Question

我有一个需要转换为45 x 45阵列浮点数的数字列表。

gauss_matrix = [list of 2025 float numbers]
mat_template = np.zeros([45, 45])
for rows in np.arange(45):
    for columns in np.arange(45):
          mat_template[rows, columns] = float(gauss_matrix[np.arange(2025)])

这是从第0行开始，循环遍历第0-44列。然后从第2行开始，循环遍历0-44列，依此类推？

已解决的代码:(感谢您的帮助！）

   with open('gaussfilter.csv', 'r+') as gauss:  # Let's me read and write gaussfilter.csv

GaussFilterData = gauss.readlines()  # Reads the lines of the csv file
# print("GaussFilterData: \n", GaussFilterData)

GaussList = []  # Empty list which will be used to append values to from the csv file
# print("GaussList 1: \n", GaussMatrix)

for lines in GaussFilterData:  # Loops through each row of data in gaussfilter.csv
    # print("lines: \n", lines)
    for row in np.arange(45):  # Looping through each row and splitting up the list by commas, while converting each value to a float and not a string
        GaussList.append(float(lines.split(',')[row]))  # Appending each row to GaussList

# print("\nGaussList 2: \n", GaussList)

# Making the array of values from GaussList
gauss = np.zeros([45, 45])  # Matrix of zeros, the zeros are placeholders for the values in GaussList
# print("gauss: \n", gauss)

Counter = -1  # Counter has to start at -1 so that it begins at 0 when referring to row 0 and column 0
for rows in np.arange(45):  # Loop through rows 0 - 44
        for columns in np.arange(45):  # As we are looping through row 0, loop through all 45 columns
            Counter = Counter + 1  # Counter keeps track of the number of cells in the matrix (2025)
            gauss[rows][columns] = GaussList[Counter]

     print("gauss array: \n", gauss)

Answer 1

首先请不要使用'list'。因此，我将其更改为my_list。

您可以使用'np.array（）'创建数组，并且可以使用可选的'dtype'标志指定数据类型。

type AWSS3Bucket struct {
    s3.Bucket
    Location string `json:"location"`
}

您始终可以使用以下方法检查数据类型：

>>> import numpy as np
>>> my_list = [1.5, 2.5, 3.5]
>>> my_array = np.array(my_list,dtype=" ")

Answer 2

如何将dtype的{{1}}设置为GaussMatrix呢？

'float64'

将Python数字列表转换为浮点数的numpy数组

2 个答案: