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test_rolling.py
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test_rolling.py
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from __future__ import annotations
from typing import Any
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray import DataArray, Dataset, set_options
from xarray.tests import (
assert_allclose,
assert_equal,
assert_identical,
has_dask,
requires_dask,
requires_numbagg,
)
pytestmark = [
pytest.mark.filterwarnings("error:Mean of empty slice"),
pytest.mark.filterwarnings("error:All-NaN (slice|axis) encountered"),
]
@pytest.fixture(params=["numbagg", "bottleneck"])
def compute_backend(request):
if request.param == "bottleneck":
options = dict(use_bottleneck=True, use_numbagg=False)
elif request.param == "numbagg":
options = dict(use_bottleneck=False, use_numbagg=True)
else:
raise ValueError
with xr.set_options(**options):
yield request.param
class TestDataArrayRolling:
@pytest.mark.parametrize("da", (1, 2), indirect=True)
@pytest.mark.parametrize("center", [True, False])
@pytest.mark.parametrize("size", [1, 2, 3, 7])
def test_rolling_iter(self, da: DataArray, center: bool, size: int) -> None:
rolling_obj = da.rolling(time=size, center=center)
rolling_obj_mean = rolling_obj.mean()
assert len(rolling_obj.window_labels) == len(da["time"])
assert_identical(rolling_obj.window_labels, da["time"])
for i, (label, window_da) in enumerate(rolling_obj):
assert label == da["time"].isel(time=i)
actual = rolling_obj_mean.isel(time=i)
expected = window_da.mean("time")
np.testing.assert_allclose(actual.values, expected.values)
@pytest.mark.parametrize("da", (1,), indirect=True)
def test_rolling_repr(self, da) -> None:
rolling_obj = da.rolling(time=7)
assert repr(rolling_obj) == "DataArrayRolling [time->7]"
rolling_obj = da.rolling(time=7, center=True)
assert repr(rolling_obj) == "DataArrayRolling [time->7(center)]"
rolling_obj = da.rolling(time=7, x=3, center=True)
assert repr(rolling_obj) == "DataArrayRolling [time->7(center),x->3(center)]"
@requires_dask
def test_repeated_rolling_rechunks(self) -> None:
# regression test for GH3277, GH2514
dat = DataArray(np.random.rand(7653, 300), dims=("day", "item"))
dat_chunk = dat.chunk({"item": 20})
dat_chunk.rolling(day=10).mean().rolling(day=250).std()
def test_rolling_doc(self, da) -> None:
rolling_obj = da.rolling(time=7)
# argument substitution worked
assert "`mean`" in rolling_obj.mean.__doc__
def test_rolling_properties(self, da) -> None:
rolling_obj = da.rolling(time=4)
assert rolling_obj.obj.get_axis_num("time") == 1
# catching invalid args
with pytest.raises(ValueError, match="window must be > 0"):
da.rolling(time=-2)
with pytest.raises(ValueError, match="min_periods must be greater than zero"):
da.rolling(time=2, min_periods=0)
with pytest.raises(
KeyError,
match=r"\('foo',\) not found in DataArray dimensions",
):
da.rolling(foo=2)
@pytest.mark.parametrize("name", ("sum", "mean", "std", "min", "max", "median"))
@pytest.mark.parametrize("center", (True, False, None))
@pytest.mark.parametrize("min_periods", (1, None))
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
def test_rolling_wrapped_bottleneck(
self, da, name, center, min_periods, compute_backend
) -> None:
bn = pytest.importorskip("bottleneck", minversion="1.1")
# Test all bottleneck functions
rolling_obj = da.rolling(time=7, min_periods=min_periods)
func_name = f"move_{name}"
actual = getattr(rolling_obj, name)()
expected = getattr(bn, func_name)(
da.values, window=7, axis=1, min_count=min_periods
)
# Using assert_allclose because we get tiny (1e-17) differences in numbagg.
np.testing.assert_allclose(actual.values, expected)
with pytest.warns(DeprecationWarning, match="Reductions are applied"):
getattr(rolling_obj, name)(dim="time")
# Test center
rolling_obj = da.rolling(time=7, center=center)
actual = getattr(rolling_obj, name)()["time"]
