCjDv/EZQBjAGoCVYpATYhz0ZbfHeJIqeXh/oe9693zlHmW1pNBWA9UGijwcUaYhwHhpp1SlqNBysVgI5O691uU+PgIghqN4AYBTixEsfsUl0IPY7a/VqGDi+dgDm2rCswq9mDvoxXwCtgFVBwg9+0uG25dN/jaVPyJMGPqHrC7552vIoDkqMOWEGxE2XrBECtICeGOgZzZChAJ+50JQYssWPpMM7WicOTXX3ox3EtFXDwUkv16vdeBy811rbdwUu15cFdC4EzDGZGAWUM2ADMcKcetNoehe2JX5vki2Zkg8DmBFDTLx2dFM4sgXqGrU6ipgmqmmrXwZfrzArg2DNYo/BGLwChvokgTqKtt0IbvvMflsNJDyxSFkAstqigsAHEUWCjLb4V6CjAcbVNZx5vvtXVV6DMUsUXMYA5BnIE6gRVjnWv0jDjYKGKhugWA4ss7l5rECj+TZqyRpJxwgUMwI5czHAOhdoUdGjgpiwfhz/DH14Br4BXYLEVMLXObBas2bJ1pH05g5w4Xye0RQ+wW21alq9T5GwdTdexblcWmAxYI/ORtyOvIieMb34m7FnJ9uZl85MWLg9NXuyub4n3OXjJ5m5y8FLjfnHwsvACQt0A9YzCGQYyYnvCk0+4OSvkiaCoAcBBdsggX/AOwO7EcAbAxpQ06OokFigGNTLkab8juvD90envgLJG7BdBaaPQJn4C5CDTBsBQW4FDdaPwBpkbaotiIIM79wJtdFyGQV0j4cO4gx/Zo1h5g4tHz7bp9MPPt38BFbD8iGSHqqjluMAZvZDRLlUaKopp/Hs1JY6EHoccncoW47gjLZqcAHLwulmtZJgIN9YW5WqrgjoHUMcfXgGvgFcgrQpAkZO0WpUDnljFg/nJDlii8BGlIt6vv3eq6imfBtgpb2Ee7Ff4DWSFjnbAmqnG0dBknR+peCqgj3fDSusoWPznOHhZfO3q+U4HLzVW18FLjQWc5+24ozAScmgAaEbDOOxNUM9gqJ2edBwttZFDs6ynT06EAWr6OdAVCpqkigbjnktT333XKZ9u+TVJlY2BG1ij0DVqgoccY0OrORMLihuBPDwEhFR1DVQ3McBRiJNU2ADimD0KrcBVcdPlHSE65TDz7axDBeJsnBjSGLxJAh0LMZZhZKsKACfMwwWOgBtYbvP93EOGVGkTLAaKdPRiJlLprCHs2EFOHXa4f6RXwCsQVSAZmlxptzJ7lipw4pbmotaJFDqxOkeXUzuXWbly3FhB8YwqETU42TJ2YMvS+ZU2qzKwMwP+xDDIrVhrPpgdvGTzH7uDlxr3ywMf3kUUF/7IRgWgTICSZiyhplH7UzGCNKKy4ef4VCmyM4mKhi9kYXsaQMhrsDmhFTcsJg5qsrF/220tcBJTqbAxgAM4M8ZwxjJtRGVjWTfobsNPCJEF0ASrlA41SwPPpEUKsCaGOQpxuvP5diupb49XoCkVwE0CqGsE2CBYXKxVFvJpd6Q1LHRcloHKztqN83uhpAtWA2SmIZQ8ahkO21SAOv25XmnbqxcssSJH7FbShry83XhszXJFTlMODP9Sr0CHVqBaxY5BbgFBoVufAhxkjMVhyabk0e5ZbMViq5ShHf0txG8f2pojUtlUOwA+IXeHb1RZKLKpdTRjx+bHGTzt0BXLwUs2/+E5eKlxv5z61k1pjC0yy3q54w9nlDy1v09OkJbwBfzSvm5a2svhsr08zc+eLr/IqbHcqb7d1DSipGHr0xhfyJqiRnJqGNbAXrKSrU8GaqCSgWUEAa0IEJbOOgxqBNoEVY0sE2ANgI0/vAL1qgBOPmJwo5k2Zp2SNt8hiBiKmxKfjKyenAj5Nqq64bdHMAZ35wFuAGdwb17gDcKIxSJlmTYhD4eP7+Rr9do+/1yvQCdXYE3dquIAUFXPQYkzXeC8K7ZXAeDYBUyk3EEIeQA4et85ZONIHk7IxoECJ7QfNxsV/r7hwiQCO4ll3NrbyUenb7tXoLkVmA3sqFJHgbfZtDDEsmbHmmY9ogGdXClHY4nuWFG4Mt7DMmWoakyVE1mvBHwH5Q6PA9tYRyyFPLEVC7+j0lnLrFkhpyxp06qXcsfBS3OPz7m+3cFLjftl4nOvVQsBX5yvlnbM0/TkGFtfeHrlCJ8MTRM9snpKXsM/vmcMcQgWDxEqO8iWGITJDnSzNJlVMwA0gz1sleFh3kOvatwz9Xk7II1lfgDSQGGDaQM1UNaMSW4Nv8bjGALCrN03wCfFJOoZUyHAFoVpgBqM98t0PK/H1Qj12Yn+qVEFYHWKMm5CVxpM2zzJs+GcpUluL6+hxdqWOIY9uOArRKHDpr4RiGOZNiHc1ECOthoOF3KiwmHVjsxDVobflffD0ytQrwpo9o0GeFr+jVgEQgcrWAj03z/uMmN8WgI+R6am9H0yT1U9uNRAC3JrPa75OPx3LN/HnX5z4fJDQ5B7+T+kfwLeAOLg9yGPfAmZhlJHL0xkKBctfLYk8/xmVb2OBf9cr4BXYP4KWFesJMQxuBPNA8iJQI+eE6k9q0ilYo7GS5MCfqBwtqydGA7FlvMSQ6A84HhoaS5AR7J2EBgfhvxbKtk8UcZORdZOYv6RD79//g1MeYllA6el/InVfdyqsWOrWzADSzl4qXEnALxU+8AFjIXIDnMeCWANOv2sHuOOQIA1EyUaHVdLDC5CcMEOqwtaMkNRA1uAwRkJlGVIY0O/81TtXmj8coAzE9NQ1DCUwTiUCAA2gDSYx0PptMPHB56jUCnwELQdxwCyPZZ29/GK54KlJFYfANTg9eRQxqVtcpeHDTd+d3fkN6KDlNgmLKg4AXFMhYOTEB2HfQoXewx9cAEI2xSGfLceF3S4KJOLM7FM4Q68hZcGWCNqnLi7TRyEGoCOWC0U5AjMkff7BVxHHpi+0XWtgLUeT+bhyAVHCDk2VQ7a7kpHK3mtRKVCnu8yT8kdael8FVm0SLQ4I9PjcqHCly2sHu7n19V2Fd95hvKOYQ4vIeGe4fdALFjh7rPddQbMEeNBgD62rNyn5t8HuVeN/AkHPXU9VvzDvQJegdkrgN9AdMFKwptK0GPTRWmFrvDbMnoAxAuhxXkS7Fz00NUNL7mDl/lL7uBl/hqtcYmFgJeFfJVcgIvdRdsw427z6lEGN+McNgtljQAa9gkwIX1keJovsvOinEAHILG88HAJ25xw8T3AgAbPwaCswXgfj/sj2xWAGsFADaCdddMRtUEC1ExAjcCvyzKSA6KqBMzDBezafXziyie7gDE90hJZL0w1rNWG+Qje6Lx8BHRw8eoKrGwfK+20doA3IgOWp0GZ0JkhzBdQEwWdxh1rppCJIxAH/waQm6FBgAUeQk0jNgoeLuvpl8+3O+3iDg8XbzbUO/kqIVb4gws0fiZgjkw72Gmnw8+3JUMVkAsSsQ7o0zIgdBh3abHsBwAey39gvBNl7OidZvxO8OsSjsx/GwXwTOg86f5SYvWy/s00SDPAwfz8M6I5EiEcFL8IYisI6RIciY57IpFSR1U8CdBTAX0kT0LuSusyftMsQwecr4pXoI0qsONvP9jwrXHwMn/JHbzMX6OmgJeFrpaBmtjqop1UVjKsGWMlDSANAA6eyC7hcxa5sAaMsUwStGOGDQYqmgE8+TUBNgJuoLzwu8YL3S/NXh4XnghnjSwiUBeIVURVBwpw+AQ1jGMathJ7D+5OAuRA2ihABrYQybjRcFaBONKRx3JAkkBH4U0S7nS58qDZh0THfr9aJAzghIsysVkY5NFxndZlocqJsjL434JdtAnUwethiIs2uyPfE6wUcS4GLrS03bBaKWw8wB7MEZgDqKO2K5tWxY7CHn94BbwC9a3ADNATgI3eiS4HPwaERLGThEACcfQ3Bb8f+BuMsE8ofCSHB3AIywTbgVqucOOjh9U/aiFQw5X+H6atfAhThmWLOVAEdkylI789liMRlDtJuBPBHvn94W90K3t9DyT/dK9ABirg4CUDO2GWVXDwUuN+qZfipcbVmvftRT4hGA2qGlhdoKwQaMPzoKgZhe3JYA2ySngcQZwKaRjEiAUqtGsGwOGTht4AamR+Yrxhf+TBhdDD0x+pV0BCXIOSxhQ3AnMSIAfjgDRQ4MiTjymcGD45PqXv5SdOLEVNIzaSMAzgxhQJAmtkmRjiIJhaWyjHrzXsuEq9mv6B7VYB6WgToI3aKcKFl3VpkAuuJORRVY9egCnclIsysV+h4w0+Ty/aFPBAnWNAJtin7M66hfWhSxUgTQA70pUhytIxoGMhqQCoMdTBuD+8Al6BxlfAgI6pckzZE+dBBNUP/y5AxYObIgZv1OqlICc5jt8U+70xNY9aE3S+mq8SgZ8B3FiHGAE61u0lkbtjlqzItpWAPdFrZQAoVv80vrL+jV6Bzq6Ag5ds7n8HLzXul1YFL4vZbMAaWJ8AZyphjebX8GtshVLVDSxRanfR4FhVSYiqRto2K7QxSANVDexPMs3jADf+aL8KwD6lACeocABqQjaIZoSYCicAHFioBPAgBFbtVLYM1DMIZlUljj4l3BVwJlykAvBAxSVKAizLr9kwslTx+xzitN+x1m5bFIEdvsiKujPMotaBtcouxCQkVZ4KhuS1YMEysINl8e8EVkbcDce/IbNBqEontmHpnfU4NyeCN5EVK4Y9cYCyZWiwH8MfXgGvQNMrUKnemQ32mGULvx0498My+tvCIEgUPvqbAkAEoCPjAS7LdOgKY8ogKHvklyUAY7Vvxc2AVemjMMjmmhW0UrGDZaAMBCDCU2JHExk++rmu7Gn6geYr0NQKOHhpavnn/HIHLzXul04CL4stFeALFDUKa7Trj8EbgTTIsAGo4aEsg8BZVtyooiZYocI4oA3yayJ4AxtUUNgA1uDZlfcT/MXuq1Z6n+Z4xF12xC4VwRu1imiIqx53eH0cACfYqhTmKAhCho2AGJzQ8TEnnTgs4DVkg0B1o3BH83C0xbIqB0yhg/d6N6pWOop8Xa0Csc3KbBL6b2c2yKNKnJChk7BgGeCJ1DpB6YP5sCvanXIDOjIUkKMBqWLPMpWO/PuLVTlmxTLLltux/Nj1CrROBcryecS6FSt5ynJ8gr0rtmsp2JHfJ0DjRJvfselJnlYQpMMYCgEUoYOo4ByBMwHoSNiyBrgnsE+sACrL4MF7AyAKuT0Kh+IMn/LgZgVA/vAKZKECDl6ysBdmroODlxr3y9gFr+Uf5ho/xN8+awWSwEbHY4ATTcMOhVBZBA3zn9nHR6blD6gCG1XaYBxKB4wD2iBgE9k1BmpsiHn+6MwKmJVKMnGCSmCGEoezcOS1YKcy5U2Ui4MMEbkILTG8MUhjSpwuWtLTK63nEeQaddyxjJwI4sQgR5dRlY4/vAKtXoE44HRmro6peSwUWXJ2QkCyjePCSjN2LF8nZGgE1Q9eky414SILAEc7XgWLVrgrXq7gsbDkJOSJ7VhR9k7I2XFLVqsfhb7+nVSBShuXAZ4kBCqbh98XUe2YfQsgx8KdDf4EEBRyfKDiVTuX2r4wVO1OrMKJMnsAmC2W2exda8jnMcCj1q8E7Ilsponv4GX84RVIVsDBSzaPBwcvNe6XT+yxPltk8hJIO2TtndlOM9TLrZ/72FrT2yXDIR4O8rz+Hv9xrLHk874dJ+zWolmGodsPlDZRy2ZR2mg3INiiRGnDz7X6ObyumBNFg1ikGN7AqgKFjQ7zAm30NexPHcc87/wz767piAUkbDGhxrFxXBiOR/M15FisIAx0RIkjlqrYVgU1js0zUAMQA3AI5Y0FsUrb5dBpR8KPJdBYh7J8gDem1sG0hxx3xKHYcRuZVO1o5kUITq7I2ImWq7BdiZIntDyOgE+4CFMVT0FVO1DliFJHWxKL1QGWRli1Qjt0TGE5zdoJyh1T8SRUPZVwB5/p1seOO3R9g9uoAgZirN3vTHWPdt9Sm5aCHNivZTn8zgRVj+b0xGDH7FyiAAq/aRjHb85oYUIVPKLkCZBGrF0KbVRRGCGhoADS+RzpHFu1Eoqe5HulLbupf8Lnewv2bB+0Dl6yuX8cvNS4Xwo3vCTYZEKL5zFt8zwyytaZMX7ycJifuAv+xKppzqggATRDvegexMMAawYAaBjMLOHhQAA1Q324eHJQU+MuWtDbERA7yiBGQmKDwsbUNTINSMPABpBmgi+YEUJsMAd/7ABh1urj9pIIkRVoo91/tN03cmx0GQwrp3Eh7Q+vwGwViCBNGbgxq4dm5iikUSuVghx+3QKRw3xrsYzOGPDIR6oagTd691/mhy5VAm6S6h2xVsVZOa7K8eO10yowF9yZy6qlnbRCzo4odnS8EuxYdy2cKwD08/VU1OI8adESFY/BnqCe0+SdAISSeTtJ65a1Qw+v4wLNH14Br0B7VCDZkStS8URgJ9GOHaAnYedK5v0Y6JGMHgFBIdNHMnyS9rAQDC8Kn1IAOwkwE7pwCagxhY8AHUHUCoZ4qMAnCYYSWT1luT0WBK02L8v8aY89V7+tcPBSv9rW8skOXmqpHr8X4GUhDwmo5dBZgTMYhucohgxocFG/mltAC7RhiJPjC3iDNIAyCKeFimYpgxuoLaCiiVU1fqdsIfsi7WVxt9QgDGxRADXSrhlP5NcwdJsQBQ5gDTtdcksAACAASURBVD8DvJH8EZ6GDQW2KAUz1oaZ9zOfZGNaVTgMbgLAkWUDwEHGCOb7wytQTQVwV82CVxGYaN2pJkNuBzpnSBirtRdPQB3JxQnTUc4OL5dU5fTC0hdaIycBjylz+vI9fPGYC3k6BnfU7qGwx7NyqtmPvkx7VgAXRgZsksqdqHuN2atCkLLaI4KFCx2y5MIptmRBraOgJ+6+hb83AnFEhZNU5ujFUjJY2SxbepEUumclWqBX5u5YC3RX77Tn8elb5RVIVkB/o+KW65rJk2jBLt20yhU+yeUNREet1kWpGCt9RAVkECkEPRvwKQtp5t+z3lyPNDcNZi/qYgiky8RZPUlVkHbjQhpQMqC5EgaVT0Pp0woPBy/Z3EsOXmrcLwsFLwv9uim+Uz3M7Z2TkAbQBhfqT67ifBN0EWJAM8zDEQY2sLzgCTXNEgmeZVCD1s9Q14j1SUENhrA/5T2gZqG7pG7LS1tZ2KKCNUqUC4lpBBDjCXgzhryRMATEkZwRHmoHKf7jY2qbMI35orwJihvYpAzgJBU4bpeq2+5t+w9OqnLQctwyOjQ3x6Y1uwMnRsMT06zIUWWOKXEi1U7I2ZHg4pCDoxeIwWJlypwAaiQMmcdx8mSdq9RmhbDkOBTZbVZtfxj6BlZZgRkt0AO4iZQ3BnIs4yIxHUMda3muF0bRBVQAPJhnYaQKeYI9K3TGMvuDWLYM6CQVOwHexqHLcSaPdryJp92eVeWO98W8Am1SgdksXQA+krsjFq7yJ84vtNU6oA6cCAWuRI7Gi9Nq8xL7lgGfmdk91qJd1YixpStp8RJsLb9n+l9vjuMLcqoKUmuWgu1YuVOeBxR18Eouk8wLWgD0cfCSzQPdwUuN+6Xe4GWhq4cOQaOmpkmMj4rtiV8bI4E4mIbKBneWBzl3Rm1Pan8CmFkCRQ2ya2B9MlUNj3d3eZLwQvdJI5dPgppIcWPwRlQ4qrQx9Y0AHLwe7FU4mcWFa1/o3KMqG+TbaECxqXEE3IScG4E8wUrl1rhG7u32/64kzNELu5CLk8jQEWVOADW4SwYAqSqe0PUqys/Ri0S+JtTuVQHKWOccyc0Ri1V8tx9qHbFfBYijXaxiZQ7sgdbZyqFl+x+PvoULr0AUThoFloauWUGlk2yTrv+WEyHK/O+VOHNtrMCQNlLtxHfDkyGo6JplCp6o7XkiYNleE8RjtqtE9g7+nWuYaXlnLQ1pDr8JYXzhVfB3eAW8Au1SgaStK9mKPQl6BOCwyqcUMnvktyrk+ag6MQ5uBgyCAnGqxAAoqe6JOn9ZeHORz8V75XMN3pSpeQz6BPDziXu/0vCSLxs4reHfiS9cNXZsU753MV/q4GUxVUu8J2vgZaGbg4twAzVJYDPJF+MrV0NNQ6KoEXDDqpoeXJjzCYrAmABrMA5og+DgAUCcAGts3Ns7L3SvNG95s0tN8MUqII5YpQK4wTieOE4mJkm6TAnIAbxBS3B+DefJoq4JkGZ5bw9nFYRWzbCP8IVsMudGl+NjSkBOgD487hexzTsG2v2bk+HHSWiTVN1IG+Uk5JFjn8oCkO2uv7UlB5I2SAOwY3k5epGHbmohQ0dUPMnwYyyr/0YALkXZE9qUA/L4v4V2PyJ9+9KoQGXXLLuAsbvUScAjocv8N87anmub4tCuOApTTih4EtAoadGyDloSsBzuZiPFQmKWLYA5EaQsocwJiGPjOh+fwS3XEy3U06iLf4ZXwCvQXhUw2BPbuRTqJKcBgfb62cUN33AHL/OX3MHL/DVa4xKtDl4Wuvk4WcGFtz1FPYMLcb4IH4aiZhyghrNqZL4umy8Fe5O0cFZgAwvUQOgGJKAG0IYBjtikeIgLcX+0XgWQYSTBxAHYqF1KrVDRM3SRmuQLWUAdKBTGkX8TAo1xIYu7l0mVDS5UAWlwQYv8EM20CaAmAW40C8cBTusdOa2/xpW5OZE6p0KpA6hpIchydz9YDAFyVLmDUGTtcoELQ/xbiG1WgJfonAP1oXXKiUOSLQzZlGsamBwCkSObls7zNuWtf8z5FjS+AkmLloSQVlq0Enk7ZtuSf9NRvk5l9o4Gmap6J2TxhGHUTli6ZbEqubtPsnosryJS9piFIWG9spyd2VQ8lQDIg5Ybfxz5N3oF6l2BLb9/ar2/YsbnO3iZv+QOXuavkYOXGmuUhDXoBmTQRroDSbgwhrA+IVxYc2tgvRzkC+sBVtJoZg2P80U18kswT+CNwBoFNTZe46r62zNSAdyBNFAjcEbATQLgwBolNikdKrxR2xTmKcQpiadWoQ2GsEwFgAPVQYA0swGcciUOuoW4xS4jh0bHrYbduZ8JcvQiLQpJjixWUOZodytpj/z/2zsPMCmq7Itfcs4KKq5rdlWMq2DCnFAxYMK0CooSBEVQlIwokkQFRTCAiroqohgwrZiz/s2rrjmLkTRDGsL/nvvqVdc0PdPdM90Tuk/x8XWqrqr3e69q+p2699zAQyd8jVBmCDv6XRN0kHZlkTZR/5xAsIlLtYoJORBAY6KOCTnebycQe/Kuo9hgEsgwgdBMOUFalk9dKFZly6pmOQEn6sPjBKBYJS37PEh3MJ8Mfe3Pf5+S5b0oEK3ny6bX0ptoeO09d6IpWLHoHQjFgSgcRgLF0rcYwZfhQcLNkUAJBCi8VM2hQeGlnP3yyfQdpFF9LQPdQNNtUP65Nido5URqX1+jk4WYSBN7DmHGxBl41CCiBmINUqEQRaHPN2ysjuaa7gJxpr5ODEysMcEGAk5MoPFpURRsMtFbVXcbUQHHDIgj4g3S7JxYo9E2lk7lBZxgvYiAgxbGUqjcJBNCjhd0LALHIm2ipsXFI3IwHhF+zoUEqgoBE2YiETmYkPkUq2KiTmiCHAg5EVFnpXlyxL6HCR8i38Ly5IEnjkXeBN464XNE7Zjnhla4skma89PxQo6P1vFeOm4CyHOoqowfHkfuEIiP3EGqQrH0LDNZjpRBhxGpCTjOO8uJPcE6gQAU+34skkci1bSKl0l3xqNWcDgwYoYNqUvVDFK5IqlauF5AxFkvsie4RvhqNbnTQ2wJCaRHgMJLerwqam0KL+Ukfe/YtlJQoAJAIcpBwx9b1JhWzWm1ghCEGFQOaqzCTJOG8EXR0tAQaerrZ/pIkaac8BN8fcVKrQCFSBoYyPpHTLADYQaGwi7SxpXudv/XBpE0KtDAo0ZFGjxadA1EGx9dg/eCdCgv4rAqVOb7sCpu0Vec8lE4UQHHved9bnQCirHmRZyg2hREHQgvi7VCGaILXJWpSPSNeX6okBP4eziRJ4jUCfxvwu8F79PIuCqOFB4TCHgfHT8pg5gTFXLMBDk0RI6aJgeROvgcHhzms+Pu3iNix92dl7DsuPPRCapdBaIN7sJHRRxM4pxJcrRceeClY+ed99VhCXOOXhLINgGrOOPTsyLiTiJPHvPtCaplubTMmMAD0aWwCClaXiCKlUyHaAQxWDWe0HDZ+fFAuIXU68QcX1kG79SySB4XrRNLxSpeOStWXSswYg6FIOffxYUEqhIBCi9VqTdix0LhpZz9UvTptsW2gJQH8zrRiAw8Fqggs6xQvSyWafnnxSrOFKpJrRdp9K9CI53IN65XW81pg6gZFWQaBcIMBBsn1OgETD/nkj0CVuknTqzxokwo0gQpUV6wWaGiDf5QQ5xB6pMJNPq8sfYV/BeQwgKBxkQc/Y/XPtIGjyx/mb3+rKpbNh+QSNqUL5+MiBs8d/4fMC92aVSYfFokDoQdE3NgYuxe44elRdiYKWsg1ATVpyDgwAsH0QNewIH/jRNvYu9FP6uqzHhcJBAlEC/q+IgdE2mQThGYIodCT3AnPpZ+5VOt8BiIPkH6Fbblom4QgeNSLHx0jjc99aKOT60IhZvAMDlmjuy8daKCD9MsOJZJoOIIREWeYn48xXx4vDEpxBp/DYmJOGZaGvHnwTpFYTliVJvxET/qZ+iNk4NS52a6bGKPe8TnEIEhBtu6YVl1n4oVq6CF9OYw0idSaSua5lVxJLmn6kiAwkvV7DUKL+Xsl3jhJZ3N4c54oabJQKApRNWgpTr5V5FmiUXQqHAD0UYFmwJ9jvSZxhpBg2gaiDHNG+jdcU1rahgIM16sQZQN3uNSMQQwEV6u/QjfGifcOLNYmA6vUKNhiDQQaKxccxBhg0k11sOPcifWuPQURNjYYyDQhI8R8ca/R/PhiunfqrwXm4BGUqf8xDI+pcr73+DRizlOxHFGyM7oFakhiMLR6jo6Br1fR8zQOPDwCFKpwmicQMgJo3HM8JiVeKryuOGxlU4gmn7lI3acqOMmYPHROxa5YxWv3AQsapJsUTuREslmlBxWrQlMkoMqV9FSyBBTkZZo0TxIxwrurHtfnai448UhmiVzZJNA5RKIVtbCTRYfjePTtiDShO/5aB6UGA4iAF1Ej5YVVpNDRPZZqpZP34qL7In68jRW02Vcn3C9qOnTtALxx0XzBP+DNC5XitildvrrhzdhdsKOr9LlBCEfGVS5dLn3dAlQeEmXWMWsT+GlnJzLI7yks+vV+gPOCzR4XKET96WLET0joUCDaBoINYjQQCRNmOoEsSYQZCDQ+EgaCDh4v1Yt+tKk0xeZWteiF3TyCxEGj+Y5gv8m2EDEUe+RQLhxaVNBGos+hzkmTIfhX4MIBgg2zs8miK6BWBOJuKnnX+u4wPtMkcpUL+bOdhBpg8mjGRRbJIAzKQ4jdMzkOGZm7NZz/jgm6pgI5KIIEM1lPjhmahwIOT46R183VA8mTEKjAg7Em+hrPPcROowOy51xls8tiRol+1LmUTHHp2Ct0cnWCky4LMUqVu3KCTmxFCy7++5TsfS598OwSJsglQLnnzdB9e9bFA7OLx/RE1TIilbFij5nFax8HrVse1UlsL4Hj0vNspTMuIpbVoI4MFW2VCwv5PhoHi1oEaZy+fLpPrpHH5G2pb8cwzROjdk2gSaWthU8h2hsJdV9KlfxNK54oce2YZFCPq2LQk+mxhuFl0yRzOx2KLyUk2dFCS/pHCbuhBeqAINoGoukQUQNUpwK8CjuUf1oINJgHYRVm0ijvjSNdWKO8s7N1JMGURUmzgQCDR4b6HpcKp8A+tiiaSDeWIUfF22D91aqYAPxDZE1MB92ETZOtFkW+N3U0j+aiLaB74g9QsCxCBxX0tuEmkCs8alS/jU+40ICpRHwk0nvf2OiTpBiZV4dZp4dVJ9apdECQYrVqqCsuI/CgcCI1Cqk9NlEUH+kYZxaalXo2eEEHi/cQIj0z/EdpPjZ62CyideYSHIhgVwj4P0wwqidwDgZ4kyx1KsgUk4tMmJROuahg8gdLWMeijkudcsEIf0Q6Q/OLyfwzIncHce5ifMtNES1SldBCkXEX8cbKUdLnXsTZQqsuTYi2Z5cIYDfnN5cOb7alhdsLC0r4t8T+vashRePu9b4kun+dbS6VuwzjfLX/TnxOIi+CVK2TOix1K0gXQvr+AgeM2P278eEHEsfDdO/nMjjhR7bvl6fcjENtDKEl1wZ79lsB4WXctKtisJLuk1C9Ewo0KBakIoxmKhbRA3SniDQBI+r9c42JjkmxkCkQSRNYCCMCTzet/fqOSNhPOePqXR7JPvr24Q2qOrjIm4CAScQbyzqBhPjID3KpUq5Es147qNtED2Dia6JNfoHz14Hog3eg8cNBLx6mhbnBRyawma/f3NtD2HVnGDC6NI7XCSARdroWF6FdI8g8ibmieOicKyUMj4Pvu8MWn2pcUwYXZqVPaqoEy/UeK8cH4GTKDqH17lcG3VsTzyBVIUdE3kCodX8MYKS5k4QipUzXw1BNlIVx+6UB9E6+J2BO+GNatdRs2aYpDrPnKhZsi+B7IUbL+aE1bACs3IX6cOKWBzRJFBdCEDogSjj07NM0CnJmyfyGQSblUEkoPfm8ZX2kI6F55b6FYjR9dTLa8mqVRrR44UeFy3oPHh86laNMF2reCRPLLLQp4P66MLQo8fE6OIpW/66le2+oPCSbcJl2z6Fl7JxC7+VC8JLuggsggYlnDWaAiINjIQxeUd1Jwg0yyHeQKzR/3jE5BsCTEuNosHFCEJNQ1QH0iiLmFADMUeNavV9Ggmn2yMVv34xscaMiWNCjhdrEHWjf99kceEaK9kM4QaizRqdAJuXDUQZK/ntqvxAtMGk17xuzIw4EGvwvve9CUyKMY64kEBZCXh/nKihsa+c41KoghQPS6VypsY+CgdpgUinsrQqjRjwE0wflWO+OEEah0u3cpEAiNKxCJxIVar1Uq2CqBwfwVPW9vF7JFAdCXhhJxad4+6Q+4pWsdLFzow8FHIila98tI7bRsx3xyZfOilzkTkxbwvzy7EInqCiTVAdy1fC8kIPKt24qLuYEWp8OhZeU4CtjiOPx5zvBBIJPfHePLFoHhdBCE+fomLmyzGPHkT0YB2ILoWri5yIFPr+6O9gfBaKMrhxWVvLAarA4w2ZIf6Y+bITm90/CD0qB/nqW3gMUrV8upYXmPF46HPX53u3Vsn2U3gpZ7dUnvCCUHk9s6vBslyNZiHSLIeRMEQavIY4o49IgYJQA5GmSCflMBZeoxMaiDNIf4Jog4pPEGrgC9FE06FQhttF1bj/KNNNsaYaDITgEPEHzqdH+QgaH32DSa99FqRIeVPilZqOYs8R2aATX/yBctE2QfUoGMIibQrvIR0lWkUKqSY+EieIvkG6CX1uqs+YqQ5HGk2vCn05wggdN3l0RseR6Bt9jeibZavWuPLJgU+Or1plPhyIzLFHn14VpFoFE0T88PICTjQyp656d0VTrvw6nBhWh9HEY8wGgWhFLJdCFQg7/nngdxEzWPZmqIGvjomtUY+dmP+ON1bGpCgm7LiJk/PS8WammCwFFbOCNAd/t9wLtE4MiqZpxUQfTNa4kAAJVH8CzovHXYfWBv47PqpnvWgfi+px4jHMl+06FEQArUH0Tujp49JDIVhf+c5L1R9SDrYgb4WXxUsK9UfuatmgZbNydetDt20sTdWwFsJA44YqEjTQ/w1r2PN6KghwSZ8ALiJhNI2VcI4JNZiYLFkSmAjDSBhRNSrcrF2DSBoXTdOsgbt75cQaTXcJUqL8IyNr0u+TqvYNTHIhwphwE4g1TsRxRsUrEV2jXjeoJIUxY143+BweOIhgUBEHoaSIqkFEAqJurDQzhBz/GIg1WMeXAndpU748uJvYciGBbBHwhsfRRx+Z46tY2V19ROAEZcedT44XcYKULHwWpGXF0jKC9KogEsd54CAX3kXmhK+98BMxP46mYllKCCPQsjUEuN1qRiBaocaVOi/ur7Oe4IOKWeZ7BX+dmFmyT8/yJY7xur6KN4v0LoSPxnFGpT56J/Cx8K/N78KJN8W9dtYXdPx6TMmqZoONh0sCpRDYcuYt5FMFCeSd8FJQuFzOGzhBPvr0a+uOthttIHdNGSwbbdgyYfc88vSrMviaW9f77J2nbpEG9evKy29tqlEbKgzo/0IVBAqX6n8IA2pgu0Z/ADdWIaZxgxrSBIIMykEHRrUQZkyoMbEGERsUacpzfkTFGu9Zg1SXaDrUCk2RWrZcgyU0YOLPhfpDRycjDS2axnmRuPQn/V+rtqU9IWrCImoikTV4zUiJ8vRU1fmuRRcgrSQQb/DoxRt7bmXAg8pS8LuJ/Ef0DaIXkGoCg2KIMb66FMQbRBv4lClLkwoqTNXR912FKVe22adTMQqh6oyLXD+SqF8OJnoWaRM8+tehh05oiOzGu/PICTw6wnQsdxcO496nYsTSq4Iy5IHHhq0T3OUPo3QiqVdRY2QaIOf6SGT7ykrAp1m5yjTu/PWpVS5dK/a+F2rhceXKouOOuPfdceWKbRt+O/oZKtjUDtIYfBUab55cX38fiZrzO8+dWFUbv17orxNJy0rkvcPInbL2Pr9HAqkRoPCSGqeKXivvhJeJ0+6X2Y+9IA/fPlpFjwbStdco2WKzjWXqNf0Tsp/71CsyfMIMmXPblcU+33rztpbLu3jVliX2GUpAe1PaAlQSsqpCTpQpUIFm+VJ9vVTDzPURWUMQaeB50kSFmMYaRdNIDWrx2FgjaEygMbFGw1RZ/jkj5wlC+5w4ox4lgUgD0QbPERmB/oJQsyIwHIbhLLxtEE3TTPultk4YINyYWIPIGkywEfkE0QYiTvAfIg7e5+Q6I91WpTbi06acaBNE23gxJzAnhmADAQfn+BIVaBGVgwiBhQXwvtEJq44bjCs/EYVgY2lT+r9xvTr6ExeeOM7nBgKhiTfBa4vG8WlVTJ+qUmMjnw4G19JoBI6ZGVspZOezkShix3x0fJROIOKEUTtBhZ0W9euYuOkFnSZ16mhotgRVrFzEGa67Phcenjo2QYymaAXPcd7gvKKgk08jk20tCwGXzuAEGR+h41/7iB4XzeNSGixlCxF1KEkcLXEeiLqWFhEIOxYBZH47EHdifjnRyJ1Y1ZmYUBsVdHwUD8QbrOuNmG0dCEYojx4IP2VpP79DArlAgMJL1ezFvBNeDj65v3Q6uINc2qur9ciceS+ZsPLx8zMTTowhvIyadKe898z6US/4fmnCSzpdXqQ/QCHSIHomfAxEmkJUF1JxZhkqC6lgAxNHRMk00ZSaRvXEHiEA+CiaUKhRIUf//nDJMAEry7xijRoKu7LNXqzBc6Q+rdb34VUDTxszmNUfHwu132zirOlQmDyjXHfNtfpcox4g0NQPKkFhEm2vsZ4JN/SvyXD3VcnN4Yeoj76JRt7g7iXEP5QIh4CjL6VAx5ilUuk1A3cdFxeuNgEHBsctGtU2YccJM7WkKcq/qx+OiTqBFwjS7yD8hAbGQUqVpVoFpsZMoaqSwySvDsrMDoP0KO+Z44xWY0IOPke04worV+7SqSwN0fx0fJlyvfbqJGzhstW2jq9o1bx+bd2+K1Nu50aQaoU7/S7dyhl9w7MjNEy26Bx4dHjfHSeE4nOmW+XV8GRjy0nAmZnGonWKPQ/Ss6yMuY/m8f46OM/1/Zr6d61QvbEg5CAKuWDlahOAaun7BRre47a3TprWrWvvoxoNzlNkRXqRB9HN6/TWhvfhQaqlr6wVX+486sPjU7OcITOrZZVzKPDrWSJA4SVLYMu52bwTXnY+pLuMHHCOdDlqf0P37kdfyFl9r5ZXHpkiLZo1WQ8nhJchY2+T/drvJPX07vO+e7STLkcfoGadTtHIlPCSTj8iBWKZijJepEE0zTKk0WiKEyJpIM4gigZpT7gjjogLS3nSaJpoqhP8aLxIA9FGf19yySIBlLw1cQY+IzpxRhoUImuWm8GwTqzNgNhF4dj7+ggHkcXaj0hNqQ+RRifGzVVoQ/SMCTM6sa6vaWqIqEG0VF2sFxFu6jGFLYs9WvU2vUZ/pJrHTfjfTUh9JAEe1+qPUZSPN48cmBbrWEN03jKMS30NYXGtioIIrPN+N+bpoZNRRHbB3NpSq0zcgZCI6C//XiwaB59hHfw45UICVYWAr2gVijpBmpXdscfkLqiig0gdVS2lUCMiXWUrpFmJVaYwg+Tgzj6qs/1RsForuKnAo+dJc01BRmlzH6XTWKN0cHffR9wgtbXGOufHUUcrVHgRB+cJqr3VQVnzMEqH509VGTc8jupJwHnkOEHW0rGC567KTFD5KjBZxuuVFqWnaVmB0ONSM52Plp72JrQs0TKNfnu4XtSvXVvXWVOsWpalZKkIZOlYVpkGUax1AuHHvbYqd3rO19B/UaEHQrArR+wigqyMOipmVc8u4FFXEgEKL5UEPslu80p4wQ+udgd1kwnDeslRh3QwNJ9+8Z2c1GOEPHnPeNmsbev1cL3zwf/koSdekhbNm8gPP/8m819+V444sL1MGtm70oSXdIYSJvMFQRQNfGgKfBQNfGgCcQZCDaJsGugk3XvSwI+miU7kD2zfkKlN6QDPwrqWzgK/EYgxNjlWAQcpUIFQYyayOjlYpz8WFi3RibemRFlVIAg4Oolu3VTD9XXSgOgZTBLCqBr9cd+xXWObPHMhgXgC0QgcK5lsZZRRccqVWEYEDqIKEIWDEvMm+CC9Sh8hDC7RMuKYrOrvUWdirCJMi4aaKqKvnZFxIN7oXcbdNm0sLRvVYSeQQLUl4NOtzOg4SK/CHfc1ev3GueRTrpDGgXMFd/IRfQMRB2KnzuXs3MA1ex3u5mvehkvLwPniJnAQZJrpDSC870sZh545OjFr27ShbN1y/RtI1RYqD5wEqgkB88eyiLtYtawwXSsQdkz4Ma8dXxlL3XJUlF2m+ZSWnhn47+D8X67vhalelqq1VprVrS/LtShI8RQtVP50KZi+ehbSrVCWeJvmLaR1g4bVhCAPM9MEKLxkmmhmtpdXwguQIeJl1MBuckKnjkYwWcRLPOY7HnhKJky9T95/9vYw6iUzXcGtkAAJkAAJkAAJkAAJkAAJkAAJkAAJ5BqBvBNe4PFy1CF7ycCep1pfPvj4izJi4swSPV7iO/zpF96WS0beJG8/OV0aNlCDFS4kQAIkQAIkQAIkQAIkQAIkQAIkQAIkUAKBvBNeJtx8n4ktc2dcJQ0b1peuPYtXNbrhtjny9AtvyRN3jzNkN9/1iLTbbkv5587byF+Llsr5l06U2prP+egdV3NQkQAJkAAJkAAJkAAJkAAJkAAJkAAJkECpBPJOeFlSsEzOvWS8fPL5twZm49YtZdaUIbJxm1b2+ooxt8q8+a/Lh/Nn2Ouh426Xh598OYTYdqMNZPr4AVaCurRllZof/Pzrn7bdenXpXcDzsGQCi5cUqnfGatmgZbOUMXF8pYyKKyYhUJbxR6gkkCoBjq9USXG9bBBId/wVFC5Xr6oiadWiaTYOh9vMMQIcXznWodWsOemOv9/+WGRVwNKZb1QzJFX+cPNOePE9guiVVUVFstGGLUvsJPwBPm/gBPnwk6/UAauGrttC7p06rNTvfP71j3Lx8Cny3Y+/2nYvOu9EOf/MzlV+IPAAK56AH18fkubJvAAAIABJREFUffq17Ryi3l1TBpd5fA26aro8/uzrxRoCw2gYR3MhgXgC6Y6/3Q7voSag6gAat8y+ZaTssO3mBEwCxQhkenzx+sYBlg6BdMffTwv+kAGjpoY35TbdeEMZc0UP2XXHrdPZLdfNEwKZHl95fX1Tk3Er4cklZQLpjr9vf1gg5w0YL7/89pftA8EDMyYNktYbNE95n1wxMwTyVnhJBd/EaffL7MdekIdvHy2NGjaQrr2KpyXFbwN/uA/vOlDa7/YPOe/0Y2S3dlvLsuUrqSymAjsP18n0+Lps9DQT/K66/NyQZoP69QQ/ILmQQDyBdMff19//olVXtMRKsLz/3y9l5MQ75Jn7JppoyIUEogQyPb54feP4SodAuuMPkdALFy+V+24eLjW1elS/oZPl198Xypzbrkxnt1w3Twhkenzx+pYnAydDzUx3/PXV69nPOkedfFU/y8KAzcZWm7e1DA4uFUuAwkspvGHE2+ngDnJpr6621px5L8nwCTNKNOK9fMwt6g/zthrvTtMSzLUqtie5t2pHINPjC3+4Fy4ukFsnDqx2LHjAFU8g3fEXf4Qnnjdc2mgU4NRr+lf8wXOPVZ5ApscXr29Vvsur1AGmO/46nXGZ/H3TNjJtnJuI3DX7aZky4yErpMCFBOIJZHp88frGMZYOgXTG3yKdF+x73IUycXgvm9NieeyZ1wRz1o+fn6kJHTXS2TXXLScBCi+lAETp6ZEDzpEuR+1vayUrPd3x+L6mJG7UupUs+O1P2V7D7wf1OY0RB+UcpLn69UyPL/zhfv6192T3nbaVls2bWvWujh12ylV8bFc5CaQ7/qK7e+mND6TX5ddpGts42axtm3IeCb+eiwQyPb54fcvFUZK9NqU7/vxEZJcdtpLTuxwqYybfLT00crlb107ZO0huudoSyPT44vWt2g6FSjnwdMbf4qWFsk/nPjJpZB854sA97Xg/UAuN03uPlucfvJ7pRhXcgxReSgC+bt06aXdQN5kwrJdOYJ1C+OkX38lJPUaYZwa8M+KXHQ88x0K3Tj32IBNgpt45V1asXCUv6MCuS4PdCh7aVXt32Rhf9z48X7745kdpUK+uIA0EF9YrL+0uJx7thEMuJOAJlGX8Rb972KkDtNLbdjJu6AWESgLrEcjG+OL1jQMtVQJlGX/wQDi9z2jZcrNN5MNPv5JaGrV8z41D6F+VKvQ8Wi8b44vXtzwaQOVsalnG38nnj5Rvf/hF+nbvInXq1LaIF8wRKLyUszPK8HUKL6VAg6I4amA3OaFTR1srWcQLhBeYsR13xL62Pox2T+g+1HKGd9p+yzJ0D7+SywSyPb56DJyoJdCXMEc9lwdROdqW7vjzu5r71CsyZOxtMn/2pFKNoMtxaPxqDhDI9vji9S0HBkkWm5Du+Dv0lEvk4P12l8H9zhRUCuk3bLK89/EX8u4ztzJ1PIv9VF03ne3xxetbdR0ZFXPc6Y4/XNPgC4NrWuNGDaSoaLV89uX3TDWqmO4qthcKL6VARw4d0jUG9jzV1nrw8RdlxMSZJQ5UrH/s4fvKxT1OsvV9hMydN1whe+yyXSV0L3dZlQlke3xhrL79/mfyxN3jqjIGHlslEUh3/OEwi1avkQO69NNw1fYy4pKzK+nIudvqQCDb44vXt+owCirvGNMZf0sKlsnex/SWsYPPl86H72MH/e5Hn8tZfcfI/dNHSLvttqi8hnDPVZJAtscXr29VsturzEGlM/4SHfSx5wyRhg3qWWAAl4olQOGlFN4Tbr7PxJa5M66Shg3rmws0SnB5M8mzL7rGqnkgygXLpOkPyKw5/7EqSE2bNDKR5pW3PpJXH5miA7x+xfYs91blCSQbXzfcNkfNmt8KhZNk42vY+Bly+gmHyNaa7vahlqju1n+snNHlMPMZ4kIC8QSSjb/46xu+f8cDT9l17oU5N6iPUBNCJYESCSQbX/HXt2Tji9c3DrZ0CCQbf/HXtw5H9zI/vlsnXiqN9ffeCK3Y9uIb78tLD09mxEs64PNk3WTjK/76lmx88fqWJwMnQ81MNv7ir2/weYGJLipT/lttCW66Y64wKCBDnZHmZii8lAIMd0FQYvCTz7+1tTZu3VJmTRkiG7dpZa8P6HKRGUvOmjLYXq9cVSQ9B10rb733mb2GmnjTmP5WXpoLCcQTSDa+rhhzq8yb/7p8OH9GSuMLodK//PZXuBsY604aeaGNQy4kkO74i7++LV+xSvY5to+Je77SG6mSQEkE0r2+JRtfvL5xrKVDINn4i7++IQR/wtT7zPeglpaT3m6rzeSy3l1lz135+y0d7vmybrLxFf/7Ldn44vUtX0ZOZtqZbPzFX9/+89I7cvHwG23nuGk2fmhP2XuPHTNzMNxKWgQovKSA669FS2VVUVHKfgYo3YWT4m+bbMgyXSnwzfdVMjm+kMf5x1+LTBxklFW+j6zU2p/u+Ettq1yLBByBTI4vXt84qtIlkO74w283+B+0atE03V1x/TwkkMnxxetbHg6gcjY51fGHNPEffv5NWrdqbh4vXCqPAIWXymPPPZMACZAACZAACZAACZAACZAACZAACeQ4AQovOd7BbB4JkAAJkAAJkAAJkAAJkAAJkAAJkEDlEaDwUnnsuWcSIAESIAESIAESIAESIAESIAESIIEcJ0DhJcc7mM0jARIgARIgARIgARIgARIgARIgARKoPAIUXiqPPfdMAiRAAiRAAiRAAiRAAiRAAiRAAiSQ4wQovOR4B7N5JEACJEACJEACJEACJEACJEACJEAClUeAwkvlseeeSYAESIAESIAESIAESIAESIAESIAEcpwAhZcc72A2jwRIgARIgARIgARIgARIgARIgARIoPIIUHipPPbcMwmQAAmQAAmQAAmQAAmQAAmQAAmQQI4ToPCS4x3M5pEACZAACZAACZAACZAACZAACZAACVQeAQovlceeeyYBEiABEiABEiABEiABEiABEiABEshxAhRecryD2TwSIAESIAESIAESIAESIAESIAESIIHKI0DhpfLYc88kQAIkQAIkQAIkQAIkQAIkQAIkQAI5ToDCS453MJtHAiRAAiRAAiRAAiRAAiRAAiRAAiRQeQQovFQee+6ZBEiABEiABEiABKolgXV61DWq5ZHzoEmABEiABEig4glQeKl45twjCZAACZAACZAACZAACZAACZAACZBAnhCg8JInHc1mkgAJ5DaBxUsK5aU3PrBGHtJxd2nYoH7Y4Lfe+8yet9/tH5UO4aEnXpLmTRvLwfvtXmnHAh5Dx90md04eLBu3bllpx5HOjk+9YJQceVB76da1Uzpfy/q6r7/zX/n6+1/kjC6HZn1fVX0H6bKoqHF44eAbZLO2reWyPqdVSYTf/rBA7nnoP/LMi+9IM702PHrH1RV2nOn2WYUdGHdEAiRAAiSQcwQovORcl7JBJEAC+Ujg/f9+KWf0ucqafu5pR8klF5wSYji992h7fu/UYRWGZszku2X24y/Ke8/cWmyfe3a6QLbcbBO5f/qICjuW+B3956V35OLhN8rjd10jW2y2cbmP46gzB8nfN91Ibh7bv9zbKmkDux3eQ048an8ZevFZWdtHaRt+4bX3pc/g6+XRO8fIVn/fJFy179DJ8twr78p/X7ijUo6rPDstqU1l3Wa6LDI9Dks67sO7DpStt2grU6/J3vgsKzN8D+fPoiUFckKnjrJ27ToZlAWBaPmKVbLHkefL4H5nqEh4WE6M3/Iw53dJgARIgAQqngCFl4pnzj2SAAmQQMYJeOGlZfMmsnhpobz40A3SolkT209lCC9XXT9L5mh0S7zwsmhxgdSsVVOaNm6YcQapbjDTE95OZ1ymwksbmTZuQKqHkPZ6lS28QFyBsIBohK02bxsef+GyFbJyVZFg3FW3paQ2lbUd6bLI9DisjsLLgt//kkNOvkTGD+spRx+yV1nRJ/3esuUrBaLv5ReeLmeddHhOjN+kjeYKJEACJEACVYoAhZcq1R08GBIgARIoGwEvvIwbcoEMunq6nNL5QBkx4JyEwsuaNWvlpjseltmPvSB/LVoqG7RsJhedd6J00YgKvzyo0SpTZjwkf/y1WMP/G2mqQhtLy7lu1IW2yvW3PigPPPa8FBQut9eIYrm87+my1+47yFPPvyVXXHOrrNIJOVIcsGDbPc44RvoNmyx/26S1XNqrq03ka6g75+TR/cL9rl6zRpBWc4imIvU+53hJ5VgTEXv6hbf1GGfL9z/9Jo0bNZDjjtjX9lmnTm1JNOEtbX1sH2LWNVPusXQutBk8zjv9aPn8qx/kztlPSy0Vk9putIEdyqiB3WXturUy6to7jNf9jz4v7330hRxx0J7S61/HybsffS7DJ8yUbzRFB8d2lE44r9AJYd26dez7YHDttAfkwcdfEEwYEZWDdU87/pAw4gWpZaMm3Skvv/mBrQMxZPRl3WWXHbYqcQCV1mf+SxAjrr9tjnz7gzu2DrttL91PO1ouuGyiYJ9tNmwh9fQ4t95iU5lyVT+5/d9PCCJHZk0ZLLfe87g88vSrcv+0EdKoYSzVbcTEmbLgt79k+ngnTCVjHd+AN979xFj+6+Qj5C5ljT4FkxuuvFD+78PP5Za7H5Pf/lwkhx+wp/Q798RwzJXW3p8W/CEnnz8iYZuSjTm085W3PrJJ/PRZj8mX3/4kV/Q9Qz778vuQRbJzBJ8nE16QijRi4gzt13Nlj122C7HMmfeScp8n99w01MTVZOMpGvGC8/msvlfL8P5ny9577BiOt87/ukL6dDtBjjl0bykr73T7FsdyQvehdg3y46q7Ruvh+pDo3Nl2i7/J1ZNn2TUJfYTr1vlnHlMsguUr7Ysrr7tLPvn8Wz2P1sqO224ul/buKmNuuFs+/t83Nqa9SDjntivl33OfK9ZnaMO9D8/Xfn3U9oN99PzXsXbuYVm2fIWceN5wi855/f/+a+Nv0403tGsb3uNCAiRAAiRAAiURoPDCsUECJEACOUDACy+YjEE0efjJl+XZByaZWBIf8TJ03O3y6DOvyrGH72sTukeefkUwybt90mUmnMx96hUZMvY2++6ZJx5uQsOds5+ySciT94w3Wpj0r9O0gB2221xWr14jM3QiiMnvy3OnyC+//imDVXj5n4oSmAhjgSCw1z93kOgk8Oa7HpEbZzxcLOXHT0ZnXDfIJv3JjjVR10H4GTBqquyzRzs5+tC95J0P/mc8fApW/IQ32fqY5B1x2kD5RcWDw/bfQ/6587by7Mv/Z1z6qDgEoatZk0ZycueD7HA6HdxeJ37f2TFgwURv263+Jnvu8g857IA95NizB5uYdc4pR8qHn3wtz7/2nvnyeAEKbcbxbqffOfHo/VUEWWCTQS+8+ONZqvuHt0qrFs1k5n1POP4PT7FtJ1pK6zMc/xPz35RLR99s/XzmiYeZ2HTPQ8/KxOG9ZN6zb6hg8pYdw4atmts6ODZMkB9/9nV5+8np8sEnX9lYG9zvzNDz5c+FS2T/E/qF7JOxLq0/8Rkmt5u0aSW33jvPhD0IXkjBaq4CBMSIIw9sb9ETycaoaEke8EjUpmRjzvcP9oFIJ4gG3U7tJM+/+l7IItn+wTuZ8IJIIkRp7Nd+p2JpQoeecol5oUA4+Oq7n5OOp+g5B8EJrycM66WCXwfjVKTn766HnisDe55qHkK+j9LlnW7fQsQYde2dxgzCLASMDrtvbyJdonMH4+7FN96360KD+vVk3vw35I3/+0RumTBQ9t2znfi2QcA89diD9LxoKvc/8pwJm+A1afoDdn1rr9/H0l3bipRIP37xHtaHcINz71gVax9VIRHXsRGXnC2n6DYhPu5zbB/7vt/WMy++baLbq4/cqOOwccJzj2+SAAmQAAmQAIUXjgESIAESyAECUeEFE5iDT+4vh3bcQyaN7F1MePntj0Vy0EkX66TjKBnQ0/nAIMJid/UQ8ZPWjsf3lTVr18prj94Ukuk56Fr57sdfQ+HFf4Dv4s7wy29+KCMn3iEzr7vcTHxLSjWKTgL9pBziASbrWM6+6BoTGpAqlcqxJuq6A7pcZJOuh2533jZYzuo7xqI4IAzFT3iTrY8oDghJfvLltwmBaWMVARKlGvlJaLynxCUjp9pkH8fh77xj29jHs/dfK/Xq1RXwP2if3eTGMReFxx9NNcJEcdBV00OhDCvhbj4iha6+/Dw5/sj9Sh3RJfUZ9ruqaLW8Oe/m8PsQl9auWyfvvP9ZwlSjqPCCL0EUwMT3ibvH2TYQHYLIk+dmX2cCRTLWiQ7cs7zv5uGy0/Zb2iqYRCPa5ul/T7AJO5Zh42eoQevbxY4f75fU3kSpRqmMOS/M3HPj0PB4sJ94Fr4tJe0/mfCC7+Ocmq2RTzgXIaj5fkZk2zGH7S3JxhPGZ1mFl3R5l6VvEV1z7iXjJbqvks4dz3OdjkdEyfyuQiOiTxBtcnGPk+ycwLkBcdhH2sEzBus10dTGRKlG8X0GUaW+noMYr35Bu4pWr7Y+8MLLhd1PsOg1LNj+gSdebBFn0ajBUk9CfkgCJEACJJB3BCi85F2Xs8EkQAK5SCAqvOy649YyYep9cscDT5kZ6jCNoMACc10IJD0HTbKQ+2g6yK+/L5R2220hd2nKCEQYRBZcNejcEFW88ILohhGaLvPFNz8WwwkDzwP23iUl4QVfxHbf1Gibt5+cJgt1MoUJDIyBEZ2S7FgTGfQiCgIiBSb/LSJ3n316AkxgoxNepAclWx93xRH5ATEIkR7xS2nCS3QSiO/BSBTHiGgkv/iJJtghjefcAePXE1CiwsvYG++VWQ8+Y0KGXxAFgzYiLaJv9y4Jh3hpfYYoAJiPdj58Hxk7+Pz1vl+SH0r8xBXVacZMvsdEL0QNYNK6taZBIZoqlb5JdOCeT5Sl38/rj08N/YIQPYUoKm/0m2yMJmpTKmMOwgvGUFSgwnHHs0i2/1SEF6TOHHvOEEuTO+fUI00AfFIjut56YprUqV0r6XjCuVhW4SUd3mXt29KEl/hzB+Mb1che00paGO9+8cItzi347OA8jV9K8niJ9pk34IV4AhHFLz7C6V01Cl+hJr0QZ+JF2B0PPEfTnjpbyiYXEiABEiABEkhEgMILxwUJkAAJ5ACBeOEFkQqYIHTYbQedjDgfFggv8Ne4ZORN5uewZVxFH4gKO2z7d70z3NNSRXwUCr4bFV5wtxnRERAtBvU5Xf6xzWaycPFSi7hIV3hBqgCEBkTmfPXtz+o9M1dee0zv7msqRrJjjfpe+C5cUrBM9j6mt5WrjjfrrKGGMkccuGcx4aWVtjnZ+oikQBlsiEPRMt1+n+kIL4gIadiwQbGSufCN6XX5dZZqBG8YVFy6/VpN+9LULL9EhRd4piCd7NoRvdcbudtsuWmxqkN+hWR9tmu7rWWfzn3MLwNROvFLqsILJr57d+6tnjr76d3/jnLmhVeHYyKVvkl0KiYSXv49d76Je1HhxaeuQXhJ1l4IEonalMqYS0V4SWX/qQgv4NHl3GGyRNO+5mkUEaI2uh53cHhuJhtPSGFLJLyMG6oRM+rngqWkVKOo8JGMd1n7Nh3hBe34XcWXy7XqEc79jVq3kv30OnTyMQcYD7CorylIqFYWv6QivPg2nK1eQtHS2xASIfTh/C8qWkPhJQf+XrIJJEACJFAZBCi8VAZ17pMESIAEMkwgXnjB5mE6eoMapSL6Y/utNzPhxXtCIDQfIfrRBSH8ECcwyYeo8phGy+A13j9NvTsWa8lXTMb85DQqDnz5zU9yXLch4SR74rT7zQj1w/kziu0jUWlbiDgox/zdjwvUP2U7uV5NU7GkcqyJMOL4d9aUlDtvuCJh++InvMnWR3QJokwmjexjwo1fcNcdHiOYGCOVIbq/RGIBvtdj4ES9Y/9xMRHHR9Q8MvNqTfUpUtPXkZbGgHQGv0SFFx/tMfuWkSqUbZ6wjfFcUukz7KOV+tFEo3GwHaRrvPXepyaQIcoIkVF+SZRec9noafKUplMduM+u5q8D7wuMIyzJWCfqz7IIL6m014t+0TalMuZSEV5S2X+qwov3XDr5mAMt7SgqiCQbTygjHT3n4NsDgS1a3cdHq8R7vKQjvJS1b1MVXvxxw4MGxxk9L7zwcsFl15rpcbzXCs5TpMvBxyb+uhc/fv21LyreIJIG/k6o0OZTjRjxkuE/YNwcCZAACeQBAQovedDJbCIJkEDuE0gkvMCcc7/jLrSqNzC3hfCCBQaoSIPABGZfNe78VUu6YqIIEWHUwG7myQFvDkTA7L/XLvKm+jDAuBK+CZiMeU8DmH6eqxVvfv71D5l8+xzdzsJQeIFZb7f+Y22Ch9SnGjVr2IQ9kfDiBSIc2903DpHd2m0TdliyY03Usz7yAelSXY8/2FJcMMmGIS5SYOInvMnWRzTPwVrytkH9upZOsPc/dzQj1bfV9wQpNOA1Q81tbxpzsaYK1dWqLBsaXxiExqdLvPr2x3L+pRPtjn2vs4+Tjz/7xqJ8tvjbRnZsEDkO6NJPCjRyBBVbUMnlrgefNgHDm+vizvwh6uGDCk3DLv6XVhhqKxC+YMB76nEHhZEMUTap9BkqKaEdiBbCXf/lK1basfU//2TrQ/MBOqi9GbBiAgpD00TCy0effi1de11pu4f5MKpT+SUZ60wJL6m0F+dHojYlG3OpCC+p7D9V4QXjt4NGceERHjfwQ/FLsvGE9eLPueO7DZUlBYVy9aDzzJD5ppkP2/ldXuGlLH2bqvCCdsC3qqYKeMO0ItMa9ZaaNecZMwX30XleSEOFr956bsHfBpXbWm/Q3Ey+4QcDQ99rNJUO5/R+7XfWakezipnrTrhZUzTvf8qqwnVW8/HH/vOaPKBVybzgQ+El9/+WsoUkQAIkkC0CFF6yRZbbJQESIIEKJOCFF4gr0ZLCPkUgKrxg0oF0lfkvvxseIaJikGKCu+qY/KOU9GNa+QhLxw47q6nnt/r+WqukgsWLDd5roWOHndST5SO5eWx/E2vwPrxkEN2BBRVBcJc4kfBik6Dj+loVpfhoi2THmggx9j31zrkmHkW9ILxvDdqNstbzZo2VzVXwSLY+9vGuloMeeOVUE5f84qskwQy437Apmir1k32ElKHVa1Y749OI+av/Hrx34MHjl220NDO4YaKIBX3Zrf84m2hjgVkxytaiUsuQi86y91BF5TI1E/X7xHvwfLnhyr7FDF+jfJL1WZEa6yKy5z6t7OIX9AkqTKF8NsS1mTopxXEhIuqZ+yZaZaDHdXKKqkbRBelXKPsc74uTCuv4PoUZcTxLX30GPivwK8Iy7a5Hbdx6j5dk7bW+StCmZGOuJBPfeBbJ9h8/DuPbHX09WivtoF+QkneEVm6KLsnGU/w5hxQrnP9Ih8KC8wJVtLyPTFl5l6Vv39RIqu461qNRR4n2j+OEUAXRy5ewRyoexJZoetwDKrSg7Ls/dxo2qCdjruhh1cjAe+S1sXZj7FyrJs3R8QsxDhXdnnzuzRAxUhZHq98V/Jd85M2IAeeYOOMXeLxccFbnsIpbaX3Jz0iABEiABPKTAIWX/Ox3tpoESIAErNrLzwv+VN+SeglNYz0iTKg6ntDXyjOjtLBfYEb5/U+/WoRHIu8TrIdJUoF6zLTZoEWYblIW9Kkea3TbSJFaoELJOhWM2mzY0iJ6SltSWR93vDH52mSjVlK7Vq1im4P5J/bRQksbJ1vgq/GdCjYoP5vIsBfMUYUJpaJLK1GLaCZELMGrpqmmOyVbUukz7BsRENhe/L7RDyj3C54wdy3rkgrrsm47+r1U2ltSm8oy5uKPOZX9Z6KdycZTon0grWqTNhtYJFcml2z2Lbb9hUZ34ZzxVcESHfsCPSewJLru/KxjG+O6pGsWvofImB9+/r3Ua1smmXFbJEACJEACuU+Awkvu9zFbSAIkQAJpEUD6Asr1/nPnbW1y8pym1SCyIpGnSFob5sokQAIkQAIkQAIkQAIkkIcEKLzkYaezySRAAiRQGoH/ffWDhfT/tOB3qVWzpmz5903kQq2CtOeu/yA4EiABEiABEiABEiABEiCBNAlQeEkTGFcnARIgARIgARIgARIgARIgARIgARIggVQJUHhJlRTXIwESIAESIAESIAESIAESIAESIAESIIE0CVB4SRMYVycBEiABEiABEiABEiABEiABEiABEiCBVAlQeEmVFNcjARIgARIgARIgARIgARIgARIgARIggTQJUHhJExhXJwESIAESIAESIAESIAESIAESIAESIIFUCVB4SZUU1yMBEiABEiABEiABEiABEiABEiABEiCBNAlQeEkTGFcnARIgARIgARIgARIgARIgARIgARIggVQJVKjwslaPqmaqR8b1SIAESIAESIAESIAESIAESIAESIAESKCaE6hQ4aWas+LhkwAJkAAJkAAJkAAJkAAJkAAJkAAJkEBaBCi8pIWLK5MACZAACZAACZAACZAACZAACZAACZBA6gQovKTOKuNrMvUq40i5QRIgARIgARIgARIgARIgARIgARKoUgQovFSp7uDBkAAJkAAJkAAJkAAJkAAJkAAJkAAJ5BIBCi+51JtsCwkUI8CYKg4IEiABEiABEiABEiABEiABEqhsAhReKrsHuH8SIAESIAESIAESIAESIAESIAESIIGcJUDhJWe7lg0jARIgARIgARIgARIgARIgARIgARKobAIUXiq7B7h/EiABEiCBvCfAxMC8HwIEQAIkQAIkQAIkkMMEKLzkcOeyaSSQEwTWaStq5ERL2AgSIAESIAESIAESIAESIIE8JEDhJQ87nU0mARIgARIgARIgARIgARIgARIgARKoGAIUXiqGM/dCAiRAAiRAAiRAAiRAAiRAAiRAAiSQhwQovORhp7PJJEACJEACJEACJEACJEACJEACJEACFUOAwkvFcOZeSIAESIAESIAESIAESIAESIAESIAE8pAAhZc87HQ2mQRIgARIgARIgARIgAS0oGyxAAABsElEQVRIgARIgARIoGIIUHipGM7cCwmQAAmQAAmQAAmQAAmQAAmQAAmQQB4SoPCSh53OJpMACZAACZAACZAACZAACZAACZAACVQMAQovFcOZeyEBEiABEiABEshXAmu14TXztfFsNwmQAAmQAAmQAIUXjgESIAESIAESIAESIAESIAESIAESIAESyBIBCi9ZAsvNkgAJkAAJkAAJkAAJkAAJkAAJkAAJkACFF44BEiABEiABEiABEiABEiABEiABEiABEsgSAQovWQLLzZIACZAACZAACZAACZAACZAACZAACZAAhReOARIgARIgARIgARIgARIgARIgARIgARLIEgEKL1kCy82SAAmQAAmQAAmQAAmQAAmQAAmQAAmQAIUXjgESIAESIAESIAESIAESIAESIAESIAESyBIBCi9ZAsvNkgAJkAAJkAAJkAAJkAAJkAAJkAAJkACFF44BEiABEiABEiABEiABEiABEiABEiABEsgSAQovWQLLzZIACZAACZAACZAACZAACZAACZAACZAAhReOARIgARIgARIgARIgARIgARIgARIgARLIEoH/B1x8y4Sfnf0dAAAAAElFTkSuQmCC", - "text/html": [ - "
