issues: 305702311

Code Sample, a copy-pastable example if possible

From @RayPalmerTech in https://github.com/kwgoodman/bottleneck/issues/186: ```python import numpy as np import pandas as pd import time import bottleneck as bn import xarray import matplotlib.pyplot as plt

N = 30000200 # Number of datapoints Fs = 30000 # sample rate T=1/Fs # sample period duration = N/Fs # duration in s t = np.arange(0,duration,T) # time vector DATA = np.random.randn(N,)+5np.sin(2np.pi0.01t) # Example noisy sine data and window size w = 330000

def using_bottleneck_mean(data,width): return bn.move_mean(a=data,window=width,min_count = 1)

def using_pandas_rolling_mean(data,width): return np.asarray(pd.DataFrame(data).rolling(window=width,center=True,min_periods=1).mean()).ravel()

def using_xarray_mean(data,width): return xarray.DataArray(data,dims='x').rolling(x=width,min_periods=1, center=True).mean()

start=time.time() A = using_bottleneck_mean(DATA,w) print('Bottleneck: ', time.time()-start, 's') start=time.time() B = using_pandas_rolling_mean(DATA,w) print('Pandas: ',time.time()-start,'s') start=time.time() C = using_xarray_mean(DATA,w) print('Xarray: ',time.time()-start,'s') ```

This results in: Bottleneck: 0.0867006778717041 s Pandas: 0.563546895980835 s Xarray: 25.133142709732056 s

Somehow xarray is way slower than pandas and bottleneck, even though it's using bottleneck under the hood!

Problem description

Profiling shows that the majority of time is spent in xarray.core.rolling.DataArrayRolling._setup_windows. Monkey-patching that method with a dummy rectifies the issue: xarray.core.rolling.DataArrayRolling._setup_windows = lambda *args: None

Now we obtain: Bottleneck: 0.06775331497192383 s Pandas: 0.48262882232666016 s Xarray: 0.1723031997680664 s

The solution is to make setting up windows done lazily (in __iter__), instead of doing it in the constructor.

Output of xr.show_versions()