diff --git a/extreme_data/meteo_france_data/scm_models_data/visualization/main_study_visualizer.py b/extreme_data/meteo_france_data/scm_models_data/visualization/main_study_visualizer.py
index 9e346c0c97042fa70054c1a72607257a68adb5c2..9130c65cc940f8c8f8dc0586288e38cc8f2123d6 100644
--- a/extreme_data/meteo_france_data/scm_models_data/visualization/main_study_visualizer.py
+++ b/extreme_data/meteo_france_data/scm_models_data/visualization/main_study_visualizer.py
@@ -18,9 +18,11 @@ from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranS
SafranRainfall, \
SafranTemperature, SafranPrecipitation, SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, \
SafranSnowfall7Days, SafranPrecipitation1Day, SafranPrecipitation3Days, SafranPrecipitation5Days, \
- SafranPrecipitation7Days, SafranDateFirstSnowfall
+ SafranPrecipitation7Days, SafranDateFirstSnowfall, SafranSnowfallCenterOnDay1dayMeanRate, \
+ SafranSnowfallCenterOnDay1day
from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2020, \
SafranSnowfall2019
+from extreme_data.meteo_france_data.scm_models_data.safran.safran_variable import SafranSnowfallVariableCenterOnDay
from extreme_data.meteo_france_data.scm_models_data.visualization.study_visualizer import \
StudyVisualizer
from projects.exceeding_snow_loads.section_discussion.crocus_study_comparison_with_eurocode import \
@@ -39,13 +41,14 @@ SCM_STUDIES = [SafranSnowfall, CrocusSweTotal, CrocusDepth, CrocusSwe3Days]
SCM_STUDIES_NAMES = [get_display_name_from_object_type(k) for k in SCM_STUDIES]
SCM_STUDY_NAME_TO_SCM_STUDY = dict(zip(SCM_STUDIES_NAMES, SCM_STUDIES))
-
# I keep the scm study separated from the adamont study (for the tests)
SCM_STUDY_CLASS_TO_ABBREVIATION = {
SafranSnowfall: 'SF3',
SafranSnowfall1Day: 'daily snowfall',
SafranSnowfall2020: 'daily snowfall',
SafranSnowfall2019: 'daily snowfall',
+ SafranSnowfallCenterOnDay1dayMeanRate: 'daily snowfall',
+ SafranSnowfallCenterOnDay1day: 'daily snowfall',
GapBetweenSafranSnowfall2019And2020: 'daily snowfall\n bias = SAFRAN 2020 minus SAFRAN 2019',
GapBetweenSafranSnowfall2019AndMySafranSnowfall2019Recentered: 'daily snowfall\n my SAFRAN 2019 recentered minus SAFRAN 2019',
GapBetweenSafranSnowfall2019AndMySafranSnowfall2019NotRecentered: 'daily snowfall\n my SAFRAN 2019 notrecentered minus SAFRAN 2019',
diff --git a/projects/projected_snowfall/comparison_with_scm/main_comparison_on_quantile_period.py b/projects/projected_snowfall/comparison_with_scm/main_comparison_on_quantile_period.py
index a4289f85b2d2adc4ecea3954fe8f8fd18eb62979..8317e6e9f54391a8b8db6c5db309f8dca3f6076d 100644
--- a/projects/projected_snowfall/comparison_with_scm/main_comparison_on_quantile_period.py
+++ b/projects/projected_snowfall/comparison_with_scm/main_comparison_on_quantile_period.py
@@ -1,4 +1,5 @@
import numpy as np
+import os.path as op
import matplotlib
from extreme_data.meteo_france_data.adamont_data.adamont.adamont_snowfall import AdamontSnowfall
@@ -6,8 +7,11 @@ from extreme_data.meteo_france_data.adamont_data.adamont_gcm_rcm_couples import
get_year_min_and_year_max_used_to_compute_quantile
from extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario, gcm_rcm_couple_to_color, \
gcm_rcm_couple_to_str, load_gcm_rcm_couples
-from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day
-from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2020
+from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, \
