I'm trying to convert a radial distribution function for my use, but the code I'm looking at uses a cKDTree. The problem is that I want to use only numpy in my function.
Does anyone know an equivalent function in numpy that can be used or a way to make an equivalent "tree"?
Related
I need to convolve a (128,128,128) numpy array m with a (35,35,35) kernel k.
I am looking for something like Matlab's convn function
convn(m,k,'same')
The most similar function I found in python is
ndimage.convolve()
but is not the same.
Can anyone suggest a method of doing this as easy as calling that function in Matlab?
I am trying to implement a discrete wavelet transform (DWT) in 3D, and I have found the MATLAB equivalent, wavedec3. Does anyone know if there is a Python equivalent I can use rather than going ahead and writing my own?
I used pywt the wavedecn(array, method, level) and it does what I wanted to do: perform discrete wavelet transform on a 3D array in multi-levels.
In numpy is there any built-in function to calculate moving skewness of numpy array? I know there are basic functions like mean, median, mode, min, max etc. But I wonder if there are any functions for calculating moving skewness, kurtosis and higher moments?
You should use SciPy for calculating skewness, kurtosis, etc.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html
Already got solution here. There is no built-in function in numpy but we can use scipy and numpy combine to achieve the goal.
I Wanted to solve matrix inverse without calling numpy into python. I want to know if it possible or not.
your question title:
What is the easiest way to solve matrix inverse using python
import numpy
numpy.linalg.inv(your_matrix)
or the same with scipy instead of numpy -- that's definitely the easiest, for you as a programmer.
What is your reason not to use numpy?
You can of course look for an algorithm and implement it manually. But the built-in function are based on the Fortran LAPACK algorithms, which are tested and optimized for the last 50 years... they will be hard to surpass...
I'm translating some code from MATLAB to Python and I'm stuck with the corrmtx() MATLAB function. Is there any similar function in Python, or how could I replace it?
The spectrum package has such a function.
How about:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.toeplitz.html
The matlab docs for corrmtx state:
X = corrmtx(x,m) returns an (n+m)-by-(m+1) rectangular Toeplitz matrix
X, such that X'X is a (biased) estimate of the autocorrelation matrix
for the length n data vector x.
The scipy function gives the Toeplitz matrix, although I'm not sure if the implementations are identical.
Here is a list of alternatives that can help you in translating your code, all of which contain that function:
scipy (toeplitz | corrmtx)
spectrum (corrmtx)
The following is a link to another post that tells you how to use numpy for the auto correlation since it seems to be the default funcationality of corrmtx
Additional Information:
Finding the correlation matrix in Python
Unbiased Estimation of Covariance Matrix