From f1345ab385d62e222d477fa41ffa68062081420a Mon Sep 17 00:00:00 2001
From: Le Roux Erwan <erwan.le-roux@irstea.fr>
Date: Tue, 15 Sep 2020 09:59:54 +0200
Subject: [PATCH] [contrasting] add a pre-computed of the max_abs value for the
shape parameter & for the various moments
---
.../scm_models_data/abstract_study.py | 20 ++-
.../visualization/plot_utils.py | 13 +-
.../visualization/study_visualizer.py | 8 +-
.../altitudes_fit/altitudes_studies.py | 2 +-
.../altitudes_fit/main_altitudes_studies.py | 122 +++++++------
...es_visualizer_for_non_stationary_models.py | 160 +++++++++---------
6 files changed, 178 insertions(+), 147 deletions(-)
diff --git a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
index 44228b3f..998e53b4 100644
--- a/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
+++ b/extreme_data/meteo_france_data/scm_models_data/abstract_study.py
@@ -17,6 +17,7 @@ import pandas as pd
from PIL import Image
from PIL import ImageDraw
from matplotlib.colors import Normalize
+from matplotlib.patches import Polygon
from netCDF4 import Dataset
from extreme_data.edf_data.weather_types import load_df_weather_types
@@ -98,6 +99,7 @@ class AbstractStudy(object):
""" Time """
+
@cached_property
def year_to_first_index_and_last_index(self):
year_to_first_index_and_last_index = OrderedDict()
@@ -546,15 +548,15 @@ class AbstractStudy(object):
ax.fill(*coords_list, **{'color': default_color_for_missing_massif})
# For the moment we comment all the part of this code
- # # Add a hatch to visualize the mean & variance variation sign
- # hatch_list = ['//', '\\\\']
- # if massif_name_to_hatch_boolean_list is not None:
- # if massif_name in massif_name_to_hatch_boolean_list:
- # a = np.array(coords_list).transpose()
- # hatch_boolean_list = massif_name_to_hatch_boolean_list[massif_name]
- # for hatch, is_hatch in zip(hatch_list, hatch_boolean_list):
- # if is_hatch:
- # ax.add_patch(Polygon(xy=a, fill=False, hatch=hatch))
+ # Add a hatch to visualize the mean & variance variation sign
+ hatch_list = ['//', '\\\\']
+ if massif_name_to_hatch_boolean_list is not None:
+ if massif_name in massif_name_to_hatch_boolean_list:
+ a = np.array(coords_list).transpose()
+ hatch_boolean_list = massif_name_to_hatch_boolean_list[massif_name]
+ for hatch, is_hatch in zip(hatch_list, hatch_boolean_list):
+ if is_hatch:
+ ax.add_patch(Polygon(xy=a, fill=False, hatch=hatch))
if show_label:
# Improve some explanation on the X axis and on the Y axis
diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py b/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
index f94aa78a..5bb951b5 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/plot_utils.py
@@ -35,8 +35,9 @@ def plot_against_altitude(x_ticks, ax, massif_id, massif_name, values, altitude=
def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_method, add_x_label=True,
- negative_and_positive_values=True, massif_name_to_text=None):
- max_abs_change = max([abs(e) for e in massif_name_to_value.values()])
+ negative_and_positive_values=True, massif_name_to_text=None, add_colorbar=True, max_abs_change=None):
+ if max_abs_change is None:
+ max_abs_change = max([abs(e) for e in massif_name_to_value.values()])
if negative_and_positive_values:
ticks, labels = ticks_values_and_labels_for_percentages(graduation=graduation, max_abs_change=max_abs_change)
min_ratio = -max_abs_change
@@ -52,6 +53,11 @@ def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_metho
for m, v in massif_name_to_value.items()}
ticks_values_and_labels = ticks, labels
ax = plt.gca()
+
+ massif_name_to_hatch_boolean_list = {}
+ for massif_name in set(AbstractStudy.all_massif_names()) - set(list(massif_name_to_value.keys())):
+ massif_name_to_hatch_boolean_list[massif_name] = [True, True]
+
AbstractStudy.visualize_study(ax=ax,
massif_name_to_value=massif_name_to_value,
massif_name_to_color=massif_name_to_color,
