import math
import os
import os.path as op
from collections import OrderedDict
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
BLOCK_MAXIMA_DISPLAY_NAME = 'block maxima '
class StudyVisualizer(object):
def __init__(self, study: AbstractStudy, show=True, save_to_file=False, only_one_graph=False, only_first_row=False,
vertical_kde_plot=False, year_for_kde_plot=None, plot_block_maxima_quantiles=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.plot_name = None
# KDE PLOT ARGUMENTS
self.vertical_kde_plot = vertical_kde_plot
self.year_for_kde_plot = year_for_kde_plot
self.plot_block_maxima_quantiles = plot_block_maxima_quantiles
self.window_size_for_smoothing = 21
# PLOT ARGUMENTS
self.show = False if self.save_to_file else show
if self.only_one_graph:
self.figsize = (6.0, 4.0)
elif self.only_first_row:
self.figsize = (8.0, 6.0)
else:
self.figsize = (16.0, 10.0)
self.subplot_space = 0.05
self.coef_zoom_map = 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=self.subplot_space, wspace=self.subplot_space)
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)
self.plot_name = ' Empirical distribution '
self.plot_name += 'with all available data' if self.year_for_kde_plot is None else \
'for the year {}'.format(self.year_for_kde_plot)
self.show_or_save_to_file()
def visualize_experimental_law(self, ax, massif_id):
# Display the experimental law for a given massif
if self.year_for_kde_plot is not None:
all_massif_data = self.study.year_to_daily_time_serie[self.year_for_kde_plot][:, massif_id]
else:
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)
# Display an histogram on the background (with 100 bins, for visibility, and to check 0.9 quantiles)
ax2 = ax.twiny() if self.vertical_kde_plot else ax.twinx()
color_hist = 'k'
orientation = "horizontal" if self.vertical_kde_plot else 'vertical'
ax2.hist(all_massif_data, bins=50, density=True, histtype='step', color=color_hist, orientation=orientation)
label_function = ax2.set_xlabel if self.vertical_kde_plot else ax2.set_ylabel
# Do not display this label in the vertical plot
if not self.vertical_kde_plot:
label_function('normalized histogram', color=color_hist)
# Kde plot, and retrieve the data forming the line
color_kde = 'b'
sns.kdeplot(all_massif_data, bw=1, ax=ax, color=color_kde, vertical=self.vertical_kde_plot).set(xlim=0)
data_x, data_y = ax.lines[0].get_data()
# Plot the mean and median points
name_to_xlevel_and_color = OrderedDict()
name_to_xlevel_and_color['median'] = (np.median(all_massif_data), 'chartreuse')
name_to_xlevel_and_color['mean'] = (np.mean(all_massif_data), 'g')
# Plot some specific "extreme" quantiles with 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))]
name_to_xlevel_and_color[name] = (x_level, color)
# Plot some additional quantiles from the correspond Annual Maxima law
if self.plot_block_maxima_quantiles:
# This formula can only be applied if we have a daily time serie
assert len(self.study.year_to_daily_time_serie[1958]) in [365, 366]
p = p ** (1 / 365)
x_level = all_massif_data[int(p * len(all_massif_data))]
name_to_xlevel_and_color[BLOCK_MAXIMA_DISPLAY_NAME + name] = (x_level, color)
for name, (xi, color) in name_to_xlevel_and_color.items():
if self.vertical_kde_plot:
yi = xi
xi = np.interp(yi, data_y, data_x)
else:
yi = np.interp(xi, data_x, data_y)
marker = "x" if BLOCK_MAXIMA_DISPLAY_NAME in name else "o"
ax.scatter([xi], [yi], color=color, marker=marker, label=name)
label_function = ax.set_xlabel if self.vertical_kde_plot else ax.set_ylabel
label_function('Probability Density function f(x)', color=color_kde)
xlabel = 'x = {}'.format(self.study.title) if self.only_one_graph else 'x'
label_function = ax.set_ylabel if self.vertical_kde_plot else ax.set_xlabel
label_function(xlabel)
# Take all the ticks
# sorted_x_levels = sorted(list([x_level for x_level, _ in name_to_xlevel_and_color.values()]))
# extraticks = [float(float_to_str_with_only_some_significant_digits(x, nb_digits=2))
# for x in sorted_x_levels]
# Display only some specific ticks
extraticks_names = ['mean', AbstractParams.QUANTILE_100]
if self.plot_block_maxima_quantiles:
extraticks_names += [name for name in name_to_xlevel_and_color.keys() if BLOCK_MAXIMA_DISPLAY_NAME in name]
extraticks = [name_to_xlevel_and_color[name][0] for name in extraticks_names]
set_ticks_function = ax.set_yticks if self.vertical_kde_plot else ax.set_xticks
# Round up the ticks with a given number of significative digits
extraticks = [float(float_to_str_with_only_some_significant_digits(t, nb_digits=2)) for t in extraticks]
set_ticks_function(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)
self.plot_name = ' mean with sliding window of size {}'.format(self.window_size_for_smoothing)
self.show_or_save_to_file()
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 with sliding window of size {}'.format(self.window_size_for_smoothing), 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):
default_covariance_function = CovarianceFunction.cauchy
plot_name = 'Full Likelihood with Linear marginals and max stable dependency structure'
plot_name += '\n(with {} covariance structure when a covariance is needed)'.format(str(default_covariance_function).split('.')[-1])
self.plot_name = plot_name
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=self.subplot_space, wspace=self.subplot_space)
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)
# Add the label
self.show_or_save_to_file()
def fit_and_visualize_estimator(self, estimator, axes=None, title=None):
estimator.fit()
margin_fct = estimator.margin_function_fitted
axes = margin_fct.visualize_function(show=False, axes=axes, title='')
def get_lim_array(ax):
return np.array([np.array(ax.get_xlim()), np.array(ax.get_ylim())])
for ax in axes:
old_lim = get_lim_array(ax)
self.study.visualize(ax, fill=False, show=False)
new_lim = get_lim_array(ax)
assert 0 <= self.coef_zoom_map <= 1
updated_lim = new_lim * self.coef_zoom_map + (1 - self.coef_zoom_map) * old_lim
for i, method in enumerate([ax.set_xlim, ax.set_ylim]):
method(updated_lim[i, 0], updated_lim[i, 1])
ax.tick_params(axis=u'both', which=u'both', length=0)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_aspect('equal')
ax0 = axes[0]
ax0.get_yaxis().set_visible(True)
# todo: manage to remove ticks on ylabel
# finally it's good because it differntiate it from the other labels
# maybe i could put it in bold
ax0.set_ylabel(title)
ax0.tick_params(axis=u'both', which=u'both', length=0)
def show_or_save_to_file(self):
assert self.plot_name is not None
title = self.study.title
title += '\n' + self.plot_name
if self.only_one_graph:
plt.suptitle(self.plot_name)
else:
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(self.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=self.subplot_space, wspace=self.subplot_space)
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)