update

language_learning/2020.08.29_several_python_functions/read_txt_file/1D_data.txt

@@ -0,0 +1,4 @@
+1   1.2   2.4
+2   5.5   3.2
+3   6.7   7.1
+4   3.6   4.9

language_learning/2020.08.29_several_python_functions/read_txt_file/2D_data.txt

@@ -0,0 +1,4 @@
+0      1      2      3      4
+1     1.3    2.7    6.7    8.3
+2     4.3    2.9    5.4    7.4
+3     9.1    8.2    2.6    3.1

language_learning/2020.08.29_several_python_functions/read_txt_file/read_1D_data.py

@@ -0,0 +1,47 @@
+import numpy as np  
+import matplotlib.pyplot as plt
+# import os
+# os.chdir('D:/data')  # 设置路径
+
+
+def main():  
+    x, y = read_one_dimension('1D_data.txt')
+    for dim0 in range(y.shape[1]):
+        plt.plot(x, y[:, dim0], '-k')
+    plt.show()
+
+
+def read_one_dimension(file_name):
+    f = open(file_name, 'r')
+    text = f.read()
+    f.close()
+    row_list = np.array(text.split('\n'))  # 根据“回车”分割成每一行
+    # print('文本格式：')
+    # print(text)
+    # print('row_list:')
+    # print(row_list)
+    # print('column:')
+    dim_column = np.array(row_list[0].split()).shape[0] # 列数
+    x = np.array([])
+    y = np.array([])
+    for row in row_list:
+        column = np.array(row.split())  # 每一行根据“空格”继续分割
+        # print(column)
+        if column.shape[0] != 0:  # 解决最后一行空白的问题
+            x = np.append(x, [float(column[0])], axis=0)  # 第一列为x数据
+            y_row = np.zeros(dim_column-1)
+            for dim0 in range(dim_column-1):
+                y_row[dim0] = float(column[dim0+1])
+            if np.array(y).shape[0] == 0:
+                y = [y_row]
+            else:
+                y = np.append(y, [y_row], axis=0)
+    # print('x:')
+    # print(x)
+    # print('y:')
+    # print(y)
+    return x, y
+
+
+if __name__ == '__main__': 
+    main()

language_learning/2020.08.29_several_python_functions/read_txt_file/read_2D_data.py

@@ -0,0 +1,69 @@
+import numpy as np
+# import os
+# os.chdir('D:/data')  # 设置路径
+
+
+def main():  
+    x1, x2, matrix = read_two_dimension('2D_data.txt')
+    plot_matrix(x1, x2, matrix)
+
+
+def read_two_dimension(file_name):
+    f = open(file_name, 'r')
+    text = f.read()
+    f.close()
+    row_list = np.array(text.split('\n'))  # 根据“回车”分割成每一行
+    # print('文本格式：')
+    # print(text)
+    # print('row_list:')
+    # print(row_list)
+    # print('column:')
+    dim_column = np.array(row_list[0].split()).shape[0] # 列数
+    x1 = np.array([])
+    x2 = np.array([])
+    matrix = np.array([])
+    for i0 in range(row_list.shape[0]):
+        column = np.array(row_list[i0].split())  # 每一行根据“空格”继续分割
+        # print(column)
+        if i0 == 0:
+            x1_str = column[1::]  # x1坐标（去除第一个在角落的值）
+            x1 = np.zeros(x1_str.shape[0])
+            for i00 in range(x1_str.shape[0]):
+                x1[i00] = float(x1_str[i00])  # 字符串转浮点
+        elif column.shape[0] != 0:  # 解决最后一行空白的问题
+            x2 = np.append(x2, [float(column[0])], axis=0)  # 第一列为x数据
+            matrix_row = np.zeros(dim_column-1)
+            for dim0 in range(dim_column-1):
+                matrix_row[dim0] = float(column[dim0+1])
+            if np.array(matrix).shape[0] == 0:
+                matrix = [matrix_row]
+            else:
+                matrix = np.append(matrix, [matrix_row], axis=0)
+    # print('x1:')
+    # print(x1)
+    # print('x2:')
+    # print(x2)
+    # print('matrix:')
+    # print(matrix)
+    return x1, x2, matrix
+
+
+
+def plot_matrix(x1, x2, matrix):
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+    from matplotlib import cm
+    from matplotlib.ticker import LinearLocator, FormatStrFormatter
+    fig = plt.figure()
+    ax = fig.gca(projection='3d')
+    x1, x2 = np.meshgrid(x1, x2)
+    ax.plot_surface(x1, x2, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False) 
+    plt.xlabel('x1')
+    plt.ylabel('x2') 
+    ax.set_zlabel('z')  
+    plt.show()
+
+
+
+if __name__ == '__main__': 
+    main()