# 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
from itertools import product
import numpy as np
import pandas as pd
import climind.data_types.grid as gd
import climind.data_types.timeseries as ts
from climind.data_manager.metadata import CombinedMetadata
import copy
from climind.readers.generic_reader import read_ts
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def read_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
years = []
anomalies = []
with open(filename[0], 'r') as f:
f.readline()
f.readline()
for line in f:
columns = line.split()
if len(columns) != 4:
break
year = columns[0]
years.append(int(year))
anomalies.append(float(columns[1]))
metadata.creation_message()
return ts.TimeSeriesAnnual(years, anomalies, metadata=metadata)
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def read_one_month(filehandle):
year_month = filehandle.readline()
columns = year_month.split()
year = int(columns[1])
month = int(columns[0])
outarray = np.zeros((1, 36, 72))
for i in range(36):
line = filehandle.readline().rstrip()
columns = line.split()
columns = np.array([float(x) for x in columns])
outarray[0, i, :] = columns[:]
return year, month, outarray
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def read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) -> gd.GridMonthly:
years = []
months = []
number_of_months = (2020 - 1850 + 1) * 12
data_array = np.zeros((number_of_months, 36, 72))
times = pd.date_range(start=f'1850-01-01', freq='1MS', periods=number_of_months)
count = 0
with open(filename[0], 'r') as f:
for y, m in product(range(1850, 2021), range(1, 13)):
year, month, month_array = read_one_month(f)
data_array[count, :, :] = month_array[0, :, :]
count += 1
if y != year or m != month:
print(f"mismatch {y} {year} or {m} {month}")
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
data_array = np.roll(data_array, 36, 2)
ds = gd.make_xarray(data_array, times, latitudes, longitudes)
# update encoding
for key in ds.data_vars:
ds[key].encoding.update({'zlib': True, '_FillValue': -1e30})
metadata.creation_message()
return gd.GridMonthly(ds, metadata)
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def read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs):
return read_monthly_grid(filename, metadata)
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def read_monthly_1x1_grid(filename: Path, metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
df = read_monthly_grid(filename, metadata)
df = df.df
# regrid to 1x1
lats = np.arange(-89.5, 90.5, 1.0)
lons = np.arange(-179.5, 180.5, 1.0)
# Copy 5-degree grid cell value into all one degree cells
grid = np.repeat(df.tas_mean, 5, 1)
grid = np.repeat(grid, 5, 2)
df = gd.make_xarray(grid, df.time.data, lats, lons)
metadata.creation_message()
metadata['history'].append("Regridded to 1 degree latitude-longitude resolution")
return gd.GridMonthly(df, metadata)