1、封装成命令行程序，接受2个参数：csv_path、start_row；
2、重构代码，通过线性回归后得到的bin1和bin2的gain和offset，计算bin1和bin2的波长，并写入到excel中以便检查； 3、wave_sn0031_sample.txt是 示例波长文件；
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tangchao0503
112
main.py
112
main.py
@ -3,58 +3,30 @@ import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from sklearn import datasets, linear_model
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import argparse
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def get_data(file_name):
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# data = pd.read_csv(file_name, header = None)
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def read_data(file_name, start_row):
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data = pd.read_csv(file_name, header=None)
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# data = pd.read_excel(file_name)
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# X_parameter = []
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# Y_parameter = []
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# for single_square_feet, single_price_value in zip(data['square_feet'], data['price']):
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# X_parameter.append([float(single_square_feet)])
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# Y_parameter.append(float(single_price_value))
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row = list(range(340, 340 + 300))
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wave = [399.959, 401.958, 403.958, 405.957, 407.957, 409.957, 411.956, 413.956, 415.955, 417.955, 419.954, 421.954,
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423.954, 425.953, 427.953, 429.952, 431.952, 433.951, 435.951, 437.951, 439.95, 441.95, 443.949, 445.949,
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447.948, 449.948, 451.947, 453.947, 455.947, 457.946, 459.946, 461.945, 463.945, 465.944, 467.944, 469.944,
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471.943, 473.943, 475.942, 477.942, 479.941, 481.941, 483.94, 485.94, 487.94, 489.939, 491.939, 493.938,
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495.938, 497.937, 499.937, 501.937, 503.936, 505.936, 507.935, 509.935, 511.934, 513.934, 515.933, 517.933,
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519.933, 521.932, 523.932, 525.931, 527.931, 529.93, 531.93, 533.93, 535.929, 537.929, 539.928, 541.928,
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543.927, 545.927, 547.927, 549.926, 551.926, 553.925, 555.925, 557.924, 559.924, 561.923, 563.923, 565.923,
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567.922, 569.922, 571.921, 573.921, 575.92, 577.92, 579.92, 581.919, 583.919, 585.918, 587.918, 589.917,
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591.917, 593.917, 595.916, 597.916, 599.915, 601.915, 603.914, 605.914, 607.913, 609.913, 611.913, 613.912,
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615.912, 617.911, 619.911, 621.91, 623.91, 625.909, 627.909, 629.909, 631.908, 633.908, 635.907, 637.907,
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639.906, 641.906, 643.906, 645.905, 647.905, 649.904, 651.904, 653.903, 655.903, 657.903, 659.902, 661.902,
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663.901, 665.901, 667.9, 669.9, 671.899, 673.899, 675.899, 677.898, 679.898, 681.897, 683.897, 685.896,
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687.896, 689.896, 691.895, 693.895, 695.894, 697.894, 699.893, 701.893, 703.893, 705.892, 707.892, 709.891,
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711.891, 713.89, 715.89, 717.889, 719.889, 721.889, 723.888, 725.888, 727.887, 729.887, 731.886, 733.886,
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735.886, 737.885, 739.885, 741.884, 743.884, 745.883, 747.883, 749.883, 751.882, 753.882, 755.881, 757.881,
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759.88, 761.88, 763.879, 765.879, 767.879, 769.878, 771.878, 773.877, 775.877, 777.876, 779.876, 781.876,
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783.875, 785.875, 787.874, 789.874, 791.873, 793.873, 795.872, 797.872, 799.872, 801.871, 803.871, 805.87,
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807.87, 809.869, 811.869, 813.869, 815.868, 817.868, 819.867, 821.867, 823.866, 825.866, 827.866, 829.865,
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831.865, 833.864, 835.864, 837.863, 839.863, 841.862, 843.862, 845.862, 847.861, 849.861, 851.86, 853.86,
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855.859, 857.859, 859.858, 861.858, 863.858, 865.857, 867.857, 869.856, 871.856, 873.855, 875.855, 877.855,
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879.854, 881.854, 883.853, 885.853, 887.852, 889.852, 891.852, 893.851, 895.851, 897.85, 899.85, 901.849,
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903.849, 905.848, 907.848, 909.848, 911.847, 913.847, 915.846, 917.846, 919.845, 921.845, 923.845, 925.844,
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927.844, 929.843, 931.843, 933.842, 935.842, 937.841, 939.841, 941.841, 943.84, 945.84, 947.839, 949.839,
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951.838, 953.838, 955.838, 957.837, 959.837, 961.836, 963.836, 965.835, 967.835, 969.835, 971.834, 973.834,
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975.833, 977.833, 979.832, 981.832, 983.831, 985.831, 987.831, 989.83, 991.83, 993.829, 995.829, 997.828]
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row_bin1 = []
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wave_bin1 = []