" + "image/svg+xml": [ + "0.60.650.70.750.80.850.90.50.550.60.650.70.750.80.40.81.21.622.4Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -11309,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_electrode_design.ipynb b/examples/notebooks/spm_electrode_design.ipynb index f931aced6..8b44b8f56 100644 --- a/examples/notebooks/spm_electrode_design.ipynb +++ b/examples/notebooks/spm_electrode_design.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "metadata": { "id": "SQdt4brD04p1" }, @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "metadata": { "id": "zuvGHWID04p_" }, @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "metadata": { "id": "WPCybXIJ04qA" }, @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": { "id": "etMzRtx404qA" }, @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "metadata": { "id": "N3FtAhrT04qB" }, @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -208,9 +208,9 @@ "output_type": "stream", "text": [ "Halt: Maximum number of iterations (5) reached.\n", - "Estimated parameters: [7.38910820e-05 2.07135001e-06]\n", - "Initial gravimetric energy density: 386.13 Wh.kg-1\n", - "Optimised gravimetric energy density: 397.79 Wh.kg-1\n" + "Estimated parameters: [6.92072167e-05 2.26386226e-06]\n", + "Initial gravimetric energy density: 386.81 Wh.kg-1\n", + "Optimised gravimetric energy density: 404.95 Wh.kg-1\n" ] } ], @@ -245,13095 +245,25 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": { "id": "ZVfozY0A04qC" }, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857, - 3510.522507468857, - 3510, - 3505, - 3500, - 3495, - 3490, - 3485, - 3480, - 3475, - 3470, - 3465, - 3460, - 3455, - 3450, - 3445, - 3440, - 3435, - 3430, - 3425, - 3420, - 3415, - 3410, - 3405, - 3400, - 3395, - 3390, - 3385, - 3380, - 3375, - 3370, - 3365, - 3360, - 3355, - 3350, - 3345, - 3340, - 3335, - 3330, - 3325, - 3320, - 3315, - 3310, - 3305, - 3300, - 3295, - 3290, - 3285, - 3280, - 3275, - 3270, - 3265, - 3260, - 3255, - 3250, - 3245, - 3240, - 3235, - 3230, - 3225, - 3220, - 3215, - 3210, - 3205, - 3200, - 3195, - 3190, - 3185, - 3180, - 3175, - 3170, - 3165, - 3160, - 3155, - 3150, - 3145, - 3140, - 3135, - 3130, - 3125, - 3120, - 3115, - 3110, - 3105, - 3100, - 3095, - 3090, - 3085, - 3080, - 3075, - 3070, - 3065, - 3060, - 3055, - 3050, - 3045, - 3040, - 3035, - 3030, - 3025, - 3020, - 3015, - 3010, - 3005, - 3000, - 2995, - 2990, - 2985, - 2980, - 2975, - 2970, - 2965, - 2960, - 2955, - 2950, - 2945, - 2940, - 2935, - 2930, - 2925, - 2920, - 2915, - 2910, - 2905, - 2900, - 2895, - 2890, - 2885, - 2880, - 2875, - 2870, - 2865, - 2860, - 2855, - 2850, - 2845, - 2840, - 2835, - 2830, - 2825, - 2820, - 2815, - 2810, - 2805, - 2800, - 2795, - 2790, - 2785, - 2780, - 2775, - 2770, - 2765, - 2760, - 2755, - 2750, - 2745, - 2740, - 2735, - 2730, - 2725, - 2720, - 2715, - 2710, - 2705, - 2700, - 2695, - 2690, - 2685, - 2680, - 2675, - 2670, - 2665, - 2660, - 2655, - 2650, - 2645, - 2640, - 2635, - 2630, - 2625, - 2620, - 2615, - 2610, - 2605, - 2600, - 2595, - 2590, - 2585, - 2580, - 2575, - 2570, - 2565, - 2560, - 2555, - 2550, - 2545, - 2540, - 2535, - 2530, - 2525, - 2520, - 2515, - 2510, - 2505, - 2500, - 2495, - 2490, - 2485, - 2480, - 2475, - 2470, - 2465, - 2460, - 2455, - 2450, - 2445, - 2440, - 2435, - 2430, - 2425, - 2420, - 2415, - 2410, - 2405, - 2400, - 2395, - 2390, - 2385, - 2380, - 2375, - 2370, - 2365, - 2360, - 2355, - 2350, - 2345, - 2340, - 2335, - 2330, - 2325, - 2320, - 2315, - 2310, - 2305, - 2300, - 2295, - 2290, - 2285, - 2280, - 2275, - 2270, - 2265, - 2260, - 2255, - 2250, - 2245, - 2240, - 2235, - 2230, - 2225, - 2220, - 2215, - 2210, - 2205, - 2200, - 2195, - 2190, - 2185, - 2180, - 2175, - 2170, - 2165, - 2160, - 2155, - 2150, - 2145, - 2140, - 2135, - 2130, - 2125, - 2120, - 2115, - 2110, - 2105, - 2100, - 2095, - 2090, - 2085, - 2080, - 2075, - 2070, - 2065, - 2060, - 2055, - 2050, - 2045, - 2040, - 2035, - 2030, - 2025, - 2020, - 2015, - 2010, - 2005, - 2000, - 1995, - 1990, - 1985, - 1980, - 1975, - 1970, - 1965, - 1960, - 1955, - 1950, - 1945, - 1940, - 1935, - 1930, - 1925, - 1920, - 1915, - 1910, - 1905, - 1900, - 1895, - 1890, - 1885, - 1880, - 1875, - 1870, - 1865, - 1860, - 1855, - 1850, - 1845, - 1840, - 1835, - 1830, - 1825, - 1820, - 1815, - 1810, - 1805, - 1800, - 1795, - 1790, - 1785, - 1780, - 1775, - 1770, - 1765, - 1760, - 1755, - 1750, - 1745, - 1740, - 1735, - 1730, - 1725, - 1720, - 1715, - 1710, - 1705, - 1700, - 1695, - 1690, - 1685, - 1680, - 1675, - 1670, - 1665, - 1660, - 1655, - 1650, - 1645, - 1640, - 1635, - 1630, - 1625, - 1620, - 1615, - 1610, - 1605, - 1600, - 1595, - 1590, - 1585, - 1580, - 1575, - 1570, - 1565, - 1560, - 1555, - 1550, - 1545, - 1540, - 1535, - 1530, - 1525, - 1520, - 1515, - 1510, - 1505, - 1500, - 1495, - 1490, - 1485, - 1480, - 1475, - 1470, - 1465, - 1460, - 1455, - 1450, - 1445, - 1440, - 1435, - 1430, - 1425, - 1420, - 1415, - 1410, - 1405, - 1400, - 1395, - 1390, - 1385, - 1380, - 1375, - 1370, - 1365, - 1360, - 1355, - 1350, - 1345, - 1340, - 1335, - 1330, - 1325, - 1320, - 1315, - 1310, - 1305, - 1300, - 1295, - 1290, - 1285, - 1280, - 1275, - 1270, - 1265, - 1260, - 1255, - 1250, - 1245, - 1240, - 1235, - 1230, - 1225, - 1220, - 1215, - 1210, - 1205, - 1200, - 1195, - 1190, - 1185, - 1180, - 1175, - 1170, - 1165, - 1160, - 1155, - 1150, - 1145, - 1140, - 1135, - 1130, - 1125, - 1120, - 1115, - 1110, - 1105, - 1100, - 1095, - 1090, - 1085, - 1080, - 1075, - 1070, - 1065, - 1060, - 1055, - 1050, - 1045, - 1040, - 1035, - 1030, - 1025, - 1020, - 1015, - 1010, - 1005, - 1000, - 995, - 990, - 985, - 980, - 975, - 970, - 965, - 960, - 955, - 950, - 945, - 940, - 935, - 930, - 925, - 920, - 915, - 910, - 905, - 900, - 895, - 890, - 885, - 880, - 875, - 870, - 865, - 860, - 855, - 850, - 845, - 840, - 835, - 830, - 825, - 820, - 815, - 810, - 805, - 800, - 795, - 790, - 785, - 780, - 775, - 770, - 765, - 760, - 755, - 750, - 745, - 740, - 735, - 730, - 725, - 720, - 715, - 710, - 705, - 700, - 695, - 690, - 685, - 680, - 675, - 670, - 665, - 660, - 655, - 650, - 645, - 640, - 635, - 630, - 625, - 620, - 615, - 610, - 605, - 600, - 595, - 590, - 585, - 580, - 575, - 570, - 565, - 560, - 555, - 550, - 545, - 540, - 535, - 530, - 525, - 520, - 515, - 510, - 505, - 500, - 495, - 490, - 485, - 480, - 475, - 470, - 465, - 460, - 455, - 450, - 445, - 440, - 435, - 430, - 425, - 420, - 415, - 410, - 405, - 400, - 395, - 390, - 385, - 380, - 375, - 370, - 365, - 360, - 355, - 350, - 345, - 340, - 335, - 330, - 325, - 320, - 315, - 310, - 305, - 300, - 295, - 290, - 285, - 280, - 275, - 270, - 265, - 260, - 255, - 250, - 245, - 240, - 235, - 230, - 225, - 220, - 215, - 210, - 205, - 200, - 195, - 190, - 185, - 180, - 175, - 170, - 165, - 160, - 155, - 150, - 145, - 140, - 135, - 130, - 125, - 120, - 115, - 110, - 105, - 100, - 95, - 90, - 85, - 80, - 75, - 70, - 65, - 60, - 55, - 50, - 45, - 40, - 35, - 30, - 25, - 20, - 15, - 10, - 5, - 0 - ], - "y": [ - 4.0781535600043215, - 4.062128492127174, - 4.052857122903849, - 4.045468006198349, - 4.039164655238445, - 4.033627171418397, - 4.028679583717739, - 4.024206701988653, - 4.020126671141043, - 4.016378976602813, - 4.0129172388855325, - 4.009704860323629, - 4.006712148243633, - 4.003914931880226, - 4.001293401720541, - 3.99883105267252, - 3.996513935514436, - 3.994329756382196, - 3.992268199397369, - 3.990320376053805, - 3.9884786151773146, - 3.9867362494432466, - 3.985087257169886, - 3.983526273017628, - 3.982048410059068, - 3.9806493509083007, - 3.9793252039606934, - 3.978072411222857, - 3.976887703615617, - 3.9757680144916834, - 3.974710494221364, - 3.9737124647122415, - 3.9727714087989336, - 3.9718849370518097, - 3.971050756685545, - 3.970266658014845, - 3.969530495605136, - 3.968840185890992, - 3.9681936922444754, - 3.9675890165982617, - 3.9670241906840658, - 3.966497268901439, - 3.9660063241849626, - 3.9655494426727618, - 3.965124722491505, - 3.9647302700948246, - 3.964364200491103, - 3.96402463621366, - 3.963709707556047, - 3.9634175509708824, - 3.963146309998812, - 3.9628941362300134, - 3.9626591937709392, - 3.962439660679193, - 3.962233730227712, - 3.962039611945965, - 3.9618555334371224, - 3.961679745579746, - 3.961510524327599, - 3.961346175226278, - 3.961185033115423, - 3.961025465675149, - 3.9608658760771354, - 3.960704706406239, - 3.960540438943686, - 3.9603716016500656, - 3.9601967674705296, - 3.9600145563644937, - 3.9598236409958183, - 3.959622743439794, - 3.95941063977861, - 3.9591861609960928, - 3.9589481906857658, - 3.958695677190458, - 3.9584276269293177, - 3.958143105106755, - 3.957841219978721, - 3.9575211653766007, - 3.9571821882635025, - 3.956823597460946, - 3.95644474094881, - 3.9560450489014496, - 3.9556240092170705, - 3.9551811668294734, - 3.954716112093187, - 3.954228495561752, - 3.953718036274191, - 3.9531845023818577, - 3.952627716265804, - 3.952047553288323, - 3.951443940452529, - 3.95081681939006, - 3.9501662187187385, - 3.9494922074961463, - 3.9487948939344584, - 3.9480744285682583, - 3.947331002452851, - 3.946564842068588, - 3.9457761724734417, - 3.944965309992684, - 3.9441325800465257, - 3.9432783377421696, - 3.942402965889137, - 3.941506873027282, - 3.9405904873417623, - 3.939654253971517, - 3.93869865012846, - 3.937724165047615, - 3.9367313037486937, - 3.9357205852191233, - 3.9346925406436792, - 3.933647718924989, - 3.932586674408453, - 3.9315099636900217, - 3.9304181529211393, - 3.929311812830394, - 3.9281915173138953, - 3.9270578420856808, - 3.9259113661614737, - 3.924752671067227, - 3.923582336475441, - 3.922400932133523, - 3.921209026068122, - 3.920007184672868, - 3.9187959711483114, - 3.917575932353624, - 3.916347613554031, - 3.915111551967116, - 3.9138682760725696, - 3.912618304973568, - 3.91136214780803, - 3.9101003032077903, - 3.908833258803867, - 3.9075614907756258, - 3.9062854634420208, - 3.9050056288924857, - 3.903722426655612, - 3.9024362838142888, - 3.9011476169194825, - 3.8998568227693142, - 3.8985642880087874, - 3.8972703855527144, - 3.895975474433693, - 3.8946798996721896, - 3.893383992166422, - 3.8920880685998727, - 3.8907924313642135, - 3.889497368495429, - 3.888203153621002, - 3.88691004591595, - 3.885618290065571, - 3.8843281162328154, - 3.883039740028083, - 3.881753362479445, - 3.880469170001101, - 3.879187334358151, - 3.877908012625472, - 3.8766313471388174, - 3.875357465436043, - 3.874086480186543, - 3.8728184902884193, - 3.871553577419355, - 3.870291808871612, - 3.869033236830797, - 3.867777898016867, - 3.866525813502974, - 3.8652769885098297, - 3.864031412173953, - 3.862789057288426, - 3.8615498800148944, - 3.860313819565679, - 3.8590807978551904, - 3.857850719119923, - 3.856623469506763, - 3.8553989166295626, - 3.8541769090945737, - 3.852957275995444, - 3.851739826379542, - 3.850524348687568, - 3.8493106101695633, - 3.848098356281136, - 3.846887310064644, - 3.845677171521611, - 3.844467616983481, - 3.843258298489774, - 3.8420488431839814, - 3.840838852739704, - 3.839627902831415, - 3.838415542666537, - 3.8372012945979743, - 3.8359846538389095, - 3.834765088304472, - 3.833542038607813, - 3.832314918241333, - 3.831083113976733, - 3.829845986520855, - 3.828602871467264, - 3.827353080586043, - 3.8260959034970865, - 3.8248306097736746, - 3.8235564515767657, - 3.822272666807052, - 3.8209784823430417, - 3.8196731188882183, - 3.818355795729425, - 3.817025736386133, - 3.815682174975635, - 3.8143243633068846, - 3.8129515787012234, - 3.811563132520345, - 3.8101583793612583, - 3.808736726853778, - 3.807297645969768, - 3.8058406817238137, - 3.80436546411492, - 3.802871719126947, - 3.8013592795750033, - 3.799828095556136, - 3.798278244237408, - 3.796709938695079, - 3.795123535506046, - 3.7935195407902023, - 3.791898614409963, - 3.7902615720536934, - 3.788609384963118, - 3.7869431771111666, - 3.7852642196964066, - 3.7835739228902656, - 3.781873824853354, - 3.7801655781220935, - 3.778450933554908, - 3.77673172211256, - 3.775009834826224, - 3.7732872013754, - 3.7715657677511754, - 3.7698474735163607, - 3.7681342291899154, - 3.766427894278298, - 3.764730256450638, - 3.763043012383892, - 3.76136775030427, - 3.759705935267657, - 3.7580588967027095, - 3.756427818706251, - 3.754813733065729, - 3.753217515064813, - 3.7516398818772, - 3.750081393439029, - 3.748542455571776, - 3.747023325098422, - 3.7455241166687054, - 3.744044810994625, - 3.742585264194985, - 3.741145217955012, - 3.7397243102231448, - 3.7383220861894384, - 3.7369380093171713, - 3.735571472229128, - 3.734221807281244, - 3.732888296687621, - 3.731570182090632, - 3.730266673498163, - 3.728976957535277, - 3.727700204980097, - 3.7264355775731017, - 3.7251822341053327, - 3.7239393358039874, - 3.7227060510445447, - 3.7214815594261057, - 3.720265055252273, - 3.7190557504633626, - 3.7178528770675903, - 3.7166556891193983, - 3.7154634642923026, - 3.7142755050922776, - 3.713091139755458, - 3.7119097228712814, - 3.710730635772411, - 3.709553286711547, - 3.708377110886087, - 3.707201570312106, - 3.7060261535853862, - 3.704850375550881, - 3.703673776900308, - 3.7024959237151753, - 3.701316406970347, - 3.7001348420108537, - 3.6989508680127496, - 3.6977641474369536, - 3.696574365483474, - 3.695381229551866, - 3.694184468712492, - 3.692983833192168, - 3.6917790938766006, - 3.6905700418313105, - 3.689356487841866, - 3.6881382619737697, - 3.686915213151619, - 3.6856872087568857, - 3.684454134243129, - 3.6832158927672474, - 3.6819724048350486, - 3.680723607959318, - 3.679469456328282, - 3.6782099204824057, - 3.676944986997304, - 3.675674658170475, - 3.6743989517097777, - 3.673117900421307, - 3.671831551894593, - 3.6705399681830535, - 3.6692432254777265, - 3.6679414137723967, - 3.666634636518434, - 3.665323010267701, - 3.664006664302177, - 3.662685740248972, - 3.661360391679693, - 3.660030783693226, - 3.658697092481317, - 3.657359504876294, - 3.656018217880785, - 3.654673438179195, - 3.6533253816311055, - 3.651974272746874, - 3.650620344145859, - 3.649263835998055, - 3.6479049954498697, - 3.64654407603522, - 3.6451813370730024, - 3.6438170430523886, - 3.642451463007464, - 3.6410848698827625, - 3.63971753989153, - 3.638349751868498, - 3.636981786619237, - 3.6356139262679137, - 3.6342464536056944, - 3.632879651441849, - 3.631513801959559, - 3.6301491860787576, - 3.628786082827955, - 3.627424768727187, - 3.626065517184118, - 3.624708597905212, - 3.623354276323971, - 3.622002813047884, - 3.620654463325893, - 3.6193094765378895, - 3.617968095707732, - 3.6166305570410184, - 3.6152970894888607, - 3.613967914338624, - 3.612643244832525, - 3.611323285814783, - 3.610008233407862, - 3.6086982747181993, - 3.607393587571689, - 3.606094340279004, - 3.6048006914304, - 3.603512789720329, - 3.602230773801203, - 3.6009547721656663, - 3.5996849030565214, - 3.5984212744049477, - 3.597163983794369, - 3.5959131184501274, - 3.5946687552535765, - 3.5934309607795667, - 3.5921997913561636, - 3.590975293145315, - 3.5897575022431543, - 3.5885464447987476, - 3.58734213714977, - 3.5861445859738885, - 3.584953788454368, - 3.5837697324585904, - 3.582592396728057, - 3.581421751078609, - 3.5802577566094453, - 3.579100365919649, - 3.577949523331005, - 3.576805165115871, - 3.575667219728831, - 3.5745356080411113, - 3.573410243576644, - 3.572291032748701, - 3.571177875096211, - 3.5700706635187833, - 3.568969284509659, - 3.567873618385751, - 3.5667835395141276, - 3.5656989165341613, - 3.564619612574924, - 3.5635454854671544, - 3.5624763879494097, - 3.5614121678680313, - 3.5603526683703404, - 3.5592977280912264, - 3.558247181332565, - 3.5572008582352743, - 3.556158584944156, - 3.555120183765203, - 3.5540854733155114, - 3.553054268665782, - 3.552026381475508, - 3.551001620120972, - 3.549979789816222, - 3.548960692727279, - 3.5479441280797843, - 3.5469298922604047, - 3.5459177789123357, - 3.54490757902527, - 3.5438990810201907, - 3.542892070829531, - 3.541886331973032, - 3.5408816456299164, - 3.539877790707815, - 3.5388745439091256, - 3.5378716797952148, - 3.536868970849262, - 3.5358661875382458, - 3.5348630983748404, - 3.5338594699798067, - 3.5328550671457184, - 3.5318496529026273, - 3.530842988586536, - 3.529834833911404, - 3.528824947045443, - 3.5278130846926485, - 3.52679900218025, - 3.525782453553013, - 3.5247631916752344, - 3.523740968341321, - 3.5227155343957404, - 3.52168663986339, - 3.520654034091064, - 3.519617465901109, - 3.5185766837579777, - 3.5175314359486443, - 3.516481470777728, - 3.5154265367780937, - 3.5143663829378275, - 3.513300758944305, - 3.5122294154461127, - 3.511152104333493, - 3.5100685790379904, - 3.508978594851838, - 3.5078819092676476, - 3.5067782823387015, - 3.5056674770604, - 3.504549259772839, - 3.503423400584881, - 3.502289673819573, - 3.5011478584808287, - 3.4999977387411385, - 3.4988391044498233, - 3.4976717516612643, - 3.496495483182384, - 3.495310109138316, - 3.494115447555253, - 3.492911324958987, - 3.491697576987634, - 3.4904740490167594, - 3.48924059679486, - 3.4879970870869235, - 3.486743398323682, - 3.4854794212537072, - 3.4842050595955354, - 3.482920230686549, - 3.4816248661252818, - 3.480318912403507, - 3.4790023315243683, - 3.4776751016025056, - 3.4763372174421123, - 3.4749886910886008, - 3.4736295523495304, - 3.472259849280337, - 3.4708796486302957, - 3.4694890362443003, - 3.4680881174159053, - 3.4666770171871777, - 3.4652558805911324, - 3.463824872832561, - 3.462384179403261, - 3.460934006128083, - 3.459474579138267, - 3.4580061447691928, - 3.4565289693797165, - 3.4550433390910085, - 3.4535495594431485, - 3.4520479549682195, - 3.450538868679267, - 3.449022661475053, - 3.447499711461173, - 3.445970413188571, - 3.4444351768113988, - 3.4428944271664896, - 3.4413486027776026, - 3.4397981547881065, - 3.438243545826284, - 3.4366852488082644, - 3.435123745683842, - 3.4335595261311216, - 3.431993086206317, - 3.4304249269554377, - 3.4288555529948748, - 3.4272854710682346, - 3.4257151885868966, - 3.4241452121619247, - 3.4225760461350117, - 3.4210081911160173, - 3.419442142534674, - 3.4178783892137656, - 3.4163174119708324, - 3.414759682255156, - 3.4132056608263377, - 3.411655796480413, - 3.4101105248288035, - 3.408570267135024, - 3.4070354292132636, - 3.405506400392595, - 3.403983552549601, - 3.4024672392117923, - 3.400957794733415, - 3.399455533544512, - 3.3979607494735324, - 3.39647371514301, - 3.3949946814372924, - 3.3935238770405647, - 3.3920615080428966, - 3.390607757611462, - 3.389162785723548, - 3.3877267289574102, - 3.3862997003367714, - 3.384881789224161, - 3.3834730612579964, - 3.382073558328104, - 3.3806832985838384, - 3.379302276469116, - 3.3779304627780213, - 3.3765678047249033, - 3.375214226022518, - 3.3738696269617083, - 3.372533884486157, - 3.3712068522556384, - 3.369888360691123, - 3.3685782169952594, - 3.3672762051416316, - 3.365982085826301, - 3.3646955963751912, - 3.363416450600904, - 3.3621443386026506, - 3.360878926503067, - 3.359619856115685, - 3.3583667445370065, - 3.3571191836572125, - 3.3558767395835076, - 3.3546389519703874, - 3.353405333251112, - 3.352175367764781, - 3.3509485107736836, - 3.3497241873656023, - 3.3485017912361044, - 3.34728068334599, - 3.346060190449433, - 3.344839603488674, - 3.3436181758516086, - 3.3423951214890755, - 3.3411696128893005, - 3.339940778907851, - 3.3387077024521448, - 3.3374694180208495, - 3.3362249090997276, - 3.334973105417095, - 3.333712880063687, - 3.332443046484157, - 3.3311623553495604, - 3.329869491323346, - 3.328563069736361, - 3.32724163319048, - 3.325903648114467, - 3.3245475013007426, - 3.3231714964572268, - 3.321773850814463, - 3.320352691835222, - 3.3189060540813884, - 3.317431876301088, - 3.3159279988083807, - 3.314392161237326, - 3.312822000763001, - 3.3112150508927645, - 3.3095687409427863, - 3.307880396326462, - 3.306147239793123, - 3.304366393766847, - 3.3025348839458797, - 3.300649644332875, - 3.2987075238735573, - 3.2967052948870874, - 3.294639663473017, - 3.292507282077737, - 3.290304764395832, - 3.288028702768201, - 3.2856756882176765, - 3.2832423332335283, - 3.2807252973771406, - 3.278121315731635, - 3.2754272301574874, - 3.2726400232438095, - 3.269756854760963, - 3.2667751003251047, - 3.2636923918801815, - 3.2605066594903542, - 3.25721617381772, - 3.2538195885414565, - 3.250315981858906, - 3.246704896102827, - 3.242986374418139, - 3.239160993372672, - 3.2352298903369876, - 3.2311947844644098, - 3.227057990140412, - 3.222822421854606, - 3.218491589581512, - 3.214069583938895, - 3.209561050621223, - 3.204971153876343, - 3.2003055290966023, - 3.195570224919974, - 3.1907716355687565, - 3.185916424477614, - 3.1810114405627146, - 3.176063628744403, - 3.1710799365422058, - 3.1660672187022643, - 3.1610321418847236, - 3.155981091428758, - 3.1509200821259715, - 3.145854674774099, - 3.140789900061202, - 3.1357301910580246, - 3.130679325287935, - 3.1256403770153307, - 3.120615680061021, - 3.1156068011316647, - 3.110614523353324, - 3.105638839437114, - 3.1006789536860233, - 3.0957332918802307, - 3.090799517956329, - 3.085874556322205, - 3.0809546186207895, - 3.076035233767983, - 3.0711112801359244, - 3.06617701882649, - 3.061226127073741, - 3.056251730921822, - 3.051246436440277, - 3.0462023588562612, - 3.041111149098527, - 3.035964017357659, - 3.0307517533678836, - 3.025464743206662, - 3.0200929824875216, - 3.0146260858892946, - 3.0090532930207265, - 3.0033634706642856, - 2.9975451114773475, - 2.9915863292539195, - 2.985474850866622, - 2.9791980050182136, - 2.9727427079349993, - 2.966095446132939, - 2.9592422563810845, - 2.952168702977978, - 2.9448598524448917, - 2.937300245726268, - 2.9294738679728756, - 2.92136411596758, - 2.9129537632373315, - 2.9042249228786146, - 2.895159008107192, - 2.8857366905264774, - 2.8759378560928313, - 2.8657415587400674, - 2.8551259716095316, - 2.8440683358167096, - 2.8325449066694137, - 2.820530897237168, - 2.808000419155303, - 2.794926420531019, - 2.781280620801645, - 2.7670334423774694, - 2.752153938882235, - 2.7366097197836883, - 2.7203668711836975, - 2.7033898725116132, - 2.68564150883573, - 2.6670827784741347, - 2.64767279554727, - 2.627368687068769, - 2.60612548411602, - 2.5838960065557774, - 2.547832201997476, - 2.570061679557719, - 2.591304882510468, - 2.6116089909889686, - 2.6310189739158334, - 2.6495777042774287, - 2.667326067953312, - 2.684303066625396, - 2.700545915225387, - 2.7160901343239336, - 2.730969637819168, - 2.7452168162433437, - 2.758862615972718, - 2.771936614597002, - 2.7844670926788666, - 2.7964811021111124, - 2.8080045312584083, - 2.8190621670512304, - 2.829677754181766, - 2.83987405153453, - 2.849672885968176, - 2.8590952035488906, - 2.8681611183203133, - 2.87688995867903, - 2.885300311409279, - 2.8934100634145743, - 2.9012364411679665, - 2.9087960478865904, - 2.9161048984196767, - 2.9231784518227832, - 2.930031641574638, - 2.936678903376698, - 2.9431342004599124, - 2.9494110463083207, - 2.9555225246956183, - 2.961481306919046, - 2.9672996661059843, - 2.9729894884624253, - 2.9785622813309933, - 2.9840291779292203, - 2.9894009386483607, - 2.9946879488095823, - 2.999900212799358, - 3.005047344540226, - 3.01013855429796, - 3.015182631881976, - 3.0201879263635205, - 3.0251623225154396, - 3.0301132142681886, - 3.035047475577623, - 3.0399714292096816, - 3.044890814062488, - 3.049810751763904, - 3.0547357133980277, - 3.0596694873219294, - 3.064615149127722, - 3.0695750348788127, - 3.074550718795023, - 3.0795429965733634, - 3.08455187550272, - 3.0895765724570294, - 3.0946155207296338, - 3.0996663864997234, - 3.104726095502901, - 3.109790870215798, - 3.1148562775676703, - 3.1199172868704568, - 3.1249683373264223, - 3.130003414143963, - 3.1350161319839045, - 3.1399998241861016, - 3.1449476360044133, - 3.149852619919313, - 3.1547078310104553, - 3.1595064203616725, - 3.164241724538301, - 3.1689073493180415, - 3.173497246062922, - 3.1780057793805936, - 3.1824277850232106, - 3.186758617296305, - 3.190994185582111, - 3.1951309799061085, - 3.1991660857786863, - 3.203097188814371, - 3.2069225698598376, - 3.2106410915445256, - 3.2142521773006045, - 3.217755783983155, - 3.2211523692594186, - 3.224442854932053, - 3.22762858732188, - 3.2307112957668034, - 3.233693050202662, - 3.2365762186855083, - 3.239363425599186, - 3.242057511173334, - 3.2446614928188393, - 3.247178528675227, - 3.2496118836593753, - 3.2519648982099, - 3.2542409598375306, - 3.2564434775194355, - 3.258575858914716, - 3.260641490328786, - 3.262643719315256, - 3.2645858397745737, - 3.2664710793875784, - 3.2683025892085458, - 3.2700834352348216, - 3.27181659176816, - 3.273504936384485, - 3.275151246334463, - 3.2767581962046997, - 3.2783283566790247, - 3.2798641942500795, - 3.281368071742787, - 3.282842249523087, - 3.284288887276922, - 3.285710046256162, - 3.2871076918989255, - 3.2884836967424413, - 3.2898398435561655, - 3.2911778286321787, - 3.29249926517806, - 3.2938056867650447, - 3.295098550791259, - 3.2963792419258557, - 3.2976490755053858, - 3.2989093008587935, - 3.3001611045414263, - 3.301405613462548, - 3.3026438978938435, - 3.3038769743495497, - 3.305105808330999, - 3.306331316930774, - 3.3075543712933073, - 3.3087757989303728, - 3.309996385891132, - 3.3112168787876888, - 3.312437986677803, - 3.313660382807301, - 3.3148847062153823, - 3.31611156320648, - 3.3173415286928107, - 3.318575147412086, - 3.3198129350252064, - 3.3210553790989112, - 3.322302939978705, - 3.3235560515573837, - 3.324815121944766, - 3.3260805340443493, - 3.327352646042603, - 3.32863179181689, - 3.3299182812679997, - 3.3312124005833303, - 3.332514412436958, - 3.333824556132822, - 3.335143047697337, - 3.3364700799278557, - 3.337805822403407, - 3.339150421464218, - 3.340504000166602, - 3.34186665821972, - 3.343238471910815, - 3.344619494025537, - 3.3460097537698026, - 3.347409256699695, - 3.34881798466586, - 3.35023589577847, - 3.351662924399109, - 3.353098981165247, - 3.354543953053161, - 3.3559977034845954, - 3.3574600724822634, - 3.358930876878991, - 3.3604099105847087, - 3.361896944915231, - 3.3633917289862105, - 3.364893990175114, - 3.366403434653491, - 3.3679197479912997, - 3.3694425958342937, - 3.3709716246549624, - 3.3725064625767227, - 3.3740467202705022, - 3.3755919919221116, - 3.3771418562680364, - 3.3786958776968548, - 3.380253607412531, - 3.3818145846554644, - 3.3833783379763727, - 3.384944386557716, - 3.3865122415767104, - 3.3880814076036234, - 3.3896513840285953, - 3.3912216665099333, - 3.3927917484365735, - 3.3943611223971364, - 3.395929281648016, - 3.3974957215728203, - 3.399059941125541, - 3.400621444249963, - 3.402179741267983, - 3.403734350229805, - 3.4052847982193013, - 3.4068306226081884, - 3.4083713722530975, - 3.4099066086302696, - 3.411435906902872, - 3.412958856916752, - 3.414475064120966, - 3.415984150409918, - 3.417485754884847, - 3.418979534532707, - 3.4204651648214153, - 3.4219423402108915, - 3.4234107745799656, - 3.4248702015697816, - 3.4263203748449595, - 3.42776106827426, - 3.429192076032831, - 3.4306132126288764, - 3.432024312857604, - 3.433425231685999, - 3.4348158440719945, - 3.4361960447220357, - 3.437565747791229, - 3.4389248865302995, - 3.440273412883811, - 3.4416112970442043, - 3.442938526966067, - 3.4442551078452057, - 3.4455610615669805, - 3.4468564261282477, - 3.448141255037234, - 3.449415616695406, - 3.450679593765381, - 3.451933282528622, - 3.4531767922365586, - 3.454410244458458, - 3.4556337724293327, - 3.456847520400686, - 3.458051642996952, - 3.459246304580015, - 3.460431678624083, - 3.461607947102963, - 3.462775299891522, - 3.4639339341828372, - 3.4650840539225274, - 3.4662258692612715, - 3.4673595960265793, - 3.468485455214538, - 3.4696036725020987, - 3.4707144777804, - 3.4718181047093464, - 3.4729147902935367, - 3.474004774479689, - 3.4750882997751913, - 3.4761656108878114, - 3.4772369543860036, - 3.4783025783795263, - 3.4793627322197924, - 3.480417666219427, - 3.481467631390343, - 3.4825128791996764, - 3.4835536613428073, - 3.484590229532763, - 3.485622835305089, - 3.486651729837439, - 3.4876771637830197, - 3.488699387116933, - 3.4897186489947116, - 3.490735197621949, - 3.4917492801343473, - 3.492761142487142, - 3.493771029353103, - 3.4947791840282347, - 3.495785848344326, - 3.496791262587417, - 3.4977956654215054, - 3.498799293816539, - 3.4998023829799445, - 3.5008051662909607, - 3.5018078752369135, - 3.5028107393508243, - 3.5038139861495137, - 3.504817841071615, - 3.5058225274147308, - 3.5068282662712296, - 3.5078352764618894, - 3.508843774466969, - 3.5098539743540345, - 3.5108660877021034, - 3.511880323521483, - 3.512896888168978, - 3.513915985257921, - 3.514937815562671, - 3.5159625769172065, - 3.5169904641074807, - 3.51802166875721, - 3.519056379206902, - 3.520094780385855, - 3.521137053676973, - 3.522183376774264, - 3.523233923532925, - 3.524288863812039, - 3.52534836330973, - 3.5264125833911084, - 3.527481680908853, - 3.528555808016623, - 3.52963511197586, - 3.5307197349558264, - 3.53180981382745, - 3.5329054799513577, - 3.534006858960482, - 3.53511407053791, - 3.5362272281903997, - 3.537346439018343, - 3.53847180348281, - 3.5396034151705296, - 3.54074136055757, - 3.5418857187727033, - 3.543036561361348, - 3.544193952051144, - 3.5453579465203076, - 3.546528592169756, - 3.547705927900289, - 3.548889983896067, - 3.5500807814155872, - 3.5512783325914685, - 3.5524826402404464, - 3.553693697684853, - 3.5549114885870137, - 3.5561359867978624, - 3.5573671562212654, - 3.5586049506952753, - 3.559849313891826, - 3.561100179236068, - 3.5623574698466465, - 3.56362109849822, - 3.564890967607365, - 3.566166969242902, - 3.5674489851620277, - 3.5687368868720983, - 3.570030535720703, - 3.571329783013388, - 3.572634470159898, - 3.5739444288495608, - 3.575259481256482, - 3.576579440274224, - 3.5779041097803224, - 3.5792332849305595, - 3.580566752482717, - 3.5819042911494305, - 3.583245671979588, - 3.5845906587675915, - 3.5859390084895826, - 3.5872904717656695, - 3.5886447933469108, - 3.590001712625817, - 3.591360964168886, - 3.592722278269654, - 3.5940853815204563, - 3.595449997401258, - 3.5968158468835476, - 3.598182649047393, - 3.599550121709612, - 3.6009179820609356, - 3.6022859473101967, - 3.603653735333229, - 3.605021065324461, - 3.6063876584491625, - 3.6077532384940874, - 3.609117532514701, - 3.610480271476919, - 3.611841190891568, - 3.613200031439754, - 3.6145565395875576, - 3.6159104681885728, - 3.617261577072804, - 3.6186096336208937, - 3.619954413322484, - 3.621295700317993, - 3.622633287923015, - 3.6239669791349254, - 3.625296587121391, - 3.626621935690671, - 3.627942859743875, - 3.629259205709399, - 3.630570831960133, - 3.6318776092140954, - 3.6331794209194257, - 3.634476163624752, - 3.635767747336292, - 3.6370540958630055, - 3.638335147151477, - 3.6396108536121736, - 3.640881182439003, - 3.642146115924104, - 3.6434056517699807, - 3.6446598034010167, - 3.6459086002767473, - 3.647152088208946, - 3.648390329684828, - 3.649623404198584, - 3.6508514085933177, - 3.652074457415469, - 3.653292683283565, - 3.6545062372730097, - 3.6557152893182994, - 3.6569200286338663, - 3.6581206641541906, - 3.659317424993565, - 3.660510560925174, - 3.661700342878652, - 3.662887063454448, - 3.664071037452553, - 3.665252602412046, - 3.666432119156874, - 3.667609972342007, - 3.66878657099258, - 3.6699623490270854, - 3.6711377657538047, - 3.6723133063277857, - 3.673489482153246, - 3.67466683121411, - 3.67584591831298, - 3.677027335197157, - 3.678211700533976, - 3.6793996597340017, - 3.680591884561097, - 3.6817890725092894, - 3.6829919459050617, - 3.684201250693971, - 3.685417754867804, - 3.6866422464862434, - 3.687875531245686, - 3.6891184295470314, - 3.6903717730148, - 3.691636400421795, - 3.692913152976976, - 3.6942028689398616, - 3.695506377532331, - 3.69682449212932, - 3.6981580027229426, - 3.6995076676708263, - 3.70087420475887, - 3.702258281631137, - 3.703660505664843, - 3.705081413396711, - 3.706521459636684, - 3.7079810064363232, - 3.709460312110404, - 3.7109595205401207, - 3.712478651013475, - 3.714017588880728, - 3.7155760773188984, - 3.717153710506511, - 3.718749928507428, - 3.7203640141479504, - 3.721995092144408, - 3.723642130709356, - 3.725303945745969, - 3.726979207825591, - 3.728666451892338, - 3.730364089719997, - 3.732070424631614, - 3.733783668958059, - 3.735501963192874, - 3.737223396817099, - 3.738946030267923, - 3.7406679175542585, - 3.742387128996606, - 3.744101773563792, - 3.745810020295053, - 3.747510118331964, - 3.7492004151381058, - 3.7508793725528657, - 3.7525455804048167, - 3.754197767495392, - 3.7558348098516623, - 3.7574557362319014, - 3.7590597309477447, - 3.7606461341367776, - 3.762214439679107, - 3.7637642909978344, - 3.765295475016702, - 3.7668079145686457, - 3.768301659556619, - 3.769776877165512, - 3.7712338414114663, - 3.772672922295477, - 3.774094574802957, - 3.775499327962044, - 3.776887774142922, - 3.7782605587485834, - 3.7796183704173343, - 3.780961931827832, - 3.7822919911711232, - 3.783609314329917, - 3.78491467778474, - 