import math import os import os.path as op import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy from experiment.utils import average_smoothing_with_sliding_window from extreme_estimator.estimator.full_estimator.abstract_full_estimator import \ FullEstimatorInASingleStepWithSmoothMargin from extreme_estimator.estimator.margin_estimator.abstract_margin_estimator import SmoothMarginEstimator from extreme_estimator.extreme_models.margin_model.smooth_margin_model import LinearAllParametersAllDimsMarginModel from extreme_estimator.extreme_models.max_stable_model.abstract_max_stable_model import CovarianceFunction, \ AbstractMaxStableModelWithCovarianceFunction from extreme_estimator.margin_fits.abstract_params import AbstractParams from extreme_estimator.margin_fits.gev.gev_params import GevParams from extreme_estimator.margin_fits.gev.gevmle_fit import GevMleFit from extreme_estimator.margin_fits.gpd.gpd_params import GpdParams from extreme_estimator.margin_fits.gpd.gpdmle_fit import GpdMleFit from extreme_estimator.margin_fits.plot.create_shifted_cmap import get_color_rbga_shifted from spatio_temporal_dataset.dataset.abstract_dataset import AbstractDataset from test.test_utils import load_test_max_stable_models from utils import get_display_name_from_object_type, VERSION_TIME, float_to_str_with_only_some_significant_digits class StudyVisualizer(object): def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False): self.only_first_row = only_first_row self.only_one_graph = only_one_graph self.save_to_file = save_to_file self.study = study self.show = False if self.save_to_file else show self.window_size_for_smoothing = 21 if self.only_one_graph: self.figsize = (6.0, 4.0) elif self.only_first_row: self.figsize = (16.0, 6.0) else: self.figsize = (16.0, 10.0) @property def observations(self): return self.study.observations_annual_maxima @property def coordinates(self): return self.study.massifs_coordinates @property def dataset(self): return AbstractDataset(self.observations, self.coordinates) # Graph for each massif / or groups of massifs def visualize_massif_graphs(self, visualize_function): if self.only_one_graph: fig, ax = plt.subplots(1, 1, figsize=self.figsize) visualize_function(ax, 0) else: nb_columns = 5 nb_rows = 1 if self.only_first_row else math.ceil(len(self.study.safran_massif_names) / nb_columns) fig, axes = plt.subplots(nb_rows, nb_columns, figsize=self.figsize) fig.subplots_adjust(hspace=1.0, wspace=1.0) if self.only_first_row: for massif_id, massif_name in enumerate(self.study.safran_massif_names[:nb_columns]): ax = axes[massif_id] visualize_function(ax, massif_id) else: for massif_id, massif_name in enumerate(self.study.safran_massif_names): row_id, column_id = massif_id // nb_columns, massif_id % nb_columns ax = axes[row_id, column_id] visualize_function(ax, massif_id) def visualize_all_experimental_law(self): self.visualize_massif_graphs(self.visualize_experimental_law) plot_name = ' Empirical distribution with all available data' self.show_or_save_to_file(plot_name) def visualize_experimental_law(self, ax, massif_id): # Display the experimental law for a given massif all_massif_data = np.concatenate([data[:, massif_id] for data in self.study.year_to_daily_time_serie.values()]) all_massif_data = np.sort(all_massif_data) # Kde plot, and retrieve the data forming the line color_kde = 'b' sns.kdeplot(all_massif_data, bw=1, ax=ax, color=color_kde).set(xlim=0) data_x, data_y = ax.lines[0].get_data() # Plot the mean point in green x_level_to_color = { np.mean(all_massif_data): ('g', 'mean'), } # Plot some specific quantiles in their color for p, color, name in zip(AbstractParams.QUANTILE_P_VALUES, AbstractParams.QUANTILE_COLORS, AbstractParams.QUANTILE_NAMES): x_level = all_massif_data[int(p * len(all_massif_data))] x_level_to_color[x_level] = (color, name) for xi, (color, name) in x_level_to_color.items(): yi = np.interp(xi, data_x, data_y) ax.scatter([xi], [yi], color=color, marker="o", label=name) ax.set_ylabel('Probability Density function f(x)', color=color_kde) xlabel = 'x = {}'.format(self.study.title) if self.only_one_graph else 'x' ax.set_xlabel(xlabel) extraticks = [float(float_to_str_with_only_some_significant_digits(x, nb_digits=2)) for x in sorted(list(x_level_to_color.keys()))] if not self.only_one_graph: extraticks = [extraticks[0], extraticks[-1]] ax.set_xticks(extraticks) if not self.only_one_graph: ax.set_title(self.study.safran_massif_names[massif_id]) ax.legend() def visualize_all_mean_and_max_graphs(self): self.visualize_massif_graphs(self.visualize_mean_and_max_graph) plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing) self.show_or_save_to_file(plot_name) def visualize_mean_and_max_graph(self, ax, massif_id): # Display the graph of the max on top color_maxima = 'r' tuples_x_y = [(year, annual_maxima[massif_id]) for year, annual_maxima in self.study.year_to_annual_maxima.items()] x, y = list(zip(*tuples_x_y)) ax2 = ax.twinx() ax2.plot(x, y, color=color_maxima) ax2.set_ylabel('maxima', color=color_maxima) # Display the mean graph # Counting the sum of 3-consecutive days of snowfall does not have any physical meaning, # as we are counting twice some days color_mean = 'g' tuples_x_y = [(year, np.mean(data[:, massif_id])) for year, data in self.study.year_to_daily_time_serie.items()] x, y = list(zip(*tuples_x_y)) x, y = average_smoothing_with_sliding_window(x, y, window_size_for_smoothing=self.window_size_for_smoothing) ax.plot(x, y, color=color_mean) ax.set_ylabel('mean', color=color_mean) ax.set_xlabel('year') ax.set_title(self.study.safran_massif_names[massif_id]) def visualize_linear_margin_fit(self, only_first_max_stable=False): plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure' default_covariance_function = CovarianceFunction.cauchy max_stable_models = load_test_max_stable_models(default_covariance_function=default_covariance_function) if only_first_max_stable: max_stable_models = max_stable_models[:1] fig, axes = plt.subplots(len(max_stable_models) + 1, len(GevParams.SUMMARY_NAMES), figsize=self.figsize) fig.subplots_adjust(hspace=1.0, wspace=1.0) margin_class = LinearAllParametersAllDimsMarginModel # Plot the smooth margin only margin_model = margin_class(coordinates=self.coordinates) estimator = SmoothMarginEstimator(dataset=self.dataset, margin_model=margin_model) self.fit_and_visualize_estimator(estimator, axes[0], title='without max stable') # Plot the smooth margin fitted with a max stable for i, max_stable_model in enumerate(max_stable_models, 1): margin_model = margin_class(coordinates=self.coordinates) estimator = FullEstimatorInASingleStepWithSmoothMargin(self.dataset, margin_model, max_stable_model) title = get_display_name_from_object_type(type(max_stable_model)) if isinstance(max_stable_model, AbstractMaxStableModelWithCovarianceFunction): title += ' ' + str(default_covariance_function).split('.')[-1] self.fit_and_visualize_estimator(estimator, axes[i], title=title) self.show_or_save_to_file(plot_name) def fit_and_visualize_estimator(self, estimator, axes=None, title=None): estimator.fit() axes = estimator.margin_function_fitted.visualize_function(show=False, axes=axes, title=title) for ax in axes: self.study.visualize(ax, fill=False, show=False) def show_or_save_to_file(self, plot_name): title = self.study.title title += '\n' + plot_name if not self.only_one_graph: plt.suptitle(title) if self.show: plt.show() if self.save_to_file: filename = "{}/{}".format(VERSION_TIME, '_'.join(self.study.title.split())) if not self.only_one_graph: filename += "/{}".format('_'.join(plot_name.split())) filepath = op.join(self.study.result_full_path, filename + '.png') dir = op.dirname(filepath) if not op.exists(dir): os.makedirs(dir, exist_ok=True) plt.savefig(filepath) def visualize_independent_margin_fits(self, threshold=None, axes=None): if threshold is None: params_names = GevParams.SUMMARY_NAMES df = self.df_gev_mle_each_massif # todo: understand how Maurienne could be negative # print(df.head()) else: params_names = GpdParams.SUMMARY_NAMES df = self.df_gpd_mle_each_massif(threshold) if axes is None: fig, axes = plt.subplots(1, len(params_names)) fig.subplots_adjust(hspace=1.0, wspace=1.0) for i, gev_param_name in enumerate(params_names): ax = axes[i] massif_name_to_value = df.loc[gev_param_name, :].to_dict() # Compute the middle point of the values for the color map values = list(massif_name_to_value.values()) colors = get_color_rbga_shifted(ax, gev_param_name, values) massif_name_to_fill_kwargs = {massif_name: {'color': color} for massif_name, color in zip(self.study.safran_massif_names, colors)} self.study.visualize(ax=ax, massif_name_to_fill_kwargs=massif_name_to_fill_kwargs, show=False) if self.show: plt.show() def visualize_cmap(self, massif_name_to_value): orig_cmap = plt.cm.coolwarm # shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted') massif_name_to_fill_kwargs = {massif_name: {'color': orig_cmap(value)} for massif_name, value in massif_name_to_value.items()} self.study.visualize(massif_name_to_fill_kwargs=massif_name_to_fill_kwargs) """ Statistics methods """ @property def df_gev_mle_each_massif(self): # Fit a margin_fits on each massif massif_to_gev_mle = { massif_name: GevMleFit(self.study.observations_annual_maxima.loc[massif_name]).gev_params.summary_serie for massif_name in self.study.safran_massif_names} return pd.DataFrame(massif_to_gev_mle, columns=self.study.safran_massif_names) def df_gpd_mle_each_massif(self, threshold): # Fit a margin fit on each massif massif_to_gev_mle = {massif_name: GpdMleFit(self.study.df_all_snowfall_concatenated[massif_name], threshold=threshold).gpd_params.summary_serie for massif_name in self.study.safran_massif_names} return pd.DataFrame(massif_to_gev_mle, columns=self.study.safran_massif_names)