# Climate indicator manager - a package for managing and building climate indicator dashboards.
# Copyright (c) 2023 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 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:
"""
The PSL monthly format has three main sections. The first line has the start and end years, then there is a
data section with each row being a year and 13 columns year and 12 months of data. Finally, there's a metadata
section at the end. The first line of the metadata gives the missing data indicator.
Parameters
----------
filename: List[Path]
List of paths for the filenames
metadata: CombinedMetadata
Metadata object
Returns
-------
ts.TimeSeriesMonthly
Monthly time series read from the file
"""
years = []
months = []
anomalies = []
with open(filename[0], 'r') as f:
# First line has start and end years
line = f.readline()
columns = line.split()
first_year = int(columns[0])
last_year = int(columns[1])
# Skip over the data to get to the missing data flag
for year in range(first_year, last_year + 1):
f.readline()
# Get the missing data indicator
missing_flag_line = f.readline()
missing_flag = float(missing_flag_line)
# Reopen the file and read the data
with open(filename[0], 'r') as f:
# Skip the header
f.readline()
# Read all years of data
for year in range(first_year, last_year + 1):
line = f.readline()
columns = line.split()
n_columns = len(columns)
for i in range(1, n_columns):
value = float(columns[i])
if value != missing_flag:
years.append(int(columns[0]))
months.append(int(i))
anomalies.append(value)
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
[docs]
def read_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
monthly = read_monthly_ts(filename, metadata)
annual = monthly.make_annual()
return annual