# Using assert_allclose because we get tiny (1e-17) differences in numbagg.
assert_allclose(actual, da["time"])
@requires_dask
@pytest.mark.parametrize("name", ("mean", "count"))
@pytest.mark.parametrize("center", (True, False, None))
@pytest.mark.parametrize("min_periods", (1, None))
@pytest.mark.parametrize("window", (7, 8))
@pytest.mark.parametrize("backend", ["dask"], indirect=True)
def test_rolling_wrapped_dask(self, da, name, center, min_periods, window) -> None:
# dask version
rolling_obj = da.rolling(time=window, min_periods=min_periods, center=center)
actual = getattr(rolling_obj, name)().load()
if name != "count":
with pytest.warns(DeprecationWarning, match="Reductions are applied"):
getattr(rolling_obj, name)(dim="time")
# numpy version
rolling_obj = da.load().rolling(
time=window, min_periods=min_periods, center=center
)
expected = getattr(rolling_obj, name)()
# using all-close because rolling over ghost cells introduces some
# precision errors
assert_allclose(actual, expected)
# with zero chunked array GH:2113
rolling_obj = da.chunk().rolling(
time=window, min_periods=min_periods, center=center
)
actual = getattr(rolling_obj, name)().load()
assert_allclose(actual, expected)
@pytest.mark.parametrize("center", (True, None))
def test_rolling_wrapped_dask_nochunk(self, center) -> None:
# GH:2113
pytest.importorskip("dask.array")
da_day_clim = xr.DataArray(
np.arange(1, 367), coords=[np.arange(1, 367)], dims="dayofyear"
)
expected = da_day_clim.rolling(dayofyear=31, center=center).mean()
actual = da_day_clim.chunk().rolling(dayofyear=31, center=center).mean()
assert_allclose(actual, expected)
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
def test_rolling_pandas_compat(
self, center, window, min_periods, compute_backend
) -> None:
s = pd.Series(np.arange(10))
da = DataArray.from_series(s)
if min_periods is not None and window < min_periods:
min_periods = window
s_rolling = s.rolling(window, center=center, min_periods=min_periods).mean()
da_rolling = da.rolling(
index=window, center=center, min_periods=min_periods
).mean()
da_rolling_np = da.rolling(
index=window, center=center, min_periods=min_periods
).reduce(np.nanmean)
np.testing.assert_allclose(s_rolling.values, da_rolling.values)
np.testing.assert_allclose(s_rolling.index, da_rolling["index"])
np.testing.assert_allclose(s_rolling.values, da_rolling_np.values)
np.testing.assert_allclose(s_rolling.index, da_rolling_np["index"])
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
def test_rolling_construct(self, center: bool, window: int) -> None:
s = pd.Series(np.arange(10))
da = DataArray.from_series(s)
s_rolling = s.rolling(window, center=center, min_periods=1).mean()
da_rolling = da.rolling(index=window, center=center, min_periods=1)
da_rolling_mean = da_rolling.construct("window").mean("window")
np.testing.assert_allclose(s_rolling.values, da_rolling_mean.values)
np.testing.assert_allclose(s_rolling.index, da_rolling_mean["index"])
# with stride
da_rolling_mean = da_rolling.construct("window", stride=2).mean("window")
np.testing.assert_allclose(s_rolling.values[::2], da_rolling_mean.values)
np.testing.assert_allclose(s_rolling.index[::2], da_rolling_mean["index"])
# with fill_value
da_rolling_mean = da_rolling.construct("window", stride=2, fill_value=0.0).mean(
"window"
)
assert da_rolling_mean.isnull().sum() == 0
assert (da_rolling_mean == 0.0).sum() >= 0
@pytest.mark.parametrize("da", (1, 2), indirect=True)
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
@pytest.mark.parametrize("name", ("sum", "mean", "std", "max"))