3.786208862248751, - 3.787492647018464, - 3.788766805215374, - 3.7900320989387857, - 3.791289276027742, - 3.792539066908963, - 3.7937821819625537, - 3.795019309418432, - 3.796251113683032, - 3.797478234049512, - 3.7987012837461704, - 3.799920849280608, - 3.8011374900396735, - 3.802351738108236, - 3.803564098273114, - 3.804775048181402, - 3.80598503862568, - 3.8071944939314726, - 3.80840381242518, - 3.8096133669633097, - 3.810823505506343, - 3.812034551722835, - 3.813246805611262, - 3.8144605441292665, - 3.8156760218212407, - 3.8168934714371425, - 3.818113104536272, - 3.8193351120712618, - 3.8205596649484623, - 3.8217869145616223, - 3.823016993296889, - 3.8242500150073777, - 3.825486075456593, - 3.826725252730124, - 3.8279676076156512, - 3.829213183951528, - 3.830462008944673, - 3.8317140934585656, - 3.832969432272496, - 3.8342280043133106, - 3.835489772861054, - 3.836754685730118, - 3.838022675628242, - 3.8392936608777415, - 3.840567542580516, - 3.841844208067171, - 3.8431235297998496, - 3.844405365442799, - 3.845689557921143, - 3.8469759354697817, - 3.848264311674514, - 3.8495544855072703, - 3.8508462413576487, - 3.8521393490627007, - 3.853433563937127, - 3.854728626805912, - 3.8560242640415714, - 3.8573201876081207, - 3.858616095113888, - 3.859911669875392, - 3.861206580994413, - 3.862500483450486, - 3.8637930182110134, - 3.8650838123611817, - 3.866372479255987, - 3.867658622097311, - 3.868941824334184, - 3.870221658883719, - 3.871497686217325, - 3.8727694542455655, - 3.874036498649489, - 3.875298343249728, - 3.8765545004152666, - 3.877804471514268, - 3.8790477474088143, - 3.88028380899573, - 3.881512127795323, - 3.88273216659001, - 3.883943380114567, - 3.885145221509821, - 3.8863371275752217, - 3.8875185319171393, - 3.8886888665089256, - 3.889847561603172, - 3.890994037527379, - 3.892127712755594, - 3.8932480082720926, - 3.894354348362838, - 3.89544615913172, - 3.896522869850152, - 3.8975839143666873, - 3.898628736085378, - 3.899656780660822, - 3.900667499190392, - 3.9016603604893136, - 3.9026348455701583, - 3.903590449413216, - 3.904526682783461, - 3.9054430684689807, - 3.906339161330836, - 3.907214533183869, - 3.908068775488224, - 3.908901505434383, - 3.909712367915141, - 3.9105010375102864, - 3.9112671978945497, - 3.9120106240099575, - 3.912731089376157, - 3.913428402937845, - 3.914102414160437, - 3.914753014831758, - 3.9153801358942273, - 3.9159837487300218, - 3.916563911707503, - 3.917120697823556, - 3.9176542317158902, - 3.918164691003451, - 3.9186523075348862, - 3.919117362271172, - 3.919560204658769, - 3.919981244343148, - 3.920380936390509, - 3.9207597929026448, - 3.9211183837052017, - 3.921457360818299, - 3.92177741542042, - 3.922079300548454, - 3.922363822371016, - 3.9226318726321567, - 3.922884386127465, - 3.923122356437791, - 3.923346835220309, - 3.923558938881493, - 3.923759836437517, - 3.923950751806192, - 3.924132962912229, - 3.924307797091765, - 3.924476634385384, - 3.924640901847938, - 3.924802071518834, - 3.924961661116848, - 3.9251212285571215, - 3.925282370667978, - 3.925446719769298, - 3.925615941021445, - 3.925791728878821, - 3.925975807387664, - 3.9261699256694103, - 3.9263758561208912, - 3.926595389212638, - 3.926830331671712, - 3.9270825054405103, - 3.927353746412581, - 3.9276459029977455, - 3.9279608316553585, - 3.9283003959328022, - 3.9286664655365233, - 3.929060917933204, - 3.929485638114461, - 3.9299425196266617, - 3.9304334643431376, - 3.930960386125764, - 3.93152521203996, - 3.932129887686174, - 3.9327763813326904, - 3.933466691046835, - 3.934202853456543, - 3.934986952127243, - 3.935821132493508, - 3.936707604240632, - 3.93764866015394, - 3.9386466896630625, - 3.939704209933382, - 3.9408238990573152, - 3.942008606664556, - 3.943261399402392, - 3.944585546349999, - 3.9459846055007666, - 3.9474624684593262, - 3.9490234526115846, - 3.9506724448849457, - 3.952414810619014, - 3.954256571495504, - 3.9562043948390673, - 3.9582659518238943, - 3.960450130956135, - 3.962767248114219, - 3.965229597162239, - 3.967851127321925, - 3.970648343685331, - 3.973641055765328, - 3.976853434327231, - 3.980315172044512, - 3.984062866582742, - 3.988142897430352, - 3.992615779159438, - 3.997563366860096, - 4.003100850680144, - 4.009404201640048, - 4.016793318345548, - 4.026064687568873, - 4.04208975544602 - ] - }, - { - "mode": "markers", - "name": "Initial", - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857 - ], - "y": [ - 4.051091197755109, - 4.026475108438385, - 4.008477230182524, - 3.994743960024916, - 3.9838543280120633, - 3.9749913244469983, - 3.9676532382673617, - 3.9615083809432377, - 3.9563232224458926, - 3.951925843627623, - 3.948184747176896, - 3.944995727609847, - 3.942273785922641, - 3.939948635878476, - 3.937961382079423, - 3.936261890108397, - 3.934807038436865, - 3.933559012260305, - 3.9324851327226193, - 3.931556777388467, - 3.9307488615214665, - 3.930039338828893, - 3.9294086705095816, - 3.9288396300260153, - 3.928316953040113, - 3.927827287088335, - 3.927358907257412, - 3.926901554605048, - 3.926446275028168, - 3.925985271887887, - 3.925511816401267, - 3.925020127680449, - 3.924505306614829, - 3.923963235167639, - 3.92339049602856, - 3.922784301368551, - 3.92214242468456, - 3.921463149377828, - 3.920745212041869, - 3.919987756314304, - 3.9191902863636656, - 3.9183526240114017, - 3.91747487089897, - 3.9165573733152192, - 3.915600692297605, - 3.914605573809794, - 3.913572923313997, - 3.9125037831662377, - 3.911399310270373, - 3.9102607556047166, - 3.9090894459581422, - 3.907886767570613, - 3.9066541544107505, - 3.9053930749232113, - 3.9041050217496687, - 3.902791498765284, - 3.9014540120117593, - 3.900094062228471, - 3.898713139278162, - 3.897312716992423, - 3.895894245743106, - 3.894459148584555, - 3.8930088156677343, - 3.891544603279242, - 3.890067830266237, - 3.8885797757484504, - 3.8870816803782287, - 3.88557474089635, - 3.884060108999762, - 3.8825388980826334, - 3.881012166605889, - 3.8794809390568865, - 3.877946178699285, - 3.876408816563815, - 3.874869745029061, - 3.873329814907405, - 3.871789812202043, - 3.8702504832112647, - 3.8687125655143606, - 3.8671767461867, - 3.865643662651416, - 3.8641138779356594, - 3.862587984105185, - 3.861066515208374, - 3.859549972965006, - 3.858038757021709, - 3.856533326009225, - 3.855034079108866, - 3.8535413880321694, - 3.8520555988992906, - 3.85057703381059, - 3.849105979973825, - 3.8476426021317303, - 3.8461872023175623, - 3.844739998262977, - 3.84330118798268, - 3.84187095100722, - 3.8404494495742183, - 3.839036803109904, - 3.837633047858334, - 3.83623833876022, - 3.8348527571203497, - 3.833476369163409, - 3.8321092268344943, - 3.830751368565041, - 3.829402806176272, - 3.8280635288487503, - 3.826733548590174, - 3.825412849161585, - 3.824101403107537, - 3.822799172278868, - 3.821506108327485, - 3.820222167080443, - 3.8189472934050097, - 3.817681406105694, - 3.8164244260533033, - 3.8151762667582303, - 3.813936834750651, - 3.8127060299372175, - 3.8114837495043346, - 3.81027054905092, - 3.8090652828279663, - 3.807868089955092, - 3.806678856713983, - 3.805497534088226, - 3.804323997614932, - 3.803158151099869, - 3.801999650606534, - 3.8008484387023658, - 3.7997044962828057, - 3.798567705214392, - 3.797437943275455, - 3.796315084344113, - 3.7951989985711463, - 3.794088996867595, - 3.7929853416497026, - 3.791887908309829, - 3.7907965552699583, - 3.78971113776844, - 3.788631507942738, - 3.7875573787766825, - 3.786488252006753, - 3.785424306903648, - 3.784365385273782, - 3.783311325912911, - 3.782261964601563, - 3.7812171340859497, - 3.780176392493644, - 3.779139509198147, - 3.778106504048643, - 3.777077198194828, - 3.77605140931841, - 3.775028951519508, - 3.774009635186179, - 3.7729928860143462, - 3.7719787021371074, - 3.770966949011369, - 3.769957421066427, - 3.7689499079884023, - 3.7679441944751497, - 3.7669400162932023, - 3.765936810120639, - 3.764934619510436, - 3.7639332065540065, - 3.762932326696258, - 3.761931728350295, - 3.760931152486282, - 3.759930195333132, - 3.758928452229471, - 3.757925811117531, - 3.7569219784313623, - 3.7559166506091968, - 3.7549095135169104, - 3.7539002418439975, - 3.752888281796543, - 3.7518733326775338, - 3.750855114448382, - 3.7498332501356177, - 3.7488073481150743, - 3.747777001374497, - 3.746741786765226, - 3.745700977649681, - 3.7446543206509846, - 3.743601344306701, - 3.742541552814826, - 3.741474430497341, - 3.740399441150352, - 3.7393159633105344, - 3.738223260991708, - 3.737120883455387, - 3.7360082066211655, - 3.734884583843864, - 3.7337493459344744, - 3.732601801386736, - 3.7314411097545257, - 3.730266510446479, - 3.729077336385026, - 3.7278728136012673, - 3.7266521512996142, - 3.725414544153301, - 3.7241591751054663, - 3.722885033362144, - 3.7215913832968206, - 3.720277424876117, - 3.7189423329676337, - 3.7175852938632454, - 3.716205512017186, - 3.7148021941032567, - 3.713374434277096, - 3.711921675709079, - 3.710443276687608, - 3.708938658096601, - 3.7074073141188975, - 3.705848823306803, - 3.704262785403982, - 3.7026489130296993, - 3.701007194809328, - 3.699337657639283, - 3.69764046691591, - 3.69591593538358, - 3.69416453064445, - 3.6923867580814673, - 3.690583438783865, - 3.688755576600643, - 3.686904295725777, - 3.68503088266543, - 3.6831367801549986, - 3.681223578288515, - 3.679292844367936, - 3.677346528980446, - 3.675386581846091, - 3.673415036909972, - 3.6714339936213336, - 3.6694455951366183, - 3.667452005719403, - 3.665455387859761, - 3.6634578796457022, - 3.661461572908639, - 3.659468493270011, - 3.6574808301781303, - 3.655500141026384, - 3.653528020671549, - 3.651566090477876, - 3.64961591970504, - 3.64767874777366, - 3.645755738233628, - 3.643847926633869, - 3.641956175838086, - 3.6400812044743103, - 3.6382235792075512, - 3.636383729905451, - 3.63456201156556, - 3.632758638178245, - 3.630973719334009, - 3.6292072706146543, - 3.62745922416492, - 3.62572943293978, - 3.624017597023729, - 3.6223235370089006, - 3.620646953545162, - 3.618987512818841, - 3.6173448546654017, - 3.615718599959673, - 3.614108357261962, - 3.6125136404784928, - 3.6109340876597806, - 3.609369325838584, - 3.6078189662453033, - 3.606282630270269, - 3.604759952019108, - 3.603250580329514, - 3.6017541301979135, - 3.6002702598964644, - 3.598798717886237, - 3.5973392233627743, - 3.595891514048498, - 3.59445534601216, - 3.5930304932765877, - 3.5916167256893132, - 3.59021375777888, - 3.588821495027339, - 3.5874397758059207, - 3.5860684525048763, - 3.584707390518293, - 3.5833564672043288, - 3.5820155708364867, - 3.5806845995590013, - 3.579363460357595, - 3.5780520680548618, - 3.5767503443379676, - 3.5754582168247135, - 3.5741756181729234, - 3.5729024852367215, - 3.5716387582723956, - 3.5703843786717067, - 3.569139197397881, - 3.567903239965505, - 3.5666764526754458, - 3.5654587820698236, - 3.564250174448393, - 3.5630505754340014, - 3.5618599295855256, - 3.5606781800562737, - 3.5595052682959345, - 3.558341133793797, - 3.5571857138610103, - 3.556038943449582, - 3.5549007550057468, - 3.5537710783553624, - 3.552649840619015, - 3.551536966154549, - 3.5504323653972203, - 3.549335964261777, - 3.5482476804307517, - 3.5471674270219764, - 3.5460951144407016, - 3.5450306504415687, - 3.5439739402037516, - 3.542924886417629, - 3.54188338938139, - 3.540849347106075, - 3.539822655427651, - 3.5388032081248264, - 3.537790897041368, - 3.53678561221188, - 3.535787241989854, - 3.534795673177249, - 3.533810807152796, - 3.532832522743972, - 3.5318606962228953, - 3.5308952096451365, - 3.529935944152046, - 3.5289827800934086, - 3.5280355971470025, - 3.5270942744345337, - 3.5261586906337223, - 3.5252287240861824, - 3.5243042529008446, - 3.5233851550527304, - 3.522471308476922, - 3.5215625911575374, - 3.5206588812116895, - 3.5197600569683063, - 3.518866008856703, - 3.5179766183813097, - 3.5170917550793694, - 3.5162112990482166, - 3.515335130909995, - 3.514463131859937, - 3.513595183709622, - 3.512731168925286, - 3.5118709706613815, - 3.511014472789345, - 3.5101615599217624, - 3.5093121174320796, - 3.5084660314698612, - 3.507623188971874, - 3.506783477668947, - 3.5059468180895785, - 3.505113070808307, - 3.5042821130253867, - 3.503453839714827, - 3.502628155560913, - 3.5018049461217235, - 3.5009841054208373, - 3.500165530277254, - 3.499349115100144, - 3.4985347554550756, - 3.4977223480154103, - 3.496911790045252, - 3.4961029793646645, - 3.4952958143124095, - 3.494490183046301, - 3.493685963193977, - 3.4928830832865994, - 3.492081443342534, - 3.491280943677112, - 3.4904814848525296, - 3.4896829676259076, - 3.488885292895574, - 3.488088361645684, - 3.487292074889229, - 3.486496333609527, - 3.4857010387002414, - 3.484906090904042, - 3.484111390749972, - 3.483316838489538, - 3.4825223340317217, - 3.481727776876827, - 3.4809330660494093, - 3.4801381000301843, - 3.4793427766871985, - 3.4785469932061286, - 3.4777506460199876, - 3.4769535985941307, - 3.4761557414408126, - 3.4753569991425586, - 3.474557264427684, - 3.4737564289118295, - 3.4729543830256997, - 3.47215101594281, - 3.47134621550744, - 3.4705398681629087, - 3.4697318588802983, - 3.4689220710878637, - 3.4681103866012, - 3.4672966855544183, - 3.46648084633252, - 3.465662745505131, - 3.464842257761884, - 3.4640192558496175, - 3.463193610511731, - 3.4623651904298556, - 3.4615338621682414, - 3.4606994901210815, - 3.4598619364631267, - 3.459021017770725, - 3.458176615144849, - 3.4573285985856432, - 3.4564768209831165, - 3.4556211328223383, - 3.4547613821617738, - 3.453897414617772, - 3.4530290733556592, - 3.4521561990879936, - 3.451278630080486, - 3.4503962021661847, - 3.4495087487685057, - 3.448616100933651, - 3.447718087373124, - 3.446814534516931, - 3.445905266578167, - 3.4449901056296217, - 3.4440688716931342, - 3.443141382842379, - 3.4422074553197946, - 3.4412669036684034, - 3.440319540879151, - 3.439365132738992, - 3.4384035302951923, - 3.437434544014805, - 3.436457982493587, - 3.4354736537702832, - 3.434481365582195, - 3.433480925640874, - 3.432472141928711, - 3.4314548230167956, - 3.430428778404429, - 3.429393818880761, - 3.4283497569087378, - 3.4272964070315592, - 3.426233586301763, - 3.425161114732811, - 3.424078815773117, - 3.4229865168021365, - 3.421884049648113, - 3.4207712511268893, - 3.4196479636009283, - 3.418514035557666, - 3.4173693168017603, - 3.4162136537020302, - 3.4150469354587796, - 3.413869041184869, - 3.4126798592517784, - 3.411479287952255, - 3.410267236169707, - 3.409043624051654, - 3.4078083836842747, - 3.406561459764918, - 3.405302810269166, - 3.4040324071088595, - 3.402750236777333, - 3.4014563009777765, - 3.400150617230701, - 3.398833219456096, - 3.3975041585259453, - 3.396163502782571, - 3.39481133780715, - 3.393447766242977, - 3.392072909628145, - 3.390686911461197, - 3.3892899365425797, - 3.3878821635541785, - 3.386463793818072, - 3.3850350485011735, - 3.383596168626729, - 3.3821474150101456, - 3.380689068116281, - 3.3792214278358483, - 3.377744812537824, - 3.3762595581598513, - 3.3747660228482967, - 3.373264580287283, - 3.3717556212071345, - 3.370239552654385, - 3.3687167971794914, - 3.3671877919443203, - 3.36565298775243, - 3.3641128480057008, - 3.362567847591306, - 3.361018471703969, - 3.359465214608614, - 3.3579085783493814, - 3.3563490714111524, - 3.354787207340347, - 3.35322350333206, - 3.351658478790755, - 3.3500926538722116, - 3.3485265480142403, - 3.3469606741490447, - 3.345395535521542, - 3.3438316543043887, - 3.3422695324177267, - 3.3407096638191893, - 3.3391525331271783, - 3.3375986142843015, - 3.336048369266992, - 3.3345022468468613, - 3.332960681408968, - 3.331424091831286, - 3.3298928804292927, - 3.3283674319687915, - 3.3268481127495177, - 3.325335269761191, - 3.3238292299133545, - 3.3223302993391575, - 3.32083876277302, - 3.319354883001166, - 3.317878900383496, - 3.3164110324446137, - 3.314951473531292, - 3.3135003945330204, - 3.3120579426619097, - 3.310624241287626, - 3.3091993898227643, - 3.307783463653505, - 3.306376514110241, - 3.3049785684724733, - 3.3035896300020595, - 3.302209677998705, - 3.300838667871357, - 3.29947653121914, - 3.298123175915253, - 3.2967784861872893, - 3.2954423226873084, - 3.2941145225450295, - 3.2927948993974967, - 3.291483243388616, - 3.290179300844195, - 3.288882831898938, - 3.2875935578187336, - 3.2863111739368027, - 3.285035351410079, - 3.2837657369016027, - 3.2825019521880026, - 3.2812435936859425, - 3.2799902318915923, - 3.278741410727174, - 3.2774966467888813, - 3.276255428490454, - 3.275017215096959, - 3.2737814356433996, - 3.272547487733064, - 3.2713147362106936, - 3.2700825117058967, - 3.2688501090425017, - 3.2676167855101235, - 3.2663817589945596, - 3.265144205964376, - 3.2639032593117134, - 3.262658006046384, - 3.261407484843172, - 3.2601506834438103, - 3.2588865359163943, - 3.257613919776889, - 3.2563316529794983, - 3.255038490784942, - 3.2537331225187915, - 3.252414168234902, - 3.2510801753030725, - 3.2497296149441044, - 3.248360878740259, - 3.246972275154833, - 3.2455620261003344, - 3.2441282636018673, - 3.242669026609765, - 3.241182256769195, - 3.239665797926234, - 3.2381173938485084, - 3.2365346846230834, - 3.234915206080872, - 3.2332563891572264, - 3.2315555600522883, - 3.2298099413292873, - 3.228016654100678, - 3.2261727214628637, - 3.2242750739602313, - 3.2223205542316817, - 3.2203059267871152, - 3.2182278883766293, - 3.2160830802221567, - 3.2138681010291426, - 3.2115795261840336, - 3.2092139268908633, - 3.2067678927734495, - 3.2042380573190137, - 3.2016211261933103, - 3.198913908397262, - 3.196113350163349, - 3.1932165714062855, - 3.190220904447609, - 3.187123934628521, - 3.183923542311989, - 3.1806179456561554, - 3.177205743420512, - 3.1736859569486833, - 3.170058070362576, - 3.166322064855059, - 3.1624784608556338, - 3.1585283380342344, - 3.1544733594039984, - 3.150315785936714, - 3.146058482389281, - 3.141704913632553, - 3.1372591307225486, - 3.132725746181483, - 3.128109898226134, - 3.1234172039855896, - 3.1186537020771947, - 3.11382578524601, - 3.108940124102552, - 3.104003583300486, - 3.0990231317637873, - 3.0940057487874557, - 3.088958327984548, - 3.0838875811272164, - 3.078799943925715, - 3.0737014857074643, - 3.068597824802668, - 3.063494051222941, - 3.0583946579467725, - 3.0533034818154463, - 3.0482236547113013, - 3.0431575653533103, - 3.0381068317181903, - 3.033072283792399, - 3.0280539560924873, - 3.0230510891662874, - 3.018062139110873, - 3.01308479401631, - 3.0081159961670028, - 3.0031519688011254, - 2.99818824623844, - 2.993219706231952, - 2.988240603471948, - 2.9832446032652142, - 2.978224814520922, - 2.9731738212912053, - 2.968083712233682, - 2.96294610748013, - 2.9577521825067516, - 2.9524926887042353, - 2.9471579704381172, - 2.9417379784707047, - 2.9362222796848605, - 2.9306000631068825, - 2.9248601422714695, - 2.918990954006901, - 2.9129805500974904, - 2.90681659862296, - 2.900486366186482, - 2.8939767029821564, - 2.887274026805922, - 2.880364302902069, - 2.8732330210943173, - 2.865865170306786, - 2.8582452105663556, - 2.8503570425630795, - 2.842183974829324, - 2.833708688581958, - 2.8249132002552466, - 2.8157788217355195, - 2.806286118291993, - 2.7964148641817967, - 2.7861439958911416, - 2.775451562958369, - 2.7643146763089894, - 2.752709454016846, - 2.7406109643896723, - 2.7279931662612316, - 2.714828846355646, - 2.7010895535721993, - 2.6867455300209717, - 2.671765638619922, - 2.656117287043286, - 2.639766347787585, - 2.6226770740957828, - 2.6048120114503917, - 2.586131904312632, - 2.5665955977449313, - 2.5461599335074667, - 2.524779640163713, - 2.5024072166621973, - 2.500010012082775 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Optimised", - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857 - ], - "y": [ - 4.060121657725171, - 4.0440965898480234, - 4.034825220624699, - 4.027436103919198, - 4.021132752959295, - 4.015595269139246, - 4.010647681438589, - 4.006174799709503, - 4.002094768861892, - 3.998347074323662, - 3.9948853366063815, - 3.9916729580444783, - 3.9886802459644817, - 3.985883029601075, - 3.9832614994413897, - 3.9807991503933695, - 3.9784820332352857, - 3.976297854103046, - 3.974236297118218, - 3.9722884737746544, - 3.970446712898164, - 3.968704347164096, - 3.9670553548907352, - 3.9654943707384778, - 3.964016507779917, - 3.96261744862915, - 3.961293301681543, - 3.9600405089437065, - 3.958855801336466, - 3.9577361122125327, - 3.956678591942213, - 3.955680562433091, - 3.954739506519783, - 3.9538530347726586, - 3.9530188544063942, - 3.952234755735694, - 3.9514985933259856, - 3.950808283611841, - 3.9501617899653247, - 3.949557114319111, - 3.9489922884049142, - 3.948465366622288, - 3.947974421905812, - 3.947517540393611, - 3.9470928202123545, - 3.946698367815674, - 3.946332298211953, - 3.945992733934509, - 3.945677805276896, - 3.945385648691732, - 3.945114407719662, - 3.9448622339508623, - 3.9446272914917886, - 3.944407758400042, - 3.944201827948562, - 3.9440077096668142, - 3.943823631157972, - 3.943647843300595, - 3.943478622048449, - 3.943314272947128, - 3.943153130836272, - 3.9429935633959983, - 3.9428339737979847, - 3.942672804127088, - 3.9425085366645343, - 3.942339699370915, - 3.942164865191379, - 3.941982654085342, - 3.941791738716668, - 3.941590841160643, - 3.941378737499459, - 3.941154258716942, - 3.940916288406615, - 3.9406637749113074, - 3.940395724650167, - 3.940111202827605, - 3.9398093176995705, - 3.93948926309745, - 3.939150285984352, - 3.9387916951817954, - 3.938412838669659, - 3.938013146622299, - 3.93759210693792, - 3.937149264550323, - 3.936684209814037, - 3.9361965932826015, - 3.935686133995041, - 3.9351526001027066, - 3.9345958139866535, - 3.934015651009173, - 3.933412038173378, - 3.9327849171109097, - 3.932134316439588, - 3.931460305216995, - 3.930762991655308, - 3.930042526289108, - 3.9292991001737, - 3.928532939789437, - 3.927744270194291, - 3.9269334077135336, - 3.926100677767375, - 3.925246435463019, - 3.9243710636099864, - 3.9234749707481313, - 3.922558585062612, - 3.9216223516923665, - 3.92066674784931, - 3.919692262768464, - 3.918699401469542, - 3.9176886829399726, - 3.9166606383645286, - 3.915615816645838, - 3.9145547721293026, - 3.913478061410871, - 3.9123862506419886, - 3.9112799105512432, - 3.9101596150347446, - 3.90902593980653, - 3.907879463882322, - 3.906720768788076, - 3.90555043419629, - 3.904369029854372, - 3.9031771237889714, - 3.9019752823937175, - 3.9007640688691607, - 3.8995440300744737, - 3.898315711274881, - 3.8970796496879654, - 3.895836373793419, - 3.8945864026944177, - 3.8933302455288783, - 3.89206840092864, - 3.890801356524716, - 3.889529588496475, - 3.88825356116287, - 3.886973726613334, - 3.8856905243764617, - 3.884404381535138, - 3.883115714640332, - 3.881824920490164, - 3.8805323857296368, - 3.879238483273564, - 3.8779435721545426, - 3.876647997393039, - 3.8753520898872713, - 3.874056166320722, - 3.872760529085063, - 3.871465466216278, - 3.8701712513418514, - 3.8688781436367994, - 3.867586387786421, - 3.8662962139536647, - 3.865007837748932, - 3.863721460200294, - 3.86243726772195, - 3.861155432079, - 3.8598761103463217, - 3.858599444859667, - 3.857325563156892, - 3.856054577907393, - 3.8547865880092687, - 3.853521675140205, - 3.852259906592461, - 3.8510013345516465, - 3.849745995737716, - 3.848493911223823, - 3.847245086230678, - 3.845999509894802, - 3.8447571550092743, - 3.843517977735744, - 3.842281917286528, - 3.84104889557604, - 3.839818816840773, - 3.838591567227613, - 3.837367014350412, - 3.836145006815423, - 3.834925373716293, - 3.8337079241003913, - 3.832492446408417, - 3.8312787078904127, - 3.8300664540019858, - 3.8288554077854937, - 3.82764526924246, - 3.8264357147043304, - 3.825226396210623, - 3.824016940904831, - 3.8228069504605537, - 3.821596000552264, - 3.8203836403873863, - 3.819169392318824, - 3.817952751559759, - 3.816733186025321, - 3.8155101363286623, - 3.8142830159621823, - 3.8130512116975823, - 3.811814084241704, - 3.8105709691881136, - 3.809321178306893, - 3.808064001217936, - 3.806798707494524, - 3.805524549297614, - 3.8042407645279015, - 3.80294658006389, - 3.801641216609067, - 3.800323893450274, - 3.7989938341069824, - 3.797650272696485, - 3.796292461027734, - 3.7949196764220727, - 3.7935312302411943, - 3.792126477082108, - 3.790704824574627, - 3.789265743690617, - 3.787808779444662, - 3.7863335618357694, - 3.784839816847797, - 3.7833273772958527, - 3.781796193276985, - 3.7802463419582577, - 3.778678036415928, - 3.7770916332268953, - 3.775487638511052, - 3.773866712130813, - 3.7722296697745423, - 3.7705774826839673, - 3.768911274832016, - 3.767232317417256, - 3.765542020611115, - 3.763841922574203, - 3.762133675842943, - 3.7604190312757577, - 3.758699819833409, - 3.756977932547074, - 3.7552552990962496, - 3.7535338654720247, - 3.75181557123721, - 3.7501023269107647, - 3.748395991999147, - 3.746698354171488, - 3.7450111101047416, - 3.743335848025119, - 3.7416740329885063, - 3.740026994423559, - 3.738395916427101, - 3.7367818307865783, - 3.7351856127856617, - 3.733607979598049, - 3.7320494911598785, - 3.7305105532926257, - 3.7289914228192713, - 3.727492214389555, - 3.726012908715474, - 3.7245533619158344, - 3.7231133156758616, - 3.721692407943994, - 3.720290183910288, - 3.7189061070380207, - 3.717539569949978, - 3.716189905002093, - 3.7148563944084705, - 3.7135382798114818, - 3.712234771219012, - 3.7109450552561265, - 3.7096683027009463, - 3.70840367529395, - 3.707150331826182, - 3.7059074335248368, - 3.704674148765394, - 3.7034496571469546, - 3.702233152973122, - 3.701023848184212, - 3.69982097478844, - 3.698623786840248, - 3.697431562013152, - 3.696243602813127, - 3.695059237476307, - 3.693877820592131, - 3.692698733493261, - 3.691521384432397, - 3.690345208606936, - 3.6891696680329553, - 3.687994251306236, - 3.6868184732717304, - 3.6856418746211577, - 3.6844640214360247, - 3.683284504691197, - 3.682102939731703, - 3.680918965733599, - 3.679732245157803, - 3.678542463204324, - 3.677349327272715, - 3.676152566433341, - 3.674951930913017, - 3.67374719159745, - 3.67253813955216, - 3.671324585562715, - 3.670106359694619, - 3.668883310872468, - 3.667655306477734, - 3.6664222319639785, - 3.6651839904880967, - 3.663940502555898, - 3.6626917056801673, - 3.6614375540491313, - 3.660178018203255, - 3.6589130847181535, - 3.657642755891324, - 3.656367049430627, - 3.655085998142156, - 3.6537996496154426, - 3.652508065903903, - 3.651211323198576, - 3.649909511493246, - 3.648602734239283, - 3.6472911079885497, - 3.645974762023026, - 3.6446538379698215, - 3.6433284894005418, - 3.641998881414076, - 3.640665190202166, - 3.639327602597143, - 3.6379863156016343, - 3.636641535900045, - 3.635293479351955, - 3.6339423704677234, - 3.632588441866708, - 3.631231933718905, - 3.629873093170719, - 3.6285121737560697, - 3.627149434793852, - 3.625785140773238, - 3.624419560728313, - 3.623052967603612, - 3.621685637612379, - 3.6203178495893473, - 3.6189498843400862, - 3.6175820239887626, - 3.616214551326544, - 3.6148477491626982, - 3.613481899680409, - 3.612117283799607, - 3.610754180548805, - 3.6093928664480366, - 3.608033614904967, - 3.6066766956260614, - 3.60532237404482, - 3.603970910768733, - 3.602622561046742, - 3.601277574258739, - 3.599936193428581, - 3.598598654761868, - 3.59726518720971, - 3.595936012059473, - 3.5946113425533746, - 3.593291383535633, - 3.5919763311287114, - 3.5906663724390486, - 3.5893616852925385, - 3.5880624379998536, - 3.586768789151249, - 3.5854808874411783, - 3.5841988715220525, - 3.5829228698865156, - 3.581653000777371, - 3.580389372125797, - 3.5791320815152186, - 3.5778812161709768, - 3.576636852974426, - 3.575399058500416, - 3.574167889077013, - 3.5729433908661643, - 3.5717255999640036, - 3.570514542519597, - 3.569310234870619, - 3.568112683694738, - 3.5669218861752174, - 3.56573783017944, - 3.5645604944489064, - 3.563389848799458, - 3.5622258543302947, - 3.5610684636404986, - 3.559917621051854, - 3.558773262836721, - 3.55763531744968, - 3.5565037057619606, - 3.5553783412974935, - 3.5542591304695503, - 3.5531459728170605, - 3.5520387612396327, - 3.5509373822305084, - 3.5498417161066005, - 3.548751637234977, - 3.54766701425501, - 3.5465877102957735, - 3.545513583188004, - 3.544444485670259, - 3.5433802655888806, - 3.5423207660911897, - 3.5412658258120757, - 3.5402152790534145, - 3.5391689559561237, - 3.5381266826650055, - 3.5370882814860525, - 3.5360535710363608, - 3.5350223663866314, - 3.533994479196357, - 3.5329697178418216, - 3.5319478875370716, - 3.530928790448129, - 3.5299122258006337, - 3.528897989981254, - 3.527885876633185, - 3.5268756767461196, - 3.52586717874104, - 3.52486016855038, - 3.5238544296938814, - 3.5228497433507657, - 3.5218458884286643, - 3.520842641629975, - 3.519839777516064, - 3.5188370685701114, - 3.517834285259095, - 3.5168311960956897, - 3.515827567700656, - 3.514823164866568, - 3.5138177506234767, - 3.512811086307386, - 3.5118029316322534, - 3.5107930447662925, - 3.509781182413498, - 3.5087670999010996, - 3.5077505512738623, - 3.506731289396084, - 3.5057090660621704, - 3.5046836321165897, - 3.5036547375842395, - 3.5026221318119135, - 3.501585563621958, - 3.500544781478827, - 3.4994995336694936, - 3.4984495684985775, - 3.497394634498943, - 3.496334480658677, - 3.495268856665154, - 3.494197513166962, - 3.493120202054342, - 3.49203667675884, - 3.4909466925726873, - 3.489850006988497, - 3.488746380059551, - 3.4876355747812493, - 3.4865173574936885, - 3.48539149830573, - 3.484257771540422, - 3.483115956201678, - 3.481965836461988, - 3.4808072021706726, - 3.4796398493821137, - 3.4784635809032336, - 3.4772782068591654, - 3.4760835452761025, - 3.4748794226798365, - 3.4736656747084833, - 3.4724421467376088, - 3.4712086945157092, - 3.469965184807773, - 3.4687114960445316, - 3.4674475189745566, - 3.4661731573163848, - 3.4648883284073984, - 3.463592963846131, - 3.4622870101243564, - 3.4609704292452177, - 3.459643199323355, - 3.4583053151629617, - 3.45695678880945, - 3.45559765007038, - 3.4542279470011863, - 3.452847746351145, - 3.4514571339651496, - 3.450056215136754, - 3.448645114908027, - 3.447223978311982, - 3.4457929705534105, - 3.44435227712411, - 3.442902103848932, - 3.441442676859116, - 3.439974242490042, - 3.438497067100566, - 3.437011436811858, - 3.435517657163998, - 3.434016052689069, - 3.4325069664001164, - 3.4309907591959026, - 3.4294678091820225, - 3.4279385109094203, - 3.426403274532248, - 3.424862524887339, - 3.423316700498452, - 3.421766252508956, - 3.4202116435471335, - 3.418653346529114, - 3.4170918434046915, - 3.415527623851971, - 3.4139611839271664, - 3.412393024676287, - 3.410823650715724, - 3.409253568789084, - 3.407683286307746, - 3.406113309882774, - 3.404544143855861, - 3.402976288836866, - 3.4014102402555233, - 3.399846486934615, - 3.3982855096916817, - 3.3967277799760054, - 3.395173758547187, - 3.393623894201262, - 3.392078622549653, - 3.3905383648558733, - 3.389003526934113, - 3.3874744981134444, - 3.3859516502704503, - 3.3844353369326416, - 3.3829258924542644, - 3.381423631265361, - 3.379928847194382, - 3.3784418128638594, - 3.376962779158142, - 3.375491974761414, - 