Xarray rolling mean with weights

Question

When I do running / rolling mean with weights in numpy, I e.g. do something like this:

data = np.random.random(100)  # Example data...
weights = np.array([1,         


        

Answer 1

If you want a Gaussian-like filter, another hack is to apply the rolling mean recursively.

Infinite recursions of the boxcar filter (i.e., our rolling mean) becomes a Gaussian filter. See B-spline in wikipedia for the detail.

Example:

x = xr.DataArray([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], dims=['x'])

# With window=2
tmp = x
plt.plot(tmp, '-ok', label='original')
for i in range(3):
    tmp = tmp.rolling(x=2, min_periods=1).mean()
    plt.plot(tmp, '-o', label='{}-times'.format(i+1))
plt.legend()

# with window=3, center=True
tmp = x
plt.plot(tmp, '--ok', label='original')
for i in range(3):
    tmp = tmp.rolling(x=3, center=True, min_periods=1).mean()
    plt.plot(tmp, '-o', label='{}-times'.format(i+1))
plt.legend()

Note: if you want to centralize the result, use the odd window size.