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for i in range(start_row, start_row + 300):
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row_bin1.append([float(i)])
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for i in range(data.shape[1]):
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wave_bin1.append(data.iloc[0][i])
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row_bin2 = list(range(170, 170 + 150))
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row_bin2 = []
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wave_bin2 = []
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for i in range(0, len(wave), 2):
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for i in np.arange(start_row / 2, start_row / 2 + 150):
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row_bin2.append([float(i)])
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for i in range(0, len(wave_bin1), 2):
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# print(i)
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# print(wave[i:i + 2])
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wave_bin2.append((wave[i] + wave[i+1])/2)
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wave_bin2.append((wave_bin1[i] + wave_bin1[i+1])/2)
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X_parameter = []
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Y_parameter = []
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for single_square_feet, single_price_value in zip(row_bin2, wave_bin2):
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X_parameter.append([float(single_square_feet)])
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Y_parameter.append(float(single_price_value))
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return X_parameter, Y_parameter
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return row_bin1, wave_bin1, row_bin2, wave_bin2
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def plot(x, y, regre):
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@ -70,19 +42,55 @@ def linearRegression(X_parameters, Y_parameters):#
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regr.fit(X_parameters, Y_parameters)
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# 绘图
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plot(X_parameters, Y_parameters, regr)
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# plot(X_parameters, Y_parameters, regr)
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return regr
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if __name__ == "__main__":
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x, y = get_data(r'D:\PycharmProjects\linear_regression\123.xlsx')
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regr = linearRegression(x, y)
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parser = argparse.ArgumentParser()
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parser.add_argument("csv_path", help="Path of csv file which contains wavelength.")
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parser.add_argument("start_row", help="Start row of coning 410 sensor.")
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args = parser.parse_args()
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yPredicted = []
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for i in x:
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xxxx = regr.predict(i[0])
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yPredicted.append(xxxx[0])
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row_bin1, wave_bin1, row_bin2, wave_bin2 = read_data(args.csv_path, int(args.start_row))
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regr_bin1 = linearRegression(row_bin1, wave_bin1)
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regr_bin2 = linearRegression(row_bin2, wave_bin2)
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print("Intercept value ", regr.intercept_)
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print("coefficient", regr.coef_)
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# bin1 calculate
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yCalculated_bin1 = []
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a_bin1 = regr_bin1.coef_[0]
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b_bin1 = regr_bin1.intercept_
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for i in range(len(row_bin1)):
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yCalculated_bin1.append(a_bin1 * row_bin1[i][0] + b_bin1)
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# bin2 calculate
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yCalculated_bin2 = []
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a_bin2 = regr_bin2.coef_[0]
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b_bin2 = regr_bin2.intercept_
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for i in range(len(row_bin2)):
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yCalculated_bin2.append(a_bin2 * row_bin2[i][0] + b_bin2)
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#
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df_bin1 = pd.DataFrame(list(zip(wave_bin1, yCalculated_bin1)),
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columns=['original', 'Calculated_bin1'])
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df_bin2 = pd.DataFrame(list(zip(yCalculated_bin2)), columns=['Calculated_bin2'])
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path_tmp = args.csv_path.split(".")
