# 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
from typing import List
import numpy as np
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(filenames: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
anomalies = []
time = []
for filename in filenames:
with open(filename, 'r') as f:
f.readline()
for line in f:
columns = line.split(',')
year = columns[0]
month = columns[1]
if len(columns) == 7:
data = float(columns[5])
else:
data = float(columns[4])
years.append(int(year))
months.append(int(month))
time.append(float(year) + (float(month) - 1) / 12.)
if data == -9999:
anomalies.append(np.nan)
else:
anomalies.append(data)
# Sort based on time axis
anomalies = [x for _, x in sorted(zip(time, anomalies))]
years = [x for _, x in sorted(zip(time, years))]
months = [x for _, x in sorted(zip(time, months))]
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
[docs]
def read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
days = []
extents = []
with open(filenames[0], 'r') as f:
f.readline()
f.readline()
for line in f:
columns = line.split(',')
years.append(int(columns[0]))
months.append(int(columns[1]))
days.append(int(columns[2]))
extents.append(float(columns[3]))
metadata.creation_message()
return ts.TimeSeriesIrregular(years, months, days, extents, metadata=metadata)