def test_rolling_reduce(
self, da, center, min_periods, window, name, compute_backend
) -> None:
if min_periods is not None and window < min_periods:
min_periods = window
if da.isnull().sum() > 1 and window == 1:
# this causes all nan slices
window = 2
rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
# add nan prefix to numpy methods to get similar # behavior as bottleneck
actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
expected = getattr(rolling_obj, name)()
assert_allclose(actual, expected)
assert actual.dims == expected.dims
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
@pytest.mark.parametrize("name", ("sum", "max"))
def test_rolling_reduce_nonnumeric(
self, center, min_periods, window, name, compute_backend
) -> None:
da = DataArray(
[0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time"
).isnull()
if min_periods is not None and window < min_periods:
min_periods = window
rolling_obj = da.rolling(time=window, center=center, min_periods=min_periods)
# add nan prefix to numpy methods to get similar behavior as bottleneck
actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
expected = getattr(rolling_obj, name)()
assert_allclose(actual, expected)
assert actual.dims == expected.dims
def test_rolling_count_correct(self, compute_backend) -> None:
da = DataArray([0, np.nan, 1, 2, np.nan, 3, 4, 5, np.nan, 6, 7], dims="time")
kwargs: list[dict[str, Any]] = [
{"time": 11, "min_periods": 1},
{"time": 11, "min_periods": None},
{"time": 7, "min_periods": 2},
]
expecteds = [
DataArray([1, 1, 2, 3, 3, 4, 5, 6, 6, 7, 8], dims="time"),
DataArray(
[
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
],
dims="time",
),
DataArray([np.nan, np.nan, 2, 3, 3, 4, 5, 5, 5, 5, 5], dims="time"),
]
for kwarg, expected in zip(kwargs, expecteds):
result = da.rolling(**kwarg).count()
assert_equal(result, expected)
result = da.to_dataset(name="var1").rolling(**kwarg).count()["var1"]
assert_equal(result, expected)
@pytest.mark.parametrize("da", (1,), indirect=True)
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1))
@pytest.mark.parametrize("name", ("sum", "mean", "max"))
def test_ndrolling_reduce(
self, da, center, min_periods, name, compute_backend
) -> None:
rolling_obj = da.rolling(time=3, x=2, center=center, min_periods=min_periods)
actual = getattr(rolling_obj, name)()
expected = getattr(
getattr(
da.rolling(time=3, center=center, min_periods=min_periods), name
)().rolling(x=2, center=center, min_periods=min_periods),
name,
)()
assert_allclose(actual, expected)
assert actual.dims == expected.dims
if name in ["mean"]:
# test our reimplementation of nanmean using np.nanmean
expected = getattr(rolling_obj.construct({"time": "tw", "x": "xw"}), name)(
["tw", "xw"]
)
count = rolling_obj.count()
if min_periods is None:
min_periods = 1
assert_allclose(actual, expected.where(count >= min_periods))
@pytest.mark.parametrize("center", (True, False, (True, False)))
@pytest.mark.parametrize("fill_value", (np.nan, 0.0))
def test_ndrolling_construct(self, center, fill_value) -> None:
da = DataArray(
np.arange(5 * 6 * 7).reshape(5, 6, 7).astype(float),
dims=["x", "y", "z"],
coords={"x": ["a", "b", "c", "d", "e"], "y": np.arange(6)},
)
actual = da.rolling(x=3, z=2, center=center).construct(
x="x1", z="z1", fill_value=fill_value
)
if not isinstance(center, tuple):
center = (center, center)
expected = (
da.rolling(x=3, center=center[0])
.construct(x="x1", fill_value=fill_value)
.rolling(z=2, center=center[1])
.construct(z="z1", fill_value=fill_value)
)
assert_allclose(actual, expected)