+ SafranSnowfallCenterOnDay1dayMeanRate, SafranSnowfallCenterOnDay1day
+from extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2020, \
+ SafranSnowfall2019
+from extreme_data.meteo_france_data.scm_models_data.safran.safran_variable import SafranSnowfallVariableCenterOnDay
matplotlib.use('Agg')
@@ -22,6 +26,7 @@ def compute_bias_and_display_it(ax,
adamont_altitude_studies: AltitudesStudies,
gcm_rcm_couple,
massif_names=None,
+ relative_bias=False,
):
bias_in_the_mean_maxima = []
altitudes = []
@@ -44,6 +49,8 @@ def compute_bias_and_display_it(ax,
if m in adamont_study.massif_name_to_annual_maxima]))
bias = mean_maxima_adamont - mean_maxima_reanalysis
+ if relative_bias:
+ bias *= 100 / mean_maxima_reanalysis
bias_in_the_mean_maxima.append(bias)
color = gcm_rcm_couple_to_color[gcm_rcm_couple]
@@ -55,12 +62,14 @@ def compute_bias_and_display_it(ax,
def main_comparaison_plot():
altitudes = [600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600][:]
- for adamont_version in [1, 2][1:]:
+
+ for adamont_version in [1, 2][:]:
print('version:', adamont_version)
gcm_rcm_couples = load_gcm_rcm_couples(adamont_scenario=AdamontScenario.histo, adamont_version=adamont_version)
- study_class = SafranSnowfall2020 if adamont_version == 2 else SafranSnowfall1Day
+ study_class_for_adamont_v1 = SafranSnowfall1Day
+ study_class = SafranSnowfall2020 if adamont_version == 2 else study_class_for_adamont_v1
comparaison_study_class = 'SAFRAN 2020' if adamont_version == 2 else 'SAFRAN 2019'
# Faster to load once the two cases
@@ -78,61 +87,67 @@ def main_comparaison_plot():
else:
list_of_massis_names = [None] + [[m] for m in reanalysis_altitude_studies_1981.study.all_massif_names()]
- for massif_names in list_of_massis_names[:]:
- ax = plt.gca()
- bias_in_the_mean = []
- list_altitudes_for_bias = []
- for gcm_rcm_couple in gcm_rcm_couples:
- print(massif_names, gcm_rcm_couple)
- gcm, rcm = gcm_rcm_couple
- years_reanalysis, years_model = get_year_min_and_year_max_used_to_compute_quantile(gcm)
- assert years_reanalysis[0] in [1981, 1988]
- if years_reanalysis[0] == 1981:
- reanalysis_altitude_studies = reanalysis_altitude_studies_1981
- else:
- reanalysis_altitude_studies = reanalysis_altitude_studies_1988
- adamont_altitude_studies = AltitudesStudies(study_class=AdamontSnowfall,
- altitudes=altitudes,
- year_min=years_model[0],
- year_max=years_model[1],
- scenario=AdamontScenario.histo,
- gcm_rcm_couple=gcm_rcm_couple,
- adamont_version=adamont_version)
- bias, altitudes_for_bias = compute_bias_and_display_it(ax, reanalysis_altitude_studies,
- adamont_altitude_studies, gcm_rcm_couple, massif_names)
- bias_in_the_mean.append(bias)
- list_altitudes_for_bias.append(altitudes_for_bias)
-
- # Assert the all the bias have been computed for the same altitudes
- altitudes_for_bias = list_altitudes_for_bias[0]
- for alti in list_altitudes_for_bias:
- assert alti == altitudes_for_bias
-
- bias_in_the_mean = np.array(bias_in_the_mean)
- min_bias, median_bias, max_bias = [f(bias_in_the_mean, axis=0) for f in [np.min, np.median, np.max]]
-
- # Plot the range for the bias, and the median
- ax.yaxis.set_ticks(altitudes)
- color = 'k'
- ax.plot(median_bias, altitudes_for_bias, label='Median bias', color=color, linewidth=4)
- # ax.fill_betweenx(altitudes, min_bias, max_bias, label='Range for the bias', alpha=0.2, color='whitesmoke')
- ax.vlines(0, ymin=altitudes[0], ymax=altitudes[-1], color='k', linestyles='dashed')
- study_str = STUDY_CLASS_TO_ABBREVIATION[type(reanalysis_altitude_studies.study)]
