# -*- coding: utf-8 -*-
"""
Created on Fri Apr 5 18:08:57 2024
@author: Alexandre Kenshilik Coche
@contact: alexandre.co@hotmail.fr
Based on the work of Ronan Abhervé and Loic Duffar (https://github.com/loicduffar)
"""
"""
WORKFLOW:
import SIM2_tools as smt
sim_folder = r"D:\2- Postdoc\2- Travaux\1- Veille\4- Donnees\8- Meteo\Surfex\SIM2"
<1> Pour traiter les données par paquets, adapter la variable batch_var au
début de la fonction to_netcdf()
<2> smt.folder_to_netcdf(os.path.join(sim_folder, "csv"))
<3> smt.merge_folder(os.path.join(sim_folder, "netcdf"))
<4> Supprimer si besoin les fichiers dans \netcdf\
<5> smt.compress_folder(os.path.join(sim_folder, "merged"))
<6> smt.clip_folder(os.path.join(sim_folder, "compressed"),
os.path.join(sim_folder, "Bretagne_rectangle.shp"))
<7> Déplacer les clipped dans \Bretagne\
<8> smt.clip_folder(os.path.join(sim_folder, "merged"),
os.path.join(sim_folder, "EBR_rectangle.shp")
<9> Déplacer les clipped dans \EBR\
<10> Déplacer les compressed dans done
<11> Déplacer les merged sur le DDext
"""
#%% Imports
import os
import re
import datetime
import pandas as pd
import numpy as np
import xarray as xr
xr.set_options(keep_attrs = True)
# import rioxarray as rio # Not necessary, the rio module from xarray is enough
import geopandas as gpd
from shapely.geometry import mapping
from geop4th import geobricks as geo
import plotly.graph_objects as go
# import plotly.offline as offline
from PIL import Image
#%% Convert to NetCDF
[docs]
def to_netcdf(csv_file_path):
start_time = datetime.datetime.now()
print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
root_folder = os.path.split(os.path.split(csv_file_path)[0])[0]
# =============================================================================
# coords_filepath = os.path.join(
# root_folder, 'coordonnees_grille_safran_lambert-2-etendu.csv')
# =============================================================================
# Needed columns
usecols = ['LAMBX', 'LAMBY', 'DATE']
# Units and long names (from liste_parametres.odt https://www.data.gouv.fr/fr/datasets/r/d1ffaf5e-7d15-4fb5-a34c-f76aaf417b46)
units_by_var = {
'PRENEI_Q': ['mm', 'Précipitations solides (cumul quotidien 06-06 UTC)'],
'PRELIQ_Q': ['mm', 'Précipitations liquides (cumul quotidien 06-06 UTC)'],
'T_Q': ['°C','Température (moyenne quotidienne)'],
'FF_Q': ['m/s', 'Vitesse du vent (moyenne quotidienne)'],
'Q_Q': ['g/kg','Humidité spécifique (moyenne quotidienne)'],
'DLI_Q': ['J/cm2', 'Rayonnement atmosphérique (cumul quotidien)'],
'SSI_Q': ['J/cm2', 'Rayonnement visible (cumul quotidien)'],
'HU_Q': ['%', 'Humidité relative (moyenne quotidienne)'],
'EVAP_Q': ['mm', 'Evapotranspiration réelle (cumul quotidien 06-06 UTC)'],
'ETP_Q': ['mm', 'Evapotranspiration potentielle (formule de Penman-Monteith)'],
'PE_Q': ['mm', 'Pluies efficaces (cumul quotidien)'],
'SWI_Q': ['%', "Indice d'humidité des sols (moyenne quotidienne 06-06 UTC)"],
'DRAINC_Q': ['mm', 'Drainage (cumul quotidien 06-06 UTC)'],
'RUNC_Q': ['mm', 'Ruissellement (cumul quotidien 06-06 UTC)'],
'RESR_NEIGE_Q': ['mm', 'Equivalent en eau du manteau neigeux (moyenne quotidienne 06-06 UTC)'],
'RESR_NEIGE6_Q': ['mm', 'Equivalent en eau du manteau neigeux à 06 UTC'],
'HTEURNEIGE_Q': ['m', 'Epaisseur du manteau neigeux (moyenne quotidienne 06-06 UTC)'],
'HTEURNEIGE6_Q': ['m', 'Epaisseur du manteau à 06 UTC'],
'HTEURNEIGEX_Q': ['m', 'Epaisseur du manteau neigeux maximum au cours de la journée'],
'SNOW_FRAC_Q': ['%', 'Fraction de maille recouverte par la neige (moyenne quotidienne 06-06 UTC)'],
'ECOULEMENT_Q': ['mm', 'Ecoulement à la base du manteau neigeux'],
'WG_RACINE_Q': ['mm','Contenu en eau liquide dans la couche racinaire à 06 UTC'],
'WGI_RACINE_Q': ['mm', 'Contenu en eau gelée dans la couche de racinaire à 06 UTC'],
'TINF_H_Q': ['°C', 'Température minimale des 24 températures horaires'],
'TSUP_H_Q': ['°C', 'Température maximale des 24 températures horaires'],
'PRETOT_Q': ['mm', 'Précipitations totales (cumul quotidien 06-06 UTC)'],
}
# NB: Cumulated values (day 1) are summed from 06:00 UTC (day 1) to 06:00 UTC (day 2)
# Therefore, days correspond to Central Standard Time days.
#%%% BATCH (User-defined)
#########################
# Process all variables at once:
# batch_var = list(units_by_var)[0:-1]
# Process by batch:
batch_var = list(units_by_var)[0:4]
# batch_var = list(units_by_var)[4:8]
# batch_var = list(units_by_var)[8:12]
# batch_var = list(units_by_var)[12:16]
# batch_var = list(units_by_var)[16:20]
# batch_var = list(units_by_var)[20:-1] # the last variable (Precip) is used-defined
#%%% Loading
print("Loading...")
