# 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/>.
from pathlib import Path
import xarray as xa
import numpy as np
from typing import Tuple
import climind.data_types.grid as gd
import climind.data_types.timeseries as ts
from climind.readers.generic_reader import get_last_modified_time
from climind.readers.generic_reader_utils import find_latest, get_latest_filename_and_url
from climind.data_manager.metadata import CombinedMetadata
import copy
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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)
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: Path, metadata: CombinedMetadata) -> gd.GridMonthly:
df = xa.open_dataset(filename)
number_of_months = len(df.time.data)
target_grid = np.zeros((number_of_months, 36, 72))
for m in range(number_of_months):
target_grid[m, :, :] = df.anom.data[m, 0, :, :]
# shift longitudes to match HadCRUT convention of -180 to 180
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 = df.time.data
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()
return gd.GridMonthly(ds, metadata)
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def read_monthly_1x1_grid(filename: Path, metadata: CombinedMetadata) -> gd.GridMonthly:
df = read_monthly_grid(filename, metadata)
df = df.df
# 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: Path, metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
"""
Read in monthly file
Parameters
----------
filename : Path
Path of monthly file
metadata : dict
Dictionary containing metadata
Returns
-------
ts.TimeSeriesMonthly
"""
years = []
months = []
anomalies = []
uncertainties = []
with open(filename, 'r') as f:
for line in f:
columns = line.split()
years.append(int(columns[0]))
months.append(int(columns[1]))
anomalies.append(float(columns[2]))
uncertainty_value = float(columns[3])
if uncertainty_value >= 0.0:
uncertainties.append(np.sqrt(uncertainty_value))
selected_file, selected_url = get_latest_filename_and_url(filename, metadata['url'][0])
metadata['filename'][0] = selected_file
metadata['url'][0] = selected_url
metadata.creation_message()
if len(uncertainties) == len(anomalies):
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata, uncertainty=uncertainties)
else:
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
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def read_annual_ts(filename: Path, metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
"""
Read in annual file
Parameters
----------
filename : Path
Filename for annual file
metadata : dict
Dictionary containing metadata
Returns
-------
ts.TimeSeriesAnnual
"""
years = []
anomalies = []
uncertainties = []
with open(filename, 'r') as f:
for line in f:
columns = line.split()
years.append(int(columns[0]))
anomalies.append(float(columns[1]))
variance = float(columns[2])
if variance >= 0:
uncertainties.append(np.sqrt(variance))
else:
uncertainties.append(np.nan)
selected_file, selected_url = get_latest_filename_and_url(filename, metadata['url'][0])
metadata['filename'][0] = selected_file
metadata['url'][0] = selected_url
metadata.creation_message()
return ts.TimeSeriesAnnual(years, anomalies, metadata=metadata, uncertainty=uncertainties)