0.1.112_增加函数
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[metadata]
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# replace with your username:
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name = guan
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version = 0.1.111
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version = 0.1.112
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author = guanjihuan
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author_email = guanjihuan@163.com
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description = An open source python package
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@ -1,6 +1,6 @@
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Metadata-Version: 2.1
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Name: guan
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Version: 0.1.111
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Version: 0.1.112
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Summary: An open source python package
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Home-page: https://py.guanjihuan.com
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Author: guanjihuan
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@ -123,6 +123,62 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
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end = time.time()
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print('Total running time = '+str((end-start)/60)+' min')
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# 将XYZ数据转成矩阵数据（说明：x_array/y_array的输入和输出不一样。要求z_array数据中y对应的数据为小循环，x对应的数据为大循环）
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def convert_xyz_data_into_matrix_data(x_array, y_array, z_array):
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import numpy as np
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x_array_input = np.array(x_array)
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y_array_input = np.array(y_array)
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x_array = np.array(list(set(x_array_input)))
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y_array = np.array(list(set(y_array_input)))
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z_array = np.array(z_array)
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len_x = len(x_array)
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len_y = len(y_array)
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matrix = np.zeros((len_x, len_y))
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for ix in range(len_x):
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for iy in range(len_y):
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matrix[ix, iy] = z_array[ix*len_y+iy]
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return x_array, y_array, matrix
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# 将矩阵数据转成XYZ数据（说明：x_array/y_array的输入和输出不一样。生成的z_array数据中y对应的数据为小循环，x对应的数据为大循环）
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def convert_matrix_data_into_xyz_data(x_array, y_array, matrix):
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import numpy as np
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x_array_input = np.array(x_array)
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y_array_input = np.array(y_array)
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matrix = np.array(matrix)
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len_x = len(x_array_input)
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len_y = len(y_array_input)
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x_array = np.zeros((len_x*len_y))
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y_array = np.zeros((len_x*len_y))
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z_array = np.zeros((len_x*len_y))
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for ix in range(len_x):
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for iy in range(len_y):
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x_array[ix*len_y+iy] = x_array_input[ix]
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y_array[ix*len_y+iy] = y_array_input[iy]
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z_array[ix*len_y+iy] = matrix[ix, iy]
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return x_array, y_array, z_array
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# 通过定义计算R^2（基于实际值和预测值）
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def calculate_R2_with_definition(y_true_array, y_pred_array):
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import numpy as np
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y_mean = np.mean(y_true_array)
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SS_tot = np.sum((y_true_array - y_mean) ** 2)
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SS_res = np.sum((y_true_array - y_pred_array) ** 2)
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R2 = 1 - (SS_res / SS_tot)
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return R2
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# 通过sklearn计算R^2，和定义的计算结果一致
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def calculate_R2_with_sklearn(y_true_array, y_pred_array):
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from sklearn.metrics import r2_score
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R2 = r2_score(y_true_array, y_pred_array)
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return R2
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# 通过scipy计算线性回归后的R^2（基于线性回归模型）
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def calculate_R2_after_linear_regression_with_scipy(y_true_array, y_pred_array):
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from scipy import stats
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slope, intercept, r_value, p_value, std_err = stats.linregress(y_true_array, y_pred_array)
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R2 = r_value**2
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return R2
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# 获取函数或类的源码（返回字符串）
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def get_source(name):
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import inspect
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@ -102,7 +102,7 @@ def read_text_files_in_directory(directory='./', file_format='.md'):
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file_list.append(root+'/'+files[i0])
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content_array = []
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for file in file_list:
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with open(file, 'r') as f:
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with open(file, 'r', encoding='UTF-8') as f:
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content_array.append(f.read())
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return file_list, content_array
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@ -118,9 +118,9 @@ def find_words_in_multiple_files(words, directory='./', file_format='.md'):
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return file_list_with_words
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# 复制一份文件
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def copy_file(file1='./a.txt', file2='./b.txt'):
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def copy_file(old_file='./a.txt', new_file='./b.txt'):
