SciPy

This section shows useful SciPy functions:

scipy.fftpack

fftshift(x, axes=None): This function shifts the zero-frequency component to the center of the spectrum
rfft(x, n=None, axis=-1, overwrite_x=0): This function performs a discrete Fourier transform of an array containing real values

detrend(data, axis=-1, type='linear', bp=0): This function removes the linear trend or a constant from the data
medfilt(volume, kernel_size=None): This function applies a median filter on an array
wiener(im, mysize=None, noise=None): This function applies a Wiener filter on an array

anderson(x, dist='norm'): This function performs the Anderson-Darling test for data coming from a specified distribution
kruskal(*args): This function performs the Kruskal-Wallis H test for data
normaltest(a, axis=0): This function tests whether data complies to the normal distribution
scoreatpercentile(a, per, limit=(), interpolation_method='fraction'): This function computes the score at a specified percentile of the input array
shapiro(x, a=None, reta=False): This function applies the Shapiro-Wilk test for normality

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