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import numpy as np
import pandas as pd
import xarray as xr
from . import _skip_slow, parameterized, randn, requires_dask
nx = 3000
long_nx = 30000
ny = 200
nt = 1000
window = 20
randn_xy = randn((nx, ny), frac_nan=0.1)
randn_xt = randn((nx, nt))
randn_t = randn((nt,))
randn_long = randn((long_nx,), frac_nan=0.1)
class Rolling:
def setup(self, *args, **kwargs):
self.ds = xr.Dataset(
{
"var1": (("x", "y"), randn_xy),
"var2": (("x", "t"), randn_xt),
"var3": (("t",), randn_t),
},
coords={
"x": np.arange(nx),
"y": np.linspace(0, 1, ny),
"t": pd.date_range("1970-01-01", periods=nt, freq="D"),
"x_coords": ("x", np.linspace(1.1, 2.1, nx)),
},
)
self.da_long = xr.DataArray(
randn_long, dims="x", coords={"x": np.arange(long_nx) * 0.1}
)
@parameterized(
["func", "center", "use_bottleneck"],
(["mean", "count"], [True, False], [True, False]),
)
def time_rolling(self, func, center, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
getattr(self.ds.rolling(x=window, center=center), func)().load()
@parameterized(
["func", "pandas", "use_bottleneck"],
(["mean", "count"], [True, False], [True, False]),
)
def time_rolling_long(self, func, pandas, use_bottleneck):
if pandas:
se = self.da_long.to_series()
getattr(se.rolling(window=window, min_periods=window), func)()
else:
with xr.set_options(use_bottleneck=use_bottleneck):
getattr(
self.da_long.rolling(x=window, min_periods=window), func
)().load()
@parameterized(
["window_", "min_periods", "use_bottleneck"], ([20, 40], [5, 5], [True, False])
)
def time_rolling_np(self, window_, min_periods, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
self.ds.rolling(x=window_, center=False, min_periods=min_periods).reduce(
np.nansum
).load()
@parameterized(
["center", "stride", "use_bottleneck"], ([True, False], [1, 1], [True, False])
)
def time_rolling_construct(self, center, stride, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
self.ds.rolling(x=window, center=center).construct(
"window_dim", stride=stride
).sum(dim="window_dim").load()
class RollingDask(Rolling):
def setup(self, *args, **kwargs):
requires_dask()
# TODO: Lazily skipped in CI as it is very demanding and slow.
# Improve times and remove errors.
_skip_slow()
super().setup(**kwargs)
self.ds = self.ds.chunk({"x": 100, "y": 50, "t": 50})
self.da_long = self.da_long.chunk({"x": 10000})
class RollingMemory:
def setup(self, *args, **kwargs):
self.ds = xr.Dataset(
{
"var1": (("x", "y"), randn_xy),
"var2": (("x", "t"), randn_xt),
"var3": (("t",), randn_t),
},
coords={
"x": np.arange(nx),
"y": np.linspace(0, 1, ny),
"t": pd.date_range("1970-01-01", periods=nt, freq="D"),
"x_coords": ("x", np.linspace(1.1, 2.1, nx)),
},
)
class DataArrayRollingMemory(RollingMemory):
@parameterized(["func", "use_bottleneck"], (["sum", "max", "mean"], [True, False]))
def peakmem_ndrolling_reduce(self, func, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
roll = self.ds.var1.rolling(x=10, y=4)
getattr(roll, func)()
@parameterized(["func", "use_bottleneck"], (["sum", "max", "mean"], [True, False]))
def peakmem_1drolling_reduce(self, func, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
roll = self.ds.var3.rolling(t=100)
getattr(roll, func)()
@parameterized(["stride"], ([None, 5, 50]))
def peakmem_1drolling_construct(self, stride):
self.ds.var2.rolling(t=100).construct("w", stride=stride)
self.ds.var3.rolling(t=100).construct("w", stride=stride)
class DatasetRollingMemory(RollingMemory):
@parameterized(["func", "use_bottleneck"], (["sum", "max", "mean"], [True, False]))
def peakmem_ndrolling_reduce(self, func, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
roll = self.ds.rolling(x=10, y=4)
getattr(roll, func)()
@parameterized(["func", "use_bottleneck"], (["sum", "max", "mean"], [True, False]))
def peakmem_1drolling_reduce(self, func, use_bottleneck):
with xr.set_options(use_bottleneck=use_bottleneck):
roll = self.ds.rolling(t=100)
getattr(roll, func)()
@parameterized(["stride"], ([None, 5, 50]))
def peakmem_1drolling_construct(self, stride):
self.ds.rolling(t=100).construct("w", stride=stride)
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