numpy

问题 Given an array of the form array([list([21603, 125, 737, 579, 2065, 10399, 1175, 0, 0, 0]), ... list([1896, 3917, 498, 296, 1452, 523, 754, 450, 3795, 341])], dtype=object) How do you prepare it to be consumed by a Keras model in TensorFlow 2.0 RC0? In its current form it throws the error ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list). and I can't seem to get it in the form I'm familiar with, array([[21603, 125, 737, 579, 2065, 10399, 1175, 0, 0, 0], ..

问题 I have an n x n x n numpy array that contains density values on a cubic grid. I'm trying to align the principal axes of inertia of the density map with the cartesian x,y,z axes of the grid. I have the following so far: import numpy as np from scipy import ndimage def center_rho(rho): """Move density map so its center of mass aligns with the center of the grid""" rhocom = np.array(ndimage.measurements.center_of_mass(rho)) gridcenter = np.array(rho.shape)/2. shift = gridcenter-rhocom rho =

问题 I have an n x n x n numpy array that contains density values on a cubic grid. I'm trying to align the principal axes of inertia of the density map with the cartesian x,y,z axes of the grid. I have the following so far: import numpy as np from scipy import ndimage def center_rho(rho): """Move density map so its center of mass aligns with the center of the grid""" rhocom = np.array(ndimage.measurements.center_of_mass(rho)) gridcenter = np.array(rho.shape)/2. shift = gridcenter-rhocom rho =

问题 I have the following list. vector = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] inserted_elements = [2, 2, 2, 2, 2] I want to get the following by inserting every two elements. output = [1, 2, 2, 3, 4, 2, 5, 6, 2, 7, 8, 2, 9, 10, 2] Not only the Python list but also the answer using numpy array is fine. 回答1: Here's an itertools based approach, which also works for an arbitrary number of elements to be inserted from one list to the other. For this I've defined a generator function, which will insert and

问题 Okay, so I found out that you can have arrays with 0s in their shape. For the case where you have 0 as the only dimension, this makes sense to me. It is an empty array. np.zeros(0) But the case where you have something like this: np.zeros((0, 100)) Is confusing for me. Why is it defined like this? 回答1: As far as I know it's just a redundant way to express an empty array. It doesn't seems to matter for python if you have rows of "emptiness". Let's say we have a give array a: import numpy as np

问题 My objective is the following: given a list of numbers (int. or float) (1D), I need to find N amount of numbers that are closest to each other in this list. For example: Given sample_list = [1, 8, 14, 15, 17, 100, 35] and N = 3 , the desired function should return a list of [14, 15, 17] , because the 3 "nearest neighbors" in this list are 14, 15, and 17. So far, I have tackled the problem the following way, being forced to make several compromises in my approach (after unsuccessfully

问题 I noticed that in can be used between numpy arrays. However its meaning can be a bit counter-intuitive. import numpy as np np.array([0]) in np.array([1, 2]) # False np.array([0]) in np.array([0, 1]) # True np.array([0, 1]) in np.array([0]) # True -- somewhat surprisingly So it seems that it behaves like np.any(np.isin(·, ·)) rather than the somewhat more intuitive np.all(np.isin(·, ·)) . Is this really the case? What is the rationale behind this choice? 回答1: As the "in" operator behaviour is

问题 I noticed that in can be used between numpy arrays. However its meaning can be a bit counter-intuitive. import numpy as np np.array([0]) in np.array([1, 2]) # False np.array([0]) in np.array([0, 1]) # True np.array([0, 1]) in np.array([0]) # True -- somewhat surprisingly So it seems that it behaves like np.any(np.isin(·, ·)) rather than the somewhat more intuitive np.all(np.isin(·, ·)) . Is this really the case? What is the rationale behind this choice? 回答1: As the "in" operator behaviour is

问题 I have the following data set in a numpy array: Array 1: [[a, 1, 20] [a, 3, 40] [b, 1, 20] [b, 2, 40] [c, 5, 90]] Array 2: [[a, 2] [a, 5]] What I'm trying to accomplish is the following: Array2[0,0]=a , and Array2[0,1]=2 I want to interpolate from the first array to find a,2,30 . To do this I'm using np.where(Array1==item)[0] which looks for 'a' , I can't interpolate though because the dtype used to import was a string, not an int. It's been a while since I've used Numpy so if I'm completely

问题 On an RPi2, I upgraded all my packages with pip-review and numpy does not work anymore. I tried to uninstall and reinstall numpy though pip and apt but I'm getting no luck. Apt installs numpy 1.12.1, if I try to install that it does not install through pip. Pip installs numpy 1.16.4 fine, but when I go to run it I get the error below. I have already run: sudo apt install libc6 libatlas-base-dev Any help is appreciated. Python 3.7.3 (default, May 8 2019, 18:07:21) [GCC 6.3.0 20170516] on linux