# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import xarray as xr
from earth2studio.data.utils import prep_data_inputs, prep_forecast_inputs
from earth2studio.utils.type import LeadTimeArray, TimeArray, VariableArray
[docs]
class Random:
"""A randomly generated normally distributed data. Primarily useful for testing.
Parameters
----------
domain_coords: OrderedDict[str, np.ndarray]
Domain coordinates that the random data will assume (such as lat, lon).
"""
def __init__(
self,
domain_coords: OrderedDict[str, np.ndarray],
):
self.domain_coords = domain_coords
# Check for regular vs. curvilinear coordinates
_, value = list(self.domain_coords.items()).pop()
value = np.array(value)
self.curv = len(value.shape) > 1
if self.curv:
self.domain_coord_shape = value.shape
[docs]
def __call__(
self,
time: datetime | list[datetime] | TimeArray,
variable: str | list[str] | VariableArray,
) -> xr.DataArray:
"""Retrieve random gaussian data.
Parameters
----------
time : datetime | list[datetime] | TimeArray
Timestamps to return data for.
variable : str | list[str] | VariableArray
Strings or list of strings that refer to variables to return.
Returns
-------
xr.DataArray
Random data array
"""
time, variable = prep_data_inputs(time, variable)
shape = [len(time), len(variable)]
coords = {"time": time, "variable": variable}
if self.curv:
shape.extend(self.domain_coord_shape)
dims = ["time", "variable", "y", "x"]
coords = coords | {
"lat": (("y", "x"), self.domain_coords["lat"]),
"lon": (("y", "x"), self.domain_coords["lon"]),
}
da = xr.DataArray(data=np.random.randn(*shape), dims=dims, coords=coords)
else:
for key, value in self.domain_coords.items():
shape.append(len(value))
coords[key] = value
da = xr.DataArray(
data=np.random.randn(*shape), dims=list(coords), coords=coords
)
return da
class Random_FX:
"""A randomly generated normally distributed data. Primarily useful for testing.
Parameters
----------
domain_coords: OrderedDict[str, np.ndarray]
Domain coordinates that the random data will assume (such as lat, lon).
"""
def __init__(
self,
domain_coords: OrderedDict[str, np.ndarray],
):
self.domain_coords = domain_coords
# Check for regular vs. curvilinear coordinates
_, value = list(self.domain_coords.items()).pop()
value = np.array(value)
self.curv = len(value.shape) > 1
if self.curv:
self.domain_coord_shape = value.shape
def __call__( # type: ignore[override]
self,
time: datetime | list[datetime] | TimeArray,
lead_time: timedelta | list[timedelta] | LeadTimeArray,
variable: str | list[str] | VariableArray,
) -> xr.DataArray:
"""Retrieve random gaussian data.
Parameters
----------
time : datetime | list[datetime] | TimeArray
Timestamps to return data for.
variable : str | list[str] | VariableArray
Strings or list of strings that refer to variables to return.
Returns
-------
xr.DataArray
Random data array
"""
time, lead_time, variable = prep_forecast_inputs(time, lead_time, variable)
shape = [len(time), len(lead_time), len(variable)]
coords = {"time": time, "lead_time": lead_time, "variable": variable}
if self.curv:
shape.extend(self.domain_coord_shape)
dims = ["time", "lead_time", "variable", "y", "x"]
coords = coords | {
"lat": (("y", "x"), self.domain_coords["lat"]),
"lon": (("y", "x"), self.domain_coords["lon"]),
}
da = xr.DataArray(data=np.random.randn(*shape), dims=dims, coords=coords)
else:
for key, value in self.domain_coords.items():
shape.append(len(value))
coords[key] = value
da = xr.DataArray(
data=np.random.randn(*shape), dims=list(coords), coords=coords
)
return da
