# 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 xarray as xa
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.TimeSeriesMonthly:
years = []
months = []
anomalies = []
with open(filename[0], 'r') as f:
for _ in range(7):
f.readline()
for line in f:
columns = line.split()
year = columns[1]
month = columns[2]
data = float(columns[4])
years.append(int(year))
months.append(int(month))
if data == -999:
anomalies.append(np.nan)
else:
anomalies.append(data / 1e6)
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
[docs]
def read_irregular_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesIrregular:
df = xa.open_dataset(filename[0])
years = df.time.dt.year.data.tolist()
months = df.time.dt.month.data.tolist()
days = df.time.dt.day.data.tolist()
anomalies = df.sie.data.tolist()
metadata.creation_message()
return ts.TimeSeriesIrregular(years, months, days, anomalies, metadata=metadata)