@@ -59,7 +65,7 @@ def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_metho
axis_off=False,
cmap=cmap,
show_label=False,
- add_colorbar=True,
+ add_colorbar=add_colorbar,
show=False,
vmin=min_ratio,
vmax=max_ratio,
@@ -68,6 +74,7 @@ def load_plot(cmap, graduation, label, massif_name_to_value, altitude, fit_metho
fontsize_label=10,
massif_name_to_text=massif_name_to_text,
add_text=massif_name_to_text is not None,
+ massif_name_to_hatch_boolean_list=massif_name_to_hatch_boolean_list
)
ax.get_xaxis().set_visible(True)
ax.set_xticks([])
diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py b/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
index 73881938..2ca68c25 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/study_visualizer.py
@@ -716,13 +716,15 @@ class StudyVisualizer(VisualizationParameters):
# PLot functions that should be common
def plot_map(self, cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label=True,
- negative_and_positive_values=True, massif_name_to_text=None, altitude=None):
+ negative_and_positive_values=True, massif_name_to_text=None, altitude=None, add_colorbar=True,
+ max_abs_change=None):
if altitude is None:
altitude = self.study.altitude
if len(massif_name_to_value) > 0:
load_plot(cmap, graduation, label, massif_name_to_value, altitude, fitmethod_to_str(fit_method),
add_x_label=add_x_label, negative_and_positive_values=negative_and_positive_values,
- massif_name_to_text=massif_name_to_text)
+ massif_name_to_text=massif_name_to_text,
+ add_colorbar=add_colorbar, max_abs_change=max_abs_change)
self.plot_name = plot_name
# self.show_or_save_to_file(add_classic_title=False, tight_layout=True, no_title=True, dpi=500)
self.show_or_save_to_file(add_classic_title=False, no_title=True, dpi=500, tight_layout=True)
@@ -736,5 +738,5 @@ class StudyVisualizer(VisualizationParameters):
plot_name2 = '{}/{}'.format(plot_name.split()[0], plot_name)
for plot_name in [plot_name1, plot_name2]:
self.plot_map(cmap, fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label, negative_and_positive_values,
- massif_name_to_text)
+ massif_name_to_text, )
diff --git a/projects/altitude_spatial_model/altitudes_fit/altitudes_studies.py b/projects/altitude_spatial_model/altitudes_fit/altitudes_studies.py
index 4c4fffa4..fff0c988 100644
--- a/projects/altitude_spatial_model/altitudes_fit/altitudes_studies.py
+++ b/projects/altitude_spatial_model/altitudes_fit/altitudes_studies.py
@@ -43,7 +43,7 @@ class AltitudesStudies(object):
# Dataset Loader
def spatio_temporal_dataset(self, massif_name, s_split_spatial: pd.Series = None,
- s_split_temporal: pd.Series = None, top_n_values_to_remove=None):
+ s_split_temporal: pd.Series = None):
coordinate_values_to_maxima = {}
massif_altitudes = self.massif_name_to_altitudes[massif_name]
if len(massif_altitudes) == 0:
diff --git a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
index 9ea4741b..e783ea88 100644
--- a/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
+++ b/projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py
@@ -1,3 +1,5 @@
+from typing import List
+
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
@@ -28,37 +30,6 @@ from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitudes_s
AltitudesStudiesVisualizerForNonStationaryModels
-def plot_time_series(studies, massif_names=None):
- studies.plot_maxima_time_series(massif_names=massif_names)
-
-
-def plot_moments(studies, massif_names=None):
- for std in [True, False][:]:
- for change in [True, False, None]:
- studies.plot_mean_maxima_against_altitude(massif_names=massif_names, std=std, change=change)
-
-
-def plot_altitudinal_fit(studies, massif_names=None):
- # model_classes = ALTITUDINAL_GEV_MODELS_LOCATION_ONLY_SCALE_ALTITUDES
- # model_classes = ALTITUDINAL_GEV_MODELS_LOCATION_QUADRATIC_MINIMUM
- model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
- # model_classes = ALTITUDINAL_GEV_MODELS_LOCATION
- # model_classes = ALTITUDINAL_GEV_MODELS_LOCATION_CUBIC_MINIMUM