sub_time = datetime.datetime.now()
print(" (Can take > 1 min per parameter for a whole decade)")
df = pd.read_csv(csv_file_path, sep=';',
usecols=usecols + batch_var,
header=0, decimal='.',
parse_dates=['DATE'],
# date_format='%Y%m%d', # Not available before pandas 2.0.0
)
now = datetime.datetime.now()
print(" Loading time:", now - sub_time)
#%%% Formatting
print("\nFormatting...")
sub_time = datetime.datetime.now()
df.rename(columns = {'LAMBX': 'x', 'LAMBY': 'y', 'DATE': 'time'}, inplace = True)
df[['x', 'y']] = df[['x', 'y']]*100 # convert hm to m
df.set_index(['time', 'y', 'x'], inplace = True)
# Add new quantities if needed
if ('PRENEI_Q' in df.columns) & ('PRELIQ_Q' in df.columns):
df['PRETOT_Q'] = df['PRENEI_Q'] + df['PRELIQ_Q']
print(" New column added: PRETOT_Q = PRENEI_Q + PRELIQ_Q")
ds = df.to_xarray()
# Continuous axis
ds = ds.reindex(x = range(ds.x.min().values, ds.x.max().values + 8000, 8000))
ds = ds.reindex(y = range(ds.y.min().values, ds.y.max().values + 8000, 8000))
# Include CRS
ds.rio.write_crs(27572, inplace = True)
# Standard attributes
ds.x.attrs = {'standard_name': 'projection_x_coordinate',
'long_name': 'x coordinate of projection',
'units': 'Meter'}
ds.y.attrs = {'standard_name': 'projection_y_coordinate',
'long_name': 'y coordinate of projection',
'units': 'Meter'}
now = datetime.datetime.now()
print(" Formatting time:", now - sub_time)
#%%% Export
print("\nExporting...")
sub_time = datetime.datetime.now()
if not os.path.exists(os.path.join(root_folder, "netcdf")):
os.mkdir(os.path.join(root_folder, "netcdf"))
for var in list(ds.data_vars): # batch_var:
# Include metadata
ds[var].attrs = {'standard_name': var,
'long_name': units_by_var[var][1],
'units': units_by_var[var][0]}
ds_var = ds[[var]]
csv_name = os.path.splitext(os.path.split(csv_file_path)[-1])[0].replace('QUOT_', '')
ds_var.to_netcdf(os.path.join(root_folder, 'netcdf', '_'.join([var, csv_name]) + '.nc'))
print(f" {var} exported")
now = datetime.datetime.now()
print(" Formatting time:", now - sub_time)
# afficher la durée d'éxécution
now = datetime.datetime.now()
print("\nEnd time:", now.strftime("%Y-%m-%d %H:%M"))
print("Total time:", now - start_time)
#%% Convert whole folder to netcdf
[docs]
def folder_to_netcdf(folder):
"""
Parameters
----------
folder : str
Folder containing the .csv files.
Returns
-------
None. Creates the .nc files in the folder 'netcdf'
"""
filelist = [f for f in os.listdir(folder)
if (os.path.isfile(os.path.join(folder, f))) & (os.path.splitext(f)[-1] == '.csv')]
for f in filelist:
filename = os.path.splitext(f)[0]
sim_pattern = re.compile('SIM2_')
years = sim_pattern.split(filename)[-1]
print(f"\n{'-'*len(years)}\n{years}\n{'-'*len(years)}")
to_netcdf(os.path.join(folder, f))
#%% Merge
[docs]
def merge(filelist):
root_folder = os.path.split(os.path.split(filelist[0])[0])[0]
print('\nMerging files...')
with xr.open_dataset(
filelist[0], decode_coords = 'all', decode_times = True) as ds_merged:
ds_merged.load() # to unlock the resource
print(f" {os.path.split(filelist[0])[-1]}")
encod = ds_merged[list(ds_merged.data_vars)[0]].encoding
for f in filelist[1:]:
with xr.open_dataset(
f, decode_coords = 'all', decode_times = True) as ds:
ds_merged = ds.combine_first(ds_merged)
print(f" {os.path.split(f)[-1]}")
ds_merged = ds_merged.sortby('time')
# Export
ds_merged[list(ds_merged.data_vars)[0]].encoding = encod
yearset = set()
sim_pattern = re.compile('_SIM2_')
year_pattern = re.compile('\d{4,8}')
for f in filelist:
filename = os.path.split(os.path.splitext(f)[0])[-1]
var, years = sim_pattern.split(filename)
yearset.update(year_pattern.findall(years))
if not os.path.exists(os.path.join(root_folder, "merged")):
os.mkdir(os.path.join(root_folder, "merged"))
new_filepath = os.path.join(
root_folder,
"merged",
'_'.join([var, 'SIM2', sorted(yearset)[0], sorted(yearset)[-1]]) + '.nc'
)
ds_merged.to_netcdf(new_filepath)
#%% Merge whole folder netcdf files
[docs]
def merge_folder(folder):
start_time = datetime.datetime.now()
print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
filelist = [f for f in os.listdir(folder)
if (os.path.isfile(os.path.join(folder, f))) & (os.path.splitext(f)[-1] == '.nc')]
varlist = set()
# Extract all variables
for f in filelist:
filename = os.path.splitext(f)[0]
sim_pattern = re.compile('_SIM2_')
var, _ = sim_pattern.split(filename)
varlist.add(var)
for v in varlist:
print(f"\n{'-'*len(v)}\n{v}\n{'-'*len(v)}")
sub_time = datetime.datetime.now()
# Extract all years
yearlist = []
sim_pattern = re.compile('_SIM2_')
for f in filelist:
filename = os.path.splitext(f)[0]
var, years = sim_pattern.split(filename)
if var == v:
yearlist.append(years)
print(f" {', '.join(yearlist)}")
files_to_merge = [os.path.join(folder, v + '_SIM2_' + y + '.nc') for y in yearlist]
merge(files_to_merge)
now = datetime.datetime.now()
print("\n Merging time:", now - sub_time)
# afficher la durée d'éxécution
now = datetime.datetime.now()
print("\nEnd time:", now.strftime("%Y-%m-%d %H:%M"))
print("Total time:", now - start_time)
#%% Compress
[docs]
def compress(filepath):
root_folder = os.path.split(os.path.split(filepath)[0])[0]
with xr.open_dataset(filepath, decode_times = True,
decode_coords = 'all') as ds:
ds.load() # to unlock the resource
# Discretization compression (lossy):
var = list(ds.data_vars)[0]
bound_max = float(ds[var].max())
bound_min = float(ds[var].min())
if bound_min<0: bound_min = bound_min*1.1
elif bound_min>0: bound_min = bound_min/1.1
else: bound_min = bound_min - 0.01*bound_max
scale_factor, add_offset = geo.compute_scale_and_offset(
bound_min, bound_max, 16)
ds[var].encoding['scale_factor'] = scale_factor
ds[var].encoding['add_offset'] = add_offset
ds[var].encoding['dtype'] = 'int16'
ds[var].encoding['_FillValue'] = -32768
print(" Compression x4 (lossy)")
# Export
if not os.path.exists(os.path.join(root_folder, "compressed")):
os.mkdir(os.path.join(root_folder, "compressed"))
filename = os.path.splitext(os.path.split(filepath)[-1])[0]
new_filepath = os.path.join(
root_folder, 'compressed', filename + '_comp.nc')
ds.to_netcdf(new_filepath)
#%% Compress whole folder
[docs]
def compress_folder(folder):
start_time = datetime.datetime.now()
print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
filelist = [f for f in os.listdir(folder)
if (os.path.isfile(os.path.join(folder, f))) & (os.path.splitext(f)[-1] == '.nc')]
print("\nCompressing...")