@pytest.mark.parametrize(
"funcname, argument",
[
("reduce", (np.mean,)),
("mean", ()),
("construct", ("window_dim",)),
("count", ()),
],
)
def test_rolling_keep_attrs(self, funcname, argument) -> None:
attrs_da = {"da_attr": "test"}
data = np.linspace(10, 15, 100)
coords = np.linspace(1, 10, 100)
da = DataArray(
data, dims=("coord"), coords={"coord": coords}, attrs=attrs_da, name="name"
)
# attrs are now kept per default
func = getattr(da.rolling(dim={"coord": 5}), funcname)
result = func(*argument)
assert result.attrs == attrs_da
assert result.name == "name"
# discard attrs
func = getattr(da.rolling(dim={"coord": 5}), funcname)
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
assert result.name == "name"
# test discard attrs using global option
func = getattr(da.rolling(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument)
assert result.attrs == {}
assert result.name == "name"
# keyword takes precedence over global option
func = getattr(da.rolling(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument, keep_attrs=True)
assert result.attrs == attrs_da
assert result.name == "name"
func = getattr(da.rolling(dim={"coord": 5}), funcname)
with set_options(keep_attrs=True):
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
assert result.name == "name"
@requires_dask
@pytest.mark.parametrize("dtype", ["int", "float32", "float64"])
def test_rolling_dask_dtype(self, dtype) -> None:
data = DataArray(
np.array([1, 2, 3], dtype=dtype), dims="x", coords={"x": [1, 2, 3]}
)
unchunked_result = data.rolling(x=3, min_periods=1).mean()
chunked_result = data.chunk({"x": 1}).rolling(x=3, min_periods=1).mean()
assert chunked_result.dtype == unchunked_result.dtype
@requires_numbagg
class TestDataArrayRollingExp:
@pytest.mark.parametrize("dim", ["time", "x"])
@pytest.mark.parametrize(
"window_type, window",
[["span", 5], ["alpha", 0.5], ["com", 0.5], ["halflife", 5]],
)
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
@pytest.mark.parametrize("func", ["mean", "sum", "var", "std"])
def test_rolling_exp_runs(self, da, dim, window_type, window, func) -> None:
da = da.where(da > 0.2)
rolling_exp = da.rolling_exp(window_type=window_type, **{dim: window})
result = getattr(rolling_exp, func)()
assert isinstance(result, DataArray)
@pytest.mark.parametrize("dim", ["time", "x"])
@pytest.mark.parametrize(
"window_type, window",
[["span", 5], ["alpha", 0.5], ["com", 0.5], ["halflife", 5]],
)
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
def test_rolling_exp_mean_pandas(self, da, dim, window_type, window) -> None:
da = da.isel(a=0).where(lambda x: x > 0.2)
result = da.rolling_exp(window_type=window_type, **{dim: window}).mean()
assert isinstance(result, DataArray)
pandas_array = da.to_pandas()
assert pandas_array.index.name == "time"
if dim == "x":
pandas_array = pandas_array.T
expected = xr.DataArray(
pandas_array.ewm(**{window_type: window}).mean()
).transpose(*da.dims)
assert_allclose(expected.variable, result.variable)
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
@pytest.mark.parametrize("func", ["mean", "sum"])
def test_rolling_exp_keep_attrs(self, da, func) -> None:
attrs = {"attrs": "da"}
da.attrs = attrs
# Equivalent of `da.rolling_exp(time=10).mean`
rolling_exp_func = getattr(da.rolling_exp(time=10), func)
# attrs are kept per default
result = rolling_exp_func()
assert result.attrs == attrs
# discard attrs
result = rolling_exp_func(keep_attrs=False)
assert result.attrs == {}
# test discard attrs using global option
with set_options(keep_attrs=False):
result = rolling_exp_func()