- # model_classes = ALTITUDINAL_GEV_MODELS_QUADRATIC
- visualizer = AltitudesStudiesVisualizerForNonStationaryModels(studies=studies,
- model_classes=model_classes,
- massif_names=massif_names,
- show=False,
- temporal_covariate_for_fit=None,
- # temporal_covariate_for_fit=MeanAlpsTemperatureCovariate,
- top_n_values_to_remove=None,
- )
- # Plot the results for the model that minimizes the individual aic
- plot_individual_aic(visualizer)
- # Plot the results for the model that minimizes the total aic
- # plot_total_aic(model_classes, visualizer)
-
-
def main():
altitudes = [900, 1200, 1500, 1800, 2100, 2400, 2700, 3000][4:6]
# todo: l ecart pour les saisons de l automne ou de sprint
@@ -74,38 +45,87 @@ def main():
study_classes = [SafranPrecipitation1Day, SafranPrecipitation3Days, SafranPrecipitation5Days,
SafranPrecipitation7Days][:]
study_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranPrecipitation1Day
- , SafranPrecipitation3Days][:1]
+ , SafranPrecipitation3Days][:1]
altitudes = [1800, 2100, 2400]
study_classes = [SafranSnowfall1Day, SafranSnowfall3Days][:1]
# Common parameters
# altitudes = [600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600]
massif_names = None
+ massif_names = ['Mercantour', 'Vercors', 'Ubaye']
seasons = [Season.annual, Season.winter, Season.spring, Season.automn][:1]
- all_groups = altitudes_for_groups[:]
- for altitudes in all_groups:
- main_loop(altitudes, massif_names, seasons, study_classes)
+ main_loop(altitudes_for_groups, massif_names, seasons, study_classes)
-def main_loop(altitudes, massif_names, seasons, study_classes):
+def main_loop(altitudes_list, massif_names, seasons, study_classes):
+ assert isinstance(altitudes_list, List)
+ assert isinstance(altitudes_list[0], List)
for season in seasons:
for study_class in study_classes:
- if issubclass(study_class, SimulationStudy):
- for ensemble_idx in list(range(14))[:1]:
- studies = AltitudesStudies(study_class, altitudes, season=season,
- ensemble_idx=ensemble_idx)
- plot_studies(massif_names, season, studies, study_class)
- else:
- studies = AltitudesStudies(study_class, altitudes, season=season)
- plot_studies(massif_names, season, studies, study_class)
-
-
-def plot_studies(massif_names, season, studies, study_class):
- print('inner loop', season, study_class)
- # plot_time_series(studies, massif_names)
- # plot_moments(studies, massif_names)
- plot_altitudinal_fit(studies, massif_names)
+ # if issubclass(study_class, SimulationStudy):
+ # for ensemble_idx in list(range(14))[:1]:
+ # studies = AltitudesStudies(study_class, altitudes, season=season,
+ # ensemble_idx=ensemble_idx)
+ # plot_studies(massif_names, season, studies, study_class)
+ # else:
+ visualizer_list = load_visualizer_list(season, study_class, altitudes_list, massif_names)
+ for visualizer in visualizer_list:
+ plots(massif_names, season, visualizer)
+
+
+def load_visualizer_list(season, study_class, altitudes_list, massif_names):
+ model_classes = ALTITUDINAL_GEV_MODELS_BASED_ON_POINTWISE_ANALYSIS
+ visualizer_list = []
+ # Load all studies
+ for altitudes in altitudes_list:
+ print('here', altitudes)
+ studies = AltitudesStudies(study_class, altitudes, season=season)
+ visualizer = AltitudesStudiesVisualizerForNonStationaryModels(studies=studies,
+ model_classes=model_classes,
+ massif_names=massif_names,
+ show=False,
+ temporal_covariate_for_fit=None,
+ # temporal_covariate_for_fit=MeanAlpsTemperatureCovariate,
+ )
+ visualizer_list.append(visualizer)
+ # Compute the max abs for all metrics
+ d = {}
+ for method_name in AltitudesStudiesVisualizerForNonStationaryModels.moment_names:
+ for order in AltitudesStudiesVisualizerForNonStationaryModels.orders:
+ c = (method_name, order)
+ max_abs = max([
+ max([abs(e) for e in v.method_name_and_order_to_d(method_name, order).values()
+ ]) for v in visualizer_list])
+ d[c] = max_abs
+ # Assign the max abs dictionary
