numpy

问题 I want to get dataset with format {N, 16, 512, 128} as 4D numpy array from hdf5 file. N is a number of 3D arrays with {16, 512, 128} format. I try to do this: import os import sys import h5py as h5 import numpy as np import subprocess import re file_name = sys.argv[1] path = sys.argv[2] f = h5.File(file_name, 'r') data = f[path] print(data.shape) #{27270, 16, 512, 128} print(data.dtype) #"<u4" data = np.array(data, dtype=np.uint32) print(data.shape) Unfortunately, after data = np.array(data,

问题 Example code: import numpy as np import math import time x=np.ones((2000,2000)) start = time.time() print(np.linalg.norm(x, 2)) end = time.time() print("time 1: " + str(end - start)) start = time.time() print(math.sqrt(np.sum(x*x))) end = time.time() print("time 2: " + str(end - start)) The output (on my machine) is: 1999.999999999991 time 1: 3.216777801513672 2000.0 time 2: 0.015042781829833984 It shows that np.linalg.norm() takes more than 3s to solve it, while the direct solution takes

问题 I have a numpy array of shape (100, 100, 6) . For the purposes of my work, I have to pad this array to have the 3rd dimension of 8 instead of 6, so the final shape should be (100, 100, 8) . I want to pad using zeros, something like this. np.zeros((100, 100, 2)) I have tried both numpy append and concatenate along axes 0 and 1, but it's not resulting in the way I want the zero padding to be appended. Could someone please forward me to the right direction? Thank you. 回答1: You can use np.dstack(

问题 I need to find all the different intersections between two partitions of the same set. For example, if we have the following two partitions of the same set x = [[1, 2], [3, 4, 5], [6, 7, 8, 9, 10]] y = [[1, 3, 6, 7], [2, 4, 5, 8, 9, 10]] the required result is [[1], [2], [3], [4, 5], [6, 7], [8, 9, 10]]. In detail, we calculate the cartesian product between every subset of x and y, and for each of these products, we classify the elements in new subsets accordingly if they belong to the

问题 A minimal numpy structured array generator: import numpy as np index = np.arange(4) A = np.stack((np.sin(index), np.cos(index)),axis=1) B = np.eye(4).astype(int) C = np.array([1, 0, 1, 0], dtype=bool) goodies = [(a, b, c, d) for a, b, c, d in zip(index, A, B, C)] dt = [('index', 'int'), ('two_floats', 'float', 2), ('four_ints', 'int', 4), ('and_a_bool', 'bool')] s = np.array(goodies, dtype=dt) generates the minimal numpy structured array: array([(0, [ 0. , 1. ], [1, 0, 0, 0], True), (1, [ 0

问题 I am trying to make python run standard deviation functions faster with numba and numpy. However the problem is the for loop is very slow and I need alternatives so that I could make the code much faster. I iterated numba to the already existing numpy version however there is not much of a gain in performance. My original list_ has million of values within it thus it is taking a very long time to compute the standard deviation function. The list_ function down below is a very short numpy

问题 I have a big list of floats and integers as given below. I want to find the length of each sublist by neglecting empty or single elements. big_list = [[137.83,81.80,198.56],0.0,[200.37,151.55,165.26, 211.84], 0.0,[1,2,3],4,[5,6,0,5,7,8],0,[2,1,4,5],[9,1,-2]] My present code: list_len = [] for i in big_list: list_len.append(len(i)) Present output: TypeError: object of type 'numpy.float64' has no len() Expected output: list_len = [3,4,3,6,4,3] # list_len should neglect elements like 0, 4 in big

问题 Question What numpy function to use for mathematical dot product in the case below? Backpropagation for a Linear Layer 回答1: Define sample (2,3) array: In [299]: dldx = np.arange(6).reshape(2,3) In [300]: w Out[300]: array([[0.1, 0.2, 0.3], [0. , 0. , 0. ]]) Element wise multiplication: In [301]: dldx*w Out[301]: array([[0. , 0.2, 0.6], [0. , 0. , 0. ]]) and summing on the last axis (size 3) produces a 2 element array: In [302]: (dldx*w).sum(axis=1) Out[302]: array([0.8, 0. ]) Your (6) is the

问题 I'm struggling to find a solution for streaming synthesized audio from a Python server. The synthesized audio is incrementally generated and returned as a np.float32 NumPy array. It then needs to be transformed from a NumPy array into an MP3 chunk. Finally, the MP3 chunk is served via flask . Here is some pseudo-code: import numpy from flask import Flask from flask import Response app = Flask(__name__) sample_rate = 24000 def pcm_to_mp3(): raise NotImplementedError() def get_synthetic_audio()

问题 I have a df like this: a=[1,2,10,11,15,16,17,18,30] b=[5,6,7,8,9,1,2,3,4] df=pd.DataFrame(list(zip(a,b)),columns=['s','i']) Using a I need to add elements of b. Result I would like: (1-2)=5+6=11 (10-11)=7+8=15 (15-18)=9+1+2+3=15 (30)=4 My idea was to create a list of values that are continuous, take the difference(+1) and use it to calculate the sum of the corresponding b elements. #find continuous integer def r (nums): nums= list(df['s']) gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s+1