i = 0
for f in filelist:
i += 1
print(f"\n {'-'*len(f)}\n {f} ({i}/{len(filelist)})\n {'-'*len(f)}")
sub_time = datetime.datetime.now()
compress(os.path.join(folder, f))
now = datetime.datetime.now()
print(" Compression time:", now - sub_time)
# afficher la durée d'éxécution
now = datetime.datetime.now()
print("\nEnd time:", now.strftime("%Y-%m-%d %H:%M"))
print("Total time:", now - start_time)
#%% Clip
[docs]
def clip(filepath, maskpath):
root_folder = os.path.split(os.path.split(filepath)[0])[0]
mask = gpd.read_file(maskpath)
with xr.open_dataset(filepath, decode_times = True,
decode_coords = 'all') as ds:
ds.load() # to unlock the resource
clipped_ds = ds.rio.clip(mask.geometry.apply(mapping),
mask.crs, all_touched = True)
# Export
if not os.path.exists(os.path.join(root_folder, "clipped")):
os.mkdir(os.path.join(root_folder, "clipped"))
filename = os.path.splitext(os.path.split(filepath)[-1])[0]
new_filepath = os.path.join(
root_folder, 'clipped', filename + '_clipped.nc')
clipped_ds.to_netcdf(new_filepath)
#%% Clip whole folder
[docs]
def clip_folder(folder, maskpath):
start_time = datetime.datetime.now()
print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
filelist = [f for f in os.listdir(folder)
if (os.path.isfile(os.path.join(folder, f))) & (os.path.splitext(f)[-1] == '.nc')]
maskname = os.path.splitext(os.path.split(maskpath)[-1])[0]
print(f"\nClipping on {maskname}...")
i = 0
for f in filelist:
i += 1
print(f"\n {'-'*len(f)}\n {f} ({i}/{len(filelist)})\n {'-'*len(f)}")
sub_time = datetime.datetime.now()
clip(os.path.join(folder, f), maskpath)
now = datetime.datetime.now()
print(" Clipping time:", now - sub_time)
# afficher la durée d'éxécution
now = datetime.datetime.now()
print("\nEnd time:", now.strftime("%Y-%m-%d %H:%M"))
print("Total time:", now - start_time)
#%% Plot synthetic maps
[docs]
def plot_map(var, *, file_folder = None, mode = "sum", timemode = 'annual'):
"""
Generates interactive maps from SIM2 data (html).
Example
-------
import SIM2_tools as smt
smt.plot_map('PRETOT', mode = "sum", timemode = 'annual')
for timemode in ['JJA', 'SON', 'DJF', 'MAM']:
smt.plot_map('SWI', mode = "mean", timemode = timemode, file_folder = folder)
Parameters
----------
var : str
SIM2 variables:
'ETP' | 'EVAP' | 'PRELIQ' | 'PRENEI' | 'PRETOT' | 'DRAINC' | 'RUNC' |
'T' | 'TINF_H' | 'TSUP_H' | 'WG_RACINE' | 'WGI_RACINE' | 'SWI' ...
mode : str, optional
'sum' | 'min' | 'max' | 'mean' | 'ratio' | 'ratio_precip' |
'mean_cumdiff' | 'sum_cumdiff' | 'min_cumdiff' | 'max_cumdiff'
'mean_cumdiff_ratio' | 'sum_cumdiff_ratio'
'mean_deficit' | 'sum_deficit'
The default is "sum".
timemode : str, optional
'annual' | 'ONDJFM' | 'AMJJAS' | 'DJF' | 'MAM' | 'JJA' | 'SON'. The default is 'annual'.
Returns
-------
None. Creates the html maps.