3.374029605763746, - 3.3725758553323115, - 3.3711308834443976, - 3.3696948266782596, - 3.3682677980576208, - 3.3668498869450105, - 3.3654411589788458, - 3.364041656048953, - 3.362651396304688, - 3.3612703741899654, - 3.3598985604988707, - 3.3585359024457526, - 3.357182323743368, - 3.3558377246825577, - 3.3545019822070063, - 3.353174949976488, - 3.351856458411973, - 3.3505463147161088, - 3.349244302862481, - 3.3479501835471503, - 3.3466636940960406, - 3.3453845483217535, - 3.3441124363235, - 3.342847024223917, - 3.3415879538365343, - 3.340334842257856, - 3.339087281378062, - 3.337844837304357, - 3.336607049691237, - 3.335373430971962, - 3.3341434654856306, - 3.332916608494533, - 3.3316922850864517, - 3.3304698889569537, - 3.3292487810668394, - 3.3280282881702825, - 3.3268077012095234, - 3.325586273572458, - 3.324363219209925, - 3.32313771061015, - 3.3219088766287004, - 3.320675800172994, - 3.319437515741699, - 3.318193006820577, - 3.316941203137944, - 3.3156809777845364, - 3.3144111442050064, - 3.3131304530704098, - 3.3118375890441953, - 3.3105311674572104, - 3.3092097309113293, - 3.307871745835316, - 3.306515599021592, - 3.305139594178076, - 3.303741948535313, - 3.302320789556072, - 3.3008741518022378, - 3.2993999740219375, - 3.29789609652923, - 3.2963602589581753, - 3.2947900984838503, - 3.293183148613614, - 3.2915368386636357, - 3.2898484940473107, - 3.288115337513972, - 3.2863344914876964, - 3.284502981666729, - 3.2826177420537244, - 3.2806756215944066, - 3.2786733926079368, - 3.2766077611938664, - 3.274475379798586, - 3.2722728621166812, - 3.2699968004890505, - 3.267643785938526, - 3.2652104309543777, - 3.26269339509799, - 3.2600894134524845, - 3.2573953278783367, - 3.254608120964659, - 3.251724952481813, - 3.248743198045954, - 3.245660489601031, - 3.2424747572112036, - 3.2391842715385692, - 3.235787686262306, - 3.232284079579755, - 3.228672993823676, - 3.224954472138988, - 3.2211290910935215, - 3.217197988057837, - 3.213162882185259, - 3.2090260878612615, - 3.2047905195754556, - 3.200459687302361, - 3.196037681659744, - 3.1915291483420725, - 3.186939251597192, - 3.1822736268174516, - 3.177538322640823, - 3.172739733289606, - 3.1678845221984635, - 3.162979538283564, - 3.158031726465252, - 3.153048034263055, - 3.1480353164231136, - 3.143000239605573, - 3.1379491891496074, - 3.132888179846821, - 3.127822772494949, - 3.122757997782051, - 3.117698288778874, - 3.1126474230087844, - 3.10760847473618, - 3.1025837777818706, - 3.097574898852514, - 3.0925826210741736, - 3.0876069371579633, - 3.0826470514068727, - 3.07770138960108, - 3.0727676156771784, - 3.0678426540430546, - 3.062922716341639, - 3.058003331488832, - 3.0530793778567737, - 3.048145116547339, - 3.0431942247945902, - 3.038219828642671, - 3.0332145341611265, - 3.0281704565771106, - 3.023079246819377, - 3.017932115078509, - 3.012719851088733, - 3.0074328409275113, - 3.002061080208371, - 2.996594183610144, - 2.991021390741576, - 2.985331568385135, - 2.979513209198197, - 2.973554426974769, - 2.9674429485874714, - 2.961166102739063, - 2.9547108056558486, - 2.9480635438537885, - 2.941210354101934, - 2.9341368006988273, - 2.926827950165741, - 2.919268343447117, - 2.911441965693725, - 2.9033322136884294, - 2.894921860958181, - 2.886193020599464, - 2.877127105828041, - 2.8677047882473268, - 2.8579059538136806, - 2.847709656460917, - 2.837094069330381, - 2.826036433537559, - 2.814513004390263, - 2.8024989949580172, - 2.7899685168761525, - 2.7768945182518685, - 2.7632487185224943, - 2.7490015400983188, - 2.7341220366030843, - 2.7185778175045376, - 2.702334968904547, - 2.6853579702324626, - 2.6676096065565793, - 2.649050876194984, - 2.6296408932681192, - 2.6093367847896185, - 2.5880935818368696, - 2.565864104276627 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - } - }, - "yaxis": { - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Voltage [V]" - } - } - } - } + "image/svg+xml": [ + "01000200030002.42.62.833.23.43.63.84ReferenceOptimisedOptimised ComparisonTime / sVoltage / V" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857, - 3510.522507468857, - 3510, - 3505, - 3500, - 3495, - 3490, - 3485, - 3480, - 3475, - 3470, - 3465, - 3460, - 3455, - 3450, - 3445, - 3440, - 3435, - 3430, - 3425, - 3420, - 3415, - 3410, - 3405, - 3400, - 3395, - 3390, - 3385, - 3380, - 3375, - 3370, - 3365, - 3360, - 3355, - 3350, - 3345, - 3340, - 3335, - 3330, - 3325, - 3320, - 3315, - 3310, - 3305, - 3300, - 3295, - 3290, - 3285, - 3280, - 3275, - 3270, - 3265, - 3260, - 3255, - 3250, - 3245, - 3240, - 3235, - 3230, - 3225, - 3220, - 3215, - 3210, - 3205, - 3200, - 3195, - 3190, - 3185, - 3180, - 3175, - 3170, - 3165, - 3160, - 3155, - 3150, - 3145, - 3140, - 3135, - 3130, - 3125, - 3120, - 3115, - 3110, - 3105, - 3100, - 3095, - 3090, - 3085, - 3080, - 3075, - 3070, - 3065, - 3060, - 3055, - 3050, - 3045, - 3040, - 3035, - 3030, - 3025, - 3020, - 3015, - 3010, - 3005, - 3000, - 2995, - 2990, - 2985, - 2980, - 2975, - 2970, - 2965, - 2960, - 2955, - 2950, - 2945, - 2940, - 2935, - 2930, - 2925, - 2920, - 2915, - 2910, - 2905, - 2900, - 2895, - 2890, - 2885, - 2880, - 2875, - 2870, - 2865, - 2860, - 2855, - 2850, - 2845, - 2840, - 2835, - 2830, - 2825, - 2820, - 2815, - 2810, - 2805, - 2800, - 2795, - 2790, - 2785, - 2780, - 2775, - 2770, - 2765, - 2760, - 2755, - 2750, - 2745, - 2740, - 2735, - 2730, - 2725, - 2720, - 2715, - 2710, - 2705, - 2700, - 2695, - 2690, - 2685, - 2680, - 2675, - 2670, - 2665, - 2660, - 2655, - 2650, - 2645, - 2640, - 2635, - 2630, - 2625, - 2620, - 2615, - 2610, - 2605, - 2600, - 2595, - 2590, - 2585, - 2580, - 2575, - 2570, - 2565, - 2560, - 2555, - 2550, - 2545, - 2540, - 2535, - 2530, - 2525, - 2520, - 2515, - 2510, - 2505, - 2500, - 2495, - 2490, - 2485, - 2480, - 2475, - 2470, - 2465, - 2460, - 2455, - 2450, - 2445, - 2440, - 2435, - 2430, - 2425, - 2420, - 2415, - 2410, - 2405, - 2400, - 2395, - 2390, - 2385, - 2380, - 2375, - 2370, - 2365, - 2360, - 2355, - 2350, - 2345, - 2340, - 2335, - 2330, - 2325, - 2320, - 2315, - 2310, - 2305, - 2300, - 2295, - 2290, - 2285, - 2280, - 2275, - 2270, - 2265, - 2260, - 2255, - 2250, - 2245, - 2240, - 2235, - 2230, - 2225, - 2220, - 2215, - 2210, - 2205, - 2200, - 2195, - 2190, - 2185, - 2180, - 2175, - 2170, - 2165, - 2160, - 2155, - 2150, - 2145, - 2140, - 2135, - 2130, - 2125, - 2120, - 2115, - 2110, - 2105, - 2100, - 2095, - 2090, - 2085, - 2080, - 2075, - 2070, - 2065, - 2060, - 2055, - 2050, - 2045, - 2040, - 2035, - 2030, - 2025, - 2020, - 2015, - 2010, - 2005, - 2000, - 1995, - 1990, - 1985, - 1980, - 1975, - 1970, - 1965, - 1960, - 1955, - 1950, - 1945, - 1940, - 1935, - 1930, - 1925, - 1920, - 1915, - 1910, - 1905, - 1900, - 1895, - 1890, - 1885, - 1880, - 1875, - 1870, - 1865, - 1860, - 1855, - 1850, - 1845, - 1840, - 1835, - 1830, - 1825, - 1820, - 1815, - 1810, - 1805, - 1800, - 1795, - 1790, - 1785, - 1780, - 1775, - 1770, - 1765, - 1760, - 1755, - 1750, - 1745, - 1740, - 1735, - 1730, - 1725, - 1720, - 1715, - 1710, - 1705, - 1700, - 1695, - 1690, - 1685, - 1680, - 1675, - 1670, - 1665, - 1660, - 1655, - 1650, - 1645, - 1640, - 1635, - 1630, - 1625, - 1620, - 1615, - 1610, - 1605, - 1600, - 1595, - 1590, - 1585, - 1580, - 1575, - 1570, - 1565, - 1560, - 1555, - 1550, - 1545, - 1540, - 1535, - 1530, - 1525, - 1520, - 1515, - 1510, - 1505, - 1500, - 1495, - 1490, - 1485, - 1480, - 1475, - 1470, - 1465, - 1460, - 1455, - 1450, - 1445, - 1440, - 1435, - 1430, - 1425, - 1420, - 1415, - 1410, - 1405, - 1400, - 1395, - 1390, - 1385, - 1380, - 1375, - 1370, - 1365, - 1360, - 1355, - 1350, - 1345, - 1340, - 1335, - 1330, - 1325, - 1320, - 1315, - 1310, - 1305, - 1300, - 1295, - 1290, - 1285, - 1280, - 1275, - 1270, - 1265, - 1260, - 1255, - 1250, - 1245, - 1240, - 1235, - 1230, - 1225, - 1220, - 1215, - 1210, - 1205, - 1200, - 1195, - 1190, - 1185, - 1180, - 1175, - 1170, - 1165, - 1160, - 1155, - 1150, - 1145, - 1140, - 1135, - 1130, - 1125, - 1120, - 1115, - 1110, - 1105, - 1100, - 1095, - 1090, - 1085, - 1080, - 1075, - 1070, - 1065, - 1060, - 1055, - 1050, - 1045, - 1040, - 1035, - 1030, - 1025, - 1020, - 1015, - 1010, - 1005, - 1000, - 995, - 990, - 985, - 980, - 975, - 970, - 965, - 960, - 955, - 950, - 945, - 940, - 935, - 930, - 925, - 920, - 915, - 910, - 905, - 900, - 895, - 890, - 885, - 880, - 875, - 870, - 865, - 860, - 855, - 850, - 845, - 840, - 835, - 830, - 825, - 820, - 815, - 810, - 805, - 800, - 795, - 790, - 785, - 780, - 775, - 770, - 765, - 760, - 755, - 750, - 745, - 740, - 735, - 730, - 725, - 720, - 715, - 710, - 705, - 700, - 695, - 690, - 685, - 680, - 675, - 670, - 665, - 660, - 655, - 650, - 645, - 640, - 635, - 630, - 625, - 620, - 615, - 610, - 605, - 600, - 595, - 590, - 585, - 580, - 575, - 570, - 565, - 560, - 555, - 550, - 545, - 540, - 535, - 530, - 525, - 520, - 515, - 510, - 505, - 500, - 495, - 490, - 485, - 480, - 475, - 470, - 465, - 460, - 455, - 450, - 445, - 440, - 435, - 430, - 425, - 420, - 415, - 410, - 405, - 400, - 395, - 390, - 385, - 380, - 375, - 370, - 365, - 360, - 355, - 350, - 345, - 340, - 335, - 330, - 325, - 320, - 315, - 310, - 305, - 300, - 295, - 290, - 285, - 280, - 275, - 270, - 265, - 260, - 255, - 250, - 245, - 240, - 235, - 230, - 225, - 220, - 215, - 210, - 205, - 200, - 195, - 190, - 185, - 180, - 175, - 170, - 165, - 160, - 155, - 150, - 145, - 140, - 135, - 130, - 125, - 120, - 115, - 110, - 105, - 100, - 95, - 90, - 85, - 80, - 75, - 70, - 65, - 60, - 55, - 50, - 45, - 40, - 35, - 30, - 25, - 20, - 15, - 10, - 5, - 0 - ], - "y": [ - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081 - ] - }, - { - "mode": "markers", - "name": "Initial", - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857 - ], - "y": [ - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965, - 5.114173249321965 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Optimised", - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 215, - 220, - 225, - 230, - 235, - 240, - 245, - 250, - 255, - 260, - 265, - 270, - 275, - 280, - 285, - 290, - 295, - 300, - 305, - 310, - 315, - 320, - 325, - 330, - 335, - 340, - 345, - 350, - 355, - 360, - 365, - 370, - 375, - 380, - 385, - 390, - 395, - 400, - 405, - 410, - 415, - 420, - 425, - 430, - 435, - 440, - 445, - 450, - 455, - 460, - 465, - 470, - 475, - 480, - 485, - 490, - 495, - 500, - 505, - 510, - 515, - 520, - 525, - 530, - 535, - 540, - 545, - 550, - 555, - 560, - 565, - 570, - 575, - 580, - 585, - 590, - 595, - 600, - 605, - 610, - 615, - 620, - 625, - 630, - 635, - 640, - 645, - 650, - 655, - 660, - 665, - 670, - 675, - 680, - 685, - 690, - 695, - 700, - 705, - 710, - 715, - 720, - 725, - 730, - 735, - 740, - 745, - 750, - 755, - 760, - 765, - 770, - 775, - 780, - 785, - 790, - 795, - 800, - 805, - 810, - 815, - 820, - 825, - 830, - 835, - 840, - 845, - 850, - 855, - 860, - 865, - 870, - 875, - 880, - 885, - 890, - 895, - 900, - 905, - 910, - 915, - 920, - 925, - 930, - 935, - 940, - 945, - 950, - 955, - 960, - 965, - 970, - 975, - 980, - 985, - 990, - 995, - 1000, - 1005, - 1010, - 1015, - 1020, - 1025, - 1030, - 1035, - 1040, - 1045, - 1050, - 1055, - 1060, - 1065, - 1070, - 1075, - 1080, - 1085, - 1090, - 1095, - 1100, - 1105, - 1110, - 1115, - 1120, - 1125, - 1130, - 1135, - 1140, - 1145, - 1150, - 1155, - 1160, - 1165, - 1170, - 1175, - 1180, - 1185, - 1190, - 1195, - 1200, - 1205, - 1210, - 1215, - 1220, - 1225, - 1230, - 1235, - 1240, - 1245, - 1250, - 1255, - 1260, - 1265, - 1270, - 1275, - 1280, - 1285, - 1290, - 1295, - 1300, - 1305, - 1310, - 1315, - 1320, - 1325, - 1330, - 1335, - 1340, - 1345, - 1350, - 1355, - 1360, - 1365, - 1370, - 1375, - 1380, - 1385, - 1390, - 1395, - 1400, - 1405, - 1410, - 1415, - 1420, - 1425, - 1430, - 1435, - 1440, - 1445, - 1450, - 1455, - 1460, - 1465, - 1470, - 1475, - 1480, - 1485, - 1490, - 1495, - 1500, - 1505, - 1510, - 1515, - 1520, - 1525, - 1530, - 1535, - 1540, - 1545, - 1550, - 1555, - 1560, - 1565, - 1570, - 1575, - 1580, - 1585, - 1590, - 1595, - 1600, - 1605, - 1610, - 1615, - 1620, - 1625, - 1630, - 1635, - 1640, - 1645, - 1650, - 1655, - 1660, - 1665, - 1670, - 1675, - 1680, - 1685, - 1690, - 1695, - 1700, - 1705, - 1710, - 1715, - 1720, - 1725, - 1730, - 1735, - 1740, - 1745, - 1750, - 1755, - 1760, - 1765, - 1770, - 1775, - 1780, - 1785, - 1790, - 1795, - 1800, - 1805, - 1810, - 1815, - 1820, - 1825, - 1830, - 1835, - 1840, - 1845, - 1850, - 1855, - 1860, - 1865, - 1870, - 1875, - 1880, - 1885, - 1890, - 1895, - 1900, - 1905, - 1910, - 1915, - 1920, - 1925, - 1930, - 1935, - 1940, - 1945, - 1950, - 1955, - 1960, - 1965, - 1970, - 1975, - 1980, - 1985, - 1990, - 1995, - 2000, - 2005, - 2010, - 2015, - 2020, - 2025, - 2030, - 2035, - 2040, - 2045, - 2050, - 2055, - 2060, - 2065, - 2070, - 2075, - 2080, - 2085, - 2090, - 2095, - 2100, - 2105, - 2110, - 2115, - 2120, - 2125, - 2130, - 2135, - 2140, - 2145, - 2150, - 2155, - 2160, - 2165, - 2170, - 2175, - 2180, - 2185, - 2190, - 2195, - 2200, - 2205, - 2210, - 2215, - 2220, - 2225, - 2230, - 2235, - 2240, - 2245, - 2250, - 2255, - 2260, - 2265, - 2270, - 2275, - 2280, - 2285, - 2290, - 2295, - 2300, - 2305, - 2310, - 2315, - 2320, - 2325, - 2330, - 2335, - 2340, - 2345, - 2350, - 2355, - 2360, - 2365, - 2370, - 2375, - 2380, - 2385, - 2390, - 2395, - 2400, - 2405, - 2410, - 2415, - 2420, - 2425, - 2430, - 2435, - 2440, - 2445, - 2450, - 2455, - 2460, - 2465, - 2470, - 2475, - 2480, - 2485, - 2490, - 2495, - 2500, - 2505, - 2510, - 2515, - 2520, - 2525, - 2530, - 2535, - 2540, - 2545, - 2550, - 2555, - 2560, - 2565, - 2570, - 2575, - 2580, - 2585, - 2590, - 2595, - 2600, - 2605, - 2610, - 2615, - 2620, - 2625, - 2630, - 2635, - 2640, - 2645, - 2650, - 2655, - 2660, - 2665, - 2670, - 2675, - 2680, - 2685, - 2690, - 2695, - 2700, - 2705, - 2710, - 2715, - 2720, - 2725, - 2730, - 2735, - 2740, - 2745, - 2750, - 2755, - 2760, - 2765, - 2770, - 2775, - 2780, - 2785, - 2790, - 2795, - 2800, - 2805, - 2810, - 2815, - 2820, - 2825, - 2830, - 2835, - 2840, - 2845, - 2850, - 2855, - 2860, - 2865, - 2870, - 2875, - 2880, - 2885, - 2890, - 2895, - 2900, - 2905, - 2910, - 2915, - 2920, - 2925, - 2930, - 2935, - 2940, - 2945, - 2950, - 2955, - 2960, - 2965, - 2970, - 2975, - 2980, - 2985, - 2990, - 2995, - 3000, - 3005, - 3010, - 3015, - 3020, - 3025, - 3030, - 3035, - 3040, - 3045, - 3050, - 3055, - 3060, - 3065, - 3070, - 3075, - 3080, - 3085, - 3090, - 3095, - 3100, - 3105, - 3110, - 3115, - 3120, - 3125, - 3130, - 3135, - 3140, - 3145, - 3150, - 3155, - 3160, - 3165, - 3170, - 3175, - 3180, - 3185, - 3190, - 3195, - 3200, - 3205, - 3210, - 3215, - 3220, - 3225, - 3230, - 3235, - 3240, - 3245, - 3250, - 3255, - 3260, - 3265, - 3270, - 3275, - 3280, - 3285, - 3290, - 3295, - 3300, - 3305, - 3310, - 3315, - 3320, - 3325, - 3330, - 3335, - 3340, - 3345, - 3350, - 3355, - 3360, - 3365, - 3370, - 3375, - 3380, - 3385, - 3390, - 3395, - 3400, - 3405, - 3410, - 3415, - 3420, - 3425, - 3430, - 3435, - 3440, - 3445, - 3450, - 3455, - 3460, - 3465, - 3470, - 3475, - 3480, - 3485, - 3490, - 3495, - 3500, - 3505, - 3510, - 3510.522507468857 - ], - "y": [ - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081, - 5.106005404082081 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - } - }, - "yaxis": { - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Current [A]" - } - } - } - } + "image/svg+xml": [ + "010002000300044.555.56ReferenceOptimisedOptimised ComparisonTime / sCurrent / A" + ] }, "metadata": {}, "output_type": "display_data" @@ -13342,7 +272,7 @@ "source": [ "if cost.update_capacity:\n", " problem._model.approximate_capacity(x)\n", - "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\")" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { @@ -13358,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13370,1046 +300,9 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "type": "contour", - "x": [ - 0.000065, - 0.0000825, - 0.0001 - ], - "y": [ - 2e-6, - 5.5e-6, - 9e-6 - ], - "z": [ - [ - -411.2298715307869, - -384.5514548667088, - -360.1243749344209 - ], - [ - -389.45853879647154, - -375.6347882136447, - -353.0562652976013 - ], - [ - -297.51446838856344, - -361.0290869712437, - -342.44425194479135 - ] - ] - }, - { - "marker": { - "color": "red", - "line": { - "color": "midnightblue", - "width": 1 - }, - "showscale": false, - "size": 12, - "symbol": "x" - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.00007503043032178322 - ], - "y": [ - 5.213989386373176e-6 - ] - }, - { - "marker": { - "color": [ - 0, - 0.034482758620689655, - 0.06896551724137931, - 0.10344827586206896, - 0.13793103448275862, - 0.1724137931034483, - 0.20689655172413793, - 0.2413793103448276, - 0.27586206896551724, - 0.3103448275862069, - 0.3448275862068966, - 0.3793103448275862, - 0.41379310344827586, - 0.4482758620689655, - 0.4827586206896552, - 0.5172413793103449, - 0.5517241379310345, - 0.5862068965517241, - 0.6206896551724138, - 0.6551724137931034, - 0.6896551724137931, - 0.7241379310344828, - 0.7586206896551724, - 0.7931034482758621, - 0.8275862068965517, - 0.8620689655172413, - 0.896551724137931, - 0.9310344827586208, - 0.9655172413793104 - ], - "colorscale": [ - [ - 0, - "rgb(255,255,229)" - ], - [ - 0.125, - "rgb(255,247,188)" - ], - [ - 0.25, - "rgb(254,227,145)" - ], - [ - 0.375, - "rgb(254,196,79)" - ], - [ - 0.5, - "rgb(254,153,41)" - ], - [ - 0.625, - "rgb(236,112,20)" - ], - [ - 0.75, - "rgb(204,76,2)" - ], - [ - 0.875, - "rgb(153,52,4)" - ], - [ - 1, - "rgb(102,37,6)" - ] - ], - "showscale": false - }, - "mode": "markers", - "showlegend": false, - "type": "scatter", - "x": [ - 0.00007503043032178322, - 0.00009802702542268024, - 0.00009961571599618376, - 0.00009696983570692418, - 0.00008395762998574042, - 0.00008911496694118421, - 0.00007515805406653971, - 0.00009204596401393966, - 0.00008057020125259683, - 0.00008386969615225464, - 0.00007254052989342064, - 0.00007935751972288362, - 0.0000750001946075022, - 0.00008577128815953699, - 0.00006982413489087496, - 0.00006640947572511218, - 0.00006612369542972039, - 0.00007061848272252423, - 0.00007472708479047697, - 0.0000738910819935762, - 0.00006523593718322669, - 0.00008483575733667275, - 0.00007033181106801437, - 0.00006562638003355986, - 0.00007040332572662624, - 0.00007229780199113263, - 0.00008264638468653952, - 0.00007440284793077438, - 0.00006533356141242215 - ], - "y": [ - 5.213989386373176e-6, - 6.987440787316741e-6, - 2.64453786570932e-6, - 3.389077859556841e-6, - 3.817668779124727e-6, - 5.687676535722202e-6, - 5.269200008333387e-6, - 5.489880994504372e-6, - 3.4059598243459385e-6, - 5.8057531438234875e-6, - 5.433979620135304e-6, - 5.189789142120092e-6, - 5.197278021663849e-6, - 3.6326734178928816e-6, - 5.035835393184889e-6, - 8.106091461340278e-6, - 6.696212143249081e-6, - 4.942164056645866e-6, - 5.279709597217932e-6, - 2.0713500143226936e-6, - 5.9660067637513625e-6, - 4.586549463916286e-6, - 6.3368993449299045e-6, - 5.485105039355238e-6, - 5.140156754229586e-6, - 5.3613255870193305e-6, - 2.844730658911555e-6, - 3.36938348462524e-6, - 5.694287622969207e-6 - ] - } - ], - "layout": { - "height": 600, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.000065, - 0.0001 - ], - "title": { - "text": "Positive electrode thickness [m]" - } - }, - "yaxis": { - "range": [ - 2e-6, - 9e-6 - ], - "title": { - "text": "Positive particle radius [m]" - } - } - } - } + "image/svg+xml": [ + "70μ80μ90μ100μ−400−380−360−340−320Cost LandscapePositive electrode thickness [m]Positive particle radius [m]" + ] }, "metadata": {}, "output_type": "display_data" @@ -14440,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index 1b399872c..d106fed38 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -30,27 +30,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", - "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", - "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", - "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", - "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", - "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", - "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", - "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", - "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", - "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", - "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", - "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", - "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", - "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", - "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", - "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", + "Requirement already satisfied: pip in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (24.0)\n", + "Requirement already satisfied: ipywidgets in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (8.1.2)\n", + "Requirement already satisfied: comm>=0.1.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (0.2.1)\n", + "Requirement already satisfied: ipython>=6.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (8.20.0)\n", + "Requirement already satisfied: traitlets>=4.3.1 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (5.14.1)\n", + "Requirement already satisfied: widgetsnbextension~=4.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (4.0.10)\n", + "Requirement already satisfied: jupyterlab-widgets~=3.0.10 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipywidgets) (3.0.10)\n", + "Requirement already satisfied: decorator in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", + "Requirement already satisfied: jedi>=0.16 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", + "Requirement already satisfied: matplotlib-inline in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", + "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n", + "Requirement already satisfied: pygments>=2.4.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", + "Requirement already satisfied: stack-data in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", + "Requirement already satisfied: pexpect>4.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", + "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", + "Requirement already satisfied: executing>=1.2.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", + "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "metadata": { "id": "SQdt4brD04p1" }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "metadata": { "id": "sBasxv8U04p3" }, @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 6, "metadata": { "id": "zuvGHWID04p_" }, @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 7, "metadata": { "id": "WPCybXIJ04qA" }, @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 8, "metadata": { "id": "etMzRtx404qA" }, @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 9, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -306,10 +306,10 @@ { "data": { "text/plain": [ - "array([0.74999764, 0.66481528])" + "array([0.75175662, 0.66487043])" ] }, - "execution_count": 46, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -354,4529 +354,39 @@ "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898, - 898, - 896, - 894, - 892, - 890, - 888, - 886, - 884, - 882, - 880, - 878, - 876, - 874, - 872, - 870, - 868, - 866, - 864, - 862, - 860, - 858, - 856, - 854, - 852, - 850, - 848, - 846, - 844, - 842, - 840, - 838, - 836, - 834, - 832, - 830, - 828, - 826, - 824, - 822, - 820, - 818, - 816, - 814, - 812, - 810, - 808, - 806, - 804, - 802, - 800, - 798, - 796, - 794, - 792, - 790, - 788, - 786, - 784, - 782, - 780, - 778, - 776, - 774, - 772, - 770, - 768, - 766, - 764, - 762, - 760, - 758, - 756, - 754, - 752, - 750, - 748, - 746, - 744, - 742, - 740, - 738, - 736, - 734, - 732, - 730, - 728, - 726, - 724, - 722, - 720, - 718, - 716, - 714, - 712, - 710, - 708, - 706, - 704, - 702, - 700, - 698, - 696, - 694, - 692, - 690, - 688, - 686, - 684, - 682, - 680, - 678, - 676, - 674, - 672, - 670, - 668, - 666, - 664, - 662, - 660, - 658, - 656, - 654, - 652, - 650, - 648, - 646, - 644, - 642, - 640, - 638, - 636, - 634, - 632, - 630, - 628, - 626, - 624, - 622, - 620, - 618, - 616, - 614, - 612, - 610, - 608, - 606, - 604, - 602, - 600, - 598, - 596, - 594, - 592, - 590, - 588, - 586, - 584, - 582, - 580, - 578, - 576, - 574, - 572, - 570, - 568, - 566, - 564, - 562, - 560, - 558, - 556, - 554, - 552, - 550, - 548, - 546, - 544, - 542, - 540, - 538, - 536, - 534, - 532, - 530, - 528, - 526, - 524, - 522, - 520, - 518, - 516, - 514, - 512, - 510, - 508, - 506, - 504, - 502, - 500, - 498, - 496, - 494, - 492, - 490, - 488, - 486, - 484, - 482, - 480, - 478, - 476, - 474, - 472, - 470, - 468, - 466, - 464, - 462, - 460, - 458, - 456, - 454, - 452, - 450, - 448, - 446, - 444, - 442, - 440, - 438, - 436, - 434, - 432, - 430, - 428, - 426, - 424, - 422, - 420, - 418, - 416, - 414, - 412, - 410, - 408, - 406, - 404, - 402, - 400, - 398, - 396, - 394, - 392, - 390, - 388, - 386, - 384, - 382, - 380, - 378, - 376, - 374, - 372, - 370, - 368, - 366, - 364, - 362, - 360, - 358, - 356, - 354, - 352, - 350, - 348, - 346, - 344, - 342, - 340, - 338, - 336, - 334, - 332, - 330, - 328, - 326, - 324, - 322, - 320, - 318, - 316, - 314, - 312, - 310, - 308, - 306, - 304, - 302, - 300, - 298, - 296, - 294, - 292, - 290, - 288, - 286, - 284, - 282, - 280, - 278, - 276, - 274, - 272, - 270, - 268, - 266, - 264, - 262, - 260, - 258, - 256, - 254, - 252, - 250, - 248, - 246, - 244, - 242, - 240, - 238, - 236, - 234, - 232, - 230, - 228, - 226, - 224, - 222, - 220, - 218, - 216, - 214, - 212, - 210, - 208, - 206, - 204, - 202, - 200, - 198, - 196, - 194, - 192, - 190, - 188, - 186, - 184, - 182, - 180, - 178, - 176, - 174, - 172, - 170, - 168, - 166, - 164, - 162, - 160, - 158, - 156, - 154, - 152, - 150, - 148, - 146, - 144, - 142, - 140, - 138, - 136, - 134, - 132, - 130, - 128, - 126, - 124, - 122, - 120, - 118, - 116, - 114, - 112, - 110, - 108, - 106, - 104, - 102, - 100, - 98, - 96, - 94, - 92, - 90, - 88, - 86, - 84, - 82, - 80, - 78, - 76, - 74, - 72, - 70, - 68, - 66, - 64, - 62, - 60, - 58, - 56, - 54, - 52, - 50, - 48, - 46, - 44, - 42, - 40, - 38, - 36, - 34, - 32, - 30, - 28, - 26, - 24, - 22, - 20, - 18, - 16, - 14, - 12, - 10, - 8, - 6, - 4, - 2, - 0 - ], - "y": [ - 4.064458320501508, - 4.056052762158592, - 4.0487962686414525, - 4.042585833976561, - 4.037232835097531, - 4.03257712970519, - 4.028491518754141, - 4.024876559034254, - 4.021655606108165, - 4.018768293806505, - 4.0161666152952, - 4.013812407545703, - 4.011673680124297, - 4.009724155345706, - 4.007942048853279, - 4.0063088634324, - 4.004809540399291, - 4.003430513850505, - 4.002161003009127, - 4.00099071495522, - 3.999911206578099, - 3.998914490911029, - 3.997993673000907, - 3.997142440201619, - 3.9963551640380586, - 3.995626744209389, - 3.994952582500556, - 3.9943283869179536, - 3.993750347234406, - 3.9932150343474624, - 3.9927191146525662, - 3.9922594704301138, - 3.991833495066583, - 3.991438622892807, - 3.991072175007909, - 3.9907320620325617, - 3.990416208314984, - 3.990122486847953, - 3.989849085201429, - 3.9895942776052857, - 3.989356403748299, - 3.9891339051225514, - 3.9889253460706713, - 3.9887293622396625, - 3.9885446620475777, - 3.988370042887224, - 3.9882043692063527, - 3.988046556437383, - 3.987895599753707, - 3.9877505488709857, - 3.9876104877926246, - 3.987474563770338, - 3.987341987405048, - 3.987212010227377, - 3.987083929835911, - 3.986957062048655, - 3.986830746317142, - 3.986704419111199, - 3.9865775282169262, - 3.986449557410258, - 3.986320016059678, - 3.9861883901258057, - 3.986054281506057, - 3.9859172878067146, - 3.985777034320949, - 3.98563317306535, - 3.9854853411017994, - 3.98533324550752, - 3.9851766042405945, - 3.985015153703548, - 3.9848486507603735, - 3.9846768613849513, - 3.984499575648364, - 3.984316606562869, - 3.984127780152747, - 3.983932938572084, - 3.983731936157834, - 3.983524639327555, - 3.983310935992475, - 3.983090724224544, - 3.982863915043445, - 3.9826304286909298, - 3.9823901783417233, - 3.9821431200521817, - 3.9818892083244712, - 3.9816284081961326, - 3.9813606949008102, - 3.981086053552698, - 3.9808044788533095, - 3.98051597481893, - 3.980220475910093, - 3.97991803830976, - 3.9796086855171766, - 3.97929244540802, - 3.978969352848035, - 3.978639449466783, - 3.978302783445445, - 3.977959409317745, - 3.9776093142950497, - 3.9772525721505576, - 3.976889263121147, - 3.976519452983738, - 3.976143211705376, - 3.975760613258723, - 3.9753717354474314, - 3.974976659741225, - 3.9745754225298366, - 3.974168089731364, - 3.973754776343832, - 3.973335568596183, - 3.972910555189912, - 3.972479827221868, - 3.9720434781174503, - 3.9716016035738946, - 3.9711543015129434, - 3.970701672042251, - 3.9702438174253305, - 3.969780842058975, - 3.969312852458187, - 3.968839891663136, - 3.968361980209246, - 3.967879312144116, - 3.967391989448715, - 3.966900115024439, - 3.9664037926668496, - 3.965903127044247, - 3.965398223681156, - 3.9648891889459774, - 3.96437613004287, - 3.963859155007252, - 3.9633383727048086, - 3.9628138928337022, - 3.9622857848538486, - 3.961753980259769, - 3.961218726281947, - 3.960680121569508, - 3.960138264308483, - 3.959593252183619, - 3.9590451823422463, - 3.9584941513605822, - 3.957940255211827, - 3.957383589236448, - 3.95682424811409, - 3.956262325837444, - 3.9556979156876424, - 3.955131109534443, - 3.9545619006722488, - 3.953990446415313, - 3.953416831562608, - 3.9528411396133576, - 3.952263452732252, - 3.951683851716143, - 3.9511024159620898, - 3.950519223436771, - 3.949934350647213, - 3.949347872612788, - 3.948759862838432, - 3.9481703932891494, - 3.947579534365659, - 3.946987412993837, - 3.9463940715242063, - 3.9457995688487766, - 3.9452039729434447, - 3.944607350451607, - 3.944009766677483, - 3.943411285580378, - 3.9428119697695823, - 3.942211880499981, - 3.941611077668443, - 3.941009619810741, - 3.9404075640991514, - 3.939804966340691, - 3.939201929083719, - 3.938598503865509, - 3.937994723684075, - 3.9373906430981624, - 3.936786315642902, - 3.9361817938418806, - 3.935577129219407, - 3.9349723723129904, - 3.934367572685963, - 3.9337627789402503, - 3.933158038729264, - 3.9325533987709185, - 3.93194890486068, - 3.931344598813826, - 3.9307405225462806, - 3.930136721857109, - 3.929533238613641, - 3.9289301137972985, - 3.9283273875159335, - 3.927725099016159, - 3.927123286695714, - 3.9265219881157174, - 3.925921240012984, - 3.925321078312248, - 3.924721538138302, - 3.924122653828175, - 3.923524450555332, - 3.9229269418525656, - 3.922330176457931, - 3.921734184020202, - 3.921138993328944, - 3.920544632322312, - 3.9199511280949046, - 3.9193585069055064, - 3.9187667941848825, - 3.918176014543489, - 3.917586191779196, - 3.9169973488849457, - 3.916409508056379, - 3.915822691254781, - 3.915236924543753, - 3.9146522238098895, - 3.914068608443516, - 3.913486097099391, - 3.912904707704774, - 3.912324457467403, - 3.911745362883411, - 3.911167439745198, - 3.910590703149161, - 3.910015167503445, - 3.909440846535554, - 3.9088677532999023, - 