There are several tools for converting Matlab to Python code. The only one that’s seen recent activity (last commit from June 2018) is Small Matlab to Python compiler (also developed here: SMOP@chiselapp). Other options include: LiberMate: translate from Matlab to Python and SciPy (Requires Python 2, last update 4 years ago). OMPC: Matlab to Python … Read more
You can use numpy.piecewise() to create the piecewise function and then use curve_fit(), Here is the code from scipy import optimize import matplotlib.pyplot as plt import numpy as np %matplotlib inline x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15], dtype=float) y = np.array([5, 7, 9, 11, … Read more
import matplotlib.pyplot as plt import numpy as np mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) hist, bins = np.histogram(x, bins=50) width = 0.7 * (bins[1] – bins[0]) center = (bins[:-1] + bins[1:]) / 2 plt.bar(center, hist, align=’center’, width=width) plt.show() The object-oriented interface is also straightforward: fig, ax = plt.subplots() ax.bar(center, … Read more
You can look at it this way: Suppose x is m by n. The possible pairs of m rows, chosen two at a time, is itertools.combinations(range(m), 2), e.g, for m=3: >>> import itertools >>> list(combinations(range(3),2)) [(0, 1), (0, 2), (1, 2)] So if d = pdist(x), the kth tuple in combinations(range(m), 2)) gives the indices … Read more
The function scipy.signal.find_peaks, as its name suggests, is useful for this. But it’s important to understand well its parameters width, threshold, distance and above all prominence to get a good peak extraction. According to my tests and the documentation, the concept of prominence is “the useful concept” to keep the good peaks, and discard the … Read more
As other folks have mentioned, for a really large file, you’re better off iterating. However, you do commonly want the entire thing in memory for various reasons. genfromtxt is much less efficient than loadtxt (though it handles missing data, whereas loadtxt is more “lean and mean”, which is why the two functions co-exist). If your … 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))
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