Prerequisite: sudo apt-get install build-essential gfortran libatlas-base-dev python-pip python-dev sudo pip install –upgrade pip Actual packages: sudo pip install numpy sudo pip install scipy Optional packages: sudo pip install matplotlib OR sudo apt-get install python-matplotlib sudo pip install -U scikit-learn sudo pip install pandas src
Correlation (default ‘valid’ case) between two 2D arrays: You can simply use matrix-multiplication np.dot like so – out = np.dot(arr_one,arr_two.T) Correlation with the default “valid” case between each pairwise row combinations (row1,row2) of the two input arrays would correspond to multiplication result at each (row1,row2) position. Row-wise Correlation Coefficient calculation for two 2D arrays: def … Read more
Matplotlib doesn’t work with pixels directly, but rather physical sizes and DPI. If you want to display a figure with a certain pixel size, you need to know the DPI of your monitor. For example this link will detect that for you. If you have an image of 3841×7195 pixels it is unlikely that you … Read more
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
For fitting y = A + B log x, just fit y against (log x). >>> x = numpy.array([1, 7, 20, 50, 79]) >>> y = numpy.array([10, 19, 30, 35, 51]) >>> numpy.polyfit(numpy.log(x), y, 1) array([ 8.46295607, 6.61867463]) # y ≈ 8.46 log(x) + 6.62 For fitting y = AeBx, take the logarithm of both … Read more
An import is required, import scipy.io… import scipy.io mat = scipy.io.loadmat(‘file.mat’)
UPDATE: more efficient solutions have been proposed, uniform_filter1d from scipy being probably the best among the “standard” 3rd-party libraries, and some newer or specialized libraries are available too. You can use np.convolve for that: np.convolve(x, np.ones(N)/N, mode=”valid”) Explanation The running mean is a case of the mathematical operation of convolution. For the running mean, you … Read more
Distribution Fitting with Sum of Square Error (SSE) This is an update and modification to Saullo’s answer, that uses the full list of the current scipy.stats distributions and returns the distribution with the least SSE between the distribution’s histogram and the data’s histogram. Example Fitting Using the El Niño dataset from statsmodels, the distributions are … Read more
To sort by the second column of a: a[a[:, 1].argsort()]
You can use Numpy’s genfromtxt() method to do so, by setting the delimiter kwarg to a comma. from numpy import genfromtxt my_data = genfromtxt(‘my_file.csv’, delimiter=”,”) More information on the function can be found at its respective documentation.