# 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 xarray as xa
import numpy as np
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:
df = xa.open_dataset(filename[0])
correction = df.tpa_correction_to_substract.values
anomalies = df.msl.values - correction
anomalies = [x * 1000 for x in anomalies]
anomalies = savgol_filter(anomalies, 9, 1)
anomalies = anomalies - np.mean(anomalies[0:3]) - 2
uncertainty = df.envelop.values.tolist()
uncertainty = [x * 1000 for x in uncertainty]
years = df.time.dt.year.data.tolist()
months = df.time.dt.month.data.tolist()
days = df.time.dt.day.data.tolist()
metadata.creation_message()
metadata['history'].append("Filtered with a 9-point Savgol filter of order 1")
outseries = ts.TimeSeriesIrregular(years, months, days, anomalies, uncertainty=uncertainty, metadata=metadata)
return outseries
[docs]
def read_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
ts = read_monthly_ts(filename, metadata)
ts = ts.make_monthly()
ts = ts.make_annual()
return ts