scikit-image has implemented a working version of downsampling here, although they shy away from calling it downsampling for it not being a downsampling in terms of DSP, if I understand correctly: http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.block_reduce but it works very well, and it is the only downsampler that I found in Python that can deal with np.nan in the … Read more
SciPy has added an inverse Box-Cox transformation. https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.inv_boxcox.html scipy.special.inv_boxcox scipy.special.inv_boxcox(y, lmbda) = Compute the inverse of the Box-Cox transformation. Find x such that: y = (x**lmbda – 1) / lmbda if lmbda != 0 log(x) if lmbda == 0 Parameters: y : array_like Data to be transformed. lmbda : array_like Power parameter of the Box-Cox … Read more
You can remove NaNs using a mask: mask = ~np.isnan(varx) & ~np.isnan(vary) slope, intercept, r_value, p_value, std_err = stats.linregress(varx[mask], vary[mask])
Use import scipy.stats. Then it works. Importing a package does not automatically import all the subpackages/modules. In these cases you have to do this explicitly.
mask = np.ones(len(data), np.bool) mask[sample_indexes] = 0 other_data = data[mask] not the most elegant for what perhaps should be a single-line statement, but its fairly efficient, and the memory overhead is minimal too. If memory is your prime concern, np.delete would avoid the creation of the mask, and fancy-indexing creates a copy anyway. On second … Read more
My usual work flow is to use a virtualenv to have a Python distribution with up-to-date packages. Within this environment you can than install and update all packages you need with pip and without any sudo calls. So if you only need SciPy (and NumPy) this would be: $ sudo apt-get install python-virtualenv python-pip $ … Read more
If you can use sympy, Matrix.rref() can do it: In [8]: sympy.Matrix(np.random.random((4,4))).rref() Out[8]: ([1, 1.42711055402454e-17, 0, -1.38777878078145e-17] [0, 1.0, 0, 2.22044604925031e-16] [0, -2.3388341405089e-16, 1, -2.22044604925031e-16] [0, 3.65674099486992e-17, 0, 1.0], [0, 1, 2, 3])
I copied your example and tried a little bit. Looks like if you stick with BFGS solver, after a few iteration the mu+ alpha * r will have some negative numbers, and that’s how you get the RuntimeWarning. The easiest fix I can think of is to switch to Nelder Mead solver. res = minimize(loglikelihood, … Read more
The output of twoD_Gaussian needs to be 1D. What you can do is add a .ravel() onto the end of the last line, like this: def twoD_Gaussian(xy, amplitude, xo, yo, sigma_x, sigma_y, theta, offset): x, y = xy xo = float(xo) yo = float(yo) a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2) b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2) c … Read more
I see you’re using numpy arrays. In that case, you can also do this: input_seq[[ix1, ix2]] = input_seq[[ix2, ix1]]