# 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 climind.data_types.timeseries as ts
from climind.readers.generic_reader import get_last_modified_time
import copy
from climind.data_manager.metadata import CombinedMetadata
[docs]
def read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs):
filenames = []
for filename in out_dir.glob(metadata['filename'][1]):
filenames.append(filename)
filenames.sort()
filename = filenames[-1]
construction_metadata = copy.deepcopy(metadata)
construction_metadata.dataset['last_modified'] = [get_last_modified_time(filename)]
if metadata['type'] == 'timeseries':
if metadata['time_resolution'] == 'monthly':
raise NotImplementedError
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':
raise NotImplementedError
[docs]
def read_annual_ts(filename: Path, metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
df = xa.open_dataset(filename)
years = df.time.dt.year.data.tolist()
# Double uncertainties to get 95% range
if metadata['variable'] == 'ohc':
if 'ocean_heat_content_0-700m' in df:
data = df['ocean_heat_content_0-700m'].values.tolist()
uncertainty = (2 * df['ocean_heat_content_0-700m_uncertainty']).data.tolist()
elif 'ohc_mean_0-700m' in df:
data = (df['ohc_mean_0-700m'] / 1e21).values.tolist()
uncertainty = (2 * df['ohc_std_0-700m'] / 1e21).data.tolist()
elif metadata['variable'] == 'ohc2k':
if 'ocean_heat_content_0-2000m' in df:
data = df['ocean_heat_content_0-2000m'].values.tolist()
uncertainty = (2 * df['ocean_heat_content_0-2000m_uncertainty']).data.tolist()
elif 'ohc_mean_0-2000m' in df:
data = (df['ohc_mean_0-2000m'] / 1e21).values.tolist()
uncertainty = (2 * df['ohc_std_0-2000m'] / 1e21).data.tolist()
else:
raise ValueError(f"Variable {metadata['variable']} unrecognised")
metadata.creation_message()
return ts.TimeSeriesAnnual(years, data, metadata=metadata, uncertainty=uncertainty)