"""
start_time = datetime.datetime.now()
# print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
print("The generation of the interactive graphic file can take 1 min")
#%%% Loading
# Finding the most recent file
if file_folder is None:
file_folder = input("file folder: ")
# Personal shortcut @Alexandre:
if file_folder == '':
file_folder = r"D:\2- Postdoc\2- Travaux\1- Veille\4- Donnees\8- Meteo\Surfex\SIM2\compressed"
root_folder = os.path.split(file_folder)[0]
var_pattern = re.compile(f"^{var}_Q")
filelist = [f for f in os.listdir(file_folder)
if (os.path.isfile(os.path.join(file_folder, f))) \
& (os.path.splitext(f)[-1] == '.nc') \
& (len(var_pattern.findall(f)) > 0)]
for f in filelist:
print(f)
# sim_pattern = re.compile('\d{4,6}')
# years = int(sim_pattern.findall(f)[-1])
file_suffix = 'Q_' + var_pattern.split(f)[1][1:]
print(f" . File suffix: {file_suffix}")
filename = f"{var}_{file_suffix}"
filepath = os.path.join(file_folder, filename)
# Loading as xarray.dataset
with xr.open_dataset(
filepath, decode_coords = 'all', decode_times = True) as ds:
ds.load()
main_var = list(ds.data_vars)[0]
# xres = float(ds.x[1] - ds.x[0])
# xres = ds.rio.transform()[0]
# yres = float(ds.y[1] - ds.y[0])
# yres = ds.rio.transform()[4]
xres, yres = ds.rio.resolution()
#%%% Initializations
last_year = ds.time.max().dt.year.item()
# The last incomplete hydrological year is not taken into account
if timemode == 'ONDJFM':
if ds.time.max() < pd.to_datetime(f"{last_year}-03-31", format = "%Y-%m-%d"):
last_year = last_year-1
else:
if ds.time.max() < pd.to_datetime(f"{last_year}-09-30", format = "%Y-%m-%d"):
last_year = last_year-1
# Hydrological years (start and end years for each decade)
annual_bins = [
((1958, 1959), (1969, 1970)),
((1965, 1966), (1974, 1975)),
((1970, 1971), (1979, 1980)),
((1975, 1976), (1984, 1985)),
((1980, 1981), (1989, 1990)),
((1985, 1986), (1994, 1995)),
((1990, 1991), (1999, 2000)),
((1995, 1996), (2004, 2005)),
((2000, 2001), (2009, 2010)),
((2005, 2006), (2014, 2015)),
((2010, 2011), (2019, 2020)),
((2015, 2016), (last_year - 1, last_year)),
]
suf_title = []
suf_slider = []
suf_timemode = {
'sum': {'annual': 'cumuls annuels',
'ONDJFM': 'cumuls oct-mar',
'AMJJAS': 'cumuls avr-sep',
'DJF': 'cumuls dec-fev',
'MAM': 'cumuls mar-mai',
'JJA': 'cumuls juin-aout',
'SON': 'cumuls sept-nov'},
'min': {'annual': 'minimums annuels',
'ONDJFM': 'minimums oct-mar',
'AMJJAS': 'minimums avr-sep',
'DJF': 'minimums dec-fev',
'MAM': 'minimums mar-mai',
'JJA': 'minimums juin-aout',
'SON': 'minimums sept-nov'},
'max': {'annual': 'maximums annuels',
'ONDJFM': 'maximums oct-mar',
'AMJJAS': 'maximums avr-sep',
'DJF': 'maximums dec-fev',
'MAM': 'maximums mar-mai',
'JJA': 'maximums juin-aout',
'SON': 'maximums sept-nov'},
'mean': {'annual': 'moyennes annuelles',
'ONDJFM': 'moyennes oct-mar',
'AMJJAS': 'moyennes avr-sep',
'DJF': 'moyennes dec-fev',
'MAM': 'moyennes mar-mai',
'JJA': 'moyennes juin-aout',
'SON': 'moyennes sept-nov'},
'ratio': {'annual': 'obsolète (cumuls annuels / cumuls annuels)',
'ONDJFM': 'cumuls oct-mar / cumuls annuels',
'AMJJAS': 'cumuls avr-sep / cumuls annuels',
'DJF': 'cumuls dec-fev / cumuls annuels',
'MAM': 'cumuls mar-mai / cumuls annuels',
'JJA': 'cumuls juin-aout / cumuls annuels',
'SON': 'cumuls sept-nov / cumuls annuels'},
'ratio_precip': {'annual': 'cumuls annuels / précipitations annuelles',
'ONDJFM': 'cumuls oct-mar / précipitations annuelles',
'AMJJAS': 'cumuls avr-sep / précipitations annuelles',
'DJF': 'cumuls dec-fev / précipitations annuelles',
'MAM': 'cumuls mar-mai / précipitations annuelles',
'JJA': 'cumuls juin-aout / précipitations annuelles',
'SON': 'cumuls sept-nov / précipitations annuelles'},
'mean_cumdiff': {'annual': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'sum_cumdiff': {'annual': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'min_cumdiff': {'annual': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de minimums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'max_cumdiff': {'annual': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de maximums avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'mean_cumdiff_ratio': {'annual': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de moyennes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'sum_cumdiff_ratio': {'annual': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]}',
'ONDJFM': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (oct-mar)',
'AMJJAS': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (avr-sep)',
'DJF': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (dec-fev)',
'MAM': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (mar-mai)',
'JJA': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (juin-aout)',
'SON': f'écarts de sommes avec la période {annual_bins[0][0][0]}-{annual_bins[0][1][1]} (sept-nov)'},
'mean_deficit': {'annual': 'déficits annuels moyens (précipitations - grandeur)',
'ONDJFM': 'déficits oct-mar moyens (précipitations - grandeur)',
'AMJJAS': 'déficits avr-sep moyens (précipitations - grandeur)',
