Using numpy.memmap you create arrays directly mapped into a file: import numpy a = numpy.memmap(‘test.mymemmap’, dtype=”float32″, mode=”w+”, shape=(200000,1000)) # here you will see a 762MB file created in your working directory You can treat it as a conventional array: a += 1000. It is possible even to assign more arrays to the same file, controlling … Read more
See the relevant documentation in general and specifically from matplotlib.ticker import FormatStrFormatter fig, ax = plt.subplots() ax.yaxis.set_major_formatter(FormatStrFormatter(‘%.2f’))
imread is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use imageio.imread instead. import imageio im = imageio.imread(‘astronaut.png’) im.shape # im is a numpy array (512, 512, 3) imageio.imwrite(‘imageio:astronaut-gray.jpg’, im[:, :, 0])
for numerical solution, you can use fsolve: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html#scipy.optimize.fsolve from scipy.optimize import fsolve import math def equations(p): x, y = p return (x+y**2-4, math.exp(x) + x*y – 3) x, y = fsolve(equations, (1, 1)) print equations((x, y))
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
It’s probably faster and easier to use numpy.digitize(): import numpy data = numpy.random.random(100) bins = numpy.linspace(0, 1, 10) digitized = numpy.digitize(data, bins) bin_means = [data[digitized == i].mean() for i in range(1, len(bins))] An alternative to this is to use numpy.histogram(): bin_means = (numpy.histogram(data, bins, weights=data)[0] / numpy.histogram(data, bins)[0]) Try for yourself which one is faster… … Read more
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