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Le Roux Erwan authored9818f99a
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import os
import os.path as op
from collections import OrderedDict
import matplotlib.pyplot as plt
import pandas as pd
from netCDF4 import Dataset
from extreme_estimator.gev.gevmle_fit import GevMleFit
from safran_study.massif import safran_massif_names_from_datasets
from safran_study.snowfall_annual_maxima import SafranSnowfall
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
from spatio_temporal_dataset.coordinates.spatial_coordinates.abstract_spatial_coordinates import \
AbstractSpatialCoordinates
from utils import get_full_path, cached_property
class Safran(object):
def __init__(self, nb_days_of_snowfall=1):
self.safran_altitude = None
self.nb_days_of_snowfall = nb_days_of_snowfall
def write_to_file(self, df):
if not op.exists(self.result_full_path):
os.makedirs(self.result_full_path, exist_ok=True)
df.to_csv(op.join(self.result_full_path, 'merged_array_{}_altitude.csv'.format(self.safran_altitude)))
""" Visualization methods """
def visualize(self):
df_massif = pd.read_csv(op.join(self.map_full_path, 'massifsalpes.csv'))
coord_tuples = [(row_massif['idx'], row_massif[AbstractCoordinates.COORDINATE_X],
row_massif[AbstractCoordinates.COORDINATE_Y])
for _, row_massif in df_massif.iterrows()]
for massif_idx in set([tuple[0] for tuple in coord_tuples]):
l = [coords for idx, *coords in coord_tuples if idx == massif_idx]
l = list(zip(*l))
plt.plot(*l, color='black')
plt.fill(*l)
self.massifs_coordinates.visualization_2D()
""" Statistics methods """
@property
def df_gev_mle_each_massif(self):
# Fit a gev n each massif
massif_to_gev_mle = {massif_name: GevMleFit(self.df_annual_maxima[massif_name]).gev_params.to_serie()
for massif_name in self.safran_massif_names}
return pd.DataFrame(massif_to_gev_mle)
""" Annual maxima of snowfall """
@property
def df_annual_maxima(self):
return pd.DataFrame(self.year_to_annual_maxima, index=self.safran_massif_names).T
""" Load some attributes only once """
@cached_property
def year_to_annual_maxima(self):
year_to_safran_snowfall = {year: SafranSnowfall(dataset) for year, dataset in
self.year_to_dataset_ordered_dict.items()}
year_to_annual_maxima = OrderedDict()
for year in self.year_to_dataset_ordered_dict.keys():
year_to_annual_maxima[year] = year_to_safran_snowfall[year].annual_maxima_of_snowfall(
self.nb_days_of_snowfall)
return year_to_annual_maxima
@cached_property
def safran_massif_names(self):
# Load the names of the massif as defined by SAFRAN
return safran_massif_names_from_datasets(self.year_to_dataset_ordered_dict.values())
@cached_property
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('_')[1][:4]), f) for f in os.listdir(self.safran_full_path) if f.endswith('.nc')]
for year, nc_file in sorted(nc_files, key=lambda t: t[0]):
year_to_dataset[year] = Dataset(op.join(self.safran_full_path, nc_file))
return year_to_dataset
@cached_property
def massifs_coordinates(self) -> AbstractSpatialCoordinates:
# Coordinate object that represents the massif coordinates in Lambert extended
df_centroid = pd.read_csv(op.join(self.map_full_path, 'coordonnees_massifs_alpes.csv'))
for coord_column in [AbstractCoordinates.COORDINATE_X, AbstractCoordinates.COORDINATE_Y]:
df_centroid.loc[:, coord_column] = df_centroid[coord_column].str.replace(',', '.').astype(float)
# Assert that the massif names are the same between SAFRAN and the coordinate file
assert not set(self.safran_massif_names).symmetric_difference(set(df_centroid['NOM']))
# Build coordinate object from df_centroid
return AbstractSpatialCoordinates.from_df(df_centroid)
""" Some properties """
@property
def relative_path(self) -> str:
return r'local/spatio_temporal_datasets'
@property
def full_path(self) -> str:
return get_full_path(relative_path=self.relative_path)
@property
def safran_full_path(self) -> str:
return op.join(self.full_path, 'safran-crocus_{}'.format(self.safran_altitude), 'Safran')
@property
def map_full_path(self) -> str:
return op.join(self.full_path, 'map')
@property
def result_full_path(self) -> str:
return op.join(self.full_path, 'results')