# 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 pandas as pd
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
dates = []
mass_balance = []
with open(filename[0], 'r') as in_file:
in_file.readline()
for line in in_file:
if int(line[0:4]) > 1985:
columns = line.split(',')
dates.append(columns[0])
mass_balance.append(float(columns[1]))
parsed_dates = pd.to_datetime(dates, format='%Y-%m-%d')
years = parsed_dates.year.tolist()
months = parsed_dates.month.tolist()
dico = {'year': years, 'month': months, 'data': mass_balance}
df = pd.DataFrame(dico)
mdf_year = df.groupby(['year', 'month'])['year'].mean()
mdf_month = df.groupby(['year', 'month'])['month'].mean()
mdf_data = df.groupby(['year', 'month'])['data'].sum()
mdf_year = mdf_year.astype(int)
mdf_month = mdf_month.astype(int)
if not first_diff:
mdf_data = mdf_data.cumsum()
years = mdf_year.values.tolist()
months = mdf_month.values.tolist()
mass_balance = mdf_data.values.tolist()
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, mass_balance, metadata=metadata)
[docs]
def read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> ts.TimeSeriesAnnual:
if 'first_difference' in kwargs:
first_diff = kwargs['first_difference']
else:
first_diff = False
years = []
mass_balance = []
with open(filename[0], 'r') as in_file:
in_file.readline()
for line in in_file:
columns = line.split(',')
years.append(int(columns[0]))
mass_balance.append(float(columns[1]))
if not first_diff:
dico = {'year': years, 'data': mass_balance}
df = pd.DataFrame(dico)
mdf_year = df['year'].astype(int)
mdf_data = df['data'].cumsum()
years = mdf_year.values.tolist()
mass_balance = mdf_data.values.tolist()
metadata.creation_message()
return ts.TimeSeriesAnnual(years, mass_balance, metadata=metadata)