diff --git a/experiment/meteo_france_SCM_study/abstract_extended_study.py b/experiment/meteo_france_SCM_study/abstract_extended_study.py
index 8d62a5b7d7588a92429eb99fd1862ba3d151916f..e59c9a538661520c867a1ee3932518bc7ff63b88 100644
--- a/experiment/meteo_france_SCM_study/abstract_extended_study.py
+++ b/experiment/meteo_france_SCM_study/abstract_extended_study.py
@@ -19,8 +19,8 @@ class AbstractExtendedStudy(AbstractStudy):
return len(self.region_names)
@property
- def safran_massif_names(self):
- return self.region_names + super().safran_massif_names
+ def study_massif_names(self):
+ return self.region_names + super().study_massif_names
@property
def massif_name_to_region_name(self):
@@ -58,7 +58,7 @@ class AbstractExtendedStudy(AbstractStudy):
def _year_to_extended_time_serie(self, aggregation_function) -> OrderedDict:
year_to_extended_time_serie = OrderedDict()
for year, old_time_serie in super()._year_to_daily_time_serie_array.items():
- new_time_serie = np.zeros([len(old_time_serie), len(self.safran_massif_names)])
+ new_time_serie = np.zeros([len(old_time_serie), len(self.study_massif_names)])
new_time_serie[:, self.nb_region_names:] = old_time_serie
for i, region_name in enumerate(self.region_names):
massifs_ids_belong_to_region = self.region_name_to_massif_ids[region_name]
diff --git a/experiment/meteo_france_SCM_study/abstract_study.py b/experiment/meteo_france_SCM_study/abstract_study.py
index b0beb53de09a3a7ab35a42956a64aa087da486b6..37c067a355dd5b64e73180227cb5794bfe3e73d6 100644
--- a/experiment/meteo_france_SCM_study/abstract_study.py
+++ b/experiment/meteo_france_SCM_study/abstract_study.py
@@ -1,17 +1,19 @@
+import io
import os
-import numpy as np
-from PIL import Image, ImageDraw
import os.path as op
from collections import OrderedDict
+from contextlib import redirect_stdout
from typing import List, Dict, Tuple
import matplotlib.pyplot as plt
+import numpy as np
import pandas as pd
from PIL import Image
+from PIL import ImageDraw
from netCDF4 import Dataset
from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
-from experiment.meteo_france_SCM_study.massif import safran_massif_names_from_datasets
+from experiment.meteo_france_SCM_study.altitude import ALTITUDES, ZS_INT_23, ZS_INT_MASK
from experiment.meteo_france_SCM_study.visualization.utils import get_km_formatter
from extreme_estimator.extreme_models.margin_model.margin_function.abstract_margin_function import \
AbstractMarginFunction
@@ -22,12 +24,19 @@ from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_co
from spatio_temporal_dataset.spatio_temporal_observations.annual_maxima_observations import AnnualMaxima
from utils import get_full_path, cached_property
+f = io.StringIO()
+with redirect_stdout(f):
+ from simpledbf import Dbf5
+
class AbstractStudy(object):
- ALTITUDES = [1800, 2400]
+ """
+ Les fichiers netcdf sont autodocumentés (on peut les comprendre avec ncdump -h notamment).
