# 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 climind.data_types.timeseries as ts
from climind.readers.generic_reader import get_last_modified_time
from climind.data_manager.metadata import CombinedMetadata
import copy
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def find_latest(out_dir: Path, filename_with_wildcards: str) -> str:
"""
Find the most recent file that matches
Parameters
----------
filename_with_wildcards : str
Filename including wildcards
out_dir : Path
Path of data directory
Returns
-------
"""
# look in directory to find all matching
filename_with_wildcards = filename_with_wildcards.replace('YYYYMMMM', '*')
list_of_files = list(out_dir.glob(filename_with_wildcards))
list_of_files.sort()
out_filename = list_of_files[-1]
return out_filename
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def read_ts(out_dir: Path, metadata: CombinedMetadata):
filename = metadata['filename'][0]
filename = find_latest(out_dir, filename)
construction_metadata = copy.deepcopy(metadata)
construction_metadata.dataset['last_modified'] = [get_last_modified_time(filename)]
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"]}')
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def read_monthly_ts(filename: str, metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
anomalies = []
with open(filename, 'r') as f:
f.readline()
for line in f:
columns = line.split()
year = columns[0]
month = columns[1]
years.append(int(year))
months.append(int(month))
anomalies.append(float(columns[2]))
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
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def read_annual_ts(filename: str, metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
monthly = read_monthly_ts(filename, metadata)
annual = monthly.make_annual()
return annual