3.9082959001853026, - 3.907725352268668, - 3.907156086330904, - 3.906588110131392, - 3.906021434977519, - 3.905456071569253, - 3.904892030002614, - 3.9043293197730007, - 3.9037679497785502, - 3.903207928323413, - 3.902649263120972, - 3.90209196129703, - 3.9015360293929464, - 3.900981473368741, - 3.9004282986061214, - 3.899876509911508, - 3.8993261115189863, - 3.8987771070932142, - 3.8982294997322926, - 3.897683291970595, - 3.897138485781557, - 3.8965950825804, - 3.896053083226816, - 3.8955124880276464, - 3.894973479409284, - 3.8944359159609543, - 3.8938997927898567, - 3.893365111244194, - 3.8928318722512505, - 3.8923000763200473, - 3.8917697235439728, - 3.8912408136033343, - 3.890713345767862, - 3.890187318899152, - 3.889662731453013, - 3.889139581481844, - 3.888617866636829, - 3.8880975841701826, - 3.887578730937284, - 3.887061303398744, - 3.886545297622442, - 3.8860307092854938, - 3.885517533676153, - 3.885005765695656, - 3.884495399859997, - 3.8839864303016713, - 3.883478851684374, - 3.882972878879071, - 3.882468322219818, - 3.8819651777314186, - 3.8814634412077695, - 3.8809631082150577, - 3.8804641740948647, - 3.8799666339672023, - 3.87947048273345, - 3.878975715079215, - 3.878482325477103, - 3.877990308189455, - 3.8774996572709486, - 3.8770103665711337, - 3.8765224297369465, - 3.8760358402150623, - 3.875550591254246, - 3.875066675907578, - 3.874584087034617, - 3.87410281730353, - 3.87362285919308, - 3.8731442049945968, - 3.8726668468138343, - 3.872190776911459, - 3.8717160031695506, - 3.871242521918264, - 3.870770328875075, - 3.870299419935004, - 3.8698297911814943, - 3.869361438897266, - 3.8688945959788623, - 3.868429009064996, - 3.867964556705881, - 3.867501261990155, - 3.867039229693138, - 3.866578384122753, - 3.8661187196459705, - 3.8656602597382905, - 3.86520299243876, - 3.8647469057736634, - 3.86429199301419, - 3.863838247102335, - 3.8633856623235183, - 3.862934232113529, - 3.862483949799157, - 3.862034808600298, - 3.861586724604013, - 3.861139753872099, - 3.860693891918763, - 3.860249131621257, - 3.859805465758453, - 3.859362887012568, - 3.858921387970775, - 3.85848096112686, - 3.8580415988826817, - 3.857603293549709, - 3.857166037350407, - 3.856729822419626, - 3.856294593903323, - 3.855860303980405, - 3.8554270131189816, - 3.854994713212551, - 3.854563396071275, - 3.85413305342295, - 3.853703676914004, - 3.853275258110362, - 3.8528477884983263, - 3.852421259485328, - 3.8519956624007, - 3.8515709884963463, - 3.8511472289473727, - 3.850724270994635, - 3.8503021681896015, - 3.849880934200909, - 3.8494605599902783, - 3.849041036442977, - 3.8486223543680897, - 3.8482045044987134, - 3.8477874774921874, - 3.847371263930169, - 3.8469558543187, - 3.84654123908821, - 3.84612740859349, - 3.845714326592301, - 3.845301911056292, - 3.844890233420811, - 3.84447928380944, - 3.844069052266989, - 3.843659528759075, - 3.84325070317168, - 3.842842565310608, - 3.842435104900903, - 3.8420283115861857, - 3.841622174927995, - 3.841216684404971, - 3.8408118294120537, - 3.840407529216243, - 3.8400037928204385, - 3.839600643781504, - 3.839198071204656, - 3.838796064104344, - 3.838394611403088, - 3.837993701930117, - 3.837593324420073, - 3.8371934675115495, - 3.836794119745636, - 3.836395269564312, - 3.8359969053088614, - 3.835599008204721, - 3.835201478594577, - 3.834804382240972, - 3.8344077066074, - 3.8340114390472624, - 3.833615566801704, - 3.833220076997311, - 3.8328249566437904, - 3.8324301926315294, - 3.8320357717290983, - 3.831641680580652, - 3.831247905703253, - 3.830854433484134, - 3.8304612501778386, - 3.8300683419033095, - 3.829675694640845, - 3.8292832942290462, - 3.8288911263615697, - 3.828499176583876, - 3.828107430289855, - 3.8277158727183354, - 3.8273244889495537, - 3.8269332435733534, - 3.8265421008445895, - 3.82615107824348, - 3.825760159841712, - 3.825369329532814, - 3.82497857102791, - 3.824587867851399, - 3.824197203336537, - 3.823806560620891, - 3.8234159226417166, - 3.8230252721312272, - 3.822634591611723, - 3.822243863390683, - 3.821853069555716, - 3.821462191969372, - 3.821071212263919, - 3.82068011183598, - 3.820288871841056, - 3.819897473187962, - 3.8195058965331543, - 3.81911412227496, - 3.81872213054771, - 3.818329896566029, - 3.8179374012742633, - 3.817544626258986, - 3.8171515504940623, - 3.8167581526584, - 3.8163644111294497, - 3.815970303976701, - 3.8155758089549856, - 3.815180903497822, - 3.814785564710601, - 3.8143897693637174, - 3.8139934938856337, - 3.813596714355898, - 3.813199406498071, - 3.8128015456726225, - 3.812403106869787, - 3.812004064702366, - 3.8116043933985018, - 3.8112040667944296, - 3.8108030583272416, - 3.8104013410276054, - 3.8099988875125126, - 3.8095956724792432, - 3.8091916667588186, - 3.8087868417184287, - 3.808381168404399, - 3.807974617414164, - 3.8075671588893902, - 3.805445345491064, - 3.8058528040158377, - 3.806259355006073, - 3.8066650283201025, - 3.807069853360492, - 3.807473859080917, - 3.8078770741141863, - 3.808279527629279, - 3.8086812449289154, - 3.8090822533961033, - 3.809482580000175, - 3.80988225130404, - 3.8102812934714607, - 3.810679732274296, - 3.811077593099745, - 3.811474900957572, - 3.8118716804873074, - 3.812267955965391, - 3.812663751312275, - 3.813059090099496, - 3.8134539955566593, - 3.813848490578375, - 3.814242597731123, - 3.814636339260074, - 3.815029737095736, - 3.815422812860659, - 3.815815587875937, - 3.816208083167702, - 3.816600317149384, - 3.8169923088766335, - 3.817384083134828, - 3.817775659789635, - 3.81816705844273, - 3.8185582984376536, - 3.818949398865593, - 3.819340378571046, - 3.81973125615739, - 3.820122049992357, - 3.820512778213397, - 3.820903458732901, - 3.8212941092433903, - 3.821684747222565, - 3.8220753899382105, - 3.8224660544530726, - 3.822856757629584, - 3.823247516134488, - 3.8236383464433854, - 3.8240292648451537, - 3.824420287446263, - 3.824811430175027, - 3.8252026755512274, - 3.825594059320009, - 3.825985616891529, - 3.8263773631855496, - 3.8267693129632434, - 3.82716148083072, - 3.827553881242519, - 3.827946528504983, - 3.828339436779512, - 3.828732620085808, - 3.829126092304927, - 3.8295198671823254, - 3.829913958330773, - 3.830308379233203, - 3.830703143245464, - 3.8310982635989848, - 3.831493753403378, - 3.831889625648936, - 3.8322858932090735, - 3.8326825688426456, - 3.83307966519625, - 3.833477194806395, - 3.833875091910535, - 3.8342734561659855, - 3.8346723063473096, - 3.8350716541132233, - 3.835471511021747, - 3.835871888531791, - 3.8362727980047615, - 3.8366742507060176, - 3.8370762578063298, - 3.8374788303831777, - 3.837881979422112, - 3.838285715817917, - 3.8386900160137274, - 3.8390948710066457, - 3.839500361529669, - 3.839906498187859, - 3.840313291502577, - 3.840720751912282, - 3.841128889773354, - 3.8415377153607486, - 3.841947238868663, - 3.842357470411114, - 3.842768420022485, - 3.8431800976579655, - 3.843592513193974, - 3.844005595195163, - 3.844419425689883, - 3.844834040920374, - 3.845249450531843, - 3.845665664093861, - 3.846082691100387, - 3.846500540969763, - 3.8469192230446505, - 3.847338746591952, - 3.847759120802582, - 3.848180354791275, - 3.848602457596309, - 3.8490254155490464, - 3.84944917509802, - 3.8498738490023734, - 3.8502994460870017, - 3.8507259751, - 3.851153444712036, - 3.851581863515678, - 3.852011240024624, - 3.852441582672949, - 3.852872899814225, - 3.8533051997206553, - 3.853738490582079, - 3.854172780504997, - 3.854608009021299, - 3.8550442239520817, - 3.855481480151383, - 3.855919785484355, - 3.8563591477285337, - 3.856799574572449, - 3.8572410736142415, - 3.8576836523601266, - 3.858127318222931, - 3.858572078520437, - 3.859017940473773, - 3.859464911205687, - 3.859912995201971, - 3.860362136400831, - 3.860812418715202, - 3.861263848925193, - 3.8617164337040086, - 3.862170179615864, - 3.862625092375337, - 3.863081179040434, - 3.863538446339964, - 3.863996906247644, - 3.8644565707244265, - 3.864917416294812, - 3.865379448591829, - 3.865842743307555, - 3.8663071956666695, - 3.866772782580536, - 3.86723962549894, - 3.867707977783168, - 3.8681776065366775, - 3.868648515476749, - 3.869120708519938, - 3.869594189771224, - 3.8700689635131327, - 3.870545033415508, - 3.8710223915962705, - 3.8715010457947536, - 3.871981003905203, - 3.8724622736362906, - 3.872944862509251, - 3.87342877785592, - 3.873914026816737, - 3.87440061633862, - 3.874888553172807, - 3.8753778438726223, - 3.8758684947911286, - 3.876360512078777, - 3.8768539016808887, - 3.877348669335124, - 3.877844820568876, - 3.8783423606965384, - 3.878841294816731, - 3.8793416278094432, - 3.879843364333092, - 3.880346508821492, - 3.8808510654807447, - 3.881357038286048, - 3.881864616903345, - 3.88237358646167, - 3.8828839522973295, - 3.883395720277827, - 3.883908895887167, - 3.884423484224116, - 3.8849394900004177, - 3.8854569175389577, - 3.885975770771856, - 3.8864960532385022, - 3.887017768083518, - 3.8875409180546865, - 3.8880655055008257, - 3.888591532369536, - 3.889119000205008, - 3.8896479101456465, - 3.890178262921721, - 3.890710058852924, - 3.891243297845868, - 3.8917779793915304, - 3.892314102562628, - 3.892851666010958, - 3.89339067462932, - 3.8939312698284896, - 3.8944732691820736, - 3.895016672383231, - 3.8955614785722688, - 3.8961076863339663, - 3.896655293694888, - 3.89720429812066, - 3.8977546965131817, - 3.898306485207795, - 3.8988596599704146, - 3.89941421599462, - 3.899970147898704, - 3.9005274497226456, - 3.9010861149250866, - 3.901646136380224, - 3.9022075063746744, - 3.902770216604288, - 3.903334258170927, - 3.903899621579193, - 3.9044662967330654, - 3.9050342729325775, - 3.905603538870342, - 3.906174086786976, - 3.906745939901577, - 3.907319033137228, - 3.907893354105118, - 3.908468889750835, - 3.909045626346872, - 3.909623549485085, - 3.910202644069077, - 3.910782894306448, - 3.911364283701065, - 3.9119467950451896, - 3.9125304104115632, - 3.9131151111454265, - 3.9137008778564546, - 3.9142876946580527, - 3.9148755354866194, - 3.91546437838087, - 3.9160542011451622, - 3.916644980786556, - 3.91723669350718, - 3.9178293146965784, - 3.9184228189239856, - 3.9190171799306177, - 3.919612370621876, - 3.920208363059605, - 3.9208051284542393, - 3.921402637157006, - 3.922000840429849, - 3.922599724739975, - 3.923199264913922, - 3.923799426614658, - 3.924400174717391, - 3.925001473297388, - 3.9256032856178327, - 3.926205574117607, - 3.926808300398972, - 3.9274114252153143, - 3.9280149084587825, - 3.9286187091479543, - 3.929222785415499, - 3.929827091462354, - 3.930431585372592, - 3.931036225330938, - 3.931640965541925, - 3.9322457592876368, - 3.932850558914664, - 3.933455315821081, - 3.9340599804435543, - 3.934664502244576, - 3.935268829699836, - 3.9358729102857497, - 3.936476690467183, - 3.9370801156853927, - 3.937683152942365, - 3.938285750700825, - 3.938887806412415, - 3.939489264270117, - 3.940090067101654, - 3.940690156371256, - 3.941289472182051, - 3.941887953279157, - 3.942485537053281, - 3.9430821595451184, - 3.9436777554504503, - 3.94427225812588, - 3.94486559959551, - 3.9454577209673327, - 3.946048579890823, - 3.946638049440106, - 3.947226059214462, - 3.947812537248886, - 3.948397410038446, - 3.948980602563763, - 3.949562038317817, - 3.9501416393339257, - 3.9507193262150313, - 3.951295018164282, - 3.9518686330169865, - 3.9524400872739225, - 3.9530092961361167, - 3.953576102289316, - 3.954140512439118, - 3.954702434715763, - 3.9552617758381214, - 3.955818441813501, - 3.956372337962256, - 3.95692336894392, - 3.957471438785293, - 3.958016450910157, - 3.958558308171182, - 3.9590969128836218, - 3.959632166861443, - 3.9601639714555223, - 3.960692079435376, - 3.9612165593064823, - 3.961737341608926, - 3.962254316644544, - 3.962767375547651, - 3.9632764102828295, - 3.9637813136459217, - 3.9642819792685233, - 3.964778301626113, - 3.965270176050389, - 3.96575749874579, - 3.96624016681092, - 3.9667180782648095, - 3.967191039059861, - 3.967659028660649, - 3.968122004027004, - 3.968579858643925, - 3.969032488114617, - 3.969479790175568, - 3.969921664719124, - 3.9703580138235415, - 3.970788741791586, - 3.971213755197857, - 3.9716329629455056, - 3.9720462763330375, - 3.9724536091315104, - 3.972854846342899, - 3.973249922049105, - 3.973638799860397, - 3.97402139830705, - 3.974397639585413, - 3.974767449722821, - 3.9751307587522313, - 3.975487500896723, - 3.9758375959194185, - 3.976180970047119, - 3.976517636068457, - 3.9768475394497087, - 3.977170632009694, - 3.9774868721188503, - 3.9777962249114336, - 3.978098662511767, - 3.978394161420604, - 3.978682665454983, - 3.978964240154372, - 3.979238881502485, - 3.9795065947978063, - 3.979767394926145, - 3.980021306653855, - 3.980268364943397, - 3.980508615292603, - 3.9807421016451183, - 3.9809689108262174, - 3.981189122594149, - 3.9814028259292287, - 3.981610122759508, - 3.9818111251737576, - 3.982005966754421, - 3.9821947931645423, - 3.982377762250038, - 3.982555047986625, - 3.9827268373620472, - 3.982893340305222, - 3.983054790842268, - 3.983211432109194, - 3.983363527703473, - 3.983511359667024, - 3.983655220922622, - 3.983795474408388, - 3.9839324681077306, - 3.984066576727479, - 3.984198202661352, - 3.984327744011931, - 3.9844557148186, - 3.9845826057128737, - 3.984708932918816, - 3.9848352486503287, - 3.9849621164375857, - 3.985090196829051, - 3.985220174006722, - 3.985352750372012, - 3.9854886743942983, - 3.985628735472659, - 3.985773786355381, - 3.985924743039057, - 3.9860825558080264, - 3.986248229488898, - 3.986422848649251, - 3.986607548841336, - 3.986803532672345, - 3.987012091724225, - 3.9872345903499737, - 3.987472464206959, - 3.9877272718031023, - 3.9880006734496263, - 3.9882943949166574, - 3.9886102486342354, - 3.9889503616095823, - 3.989316809494481, - 3.9897116816682567, - 3.990137657031787, - 3.99059730125424, - 3.991093220949136, - 3.99162853383608, - 3.9922065735196273, - 3.9928307691022296, - 3.9935049308110626, - 3.994233350639732, - 3.9950206268032926, - 3.9958718596025817, - 3.996792677512703, - 3.9977893931797728, - 3.998868901556894, - 4.0000391896108, - 4.001308700452178, - 4.002687727000964, - 4.004187050034073, - 4.0058202354549515, - 4.007602341947379, - 4.00955186672597, - 4.0116905941473755, - 4.014044801896873, - 4.016646480408178, - 4.019533792709838, - 4.022754745635927, - 4.026369705355814, - 4.030455316306863, - 4.035111021699204, - 4.040464020578233, - 4.046674455243125, - 4.053930948760264, - 4.062336507103181 - ] - }, - { - "mode": "markers", - "name": "Target", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.063766615038272, - 4.055347026526715, - 4.046888251680567, - 4.041651559638272, - 4.036084848086105, - 4.030312191583603, - 4.028432380301851, - 4.024134326208111, - 4.020718477366584, - 4.01826318716266, - 4.015124237587261, - 4.01095181116977, - 4.010959267576446, - 4.007910075821095, - 4.008017026534262, - 4.005238601258139, - 4.001827919179753, - 4.00453773200696, - 4.002402266155831, - 4.000184214612106, - 3.9995515186981914, - 3.996995332053068, - 3.995177043200375, - 3.997171418952401, - 3.994764901774827, - 3.9950074090992698, - 3.99385383278708, - 3.993730405849108, - 3.99168165972527, - 3.993483841666832, - 3.9914989522418023, - 3.9905313356256817, - 3.9933724966583672, - 3.988378129662939, - 3.989764823181861, - 3.9885611037267177, - 3.988559194360511, - 3.988269148749386, - 3.988238321593786, - 3.990224262803108, - 3.989524750957501, - 3.986268466331111, - 3.987600735285836, - 3.98893442277192, - 3.988241819106558, - 3.9864108364449686, - 3.9883946844594353, - 3.986132469131501, - 3.985430402094664, - 3.98640980081397, - 3.9853286820676863, - 3.985793611490026, - 3.9860861268514176, - 3.9851231592676433, - 3.987890614344661, - 3.985561221696603, - 3.9874416349593114, - 3.9854026779276497, - 3.9868641600563826, - 3.985872856461782, - 3.986478874582215, - 3.985894405174802, - 3.9855997912052303, - 3.984419577944207, - 3.9832170021118953, - 3.984915699700496, - 3.9858117463856986, - 3.983794186650886, - 3.984627429314067, - 3.9832290056569257, - 3.984763116181034, - 3.9844466621666785, - 3.9844363911315144, - 3.98176397217256, - 3.980290414534456, - 3.9818310102476246, - 3.982894894985081, - 3.982971837931911, - 3.981919006944154, - 3.983961961880057, - 3.981082782525831, - 3.98154101899838, - 3.982399147338916, - 3.979550316284578, - 3.980777401420939, - 3.9817483081866656, - 3.9806952719418542, - 3.9782976845379183, - 3.9786449399916815, - 3.979550712541695, - 3.980141828604427, - 3.978313381522482, - 3.977079196022803, - 3.977490151196053, - 3.978800394916369, - 3.977264631702401, - 3.978195392499222, - 3.9781289292408615, - 3.977388523661405, - 3.976341512120868, - 3.975028077105661, - 3.9770162552106942, - 3.974593009501583, - 3.974129216473858, - 3.976052740642858, - 3.9730649968801575, - 3.974648186340009, - 3.973259788713131, - 3.972909525428935, - 3.971667094500572, - 3.970678132192676, - 3.9718824064983105, - 3.971584579536206, - 3.971612072049483, - 3.970594027589356, - 3.970953962176267, - 3.968850478959772, - 3.96793477243484, - 3.969220093404579, - 3.9667098269383736, - 3.969964893281222, - 3.964931335451744, - 3.9655746184755434, - 3.963502507582772, - 3.9648711714385425, - 3.9631916296805074, - 3.96268547206851, - 3.9640645422485625, - 3.965361461979576, - 3.962080133913093, - 3.9625417670027314, - 3.962031598221051, - 3.9611772333545057, - 3.960845057803919, - 3.959800956700075, - 3.9609667573312857, - 3.960318489761386, - 3.957488219496922, - 3.956809995715678, - 3.9579831825659983, - 3.9578866449604577, - 3.956754560888196, - 3.956428963115653, - 3.9569962888941967, - 3.9546981467427, - 3.9540266171111336, - 3.9536867732929704, - 3.9538548068888857, - 3.9514663725033095, - 3.951497027004693, - 3.9502776715691903, - 3.949640979832731, - 3.948649119059432, - 3.949184283306578, - 3.95030457744666, - 3.9479656560194702, - 3.950279385899929, - 3.947687694781781, - 3.94744363187631, - 3.9447431920234624, - 3.9443165511317817, - 3.944807275915567, - 3.943244865694062, - 3.9427999396921094, - 3.944261181767336, - 3.940899458824193, - 3.940983887084527, - 3.941371232980305, - 3.94017715268593, - 3.9387703292792793, - 3.940708049106405, - 3.9382866771125222, - 3.938494927979451, - 3.938865151149417, - 3.9352511090355047, - 3.9356249653112614, - 3.9347448334114423, - 3.934248660937708, - 3.934999890721485, - 3.9341807069881622, - 3.933638661116357, - 3.93188008951488, - 3.9324857523456647, - 3.9330179676787407, - 3.932030480299193, - 3.9306754471525664, - 3.928884697838489, - 3.9287091849760745, - 3.926525830942548, - 3.928731358321464, - 3.92564062488089, - 3.927006517786558, - 3.926274975721286, - 3.925275021250464, - 3.924516673259597, - 3.925327548073805, - 3.923968241216764, - 3.9235818519272994, - 3.9220898139958313, - 3.921565038984273, - 3.9225987091943946, - 3.9216797800033665, - 3.921520181828624, - 3.918237610699066, - 3.918807161500553, - 3.919951950629526, - 3.9206064471454583, - 3.918239509668187, - 3.9156042789752896, - 3.918080896365676, - 3.9168506658565905, - 3.9143153262612738, - 3.913605507789458, - 3.913403082736573, - 3.9135153200583273, - 3.9108765522464193, - 3.9119728557148776, - 3.911912132598821, - 3.908961363578715, - 3.909016634167384, - 3.9092012824466407, - 3.908861592041687, - 3.908681595899965, - 3.9065439021343664, - 3.907340339983547, - 3.907719578079045, - 3.905908735381989, - 3.904713684667116, - 3.904939137041487, - 3.902776466434739, - 3.9034005272953696, - 3.903871026152845, - 3.90351997014436, - 3.9025636686923257, - 3.902287367974386, - 3.9002271463467824, - 3.899814768596363, - 3.9006012748057017, - 3.8979532460299713, - 3.899394797438973, - 3.89959135971848, - 3.8991091898973607, - 3.897979924758292, - 3.897051699498517, - 3.896835806675986, - 3.8954731663549063, - 3.894329346283228, - 3.893942396872436, - 3.8953462253147086, - 3.8923963185873514, - 3.892207211880495, - 3.8935137762073726, - 3.891196033638067, - 3.8924626664803985, - 3.888981558423561, - 3.892122200242589, - 3.889227660160607, - 3.888693660950026, - 3.8890744315171824, - 3.8902600706970976, - 3.8871381874743665, - 3.885953459696169, - 3.8853101171862305, - 3.885977403912898, - 3.885372358921568, - 3.88303830115534, - 3.883864022611429, - 3.884777194605211, - 3.884886131870058, - 3.8828507454739434, - 3.881152329612826, - 3.882281562617418, - 3.8824343436144257, - 3.880950911507418, - 3.8802400975631968, - 3.8804261438044265, - 3.8797094503244263, - 3.879701806038266, - 3.878017216734086, - 3.878113640891222, - 3.879236676420687, - 3.876721943585403, - 3.87634619260526, - 3.8746570682579047, - 3.8747663189332897, - 3.875818259987654, - 3.872366684272034, - 3.874743889654071, - 3.872819059744377, - 3.8713068345984434, - 3.873926199130008, - 3.871892915333893, - 3.8711244175081223, - 3.8713598981921673, - 3.8694012099360098, - 3.870680772951998, - 3.870410350122405, - 3.8698006845876, - 3.869197947278966, - 3.8671289726173734, - 3.867849205049668, - 3.867609960369193, - 3.8657174755635024, - 3.866413373541229, - 3.8662958726462495, - 3.865768218114033, - 3.864615119647024, - 3.865663083203446, - 3.8630853175548663, - 3.863250804749552, - 3.863012752253663, - 3.8630412717984552, - 3.8636740939563983, - 3.863257385837275, - 3.860796347473389, - 3.8618305287438366, - 3.859217852679095, - 3.8600162914490985, - 3.860799802162212, - 3.857819599674143, - 3.8599991753584626, - 3.856215891167527, - 3.8588699057210416, - 3.856242887048386, - 3.8564687549288856, - 3.856980792077119, - 3.8568987659743734, - 3.8575827615426177, - 3.854339866718487, - 3.855703799718036, - 3.856240195390902, - 3.853694365448336, - 3.8540068319027423, - 3.853497981977703, - 3.850435499394477, - 3.85017206561899, - 3.851213736052692, - 3.851087551682031, - 3.8518160835126376, - 3.851239845599939, - 3.848434399061387, - 3.848505136307469, - 3.847927426660232, - 3.848601324744481, - 3.8484172720067407, - 3.8483849264517263, - 3.849502686169691, - 3.845643246910342, - 3.845959113621842, - 3.8462451849937977, - 3.845550879901952, - 3.8455902243703526, - 3.843524396198685, - 3.844102840581636, - 3.8430193356027247, - 3.845356232630326, - 3.8441326660015127, - 3.840728247463448, - 3.842781228973563, - 3.841998361546287, - 3.840743800480641, - 3.841537200461888, - 3.8405529178380062, - 3.839848254375025, - 3.84073618500955, - 3.840706160120692, - 3.837318168080091, - 3.8380128126498576, - 3.840340246523285, - 3.838431692851306, - 3.839566263853612, - 3.838371158434279, - 3.835997568264091, - 3.8351879025035864, - 3.8348106580301953, - 3.836927242423968, - 3.8365504896229097, - 3.8346285665075976, - 3.837377704861987, - 3.834017967468721, - 3.8335400658457033, - 3.833025972355043, - 3.8327972681239433, - 3.831916319057857, - 3.832417328795066, - 3.8340781487453865, - 3.832821679829236, - 3.829784037023579, - 3.829763268675516, - 3.830140448119536, - 3.8290133869372607, - 3.82939005991013, - 3.830395252181539, - 3.828324937479579, - 3.8279985117214457, - 3.827093812289111, - 3.8276331600893694, - 3.825418436769915, - 3.825705259292321, - 3.827943817927957, - 3.825255418886575, - 3.823687602901275, - 3.8254157101107182, - 3.824996780345293, - 3.823449511851582, - 3.827696826247711, - 3.824926860984399, - 3.821228055618123, - 3.822878980581137, - 3.823078649056603, - 3.821781566445678, - 3.821619381508941, - 3.820300701511277, - 3.8199852660226674, - 3.818905266929785, - 3.818607949913291, - 3.818715092496598, - 3.817952541485566, - 3.818936144132713, - 3.818628032061025, - 3.8178859786511463, - 3.816627779909325, - 3.818286632226881, - 3.818000982464898, - 3.816960053249001, - 3.8150153970422416, - 3.815209564965538, - 3.815986428908258, - 3.8155537052621553, - 3.8137172652358986, - 3.814446436419453, - 3.8145333341724577, - 3.815853424246921, - 3.814031057938185, - 3.8121417883619495, - 3.8133995391295943, - 3.810958980109545, - 3.810456878830927, - 3.8098424004613185, - 3.810285380726005, - 3.809759253383596, - 3.809859493147646, - 3.807527390752216, - 3.809916535294804, - 3.8090681738034338, - 3.807577308643072, - 3.809621569556448, - 3.808249752024658, - 3.8080809062041503, - 3.8058012979439737 - ] - }, - { - "line": { - "width": 4 - }, - "mode": "lines", - "name": "Model", - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 62, - 64, - 66, - 68, - 70, - 72, - 74, - 76, - 78, - 80, - 82, - 84, - 86, - 88, - 90, - 92, - 94, - 96, - 98, - 100, - 102, - 104, - 106, - 108, - 110, - 112, - 114, - 116, - 118, - 120, - 122, - 124, - 126, - 128, - 130, - 132, - 134, - 136, - 138, - 140, - 142, - 144, - 146, - 148, - 150, - 152, - 154, - 156, - 158, - 160, - 162, - 164, - 166, - 168, - 170, - 172, - 174, - 176, - 178, - 180, - 182, - 184, - 186, - 188, - 190, - 192, - 194, - 196, - 198, - 200, - 202, - 204, - 206, - 208, - 210, - 212, - 214, - 216, - 218, - 220, - 222, - 224, - 226, - 228, - 230, - 232, - 234, - 236, - 238, - 240, - 242, - 244, - 246, - 248, - 250, - 252, - 254, - 256, - 258, - 260, - 262, - 264, - 266, - 268, - 270, - 272, - 274, - 276, - 278, - 280, - 282, - 284, - 286, - 288, - 290, - 292, - 294, - 296, - 298, - 300, - 302, - 304, - 306, - 308, - 310, - 312, - 314, - 316, - 318, - 320, - 322, - 324, - 326, - 328, - 330, - 332, - 334, - 336, - 338, - 340, - 342, - 344, - 346, - 348, - 350, - 352, - 354, - 356, - 358, - 360, - 362, - 364, - 366, - 368, - 370, - 372, - 374, - 376, - 378, - 380, - 382, - 384, - 386, - 388, - 390, - 392, - 394, - 396, - 398, - 400, - 402, - 404, - 406, - 408, - 410, - 412, - 414, - 416, - 418, - 420, - 422, - 424, - 426, - 428, - 430, - 432, - 434, - 436, - 438, - 440, - 442, - 444, - 446, - 448, - 450, - 452, - 454, - 456, - 458, - 460, - 462, - 464, - 466, - 468, - 470, - 472, - 474, - 476, - 478, - 480, - 482, - 484, - 486, - 488, - 490, - 492, - 494, - 496, - 498, - 500, - 502, - 504, - 506, - 508, - 510, - 512, - 514, - 516, - 518, - 520, - 522, - 524, - 526, - 528, - 530, - 532, - 534, - 536, - 538, - 540, - 542, - 544, - 546, - 548, - 550, - 552, - 554, - 556, - 558, - 560, - 562, - 564, - 566, - 568, - 570, - 572, - 574, - 576, - 578, - 580, - 582, - 584, - 586, - 588, - 590, - 592, - 594, - 596, - 598, - 600, - 602, - 604, - 606, - 608, - 610, - 612, - 614, - 616, - 618, - 620, - 622, - 624, - 626, - 628, - 630, - 632, - 634, - 636, - 638, - 640, - 642, - 644, - 646, - 648, - 650, - 652, - 654, - 656, - 658, - 660, - 662, - 664, - 666, - 668, - 670, - 672, - 674, - 676, - 678, - 680, - 682, - 684, - 686, - 688, - 690, - 692, - 694, - 696, - 698, - 700, - 702, - 704, - 706, - 708, - 710, - 712, - 714, - 716, - 718, - 720, - 722, - 724, - 726, - 728, - 730, - 732, - 734, - 736, - 738, - 740, - 742, - 744, - 746, - 748, - 750, - 752, - 754, - 756, - 758, - 760, - 762, - 764, - 766, - 768, - 770, - 772, - 774, - 776, - 778, - 780, - 782, - 784, - 786, - 788, - 790, - 792, - 794, - 796, - 798, - 800, - 802, - 804, - 806, - 808, - 810, - 812, - 814, - 816, - 818, - 820, - 822, - 824, - 826, - 828, - 830, - 832, - 834, - 836, - 838, - 840, - 842, - 844, - 846, - 848, - 850, - 852, - 854, - 856, - 858, - 860, - 862, - 864, - 866, - 868, - 870, - 872, - 874, - 876, - 878, - 880, - 882, - 884, - 886, - 888, - 890, - 892, - 894, - 896, - 898 - ], - "y": [ - 4.0633974138023445, - 4.054991855459428, - 4.047735361942289, - 4.041524927277397, - 4.0361719283983675, - 4.031516223006027, - 4.027430612054977, - 4.023815652335091, - 4.020594699409002, - 4.017707387107341, - 4.015105708596036, - 4.012751500846539, - 4.010612773425134, - 4.008663248646543, - 4.006881142154115, - 4.005247956733236, - 4.003748633700128, - 4.0023696071513415, - 4.001100096309964, - 3.999929808256057, - 3.998850299878936, - 3.997853584211866, - 3.996932766301744, - 3.996081533502456, - 3.995294257338895, - 3.9945658375102258, - 3.9938916758013927, - 3.9932674802187904, - 3.992689440535243, - 3.9921541276482992, - 3.991658207953403, - 3.99119856373095, - 3.99077258836742, - 3.990377716193644, - 3.990011268308746, - 3.9896711553333986, - 3.989355301615821, - 3.98906158014879, - 3.9887881785022654, - 3.9885333709061226, - 3.988295497049136, - 3.988072998423388, - 3.987864439371508, - 3.9876684555404993, - 3.9874837553484146, - 3.987309136188061, - 3.9871434625071895, - 3.98698564973822, - 3.986834693054544, - 3.986689642171822, - 3.9865495810934615, - 3.986413657071175, - 3.986281080705885, - 3.986151103528214, - 3.986023023136749, - 3.985896155349492, - 3.985769839617979, - 3.985643512412037, - 3.985516621517763, - 3.9853886507110943, - 3.985259109360515, - 3.985127483426642, - 3.9849933748068938, - 3.984856381107551, - 3.9847161276217857, - 3.984572266366187, - 3.984424434402636, - 3.984272338808357, - 3.9841156975414314, - 3.983954247004385, - 3.9837877440612104, - 3.983615954685788, - 3.983438668949201, - 3.983255699863706, - 3.983066873453584, - 3.9828720318729207, - 3.982671029458671, - 3.982463732628392, - 3.982250029293312, - 3.982029817525381, - 3.981803008344282, - 3.981569521991766, - 3.98132927164256, - 3.981082213353018, - 3.980828301625308, - 3.9805675014969695, - 3.980299788201647, - 3.980025146853535, - 3.9797435721541463, - 3.979455068119767, - 3.97915956921093, - 3.9788571316105967, - 3.9785477788180135, - 3.978231538708857, - 3.977908446148872, - 3.97757854276762, - 3.977241876746282, - 3.9768985026185817, - 3.9765484075958866, - 3.9761916654513945, - 3.975828356421984, - 3.975458546284575, - 3.975082305006213, - 3.97469970655956, - 3.974310828748268, - 3.973915753042062, - 3.9735145158306735, - 3.9731071830322007, - 3.9726938696446688, - 3.97227466189702, - 3.9718496484907497, - 3.9714189205227046, - 3.970982571418287, - 3.970540696874731, - 3.97009339481378, - 3.969640765343088, - 3.9691829107261674, - 3.968719935359812, - 3.968251945759024, - 3.9677789849639726, - 3.967301073510083, - 3.966818405444953, - 3.966331082749552, - 3.965839208325276, - 3.9653428859676865, - 3.964842220345085, - 3.9643373169819927, - 3.9638282822468143, - 3.963315223343707, - 3.962798248308089, - 3.9622774660056455, - 3.961752986134539, - 3.9612248781546855, - 3.960693073560606, - 3.960157819582785, - 3.959619214870345, - 3.95907735760932, - 3.958532345484456, - 3.957984275643083, - 3.957433244661419, - 3.956879348512664, - 3.9563226825372846, - 3.9557633414149262, - 3.955201419138281, - 3.9546370089884793, - 3.95407020283528, - 3.9535009939730856, - 3.9529295397161497, - 3.952355924863445, - 3.9517802329141944, - 3.951202546033089, - 3.95062294501698, - 3.950041509262926, - 3.949458316737609, - 3.9488734439480497, - 3.948286965913625, - 3.947698956139269, - 3.9471094865899863, - 3.946518627666496, - 3.945926506294674, - 3.945333164825043, - 3.9447386621496134, - 3.9441430662442816, - 3.943546443752444, - 3.94294885997832, - 3.942350378881214, - 3.941751063070419, - 3.9411509738008177, - 3.94055017096928, - 3.939948713111578, - 3.939346657399988, - 3.938744059641528, - 3.938141022384556, - 3.937537597166346, - 3.936933816984912, - 3.9363297363989993, - 3.935725408943739, - 3.9351208871427175, - 3.934516222520245, - 3.933911465613827, - 3.9333066659868, - 3.932701872241088, - 