+ for v in visualizer_list:
+ v._method_name_and_order_to_max_abs = d
+ # Compute the max abs for the shape parameter
+ max_abs_for_shape = max([max([abs(e) for e in v.massif_name_to_shape.values()]) for v in visualizer_list])
+ for v in visualizer_list:
+ v._max_abs_for_shape = max_abs_for_shape
+
+ return visualizer_list
+
+
+def plots(massif_names, season, visualizer):
+ studies = visualizer.studies
+ print('inner loop', season, type(studies.study))
+
+ # Plot time series
+ # studies.plot_maxima_time_series(massif_names=massif_names)
+
+ # Plot moments
+ # for std in [True, False][:]:
+ # for change in [True, False, None]:
+ # studies.plot_mean_maxima_against_altitude(massif_names=massif_names, std=std, change=change)
+
+ # Plot the results for the model that minimizes the individual aic
+ plot_individual_aic(visualizer)
+
+ # Plot the results for the model that minimizes the total aic
+ # plot_total_aic(model_classes, visualizer)
if __name__ == '__main__':
diff --git a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
index 4240af2b..ee9f2868 100644
--- a/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
+++ b/projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/altitudes_studies_visualizer_for_non_stationary_models.py
@@ -21,7 +21,7 @@ from extreme_fit.model.margin_model.polynomial_margin_model.spatio_temporal_poly
from extreme_fit.model.margin_model.utils import MarginFitMethod
from projects.altitude_spatial_model.altitudes_fit.altitudes_studies import AltitudesStudies
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.altitude_group import \
- get_altitude_group_from_altitudes
+ get_altitude_group_from_altitudes, HighAltitudeGroup
from projects.altitude_spatial_model.altitudes_fit.one_fold_analysis.one_fold_fit import \
OneFoldFit
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
@@ -36,7 +36,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
fit_method=MarginFitMethod.extremes_fevd_mle,
temporal_covariate_for_fit=None,
display_only_model_that_pass_anderson_test=True,
- top_n_values_to_remove=None):
+ ):
super().__init__(studies.study, show=show, save_to_file=not show)
self.studies = studies
self.non_stationary_models = model_classes
@@ -50,14 +50,19 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
self._massif_name_to_one_fold_fit = {}
for massif_name in self.massif_names:
if any([massif_name in study.study_massif_names for study in self.studies.altitude_to_study.values()]):
- assert top_n_values_to_remove is None
- dataset = studies.spatio_temporal_dataset(massif_name=massif_name,
- top_n_values_to_remove=top_n_values_to_remove)
+ dataset = studies.spatio_temporal_dataset(massif_name=massif_name)
old_fold_fit = OneFoldFit(massif_name, dataset, model_classes, self.fit_method,
self.temporal_covariate_for_fit,
type(self.altitude_group),
self.display_only_model_that_pass_anderson_test)
self._massif_name_to_one_fold_fit[massif_name] = old_fold_fit
+ # Cache
+ self._method_name_and_order_to_massif_name_to_value = {}
+ self._method_name_and_order_to_max_abs = {}
+ self._max_abs_for_shape = None
+
+ moment_names = ['moment', 'changes_in_the_moment', 'relative_changes_in_the_moment'][2:]
+ orders = [1, 2, None][2:]
@property
def massif_name_to_one_fold_fit(self) -> Dict[str, OneFoldFit]:
@@ -66,72 +71,60 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
or old_fold_fit.has_at_least_one_valid_non_stationary_model}
def plot_moments(self):
- for method_name in ['moment', 'changes_in_the_moment', 'relative_changes_in_the_moment']:
- for order in [1, 2, None]:
+ for method_name in self.moment_names:
+ for order in self.orders:
# self.plot_against_years(method_name, order)
self.plot_map_moment(method_name, order)
- def plot_map_moment(self, method_name, order):
- # Compute values
- massif_name_to_value = {}
- for massif_name, one_fold_fit in self.massif_name_to_one_fold_fit.items():
