aulemahal

cd xscen/

pygettext.py -o xscen/data/messages.pot xscen/*.py
cp xscen/data/messages.pot xscen/data/fr
# edit xscen/data/fr/LC_MESSAGES/xscen.po
msgfmt.py xscen/data/*/LC_MESSAGES/xscen.po

git commit

aulemahal

import sys
import dask
from dask.diagnostics import ProgressBar
import time
import xarray as xr
import glob


import fsspec
# This was last run on commit f42b0c23aa0229cee6e5a394a860f0ad1149ace6

aulemahal

# Creep fill : fill missing points with the mean of the neighbors.
#
# The process here implemented first create weights and then computes the fill by a dot product, in a fashion similar to xESMF.
# It uses a sparse matrix to stores those weights. This enables the reuse of a creep filling mask and faster parallelization over non-creeped dims.
#
import itertools
import numpy as np
import sparse as sp
import xarray as xr

aulemahal

import pygeos  # I'm not sure why, but it seems this must be imported before one of the other packages below
import xarray as xr
import cf_xarray as cfxr
from shapely.geometry import Polygon
import shapely
import ESMF
import xesmf as xe
import numpy as np

ds = xr.open_dataset('caw/series/200109/rhsmax_caw_200109_se.nc')  # some 2D grid data

aulemahal

# This function creates a xclim0.30-compliant yaml file from a pre-0.30 version.
import os
from yaml import safe_load, safe_dump

def update_yaml(raw: os.PathLike, adapted: os.PathLike):
    """Reads in a virtual module yaml that was created for xclim <= 0.29 and refactors it to fit xclim's current yaml specifications."""
    freq_names = {"annual": "A", "seasonal": "Q", "monthly": "M", "weekly": "W"}
    freq_defs = {"annual": "YS", "seasonal": "QS-DEC", "monthly": "MS", "weekly": "W"}

    with open(raw) as f:

aulemahal

"""
Convert a geopandas dataframe to a CF compliant dataset.

Follows conventions as noted here : http://cfconventions.org/Data/cf-conventions/cf-conventions-1.8/cf-conventions.html#geometries

Author: Pascal Bourgault
Date: 2021-02-05
"""
import geopandas as gpd
from numpy import arange

aulemahal

# Spatial intersection and average
# Compute weights corresponding to the intersection between grid cells and a polygon.
# Pascal bourgault, Sept 2020
from cartopy import crs
from shapely.geometry import Polygon
from shapely.ops import transform
import xarray as xr
import numpy as np
from functools import partial
import pyproj