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import shutil
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shutil.copy(file1, file2)
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shutil.copy(old_file, new_file)
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# 打开文件，替代某字符串
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def open_file_and_replace_str(file_path='./a.txt', old_str='', new_str=''):
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@ -134,7 +134,7 @@ def open_file_and_replace_str(file_path='./a.txt', old_str='', new_str=''):
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# 复制一份文件，然后再替代某字符串
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def copy_file_and_replace_str(old_file='./a.txt', new_file='./b.txt', old_str='', new_str=''):
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import guan
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guan.copy_file(file1=old_file, file2=new_file)
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guan.copy_file(old_file=old_file, new_file=new_file)
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content = guan.read_text_file(file_path=new_file)
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content = content.replace(old_str, new_str)
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f = guan.open_file(filename=new_file, file_format='', mode='overwrite')
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@ -202,6 +202,24 @@ def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
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y_array = np.append(y_array, [y_row], axis=0)
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return x_array, y_array
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# 读取文件中的XYZ数据（一行一组x, y, z）
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def read_xyz_data(filename='a', file_format='.txt'):
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import numpy as np
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f = open(filename+file_format, 'r')
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text = f.read()
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f.close()
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row_list = np.array(text.split('\n'))
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x_array = np.array([])
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y_array = np.array([])
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z_array = np.array([])
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for row in row_list:
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column = np.array(row.split())
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if column.shape[0] != 0:
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x_array = np.append(x_array, [float(column[0])], axis=0)
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y_array = np.append(y_array, [float(column[1])], axis=0)
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z_array = np.append(z_array, [float(column[2])], axis=0)
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return x_array, y_array, z_array
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# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）
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def read_two_dimensional_data(filename='a', file_format='.txt'):
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import numpy as np
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@ -260,7 +278,7 @@ def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
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matrix = np.append(matrix, [matrix_row], axis=0)
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return x_array, y_array, matrix
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# 读取文件中的二维数据（不包括x和y）
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# 读取文件中的二维数据（不包括横纵坐标）
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def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
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import numpy as np
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matrix = np.loadtxt(filename+file_format)
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@ -288,6 +306,25 @@ def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
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f.write('\n')
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i0 += 1
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# 在文件中写入XYZ数据（一行一组x, y, z）
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def write_one_dimensional_data(x_array, y_array, z_array, filename='a', file_format='.txt'):
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import guan
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with open(filename+file_format, 'w', encoding='UTF-8') as f:
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write_xyz_data_without_opening_file(x_array, y_array, z_array, f)
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# 在文件中写入XYZ数据（一行一组x, y, z）（需要输入已打开的文件）
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def write_xyz_data_without_opening_file(x_array, y_array, z_array, f):
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import numpy as np
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x_array = np.array(x_array)
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y_array = np.array(y_array)
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z_array = np.array(z_array)
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i0 = 0
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for x0 in x_array:
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f.write(str(x0)+' ')
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f.write(str(y_array[i0])+' ')
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f.write(str(z_array[i0])+'\n')
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i0 += 1
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# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）
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def write_two_dimensional_data(x_array, y_array, matrix, filename='a', file_format='.txt'):
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import guan
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@ -314,13 +351,13 @@ def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
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f.write('\n')
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i0 += 1
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# 在文件中写入二维数据（不包括x和y）
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# 在文件中写入二维数据（不包括横纵坐标）
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def write_two_dimensional_data_without_xy_array(matrix, filename='a', file_format='.txt'):
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import guan
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with open(filename+file_format, 'w', encoding='UTF-8') as f:
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guan.write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f)
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# 在文件中写入二维数据（不包括x和y）（需要输入已打开的文件）
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# 在文件中写入二维数据（不包括横纵坐标）（需要输入已打开的文件）
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def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
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for row in matrix:
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for element in row:
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