- value = one_fold_fit.__getattribute__(method_name)([self.altitude_group.reference_altitude], order=order)[0]
- massif_name_to_value[massif_name] = value
+ def method_name_and_order_to_max_abs(self, method_name, order):
+ c = (method_name, order)
+ if c not in self._method_name_and_order_to_max_abs:
+ return None
+ else:
+ return self._method_name_and_order_to_max_abs[c]
+
+ def method_name_and_order_to_d(self, method_name, order):
+ c = (method_name, order)
+ if c not in self._method_name_and_order_to_massif_name_to_value:
+ # Compute values
+ massif_name_to_value = {}
+ for massif_name, one_fold_fit in self.massif_name_to_one_fold_fit.items():
+ value = \
+ one_fold_fit.__getattribute__(method_name)([self.altitude_group.reference_altitude], order=order)[0]
+ massif_name_to_value[massif_name] = value
+ # Remove values
+ if any([np.isinf(v) for v in massif_name_to_value.values()]):
+ print("shape to large > 0.5, thus removing std that are infinite")
+ massif_name_to_value = {m: v for m, v in massif_name_to_value.items()
+ if not np.isinf(v)}
+ # Store it
+ self._method_name_and_order_to_massif_name_to_value[c] = massif_name_to_value
+ return self._method_name_and_order_to_massif_name_to_value[c]
- # Common plot settings
+ def plot_map_moment(self, method_name, order):
+ massif_name_to_value = self.method_name_and_order_to_d(method_name, order)
+ # Plot settings
moment = ' '.join(method_name.split('_'))
moment = moment.replace('moment', '{} in 2019'.format(OneFoldFit.get_moment_str(order=order)))
plot_name = '{}{} annual maxima of {}'.format(OneFoldFit.folder_for_plots, moment,
- SCM_STUDY_CLASS_TO_ABBREVIATION[
- self.studies.study_class])
+ SCM_STUDY_CLASS_TO_ABBREVIATION[
+ self.studies.study_class])
ylabel = '{} ({})'.format(plot_name, self.study.variable_unit)
# Plot the map
- if any([np.isinf(v) for v in massif_name_to_value.values()]):
- print("shape to large > 0.5, thus removing std that are infinite")
- massif_name_to_value = {m: v for m, v in massif_name_to_value.items()
- if not np.isinf(v)}
-
- print(massif_name_to_value)
- negative_and_positive_values = min(massif_name_to_value.values()) < 0
+ a_change_is_displayed = self.moment_names.index(method_name) > 0
self.plot_map(cmap=plt.cm.coolwarm, fit_method=self.fit_method, graduation=10,
label=ylabel, massif_name_to_value=massif_name_to_value,
- plot_name=plot_name, add_x_label=True, negative_and_positive_values=negative_and_positive_values,
- massif_name_to_text=None, altitude=self.altitude_group.reference_altitude)
-
-
-
- # ax.get_xaxis().set_visible(True)
- # ax.set_xticks([])
- # ax.set_xlabel('Altitude = {}m'.format(self.altitude_group.reference_altitude), fontsize=15)
-
-
- # cmap = get_shifted_map(min_ratio, max_ratio)
- # print(massif_name_to_value)
- # massif_name_to_color = {m: get_colors([v], cmap, -max_abs_change, max_abs_change)[0]
- # for m, v in massif_name_to_value.items()}
- #
- #
- # ticks_values_and_labels = ticks_values_and_labels_for_percentages(graduation, max_abs_change)
- #
- # ax = self.study.visualize_study(massif_name_to_value=massif_name_to_value,
- # replace_blue_by_white=False,
- # axis_off=False, show_label=False,
- # add_colorbar=add_colorbar,
- # # massif_name_to_marker_style=self.massif_name_to_marker_style,
- # # marker_style_to_label_name=self.selected_marker_style_to_label_name,
- # massif_name_to_color=massif_name_to_color,
- # cmap=cmap,
- # show=False,
- # ticks_values_and_labels=ticks_values_and_labels,
- # label=ylabel,
- # add_legend=False,
- # )
-
- # self.plot_name = plot_name
- # self.show_or_save_to_file(add_classic_title=False, tight_layout=True, no_title=True,
- # dpi=500)
- # ax.clear()
+ plot_name=plot_name, add_x_label=True,
+ negative_and_positive_values=a_change_is_displayed,
+ massif_name_to_text=None, altitude=self.altitude_group.reference_altitude,
+ add_colorbar=self.add_colorbar,
+ max_abs_change=self.method_name_and_order_to_max_abs(method_name, order)
+ )
+
