https://stackoverflow.com › questions › 77936

If your n-dimensional vector is in a list [[a0, a1, ..., an],[b0, b1, ..., bn],[c0, c1, ..., cn]], just convert the list to array, and than calculate the centroid like this: import numpy as np vectors = np.array(Listv) centroid = np.mean(vectors, axis=0)

https://stackoverflow.com › questions › 36955185

I have a large numpy 3d array (10000, 3, 3). in which I would like to find the center coordinates of each region (clusters with the same number). Each sub-array can have 1, 2, 3 or 4 regions. A subset of my array is: largearray= array([[[1, 0, 0], [0, 0, 2], [3, 0, 2]], [[0, 0, 4], [0, 0, 4], [0, 0, 4]],

https://blog.finxter.com › 5-best-ways-to-find-the-center-of-a-set-of-points-using-python

NumPy, a powerful numerical processing library, provides a highly efficient array structure that can be used to calculate the centroid of a set of points. With NumPy, you can perform the entire calculation in a vectorized manner, which can significantly speed up the process for large datasets.

https://python.tutorialink.com › whats-the-best-way-to-calculate-a-3d-or-n-d-centroid

centroid = average(x), average(y), average(z) where x , y and z are arrays of floating-point numbers. I seem to recall that there is a way to get a more accurate centroid, but I haven’t found a simple algorithm for doing so.

https://numpy.org › doc › stable › user › absolute_beginners.html

NumPy: the absolute basics for beginners#. Welcome to the absolute beginner’s guide to NumPy! NumPy (Numerical Python) is an open source Python library that’s widely used in science and engineering.The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data structures.

https://numpy.org › doc › stable › reference › arrays.ndarray.html

Learn how to create, index, and access ndarrays, which are multidimensional containers of items of the same type and size. Find out how to use array attributes, such as shape, strides, and flags, to manipulate the memory layout of the array.

https://www.slingacademy.com › article › how-to-use-numpys-advanced-features-for-3d...

Using NumPy’s array operations, you can tweak data points before plotting to achieve the visual effect you desire. On the Matplotlib side, you can use the vast array of customizations from the colormap to the viewing angle to improve readability and appearance.

https://docs.scipy.org › doc › scipy-0.14.0 › reference › generated › scipy.ndimage.measurements...

scipy.ndimage.measurements.center_of_mass(input, labels=None, index=None) [source] ¶. Calculate the center of mass of the values of an array at labels. Parameters: input : ndarray. Data from which to calculate center-of-mass. labels : ndarray, optional.

https://docs.scipy.org › doc › numpy-1.13.0 › reference › arrays.ndarray.html

An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N positive integers that specify the sizes of each dimension.