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
sklearn >= 0.22.0 sklearn.metrics has a mean_squared_error function with a squared kwarg (defaults to True). Setting squared to False will return the RMSE. from sklearn.metrics import mean_squared_error rms = mean_squared_error(y_actual, y_predicted, squared=False) sklearn < 0.22.0 sklearn.metrics has a mean_squared_error function. The RMSE is just the square root of whatever it returns. from sklearn.metrics import mean_squared_error … 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
Last time I checked it, the scipy __init__ method executes a from numpy import * so that the whole numpy namespace is included into scipy when the scipy module is imported. The log10 behavior you are describing is interesting, because both versions are coming from numpy. One is a ufunc, the other is a numpy.lib … 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
Conda replaces virtualenv. In my opinion it is better. It is not limited to Python but can be used for other languages too. In my experience it provides a much smoother experience, especially for scientific packages. The first time I got MayaVi properly installed on Mac was with conda. You can still use pip. In … Read more
You can simply use pip install numpy Or for python3, use pip3 install numpy
To sort by the second column of a: a[a[:, 1].argsort()]
An import is required, import scipy.io… import scipy.io mat = scipy.io.loadmat(‘file.mat’)
Use numpy.genfromtxt() by setting the delimiter kwarg to a comma: from numpy import genfromtxt my_data = genfromtxt(‘my_file.csv’, delimiter=”,”)