# 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 Tuple
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
[docs]
def read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs):
construction_metadata = copy.deepcopy(metadata)
if metadata['type'] == 'gridded':
filename = out_dir
if 'grid_resolution' in kwargs:
if kwargs['grid_resolution'] == 5:
return
if kwargs['grid_resolution'] == 1:
return
else:
return read_monthly_grid(filename, construction_metadata)
[docs]
def read_monthly_grid(filename, metadata):
combo = read_grid(filename)
metadata['history'] = [f"Gridded dataset created from file {metadata['filename']} "
f"downloaded from {metadata['url']}"]
return gd.GridMonthly(combo, metadata)
[docs]
def read_grid(filename: Path):
dataset_list = []
for year, month in itertools.product(range(1993, 2030), range(1, 13)):
filled_filename = filename / f"dt_global_twosat_phy_l4_{year}{month:02d}_vDT2021-M01.nc"
if filled_filename.exists():
dataset_list.append(xa.open_dataset(filled_filename))
combo = xa.concat(dataset_list, dim='time')
combo['sla'] = combo['sla'] * 1000.0
times = combo.time.data
latitudes = combo.latitude.data
longitudes = combo.longitude.data
target_grid = combo.sla.data
combo = gd.make_xarray(target_grid, times, latitudes, longitudes, variable='sealevel')
return combo