'DJF': 'déficits dec-fev moyens (précipitations - grandeur)',
'MAM': 'déficits mar-mai moyens (précipitations - grandeur)',
'JJA': 'déficits juin-aout moyens (précipitations - grandeur)',
'SON': 'déficits sept-nov moyens (précipitations - grandeur)'},
'sum_deficit': {'annual': 'déficits annuel cumulés (précipitations - grandeur)',
'ONDJFM': 'déficits oct-mar cumulés (précipitations - grandeur)',
'AMJJAS': 'déficits avr-sep cumulés (précipitations - grandeur)',
'DJF': 'déficits dec-fev cumulés (précipitations - grandeur)',
'MAM': 'déficits mar-mai cumulés (précipitations - grandeur)',
'JJA': 'déficits juin-aout cumulés (précipitations - grandeur)',
'SON': 'déficits sept-nov cumulés (précipitations - grandeur)'},
}
metadata_by_var = {
'PRENEI_Q': ['mm', 'Précipitations solides'],
'PRELIQ_Q': ['mm', 'Précipitations liquides'],
'T_Q': ['°C','Température moy. journalière'],
'FF_Q': ['m/s', 'Vit. vent'],
'Q_Q': ['g/kg','Humidité spécifique '],
'DLI_Q': ['J/cm2', 'Rayonnement atmosphérique '],
'SSI_Q': ['J/cm2', 'Rayonnement visible '],
'HU_Q': ['%', 'Humidité relative '],
'EVAP_Q': ['mm', 'Evapotranspiration réelle'],
'ETP_Q': ['mm', 'Evapotranspiration potentielle (Penman-Monteith)'],
'PE_Q': ['mm', 'Pluies efficaces'],
'SWI_Q': ['', 'Indice humidité des sols'],
'DRAINC_Q': ['mm', 'Recharge potentielle'],
'RUNC_Q': ['mm', 'Ruissellement'],
'RESR_NEIGE_Q': ['mm', 'Equivalent eau manteau neigeux'],
'RESR_NEIGE6_Q': ['mm', 'Equivalent eau manteau neigeux'],
'HTEURNEIGE_Q': ['m', 'Epaisseur manteau neigeux'],
'HTEURNEIGE6_Q': ['m', 'Epaisseur du manteau neigeux'],
'HTEURNEIGEX_Q': ['m', 'Epaisseur manteau neigeux maximum journalier'],
'SNOW_FRAC_Q': ['%', 'Fraction maille recouverte par neige'],
'ECOULEMENT_Q': ['mm', 'Ecoulement en base manteau neigeux'],
'WG_RACINE_Q': ['mm','Contenu en eau liquide dans couche racinaire'],
'WGI_RACINE_Q': ['mm', 'Contenu en eau gelée dans la couche de racinaire'],
'TINF_H_Q': ['°C', 'Température min. journalière'],
'TSUP_H_Q': ['°C', 'Température max. journalière'],
'PRETOT_Q': ['mm', 'Précipitations totales'],
}
fig = go.Figure()
#%%% Units & colorscale
dst_dir1 = 'valeurs'
if mode in ["sum", "min", "max", "mean",
"mean_cumdiff", "sum_cumdiff", "min_cumdiff", "max_cumdiff",
"mean_deficit", "sum_deficit"]:
unit = metadata_by_var[main_var][0]
if mode == "sum":
zmin = ds[main_var].groupby(
ds.time.dt.year).sum(min_count = 1).min(dim = ['x', 'y', 'year']).item()
zmax = ds[main_var].groupby(
ds.time.dt.year).sum(min_count = 1).max(dim = ['x', 'y', 'year']).item()
elif mode == "min":
zmin = ds[main_var].groupby(
ds.time.dt.year).min().min(dim = ['x', 'y', 'year']).item()
zmax = ds[main_var].groupby(
ds.time.dt.year).min().max(dim = ['x', 'y', 'year']).item()
elif mode == "max":
zmin = ds[main_var].groupby(
ds.time.dt.year).max().min(dim = ['x', 'y', 'year']).item()
zmax = ds[main_var].groupby(
ds.time.dt.year).max().max(dim = ['x', 'y', 'year']).item()
elif mode == "mean":
zmin = ds[main_var].groupby(
ds.time.dt.year).mean().min(dim = ['x', 'y', 'year']).item()
zmax = ds[main_var].groupby(
ds.time.dt.year).mean().max(dim = ['x', 'y', 'year']).item()
elif mode in ['mean_cumdiff', 'sum_cumdiff', 'min_cumdiff', 'max_deficit']:
# =============================================================================
# zmin = (ds[main_var].groupby(
# ds.time.dt.year).mean() - ds[main_var].groupby(
# ds.time.dt.year).mean()[{'year' : 0}]).min(dim = ['x', 'y', 'year']).item()
# zmax = (ds[main_var].groupby(
# ds.time.dt.year).mean() - ds[main_var].groupby(
# ds.time.dt.year).mean()[{'year' : 0}]).max(dim = ['x', 'y', 'year']).item()
# =============================================================================
# It is better here to use the grouped values for limits (computed later)
zmin = 0
zmax = 0
dst_dir1 = 'diff'
elif mode in ["mean_deficit", "sum_deficit"]:
zmin = 0
zmax = 0
dst_dir1 = 'deficits'
elif mode == "ratio":
unit = "%"
zmin = 0
zmax = 100
dst_dir1 = 'pourcentages'
elif mode == "ratio_precip":
unit = "%"
zmin = 0
zmax = 100
dst_dir1 = 'pourcentages precip'
elif mode in ['mean_cumdiff_ratio', 'sum_cumdiff_ratio']:
unit = "%"
zmin = 0
zmax = 0
dst_dir1 = "diff_ratio"
if var in ['T', 'TSUP_H', 'TINF_H']:
colorscale = "Plasma_r"
else:
colorscale = "Viridis_r"
if mode in ['mean_cumdiff', 'sum_cumdiff', 'min_cumdiff', 'max_cumdiff',
'mean_deficit', 'sum_deficit',
'mean_cumdiff_ratio', 'sum_cumdiff_ratio']:
colorscale = 'RdBu'
if var in ['T', 'TSUP_H', 'TINF_H', 'ETP', 'EVAP']:
colorscale = 'RdBu_r'
#%%% Prepare data
# ---- Exclude non-wanted months
ds = ds[main_var].sel(
time = slice(f"{str(annual_bins[0][0][0])}-10-01", f"{str(annual_bins[-1][1][1])}-09-30")
)
if timemode == 'ONDJFM':
final_map = ds[(ds.time.dt.month <= 3) | (ds.time.dt.month >= 10)].copy()
elif timemode == 'AMJJAS':
final_map = ds[(ds.time.dt.month >= 4) & (ds.time.dt.month <= 9)].copy()
elif timemode == 'DJF':
final_map = ds[(ds.time.dt.month <= 2) | (ds.time.dt.month >= 12)].copy()
elif timemode== 'MAM':
final_map = ds[(ds.time.dt.month >= 3) & (ds.time.dt.month <= 5)].copy()
elif timemode == 'JJA':
