# 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/>.
from pathlib import Path
from typing import List
import xarray as xa
import numpy as np
import copy
import climind.data_types.timeseries as ts
import climind.data_types.grid as gd
from climind.readers.generic_reader_utils import find_latest, get_latest_filename_and_url
from climind.data_manager.metadata import CombinedMetadata
from climind.readers.generic_reader import get_last_modified_time
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def read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs):
filename_with_wildcards = metadata['filename'][0]
filename = [find_latest(out_dir, filename_with_wildcards)]
last_modified = get_last_modified_time(filename[0])
construction_metadata = copy.deepcopy(metadata)
construction_metadata.dataset['last_modified'] = [last_modified]
if metadata['type'] == 'timeseries':
if metadata['time_resolution'] == 'monthly':
return read_monthly_ts(filename, construction_metadata)
elif metadata['time_resolution'] == 'annual':
return read_annual_ts(filename, construction_metadata)
else:
raise KeyError(f'That time resolution is not known: {metadata["time_resolution"]}')
elif metadata['type'] == 'gridded':
if 'grid_resolution' in kwargs:
if kwargs['grid_resolution'] == 1:
return read_monthly_1x1_grid(filename, construction_metadata)
if kwargs['grid_resolution'] == 5:
return read_monthly_grid(filename, construction_metadata)
else:
return read_monthly_grid(filename, construction_metadata)
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def read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) -> gd.GridMonthly:
df = xa.open_dataset(filename[0])
df = df.rename({'tas': 'tas_mean'})
#df.tas_mean.data = np.roll(df.tas_mean.data, 36, 2)
metadata['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
return gd.GridMonthly(df, metadata)
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def read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
return read_monthly_grid(filename, metadata)
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def read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
df = xa.open_dataset(filename[0])
df = df.rename({'tas': 'tas_mean'})
df.tas_mean.data = np.roll(df.tas_mean.data, 36, 2)
# regrid to 1x1
lats = np.arange(-89.5, 90.5, 1.0)
lons = np.arange(-179.5, 180.5, 1.0)
# Copy 5-degree grid cell value into all one degree cells
grid = np.repeat(df.tas_mean, 5, 1)
grid = np.repeat(grid, 5, 2)
df = gd.make_xarray(grid, df.time.data, lats, lons)
metadata.creation_message()
metadata['history'].append("Regridded to 1 degree latitude-longitude resolution")
return gd.GridMonthly(df, metadata)
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def read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
df = xa.open_dataset(filename[0])
ntimes = df.tas.data.shape[0]
years = df.time.dt.year.data.tolist()
months = df.time.dt.month.data.tolist()
anomalies = np.reshape(df.tas.data, (ntimes)).tolist()
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
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def read_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
return read_monthly_ts(filename, metadata).make_annual()