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from __future__ import annotations
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray import DataArray, Dataset, set_options
from xarray.core import duck_array_ops
from xarray.tests import (
assert_allclose,
assert_equal,
assert_identical,
has_dask,
raise_if_dask_computes,
requires_cftime,
)
def test_coarsen_absent_dims_error(ds: Dataset) -> None:
with pytest.raises(
ValueError,
match=r"Window dimensions \('foo',\) not found in Dataset dimensions",
):
ds.coarsen(foo=2)
@pytest.mark.parametrize("dask", [True, False])
@pytest.mark.parametrize(("boundary", "side"), [("trim", "left"), ("pad", "right")])
def test_coarsen_dataset(ds, dask, boundary, side):
if dask and has_dask:
ds = ds.chunk({"x": 4})
actual = ds.coarsen(time=2, x=3, boundary=boundary, side=side).max()
assert_equal(
actual["z1"], ds["z1"].coarsen(x=3, boundary=boundary, side=side).max()
)
# coordinate should be mean by default
assert_equal(
actual["time"], ds["time"].coarsen(time=2, boundary=boundary, side=side).mean()
)
@pytest.mark.parametrize("dask", [True, False])
def test_coarsen_coords(ds, dask):
if dask and has_dask:
ds = ds.chunk({"x": 4})
# check if coord_func works
actual = ds.coarsen(time=2, x=3, boundary="trim", coord_func={"time": "max"}).max()
assert_equal(actual["z1"], ds["z1"].coarsen(x=3, boundary="trim").max())
assert_equal(actual["time"], ds["time"].coarsen(time=2, boundary="trim").max())
# raise if exact
with pytest.raises(ValueError):
ds.coarsen(x=3).mean()
# should be no error
ds.isel(x=slice(0, 3 * (len(ds["x"]) // 3))).coarsen(x=3).mean()
# working test with pd.time
da = xr.DataArray(
np.linspace(0, 365, num=364),
dims="time",
coords={"time": pd.date_range("1999-12-15", periods=364)},
)
actual = da.coarsen(time=2).mean()
@requires_cftime
def test_coarsen_coords_cftime():
times = xr.date_range("2000", periods=6, use_cftime=True)
da = xr.DataArray(range(6), [("time", times)])
actual = da.coarsen(time=3).mean()
expected_times = xr.date_range("2000-01-02", freq="3D", periods=2, use_cftime=True)
np.testing.assert_array_equal(actual.time, expected_times)
@pytest.mark.parametrize(
"funcname, argument",
[
("reduce", (np.mean,)),
("mean", ()),
],
)
def test_coarsen_keep_attrs(funcname, argument) -> None:
global_attrs = {"units": "test", "long_name": "testing"}
da_attrs = {"da_attr": "test"}
attrs_coords = {"attrs_coords": "test"}
da_not_coarsend_attrs = {"da_not_coarsend_attr": "test"}
data = np.linspace(10, 15, 100)
coords = np.linspace(1, 10, 100)
ds = Dataset(
data_vars={
"da": ("coord", data, da_attrs),
"da_not_coarsend": ("no_coord", data, da_not_coarsend_attrs),
},
coords={"coord": ("coord", coords, attrs_coords)},
attrs=global_attrs,
)
# attrs are now kept per default
func = getattr(ds.coarsen(dim={"coord": 5}), funcname)
result = func(*argument)
assert result.attrs == global_attrs
assert result.da.attrs == da_attrs
assert result.da_not_coarsend.attrs == da_not_coarsend_attrs
assert result.coord.attrs == attrs_coords
assert result.da.name == "da"
assert result.da_not_coarsend.name == "da_not_coarsend"
# discard attrs
func = getattr(ds.coarsen(dim={"coord": 5}), funcname)
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
assert result.da.attrs == {}
assert result.da_not_coarsend.attrs == {}
assert result.coord.attrs == {}
assert result.da.name == "da"
assert result.da_not_coarsend.name == "da_not_coarsend"
# test discard attrs using global option
func = getattr(ds.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument)
assert result.attrs == {}
assert result.da.attrs == {}
assert result.da_not_coarsend.attrs == {}
assert result.coord.attrs == {}
assert result.da.name == "da"
assert result.da_not_coarsend.name == "da_not_coarsend"
# keyword takes precedence over global option
func = getattr(ds.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument, keep_attrs=True)
assert result.attrs == global_attrs
assert result.da.attrs == da_attrs
assert result.da_not_coarsend.attrs == da_not_coarsend_attrs
assert result.coord.attrs == attrs_coords
assert result.da.name == "da"
assert result.da_not_coarsend.name == "da_not_coarsend"
func = getattr(ds.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=True):
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
assert result.da.attrs == {}
assert result.da_not_coarsend.attrs == {}
assert result.coord.attrs == {}
assert result.da.name == "da"
assert result.da_not_coarsend.name == "da_not_coarsend"
@pytest.mark.slow
@pytest.mark.parametrize("ds", (1, 2), indirect=True)
@pytest.mark.parametrize("window", (1, 2, 3, 4))
@pytest.mark.parametrize("name", ("sum", "mean", "std", "var", "min", "max", "median"))
def test_coarsen_reduce(ds: Dataset, window, name) -> None:
# Use boundary="trim" to accommodate all window sizes used in tests
coarsen_obj = ds.coarsen(time=window, boundary="trim")
# add nan prefix to numpy methods to get similar behavior as bottleneck
actual = coarsen_obj.reduce(getattr(np, f"nan{name}"))
expected = getattr(coarsen_obj, name)()
assert_allclose(actual, expected)