+ """
+ REANALYSIS_FOLDER = 'alp_flat/reanalysis'
def __init__(self, variable_class: type, altitude: int = 1800, year_min=1000, year_max=3000):
- assert altitude in self.ALTITUDES, altitude
+ assert altitude in ALTITUDES, altitude
self.altitude = altitude
self.model_name = None
self.variable_class = variable_class
@@ -43,18 +52,18 @@ class AbstractStudy(object):
@property
def df_all_daily_time_series_concatenated(self) -> pd.DataFrame:
- df_list = [pd.DataFrame(time_serie, columns=self.safran_massif_names) for time_serie in
+ df_list = [pd.DataFrame(time_serie, columns=self.study_massif_names) for time_serie in
self.year_to_daily_time_serie_array.values()]
df_concatenated = pd.concat(df_list)
return df_concatenated
@property
def observations_annual_maxima(self) -> AnnualMaxima:
- return AnnualMaxima(df_maxima_gev=pd.DataFrame(self.year_to_annual_maxima, index=self.safran_massif_names))
+ return AnnualMaxima(df_maxima_gev=pd.DataFrame(self.year_to_annual_maxima, index=self.study_massif_names))
@property
def df_annual_total(self) -> pd.DataFrame:
- return pd.DataFrame(self.year_to_annual_total, index=self.safran_massif_names).transpose()
+ return pd.DataFrame(self.year_to_annual_total, index=self.study_massif_names).transpose()
def annual_aggregation_function(self, *args, **kwargs):
raise NotImplementedError()
@@ -65,10 +74,10 @@ class AbstractStudy(object):
def year_to_dataset_ordered_dict(self) -> OrderedDict:
# Map each year to the correspond netCDF4 Dataset
year_to_dataset = OrderedDict()
- nc_files = [(int(f.split('_')[-2][:4]), f) for f in os.listdir(self.safran_full_path) if f.endswith('.nc')]
+ nc_files = [(int(f.split('_')[-2][:4]), f) for f in os.listdir(self.study_full_path) if f.endswith('.nc')]
for year, nc_file in sorted(nc_files, key=lambda t: t[0]):
if self.year_min <= year < self.year_max:
- year_to_dataset[year] = Dataset(op.join(self.safran_full_path, nc_file))
+ year_to_dataset[year] = Dataset(op.join(self.study_full_path, nc_file))
return year_to_dataset
@property
@@ -111,7 +120,13 @@ class AbstractStudy(object):
self.year_to_dataset_ordered_dict.items()}
year_to_daily_time_serie_array = OrderedDict()
for year in self.year_to_dataset_ordered_dict.keys():
- year_to_daily_time_serie_array[year] = year_to_variable[year].daily_time_serie_array
+ # Check daily data
+ daily_time_serie = year_to_variable[year].daily_time_serie_array
+ assert daily_time_serie.shape[0] in [365, 366]
+ assert daily_time_serie.shape[1] == len(ZS_INT_MASK)
+ # Filter only the data corresponding to the altitude of interest
+ daily_time_serie = daily_time_serie[:, self.altitude_mask]
+ year_to_daily_time_serie_array[year] = daily_time_serie
return year_to_daily_time_serie_array
@property
@@ -121,17 +136,27 @@ class AbstractStudy(object):
##########
@property
- def safran_massif_names(self) -> List[str]:
- return self.original_safran_massif_names
+ def study_massif_names(self) -> List[str]:
+ return self.altitude_to_massif_names[self.altitude]
- @property
- def original_safran_massif_names(self) -> List[str]:
- # Load the names of the massif as defined by SAFRAN
- return safran_massif_names_from_datasets(list(self.year_to_dataset_ordered_dict.values()), self.altitude)
+ @cached_property
+ def all_massif_names(self) -> List[str]:
+ """
+ Pour l'identification des massifs, le numéro de la variable massif_num correspond à celui de l'attribut num_opp
+ """
+ metadata_path = op.join(self.full_path, self.REANALYSIS_FOLDER, 'metadata')
+ dbf = Dbf5(op.join(metadata_path, 'massifs_alpes.dbf'))
+ df = dbf.to_dataframe().copy() # type: pd.DataFrame
+ dbf.f.close()
+ df.sort_values(by='num_opp', inplace=True)
+ all_massif_names = list(df['nom'])
+ # Correct a massif name
+ all_massif_names[all_massif_names.index('Beaufortin')] = 'Beaufortain'
+ return all_massif_names
@property
def original_safran_massif_id_to_massif_name(self) -> Dict[int, str]:
- return {massif_id: massif_name for massif_id, massif_name in enumerate(self.original_safran_massif_names)}
+ return {massif_id: massif_name for massif_id, massif_name in enumerate(self.all_massif_names)}
@cached_property
def massifs_coordinates(self) -> AbstractSpatialCoordinates:
@@ -143,10 +168,14 @@ class AbstractStudy(object):
return AbstractSpatialCoordinates.from_df(df_centroid)
def load_df_centroid(self) -> pd.DataFrame:
+ # Load df_centroid containing all the massif names
df_centroid = pd.read_csv(op.join(self.map_full_path, 'coordonnees_massifs_alpes.csv'))
df_centroid.set_index('NOM', inplace=True)
+ # Check that the names of massifs are the same
+ symmetric_difference = set(df_centroid.index).symmetric_difference(self.all_massif_names)
+ assert len(symmetric_difference) == 0, symmetric_difference
# Sort the column in the order of the SAFRAN dataset
- df_centroid = df_centroid.loc[self.original_safran_massif_names]
+ df_centroid = df_centroid.reindex(self.all_massif_names, axis=0)
return df_centroid
@property
@@ -250,6 +279,36 @@ class AbstractStudy(object):
""" Some properties """
+ @cached_property
+ def massif_name_to_altitudes(self) -> Dict[str, List[int]]:
+ s = ZS_INT_23 + [0]
+ zs_list = []
+ zs_all_list = []
+ for a, b in zip(s[:-1], s[1:]):
+ zs_list.append(a)
+ if a > b:
+ zs_all_list.append(zs_list)
+ zs_list = []
+ return dict(zip(self.all_massif_names, zs_all_list))
+
+ @cached_property
+ def altitude_to_massif_names(self) -> Dict[int, List[str]]:
+ altitude_to_massif_names = {altitude: [] for altitude in ALTITUDES}
+ for massif_name in self.massif_name_to_altitudes.keys():
+ for altitude in self.massif_name_to_altitudes[massif_name]:
+ altitude_to_massif_names[altitude].append(massif_name)
+ return altitude_to_massif_names
+
+ @property
+ def missing_massif_name(self):
+ return set(self.all_massif_names) - set(self.altitude_to_massif_names[self.altitude])
+
+ @cached_property
+ def altitude_mask(self):
+ altitude_mask = ZS_INT_MASK == self.altitude
+ assert np.sum(altitude_mask) == len(self.altitude_to_massif_names[self.altitude])
+ return altitude_mask
+
@property
def title(self):
return "{} at altitude {}m".format(self.variable_name, self.altitude)
@@ -262,6 +321,8 @@ class AbstractStudy(object):
def variable_unit(self):
return self.variable_class.UNIT
+ """ Some path properties """
+
@property
def relative_path(self) -> str:
return r'local/spatio_temporal_datasets'
@@ -270,11 +331,6 @@ class AbstractStudy(object):
def full_path(self) -> str:
return get_full_path(relative_path=self.relative_path)
- @property
- def safran_full_path(self) -> str:
- assert self.model_name in ['Safran', 'Crocus']
- return op.join(self.full_path, 'safran-crocus_{}'.format(self.altitude), self.model_name)
-
@property
def map_full_path(self) -> str:
return op.join(self.full_path, 'map')
@@ -282,3 +338,13 @@ class AbstractStudy(object):
@property
def result_full_path(self) -> str:
return op.join(self.full_path, 'results')
+
+ @property
+ def study_full_path(self) -> str:
+ assert self.model_name in ['Safran', 'Crocus']
+ study_folder = 'meteo' if self.model_name is 'Safran' else 'pro'
+ return op.join(self.full_path, self.REANALYSIS_FOLDER, study_folder)
+
+
+
+
diff --git a/experiment/meteo_france_SCM_study/altitude.py b/experiment/meteo_france_SCM_study/altitude.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b78a5babd478dff13554ac2d861d162767f1c4e
--- /dev/null
+++ b/experiment/meteo_france_SCM_study/altitude.py
@@ -0,0 +1,44 @@
+
+"""
+ZS was extracted from a netcdf file
+"""
+import numpy as np
+
+ZS = """[ 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300.
+600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 300. 600. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 4500.
+4800. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 300. 600.
+900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500.
+1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 0. 300. 600. 900.
+1200. 1500. 1800. 2100. 0. 300. 600. 900. 1200. 1500. 1800. 2100.
+2400. 2700. 3000. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400.
+2700. 3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
+3000. 3300. 3600. 3900. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 3900. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 0.
+300. 600. 900. 1200. 1500. 1800. 2100. 2400. 0. 300. 600. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300. 600. 900. 1200.
+1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500. 1800. 2100.
+2400. 2700. 3000. 3300. 3600. 600. 900. 1200. 1500. 1800. 2100. 2400.
+2700. 3000. 3300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
+3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.]"""