3.932097132030101, - 3.9314924920717553, - 3.930887998161517, - 3.930283692114662, - 3.9296796158471174, - 3.9290758151579457, - 3.928472331914478, - 3.9278692070981354, - 3.9272664808167703, - 3.926664192316996, - 3.926062379996551, - 3.9254610814165543, - 3.924860333313821, - 3.924260171613085, - 3.9236606314391382, - 3.923061747129012, - 3.922463543856169, - 3.9218660351534025, - 3.921269269758768, - 3.920673277321039, - 3.920078086629781, - 3.9194837256231487, - 3.9188902213957415, - 3.918297600206343, - 3.9177058874857194, - 3.917115107844326, - 3.916525285080033, - 3.9159364421857825, - 3.915348601357216, - 3.9147617845556177, - 3.9141760178445897, - 3.9135913171107264, - 3.9130077017443528, - 3.912425190400228, - 3.911843801005611, - 3.91126355076824, - 3.910684456184248, - 3.9101065330460343, - 3.909529796449998, - 3.9089542608042818, - 3.908379939836391, - 3.907806846600739, - 3.907234993486139, - 3.906664445569505, - 3.9060951796317407, - 3.9055272034322286, - 3.904960528278356, - 3.90439516487009, - 3.903831123303451, - 3.9032684130738375, - 3.902707043079387, - 3.9021470216242498, - 3.901588356421809, - 3.901031054597867, - 3.9004751226937833, - 3.8999205666695778, - 3.8993673919069582, - 3.898815603212345, - 3.898265204819823, - 3.897716200394051, - 3.8971685930331295, - 3.896622385271432, - 3.896077579082394, - 3.8955341758812367, - 3.8949921765276527, - 3.8944515813284832, - 3.893912572710121, - 3.893375009261791, - 3.8928388860906935, - 3.892304204545031, - 3.8917709655520873, - 3.891239169620884, - 3.8907088168448096, - 3.890179906904171, - 3.889652439068699, - 3.889126412199989, - 3.8886018247538496, - 3.888078674782681, - 3.887556959937666, - 3.887036677471019, - 3.886517824238121, - 3.886000396699581, - 3.885484390923279, - 3.88496980258633, - 3.88445662697699, - 3.8839448589964927, - 3.883434493160834, - 3.882925523602508, - 3.882417944985211, - 3.881911972179908, - 3.881407415520655, - 3.880904271032255, - 3.8804025345086064, - 3.879902201515894, - 3.8794032673957015, - 3.878905727268039, - 3.878409576034287, - 3.877914808380052, - 3.87742141877794, - 3.876929401490292, - 3.8764387505717854, - 3.87594945987197, - 3.8754615230377834, - 3.874974933515899, - 3.874489684555083, - 3.874005769208414, - 3.8735231803354537, - 3.8730419106043663, - 3.8725619524939168, - 3.8720832982954336, - 3.871605940114671, - 3.871129870212296, - 3.8706550964703874, - 3.870181615219101, - 3.869709422175912, - 3.8692385132358407, - 3.868768884482331, - 3.868300532198103, - 3.867833689279699, - 3.8673681023658326, - 3.866903650006718, - 3.866440355290992, - 3.865978322993975, - 3.8655174774235896, - 3.8650578129468074, - 3.8645993530391274, - 3.864142085739597, - 3.8636859990745, - 3.863231086315027, - 3.862777340403172, - 3.862324755624355, - 3.861873325414366, - 3.861423043099994, - 3.860973901901134, - 3.86052581790485, - 3.860078847172936, - 3.8596329852196, - 3.859188224922094, - 3.8587445590592897, - 3.8583019803134047, - 3.857860481271612, - 3.857420054427697, - 3.8569806921835186, - 3.856542386850546, - 3.856105130651245, - 3.855668915720462, - 3.85523368720416, - 3.854799397281242, - 3.8543661064198185, - 3.853933806513388, - 3.853502489372112, - 3.853072146723787, - 3.852642770214841, - 3.852214351411199, - 3.851786881799163, - 3.851360352786165, - 3.850934755701537, - 3.850510081797183, - 3.8500863222482096, - 3.849663364295472, - 3.8492412614904383, - 3.8488200275017457, - 3.848399653291115, - 3.8479801297438136, - 3.847561447668926, - 3.8471435977995503, - 3.846726570793024, - 3.846310357231006, - 3.845894947619537, - 3.8454803323890463, - 3.845066501894326, - 3.8446534198931377, - 3.8442410043571287, - 3.843829326721648, - 3.843418377110277, - 3.843008145567826, - 3.842598622059912, - 3.842189796472517, - 3.841781658611445, - 3.84137419820174, - 3.840967404887022, - 3.840561268228832, - 3.840155777705808, - 3.8397509227128905, - 3.83934662251708, - 3.8389428861212753, - 3.838539737082341, - 3.838137164505493, - 3.837735157405181, - 3.8373337047039247, - 3.836932795230954, - 3.83653241772091, - 3.8361325608123864, - 3.8357332130464727, - 3.8353343628651486, - 3.8349359986096982, - 3.834538101505558, - 3.834140571895414, - 3.8337434755418087, - 3.8333467999082367, - 3.8329505323480992, - 3.832554660102541, - 3.832159170298148, - 3.8317640499446273, - 3.8313692859323663, - 3.830974865029936, - 3.830580773881489, - 3.83018699900409, - 3.829793526784971, - 3.8294003434786754, - 3.8290074352041463, - 3.828614787941682, - 3.828222387529883, - 3.8278302196624066, - 3.8274382698847127, - 3.827046523590692, - 3.826654966019172, - 3.8262635822503905, - 3.8258723368741903, - 3.8254811941454263, - 3.825090171544317, - 3.824699253142549, - 3.824308422833651, - 3.823917664328747, - 3.823526961152236, - 3.8231362966373736, - 3.822745653921728, - 3.8223550159425534, - 3.821964365432064, - 3.82157368491256, - 3.82118295669152, - 3.820792162856553, - 3.820401285270209, - 3.820010305564756, - 3.8196192051368167, - 3.819227965141893, - 3.818836566488798, - 3.818444989833991, - 3.8180532155757967, - 3.817661223848547, - 3.8172689898668657, - 3.8168764945751, - 3.8164837195598222, - 3.816090643794899, - 3.815697245959237, - 3.815303504430286, - 3.814909397277538, - 3.8145149022558225, - 3.814119996798659, - 3.813724658011438, - 3.8133288626645543, - 3.8129325871864705, - 3.812535807656735, - 3.812138499798908, - 3.8117406389734594, - 3.811342200170624, - 3.810943158003203, - 3.810543486699338, - 3.8101431600952664, - 3.8097421516280785, - 3.8093404343284423, - 3.8089379808133494, - 3.80853476578008, - 3.808130760059655, - 3.8077259350192656, - 3.807320261705236, - 3.806913710715001, - 3.806506252190227 - ] - } - ], - "layout": { - "autosize": false, - "height": 576, - "legend": { - "font": { - "size": 12 - }, - "x": 1, - "xanchor": "right", - "y": 1, - "yanchor": "top" - }, - "margin": { - "b": 10, - "l": 10, - "pad": 4, - "r": 10, - "t": 75 - }, - "showlegend": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "contour" - } - ], - "contourcarpet": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "contourcarpet" - } - ], - "heatmap": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmap" - } - ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "histogram2d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2d" - } - ], - "histogram2dcontour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "histogram2dcontour" - } - ], - "mesh3d": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "mesh3d" - } - ], - "parcoords": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "parcoords" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Optimised Comparison", - "x": 0.5 - }, - "width": 1024, - "xaxis": { - "autorange": true, - "range": [ - -54.313231642426395, - 952.3132316424264 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Time [s]" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - 3.7887473598078, - 4.080820553174445 - ], - "tickfont": { - "size": 12 - }, - "title": { - "font": { - "size": 12 - }, - "text": "Voltage [V]" - }, - "type": "linear" - } - } - }, - "image/png": "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", "text/html": [ - "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" + "image/svg+xml": [ + "02004006008003.83.853.93.9544.05ReferenceModelOptimised ComparisonTime / sVoltage / V" ] }, "metadata": {}, @@ -437,61 +384,8 @@ "outputs": [ { "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" + "image/svg+xml": [ + "24681012140.00050.0010.00150.0020.0025ConvergenceIterationCost" ] }, "metadata": {}, @@ -499,61 +393,8 @@ }, { "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" + "image/svg+xml": [ + "05100.660.680.70.720.740.760.7805100.660.6650.670.6750.68Negative electrode active material volume fractionPositive electrode active material volume fractionParameter ConvergenceFunction CallFunction CallNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -581,90 +422,8 @@ "outputs": [ { "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " \n", - " " + "image/svg+xml": [ + "0.50.550.60.650.70.750.80.40.450.50.550.60.650.70246810Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -672,32 +431,8 @@ }, { "data": { - "text/html": [ - "
" + "image/svg+xml": [ + "0.60.650.70.750.80.850.90.50.550.60.650.70.750.80.40.81.21.622.4Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, From d7a71a75b24dd3c245a13b2543be720a241d5ccc Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Feb 2024 09:29:43 +0000 Subject: [PATCH 04/20] fix missed deletion during merge --- setup.py | 51 --------------------------------------------------- 1 file changed, 51 deletions(-) delete mode 100644 setup.py diff --git a/setup.py b/setup.py deleted file mode 100644 index 8d53b6e92..000000000 --- a/setup.py +++ /dev/null @@ -1,51 +0,0 @@ -from distutils.core import setup -import os -from setuptools import find_packages - -# User-friendly description from README.md -current_directory = os.path.dirname(os.path.abspath(__file__)) -try: - with open(os.path.join(current_directory, "README.md"), encoding="utf-8") as f: - long_description = f.read() -except Exception: - long_description = "" - -# Defines __version__ -root = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(root, "pybop", "version.py")) as f: - exec(f.read()) - -setup( - name="pybop", - packages=find_packages("."), - version=__version__, # noqa F821 - license="BSD-3-Clause", - description="Python Battery Optimisation and Parameterisation", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/pybop-team/PyBOP", - install_requires=[ - "pybamm>=23.5", - "numpy>=1.16", - "scipy>=1.3", - "pandas>=1.0", - "pints>=0.5", - ], - extras_require={ - "plot": ["plotly>=5.0", "kaleido>=0.2"], - "all": ["pybop[plot]"], - "docs": [ - "sphinx>=6", - "pydata-sphinx-theme", - "sphinx-autobuild", - "sphinx-autoapi", - "sphinx_copybutton", - "sphinx_favicon", - "sphinx_design", - "myst-parser", - ], - }, - # https://pypi.org/classifiers/ - classifiers=[], - python_requires=">=3.8,<=3.12", -) From 4cf9108c85a11caf38f7efc94f5adee57d292e34 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 1 Mar 2024 12:30:47 +0000 Subject: [PATCH 05/20] Revamp model, problem, and cost object from numpy arrays to dictionary. Update tests, add base model classes to init, cleaner multi-signal interaction --- examples/scripts/spm_CMAES.py | 4 +- examples/scripts/spm_adam.py | 17 ++- examples/scripts/spm_scipymin.py | 4 +- examples/standalone/problem.py | 16 +-- pybop/_problem.py | 39 ++++-- pybop/costs/design_costs.py | 14 ++- pybop/costs/fitting_costs.py | 127 ++++++++++++++------ pybop/models/base_model.py | 31 +++-- pybop/models/empirical/__init__.py | 2 +- pybop/models/lithium_ion/__init__.py | 2 +- pybop/models/lithium_ion/echem_base.py | 11 +- pybop/plotting/plot_problem.py | 24 ++-- tests/integration/test_parameterisations.py | 8 +- tests/unit/test_cost.py | 6 +- tests/unit/test_models.py | 4 +- tests/unit/test_problem.py | 4 +- tests/unit/test_standalone.py | 11 +- 17 files changed, 212 insertions(+), 112 deletions(-) diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py index 170d560bc..816f6798a 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/spm_CMAES.py @@ -33,11 +33,13 @@ "Time [s]": t_eval, "Current function [A]": values["Current [A]"].data, "Voltage [V]": corrupt_values, + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, } ) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.SumSquaredError(problem) optim = pybop.Optimisation(cost, optimiser=pybop.CMAES) optim.set_max_iterations(100) diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index b8f3d2f50..56d315f47 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -20,7 +20,7 @@ ] # Generate data -sigma = 0.001 +sigma = 0.01 t_eval = np.arange(0, 900, 2) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -31,14 +31,23 @@ "Time [s]": t_eval, "Current function [A]": values["Current [A]"].data, "Voltage [V]": corrupt_values, + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, } ) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) -optim = pybop.Optimisation(cost, optimiser=pybop.Adam, verbose=True) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.Optimisation( + cost, + optimiser=pybop.Adam, + verbose=True, + allow_infeasible_solutions=True, + sigma0=sigma, +) optim.set_max_iterations(100) +optim.set_max_unchanged_iterations(20) # Run optimisation x, final_cost = optim.run() diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index 759f8c2e6..db1d783d0 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -21,12 +21,12 @@ parameters = [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.05), + prior=pybop.Gaussian(0.6, 0.02), bounds=[0.5, 0.8], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.05), + prior=pybop.Gaussian(0.48, 0.02), bounds=[0.4, 0.7], ), ] diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index 5a29138e8..bc9cd31d5 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -14,10 +14,13 @@ def __init__( model=None, check_model=True, signal=None, + default_variables=None, init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self._dataset = dataset.data # Check that the dataset contains time and current @@ -37,8 +40,7 @@ def __init__( raise ValueError( f"Time data and {signal} data must be the same length." ) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} def evaluate(self, x): """ @@ -55,7 +57,7 @@ def evaluate(self, x): The model output y(t) simulated with inputs x. """ - return x[0] * self._time_data + x[1] + return {signal: x[0] * self._time_data + x[1] for signal in self.signal} def evaluateS1(self, x): """ @@ -73,8 +75,8 @@ def evaluateS1(self, x): with given inputs x. """ - y = x[0] * self._time_data + x[1] + y = {signal: x[0] * self._time_data + x[1] for signal in self.signal} - dy = np.dstack([self._time_data, np.zeros(self._time_data.shape)]) + dy = [self._time_data, np.zeros(self._time_data.shape)] - return (np.asarray(y), np.asarray(dy)) + return (y, np.asarray(dy)) diff --git a/pybop/_problem.py b/pybop/_problem.py index 2ecc59515..e2aaee3d0 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -1,4 +1,5 @@ import numpy as np +import pybop class BaseProblem: @@ -27,6 +28,7 @@ def __init__( model=None, check_model=True, signal=["Voltage [V]"], + default_variables=[], init_soc=None, x0=None, ): @@ -45,6 +47,11 @@ def __init__( self._time_data = None self._target = None + if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): + self.default_variables = default_variables + else: + self.default_variables = [] + # Set bounds self.bounds = dict( lower=[param.bounds[0] for param in self.parameters], @@ -148,10 +155,13 @@ def __init__( dataset, check_model=True, signal=["Voltage [V]"], + default_variables=["Time [s]", "Discharge capacity [A.h]"], init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self._dataset = dataset.data self.x = self.x0 @@ -161,12 +171,13 @@ def __init__( # Unpack time and target data self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} # Add useful parameters to model if model is not None: self._model.signal = self.signal + self._model.default_variables = self.default_variables + self._model.n_parameters = self.n_parameters self._model.n_outputs = self.n_outputs self._model.n_time_data = self.n_time_data @@ -193,14 +204,14 @@ def evaluate(self, x): y : np.ndarray The model output y(t) simulated with inputs x. """ - if (x != self.x).any() and self._model.matched_parameters: + if np.any(x != self.x) and self._model.matched_parameters: for i, param in enumerate(self.parameters): param.update(value=x[i]) self._model.rebuild(parameters=self.parameters) self.x = x - y = np.asarray(self._model.simulate(inputs=x, t_eval=self._time_data)) + y = self._model.simulate(inputs=x, t_eval=self._time_data) return y @@ -229,7 +240,7 @@ def evaluateS1(self, x): t_eval=self._time_data, ) - return (np.asarray(y), np.asarray(dy)) + return (y, np.asarray(dy)) class DesignProblem(BaseProblem): @@ -255,10 +266,13 @@ def __init__( experiment, check_model=True, signal=["Voltage [V]"], + default_variables=["Time [s]", "Current [A]", "Discharge capacity [A.h]"], init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self.experiment = experiment # Build the model if required @@ -278,8 +292,9 @@ def __init__( # Add an example dataset for plotting comparison sol = self.evaluate(self.x0) - self._time_data = sol[:, -1] - self._target = sol[:, 0:-1] + self._time_data = sol["Time [s]"] + self._capacity_data = sol["Discharge capacity [A.h]"] + self._target = {key: sol[key] for key in self.signal} self._dataset = None def evaluate(self, x): @@ -307,6 +322,8 @@ def evaluate(self, x): return sol else: - predictions = [sol[signal].data for signal in self.signal + ["Time [s]"]] + predictions = {} + for signal in self.signal + self.default_variables: + predictions[signal] = sol[signal].data - return np.vstack(predictions).T + return predictions diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 5af2ddfe6..f2b452af4 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -57,9 +57,13 @@ def update_simulation_data(self, initial_conditions): if self.update_capacity: self.problem.model.approximate_capacity(self.problem.x0) solution = self.problem.evaluate(initial_conditions) - self.problem._time_data = solution[:, -1] - self.problem._target = solution[:, 0:-1] - self.dt = solution[1, -1] - solution[0, -1] + + if "Time [s]" not in solution: + raise ValueError("The solution does not contain time data.") + self.problem._time_data = solution["Time [s]"] + self.problem._capacity_data = solution["Discharge capacity [A.h]"] + self.problem._target = {key: solution[key] for key in self.problem.signal} + self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] def _evaluate(self, x, grad=None): """ @@ -123,7 +127,7 @@ def _evaluate(self, x, grad=None): self.problem.model.approximate_capacity(x) solution = self.problem.evaluate(x) - voltage, current = solution[:, 0], solution[:, 1] + voltage, current = solution["Voltage [V]"], solution["Current [A]"] negative_energy_density = -np.trapz(voltage * current, dx=self.dt) / ( 3600 * self.problem.model.cell_mass(self.parameter_set) ) @@ -181,7 +185,7 @@ def _evaluate(self, x, grad=None): self.problem.model.approximate_capacity(x) solution = self.problem.evaluate(x) - voltage, current = solution[:, 0], solution[:, 1] + voltage, current = solution["Voltage [V]"], solution["Current [A]"] negative_energy_density = -np.trapz(voltage * current, dx=self.dt) / ( 3600 * self.problem.model.cell_volume(self.parameter_set) ) diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index 8db3111c6..d8bb212a3 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -39,10 +39,23 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - if len(prediction) < len(self._target): - return np.float64(np.inf) # simulation stopped early + for key in prediction: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target + + e = np.array( + [ + np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) + for signal in prediction + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + + if self.n_outputs == 1: + return e.item() else: - return np.sqrt(np.mean((prediction - self._target) ** 2)) + return np.sum(e) def _evaluateS1(self, x): """ @@ -65,24 +78,38 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - if len(y) < len(self._target): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - else: - dy = dy.reshape( - ( - self.problem.n_time_data, - self.n_outputs, - self.n_parameters, - ) - ) - r = y - self._target - e = np.sqrt(np.mean((r) ** 2)) - de = np.mean((r.T * dy.T), axis=2) / np.sqrt( - np.mean((r.T * dy.T) ** 2, axis=2) + + for key in y: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array( + [ + y[signal] - self._target[signal] + for signal in y + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + + if self.n_outputs == 1: + r = r.reshape(self.problem.n_time_data) + dy = dy.reshape(self.n_parameters, self.problem.n_time_data) + e = np.sqrt(np.mean(r**2)) + de = np.mean((r * dy), axis=1) / np.sqrt( + np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps ) + return e.item(), de.flatten() - return e, de.flatten() + else: + r = r.reshape(self.n_outputs, self.problem.n_time_data) + e = np.sqrt(np.mean(r**2, axis=1)) + de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / np.sqrt( + np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1) + np.finfo(float).eps + ) + return np.sum(e), np.sum(de, axis=1) class SumSquaredError(BaseCost): @@ -128,13 +155,22 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - if len(prediction) < len(self._target): - return np.float64(np.inf) # simulation stopped early + for key in prediction: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target + + e = np.array( + [ + np.sum(((prediction[signal] - self._target[signal]) ** 2), axis=0) + for signal in prediction + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + if self.n_outputs == 1: + return e.item() else: - return np.sum( - (np.sum(((prediction - self._target) ** 2), axis=0)), - axis=0, - ) + return np.sum(e) def _evaluateS1(self, x): """ @@ -157,22 +193,33 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - if len(y) < len(self._target): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - else: - dy = dy.reshape( - ( - self.problem.n_time_data, - self.n_outputs, - self.n_parameters, - ) - ) - r = y - self._target - e = np.sum(np.sum(r**2, axis=0), axis=0) - de = 2 * np.sum(np.sum((r.T * dy.T), axis=2), axis=1) + for key in y: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array( + [ + y[signal] - self._target[signal] + for signal in y + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) - return e, de + if self.n_outputs == 1: + r = r.reshape(self.problem.n_time_data) + dy = dy.reshape(self.n_parameters, self.problem.n_time_data) + e = np.sum(r**2, axis=0) + de = 2 * np.sum((r * dy), axis=1) + return e.item(), de + + else: + r = r.reshape(self.n_outputs, self.problem.n_time_data) + e = np.sum(r**2, axis=0) + de = 2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) + return np.sum(e), np.sum(de, axis=1) def set_fail_gradient(self, de): """ diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index 1f56f9b4e..ab739bf77 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -58,6 +58,8 @@ def __init__(self, name="Base Model"): self.parameters = None self.dataset = None self.signal = None + self.non_parameters = None + self.default_variables = [] self.matched_parameters = {} self.non_matched_parameters = {} self.fit_keys = [] @@ -353,11 +355,14 @@ def simulate(self, inputs, t_eval) -> np.ndarray[np.float64]: self.built_model, inputs=inputs, t_eval=t_eval ) else: - return [np.inf] + return {signal: [np.inf] for signal in self.signal} - predictions = [sol[signal].data for signal in self.signal] + predictions = { + signal: sol[signal].data + for signal in (self.signal + self.default_variables) + } - return np.vstack(predictions).T + return predictions def simulateS1(self, inputs, t_eval): """ @@ -399,20 +404,20 @@ def simulateS1(self, inputs, t_eval): t_eval=t_eval, calculate_sensitivities=True, ) + predictions = {signal: sol[signal].data for signal in self.signal} - predictions = [sol[signal].data for signal in self.signal] + dy = np.asarray( + [ + sol[signal].sensitivities[key].toarray() + for signal in self.signal + for key in self.fit_keys + ] + ).reshape(self.n_parameters, self.n_time_data, self.n_outputs) - sensitivities = [ - np.array( - [[sol[signal].sensitivities[key]] for signal in self.signal] - ).reshape(len(sol[self.signal[0]].data), self.n_outputs) - for key in self.fit_keys - ] - - return np.vstack(predictions).T, np.dstack(sensitivities) + return predictions, dy else: - return [np.inf], [np.inf] + return {signal: [np.inf] for signal in self.signal}, [np.inf] def predict( self, diff --git a/pybop/models/empirical/__init__.py b/pybop/models/empirical/__init__.py index 587906276..6a28b0a98 100644 --- a/pybop/models/empirical/__init__.py +++ b/pybop/models/empirical/__init__.py @@ -1,4 +1,4 @@ # # Import lithium ion based models # -from .ecm import Thevenin +from .ecm import ECircuitModel, Thevenin diff --git a/pybop/models/lithium_ion/__init__.py b/pybop/models/lithium_ion/__init__.py index d61591b4f..4dca05ea0 100644 --- a/pybop/models/lithium_ion/__init__.py +++ b/pybop/models/lithium_ion/__init__.py @@ -1,4 +1,4 @@ # # Import lithium ion based models # -from .echem import SPM, SPMe +from .echem import EChemBaseModel, SPM, SPMe diff --git a/pybop/models/lithium_ion/echem_base.py b/pybop/models/lithium_ion/echem_base.py index 7e5c869fa..6a642ff34 100644 --- a/pybop/models/lithium_ion/echem_base.py +++ b/pybop/models/lithium_ion/echem_base.py @@ -223,9 +223,14 @@ def approximate_capacity(self, x): # Calculate average voltage positive_electrode_ocp = self._parameter_set["Positive electrode OCP [V]"] negative_electrode_ocp = self._parameter_set["Negative electrode OCP [V]"] - average_voltage = positive_electrode_ocp(mean_sto_pos) - negative_electrode_ocp( - mean_sto_neg - ) + try: + average_voltage = positive_electrode_ocp( + mean_sto_pos + ) - negative_electrode_ocp(mean_sto_neg) + except TypeError: + average_voltage = positive_electrode_ocp([mean_sto_pos]).evaluate()[0][ + 0 + ] - negative_electrode_ocp(mean_sto_neg) # Super hacky, needs to be fixed # Calculate and update nominal capacity theoretical_capacity = theoretical_energy / average_voltage diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index 57aebe048..67f63d757 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -32,37 +32,37 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): parameter_values = problem.x0 # Extract the time data and evaluate the model's output and target values - reference_time_data = problem.time_data() + xaxis_data = problem.time_data() model_output = problem.evaluate(parameter_values) target_output = problem.target() # Create a plot for each output figure_list = [] - for i in range(0, problem.n_outputs): + for i in problem.signal: default_layout_options = dict( title="Scatter Plot", xaxis_title="Time / s", - yaxis_title=pybop.StandardPlot.remove_brackets(problem.signal[i]), + yaxis_title=pybop.StandardPlot.remove_brackets(i), ) # Create a plotting dictionary if isinstance(problem, pybop.DesignProblem): trace_name = "Optimised" - opt_time_data = model_output[:, -1] + opt_time_data = model_output["Time [s]"] else: trace_name = "Model" - opt_time_data = reference_time_data + opt_time_data = xaxis_data plot_dict = pybop.StandardPlot( x=opt_time_data, - y=model_output[:, i], + y=model_output[i], layout_options=default_layout_options, trace_names=trace_name, ) target_trace = plot_dict.create_trace( - x=reference_time_data, - y=target_output[:, i], + x=xaxis_data, + y=target_output[i], name="Reference", mode="markers", showlegend=True, @@ -71,12 +71,12 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): if isinstance(problem, pybop.FittingProblem): # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) + plot_dict.sigma = np.std(model_output[i] - target_output[i]) # Convert x and upper and lower limits into lists to create a filled trace - x = reference_time_data.tolist() - y_upper = (model_output[:, i] + plot_dict.sigma).tolist() - y_lower = (model_output[:, i] - plot_dict.sigma).tolist() + x = xaxis_data.tolist() + y_upper = (model_output[i] + plot_dict.sigma).tolist() + y_lower = (model_output[i] - plot_dict.sigma).tolist() fill_trace = plot_dict.create_trace( x=x + x[::-1], diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index e9d7cd9af..10d1278c1 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -81,6 +81,7 @@ def test_spm_optimisers(self, optimiser, spm_costs, x0): if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) + parameterisation.cost.problem._model.allow_infeasible_solutions = False assert parameterisation._use_f_guessed is True parameterisation.set_max_iterations(1) x, final_cost = parameterisation.run() @@ -126,12 +127,15 @@ def spm_two_signal_cost(self, parameters, model, x0): { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, + "Bulk open-circuit voltage [V]": solution[ + "Bulk open-circuit voltage [V]" + ].data, } ) # Define the cost to optimise - signal = ["Terminal voltage [V]", "Time [s]"] + signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 29172932a..7d3a4ea63 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -122,7 +122,7 @@ def test_costs(self, cost): ) # Test type of returned value - assert type(cost([0.5])) == np.float64 + assert np.isscalar(cost([0.5])) if isinstance(cost, pybop.ObserverCost): with pytest.raises(NotImplementedError): @@ -137,7 +137,7 @@ def test_costs(self, cost): if isinstance(cost, pybop.SumSquaredError): e, de = cost.evaluateS1([0.5]) - assert type(e) == np.float64 + assert np.isscalar(e) assert type(de) == np.ndarray # Test option setting @@ -187,7 +187,7 @@ def test_energy_density_costs( cost = cost_class(problem) # Test type of returned value - assert type(cost([0.5])) == np.float64 + assert np.isscalar(cost([0.5])) assert cost([0.4]) <= 0 # Should be a viable design assert cost([0.8]) == np.inf # Should exceed active material + porosity < 1 assert cost([1.4]) == np.inf # Definitely not viable diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index ce9be93e8..2e7f7ea5e 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -202,9 +202,9 @@ def test_simulate(self): model.signal = ["y_0"] inputs = {} t_eval = np.linspace(0, 10, 100) - expected = y0 * np.exp(-k * t_eval).reshape(-1, 1) + expected = y0 * np.exp(-k * t_eval) solved = model.simulate(inputs, t_eval) - np.testing.assert_array_almost_equal(solved, expected, decimal=5) + np.testing.assert_array_almost_equal(solved["y_0"], expected, decimal=5) with pytest.raises(ValueError): ExponentialDecay(n_states=-1) diff --git a/tests/unit/test_problem.py b/tests/unit/test_problem.py index 0cdfd8bce..5b436a319 100644 --- a/tests/unit/test_problem.py +++ b/tests/unit/test_problem.py @@ -167,7 +167,7 @@ def test_problem_construct_with_model_predict( assert problem._model._built_model is not None with pytest.raises(AssertionError): np.testing.assert_allclose( - out["Terminal voltage [V]"].data, - problem_output, + out["Voltage [V]"].data, + problem_output["Voltage [V]"], atol=1e-5, ) diff --git a/tests/unit/test_standalone.py b/tests/unit/test_standalone.py index 4ba611a96..f5b0a33e6 100644 --- a/tests/unit/test_standalone.py +++ b/tests/unit/test_standalone.py @@ -53,13 +53,18 @@ def test_standalone_problem(self): # Test the Problem with a Cost rmse_cost = pybop.RootMeanSquaredError(problem) - x = rmse_cost([1, 2]) + rmse_x = rmse_cost([1, 2]) + rmse_grad_x = rmse_cost.evaluateS1([1, 2]) - np.testing.assert_allclose(x, 3.138, atol=1e-2) + np.testing.assert_allclose(rmse_x, 3.05615, atol=1e-2) + np.testing.assert_allclose(rmse_grad_x[1], [-0.81758337, 0.0], atol=1e-2) # Test the sensitivities sums_cost = pybop.SumSquaredError(problem) - sums_cost.evaluateS1([1, 