How to copy data from a numpy array to another
What is the fastest way to copy data from array b to array a, without modifying the address of array a. I need this because an external library (PyFFTW) uses a pointer to my array that cannot change.
numpy
Binary random array with a specific proportion of ones?
What is the efficient(probably vectorized with Matlab terminology) way to generate random number of zeros and ones with a specific proportion? Specially with Numpy?
Numpy: Creating a complex array from 2 real ones?
I want to combine 2 parts of the same array to make a complex array:
Numpy: Should I use newaxis or None?
In numpy one can use the ‘newaxis’ object in the slicing syntax to create an axis of length one, e.g.:
How to specify upper and lower limits when using numpy.random.normal
I want to be able to pick values from a normal distribution that only ever fall between 0 and 1. In some cases I want to be able to basically just return a completely random distribution, and in other cases I want to return values that fall in the shape of a gaussian.
NumPy/OpenCV 2: how do I crop non-rectangular region?
I have a set of points that make a shape (closed polyline). Now I want to copy/crop all pixels from some image inside this shape, leaving the rest black/transparent. How do I do this?
Weighted percentile using numpy
Is there a way to use the numpy.percentile function to compute weighted percentile? Or is anyone aware of an alternative python function to compute weighted percentile?
Interpolation over an irregular grid
So, I have three numpy arrays which store latitude, longitude, and some property value on a grid — that is, I have LAT(y,x), LON(y,x), and, say temperature T(y,x), for some limits of x and y. The grid isn’t necessarily regular — in fact, it’s tripolar.
figure of imshow() is too small
I’m trying to visualize a numpy array using imshow() since it’s similar to imagesc() in Matlab.
How to calculate a Gaussian kernel matrix efficiently in numpy?
def GaussianMatrix(X,sigma): row,col=X.shape GassMatrix=np.zeros(shape=(row,row)) X=np.asarray(X) i=0 for v_i in X: j=0 for v_j in X: GassMatrix[i,j]=Gaussian(v_i.T,v_j.T,sigma) j+=1 i+=1 return GassMatrix def Gaussian(x,z,sigma): return np.exp((-(np.linalg.norm(x-z)**2))/(2*sigma**2)) This is my current way. Is there any way I can use matrix operation to do this? X is the data points. Answers: Thank you for visiting the Q&A section on … Read more