final_map = ds[(ds.time.dt.month >= 6) & (ds.time.dt.month <= 8)].copy()
elif timemode == 'SON':
final_map = ds[(ds.time.dt.month >= 9) & (ds.time.dt.month <= 11)].copy()
elif timemode == 'annual':
final_map = ds.copy()
# ---- Groups
# <group> correspond to the start year (year n) of the hydrological year
# Hydrological years are considered to start in october (year n) and end in september (year n+1)
group = final_map.time.dt.year \
+ np.floor((final_map.time.dt.month + 2)/12) - 1
group = group.astype(int)
# ---- Aggregate values:
if mode == 'sum':
final_map = final_map.groupby(group).sum(min_count = 1)
elif mode == 'min':
final_map = final_map.groupby(group).min()
elif mode == 'max':
final_map = final_map.groupby(group).max()
elif mode == 'mean':
final_map = final_map.groupby(group).mean()
elif mode in ['mean_cumdiff', 'mean_cumdiff_ratio']:
final_map = final_map.groupby(group).mean()
elif mode in ['sum_cumdiff', 'sum_cumdiff_ratio']:
final_map = final_map.groupby(group).sum(min_count = 1)
elif mode == 'min_cumdiff':
final_map = final_map.groupby(group).min()
elif mode == 'max_cumdiff':
final_map = final_map.groupby(group).max()
elif mode == 'ratio_precip': # values expressed as pourcentages of annual PRETOT
filename_pretot = f"PRETOT_{file_suffix}"
filepath_pretot = os.path.join(file_folder, filename_pretot)
with xr.open_dataset(filepath_pretot,
decode_coords = 'all',
decode_times = True) as pretot:
pretot = pretot['PRETOT_Q'].sel(
time = slice(f"{str(annual_bins[0][0][0])}-10-01", f"{str(annual_bins[-1][1][1])}-09-30")
)
allmonths_group = pretot.time.dt.year \
+ np.floor((pretot.time.dt.month + 2)/12) - 1
allmonths_group = allmonths_group.astype(int)
final_map = ( final_map.groupby(group).sum(min_count = 1) \
/ pretot.groupby(allmonths_group).sum(min_count = 1) )*100
# =============================================================================
# # This adjusts the scale based on max among all INDIVIDUAL YEARS
# zmax = max([zmax, float(final_map.max())])
# print(" . Color scale has been adjusted")
# =============================================================================
elif mode == 'ratio': # values expressed as pourcentages of annual sums
allmonths_group = ds.time.dt.year \
+ np.floor((ds.time.dt.month + 2)/12) - 1
allmonths_group = allmonths_group.astype(int)
final_map = ( final_map.groupby(group).sum(min_count = 1) \
/ ds.groupby(allmonths_group).sum(min_count = 1) )*100
# =============================================================================
# # This adjusts the scale based on max among all INDIVIDUAL YEARS
# zmax = max([zmax, float(final_map.max())])
# print(" . Color scale has been adjusted")
# =============================================================================
elif mode == 'mean_deficit': # values expressed as differences with PRETOT
filename_pretot = f"PRETOT_{file_suffix}"
filepath_pretot = os.path.join(file_folder, filename_pretot)
with xr.open_dataset(filepath_pretot,
decode_coords = 'all',
decode_times = True) as pretot:
pretot = pretot['PRETOT_Q'].sel(
time = slice(f"{str(annual_bins[0][0][0])}-10-01", f"{str(annual_bins[-1][1][1])}-09-30")
)
if timemode == 'ONDJFM':
pretot = pretot[(pretot.time.dt.month <= 3) | (pretot.time.dt.month >= 10)].copy()
elif timemode == 'AMJJAS':
pretot = pretot[(pretot.time.dt.month >= 4) & (pretot.time.dt.month <= 9)].copy()
elif timemode == 'DJF':
pretot = pretot[(pretot.time.dt.month <= 2) | (pretot.time.dt.month >= 12)].copy()
elif timemode== 'MAM':
pretot = pretot[(pretot.time.dt.month >= 3) & (pretot.time.dt.month <= 5)].copy()
elif timemode == 'JJA':
pretot = pretot[(pretot.time.dt.month >= 6) & (pretot.time.dt.month <= 8)].copy()
elif timemode == 'SON':
pretot = pretot[(pretot.time.dt.month >= 9) & (pretot.time.dt.month <= 11)].copy()
elif timemode == 'annual':
pretot = pretot.copy()
final_map = final_map.groupby(group).mean() - \
pretot.groupby(group).mean()
elif mode == 'sum_deficit': # values expressed as differences with PRETOT
filename_pretot = f"PRETOT_{file_suffix}"
filepath_pretot = os.path.join(file_folder, filename_pretot)
with xr.open_dataset(filepath_pretot,
decode_coords = 'all',
decode_times = True) as pretot:
pretot = pretot['PRETOT_Q'].sel(
time = slice(f"{str(annual_bins[0][0][0])}-10-01", f"{str(annual_bins[-1][1][1])}-09-30")
)
if timemode == 'ONDJFM':
pretot = pretot[(pretot.time.dt.month <= 3) | (pretot.time.dt.month >= 10)].copy()
elif timemode == 'AMJJAS':
pretot = pretot[(pretot.time.dt.month >= 4) & (pretot.time.dt.month <= 9)].copy()
elif timemode == 'DJF':
pretot = pretot[(pretot.time.dt.month <= 2) | (pretot.time.dt.month >= 12)].copy()
elif timemode== 'MAM':
pretot = pretot[(pretot.time.dt.month >= 3) & (pretot.time.dt.month <= 5)].copy()
elif timemode == 'JJA':
pretot = pretot[(pretot.time.dt.month >= 6) & (pretot.time.dt.month <= 8)].copy()
elif timemode == 'SON':
pretot = pretot[(pretot.time.dt.month >= 9) & (pretot.time.dt.month <= 11)].copy()
elif timemode == 'annual':
pretot = pretot.copy()
final_map = final_map.groupby(group).sum(min_count = 1) - \