2]) + x = sums_cost.evaluateS1([1, 2]) + + np.testing.assert_allclose(x[0], 934.006734006734, atol=1e-2) + np.testing.assert_allclose(x[1], [-334.006734, 0.0], atol=1e-2) # Test incorrect number of initial parameter values with pytest.raises(ValueError): From 3428c97608d788c9342d6fd429f1eb4272e7888c Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 1 Mar 2024 14:13:02 +0000 Subject: [PATCH 06/20] Fix ukf examples, temporarily limits ukf to signal output model --- examples/scripts/exp_UKF.py | 5 ++- pybop/observers/observer.py | 64 +++++++++++++++++------------ pybop/observers/unscented_kalman.py | 3 +- 3 files changed, 42 insertions(+), 30 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index 965775ecd..77e0c6456 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -42,7 +42,8 @@ simulator = pybop.Observer(parameters, model, signal=["2y"], x0=x0) simulator._time_data = t_eval measurements = simulator.evaluate(x0) -measurements = measurements[:, 0] + +measurements = measurements["2y"] # Verification step: Compare by plotting go = pybop.PlotlyManager().go @@ -85,7 +86,7 @@ # Verification step: Find the maximum likelihood estimate given the true parameters estimation = observer.evaluate(x0) -estimation = estimation[:, 0] +estimation = estimation["2y"] # Verification step: Add the estimate to the plot line4 = go.Scatter(x=t_eval, y=estimation, name="Estimated trajectory", mode="lines") diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 0a93fe8a4..173d9f3c3 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -53,6 +53,7 @@ def __init__( self._state = model.reinit(inputs) self._model = model self._signal = self.signal + self._n_outputs = len(self._signal) def reset(self, inputs: Inputs) -> None: self._state = self._model.reinit(inputs) @@ -78,9 +79,7 @@ def observe(self, time: float, value: Optional[np.ndarray] = None) -> float: self._state = self._model.step(self._state, time) return 0.0 - def log_likelihood( - self, values: np.ndarray, times: np.ndarray, inputs: Inputs - ) -> float: + def log_likelihood(self, values: dict, times: np.ndarray, inputs: Inputs) -> float: """ Returns the log likelihood of the model given the values and inputs. @@ -93,16 +92,22 @@ def log_likelihood( inputs : Inputs The inputs to the model. """ - if len(values) != len(times): - raise ValueError("values and times must have the same length.") - log_likelihood = 0.0 - self.reset(inputs) - for t, v in zip(times, values): - try: - log_likelihood += self.observe(t, v) - except Exception: - return np.float64(-np.inf) - return log_likelihood + if self._n_outputs == 1: + signal = self._signal[0] + if len(values[signal]) != len(times): + raise ValueError("values and times must have the same length.") + log_likelihood = 0.0 + self.reset(inputs) + for t, v in zip(times, values[signal]): + try: + log_likelihood += self.observe(t, v) + except Exception: + return np.float64(-np.inf) + return log_likelihood + else: + raise ValueError( + "Obersever.log_likelihood is currently restricted to single output models." + ) def get_current_state(self) -> TimeSeriesState: """ @@ -156,17 +161,24 @@ def evaluate(self, x): inputs[param.name] = x[i] self.reset(inputs) - output = [] - if hasattr(self, "_dataset"): - ym = self._target - for i, t in enumerate(self._time_data): - self.observe(t, ym[i]) - ys = self.get_current_measure() - output.append(ys) + if self._n_outputs == 1: + signal = self._signal[0] + output = [] + if hasattr(self, "_dataset"): + ym = self._target[signal] + for i, t in enumerate(self._time_data): + self.observe(t, ym[i]) + ys = self.get_current_measure() + output.append(ys) + else: + for t in self._time_data: + self.observe(t) + ys = self.get_current_measure() + output.append(ys) + + out = {signal: np.vstack(output) for signal in self._signal} + return out else: - for t in self._time_data: - self.observe(t) - ys = self.get_current_measure() - output.append(ys) - - return np.vstack(output) + raise ValueError( + "Observer is currently restricted to single output models." + ) diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index 62b9d0a79..62e98d917 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -62,8 +62,7 @@ def __init__( self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} # Add useful parameters to model if model is not None: From 43521dae672d82ede892d4c21afa2eb4a53146a7 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sat, 2 Mar 2024 19:05:46 +0000 Subject: [PATCH 07/20] default_variables to additional_variables w/ docstrings, updt. observer tests, dict output observer.evaluate() --- examples/scripts/exp_UKF.py | 6 ++--- pybop/_problem.py | 38 ++++++++++++++++++++++------- pybop/models/base_model.py | 5 ++-- pybop/observers/observer.py | 33 +++++++++++++------------ pybop/observers/unscented_kalman.py | 5 +++- tests/unit/test_observers.py | 4 +-- 6 files changed, 57 insertions(+), 34 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index 77e0c6456..bbbc6f950 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -43,15 +43,15 @@ simulator._time_data = t_eval measurements = simulator.evaluate(x0) -measurements = measurements["2y"] - # Verification step: Compare by plotting go = pybop.PlotlyManager().go line1 = go.Scatter(x=t_eval, y=corrupt_values, name="Corrupt values", mode="markers") line2 = go.Scatter( x=t_eval, y=expected_values, name="Expected trajectory", mode="lines" ) -line3 = go.Scatter(x=t_eval, y=measurements, name="Observed values", mode="markers") +line3 = go.Scatter( + x=t_eval, y=measurements["2y"], name="Observed values", mode="markers" +) fig = go.Figure(data=[line1, line2, line3]) # Form dataset diff --git a/pybop/_problem.py b/pybop/_problem.py index e2aaee3d0..f1f0e8c82 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -16,6 +16,8 @@ class BaseProblem: Flag to indicate if the model should be checked (default: True). signal: List[str] The signal to observe. + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -28,7 +30,7 @@ def __init__( model=None, check_model=True, signal=["Voltage [V]"], - default_variables=[], + additional_variables=[], init_soc=None, x0=None, ): @@ -48,9 +50,9 @@ def __init__( self._target = None if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): - self.default_variables = default_variables + self.additional_variables = additional_variables else: - self.default_variables = [] + self.additional_variables = [] # Set bounds self.bounds = dict( @@ -146,6 +148,12 @@ class FittingProblem(BaseProblem): Dataset object containing the data to fit the model to. signal : str, optional The signal to fit (default: "Voltage [V]"). + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). + init_soc : float, optional + Initial state of charge (default: None). + x0 : np.ndarray, optional + Initial parameter values (default: None). """ def __init__( @@ -155,12 +163,13 @@ def __init__( dataset, check_model=True, signal=["Voltage [V]"], - default_variables=["Time [s]", "Discharge capacity [A.h]"], + additional_variables=[], init_soc=None, x0=None, ): + additional_variables += ["Time [s]", "Discharge capacity [A.h]"] super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self._dataset = dataset.data self.x = self.x0 @@ -176,7 +185,7 @@ def __init__( # Add useful parameters to model if model is not None: self._model.signal = self.signal - self._model.default_variables = self.default_variables + self._model.additional_variables = self.additional_variables self._model.n_parameters = self.n_parameters self._model.n_outputs = self.n_outputs self._model.n_time_data = self.n_time_data @@ -257,6 +266,16 @@ class DesignProblem(BaseProblem): List of parameters for the problem. experiment : object The experimental setup to apply the model to. + check_model : bool, optional + Flag to indicate if the model parameters should be checked for feasibility each iteration (default: True). + signal : str, optional + The signal to fit (default: "Voltage [V]"). + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). + init_soc : float, optional + Initial state of charge (default: None). + x0 : np.ndarray, optional + Initial parameter values (default: None). """ def __init__( @@ -266,12 +285,13 @@ def __init__( experiment, check_model=True, signal=["Voltage [V]"], - default_variables=["Time [s]", "Current [A]", "Discharge capacity [A.h]"], + additional_variables=[], init_soc=None, x0=None, ): + additional_variables += ["Time [s]", "Current [A]", "Discharge capacity [A.h]"] super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self.experiment = experiment @@ -323,7 +343,7 @@ def evaluate(self, x): else: predictions = {} - for signal in self.signal + self.default_variables: + for signal in self.signal + self.additional_variables: predictions[signal] = sol[signal].data return predictions diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index ab739bf77..8e291676f 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -58,8 +58,7 @@ def __init__(self, name="Base Model"): self.parameters = None self.dataset = None self.signal = None - self.non_parameters = None - self.default_variables = [] + self.additional_variables = [] self.matched_parameters = {} self.non_matched_parameters = {} self.fit_keys = [] @@ -359,7 +358,7 @@ def simulate(self, inputs, t_eval) -> np.ndarray[np.float64]: predictions = { signal: sol[signal].data - for signal in (self.signal + self.default_variables) + for signal in (self.signal + self.additional_variables) } return predictions diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 173d9f3c3..ce482ec8d 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -22,6 +22,8 @@ class Observer(BaseProblem): Flag to indicate if the model should be checked (default: True). signal: List[str] The signal to observe. + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -37,10 +39,13 @@ def __init__( model: BaseModel, check_model=True, signal=["Voltage [V]"], + additional_variables=[], init_soc=None, x0=None, ) -> None: - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, additional_variables, init_soc, x0 + ) if model._built_model is None: raise ValueError("Only built models can be used in Observers") if model.signal is None: @@ -161,24 +166,20 @@ def evaluate(self, x): inputs[param.name] = x[i] self.reset(inputs) - if self._n_outputs == 1: - signal = self._signal[0] - output = [] - if hasattr(self, "_dataset"): + output = {} + ys = [] + if hasattr(self, "_dataset"): + for signal in self._signal: ym = self._target[signal] for i, t in enumerate(self._time_data): self.observe(t, ym[i]) - ys = self.get_current_measure() - output.append(ys) - else: + ys.append(self.get_current_measure()) + output[signal] = np.vstack(ys) + else: + for signal in self._signal: for t in self._time_data: self.observe(t) - ys = self.get_current_measure() - output.append(ys) + ys.append(self.get_current_measure()) + output[signal] = np.vstack(ys) - out = {signal: np.vstack(output) for signal in self._signal} - return out - else: - raise ValueError( - "Observer is currently restricted to single output models." - ) + return output diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index 62e98d917..e0dd7b8f6 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -50,10 +50,13 @@ def __init__( dataset=None, check_model=True, signal=["Voltage [V]"], + additional_variables=[], init_soc=None, x0=None, ) -> None: - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, additional_variables, init_soc, x0 + ) if dataset is not None: self._dataset = dataset.data diff --git a/tests/unit/test_observers.py b/tests/unit/test_observers.py index ab77428c5..e2c44d74c 100644 --- a/tests/unit/test_observers.py +++ b/tests/unit/test_observers.py @@ -62,7 +62,7 @@ def test_observer(self, model, parameters, x0): observer.observe(-1) with pytest.raises(ValueError): observer.log_likelihood( - t_eval, np.array([1]), inputs=observer._state.inputs + {"2y": t_eval}, np.array([1]), inputs=observer._state.inputs ) # Test covariance @@ -81,7 +81,7 @@ def test_observer(self, model, parameters, x0): "Output": expected, } ) - observer._target = expected + observer._target = {"2y": expected} observer.evaluate(x0) @pytest.mark.unit From 67d28876c50e303eb359707f80023f38fbbc0a74 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 13:50:13 +0000 Subject: [PATCH 08/20] Fix integration test logic, add gradient landscape plots, pin pytest version due to breaking change in 8.1.0 --- examples/scripts/exp_UKF.py | 5 +- examples/scripts/spm_adam.py | 39 ++++++-- noxfile.py | 2 +- pybop/costs/fitting_costs.py | 13 +-- pybop/plotting/plot_cost2d.py | 47 ++++++++- pyproject.toml | 2 +- tests/integration/test_parameterisations.py | 100 +++++++++++--------- 7 files changed, 141 insertions(+), 67 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index bbbc6f950..d7d838cc0 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -86,10 +86,11 @@ # Verification step: Find the maximum likelihood estimate given the true parameters estimation = observer.evaluate(x0) -estimation = estimation["2y"] # Verification step: Add the estimate to the plot -line4 = go.Scatter(x=t_eval, y=estimation, name="Estimated trajectory", mode="lines") +line4 = go.Scatter( + x=t_eval, y=estimation["2y"], name="Estimated trajectory", mode="lines" +) fig.add_trace(line4) fig.show() diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index 56d315f47..78d484b45 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -20,34 +20,50 @@ ] # Generate data -sigma = 0.01 -t_eval = np.arange(0, 900, 2) -values = model.predict(t_eval=t_eval) -corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) +init_soc = 0.5 +sigma = 0.003 +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (1 second period)", + "Charge at 0.5C for 3 minutes (1 second period)", + ), + ] + * 2 +) +values = model.predict(init_soc=init_soc, experiment=experiment) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + # Form dataset dataset = pybop.Dataset( { - "Time [s]": t_eval, + "Time [s]": values["Time [s]"].data, "Current function [A]": values["Current [A]"].data, - "Voltage [V]": corrupt_values, - "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), } ) signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +problem = pybop.FittingProblem( + model, parameters, dataset, signal=signal, init_soc=init_soc +) cost = pybop.RootMeanSquaredError(problem) optim = pybop.Optimisation( cost, optimiser=pybop.Adam, verbose=True, allow_infeasible_solutions=True, - sigma0=sigma, + sigma0=0.05, ) optim.set_max_iterations(100) -optim.set_max_unchanged_iterations(20) +optim.set_max_unchanged_iterations(45) # Run optimisation x, final_cost = optim.run() @@ -64,3 +80,6 @@ # Plot the cost landscape with optimisation path pybop.plot_optim2d(optim, steps=15) + +# Plot the cost and gradient landscapes +pybop.plot_cost2d(cost, gradient=True, steps=3) diff --git a/noxfile.py b/noxfile.py index c732bc3f9..5784e90dd 100644 --- a/noxfile.py +++ b/noxfile.py @@ -33,7 +33,7 @@ def coverage(session): "--cov-report=xml", ) session.run( - "pytest", "--plots", "--cov", "--cov-append", "--cov-report=xml", "-n", "1" + "pytest", "--plots", "--cov", "--cov-append", "--cov-report=xml", "-n", "0" ) diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index d8bb212a3..eb21001a5 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -98,16 +98,17 @@ def _evaluateS1(self, x): r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) e = np.sqrt(np.mean(r**2)) - de = np.mean((r * dy), axis=1) / np.sqrt( - np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps + de = np.mean((r * dy), axis=1) / ( + np.sqrt(np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps) ) return e.item(), de.flatten() else: r = r.reshape(self.n_outputs, self.problem.n_time_data) e = np.sqrt(np.mean(r**2, axis=1)) - de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / np.sqrt( - np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1) + np.finfo(float).eps + de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / ( + np.sqrt(np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1)) + + np.finfo(float).eps ) return np.sum(e), np.sum(de, axis=1) @@ -162,7 +163,7 @@ def _evaluate(self, x, grad=None): e = np.array( [ - np.sum(((prediction[signal] - self._target[signal]) ** 2), axis=0) + np.sum(((prediction[signal] - self._target[signal]) ** 2)) for signal in prediction if signal not in ["Time [s]", "Discharge capacity [A.h]"] ] @@ -217,7 +218,7 @@ def _evaluateS1(self, x): else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - e = np.sum(r**2, axis=0) + e = np.sum(r**2, axis=1) de = 2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) return np.sum(e), np.sum(de, axis=1) diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 6e4291173..30c2ef661 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -3,7 +3,9 @@ import numpy as np -def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): +def plot_cost2d( + cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs +): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -54,6 +56,23 @@ def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): for j, yj in enumerate(y): costs[j, i] = cost(np.array([xi, yj])) + if gradient: + grad_parameter_costs = [] + + # Determine the number of gradient outputs from cost.evaluateS1 + num_gradients = len(cost.evaluateS1(np.array([x[0], y[0]]))[1]) + + # Create an array to hold each gradient output & populate + grads = [np.zeros((len(y), len(x))) for _ in range(num_gradients)] + for i, xi in enumerate(x): + for j, yj in enumerate(y): + (*current_grads,) = cost.evaluateS1(np.array([xi, yj]))[1] + for k, grad_output in enumerate(current_grads): + grads[k][j, i] = grad_output + + # Append the arrays to the grad_parameter_costs list + grad_parameter_costs.extend(grads) + # Import plotly only when needed go = pybop.PlotlyManager().go @@ -80,6 +99,32 @@ def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): elif show: fig.show() + if gradient: + grad_figs = [] + for i, grad_costs in enumerate(grad_parameter_costs): + # Update title for gradient plots + updated_layout_options = layout_options.copy() + updated_layout_options["title"] = f"Gradient for Parameter: {i+1}" + + # Create contour plot with updated layout options + grad_layout = go.Layout(updated_layout_options) + + # Create fig + grad_fig = go.Figure( + data=[go.Contour(x=x, y=y, z=grad_costs)], layout=grad_layout + ) + grad_fig.update_layout(**layout_kwargs) + + if "ipykernel" in sys.modules and show: + grad_fig.show("svg") + elif show: + grad_fig.show() + + # append grad_fig to list + grad_figs.append(grad_fig) + + return fig, grad_figs + return fig diff --git a/pyproject.toml b/pyproject.toml index 36eb03a43..3287989fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ dev = [ "nox", "nbmake", "pre-commit", - "pytest>=6", + "pytest<=8", "pytest-cov", "pytest-mock", "pytest-xdist", diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index 10d1278c1..33ad4f364 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -8,6 +8,12 @@ class TestModelParameterisation: A class to test the model parameterisation methods. """ + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.array([0.55, 0.55]) + np.random.normal( + loc=0.0, scale=0.05, size=2 + ) + @pytest.fixture def model(self): parameter_set = pybop.ParameterSet.pybamm("Chen2020") @@ -18,21 +24,17 @@ def parameters(self): return [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.02), - bounds=[0.375, 0.7], + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.375, 0.75], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.5, 0.02), - bounds=[0.375, 0.625], + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.375, 0.75], ), ] - @pytest.fixture - def x0(self): - return np.array([0.63, 0.51]) - - @pytest.fixture(params=[0.3, 0.7]) + @pytest.fixture(params=[0.4, 0.7]) def init_soc(self, request): return request.param @@ -40,20 +42,24 @@ def init_soc(self, request): def cost_class(self, request): return request.param + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + @pytest.fixture - def spm_costs(self, parameters, model, x0, cost_class, init_soc): + def spm_costs(self, model, parameters, cost_class, init_soc): # Form dataset - solution = self.getdata(model, x0, init_soc) + solution = self.getdata(model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), } ) # Define the cost to optimise - signal = ["Terminal voltage [V]"] + signal = ["Voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) @@ -74,10 +80,14 @@ def spm_costs(self, parameters, model, x0, cost_class, init_soc): ], ) @pytest.mark.integration - def test_spm_optimisers(self, optimiser, spm_costs, x0): + def test_spm_optimisers(self, optimiser, spm_costs): # Test each optimiser - parameterisation = pybop.Optimisation(cost=spm_costs, optimiser=optimiser) + initial_cost = spm_costs(spm_costs.x0) + parameterisation = pybop.Optimisation( + cost=spm_costs, optimiser=optimiser, sigma0=0.05 + ) parameterisation.set_max_unchanged_iterations(iterations=25, threshold=5e-4) + parameterisation.set_max_iterations(125) if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) @@ -102,35 +112,34 @@ def test_spm_optimisers(self, optimiser, spm_costs, x0): parameterisation.optimiser.set_population_size(-5) parameterisation.optimiser.set_population_size(5) - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() elif optimiser in [pybop.SciPyMinimize]: parameterisation.cost.problem._model.allow_infeasible_solutions = False - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() else: - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() # Assertions - np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + assert initial_cost > final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2) @pytest.fixture - def spm_two_signal_cost(self, parameters, model, x0): + def spm_two_signal_cost(self, parameters, model, cost_class): # Form dataset init_soc = 0.5 - solution = self.getdata(model, x0, init_soc) + solution = self.getdata(model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Voltage [V]": solution["Voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), "Bulk open-circuit voltage [V]": solution[ "Bulk open-circuit voltage [V]" - ].data, + ].data + + self.noise(0.002, len(solution["Time [s]"].data)), } ) @@ -139,54 +148,55 @@ def spm_two_signal_cost(self, parameters, model, x0): problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) - return pybop.SumSquaredError(problem) + return cost_class(problem) @pytest.mark.parametrize( - "optimiser", + "multi_optimiser", [ pybop.SciPyDifferentialEvolution, - pybop.IRPropMin, + pybop.Adam, pybop.CMAES, ], ) @pytest.mark.integration - def test_multiple_signals(self, optimiser, spm_two_signal_cost, x0): + def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): # Test each optimiser + initial_cost = spm_two_signal_cost(spm_two_signal_cost.x0) parameterisation = pybop.Optimisation( - cost=spm_two_signal_cost, optimiser=optimiser + cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.05 ) parameterisation.set_max_unchanged_iterations(iterations=15, threshold=5e-4) - parameterisation.set_max_iterations(100) + parameterisation.set_max_iterations(125) - if optimiser in [pybop.SciPyDifferentialEvolution]: + if multi_optimiser in [pybop.SciPyDifferentialEvolution]: parameterisation.optimiser.set_population_size(5) x, final_cost = parameterisation.run() # Assertions - np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + assert initial_cost > final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2) - @pytest.mark.parametrize("init_soc", [0.3, 0.7]) + @pytest.mark.parametrize("init_soc", [0.4, 0.7]) @pytest.mark.integration - def test_model_misparameterisation(self, parameters, model, x0, init_soc): + def test_model_misparameterisation(self, parameters, model, init_soc): # Define two different models with different parameter sets # The optimisation should fail as the models are not the same second_parameter_set = pybop.ParameterSet.pybamm("Ecker2015") second_model = pybop.lithium_ion.SPM(parameter_set=second_parameter_set) # Form dataset - solution = self.getdata(second_model, x0, init_soc) + solution = self.getdata(second_model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, } ) # Define the cost to optimise - signal = ["Terminal voltage [V]"] + signal = ["Voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) @@ -204,22 +214,20 @@ def test_model_misparameterisation(self, parameters, model, x0, init_soc): # Assertions with np.testing.assert_raises(AssertionError): np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + np.testing.assert_allclose(x, self.ground_truth, atol=2e-2) - def getdata(self, model, x0, init_soc): + def getdata(self, model, x, init_soc): model.parameter_set.update( { - "Negative electrode active material volume fraction": x0[0], - "Positive electrode active material volume fraction": x0[1], + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], } ) experiment = pybop.Experiment( [ ( - "Discharge at 1C for 3 minutes (1 second period)", - "Rest for 2 minutes (1 second period)", - "Charge at 1C for 3 minutes (1 second period)", - "Rest for 2 minutes (1 second period)", + "Discharge at 0.5C for 3 minutes (1 second period)", + "Charge at 0.5C for 3 minutes (1 second period)", ), ] * 2 From b6a073ba39a29c2147e380eb60b4391d8fe0d58f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 14:23:45 +0000 Subject: [PATCH 09/20] Add tests for gradient plots, up coverage --- tests/unit/test_plots.py | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index fff8074b0..bc721f029 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -13,18 +13,32 @@ def model(self): # Define an example model return pybop.lithium_ion.SPM() + # @pytest.fixture + # def parameters(self): + # return [ + # pybop.Parameter( + # "Negative particle radius [m]", + # prior=pybop.Gaussian(6e-06, 0.1e-6), + # bounds=[1e-6, 9e-6], + # ), + # pybop.Parameter( + # "Positive particle radius [m]", + # prior=pybop.Gaussian(4.5e-06, 0.1e-6), + # bounds=[1e-6, 9e-6], + # ), + # ] @pytest.fixture def parameters(self): return [ pybop.Parameter( - "Negative particle radius [m]", - prior=pybop.Gaussian(6e-06, 0.1e-6), - bounds=[1e-6, 9e-6], + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], ), pybop.Parameter( - "Positive particle radius [m]", - prior=pybop.Gaussian(4.5e-06, 0.1e-6), - bounds=[1e-6, 9e-6], + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + bounds=[0.4, 0.7], ), ] @@ -65,7 +79,7 @@ def cost(self, problem): @pytest.mark.unit def test_cost_plots(self, cost): # Test plotting of Cost objects - pybop.plot_cost2d(cost, steps=5) + pybop.plot_cost2d(cost, gradient=True, steps=5) @pytest.fixture def optim(self, cost): From ee4cdffce74bb053821d0a76d26ce570f0f748dd Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 16:30:12 +0000 Subject: [PATCH 10/20] Set default SciPyMinimize method to Nelder-Mead, clean-up repo --- examples/scripts/spm_scipymin.py | 4 ++-- pybop/optimisers/scipy_optimisers.py | 5 +++-- pyproject.toml | 2 +- tests/unit/test_plots.py | 14 -------------- 4 files changed, 6 insertions(+), 19 deletions(-) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index db1d783d0..759f8c2e6 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -21,12 +21,12 @@ parameters = [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.02), + prior=pybop.Gaussian(0.6, 0.05), bounds=[0.5, 0.8], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.02), + prior=pybop.Gaussian(0.48, 0.05), bounds=[0.4, 0.7], ), ] diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 7e717b81e..f4ea49a72 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -12,7 +12,8 @@ class SciPyMinimize(BaseOptimiser): Parameters ---------- method : str, optional - The type of solver to use. If not specified, defaults to 'COBYLA'. + The type of solver to use. If not specified, defaults to 'Nelder-Mead'. + Options: 'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'COBYLA', 'SLSQP', 'trust-constr', 'dogleg', 'trust-ncg', 'trust-exact', 'trust-krylov'. bounds : sequence or ``Bounds``, optional Bounds for variables as supported by the selected method. maxiter : int, optional @@ -27,7 +28,7 @@ def __init__(self, method=None, bounds=None, maxiter=None): self._max_iterations = maxiter if self.method is None: - self.method = "COBYLA" # "L-BFGS-B" + self.method = "Nelder-Mead" def _runoptimise(self, cost_function, x0, bounds): """ diff --git a/pyproject.toml b/pyproject.toml index 3287989fd..36eb03a43 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ dev = [ "nox", "nbmake", "pre-commit", - "pytest<=8", + "pytest>=6", "pytest-cov", "pytest-mock", "pytest-xdist", diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index bc721f029..d2f8387af 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -13,20 +13,6 @@ def model(self): # Define an example model return pybop.lithium_ion.SPM() - # @pytest.fixture - # def parameters(self): - # return [ - # pybop.Parameter( - # "Negative particle radius [m]", - # prior=pybop.Gaussian(6e-06, 0.1e-6), - # bounds=[1e-6, 9e-6], - # ), - # pybop.Parameter( - # "Positive particle radius [m]", - # prior=pybop.Gaussian(4.5e-06, 0.1e-6), - # bounds=[1e-6, 9e-6], - # ), - # ] @pytest.fixture def parameters(self): return [ From 66efaba7832774844a5449024e845d7daeb95b11 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 8 Mar 2024 09:55:58 +0000 Subject: [PATCH 11/20] unicode fix for win notebooks, update prediction shape checks, remove Discharge capacity as default additional_variable --- conftest.py | 5 +++ pybop/_problem.py | 11 +++---- pybop/costs/base_cost.py | 1 + pybop/costs/design_costs.py | 1 - pybop/costs/fitting_costs.py | 60 +++++++++++++----------------------- 5 files changed, 32 insertions(+), 46 deletions(-) diff --git a/conftest.py b/conftest.py index 3641cbd11..d8e79fcd8 100644 --- a/conftest.py +++ b/conftest.py @@ -1,6 +1,7 @@ import pytest import matplotlib import plotly +import sys plotly.io.renderers.default = None matplotlib.use("Template") @@ -42,6 +43,10 @@ def pytest_configure(config): config.addinivalue_line("markers", "plots: mark test as a plot test") config.addinivalue_line("markers", "notebook: mark test as a notebook test") + if sys.platform.startswith("win"): + # Set the output encoding to UTF-8 on Windows + sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf8", buffering=1) + def pytest_collection_modifyitems(config, items): options = { diff --git a/pybop/_problem.py b/pybop/_problem.py index f1f0e8c82..d1e6d394a 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -147,9 +147,9 @@ class FittingProblem(BaseProblem): dataset : Dataset Dataset object containing the data to fit the model to. signal : str, optional - The signal to fit (default: "Voltage [V]"). + The variable used for fitting (default: "Voltage [V]"). additional_variables : List[str], optional - Additional variables to observe and store in the solution (default: []). + Additional variables to observe and store in the solution (default additions are: ["Time [s]"]). