I am trying to use OpenCV with Python in order to detect squares in a live video feed from a Raspberry Pi camera. However, the cv2.GaussianBlur and cv2.Canny fu
This code generates error: IndexError: invalid index to scalar variable. at the line: results.append(RMSPE(np.expm1(y_train[testcv]), [y[1] for y in y_test])
How can I do the indexing of some arrays used as indices? I have the following six 2D arrays like this- array([[2, 0], [3, 0], [3, 1], [5, 0], [5, 1
I have two curves that have their maximum roughly at the same time, but I'd like to match them exactly. The first function, maxind, determines where the maximum
Since NumPy version 19.0, one must specify dtype=object when creating an array from "ragged" sequences. I'm faced with a large number of array calls from my own
I have an array of distances called dists. I want to select dists which are within a range. dists[(np.where(dists >= r)) and (np.where(dists <= r + dr))]
I have a numpy array of np.shape=(n,) I am attempting to iterate through each value of the array and subtract the 2nd value from the 1st, then see if the differ
I'd like to copy a numpy 2D array into a third dimension. For example, given the 2D numpy array: import numpy as np arr = np.array([[1, 2], [1, 2]]) # arr.shap
I want to scale a dataframe, which raises the error as in the title (or below). My data: df.head() timestamp open high low close volume 0 2020-06-2
I have the following code import matplotlib.pyplot as plt import numpy as np array = np.pad(np.random.rand(300,300),10,'constant', constant_values = nan) fi
I'm trying to implement a function that computes the Relu derivative for each element in a matrix, and then return the result in a matrix. I'm using Python and
Here is the way how numpy.mgrid is used. grid = np.mgrid[x1:y1:100j , x2:y2:100j, ..., xn:yn:100j] However, I find this structure very irritating. Therefore, I
I'm focusing on the special case where A is a n x d matrix (where k < d) representing an orthogonal basis for a subspace of R^d and b is known to be inside t
Seemingly simple question: I have an array with two columns, the first represents an ID and the second a count. I'd like to update it with another, similar arr
If I create an array X = np.random.rand(D, 1) it has shape (3,1): [[ 0.31215124] [ 0.84270715] [ 0.41846041]] If I create my own array A = np.array([0,1,2]
I have searched high and low and just can't find a way to do it. (It's possible I was searching for the wrong terms.) I would like to create a mask (eg: [True F
I use pandas and statsmodels to do linear regression. However, i can't find any possible way to read the results. the results are displayed but i need to do som
I would like to install Python Pandas library (0.8.1) on Mac OS X 10.6.8. This library needs Numpy>=1.6. I tried this $ sudo easy_install pandas Searching
I have been searching for an answer to this question but cannot find anything useful. I am working with the python scientific computing stack (scipy,numpy,matp
Is there a fast way in numpy to add a vector to every row or column of a matrix. Lately, I have been tiling the vector to the size of the matrix, which can use