# Climate indicator manager - a package for managing and building climate indicator dashboards.
# Copyright (c) 2022 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
from pathlib import Path
from typing import List
import xarray as xa
import pandas as pd
import numpy as np
import climind.data_types.timeseries as ts
import climind.data_types.grid as gd
from climind.readers.generic_reader import get_last_modified_time
from climind.data_manager.metadata import CombinedMetadata
from climind.readers.generic_reader import read_ts
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def read_grid(filename: List[Path]):
dataset_list = []
returned_filename = None
for year in range(1958, 2020):
filled_filename = str(filename[0]).replace('YYYY', f'{year}')
filled_filename = Path(filled_filename)
if filled_filename.exists():
field = xa.open_dataset(filled_filename, engine='cfgrib')
field = field.rename({'t2m': 'tas_mean'})
dataset_list.append(field)
returned_filename = filled_filename
for year, month in itertools.product(range(2020, 2050), range(1, 13)):
filled_filename = str(filename[1]).replace('YYYY', f'{year}')
filled_filename = Path(filled_filename.replace('MMMM', f'{month:02d}'))
if filled_filename.exists():
field = xa.open_dataset(filled_filename, engine='cfgrib')
field = field.expand_dims('time')
field = field.rename({'t2m': 'tas_mean'})
dataset_list.append(field)
returned_filename = filled_filename
combo = xa.concat(dataset_list, dim='time')
return combo, returned_filename
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def read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
ds, filled_filename = read_grid(filename)
metadata.dataset['last_modified'] = [get_last_modified_time(filled_filename)]
metadata.creation_message()
return gd.GridMonthly(ds, metadata)
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def read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
ds, filled_filename = read_grid(filename)
metadata.dataset['last_modified'] = [get_last_modified_time(filled_filename)]
jra55_125 = ds.tas_mean
number_of_months = jra55_125.shape[0]
target_grid = np.zeros((number_of_months, 36, 72))
transfer = np.zeros((5, 5)) + 1.0
transfer[0, :] = transfer[0, :] * 0.5
transfer[4, :] = transfer[4, :] * 0.5
transfer[:, 0] = transfer[:, 0] * 0.5
transfer[:, 4] = transfer[:, 4] * 0.5
transfer_sum = np.sum(transfer)
for month in range(number_of_months):
enlarged_array = np.zeros((145, 289))
enlarged_array[:, 0:288] = jra55_125[month, :, :]
enlarged_array[:, 288] = jra55_125[month, :, 0]
for xx, yy in itertools.product(range(72), range(36)):
lox = xx * 4
hix = (xx + 1) * 4
loy = yy * 4
hiy = (yy + 1) * 4
weighted = transfer * enlarged_array[loy:hiy + 1, lox:hix + 1]
grid_mean = np.sum(weighted) / transfer_sum
target_grid[month, yy, xx] = grid_mean
# flip and shift target_grid to match HadCRUT-like coords lat -90 to 90 and lon -180 to 180
target_grid = np.flip(target_grid, 1)
target_grid = np.roll(target_grid, 36, 2)
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
times = pd.date_range(start=f'{1958}-{1:02d}-01', freq='1MS', periods=number_of_months)
ds = gd.make_xarray(target_grid, times, latitudes, longitudes)
# update encoding
for key in ds.data_vars:
ds[key].encoding.update({'zlib': True, '_FillValue': -1e30})
metadata.creation_message()
metadata['history'].append("Regridded to 5 degree latitude-longitude resolution")
return gd.GridMonthly(ds, metadata)
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def read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
ds, filled_filename = read_grid(filename)
metadata.dataset['last_modified'] = [get_last_modified_time(filled_filename)]
jra55_125 = ds.tas_mean
number_of_months = jra55_125.shape[0]
target_grid = np.zeros((number_of_months, 180, 360))
for month in range(number_of_months):
enlarged_array = np.zeros((145, 289))
enlarged_array[:, 0:288] = jra55_125[month, :, :]
enlarged_array[:, 288] = jra55_125[month, :, 0]
regridded = gd.simple_regrid(enlarged_array, -180. - 1.25 / 2., -90. - 1.25 / 2., 1.25, 1.0)
target_grid[month, :, :] = regridded[:, :]
# flip and shift target_grid to match HadCRUT-like coords lat -90 to 90 and lon -180 to 180
target_grid = np.flip(target_grid, 1)
target_grid = np.roll(target_grid, 180, 2)
latitudes = np.linspace(-89.5, 89.5, 180)
longitudes = np.linspace(-179.5, 179.5, 360)
times = pd.date_range(start=f'{1958}-{1:02d}-01', freq='1MS', periods=number_of_months)
ds = gd.make_xarray(target_grid, times, latitudes, longitudes)
# update encoding
for key in ds.data_vars:
ds[key].encoding.update({'zlib': True, '_FillValue': -1e30})
metadata.creation_message()
metadata['history'].append("Regridded to 1 degree latitude-longitude resolution")
return gd.GridMonthly(ds, metadata)
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def read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
anomalies = []
with open(filename[0], 'r') as f:
for line in f:
columns = line.split()
year = columns[0][0:4]
month = columns[0][5:7]
years.append(int(year))
months.append(int(month))
anomalies.append(float(columns[1]))
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:
monthly = read_monthly_ts(filename, metadata)
annual = monthly.make_annual()
return annual