numpy

问题 I have a list of lists that looks like this: [[0], [0, 1, 2], [2], [3], [4], [5], [0, 1, 2, 3, 4, 5, 6, 7], [7], [8], [9], [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [11], [11, 12, 13, 14, 15, 16, 17, 18], [13], [14], [14, 15, 16, 17, 18], [16, 17, 18], [17], [17, 18]] I am trying to find the least number of items in the list, when concatenated, that equal the full range of the list. In this case, the full range of the list is this: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

问题 I have some C code that has the following declaration: int myfunc(int m, int n, const double **a, double **b, double *c); So a is a constant 2D array, b is a 2D array, and c is a 1D array, all dynamically allocated. b and c do not need to be anything specifically before they are passed to myfunc , and should be understood as output information. For the purposes of this question, I'm not allowed to change the declaration of myfunc . Question 1: How do I convert a given numpy array a_np into an

问题 I have some C code that has the following declaration: int myfunc(int m, int n, const double **a, double **b, double *c); So a is a constant 2D array, b is a 2D array, and c is a 1D array, all dynamically allocated. b and c do not need to be anything specifically before they are passed to myfunc , and should be understood as output information. For the purposes of this question, I'm not allowed to change the declaration of myfunc . Question 1: How do I convert a given numpy array a_np into an

问题 I have a 2D Numpy array with 3 columns. It looks something like this array([[0, 20, 1], [1,2,1], ........, [20,1,1]]) . It basically is array of list of lists. How can I convert this matrix into array([(0,20,1), (1,2,1), ........., (20,1,1)]) ? I want the output to be a array of triple. I have been trying to use tuple and map functions described in Convert numpy array to tuple, R = mydata #my data is sparse matrix of 1's and 0's #First row #R[0] = array([0,0,1,1]) #Just a sample (rows, cols)

问题 Hello this code is intended to store the coordinates of rectangles drawn with open cv and compile the results into a single image. import numpy as np import cv2 im = cv2.imread('1.jpg') im3 = im.copy() gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray,(5,5),0) thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2) contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) squares = [] for cnt in contours: if cv2.contourArea(cnt)>50: [x,y,w,h] = cv2

问题 I would like to sample a few hundred binomially distributed random variables, each with a different n and p (using the argument names as defined in the numpy.random.binomial docs). I'll be doing this repeatedly, so I'd like to vectorize the code if possible. Here's an example: import numpy as np # Made up parameters N_random_variables = 500 n_vals = np.random.random_integers(100, 200, N_random_variables) p_vals = np.random.random_sample(N_random_variables) # Can this portion be vectorized?

问题 I have a calculation using a 1D and a 2D numpy array. It has two levels of if -conditions. I was able to use np.where to avoid one if -statement and further use the slow list comprehension to iterate through each row. Ideally, I would like to vectorize the whole calculation process. Is it possible? Here is my code: import numpy as np r_base = np.linspace(0, 4, 5) np.random.seed(0) r_mat = np.array([r_base * np.random.uniform(0.9, 1.1, 5), r_base * np.random.uniform(0.9, 1.1, 5), r_base * np

问题 I have a data frame like df1 in which has four columns, suppose all the city s have date range from 2019-01-01 to 2019-07-01 , I would like to groupy city and calculating price based on its value in 2019-07-01 and pct_change : city date price pct_change 0 bj 2019-01-01 NaN NaN 1 bj 2019-02-01 NaN -0.03 2 bj 2019-03-01 NaN 0.16 3 bj 2019-04-01 NaN 0.07 4 bj 2019-05-01 NaN 0.19 5 bj 2019-06-01 NaN -0.05 6 bj 2019-07-01 6.0 -0.02 7 gz 2019-01-01 NaN NaN 8 gz 2019-02-01 NaN 0.03 9 gz 2019-03-01

问题 I have broken my standard deviation function into small chunks of std_1, std_2, std_3 etc. to optimize my code to make it run faster. Since I have over 2 million arrays on my main numpy array PC_list . I have used the numba, numpy arrays and multi processing to make the code run faster however I do not see any performance difference in the way even doe the code is broken into pieces from the main function. It takes about 57 seconds for the main function to process and the divided function to

问题 I am trying to split a list into n sublists where the size of each sublist is random (with at least one entry; assume P>I ). I used numpy.split function which works fine but does not satisfy my randomness condition. You may ask which distribution the randomness should follow. I think, it should not matter. I checked several posts which were not equivalent to my post as they were trying to split with almost equally sized chunks. If duplicate, let me know. Here is my approach: import numpy as