My Django project gives a download interface as follows: def download_item_vector(request): return HttpResponse(np.load('item_vector.npy')) I want to retur
I am writing a simple piece of code for a physics assignment where we are supposed to model a water molecule as a collection of charged particles. I am supposed
I understand that eigenvectors are only defined up to a multiplicative constant. As far as I see all numpy algorithms (e.g. linalg.eig, linalg.eigh, linalg.svd)
I am trying to merge some depth data frames, captured with a Intel realsense depth camera. The depth data frames comes in the following format: dataframe1 = [[0
I got this error file while following this tutorial: https://www.youtube.com/watch?v=yqkISICHH-U So far I have created a training dataset to feed into Tensorflo
I would like to create a vector of the same matrix in numpy (so as an array). Let's say the matrix is: w = np.array([[1,2], [3,4], [
I have two dataframes: df = pd.DataFrame([{'A': -4, 'B': -3, 'C': -2, 'D': -1, 'E': 2, 'F': 4, 'G': 8, 'H': 6, 'I': -2}]) df2 looks like this (just a cutout; i
I'm trying to constrain the function with the use of MCMC methods (emcee) without the analytical form of this function. I'm using the odeint to obtain the funct
this is my code and I have a problem. What is the solution for it, please? I try to make a screen recorder: import numpy as np import cv2 import pyautogui code
I have another question, is there a package that interpolates precipitation data taking into account mountains and oceans? I have so far used Numpy and Basemap
I need to concatenate 2 rec.arrays (same procedure I do for all other in my work). Problem I have is one of the documents I read for the array, has 2 extra vari
I am using a Semi-Supervised approach for Support Vector Machine in Python for the image classification from PASCAL VOC 2007 data. I have tried with the default
I know that, this issue is known and was already discussed. But I am encountering a strange behaviour, may be someone has idea why: When I run this: plot = df.p
I have a data frame like this: pd.DataFrame({'Material': ['Steel (16MnCr5)', 'X', 'X', 'X', 'Carbon black', 'Sulfur', 'Copper'], 'Weight': [4, 8, 0, 8, 6, 9, 3
To count the particular value of given column
My code: #importing required libraries import numpy as np from matplotlib.patches import Polygon import matplotlib.pyplot as plt #plotting data def func(x):
I have an array named fft with the length of length (800) and want to get the np.argmax(fft) and get as expected the maximum at position result (420). The data
ch4-out is the sample imageI have a numpy array that contains some image data (ch4_out). Now, I want to extract intensity profile along a particular line (horiz
I have four (nx1) dimensional arrays named a, b, c, and F. I want to run this algorithm without any loops. for i in range(n): if a[i] < b[i]: F[i
I have an array of 16 columns and 22 rows. please advice how to find all possible combinations with the following rules: I can't choose more than 1 value per ro