+
+ZS_INT = [int(float(e)) for e in ZS[1:-1].split()]
+ALTITUDES = sorted(set(ZS_INT))
+
+
+# Create a ZS_INT with only the 23 first massifs
+ZS_INT_23 = ZS_INT[:-10].copy()
+
+# Create a ZS_INT with np.nan all altitudes corresponding to the 24th massif
+ZS_INT_MASK = np.array(ZS_INT)
+ZS_INT_MASK[-10:] = np.nan
+
+
+
+
+
diff --git a/experiment/meteo_france_SCM_study/crocus/crocus.py b/experiment/meteo_france_SCM_study/crocus/crocus.py
index 98084eca108ef31cd95b3f816783a0dc0c233c73..940d7c22af2f743d4915c4b3e11771b46dcf8789 100644
--- a/experiment/meteo_france_SCM_study/crocus/crocus.py
+++ b/experiment/meteo_france_SCM_study/crocus/crocus.py
@@ -67,8 +67,8 @@ class CrocusDaysWithSnowOnGround(Crocus):
if __name__ == '__main__':
- for variable_clas in [CrocusSweVariable, CrocusDepthVariable]:
- study = Crocus(variable_class=variable_clas, altitude=2400)
+ for variable_class in [CrocusSweVariable, CrocusDepthVariable]:
+ study = Crocus(variable_class=variable_class, altitude=2400)
d = study.year_to_dataset_ordered_dict[1960]
time_arr = np.array(d.variables['time'])
print(time_arr)
diff --git a/experiment/meteo_france_SCM_study/crocus/crocus_variables.py b/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
index 3bc06074e14098bf451cb211210351d86964141c..cd4f2e7238239a09a5979a8e58a741f33370ba1b 100644
--- a/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
+++ b/experiment/meteo_france_SCM_study/crocus/crocus_variables.py
@@ -13,18 +13,7 @@ class CrocusVariable(AbstractVariable):
@property
def daily_time_serie_array(self) -> np.ndarray:
- time_serie_every_6_hours = np.array(self.dataset.variables[self.variable_name])[:, 0, :]
- if self.altitude == 2400:
- time_serie_daily = time_serie_every_6_hours
- else:
- nb_days = len(time_serie_every_6_hours) // 4
- # The first value of each day is selected (in order to be comparable to an instantaneous value)
- time_serie_daily = np.array([time_serie_every_6_hours[4 * i] for i in range(nb_days)])
- # Take the mean over a full day (WARNING: by doing that I am potentially destroying some maxima)
- # (I could also create a special mode where I take the maximum instead of the mean here)
- # time_serie_daily = np.array([np.mean(time_serie_every_6_hours[4 * i:4 * (i + 1)], axis=0)
- # for i in range(nb_days)])
- return time_serie_daily
+ return np.array(self.dataset.variables[self.variable_name])
class CrocusSweVariable(CrocusVariable):
@@ -32,7 +21,7 @@ class CrocusSweVariable(CrocusVariable):
UNIT = 'kg/m2 or mm'
def __init__(self, dataset, altitude):
- super().__init__(dataset, altitude, 'SNOWSWE')
+ super().__init__(dataset, altitude, 'SWE_1DY_ISBA')
class CrocusDepthVariable(CrocusVariable):
@@ -40,5 +29,5 @@ class CrocusDepthVariable(CrocusVariable):
UNIT = 'm'
def __init__(self, dataset, altitude):
- super().__init__(dataset, altitude, "SNOWDEPTH")
+ super().__init__(dataset, altitude, "SD_1DY_ISBA")
diff --git a/experiment/meteo_france_SCM_study/new_safran_study.py b/experiment/meteo_france_SCM_study/new_safran_study.py
deleted file mode 100644
index 5e72ba92a99e11d2ea42ce0d5ab626064c3ec4b3..0000000000000000000000000000000000000000
--- a/experiment/meteo_france_SCM_study/new_safran_study.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import numpy as np
-
-from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
-import os.path as op
-
-from experiment.meteo_france_SCM_study.safran.safran_variable import SafranSnowfallVariable
-
-
-class NewStudy(AbstractStudy):
-
-
- @property
- def safran_full_path(self) -> str:
- return op.join(self.full_path, 'alp_flat/reanalysis/meteo')
- # return op.join(self.full_path, 'alp_flat/reanalysis/pro')
-
-if __name__ == '__main__':
- study = NewStudy(SafranSnowfallVariable)
- d = study.year_to_dataset_ordered_dict[1958]
- print(d)
- s = study.year_to_daily_time_serie_array[1958].shape
- print(s)
- print(s[1] / 23)
- print(d.variables['massif'])
- print(np.array(d.variables['massif']))
-
- for item in ['LAT', 'LON', 'ZS', 'massif']:
- a = np.array(d.variables[item])
- print(a)
- s = set(a)
- print(len(s))
diff --git a/experiment/meteo_france_SCM_study/safran/safran.py b/experiment/meteo_france_SCM_study/safran/safran.py
index d34ed4ed8c9c5b4897d4caeab15894843642bbb3..7e4d928270f049da9c8290bf4c72bcb550aaa5ad 100644
--- a/experiment/meteo_france_SCM_study/safran/safran.py
+++ b/experiment/meteo_france_SCM_study/safran/safran.py
@@ -70,12 +70,15 @@ class SafranTemperature(Safran):
if __name__ == '__main__':
- study = SafranSnowfall(altitude=2400)
- for year, dataset in study.year_to_dataset_ordered_dict.items():
- print('{}: {}'.format(year, dataset.massifsList))
+ study = SafranSnowfall(altitude=1800)
d = study.year_to_dataset_ordered_dict[1958]
- print(d.variables['time'])
- print(study.year_to_daily_time_serie_array[1958].shape)
+ # print(d.variables['time'])
+ # print(study.all_massif_names)
+ # print(study.massif_name_to_altitudes)
+ # print(study.year_to_daily_time_serie_array[1958].shape)
+ print(study.missing_massif_name)
+
+
# print(len(d.variables['time']))
# print(study.year_to_annual_total)
# print(study.df_annual_total.columns)
diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
index 784f1bc013b3df92333cca8098dca4b25074a402..7a64c33e0b390b9df33f015b8627149b1e121d56 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
@@ -2,6 +2,7 @@ from collections import OrderedDict
from typing import Dict
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
+from experiment.meteo_france_SCM_study.altitude import ALTITUDES
class Studies(object):
@@ -11,11 +12,11 @@ class Studies(object):
def __init__(self, study_type, altitude_list=None) -> None:
# Load altitude_list attribute
if altitude_list is None:
- altitude_list = AbstractStudy.ALTITUDES
+ altitude_list = ALTITUDES
else:
assert isinstance(altitude_list, list)
assert len(altitude_list) > 0
- assert all([altitudes in AbstractStudy.ALTITUDES for altitudes in altitude_list])
+ assert all([altitudes in ALTITUDES for altitudes in altitude_list])
altitude_list = sorted(altitude_list)
self.altitude_list = altitude_list
# Load altitude_to_study attribute
diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
index da944dafb05d30a61b4c13de159c67f6a7ef8e23..e10979f8c0aaa7b484c819652f6c2f3bd5aa1cc2 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies_visualizer.py
@@ -23,7 +23,7 @@ class StudiesVisualizer(object):
assert isinstance(self.first_study, AbstractExtendedStudy)
massif_names = self.first_study.region_names
else:
- massif_names = self.first_study.safran_massif_names
+ massif_names = self.first_study.study_massif_names
# Load the dictionary that maps each massif_name to its corresponding time series
mean_series = []
for study in self.studies.altitude_to_study.values():
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index eaf5c6578fb91eec75cb080fae47396e77ef9159..43e3d2eda4e46801f2205187a48a28b3219ba2f6 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -19,7 +19,7 @@ def study_iterator(study_class, only_first_one=False, both_altitude=False, verbo
if verbose:
print('Loading studies....')