assert result.attrs == {}
# keyword takes precedence over global option
with set_options(keep_attrs=False):
result = rolling_exp_func(keep_attrs=True)
assert result.attrs == attrs
with set_options(keep_attrs=True):
result = rolling_exp_func(keep_attrs=False)
assert result.attrs == {}
with pytest.warns(
UserWarning,
match="Passing ``keep_attrs`` to ``rolling_exp`` has no effect.",
):
da.rolling_exp(time=10, keep_attrs=True)
class TestDatasetRolling:
@pytest.mark.parametrize(
"funcname, argument",
[
("reduce", (np.mean,)),
("mean", ()),
("construct", ("window_dim",)),
("count", ()),
],
)
def test_rolling_keep_attrs(self, funcname, argument) -> None:
global_attrs = {"units": "test", "long_name": "testing"}
da_attrs = {"da_attr": "test"}
da_not_rolled_attrs = {"da_not_rolled_attr": "test"}
data = np.linspace(10, 15, 100)
coords = np.linspace(1, 10, 100)
ds = Dataset(
data_vars={"da": ("coord", data), "da_not_rolled": ("no_coord", data)},
coords={"coord": coords},
attrs=global_attrs,
)
ds.da.attrs = da_attrs
ds.da_not_rolled.attrs = da_not_rolled_attrs
# attrs are now kept per default
func = getattr(ds.rolling(dim={"coord": 5}), funcname)
result = func(*argument)
assert result.attrs == global_attrs
assert result.da.attrs == da_attrs
assert result.da_not_rolled.attrs == da_not_rolled_attrs
assert result.da.name == "da"
assert result.da_not_rolled.name == "da_not_rolled"
# discard attrs
func = getattr(ds.rolling(dim={"coord": 5}), funcname)
result = func(*argument, keep_attrs=False)
assert result.attrs == {}
assert result.da.attrs == {}
assert result.da_not_rolled.attrs == {}
assert result.da.name == "da"
assert result.da_not_rolled.name == "da_not_rolled"
# test discard attrs using global option
func = getattr(ds.rolling(dim={"coord": 5}), funcname)
with set_options(keep_attrs=False):
result = func(*argument)
assert result.attrs == {}
assert result.da.attrs == {}
assert result.da_not_rolled.attrs == {}
assert result.da.name == "da"
assert result.da_not_rolled.name == "da_not_rolled"
# keyword takes precedence over global option
func = getattr(ds.rolling(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_rolled.attrs == da_not_rolled_attrs
assert result.da.name == "da"
assert result.da_not_rolled.name == "da_not_rolled"
func = getattr(ds.rolling(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_rolled.attrs == {}
assert result.da.name == "da"
assert result.da_not_rolled.name == "da_not_rolled"
def test_rolling_properties(self, ds) -> None:
# catching invalid args
with pytest.raises(ValueError, match="window must be > 0"):
ds.rolling(time=-2)
with pytest.raises(ValueError, match="min_periods must be greater than zero"):
ds.rolling(time=2, min_periods=0)
with pytest.raises(KeyError, match="time2"):
ds.rolling(time2=2)
with pytest.raises(
KeyError,
match=r"\('foo',\) not found in Dataset dimensions",
):
ds.rolling(foo=2)
@pytest.mark.parametrize(
"name", ("sum", "mean", "std", "var", "min", "max", "median")
)
@pytest.mark.parametrize("center", (True, False, None))
@pytest.mark.parametrize("min_periods", (1, None))
@pytest.mark.parametrize("key", ("z1", "z2"))
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
def test_rolling_wrapped_bottleneck(
self, ds, name, center, min_periods, key, compute_backend
) -> None:
bn = pytest.importorskip("bottleneck", minversion="1.1")
# Test all bottleneck functions
rolling_obj = ds.rolling(time=7, min_periods=min_periods)
func_name = f"move_{name}"
actual = getattr(rolling_obj, name)()
if key == "z1": # z1 does not depend on 'Time' axis. Stored as it is.
