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Le Roux Erwan authoredef6df62b
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from collections import OrderedDict
import matplotlib as mpl
import pandas as pd
import numpy as np
from extreme_data.meteo_france_data.scm_models_data.crocus.crocus_variables import AbstractSnowLoadVariable
from extreme_data.meteo_france_data.scm_models_data.visualization.main_study_visualizer import \
ALL_ALTITUDES_WITH_20_STATIONS_AT_LEAST
mpl.use('Agg')
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall5Days, SafranSnowfall7Days, \
SafranRainfall1Day, SafranRainfall3Days, \
SafranRainfall5Days, SafranRainfall7Days, SafranRainfall, SafranSnowfall
from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSnowLoad3Days, \
CrocusSnowLoad5Days, CrocusSnowLoad7Days, CrocusSnowLoad1Day, Crocus
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranSnowfall1Day, SafranSnowfall3Days
def compute_ratio(rainfall, snowfall):
total_precipitation = rainfall + snowfall
has_some_year_without_precipitation = (total_precipitation == 0).any()
if has_some_year_without_precipitation:
print('Nan values because we have some year without precipitation')
ratio = snowfall / total_precipitation
return ratio
def df_snow_load_maxima_parittion(rainfall_study: SafranRainfall, snowfall_study: SafranSnowfall,
snow_load_study: Crocus):
ratios = []
for year in snow_load_study.ordered_years:
maxima_index = snow_load_study.year_to_annual_maxima_index[year]
snowfall = np.diagonal(snowfall_study.year_to_daily_time_serie_array[year][maxima_index])
rainfall = np.diagonal(rainfall_study.year_to_daily_time_serie_array[year][maxima_index])
ratio = compute_ratio(rainfall, snowfall)
ratios.append(pd.Series(ratio))
df = pd.concat(ratios, axis=1)
df.index = rainfall_study.study_massif_names
df.columns = rainfall_study.ordered_years
return df
def df_snow_load_top_maxima_partition(nb_top_values, rainfall_study: SafranRainfall, snowfall_study: SafranSnowfall,
snow_load_study: Crocus):
ratios = []
for i, (massif_name, ordered_index) in enumerate(
snow_load_study.massif_name_to_annual_maxima_ordered_index.items()):
ordered_years = snow_load_study.massif_name_to_annual_maxima_ordered_years[massif_name]
top_ordered_index = ordered_index[-nb_top_values:]
top_ordered_years = ordered_years[-nb_top_values:]
# Top values only
snowfall = [snowfall_study.year_to_daily_time_serie_array[year][idx, i] for idx, year in
zip(top_ordered_index, top_ordered_years)]
rainfall = [rainfall_study.year_to_daily_time_serie_array[year][idx, i] for idx, year in
zip(top_ordered_index, top_ordered_years)]
ratio = compute_ratio(np.array(rainfall), np.array(snowfall))
ratios.append(pd.Series(ratio))
df = pd.concat(ratios, axis=1).transpose()
df.index = rainfall_study.study_massif_names
return df
def main_snow_load_maxima_partition(year_min, year_max):
rainfall_classes = [SafranRainfall1Day, SafranRainfall3Days, SafranRainfall5Days, SafranRainfall7Days]
snowfall_classes = [SafranSnowfall1Day, SafranSnowfall3Days, SafranSnowfall5Days, SafranSnowfall7Days]
snow_load_classes = [CrocusSnowLoad1Day, CrocusSnowLoad3Days, CrocusSnowLoad5Days, CrocusSnowLoad7Days]
classes = list(zip(rainfall_classes, snowfall_classes, snow_load_classes))[1:2]
nb_top = 5
for study_classes in classes:
altitude_to_s = OrderedDict()
for altitude in ALL_ALTITUDES_WITH_20_STATIONS_AT_LEAST[:]:
studies = [study_class(altitude=altitude, year_min=year_min, year_max=year_max) for study_class in
study_classes]
df = df_snow_load_top_maxima_partition(nb_top, *studies)
df2 = df.transpose().describe().transpose()
# print(df, '\n')
# print(df2.iloc[:, 1:-2])
df2.drop(columns=['count', 'std'], inplace=True)
s = df2.mean(axis=0)
# print(s)
altitude_to_s[altitude] = s
df_final = pd.DataFrame(altitude_to_s).transpose().round(2)
print(nb_top, year_min, year_max)
print(df_final)
print('\n\n')
if __name__ == '__main__':
main_snow_load_maxima_partition(1959, 2019)
# main_snow_load_maxima_partition(1959, 1989)
# main_snow_load_maxima_partition(1990, 2019)