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Commit c9c77367 authored by Le Roux Erwan's avatar Le Roux Erwan
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[projections] add relative bias, and some improvement on the name

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+79 -61
extreme_data/meteo_france_data/scm_models_data/visualization/main_study_visualizer.py
+ 5
− 2
View file @ c9c77367
......@@ -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',
......
projects/projected_snowfall/comparison_with_scm/main_comparison_on_quantile_period.py
+ 74
− 59
View file @ c9c77367
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__':
......
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