pretot.groupby(group).sum(min_count = 1)
#%%% Heatmap plot
# Add traces, one for each period
for dates in annual_bins:
# ---- Compute the decade average
temp_map = final_map.loc[{'group': slice(dates[0][0], dates[1][0])}].mean(dim = 'group')
if mode in ['mean_cumdiff', 'sum_cumdiff', 'min_cumdiff', 'max_cumdiff'] :
temp_map = temp_map - final_map.loc[{'group': slice(annual_bins[0][0][0], annual_bins[0][1][0])}].mean(dim = 'group')
# This adjusts the color based on max among all GROUPED YEARS
zmax = max([zmax, float(temp_map.max())])
zmin = min([zmin, float(temp_map.min())])
# This centers the color scale
if (zmax > 0) & (zmin < 0):
zmax = max([zmax, -zmin])
zmin = min([-zmax, zmin])
# print(" . Color scale has been adjusted")
elif mode in ['mean_cumdiff_ratio', 'sum_cumdiff_ratio']:
temp_map = (temp_map - final_map.loc[{'group': slice(annual_bins[0][0][0], annual_bins[0][1][0])}].mean(dim = 'group')) / temp_map * 100
# This adjusts the color based on max among all GROUPED YEARS
zmax = max([zmax, float(temp_map.max())])
zmin = min([zmin, float(temp_map.min())])
# This centers the color scale
if (zmax > 0) & (zmin < 0):
zmax = max([zmax, -zmin])
zmin = min([-zmax, zmin])
elif mode in ['mean_deficit', 'sum_deficit']:
# This adjusts the color based on max among all GROUPED YEARS
zmax = max([zmax, float(temp_map.max())])
zmin = min([zmin, float(temp_map.min())])
# This centers the color scale
if (zmax > 0) & (zmin < 0):
zmax = max([zmax, -zmin])
zmin = min([-zmax, zmin])
# ---- Update texts and plot annotations
if timemode == 'ONDJFM':
suf_title.append((f"oct. {dates[0][0]}", f"mar. {dates[1][1]}"))
suf_slider.append(f"{dates[0][0]}-{dates[1][1]}")
dst_dir2 = 'semestriels'
elif timemode == 'AMJJAS':
suf_title.append((f"avr. {dates[0][1]}", f"sep. {dates[1][1]}"))
suf_slider.append(f"{dates[0][1]}-{dates[1][1]}")
dst_dir2 = 'semestriels'
elif timemode == 'DJF':
suf_title.append((f"dec. {dates[0][1]}", f"fev. {dates[1][1]}"))
suf_slider.append(f"{dates[0][1]}-{dates[1][1]}")
dst_dir2 = 'saisonniers'
elif timemode == 'MAM':
suf_title.append((f"mars {dates[0][1]}", f"mai {dates[1][1]}"))
suf_slider.append(f"{dates[0][1]}-{dates[1][1]}")
dst_dir2 = 'saisonniers'
elif timemode == 'JJA':
suf_title.append((f"juin {dates[0][1]}", f"aout {dates[1][1]}"))
suf_slider.append(f"{dates[0][1]}-{dates[1][1]}")
dst_dir2 = 'saisonniers'
elif timemode == 'SON':
suf_title.append((f"sept. {dates[0][1]}", f"nov. {dates[1][1]}"))
suf_slider.append(f"{dates[0][1]}-{dates[1][1]}")
dst_dir2 = 'saisonniers'
elif timemode == 'annual':
suf_title.append((f"oct. {dates[0][0]}", f"sep. {dates[1][1]}"))
suf_slider.append(f"{dates[0][0]}-{dates[1][1]}")
dst_dir2 = 'annuels'
# ---- Add the traces
fig.add_trace(go.Heatmap(
z = temp_map.values,
x = temp_map.x.values,
y = temp_map.y.values,
name = var,
showlegend = False,
showscale = True,
coloraxis = "coloraxis",
visible = False, # only the first trace will be set to visible
))
fig.update_traces(
hoverinfo = 'all',
hovertemplate = "<b>%{z} " + unit + "</b> <br> %{x} m <br> %{y} m <br>"
)
"""
Other related functions:
https://plotly.com/python/choropleth-maps/
https://plotly.com/python/mapbox-county-choropleth/ (idem but with layout)
https://plotly.com/python/mapbox-density-heatmaps/
"""
# ---- Create and add slider
"""
cf https://plotly.com/python/sliders/
"""
steps = []
for i in range(0, len(fig.data)):
step = dict(
method="update",
args=[{"visible": [False] * len(fig.data)},
{"title": f'<b>{metadata_by_var[main_var][1]}<br>(moyennes des {suf_timemode[mode][timemode]})</b><br>de {suf_title[i][0]} à {suf_title[i][1]}'},
], # layout attribute
label = suf_slider[i],
# style = {'transform': 'rotate(45deg)'},
)
step["args"][0]["visible"][i] = True # Toggle i'th trace to "visible"
steps.append(step)
# Make 1st trace visible
fig.data[0].visible = True
sliders = [dict(
active = 0,
currentvalue = {"prefix": "période : "},
pad = {"t": 40},
steps = steps,
)]
# ---- Layout
fig.update_layout(
sliders = sliders,
title = {'font': {'size': 20},
'text': f'<b>{metadata_by_var[main_var][1]}<br>(moyenne des {suf_timemode[mode][timemode]})</b><br>de {suf_title[0][0]} à {suf_title[0][1]}', # title on the first appearing step
'xanchor': 'center',
'x': 0.5,
'yanchor': 'bottom',
'y': 0.95,
},
xaxis = {'title': {'font': {'size': 16},
'text': 'x [m]'},
'type': 'linear',
'showgrid': False,
# 'range': tuple(map(range_function[parameters[0]], xlim)),
},
yaxis = {'title': {'font': {'size': 16},
'text': 'y [m]'},
'type': 'linear',
'scaleanchor': 'x',
'showgrid': False,
# 'range': tuple(map(range_function[parameters[1]], ylim)),
},
coloraxis = {
"colorscale": colorscale,
"colorbar": {
'title': {'text': unit},
},
"cmin": zmin,
"cmax": zmax,
},
width = 775,
height = 775,
images = [{
'source': Image.open(
os.path.join(root_folder, "cartes", "graticule.png")),
'xref': "x", # "paper",
'yref': "y", # "paper",
'x': -40000, # float(ds.x.min() - xres/2),
'y': 2850000, # float(ds.y.max() + yres/2),
'sizex': 1320000, # float(ds.x.max() - ds.x.min() + xres),
'sizey': 1320000, # float(ds.y.max() - ds.y.min() + yres),
