diff --git a/extreme_data/edf_data/weather_types.py b/extreme_data/edf_data/weather_types.py
index 519b85a84079246c1d6a85bed5c8d886f34e4014..7dcbb6e03075d5613a9f6b42f5c33de3132f0535 100644
--- a/extreme_data/edf_data/weather_types.py
+++ b/extreme_data/edf_data/weather_types.py
@@ -5,23 +5,50 @@ import pandas as pd
from extreme_data.meteo_france_data.scm_models_data.utils import date_to_str
from root_utils import get_full_path
-relative_path = r'local/EDF_data/Weather_types/CatalogueTT_EDF_France0_5308.txt'
-edf_filepath = get_full_path(relative_path=relative_path)
+# Type of Weather
+
+ANTICYCLONIC = 'Anticyclonic'
+
+CENTRAL_DEPRESSION = 'Central Depression'
+
+EAST_RETURN = 'East Return'
+
+NORTHEAST_CIRCULATION = 'Northeast Circulation'
+
+SOUTH_CIRCULATION = 'South Circulation'
+
+SOUTHWEST_CIRCULATION = 'Southwest Circulation'
+
+STEADY_OCEANIC = 'Steady Oceanic'
+
+ATLANTIC_WAVE = 'Atlantic Wave'
+
+wp_int_to_wp_str = {
+ 1: ATLANTIC_WAVE,
+ 2: STEADY_OCEANIC,
+ 3: SOUTHWEST_CIRCULATION,
+ 4: SOUTH_CIRCULATION,
+ 5: NORTHEAST_CIRCULATION,
+ 6: EAST_RETURN,
+ 7: CENTRAL_DEPRESSION,
+ 8: ANTICYCLONIC,
+}
def load_df_weather_types() -> pd.DataFrame:
+ relative_path = r'local/EDF_data/Weather_types/CatalogueTT_EDF_France0_5308.txt'
+ edf_filepath = get_full_path(relative_path=relative_path)
global df
weather_types = []
with open(edf_filepath, 'rb') as f:
for i, l in enumerate(f):
if i >= 7:
l = str(l).split('"')[1:]
- wp = int(l[1][2])
+ wp_int = int(l[1][2])
+ wp_str = wp_int_to_wp_str[wp_int]
day, month, year = [int(e) for e in l[0].split('/')]
date_str = date_to_str(datetime(year=year, month=month, day=day))
- weather_types.append((date_str, wp))
+ weather_types.append((date_str, wp_str))
df = pd.DataFrame(weather_types, columns=['Date', 'WP'])
df.set_index('Date', inplace=True)
return df
-
-
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 64fd8cc22edd07f5dd93e04dcf0b0090bb51587d..ddafc5d3f181a1410346075c43b93236d25d2d58 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
@@ -82,7 +82,7 @@ class AbstractStudy(object):
# Map each year to the 'days since year-08-01 06:00:00'
year_to_days = OrderedDict()
for year in self.ordered_years:
- date = datetime.datetime(year=year-1, month=8, day=1, hour=6, minute=0, second=0)
+ date = datetime.datetime(year=year - 1, month=8, day=1, hour=6, minute=0, second=0)
days = []
for i in range(366):
days.append(date_to_str(date))
@@ -96,11 +96,19 @@ class AbstractStudy(object):
def year_to_wps(self):
assert 1954 <= self.year_min and self.year_max <= WP_PATTERN_MAX_YEAR, \
'Weather patterns are not available between {} and {}'.format(self.year_min, self.year_max)
- year_to_wps = {}
+ year_to_wps = OrderedDict()
for year, days in self.year_to_days.items():
year_to_wps[year] = self.df_weather_types.loc[days].iloc[:, 0].values
return year_to_wps
+ @cached_property
+ def year_to_wp_for_annual_maxima(self):
+ year_to_wp_for_annual_maxima = OrderedDict()
+ for year, idx in self.year_to_annual_maxima_index.items():
+ wps_for_annual_maxima = self.year_to_wps[year][idx]
+ year_to_wp_for_annual_maxima[year] = wps_for_annual_maxima
+ return year_to_wp_for_annual_maxima
+
@cached_property
def df_weather_types(self):
return load_df_weather_types()
@@ -190,8 +198,6 @@ class AbstractStudy(object):
""" Load daily observations """
-
-
@cached_property
def year_to_daily_time_serie_array(self) -> OrderedDict:
return self._year_to_daily_time_serie_array
@@ -252,7 +258,7 @@ class AbstractStudy(object):
@cached_property
def ordered_years_and_path_files(self):
- nc_files = [(int(f.split('_')[-2][:4])+1, f) for f in os.listdir(self.study_full_path) if f.endswith('.nc')]
+ nc_files = [(int(f.split('_')[-2][:4]) + 1, f) for f in os.listdir(self.study_full_path) if f.endswith('.nc')]
ordered_years, path_files = zip(*[(year, op.join(self.study_full_path, nc_file))
for year, nc_file in sorted(nc_files, key=lambda t: t[0])