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -167,7 +167,7 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Discharge capacity [A.h]"] + additional_variables += ["Time [s]"] super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -271,7 +271,7 @@ class DesignProblem(BaseProblem): signal : str, optional The signal to fit (default: "Voltage [V]"). additional_variables : List[str], optional - Additional variables to observe and store in the solution (default: []). + Additional variables to observe and store in the solution (default additions are: ["Time [s]", "Current [A]"]). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -289,7 +289,7 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Current [A]", "Discharge capacity [A.h]"] + additional_variables += ["Time [s]", "Current [A]"] super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -313,7 +313,6 @@ def __init__( # Add an example dataset for plotting comparison sol = self.evaluate(self.x0) self._time_data = sol["Time [s]"] - self._capacity_data = sol["Discharge capacity [A.h]"] self._target = {key: sol[key] for key in self.signal} self._dataset = None diff --git a/pybop/costs/base_cost.py b/pybop/costs/base_cost.py index bdafec3c4..6cbd2a19a 100644 --- a/pybop/costs/base_cost.py +++ b/pybop/costs/base_cost.py @@ -33,6 +33,7 @@ def __init__(self, problem): self.bounds = problem.bounds self.n_parameters = problem.n_parameters self.n_outputs = problem.n_outputs + self.signal = problem.signal def __call__(self, x, grad=None): """ diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index f2b452af4..e1fb38cb4 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -61,7 +61,6 @@ def update_simulation_data(self, initial_conditions): if "Time [s]" not in solution: raise ValueError("The solution does not contain time data.") self.problem._time_data = solution["Time [s]"] - self.problem._capacity_data = solution["Discharge capacity [A.h]"] self.problem._target = {key: solution[key] for key in self.problem.signal} self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index eb21001a5..3e7349110 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -39,16 +39,14 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - for key in prediction: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + for key in self.signal: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) - for signal in prediction - if signal not in ["Time [s]", "Discharge capacity [A.h]"] + for signal in self.signal ] ) @@ -79,20 +77,13 @@ def _evaluateS1(self, x): """ y, dy = self.problem.evaluateS1(x) - for key in y: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + for key in self.signal: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de - r = np.array( - [ - y[signal] - self._target[signal] - for signal in y - if signal not in ["Time [s]", "Discharge capacity [A.h]"] - ] - ) + r = np.array([y[signal] - self._target[signal] for signal in self.signal]) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) @@ -156,16 +147,14 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - for key in prediction: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + for key in self.signal: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sum(((prediction[signal] - self._target[signal]) ** 2)) - for signal in prediction - if signal not in ["Time [s]", "Discharge capacity [A.h]"] + for signal in self.signal ] ) if self.n_outputs == 1: @@ -194,20 +183,13 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - for key in y: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de - - r = np.array( - [ - y[signal] - self._target[signal] - for signal in y - if signal not in ["Time [s]", "Discharge capacity [A.h]"] - ] - ) + for key in self.signal: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array([y[signal] - self._target[signal] for signal in self.signal]) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) From 9b0373477eacd7ca84286dee38d7dea2eac99de4 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 13:00:40 +0000 Subject: [PATCH 12/20] Updt. cost2d/optim2d x0 shape/colour, revert conftest win platform unicode, add infeasible unit test --- conftest.py | 5 ---- pybop/plotting/plot_convergence.py | 30 ++++++++++----------- tests/integration/test_parameterisations.py | 4 +-- tests/unit/test_optimisation.py | 11 ++++++++ 4 files changed, 28 insertions(+), 22 deletions(-) diff --git a/conftest.py b/conftest.py index d8e79fcd8..3641cbd11 100644 --- a/conftest.py +++ b/conftest.py @@ -1,7 +1,6 @@ import pytest import matplotlib import plotly -import sys plotly.io.renderers.default = None matplotlib.use("Template") @@ -43,10 +42,6 @@ def pytest_configure(config): config.addinivalue_line("markers", "plots: mark test as a plot test") config.addinivalue_line("markers", "notebook: mark test as a notebook test") - if sys.platform.startswith("win"): - # Set the output encoding to UTF-8 on Windows - sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf8", buffering=1) - def pytest_collection_modifyitems(config, items): options = { diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index b59631798..53324b574 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -98,35 +98,35 @@ def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): # Import plotly only when needed go = pybop.PlotlyManager().go - # Plot the initial guess + # Plot the optimisation trace + optim_trace = np.array([item for sublist in optim.log for item in sublist]) + optim_trace = optim_trace.reshape(-1, 2) fig.add_trace( go.Scatter( - x=[optim.x0[0]], - y=[optim.x0[1]], + x=optim_trace[:, 0], + y=optim_trace[:, 1], mode="markers", - marker_symbol="x", marker=dict( - color="red", - line_color="midnightblue", - line_width=1, - size=12, + color=[i / len(optim_trace) for i in range(len(optim_trace))], + colorscale="YlOrBr", showscale=False, ), showlegend=False, ) ) - # Plot the optimisation trace - optim_trace = np.array([item for sublist in optim.log for item in sublist]) - optim_trace = optim_trace.reshape(-1, 2) + # Plot the initial guess fig.add_trace( go.Scatter( - x=optim_trace[:, 0], - y=optim_trace[:, 1], + x=[optim.x0[0]], + y=[optim.x0[1]], mode="markers", + marker_symbol="circle", marker=dict( - color=[i / len(optim_trace) for i in range(len(optim_trace))], - colorscale="YlOrBr", + color="mediumspringgreen", + line_color="mediumspringgreen", + line_width=1, + size=14, showscale=False, ), showlegend=False, diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index 33ad4f364..0aecf6158 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -91,7 +91,7 @@ def test_spm_optimisers(self, optimiser, spm_costs): if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) - parameterisation.cost.problem._model.allow_infeasible_solutions = False + parameterisation.cost.problem.model.allow_infeasible_solutions = False assert parameterisation._use_f_guessed is True parameterisation.set_max_iterations(1) x, final_cost = parameterisation.run() @@ -115,7 +115,7 @@ def test_spm_optimisers(self, optimiser, spm_costs): x, final_cost = parameterisation.run() elif optimiser in [pybop.SciPyMinimize]: - parameterisation.cost.problem._model.allow_infeasible_solutions = False + parameterisation.cost.problem.model.allow_infeasible_solutions = False x, final_cost = parameterisation.run() else: diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 6569d1ad5..7e1a4b10a 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -122,6 +122,17 @@ def test_halting(self, cost): with pytest.raises(ValueError): optim.set_max_unchanged_iterations(1, threshold=-1) + @pytest.mark.unit + def test_infeasible_solutions(self, cost): + # Test infeasible solutions + for optimiser in [pybop.SciPyMinimize, pybop.GradientDescent]: + optim = pybop.Optimisation( + cost=cost, optimiser=optimiser, allow_infeasible_solutions=False + ) + optim.set_max_iterations(1) + optim.run() + assert optim._iterations == 1 + @pytest.mark.unit def test_unphysical_result(self, cost): # Trigger parameters not physically viable warning From e7aef79b6b102bf0ca67096f1bee6f20f36a09fb Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 13:43:23 +0000 Subject: [PATCH 13/20] Updt SciPy & BaseOptimiser for maximum iterations limit - fixes #237 --- pybop/_optimisation.py | 5 +++-- pybop/optimisers/base_optimiser.py | 2 +- pybop/optimisers/scipy_optimisers.py | 16 ++++------------ 3 files changed, 8 insertions(+), 15 deletions(-) diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py index d5114b283..04e0b4ad2 100644 --- a/pybop/_optimisation.py +++ b/pybop/_optimisation.py @@ -175,15 +175,16 @@ def _run_pybop(self): final_cost : float The final cost associated with the best parameters. """ - x, final_cost = self.optimiser.optimise( + result = self.optimiser.optimise( cost_function=self.cost, x0=self.x0, bounds=self.bounds, maxiter=self._max_iterations, ) self.log = self.optimiser.log + self._iterations = result.nit - return x, final_cost + return result.x, self.cost(result.x) def _run_pints(self): """ diff --git a/pybop/optimisers/base_optimiser.py b/pybop/optimisers/base_optimiser.py index 29cc219a2..f8796bc99 100644 --- a/pybop/optimisers/base_optimiser.py +++ b/pybop/optimisers/base_optimiser.py @@ -38,7 +38,7 @@ def optimise(self, cost_function, x0=None, bounds=None, maxiter=None): self.cost_function = cost_function self.x0 = x0 self.bounds = bounds - self.maxiter = maxiter + self._max_iterations = maxiter # Run optimisation result = self._runoptimise(self.cost_function, x0=self.x0, bounds=self.bounds) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index f4ea49a72..5fc12ea69 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -80,7 +80,7 @@ def cost_wrapper(x): else: self.options.pop("maxiter", None) - output = minimize( + result = minimize( cost_wrapper, x0, method=self.method, @@ -89,11 +89,7 @@ def cost_wrapper(x): callback=callback, ) - # Get performance statistics - x = output.x - final_cost = cost_function(x) - - return x, final_cost + return result def needs_sensitivities(self): """ @@ -182,7 +178,7 @@ def callback(x, convergence): (lower, upper) for lower, upper in zip(bounds["lower"], bounds["upper"]) ] - output = differential_evolution( + result = differential_evolution( cost_function, bounds, strategy=self.strategy, @@ -191,11 +187,7 @@ def callback(x, convergence): callback=callback, ) - # Get performance statistics - x = output.x - final_cost = output.fun - - return x, final_cost + return result def set_population_size(self, population_size=None): """ From afd4990f84ff4acfd0d07a944beea270df9c118e Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 15:25:37 +0000 Subject: [PATCH 14/20] add infeasible cost tests, remove redundant scipyminimise maxiter options check --- pybop/optimisers/scipy_optimisers.py | 13 ++++--------- tests/unit/test_cost.py | 2 +- 2 files changed, 5 insertions(+), 10 deletions(-) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 5fc12ea69..010c653b5 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -49,9 +49,10 @@ def _runoptimise(self, cost_function, x0, bounds): A tuple (x, final_cost) containing the optimized parameters and the value of `cost_function` at the optimum. """ - # Add callback storing history of parameter values self.log = [[x0]] + self.options = {"maxiter": self._max_iterations} + # Add callback storing history of parameter values def callback(x): self.log.append([x]) @@ -74,12 +75,6 @@ def cost_wrapper(x): (lower, upper) for lower, upper in zip(bounds["lower"], bounds["upper"]) ) - # Set max iterations - if self._max_iterations is not None: - self.options = {"maxiter": self._max_iterations} - else: - self.options.pop("maxiter", None) - result = minimize( cost_wrapper, x0, @@ -158,6 +153,8 @@ def _runoptimise(self, cost_function, x0=None, bounds=None): A tuple (x, final_cost) containing the optimized parameters and the value of ``cost_function`` at the optimum. """ + self.log = [] + if bounds is None: raise ValueError("Bounds must be specified for differential_evolution.") @@ -167,8 +164,6 @@ def _runoptimise(self, cost_function, x0=None, bounds=None): ) # Add callback storing history of parameter values - self.log = [] - def callback(x, convergence): self.log.append([x]) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 7d3a4ea63..b6f4daf33 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -153,10 +153,10 @@ def test_costs(self, cost): for i in range(len(record)): assert "Non-physical point encountered" in str(record[i].message) - if isinstance(cost, pybop.RootMeanSquaredError): # Test infeasible locations cost.problem._model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf + assert cost.evaluateS1([1.1]) == (np.inf, cost._de) # Test exception for non-numeric inputs with pytest.raises(ValueError): From db284402100e0b1bb8c3ed0d4b6dad98f849ed8f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 10:08:06 +0000 Subject: [PATCH 15/20] Updt grad descent hypers for likelihood tests, add tol arg to scipy optimisers, pass optimiser final cost as is --- pybop/_optimisation.py | 8 ++++---- pybop/costs/_likelihoods.py | 2 +- pybop/optimisers/scipy_optimisers.py | 10 ++++++++-- tests/integration/test_parameterisations.py | 12 ++++++++---- 4 files changed, 21 insertions(+), 11 deletions(-) diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py index b0a819052..a7bd6732e 100644 --- a/pybop/_optimisation.py +++ b/pybop/_optimisation.py @@ -156,8 +156,6 @@ def run(self): x, final_cost = self._run_pints() elif not self.pints: x, final_cost = self._run_pybop() - if not self._minimising: - final_cost = -final_cost # Store the optimised parameters if self.cost.problem is not None: @@ -374,8 +372,10 @@ def _run_pints(self): # Store the optimised parameters self.store_optimised_parameters(x) - # Return best position and score - return x, f if self._minimising else -f + # Return best position and the score used internally, + # i.e the negative log-likelihood in the case of + # self._minimising = False + return x, f def f_guessed_tracking(self): """ diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 239535739..5af2805a9 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -96,7 +96,7 @@ def _evaluateS1(self, x, grad=None): for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) - dl = self._de * np.ones(self.n_parameters) + dl = self._dl * np.ones(self.n_parameters) return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index a3bac83d7..d3a6177c6 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -20,10 +20,11 @@ class SciPyMinimize(BaseOptimiser): Maximum number of iterations to perform. """ - def __init__(self, method=None, bounds=None, maxiter=None): + def __init__(self, method=None, bounds=None, maxiter=None, tol=1e-5): super().__init__() self.method = method self.bounds = bounds + self.tol = tol self.options = {} self._max_iterations = maxiter @@ -79,6 +80,7 @@ def cost_wrapper(x): x0, method=self.method, bounds=bounds, + tol=self.tol, options=self.options, callback=callback, ) @@ -126,8 +128,11 @@ class SciPyDifferentialEvolution(BaseOptimiser): The number of individuals in the population. Defaults to 15. """ - def __init__(self, bounds=None, strategy="best1bin", maxiter=1000, popsize=15): + def __init__( + self, bounds=None, strategy="best1bin", maxiter=1000, popsize=15, tol=1e-5 + ): super().__init__() + self.tol = tol self.strategy = strategy self._max_iterations = maxiter self._population_size = popsize @@ -178,6 +183,7 @@ def callback(x, convergence): strategy=self.strategy, maxiter=self._max_iterations, popsize=self._population_size, + tol=self.tol, callback=callback, ) diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index c9ac02b0e..73c87de45 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -70,7 +70,7 @@ def spm_costs(self, model, parameters, cost_class, init_soc): model, parameters, dataset, signal=signal, init_soc=init_soc ) if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma=[0.05, 0.05]) + return cost_class(problem, sigma=[0.03, 0.03]) else: return cost_class(problem) @@ -123,7 +123,11 @@ def test_spm_optimisers(self, optimiser, spm_costs): assert parameterisation._max_iterations == 125 elif optimiser in [pybop.GradientDescent]: - parameterisation.optimiser.set_learning_rate(0.02) + if isinstance(spm_costs, pybop.GaussianLogLikelihoodKnownSigma): + parameterisation.optimiser.set_learning_rate(1.8e-5) + parameterisation.set_min_iterations(150) + else: + parameterisation.optimiser.set_learning_rate(0.02) parameterisation.set_max_iterations(150) x, final_cost = parameterisation.run() @@ -196,9 +200,9 @@ def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): # Test each optimiser parameterisation = pybop.Optimisation( - cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.05 + cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.03 ) - parameterisation.set_max_unchanged_iterations(iterations=15, threshold=5e-4) + parameterisation.set_max_unchanged_iterations(iterations=35, threshold=5e-4) parameterisation.set_max_iterations(125) initial_cost = parameterisation.cost(spm_two_signal_cost.x0) From 61d7d7ae5d6e0d1a385a6db020ad31b947fc2cec Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 10:42:38 +0000 Subject: [PATCH 16/20] Split kaleido dependancy to avoid windows hang --- pyproject.toml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 36eb03a43..311e71c0b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,12 @@ dependencies = [ ] [project.optional-dependencies] -plot = ["plotly>=5.0", "kaleido>=0.2"] +# Split kaleido into two dependencies to avoid Windows hang +# See: https://github.com/plotly/Kaleido/issues/110 +plot = ["plotly>=5.0", + "kaleido==0.1.0.post1; sys_platform == 'win32'", + "kaleido>=0.2; sys_platform != 'win32'", +] docs = [ "pydata-sphinx-theme", "sphinx>=6", From 744d1663f32db734653c31d6970da62636ae21a0 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 12:08:31 +0000 Subject: [PATCH 17/20] small refactors and cleanup --- pybop/_problem.py | 12 +++++++++--- pybop/costs/_likelihoods.py | 11 ++++------- pybop/costs/design_costs.py | 6 +++--- pybop/models/lithium_ion/echem_base.py | 6 ++---- 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/pybop/_problem.py b/pybop/_problem.py index 3fa07ec63..de01c6007 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -49,7 +49,7 @@ def __init__( self._time_data = None self._target = None - if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): + if isinstance(model, pybop.BaseModel): self.additional_variables = additional_variables else: self.additional_variables = [] @@ -186,7 +186,10 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]"] + # Add time and remove duplicates + additional_variables.extend(["Time [s]"]) + additional_variables = list(set(additional_variables)) + super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -308,7 +311,10 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Current [A]"] + # Add time and current and remove duplicates + additional_variables.extend(["Time [s]", "Current [A]"]) + additional_variables = list(set(additional_variables)) + super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 5af2805a9..51254f7f0 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -91,7 +91,6 @@ def _evaluateS1(self, x, grad=None): Calls the problem.evaluateS1 method and calculates the log-likelihood """ - y, dy = self.problem.evaluateS1(x) for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): @@ -100,16 +99,15 @@ def _evaluateS1(self, x, grad=None): return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) + likelihood = self._evaluate(x) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = self.sigma2 * np.sum((r * dy), axis=1) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = self.sigma2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) return likelihood, np.sum(dl, axis=1) @@ -176,28 +174,27 @@ def _evaluateS1(self, x, grad=None): """ sigma = np.asarray(x[-self.n_outputs :]) if np.any(sigma <= 0): - return -np.float64(np.inf), self._de * np.ones(self.n_parameters) + return -np.float64(np.inf), -self._dl * np.ones(self.n_parameters) y, dy = self.problem.evaluateS1(x[: -self.n_outputs]) for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) - dl = self._de * np.ones(self.n_parameters) + dl = self._dl * np.ones(self.n_parameters) return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) + likelihood = self._evaluate(x) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = sigma ** (-2.0) * np.sum((r * dy), axis=1) dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = sigma ** (-2.0) * np.sum((r[:, :, np.newaxis] * dy), axis=1) dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index e1fb38cb4..16dd8a0f3 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,7 +1,7 @@ -import pybop import numpy as np import warnings +from pybop import is_numeric from pybop.costs.base_cost import BaseCost @@ -114,7 +114,7 @@ def _evaluate(self, x, grad=None): float The negative gravimetric energy density or infinity in case of infeasible parameters. """ - if not all(pybop.is_numeric(i) for i in x): + if not all(is_numeric(i) for i in x): raise ValueError("Input must be a numeric array.") try: @@ -173,7 +173,7 @@ def _evaluate(self, x, grad=None): float The negative volumetric energy density or infinity in case of infeasible parameters. """ - if not all(pybop.is_numeric(i) for i in x): + if not all(is_numeric(i) for i in x): raise ValueError("Input must be a numeric array.") try: with warnings.catch_warnings(): diff --git a/pybop/models/lithium_ion/echem_base.py b/pybop/models/lithium_ion/echem_base.py index 6a642ff34..c4664a31a 100644 --- a/pybop/models/lithium_ion/echem_base.py +++ b/pybop/models/lithium_ion/echem_base.py @@ -227,10 +227,8 @@ def approximate_capacity(self, x): average_voltage = positive_electrode_ocp( mean_sto_pos ) - negative_electrode_ocp(mean_sto_neg) - except TypeError: - average_voltage = positive_electrode_ocp([mean_sto_pos]).evaluate()[0][ - 0 - ] - negative_electrode_ocp(mean_sto_neg) # Super hacky, needs to be fixed + except Exception as e: + raise ValueError(f"Error in average voltage calculation: {e}") # Calculate and update nominal capacity theoretical_capacity = theoretical_energy / average_voltage From c1b38546872bbe2c3200b3a0bc4f90e6711d22bc Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 12:33:52 +0000 Subject: [PATCH 18/20] Updt changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7bfc4528f..043f2d30d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## Features +- [#198](https://github.com/pybop-team/PyBOP/pull/198) - Adds default subplot trace options, removes `[]` in axis plots as per SI standard, add varying signal length to quick_plot, restores design optimisation execption. +- [#224](https://github.com/pybop-team/PyBOP/pull/224) - Updated prediction objects to dictionaries, cost class calculations, added `additional_variables` argument to problem class, updated scipy.minimize defualt method to Nelder-Mead, added gradient cost landscape plots with optional argument. - [#218](https://github.com/pybop-team/PyBOP/pull/218) - Adds likelihood base class, `GaussianLogLikelihoodKnownSigma`, `GaussianLogLikelihood`, and `ProbabilityBased` cost function. As well as addition of a maximum likelihood estimation (MLE) example. - [#185](https://github.com/pybop-team/PyBOP/pull/185) - Adds a pull request template, additional nox sessions `quick` for standard tests + docs, `pre-commit` for pre-commit, `test` to run all standard tests, `doctest` for docs. - [#215](https://github.com/pybop-team/PyBOP/pull/215) - Adds `release_workflow.md` and updates `release_action.yaml` From 716c671733b8dc70244e9d4caa478df0fa12e118 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Tue, 19 Mar 2024 12:08:27 +0000 Subject: [PATCH 19/20] updt coverage, bugfix sigma check/wrap --- examples/standalone/problem.py | 4 ++-- pybop/costs/_likelihoods.py | 10 +++++----- tests/unit/test_cost.py | 1 + tests/unit/test_likelihoods.py | 11 ++++++----- 4 files changed, 14 insertions(+), 12 deletions(-) diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index bc9cd31d5..45f5a6025 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -14,12 +14,12 @@ def __init__( model=None, check_model=True, signal=None, - default_variables=None, + additional_variables=None, init_soc=None, x0=None, ): super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self._dataset = dataset.data diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 51254f7f0..25408db44 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -16,11 +16,11 @@ def set_sigma(self, sigma): Setter for sigma parameter """ - if sigma is not type(np.array([])): - try: - sigma = np.array(sigma) - except Exception: - raise ValueError("Sigma must be a numpy array") + if not isinstance(sigma, np.ndarray): + sigma = np.array([sigma]) + + if not np.isreal(sigma).all(): + raise ValueError("Sigma must contain only numeric values") if np.any(sigma <= 0): raise ValueError("Sigma must not be negative") diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 693e3944b..7dc81ebab 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -195,6 +195,7 @@ def test_energy_density_costs( assert cost([0.4]) <= 0 # Should be a viable design assert cost([0.8]) == np.inf # Should exceed active material + porosity < 1 assert cost([1.4]) == np.inf # Definitely not viable + assert cost([-0.1]) == np.inf # Should not be a viable design # Test infeasible locations cost.problem._model.allow_infeasible_solutions = False diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index 02db1e87f..2aeb8ed72 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -74,6 +74,8 @@ def test_base_likelihood_init(self, problem): assert likelihood.bounds == problem.bounds assert likelihood._n_parameters == 1 assert np.array_equal(likelihood._target, problem._target) + with pytest.raises(ValueError): + likelihood.set_sigma("Test") @pytest.mark.unit def test_base_likelihood_call_raises_not_implemented_error(self, problem): @@ -126,9 +128,8 @@ def test_gaussian_log_likelihood(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood_call_returns_negative_inf_for_non_positive_sigma( - self, problem - ): + def test_gaussian_log_likelihood_call_returns_negative_inf(self, problem): likelihood = pybop.GaussianLogLikelihood(problem) - result = likelihood(np.array([-0.5])) - assert result == -np.inf + assert likelihood(np.array([-0.5])) == -np.inf # negative value + with pytest.raises(ValueError): + assert likelihood(np.array([0.3])) == -np.inf # parameter value too small From a479136c2bdf8dbca25d67c7d8717f079b792c79 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Tue, 19 Mar 2024 15:02:20 +0000 Subject: [PATCH 20/20] coverage, bugfix model.simulateS1 --- pybop/costs/_likelihoods.py | 16 ++++++++-------- pybop/models/base_model.py | 4 +++- tests/unit/test_cost.py | 2 ++ tests/unit/test_likelihoods.py | 23 +++++++++++++++++++---- 4 files changed, 32 insertions(+), 13 deletions(-) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 93fc46e6c..dc045f0bd 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -64,18 +64,17 @@ def _evaluate(self, x, grad=None): Calls the problem.evaluate method and calculates the log-likelihood """ - prediction = self.problem.evaluate(x) + y = self.problem.evaluate(x) for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): + if len(y.get(key, [])) != len(self._target.get(key, [])): return -np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sum( self._offset - + self._multip - * np.sum((self._target[signal] - prediction[signal]) ** 2) + + self._multip * np.sum((self._target[signal] - y[signal]) ** 2) ) for signal in self.signal ] @@ -92,6 +91,7 @@ def _evaluateS1(self, x, grad=None): the log-likelihood """ y, dy = self.problem.evaluateS1(x) + for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) @@ -144,10 +144,10 @@ def _evaluate(self, x, grad=None): if np.any(sigma <= 0): return -np.inf - prediction = self.problem.evaluate(x[: -self.n_outputs]) + y = self.problem.evaluate(x[: -self.n_outputs]) for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): + if len(y.get(key, [])) != len(self._target.get(key, [])): return -np.float64(np.inf) # prediction doesn't match target e = np.array( @@ -155,8 +155,7 @@ def _evaluate(self, x, grad=None): np.sum( self._logpi - self._n_times * np.log(sigma) - - np.sum((self._target[signal] - prediction[signal]) ** 2) - / (2.0 * sigma**2) + - np.sum((self._target[signal] - y[signal]) ** 2) / (2.0 * sigma**2) ) for signal in self.signal ] @@ -173,6 +172,7 @@ def _evaluateS1(self, x, grad=None): the log-likelihood """ sigma = np.asarray(x[-self.n_outputs :]) + if np.any(sigma <= 0): return -np.float64(np.inf), -self._dl * np.ones(self.n_parameters) diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index cde839b01..0816cc211 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -410,7 +410,9 @@ def simulateS1(self, inputs, t_eval): for signal in self.signal for key in self.fit_keys ] - ).reshape(self.n_parameters, self.n_time_data, self.n_outputs) + ).reshape( + self.n_parameters, sol[self.signal[0]].data.shape[0], self.n_outputs + ) return y, dy diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 7dc81ebab..69abef2a7 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -161,6 +161,8 @@ def test_costs(self, cost): cost.problem._model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf assert cost.evaluateS1([1.1]) == (np.inf, cost._de) + assert cost([0.01]) == np.inf + assert cost.evaluateS1([0.01]) == (np.inf, cost._de) # Test exception for non-numeric inputs with pytest.raises(ValueError): diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index 2aeb8ed72..ed29f196d 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -128,8 +128,23 @@ def test_gaussian_log_likelihood(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood_call_returns_negative_inf(self, problem): + def test_gaussian_log_likelihood_returns_negative_inf(self, problem): likelihood = pybop.GaussianLogLikelihood(problem) - assert likelihood(np.array([-0.5])) == -np.inf # negative value - with pytest.raises(ValueError): - assert likelihood(np.array([0.3])) == -np.inf # parameter value too small + assert likelihood(np.array([-0.5, -0.5])) == -np.inf # negative sigma value + assert ( + likelihood.evaluateS1(np.array([-0.5, -0.5]))[0] == -np.inf + ) # negative sigma value + assert likelihood(np.array([0.01, 0.1])) == -np.inf # parameter value too small + assert ( + likelihood.evaluateS1(np.array([0.01, 0.1]))[0] == -np.inf + ) # parameter value too small + + @pytest.mark.unit + def test_gaussian_log_likelihood_known_sigma_returns_negative_inf(self, problem): + likelihood = pybop.GaussianLogLikelihoodKnownSigma( + problem, sigma=np.array([0.2]) + ) + assert likelihood(np.array([0.01])) == -np.inf # parameter value too small + assert ( + likelihood.evaluateS1(np.array([0.01]))[0] == -np.inf + ) # parameter value too small