def cmov_window(data, span, window_type):
"""Applies a centered moving window of type window_type and size span on the
data.
Returns a (subclass of) MaskedArray. The k first and k last data are always
masked (with k=span//2). When data has a missing value at position i, the
result has missing values in the interval [i-k:i+k+1].
:Parameters:
data : ndarray
Data to process. The array should be at most 2D. On 2D arrays, the window
is applied recursively on each column.
span : integer
The width of the window.
window_type : string/tuple/float
Window type (see Notes)
Notes
-----
The recognized window types are: boxcar, triang, blackman, hamming, hanning,
bartlett, parzen, bohman, blackmanharris, nuttall, barthann, kaiser (needs beta),
gaussian (needs std), general_gaussian (needs power, width), slepian (needs width).
If the window requires parameters, the window_type argument should be a tuple
with the first argument the string name of the window, and the next arguments
the needed parameters. If window_type is a floating point number, it is interpreted
as the beta parameter of the kaiser window.
Note also that only boxcar has been thoroughly tested."""
data = marray(data, copy=True, subok=True)
if data._mask is nomask:
data._mask = N.zeros(data.shape, bool_)
window = get_window(window_type, span, fftbins=False)
(n, k) = (len(data), span//2)
#
if data.ndim == 1:
data._data.flat = convolve(data._data, window)[k:n+k] / float(span)
data._mask[:] = ((convolve(getmaskarray(data), window) > 0)[k:n+k])
elif data.ndim == 2:
for i in range(data.shape[-1]):
_data = data._data[:,i]
_data.flat = convolve(_data, window)[k:n+k] / float(span)
data._mask[:,i] = (convolve(data._mask[:,i], window) > 0)[k:n+k]
else:
raise ValueError, "Data should be at most 2D"
data._mask[:k] = data._mask[-k:] = True
return data