expected = ds[key]
elif key == "z2":
expected = getattr(bn, func_name)(
ds[key].values, window=7, axis=0, min_count=min_periods
)
else:
raise ValueError
np.testing.assert_allclose(actual[key].values, expected)
# Test center
rolling_obj = ds.rolling(time=7, center=center)
actual = getattr(rolling_obj, name)()["time"]
assert_allclose(actual, ds["time"])
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
def test_rolling_pandas_compat(self, center, window, min_periods) -> None:
df = pd.DataFrame(
{
"x": np.random.randn(20),
"y": np.random.randn(20),
"time": np.linspace(0, 1, 20),
}
)
ds = Dataset.from_dataframe(df)
if min_periods is not None and window < min_periods:
min_periods = window
df_rolling = df.rolling(window, center=center, min_periods=min_periods).mean()
ds_rolling = ds.rolling(
index=window, center=center, min_periods=min_periods
).mean()
np.testing.assert_allclose(df_rolling["x"].values, ds_rolling["x"].values)
np.testing.assert_allclose(df_rolling.index, ds_rolling["index"])
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
def test_rolling_construct(self, center: bool, window: int) -> None:
df = pd.DataFrame(
{
"x": np.random.randn(20),
"y": np.random.randn(20),
"time": np.linspace(0, 1, 20),
}
)
ds = Dataset.from_dataframe(df)
df_rolling = df.rolling(window, center=center, min_periods=1).mean()
ds_rolling = ds.rolling(index=window, center=center)
ds_rolling_mean = ds_rolling.construct("window").mean("window")
np.testing.assert_allclose(df_rolling["x"].values, ds_rolling_mean["x"].values)
np.testing.assert_allclose(df_rolling.index, ds_rolling_mean["index"])
# with fill_value
ds_rolling_mean = ds_rolling.construct("window", stride=2, fill_value=0.0).mean(
"window"
)
assert (ds_rolling_mean.isnull().sum() == 0).to_dataarray(dim="vars").all()
assert (ds_rolling_mean["x"] == 0.0).sum() >= 0
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
def test_rolling_construct_stride(self, center: bool, window: int) -> None:
df = pd.DataFrame(
{
"x": np.random.randn(20),
"y": np.random.randn(20),
"time": np.linspace(0, 1, 20),
}
)
ds = Dataset.from_dataframe(df)
df_rolling_mean = df.rolling(window, center=center, min_periods=1).mean()
# With an index (dimension coordinate)
ds_rolling = ds.rolling(index=window, center=center)
ds_rolling_mean = ds_rolling.construct("w", stride=2).mean("w")
np.testing.assert_allclose(
df_rolling_mean["x"][::2].values, ds_rolling_mean["x"].values
)
np.testing.assert_allclose(df_rolling_mean.index[::2], ds_rolling_mean["index"])
# Without index (https://github.com/pydata/xarray/issues/7021)
ds2 = ds.drop_vars("index")
ds2_rolling = ds2.rolling(index=window, center=center)
ds2_rolling_mean = ds2_rolling.construct("w", stride=2).mean("w")
np.testing.assert_allclose(
df_rolling_mean["x"][::2].values, ds2_rolling_mean["x"].values
)
# Mixed coordinates, indexes and 2D coordinates
ds3 = xr.Dataset(
{"x": ("t", range(20)), "x2": ("y", range(5))},
{
"t": range(20),
"y": ("y", range(5)),
"t2": ("t", range(20)),
"y2": ("y", range(5)),
"yt": (["t", "y"], np.ones((20, 5))),
},
)
ds3_rolling = ds3.rolling(t=window, center=center)
ds3_rolling_mean = ds3_rolling.construct("w", stride=2).mean("w")
for coord in ds3.coords:
assert coord in ds3_rolling_mean.coords
@pytest.mark.slow
@pytest.mark.parametrize("ds", (1, 2), indirect=True)
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1, 2, 3))
@pytest.mark.parametrize("window", (1, 2, 3, 4))
@pytest.mark.parametrize(
"name", ("sum", "mean", "std", "var", "min", "max", "median")
)
def test_rolling_reduce(self, ds, center, min_periods, window, name) -> None:
if min_periods is not None and window < min_periods:
min_periods = window
if name == "std" and window == 1:
pytest.skip("std with window == 1 is unstable in bottleneck")
rolling_obj = ds.rolling(time=window, center=center, min_periods=min_periods)
# add nan prefix to numpy methods to get similar behavior as bottleneck
actual = rolling_obj.reduce(getattr(np, "nan%s" % name))
expected = getattr(rolling_obj, name)()
assert_allclose(actual, expected)
assert ds.dims == actual.dims