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path_new = path_tmp[0] + "_result.xlsx"
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writer = pd.ExcelWriter(path_new)
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df_bin1.to_excel(excel_writer=writer, sheet_name="bin1",
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columns=["original", "Calculated_bin1"], index=False)
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df_bin2.to_excel(excel_writer=writer, sheet_name="bin2",
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columns=["Calculated_bin2"], index=False)
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writer.save()
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writer.close()
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print("bin1")
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print("Intercept value ", regr_bin1.intercept_)
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print("coefficient", regr_bin1.coef_)
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print("bin2")
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print("Intercept value ", regr_bin2.intercept_)
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print("coefficient", regr_bin2.coef_)
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print("completed!!")
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1
wave_sn0031_sample.txt
Normal file
1
wave_sn0031_sample.txt
Normal file
@ -0,0 +1 @@
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398.83,400.83,402.83,404.83,406.83,408.82,410.82,412.82,414.82,416.82,418.82,420.82,422.82,424.82,426.82,428.82,430.82,432.82,434.81,436.81,438.81,440.81,442.81,444.81,446.81,448.81,450.81,452.81,454.81,456.81,458.81,460.81,462.8,464.8,466.8,468.8,470.8,472.8,474.8,476.8,478.8,480.8,482.8,484.8,486.8,488.8,490.79,492.79,494.79,496.79,498.79,500.79,502.79,504.79,506.79,508.79,510.79,512.79,514.79,516.78,518.78,520.78,522.78,524.78,526.78,528.78,530.78,532.78,534.78,536.78,538.78,540.78,542.78,544.77,546.77,548.77,550.77,552.77,554.77,556.77,558.77,560.77,562.77,564.77,566.77,568.77,570.77,572.76,574.76,576.76,578.76,580.76,582.76,584.76,586.76,588.76,590.76,592.76,594.76,596.76,598.75,600.75,602.75,604.75,606.75,608.75,610.75,612.75,614.75,616.75,618.75,620.75,622.75,624.75,626.74,628.74,630.74,632.74,634.74,636.74,638.74,640.74,642.74,644.74,646.74,648.74,650.74,652.74,654.73,656.73,658.73,660.73,662.73,664.73,666.73,668.73,670.73,672.73,674.73,676.73,678.73,680.73,682.72,684.72,686.72,688.72,690.72,692.72,694.72,696.72,698.72,700.72,702.72,704.72,706.72,708.71,710.71,712.71,714.71,716.71,718.71,720.71,722.71,724.71,726.71,728.71,730.71,732.71,734.71,736.7,738.7,740.7,742.7,744.7,746.7,748.7,750.7,752.7,754.7,756.7,758.7,760.7,762.7,764.69,766.69,768.69,770.69,772.69,774.69,776.69,778.69,780.69,782.69,784.69,786.69,788.69,790.68,792.68,794.68,796.68,798.68,800.68,802.68,804.68,806.68,808.68,810.68,812.68,814.68,816.68,818.67,820.67,822.67,824.67,826.67,828.67,830.67,832.67,834.67,836.67,838.67,840.67,842.67,844.67,846.66,848.66,850.66,852.66,854.66,856.66,858.66,860.66,862.66,864.66,866.66,868.66,870.66,872.65,874.65,876.65,878.65,880.65,882.65,884.65,886.65,888.65,890.65,892.65,894.65,896.65,898.65,900.64,902.64,904.64,906.64,908.64,910.64,912.64,914.64,916.64,918.64,920.64,922.64,924.64,926.64,928.63,930.63,932.63,934.63,936.63,938.63,940.63,942.63,944.63,946.63,948.63,950.63,952.63,954.62,956.62,958.62,960.62,962.62,964.62,966.62,968.62,970.62,972.62,974.62,976.62,978.62,980.62,982.61,984.61,986.61,988.61,990.61,992.61,994.61,996.61
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