# 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 Tuple, List
import itertools
import xarray as xa
import numpy as np
import climind.data_types.grid as gd
import climind.data_types.timeseries as ts
import copy
from climind.readers.generic_reader import get_last_modified_time
from climind.data_manager.metadata import CombinedMetadata
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def find_latest(out_dir: Path, filename_with_wildcards: str) -> Path:
"""
Find the most recent file that matches
Parameters
----------
filename_with_wildcards : str
Filename including wildcards
out_dir : Path
Path of data directory
Returns
-------
"""
# look in directory to find all matching
filename_with_wildcards = filename_with_wildcards.replace('YYYYMMMM', '*')
list_of_files = list(out_dir.glob(filename_with_wildcards))
list_of_files.sort()
out_filename = list_of_files[-1]
return out_filename
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def get_latest_filename_and_url(filename: Path, url: str) -> Tuple[str, str]:
"""
Get the filename and url from a filled filename Path and URL with placeholders
Parameters
----------
filename: Path
Path of filename
url: str
URL to be replaced
Returns
-------
Tuple[str, str]
The filename and the url with placeholders replaced
-------
"""
selected_file = filename.name
selected_url = url.split('/')
selected_url = selected_url[0:-1]
selected_url.append(selected_file)
selected_url = '/'.join(selected_url)
yyyy = selected_file[33:37]
mmmm = selected_file[37:39]
selected_url = selected_url.replace('YYYY', yyyy)
selected_url = selected_url.replace('MMMM', mmmm)
return selected_file, selected_url
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def read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs):
construction_metadata = copy.deepcopy(metadata)
if metadata['type'] == 'timeseries':
filename_with_wildcards = metadata['filename'][0]
filename = find_latest(out_dir, filename_with_wildcards)
construction_metadata.dataset['last_modified'] = [get_last_modified_time(filename)]
if metadata['time_resolution'] == 'monthly':
return read_monthly_ts(filename, construction_metadata)
elif metadata['time_resolution'] == 'annual':
return read_annual_ts(filename, construction_metadata)
elif metadata['time_resolution'] == 'irregular':
return read_irregular_ts([filename, out_dir / metadata['filename'][1]], construction_metadata)
else:
raise KeyError(f'That time resolution is not known: {metadata["time_resolution"]}')
elif metadata['type'] == 'gridded':
filename = out_dir / metadata['filename'][0]
if 'grid_resolution' in kwargs:
if kwargs['grid_resolution'] == 5:
return read_monthly_5x5_grid(filename, construction_metadata)
if kwargs['grid_resolution'] == 1:
return read_monthly_1x1_grid(filename, construction_metadata)
else:
return read_monthly_grid(filename, construction_metadata)
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def read_monthly_5x5_grid(filename, metadata) -> gd.GridMonthly:
combo = read_grid(filename)
number_of_months = combo.t2m.data.shape[0]
# transfer matrix for regridding, there are 20 quarter degree grid
# cells in a 5 degree grid cell, however, the ERA5 grid is offset half a
# grid cell because the first grid cell centre is at the North Pole and
# the last is at the South Pole
transfer = np.zeros((21, 21)) + 1
transfer[0, :] = transfer[0, :] * 0.5
transfer[20, :] = transfer[20, :] * 0.5
transfer[:, 0] = transfer[:, 0] * 0.5
transfer[:, 20] = transfer[:, 20] * 0.5
transfer_sum = np.sum(transfer)
enlarged_array = np.zeros((721, 1441))
target_grid = np.zeros((number_of_months, 36, 72))
for m in range(number_of_months):
if len(combo.t2m.data.shape) == 3:
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0]
else:
if np.isnan(combo.t2m.data[m, 0, 0, 0]):
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :, 1]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0, 1]
else:
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :, 0]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0, 0]
for xx, yy in itertools.product(range(72), range(36)):
lox = xx * 20
hix = (xx + 1) * 20
loy = yy * 20
hiy = (yy + 1) * 20
weighted = transfer * enlarged_array[loy:hiy + 1, lox:hix + 1]
grid_mean = np.sum(weighted) / transfer_sum
target_grid[m, yy, xx] = grid_mean
# flip and shift target_grid to match HadCRUT-like coords lat -90 to 90 and lon -180 to 180
target_grid = np.flip(target_grid, 1)
target_grid = np.roll(target_grid, 36, 2)
latitudes = np.linspace(-87.5, 87.5, 36)
longitudes = np.linspace(-177.5, 177.5, 72)
