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import numpy as np
import pandas as pd
import pytest
import xarray as xr
cp = pytest.importorskip("cupy")
@pytest.fixture
def toy_weather_data():
"""Construct the example DataSet from the Toy weather data example.
http://xarray.pydata.org/en/stable/examples/weather-data.html
Here we construct the DataSet exactly as shown in the example and then
convert the numpy arrays to cupy.
"""
np.random.seed(123)
times = pd.date_range("2000-01-01", "2001-12-31", name="time")
annual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 365.25 - 0.28))
base = 10 + 15 * annual_cycle.reshape(-1, 1)
tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)
tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3)
ds = xr.Dataset(
{
"tmin": (("time", "location"), tmin_values),
"tmax": (("time", "location"), tmax_values),
},
{"time": times, "location": ["IA", "IN", "IL"]},
)
ds.tmax.data = cp.asarray(ds.tmax.data)
ds.tmin.data = cp.asarray(ds.tmin.data)
return ds
def test_cupy_import():
"""Check the import worked."""
assert cp
def test_check_data_stays_on_gpu(toy_weather_data):
"""Perform some operations and check the data stays on the GPU."""
freeze = (toy_weather_data["tmin"] <= 0).groupby("time.month").mean("time")
assert isinstance(freeze.data, cp.core.core.ndarray)
def test_where():
from xarray.core.duck_array_ops import where
data = cp.zeros(10)
output = where(data < 1, 1, data).all()
assert output
assert isinstance(output, cp.ndarray)
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