if self.year_min <= year <= self.year_max])
@@ -609,7 +615,3 @@ class AbstractStudy(object):
mask_massif = np.array(img)
mask_french_alps += mask_massif
return ~np.array(mask_french_alps, dtype=bool)
-
-
-
-
diff --git a/test/test_extreme_data/test_edf_data/test_weather_types.py b/test/test_extreme_data/test_edf_data/test_weather_types.py
index aa5966452e281f190ad8d60f32c6b21e4a3b4468..78af1c0845113aa642b8db33b4160a5b92145831 100644
--- a/test/test_extreme_data/test_edf_data/test_weather_types.py
+++ b/test/test_extreme_data/test_edf_data/test_weather_types.py
@@ -4,7 +4,7 @@ from datetime import datetime
import pandas as pd
-from extreme_data.edf_data.weather_types import load_df_weather_types
+from extreme_data.edf_data.weather_types import load_df_weather_types, wp_int_to_wp_str, ANTICYCLONIC, STEADY_OCEANIC
from extreme_data.meteo_france_data.scm_models_data.crocus.crocus import CrocusSwe3Days
from extreme_data.meteo_france_data.scm_models_data.safran.safran import SafranTemperature, SafranPrecipitation1Day
from extreme_data.meteo_france_data.scm_models_data.utils import date_to_str
@@ -16,8 +16,8 @@ class TestWeatherTypes(unittest.TestCase):
df = load_df_weather_types()
self.assertEqual(len(df), 20354)
# Assert values
- self.assertEqual(df.iloc[0, :].values[0], 5)
- self.assertEqual(df.iloc[-1, :].values[0], 8)
+ self.assertEqual(df.iloc[0, :].values[0], wp_int_to_wp_str[5])
+ self.assertEqual(df.iloc[-1, :].values[0], wp_int_to_wp_str[8])
# Assert keys
self.assertEqual(date_to_str(datetime(year=1953, month=1, day=1)), df.index[0])
self.assertEqual(date_to_str(datetime(year=2008, month=9, day=22)), df.index[-1])
@@ -34,14 +34,14 @@ class TestWeatherTypes(unittest.TestCase):
def test_yearly_percentages(self):
# Expected Percentages come from the original paper
expected_percentages = [7, 23, 8, 18, 7, 6, 3, 28]
- wp_ids = list(range(1, 9))
- wp_to_expected_percentages = dict(zip(wp_ids, expected_percentages))
+ wp_str = [wp_int_to_wp_str[wp_int] for wp_int in range(1, 9)]
+ wp_to_expected_percentages = dict(zip(wp_str, expected_percentages))
# Compute percentages
df = load_df_weather_types()
wp_to_found_percentages = 100 * df['WP'].value_counts() / len(df)
- wp_to_found_percentages = {int(k): round(v) for k, v in wp_to_found_percentages.to_dict().items()}
+ wp_to_found_percentages = {k: round(v) for k, v in wp_to_found_percentages.to_dict().items()}
# They remove one the wp1 so that the sum of the percentages sum to 100
- wp_to_found_percentages[1] -= 1
+ wp_to_found_percentages[wp_int_to_wp_str[1]] -= 1
self.assertEqual(sum(wp_to_found_percentages.values()), 100)
# wp_to_found_percentages = wp_to_found_percentages.astype(int)
self.assertEqual(wp_to_expected_percentages, wp_to_found_percentages)
@@ -53,7 +53,7 @@ class TestWeatherTypes(unittest.TestCase):
for year, wps in study.year_to_wps.items():
daily_time_serie_array = study.year_to_daily_time_serie_array[year]
self.assertEqual(len(daily_time_serie_array), len(wps))
- mask = np.array(wps) == 8
+ mask = np.array(wps) == ANTICYCLONIC
no_rain.extend(np.max(daily_time_serie_array[mask, :], axis=1))
rain.extend(np.max(daily_time_serie_array[~mask, :], axis=1))
# For 90% of the anticyclonic days, the daily max precipitation (snowfall + rainfall) for a massifs is < 0.2mm
@@ -61,6 +61,15 @@ class TestWeatherTypes(unittest.TestCase):
self.assertLess(np.quantile(no_rain, 0.5), 0.2)
self.assertLess(5, np.quantile(rain, 0.5))
+ def test_weather_patterns_maxima(self):
+ study = SafranPrecipitation1Day(altitude=900, year_min=1954, year_max=2008)
+ s = pd.Series(np.concatenate([v for v in study.year_to_wp_for_annual_maxima.values()]))
+ storms_ranking = s.value_counts()
+ self.assertEqual(storms_ranking.index[0], STEADY_OCEANIC)
+ self.assertEqual(storms_ranking.index[-1], ANTICYCLONIC)
+ self.assertEqual(storms_ranking.values[0], 376)
+ self.assertEqual(storms_ranking.values[-1], 9)
+
if __name__ == '__main__':
unittest.main()