# make sure the order of data_var are not changed.
assert list(ds.data_vars.keys()) == list(actual.data_vars.keys())
# Make sure the dimension order is restored
for key, src_var in ds.data_vars.items():
assert src_var.dims == actual[key].dims
@pytest.mark.parametrize(
"funcname, argument",
[
("reduce", (np.mean,)),
("mean", ()),
],
)
def test_coarsen_da_keep_attrs(funcname, argument) -> None:
attrs_da = {"da_attr": "test"}
attrs_coords = {"attrs_coords": "test"}
data = np.linspace(10, 15, 100)
coords = np.linspace(1, 10, 100)
da = DataArray(
data,
dims=("coord"),
coords={"coord": ("coord", coords, attrs_coords)},
attrs=attrs_da,
name="name",
)
# attrs are now kept per default
func = getattr(da.coarsen(dim={"coord": 5}), funcname)
result = func(*argument)
assert result.attrs == attrs_da
assert da.coord.attrs == attrs_coords
assert result.name == "name"
# discard attrs
func = getattr(da.coarsen(dim={"coord": 5}), funcname)
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
# XXX: no assert?
_ = da.coord.attrs == {}
assert result.name == "name"
# test discard attrs using global option
func = getattr(da.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument)
assert result.attrs == {}
# XXX: no assert?
_ = da.coord.attrs == {}
assert result.name == "name"
# keyword takes precedence over global option
func = getattr(da.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument, keep_attrs=True)
assert result.attrs == attrs_da
# XXX: no assert?
_ = da.coord.attrs == {}
assert result.name == "name"
func = getattr(da.coarsen(dim={"coord": 5}), funcname)
with set_options(keep_attrs=True):
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
# XXX: no assert?
_ = da.coord.attrs == {}
assert result.name == "name"
@pytest.mark.parametrize("da", (1, 2), indirect=True)
@pytest.mark.parametrize("window", (1, 2, 3, 4))
@pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
def test_coarsen_da_reduce(da, window, name) -> None:
if da.isnull().sum() > 1 and window == 1:
pytest.skip("These parameters lead to all-NaN slices")
# Use boundary="trim" to accommodate all window sizes used in tests
coarsen_obj = da.coarsen(time=window, boundary="trim")
# add nan prefix to numpy methods to get similar # behavior as bottleneck
actual = coarsen_obj.reduce(getattr(np, f"nan{name}"))
expected = getattr(coarsen_obj, name)()
assert_allclose(actual, expected)
class TestCoarsenConstruct:
@pytest.mark.parametrize("dask", [True, False])
def test_coarsen_construct(self, dask: bool) -> None:
ds = Dataset(
{
"vart": ("time", np.arange(48), {"a": "b"}),
"varx": ("x", np.arange(10), {"a": "b"}),
"vartx": (("x", "time"), np.arange(480).reshape(10, 48), {"a": "b"}),
"vary": ("y", np.arange(12)),
},
coords={"time": np.arange(48), "y": np.arange(12)},
attrs={"foo": "bar"},
)
if dask and has_dask:
ds = ds.chunk({"x": 4, "time": 10})
expected = xr.Dataset(attrs={"foo": "bar"})
expected["vart"] = (
("year", "month"),
duck_array_ops.reshape(ds.vart.data, (-1, 12)),
{"a": "b"},
)
expected["varx"] = (
("x", "x_reshaped"),
duck_array_ops.reshape(ds.varx.data, (-1, 5)),
{"a": "b"},
)
expected["vartx"] = (
("x", "x_reshaped", "year", "month"),
duck_array_ops.reshape(ds.vartx.data, (2, 5, 4, 12)),
{"a": "b"},
)
expected["vary"] = ds.vary
expected.coords["time"] = (
("year", "month"),
duck_array_ops.reshape(ds.time.data, (-1, 12)),
)
with raise_if_dask_computes():
actual = ds.coarsen(time=12, x=5).construct(
{"time": ("year", "month"), "x": ("x", "x_reshaped")}
)
assert_identical(actual, expected)
with raise_if_dask_computes():
actual = ds.coarsen(time=12, x=5).construct(
time=("year", "month"), x=("x", "x_reshaped")
)
assert_identical(actual, expected)
with raise_if_dask_computes():
actual = ds.coarsen(time=12, x=5).construct(
{"time": ("year", "month"), "x": ("x", "x_reshaped")}, keep_attrs=False
)
for var in actual:
assert actual[var].attrs == {}
assert actual.attrs == {}
with raise_if_dask_computes():
actual = ds.vartx.coarsen(time=12, x=5).construct(
{"time": ("year", "month"), "x": ("x", "x_reshaped")}
)
assert_identical(actual, expected["vartx"])
with pytest.raises(ValueError):
ds.coarsen(time=12).construct(foo="bar")
with pytest.raises(ValueError):
ds.coarsen(time=12, x=2).construct(time=("year", "month"))
with pytest.raises(ValueError):
ds.coarsen(time=12).construct()
with pytest.raises(ValueError):
ds.coarsen(time=12).construct(time="bar")
with pytest.raises(ValueError):
ds.coarsen(time=12).construct(time=("bar",))
def test_coarsen_construct_keeps_all_coords(self):
da = xr.DataArray(np.arange(24), dims=["time"])
da = da.assign_coords(day=365 * da)
result = da.coarsen(time=12).construct(time=("year", "month"))
assert list(da.coords) == list(result.coords)
ds = da.to_dataset(name="T")
result = ds.coarsen(time=12).construct(time=("year", "month"))
assert list(da.coords) == list(result.coords)
|
