# 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 pandas as pd
import climind.data_types.timeseries as ts
import climind.data_types.grid as gd
from climind.data_manager.metadata import CombinedMetadata
from climind.readers.generic_reader import read_ts
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def read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) -> gd.GridMonthly:
df = xa.open_dataset(filename[0])
number_of_months = df.temperature.data.shape[1]
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
times = pd.date_range(start='1850-01-01', freq='1MS', periods=number_of_months)
target_grid = np.zeros((number_of_months, 36, 72))
ensemble_mean = np.mean(df.temperature.data, axis=0)
target_grid[:, :, :] = ensemble_mean[:, :, :]
df = gd.make_xarray(target_grid, times, latitudes, longitudes)
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])
number_of_months = df.temperature.data.shape[1]
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
times = pd.date_range(start='1850-01-01', freq='1MS', periods=number_of_months)
target_grid = np.zeros((number_of_months, 36, 72))
ensemble_mean = np.mean(df.temperature.data, axis=0)
target_grid[:, :, :] = ensemble_mean[:, :, :]
df = gd.make_xarray(target_grid, times, latitudes, longitudes)
# 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_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
years = []
anomalies = []
with open(filename[0], 'r') as f:
f.readline()
for line in f:
columns = line.split(',')
year = columns[0]
anom = columns[3]
years.append(int(year))
anomalies.append(float(anom))
metadata.creation_message()
return ts.TimeSeriesAnnual(years, anomalies, metadata=metadata)