# 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 pandas as pd
from typing import List
import climind.data_types.timeseries as ts
from climind.data_manager.metadata import CombinedMetadata
from climind.readers.generic_reader import read_ts
[docs]
def read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesIrregular:
years = []
months = []
days = []
anomalies = []
uncertainties = []
with open(filename[0], 'r') as f:
f.readline()
for line in f:
columns = line.split(',')
date = columns[0]
date_components = date.split('-')
year = int(date_components[0])
month = int(date_components[1])
day = int(date_components[2])
years.append(year)
months.append(month)
days.append(day)
anomalies.append(float(columns[1]))
uncertainties.append(float(columns[2]))
metadata.creation_message()
outseries = ts.TimeSeriesIrregular(years, months, days, anomalies,
metadata=metadata,
uncertainty=uncertainties)
return outseries