- ax.set_ylim(top=altitudes[-1] + 1300)
- study = adamont_altitude_studies.study
- ax.legend(ncol=3, prop={'size': 7})
- ax.set_ylabel('Altitude (m)', fontsize=10)
- massif_str = 'all massifs' if massif_names is None else 'the {} massif'.format(massif_names[0])
- title = 'Bias in the mean annual maxima of {} of {}\n' \
- 'for ADAMONT v{}' \
- ' against {} on the quantile mapping period ({})'.format(study_str, massif_str,
- adamont_version,
- comparaison_study_class,
- study.variable_unit)
- plot_name = title
- ax.set_xlabel(title, fontsize=10)
- reanalysis_altitude_studies.show_or_save_to_file(plot_name=plot_name, no_title=True)
- plt.close()
+ for relative_bias in [True, False][:1]:
+ for massif_names in list_of_massis_names[:]:
+ ax = plt.gca()
+ bias_in_the_mean = []
+ list_altitudes_for_bias = []
+ for gcm_rcm_couple in gcm_rcm_couples:
+ print(massif_names, gcm_rcm_couple)
+ gcm, rcm = gcm_rcm_couple
+ years_reanalysis, years_model = get_year_min_and_year_max_used_to_compute_quantile(gcm)
+ assert years_reanalysis[0] in [1981, 1988]
+ if years_reanalysis[0] == 1981:
+ reanalysis_altitude_studies = reanalysis_altitude_studies_1981
+ else:
+ reanalysis_altitude_studies = reanalysis_altitude_studies_1988
+ adamont_altitude_studies = AltitudesStudies(study_class=AdamontSnowfall,
+ altitudes=altitudes,
+ year_min=years_model[0],
+ year_max=years_model[1],
+ scenario=AdamontScenario.histo,
+ gcm_rcm_couple=gcm_rcm_couple,
+ adamont_version=adamont_version)
+ bias, altitudes_for_bias = compute_bias_and_display_it(ax, reanalysis_altitude_studies,
+ adamont_altitude_studies, gcm_rcm_couple, massif_names,
+ relative_bias)
+ bias_in_the_mean.append(bias)
+ list_altitudes_for_bias.append(altitudes_for_bias)
+
+ # Assert the all the bias have been computed for the same altitudes
+ altitudes_for_bias = list_altitudes_for_bias[0]
+ for alti in list_altitudes_for_bias:
+ assert alti == altitudes_for_bias
+
+ bias_in_the_mean = np.array(bias_in_the_mean)
+ min_bias, median_bias, max_bias = [f(bias_in_the_mean, axis=0) for f in [np.min, np.median, np.max]]
+
+ # Plot the range for the bias, and the median
+ ax.yaxis.set_ticks(altitudes)
+ color = 'k'
+ ax.plot(median_bias, altitudes_for_bias, label='Median bias', color=color, linewidth=4)
+ # ax.fill_betweenx(altitudes, min_bias, max_bias, label='Range for the bias', alpha=0.2, color='whitesmoke')
+ ax.vlines(0, ymin=altitudes[0], ymax=altitudes[-1], color='k', linestyles='dashed')
+ study_str = STUDY_CLASS_TO_ABBREVIATION[type(reanalysis_altitude_studies.study)]
+ ax.set_ylim(top=altitudes[-1] + 1300)
+ study = adamont_altitude_studies.study
+ ax.legend(ncol=3, prop={'size': 7})
+ ax.set_ylabel('Altitude (m)', fontsize=10)
+ massif_str = 'all massifs' if massif_names is None else 'the {} massif'.format(massif_names[0])
+ unit = '%' if relative_bias else study.variable_unit
+ bias_name = 'Relative bias' if relative_bias else 'Bias'
+ title = '{} in the mean annual maxima of {} of {}\n' \
+ 'for ADAMONT v{}' \
+ ' against {} on the quantile mapping period ({})'.format(bias_name,
+ study_str, massif_str,
+ adamont_version,
+ comparaison_study_class,
+ unit)
+ folder = 'relative bias' if relative_bias else 'absolute bias'
+ plot_name = op.join(folder, title)
+ ax.set_xlabel(title, fontsize=10)
+ reanalysis_altitude_studies.show_or_save_to_file(plot_name=plot_name, no_title=True)
+ plt.close()
if __name__ == '__main__':