+ @property
+ def add_colorbar(self):
+ return isinstance(self.altitude_group, HighAltitudeGroup)
def plot_against_years(self, method_name, order):
ax = plt.gca()
@@ -149,19 +142,19 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
moment = ' '.join(method_name.split('_'))
moment = moment.replace('moment', '{} in 2019'.format(OneFoldFit.get_moment_str(order=order)))
plot_name = '{}Model {} annual maxima of {}'.format(OneFoldFit.folder_for_plots, moment,
- SCM_STUDY_CLASS_TO_ABBREVIATION[self.studies.study_class])
+ SCM_STUDY_CLASS_TO_ABBREVIATION[self.studies.study_class])
ax.set_ylabel('{} ({})'.format(plot_name, self.study.variable_unit), fontsize=15)
ax.set_xlabel('altitudes', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=13)
self.studies.show_or_save_to_file(plot_name=plot_name, show=self.show, no_title=True)
ax.clear()
- def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True,
- negative_and_positive_values=True, massif_name_to_text=None):
- plot_name = '{}{}'.format(OneFoldFit.folder_for_plots, label)
- self.plot_map(cmap, self.fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label,
- negative_and_positive_values,
- massif_name_to_text)
+ # def plot_abstract_fast(self, massif_name_to_value, label, graduation=10.0, cmap=plt.cm.coolwarm, add_x_label=True,
+ # negative_and_positive_values=True, massif_name_to_text=None):
+ # plot_name = '{}{}'.format(OneFoldFit.folder_for_plots, label)
+ # self.plot_map(cmap, self.fit_method, graduation, label, massif_name_to_value, plot_name, add_x_label,
+ # negative_and_positive_values,
+ # massif_name_to_text)
@property
def massif_name_to_shape(self):
@@ -203,24 +196,31 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
graduation = (max(values) - min(values)) / 6
print(coef_name, graduation, max(values), min(values))
negative_and_positive_values = (max(values) > 0) and (min(values) < 0)
- self.plot_abstract_fast(massif_name_to_best_coef,
+ raise NotImplementedError
+ self.plot_map(massif_name_to_value=massif_name_to_best_coef,
label='{}Coef/{} plot for {} {}'.format(OneFoldFit.folder_for_plots,
- coef_name,
- SCM_STUDY_CLASS_TO_ABBREVIATION[
- type(self.study)],
- self.study.variable_unit),
+ coef_name,
+ SCM_STUDY_CLASS_TO_ABBREVIATION[
+ type(self.study)],
+ self.study.variable_unit),
add_x_label=False, graduation=graduation, massif_name_to_text=self.massif_name_to_name,
negative_and_positive_values=negative_and_positive_values)
def plot_shape_map(self):
- self.plot_abstract_fast(self.massif_name_to_shape,
- label='Shape parameter for {} maxima of {} in 2019 at {}m'.format(
- self.study.season_name,
- SCM_STUDY_CLASS_TO_ABBREVIATION[
- type(self.study)],
- self.altitude_group.reference_altitude),
- add_x_label=False, graduation=0.1, massif_name_to_text=self.massif_name_to_name,
- cmap=matplotlib.cm.get_cmap('BrBG_r'))
+
+ label = 'Shape parameter for {} maxima of {} in 2019'.format(self.study.season_name,
+ SCM_STUDY_CLASS_TO_ABBREVIATION[
+ type(self.study)])
+ self.plot_map(massif_name_to_value=self.massif_name_to_shape,
+ label=label,
+ plot_name=label,
+ add_x_label=True, graduation=0.1, massif_name_to_text=self.massif_name_to_name,
+ cmap=matplotlib.cm.get_cmap('BrBG_r'),
+ altitude=self.altitude_group.reference_altitude,
+ add_colorbar=self.add_colorbar,
+ max_abs_change=self._max_abs_for_shape,
+ fit_method=self.fit_method,
+ )
def plot_altitude_for_the_peak(self):
pass
@@ -264,7 +264,7 @@ class AltitudesStudiesVisualizerForNonStationaryModels(StudyVisualizer):
massif_name.replace('_', ' '), self.study.variable_unit))
peak_year_folder = 'Peak year ' + ylabel
plot_name = '{}{}/Peak year for {}'.format(OneFoldFit.folder_for_plots, peak_year_folder,
- massif_name.replace('_', ''))
+ massif_name.replace('_', ''))
self.studies.show_or_save_to_file(plot_name=plot_name, show=self.show, no_title=True, tight_layout=True)
plt.close()
--
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