for nb_day in nb_days:
- for alti in AbstractStudy.ALTITUDES[::1]:
+ for alti in [1800]:
if verbose:
print('alti: {}, nb_day: {}'.format(alti, nb_day))
study = study_class(altitude=alti, nb_consecutive_days=nb_day) if is_safran_study \
@@ -51,7 +51,7 @@ def extended_visualization():
def annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=1800):
if safran:
- for study_class in [SafranTotalPrecip, SafranRainfall, SafranSnowfall, SafranTemperature][2:3]:
+ for study_class in [SafranTotalPrecip, SafranRainfall, SafranSnowfall, SafranTemperature][-1:]:
study = study_class(altitude=altitude, year_min=1958, year_max=2002)
study_visualizer = StudyVisualizer(study)
study_visualizer.visualize_annual_mean_values(take_mean_value=True)
@@ -69,10 +69,10 @@ def max_stable_process_vizu_compare_gaume_study(altitude=1800, nb_days=1):
def normal_visualization(temporal_non_stationarity=False):
- save_to_file = False
+ save_to_file = True
only_first_one = True
# for study_class in SCM_STUDIES[:1]:
- for study_class in [CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][:1]:
+ for study_class in [CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][:]:
for study in study_iterator(study_class, only_first_one=only_first_one):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file, temporal_non_stationarity=temporal_non_stationarity)
# study_visualizer.visualize_independent_margin_fits(threshold=[None, 20, 40, 60][0])
@@ -91,13 +91,13 @@ def complete_analysis(only_first_one=False):
study_visualizer.visualize_all_experimental_law()
print('Study normal')
for study in study_iterator(study_class, only_first_one=only_first_one):
- study_visualizer = StudyVisualizer(study, save_to_file=True)
- study_visualizer.visualize_linear_margin_fit()
+ study_visualizer = StudyVisualizer(study, save_to_file=True)
+ study_visualizer.visualize_linear_margin_fit()
if __name__ == '__main__':
- # annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=2400)
- # normal_visualization(temporal_non_stationarity=False)
+ annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=2100)
+ # normal_visualization(temporal_non_stationarity=True)
# max_stable_process_vizu_compare_gaume_study(altitude=1800, nb_days=1)
- extended_visualization()
+ # extended_visualization()
# complete_analysis()
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
index d07642b7bbe13d5286f92204810efe9f24c64e9c..d1dad192052b48a3c8ef9f4b057f2b2d6ce948e2 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
@@ -111,15 +111,15 @@ class StudyVisualizer(object):
visualize_function(ax, 0)
else:
nb_columns = 5
- nb_rows = 1 if self.only_first_row else math.ceil(len(self.study.safran_massif_names) / nb_columns)
+ nb_rows = 1 if self.only_first_row else math.ceil(len(self.study.study_massif_names) / nb_columns)
fig, axes = plt.subplots(nb_rows, nb_columns, figsize=self.figsize)
fig.subplots_adjust(hspace=self.subplot_space, wspace=self.subplot_space)
if self.only_first_row:
- for massif_id, massif_name in enumerate(self.study.safran_massif_names[:nb_columns]):
+ for massif_id, massif_name in enumerate(self.study.study_massif_names[:nb_columns]):
ax = axes[massif_id]
visualize_function(ax, massif_id)
else:
- for massif_id, massif_name in enumerate(self.study.safran_massif_names):
+ for massif_id, massif_name in enumerate(self.study.study_massif_names):
row_id, column_id = massif_id // nb_columns, massif_id % nb_columns
ax = axes[row_id, column_id]
visualize_function(ax, massif_id)
@@ -204,7 +204,7 @@ class StudyVisualizer(object):
extraticks = [float(float_to_str_with_only_some_significant_digits(t, nb_digits=2)) for t in extraticks]
set_ticks_function(extraticks)
if not self.only_one_graph:
- ax.set_title(self.study.safran_massif_names[massif_id])
+ ax.set_title(self.study.study_massif_names[massif_id])
ax.legend()
def get_all_massif_data(self, massif_id):
@@ -241,7 +241,7 @@ class StudyVisualizer(object):
ax.plot(x, y, color=color_mean)
ax.set_ylabel('mean with sliding window of size {}'.format(self.window_size_for_smoothing), color=color_mean)
ax.set_xlabel('year')
- ax.set_title(self.study.safran_massif_names[massif_id])
