# Climate indicator manager - a package for managing and building climate indicator dashboards.
# Copyright (c) 2023 John Kennedy
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import itertools
import gzip
import tempfile
import copy
from pathlib import Path
import numpy as np
import pandas as pd
import xarray as xa
import climind.data_types.grid as gd
from climind.data_manager.metadata import CombinedMetadata
from climind.config.config import CLIMATOLOGY
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def read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs):
construction_metadata = copy.deepcopy(metadata)
if metadata['type'] == 'timeseries':
raise KeyError(f'That type is not known: {metadata["type"]}')
elif metadata['type'] == 'gridded':
filename = [out_dir / x for x in metadata['filename']]
if 'grid_resolution' in kwargs:
if kwargs['grid_resolution'] == 5:
raise KeyError(f'That space resolution is not known: {metadata["space_resolution"]}')
if kwargs['grid_resolution'] == 1:
return read_monthly_1x1_grid(filename, construction_metadata)
else:
return read_monthly_1x1_grid(filename, construction_metadata)
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def read_monthly_1x1_grid(filename, metadata) -> gd.GridMonthly:
# read appropriate climatology given climatology spec in config file
for file in filename:
if f"{CLIMATOLOGY[0]}_{CLIMATOLOGY[1]}" in str(file):
climatology = xa.open_dataset(file, decode_times=False)
climatology = climatology[['precip']]
target_climatology = np.zeros((12, 180, 360))
target_climatology[:, :, :] = np.flip(climatology.precip.data[:, :, :], 1)
dataset_list = []
for year, month in itertools.product(range(1982, 2030), range(1, 13)):
filled_filename = str(filename[0]).replace('YYYY', f'{year}')
filled_filename = Path(filled_filename.replace('MMMM', f'{month:02d}'))
filled_firstguess = str(filename[1]).replace('YYYY', f'{year}')
filled_firstguess = Path(filled_firstguess.replace('MMMM', f'{month:02d}'))
if filled_filename.exists():
df = xa.open_dataset(filled_filename)
df = df[['p']]
latitudes = np.linspace(-89.5, 89.5, 180)
longitudes = np.linspace(-179.5, 179.5, 360)
times = pd.date_range(start=f'{year}-{month:02d}-01', freq='1MS', periods=1)
target_grid = np.zeros((1, 180, 360))
target_grid[:, :, :] = np.flip(df.p.data[:, :, :], 1)
target_grid[:, :, :] = target_grid[:, :, :] - target_climatology[month - 1, :, :]
ds = gd.make_xarray(target_grid, times, latitudes, longitudes, variable='pre')
dataset_list.append(ds)
elif not (filled_filename.exists()) and filled_firstguess.exists():
df = xa.open_dataset(filled_firstguess, decode_times=False)
df = df[['p']]
latitudes = np.linspace(-89.5, 89.5, 180)
longitudes = np.linspace(-179.5, 179.5, 360)
times = pd.date_range(start=f'{year}-{month:02d}-01', freq='1MS', periods=1)
target_grid = np.zeros((1, 180, 360))
target_grid[:, :, :] = np.flip(df.p.data[:, :, :], 1)
target_grid[:, :, :] = target_grid[:, :, :] - target_climatology[month - 1, :, :]
ds = gd.make_xarray(target_grid, times, latitudes, longitudes, variable='pre')
dataset_list.append(ds)
combo = xa.concat(dataset_list, dim='time')
metadata['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
return gd.GridMonthly(combo, metadata)