'sizing': "stretch",
'opacity': 0.5,
'layer': "above", # "below"
}, {
'source': Image.open(
os.path.join(root_folder, "cartes", "departements.png")),
# os.path.join(root_folder, "cartes", "departements_blc.png")),
'xref': "x", # "paper",
'yref': "y", # "paper",
'x': -40000, # float(ds.x.min() - xres/2),
'y': 2850000, # float(ds.y.max() + yres/2),
'sizex': 1320000, # float(ds.x.max() - ds.x.min() + xres),
'sizey': 1320000, # float(ds.y.max() - ds.y.min() + yres),
'sizing': "stretch",
'opacity': 0.5,
'layer': "above", # "below"
}, {
'source': Image.open(
os.path.join(root_folder, "cartes", "France.png")),
# os.path.join(root_folder, "cartes", "France_blc.png")),
'xref': "x", # "paper",
'yref': "y", # "paper",
'x': -40000, # float(ds.x.min() - xres/2),
'y': 2850000, # float(ds.y.max() + yres/2),
'sizex': 1320000, # float(ds.x.max() - ds.x.min() + xres),
'sizey': 1320000, # float(ds.y.max() - ds.y.min() + yres),
'sizing': "stretch",
'opacity': 0.5,
'layer': "above", # "below"
}, {
# BDT limite terre-mer
'source': Image.open(
os.path.join(root_folder, "cartes", "map_background.png")),
'xref': "x", # "paper",
'yref': "y", # "paper",
'x': -40000, # float(ds.x.min() - xres/2),
'y': 2850000, # float(ds.y.max() + yres/2),
'sizex': 1320000, # float(ds.x.max() - ds.x.min() + xres),
'sizey': 1320000, # float(ds.y.max() - ds.y.min() + yres),
'sizing': "stretch",
'opacity': 1,
'layer': "below", # "below"
},
],
)
"""https://plotly.com/python/images/"""
# Credits
fig.add_annotation(xanchor = 'center',
xref = 'paper',
x = 0.38,
yref = 'paper',
y = 0.01,
yanchor = 'bottom',
font = {'size': 12},
text = "<i>Crédits : Données @Météo-France (SIM) ; Figure @Alexandre Coche</i>",
showarrow = False
)
# ---- Save the .html figures
version = 'v9'
if not os.path.exists(os.path.join(root_folder, "cartes", version, dst_dir1, dst_dir2)):
os.makedirs(os.path.join(root_folder, "cartes", version, dst_dir1, dst_dir2))
fig.write_html(os.path.join(
root_folder, "cartes", version, dst_dir1, dst_dir2,
'_'.join([var,
timemode,
mode,
datetime.datetime.now().strftime("%Y-%m-%d"),
]) + '.html'
)
)
# This creates a png figure (need kaleido package)
# =============================================================================
# fig.write_image(os.path.join(
# root_folder, "cartes", version, dst_dir1, dst_dir2,
# '_'.join([var,
# timemode,
# mode,
# datetime.datetime.now().strftime("%Y-%m-%d_%Hh%M"),
# ]) + '.png'
# )
# )
# =============================================================================
# This creates the figure AND open it in the web browser
# =============================================================================
# offline.plot(fig, filename = os.path.join(root_folder,
# "cartes", version, dst_dir1, dst_dir2,
# '_'.join([var,
# timemode,
# mode,
# datetime.datetime.now().strftime("%Y-%m-%d_%Hh%M"),
# ]) + '.html'
# ))
# =============================================================================
# div_html = offline.plot(fig, include_plotlyjs = False, output_type='div')
"""
https://stackoverflow.com/questions/36262748/save-plotly-plot-to-local-file-and-insert-into-html
Remember that you'll need to include the plotly js file for all these charts
to work.
You could include:
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
just before putting the div you got. If you put this js at the bottom of
the page, the charts won't work.
"""
#%%% Display the run time
now = datetime.datetime.now()
print("Computation time:", now - start_time)
#%%% Pipeline to plot all interesting maps
"""
start_time = datetime.datetime.now()
print("Start time: ", start_time.strftime("%Y-%m-%d %H:%M"))
n_fig = 294
print(f"The generation of all interactive graphic files can take {0.3*n_fig} min\n")
folder = r"D:\2- Postdoc\2- Travaux\1- Veille\4- Donnees\8- Meteo\SIM2\compressed"
for timemode in ['annual', 'ONDJFM', 'AMJJAS', 'DJF', 'MAM', 'JJA', 'SON']:
for var in ['EVAP', 'ETP', 'PRETOT', 'DRAINC', 'RUNC']:
smt.plot_map(var, mode = "sum", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "ratio", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "sum_cumdiff", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "sum_cumdiff_ratio", timemode = timemode, file_folder = folder)
for var in ['EVAP', 'ETP', 'DRAINC', 'RUNC']:
smt.plot_map(var, mode = "ratio_precip", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "sum_deficit", timemode = timemode, file_folder = folder)
for var in ['SWI', 'T', 'WG_RACINE']:
smt.plot_map(var, mode = "mean", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "mean_cumdiff", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "mean_cumdiff_ratio", timemode = timemode, file_folder = folder)
smt.plot_map(var, mode = "mean_deficit", timemode = timemode, file_folder = folder)
for var in ['TINF_H']:
smt.plot_map(var, mode = "min", timemode = timemode, file_folder = folder)
for var in ['TSUP_H']:
smt.plot_map(var, mode = "max", timemode = timemode, file_folder = folder)
# Display the run time:
now = datetime.datetime.now()
print("\nTotal computation time:", now - start_time)
"""