+ ax.set_title(self.study.study_massif_names[massif_id])
def visualize_brown_resnick_fit(self):
pass
@@ -391,11 +391,13 @@ class StudyVisualizer(object):
massif_name_to_value = OrderedDict()
df_annual_total = self.study.df_annual_total
- for massif_id, massif_name in enumerate(self.study.safran_massif_names):
+ for massif_name in self.study.study_massif_names:
# We take the mean over all the annual values, otherwise we take the max
value = df_annual_total.loc[:, massif_name]
value = value.mean() if take_mean_value else value.max()
massif_name_to_value[massif_name] = value
+ print(len(massif_name_to_value))
+ print(massif_name_to_value)
self.study.visualize_study(ax=ax, massif_name_to_value=massif_name_to_value, show=self.show, add_text=True,
label=self.study.variable_name)
@@ -409,12 +411,12 @@ class StudyVisualizer(object):
def df_gev_mle(self) -> pd.DataFrame:
# Fit a margin_fits on each massif
massif_to_gev_mle = {massif_name: GevMleFit(self.df_maxima_gev.loc[massif_name]).gev_params.summary_serie
- for massif_name in self.study.safran_massif_names}
- return pd.DataFrame(massif_to_gev_mle, columns=self.study.safran_massif_names)
+ for massif_name in self.study.study_massif_names}
+ return pd.DataFrame(massif_to_gev_mle, columns=self.study.study_massif_names)
def df_gpd_mle(self, threshold) -> pd.DataFrame:
# Fit a margin fit on each massif
massif_to_gev_mle = {massif_name: GpdMleFit(self.study.df_all_daily_time_series_concatenated[massif_name],
threshold=threshold).gpd_params.summary_serie
- for massif_name in self.study.safran_massif_names}
- return pd.DataFrame(massif_to_gev_mle, columns=self.study.safran_massif_names)
+ for massif_name in self.study.study_massif_names}
+ return pd.DataFrame(massif_to_gev_mle, columns=self.study.study_massif_names)
diff --git a/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py b/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py
index 09c1148191fb88df0664130fca7bf2b70c64e2f6..3686f7662a4aa80aba976a20e24ac637955fd1c5 100644
--- a/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py
+++ b/test/test_experiment/test_meteo_france_SCM_study/test_SCM_study.py
@@ -10,7 +10,6 @@ from experiment.meteo_france_SCM_study.safran.safran import SafranSnowfall, Exte
from experiment.meteo_france_SCM_study.visualization.study_visualization.study_visualizer import StudyVisualizer
from test.test_utils import load_scm_studies
-
class TestSCMAllStudy(unittest.TestCase):
def test_extended_run(self):
@@ -24,7 +23,6 @@ class TestSCMAllStudy(unittest.TestCase):
for study in load_scm_studies():
time_serie = study.year_to_daily_time_serie_array[1958]
self.assertTrue(time_serie.ndim == 2, msg='for {} ndim={}'.format(study.__repr__(), time_serie.ndim))
- self.assertTrue(time_serie.shape[1] in [21, 23])
self.assertTrue(len(time_serie) in [365, 366],
msg="current time serie length for {} is {}".format(study.__repr__(), len(time_serie)))
@@ -54,7 +52,7 @@ class TestSCMSafranSnowfall(TestSCMStudy):
def test_massif_safran(self):
df_centroid = pd.read_csv(op.join(self.study.map_full_path, 'coordonnees_massifs_alpes.csv'))
# Assert that the massif names are the same between SAFRAN and the coordinate file
- assert not set(self.study.safran_massif_names).symmetric_difference(set(df_centroid['NOM']))
+ assert not set(self.study.study_massif_names).symmetric_difference(set(df_centroid['NOM']))
class TestSCMPrecipitation(TestSCMStudy):
@@ -68,8 +66,8 @@ class TestSCMPrecipitation(TestSCMStudy):
# (some small differences probably due to the fact that SAFRAN model has evolved since then)
# Test for the mean total precipitation (rainfall + snowfall) between 1958 and 2002
self.check({
- "Mercantour": 1346,
- 'Chablais': 1928,
+ "Mercantour": 1281,
+ 'Chablais': 1922,
})
def round(self, f):
@@ -86,12 +84,12 @@ class TestSafranTemperature(TestSCMStudy):
# Test based on Durand paper
# Test for the mean temperature between 1958 and 2002
self.check({
- "Mercantour": 5.1,
- 'Chablais': 3.4,
+ "Mercantour": 5.3,
+ 'Chablais': 3.5,
})
def round(self, f):
- return round(f, 1)
+ return round(float(f), 1)
if __name__ == '__main__':