# 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
from scipy.signal import savgol_filter
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 = []
anomalies = []
with open(filename[0], 'r') as f:
if metadata['name'] != 'AVISO 2m':
f.readline()
for line in f:
columns = line.split()
# This is "decimal year" which we convert in a rough and ready way
decimal_year = float(columns[0])
year_int = int(decimal_year)
diny = 1 + int(365. * (decimal_year - year_int))
years.append(f'{year_int} {diny:03d}')
anom = float(columns[1])
if metadata['name'] == 'AVISO 2m':
anom = anom * 1000.
anomalies.append(anom)
anomalies = savgol_filter(anomalies, 9, 1)
dates = pd.to_datetime(years, format='%Y %j')
years = dates.year.tolist()
months = dates.month.tolist()
days = dates.day.tolist()
metadata.creation_message()
outseries = ts.TimeSeriesIrregular(years, months, days, anomalies,
metadata=metadata)
return outseries