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import pandas as pd
import xarray as xr
from . import parameterized, randn, requires_dask
def make_bench_data(shape, frac_nan, chunks):
vals = randn(shape, frac_nan)
coords = {"time": pd.date_range("2000-01-01", freq="D", periods=shape[0])}
da = xr.DataArray(vals, dims=("time", "x", "y"), coords=coords)
if chunks is not None:
da = da.chunk(chunks)
return da
class DataArrayMissingInterpolateNA:
def setup(self, shape, chunks, limit):
if chunks is not None:
requires_dask()
self.da = make_bench_data(shape, 0.1, chunks)
@parameterized(
["shape", "chunks", "limit"],
(
[(365, 75, 75)],
[None, {"x": 25, "y": 25}],
[None, 3],
),
)
def time_interpolate_na(self, shape, chunks, limit):
actual = self.da.interpolate_na(dim="time", method="linear", limit=limit)
if chunks is not None:
actual = actual.compute()
class DataArrayMissingBottleneck:
def setup(self, shape, chunks, limit):
if chunks is not None:
requires_dask()
self.da = make_bench_data(shape, 0.1, chunks)
@parameterized(
["shape", "chunks", "limit"],
(
[(365, 75, 75)],
[None, {"x": 25, "y": 25}],
[None, 3],
),
)
def time_ffill(self, shape, chunks, limit):
actual = self.da.ffill(dim="time", limit=limit)
if chunks is not None:
actual = actual.compute()
@parameterized(
["shape", "chunks", "limit"],
(
[(365, 75, 75)],
[None, {"x": 25, "y": 25}],
[None, 3],
),
)
def time_bfill(self, shape, chunks, limit):
actual = self.da.bfill(dim="time", limit=limit)
if chunks is not None:
actual = actual.compute()
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