# 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 numpy as np
import pandas as pd
from typing import List
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, **kwargs) -> ts.TimeSeriesMonthly:
if 'first_difference' in kwargs:
first_diff = kwargs['first_difference']
else:
first_diff = False
df = pd.read_excel(filename[0])
df = df.rename(columns={'Cumulative ice mass change (Gt)': 'data',
'Cumulative ice mass change uncertainty (Gt)': 'uncertainty'})
# Clip out missing data, which constitute quite a lof the of series
df = df[~np.isnan(df['data'])]
df['data'] = df.diff()['data']
decimal_year = df['Year'].values
year_int = decimal_year.astype(int)
months = np.rint(12. * (decimal_year - year_int) + 1.0).astype(int)
years = year_int.tolist()
months = months.tolist()
if not first_diff:
df['data'] = df.cumsum()['data']
mass_balance = df['data'].tolist()
uncertainty = df['uncertainty'].tolist()
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, mass_balance, metadata=metadata, uncertainty=uncertainty)
[docs]
def read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> ts.TimeSeriesAnnual:
monthly = read_monthly_ts(filename, metadata, **kwargs)
annual = monthly.make_annual(cumulative=True)
return annual