times = combo.time.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['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
metadata['history'].append("Regridded to 5 degree latitude-longitude resolution")
return gd.GridMonthly(ds, metadata)
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def read_monthly_1x1_grid(filename, metadata) -> gd.GridMonthly:
combo = read_grid(filename)
number_of_months = combo.t2m.data.shape[0]
# transfer matrix for regridding, there are 4 quarter degree grid
# cells in a 1 degree grid cell, however, the ERA5 grid is offset half a
# grid cell because the first grid cell centre is at the North Pole and
# the last is at the South Pole
transfer = np.zeros((5, 5)) + 1
transfer[0, :] = transfer[0, :] * 0.5
transfer[4, :] = transfer[4, :] * 0.5
transfer[:, 0] = transfer[:, 0] * 0.5
transfer[:, 4] = transfer[:, 4] * 0.5
transfer_sum = np.sum(transfer)
enlarged_array = np.zeros((721, 1441))
target_grid = np.zeros((number_of_months, 180, 360))
for m in range(number_of_months):
if len(combo.t2m.data.shape) == 3:
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0]
else:
if np.isnan(combo.t2m.data[m, 0, 0, 0]):
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :, 1]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0, 1]
else:
enlarged_array[:, 0:1440] = combo.t2m.data[m, :, :, 0]
enlarged_array[:, 1440] = combo.t2m.data[m, :, 0, 0]
for xx, yy in itertools.product(range(360), range(180)):
lox = xx * 4
hix = (xx + 1) * 4
loy = yy * 4
hiy = (yy + 1) * 4
weighted = transfer * enlarged_array[loy:hiy + 1, lox:hix + 1]
grid_mean = np.sum(weighted) / transfer_sum
target_grid[m, yy, xx] = grid_mean
# flip and shift target_grid to match HadCRUT-like coords lat -90 to 90 and lon -180 to 180
target_grid = np.flip(target_grid, 1)
target_grid = np.roll(target_grid, 180, 2)
latitudes = np.linspace(-89.5, 89.5, 180)
longitudes = np.linspace(-179.5, 179.5, 360)
times = combo.time.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['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
metadata['history'].append("Regridded to 1 degree latitude-longitude resolution")
return gd.GridMonthly(ds, metadata)
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def read_monthly_grid(filename: str, metadata) -> gd.GridMonthly:
combo = read_grid(filename)
metadata['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
return gd.GridMonthly(combo, metadata)
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def read_grid(filename: str):
dataset_list = []
for year in range(1959, 2030):
filled_filename = Path(str(filename).replace('YYYY', f'{year}'))
if filled_filename.exists():
dataset_list.append(xa.open_dataset(filled_filename))
combo = xa.concat(dataset_list, dim='time')
return combo
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def read_monthly_ts(filename: Path, metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
anomalies = []
with open(filename, 'r') as f:
for _ in range(9):
f.readline()
for line in f:
columns = line.split(',')
year = columns[0][0:4]
month = columns[0][4:6]
years.append(int(year))
months.append(int(month))
anomalies.append(float(columns[1]))
selected_file, selected_url = get_latest_filename_and_url(filename, metadata['url'][0])
metadata['filename'][0] = selected_file
metadata['url'][0] = selected_url
metadata.creation_message()
return ts.TimeSeriesMonthly(years, months, anomalies, metadata=metadata)
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def read_annual_ts(filename: Path, metadata: CombinedMetadata) -> ts.TimeSeriesAnnual:
monthly = read_monthly_ts(filename, metadata)
annual = monthly.make_annual()
return annual
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def read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) -> ts.TimeSeriesMonthly:
years = []
months = []
days = []
extents = []
clim = np.zeros((12,31))
with open(filenames[1], 'r') as f:
for _ in range(18):
f.readline()
for line in f:
columns = line.split(',')
split_date = columns[0].split('-')
month = int(split_date[1])
day = int(split_date[2])
climatology = float(columns[1]) - float(columns[4])
clim[month-1, day-1] = climatology
with open(filenames[0], 'r') as f:
for _ in range(19):
f.readline()
for line in f:
columns = line.split(',')
split_date = columns[0].split('-')
year = int(split_date[0])
month = int(split_date[1])
day = int(split_date[2])
years.append(year)
months.append(month)
days.append(day)
extents.append(float(columns[1]) - clim[month-1, day-1])
metadata.creation_message()
return ts.TimeSeriesIrregular(years, months, days, extents, metadata=metadata)