# 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("ds", (2,), indirect=True)
@pytest.mark.parametrize("center", (True, False))
@pytest.mark.parametrize("min_periods", (None, 1))
@pytest.mark.parametrize("name", ("sum", "max"))
@pytest.mark.parametrize("dask", (True, False))
def test_ndrolling_reduce(self, ds, center, min_periods, name, dask) -> None:
if dask and has_dask:
ds = ds.chunk({"x": 4})
rolling_obj = ds.rolling(time=4, x=3, center=center, min_periods=min_periods)
actual = getattr(rolling_obj, name)()
expected = getattr(
getattr(
ds.rolling(time=4, center=center, min_periods=min_periods), name
)().rolling(x=3, center=center, min_periods=min_periods),
name,
)()
assert_allclose(actual, expected)
assert actual.dims == expected.dims
# Do it in the opposite order
expected = getattr(
getattr(
ds.rolling(x=3, center=center, min_periods=min_periods), name
)().rolling(time=4, center=center, min_periods=min_periods),
name,
)()
assert_allclose(actual, expected)
assert actual.dims == expected.dims
@pytest.mark.parametrize("center", (True, False, (True, False)))
@pytest.mark.parametrize("fill_value", (np.nan, 0.0))
@pytest.mark.parametrize("dask", (True, False))
def test_ndrolling_construct(self, center, fill_value, dask) -> None:
da = DataArray(
np.arange(5 * 6 * 7).reshape(5, 6, 7).astype(float),
dims=["x", "y", "z"],
coords={"x": ["a", "b", "c", "d", "e"], "y": np.arange(6)},
)
ds = xr.Dataset({"da": da})
if dask and has_dask:
ds = ds.chunk({"x": 4})
actual = ds.rolling(x=3, z=2, center=center).construct(
x="x1", z="z1", fill_value=fill_value
)
if not isinstance(center, tuple):
center = (center, center)
expected = (
ds.rolling(x=3, center=center[0])
.construct(x="x1", fill_value=fill_value)
.rolling(z=2, center=center[1])
.construct(z="z1", fill_value=fill_value)
)
assert_allclose(actual, expected)
@requires_dask
@pytest.mark.filterwarnings("error")
@pytest.mark.parametrize("ds", (2,), indirect=True)
@pytest.mark.parametrize("name", ("mean", "max"))
def test_raise_no_warning_dask_rolling_assert_close(self, ds, name) -> None:
"""
This is a puzzle — I can't easily find the source of the warning. It
requires `assert_allclose` to be run, for the `ds` param to be 2, and is
different for `mean` and `max`. `sum` raises no warning.
"""
ds = ds.chunk({"x": 4})
rolling_obj = ds.rolling(time=4, x=3)
actual = getattr(rolling_obj, name)()
expected = getattr(getattr(ds.rolling(time=4), name)().rolling(x=3), name)()
assert_allclose(actual, expected)
@requires_numbagg
class TestDatasetRollingExp:
@pytest.mark.parametrize(
"backend", ["numpy", pytest.param("dask", marks=requires_dask)], indirect=True
)
def test_rolling_exp(self, ds) -> None:
result = ds.rolling_exp(time=10, window_type="span").mean()
assert isinstance(result, Dataset)
@pytest.mark.parametrize("backend", ["numpy"], indirect=True)
def test_rolling_exp_keep_attrs(self, ds) -> None:
attrs_global = {"attrs": "global"}
attrs_z1 = {"attr": "z1"}
ds.attrs = attrs_global
ds.z1.attrs = attrs_z1
# attrs are kept per default
result = ds.rolling_exp(time=10).mean()
assert result.attrs == attrs_global
assert result.z1.attrs == attrs_z1
# discard attrs
result = ds.rolling_exp(time=10).mean(keep_attrs=False)
assert result.attrs == {}
# TODO: from #8114 — this arguably should be empty, but `apply_ufunc` doesn't do
# that at the moment. We should change in `apply_func` rather than
# special-case it here.
#
# assert result.z1.attrs == {}
# test discard attrs using global option
with set_options(keep_attrs=False):
result = ds.rolling_exp(time=10).mean()
assert result.attrs == {}
# See above
# assert result.z1.attrs == {}
# keyword takes precedence over global option
with set_options(keep_attrs=False):
result = ds.rolling_exp(time=10).mean(keep_attrs=True)
assert result.attrs == attrs_global
assert result.z1.attrs == attrs_z1
with set_options(keep_attrs=True):
result = ds.rolling_exp(time=10).mean(keep_attrs=False)
assert result.attrs == {}
# See above
# assert result.z1.attrs == {}
with pytest.warns(
UserWarning,
match="Passing ``keep_attrs`` to ``rolling_exp`` has no effect.",
):
ds.rolling_exp(time=10, keep_attrs=True)