# 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/>.
import itertools
from pathlib import Path
import xarray as xa
import pandas as pd
import numpy as np
from typing import List
import climind.data_types.timeseries as ts
import climind.data_types.grid as gd
from climind.data_manager.metadata import CombinedMetadata
from climind.readers.generic_reader import read_ts
[docs]
def read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
return read_monthly_grid(filename, metadata)
[docs]
def read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
"""
Although Berkeley Earth is 1x1 already, the time dimension is extremely non-standard.
In order to get consistency with the other data sets regridded to 1x1, the data is copied
into a consistent xarray Dataset.
Parameters
----------
filename: str
Filename of the netcdf grid
metadata: CombinedMetadata
CombinedMetadata object holding the dataset metadata.
Returns
-------
GridMonthly
"""
df = xa.open_dataset(filename[0])
number_of_months = len(df.time.data)
latitudes = np.linspace(-89.5, 89.5, 180)
longitudes = np.linspace(-179.5, 179.5, 360)
times = pd.date_range(start=f'1850-01-01', freq='1MS', periods=number_of_months)
target_grid = np.zeros((number_of_months, 180, 360))
target_grid[:, :, :] = df.temperature.data[:, :, :]
ds = gd.make_xarray(target_grid, 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)
[docs]
def read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) -> gd.GridMonthly:
berkeley = xa.open_dataset(filename[0])
number_of_months = len(berkeley.time.data)
target_grid = np.zeros((number_of_months, 36, 72))
for m, xx, yy in itertools.product(range(number_of_months), range(72), range(36)):
transfer = np.zeros((5, 5)) + 1.0
lox = xx * 5
hix = lox + 4
loy = yy * 5
hiy = loy + 4
selection = berkeley.temperature.data[m, loy:hiy + 1, lox:hix + 1]
index = (~np.isnan(selection))
if np.count_nonzero(index) > 0:
weighted = transfer[index] * selection[index]
grid_mean = np.sum(weighted) / np.sum(transfer[index])
else:
grid_mean = np.nan
target_grid[m, yy, xx] = grid_mean
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
times = pd.date_range(start=f'1850-01-01', freq='1MS', periods=number_of_months)
ds = gd.make_xarray(target_grid, times, latitudes, longitudes)
# update encoding
for key in ds.data_vars:
ds[key].encoding.update({'zlib': True, '_FillValue': -1e30})
metadata.creation_message()
metadata['history'].append("Regridded to 5 degree latitude-longitude resolution")
return gd.GridMonthly(ds, metadata)
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def read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
anomalies = []
uncertainties = []
with open(filename[0], 'r') as f:
for _ in range(86):
f.readline()
for line in f:
columns = line.split()
if len(columns) < 2:
break
years.append(int(columns[0]))
months.append(int(columns[1]))
anomalies.append(float(columns[2]))
uncertainties.append(float(columns[3]))
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata, uncertainty=uncertainties)
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def read_annual_ts(filename: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
years = []
anomalies = []
uncertainties = []
with open(filename[0], 'r') as f:
for _ in range(58):
f.readline()
for line in f:
columns = line.split()
if len(columns) < 2:
break
years.append(int(columns[0]))
anomalies.append(float(columns[1]))
uncertainties.append(float(columns[2]))
metadata.creation_message()
return ts.TimeSeriesAnnual(years, anomalies, metadata=metadata, uncertainty=uncertainties)