# 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
from datetime import timedelta, datetime
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def convert_partial_year(number):
year = int(number)
d = timedelta(days=(number - year) * 365)
day_one = datetime(year, 1, 1)
date = d + day_one
return date
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def read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesIrregular:
anomalies = []
years = []
months = []
days = []
with open(filename[0], 'r') as f:
for i in range(52):
f.readline()
for line in f:
columns = line.split()
converted_date = convert_partial_year(float(columns[2]))
anomalies.append(float(columns[11]) + 37.64)
years.append(converted_date.year)
months.append(converted_date.month)
days.append(converted_date.day)
metadata.creation_message()
outseries = ts.TimeSeriesIrregular(years, months, days, anomalies, metadata=metadata)
return outseries
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def read_new_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesIrregular:
anomalies = []
years = []
months = []
days = []
with open(filename[0], 'r') as f:
for i in range(42):
f.readline()
for line in f:
columns = line.split()
converted_date = convert_partial_year(float(columns[0]))
anomalies.append(float(columns[1]) * 10) # convert to mm from cm
years.append(converted_date.year)
months.append(converted_date.month)
days.append(converted_date.day)
anomalies = np.array(anomalies)
anomalies = anomalies - anomalies[0]
anomalies = anomalies.tolist()
metadata.creation_message()
outseries = ts.TimeSeriesIrregular(years, months, days, anomalies, metadata=metadata)
return outseries