將輸出的多維一維數組存儲為數據文件

Question

import numpy as np 
import matplotlib.pyplot as plt 
x = open(r'''C:\Users\Documents\ex.txt''')

[INPUT ex.txt file:
   -1.642195902 0.751055263
    0.496998351 -1.306558434
   -0.490237525 -0.188855324
   -1.357284374 0.282238191
   -0.160982328 -1.115393803
    1.167022948 0.564800286
    -2.050084963    0.262537079
    0.883449292 -0.276800002
    1.017703957 -0.044710318
    0.194025051 -1.392772391
    0.209566571 -0.314937244
    1.840331474 -1.544109096
    0.493878872 -0.405593557


   ]

ls = x.readlines()

x1 = np.array([])
x2 = np.array([])
for l in ls: 

    col = l.split()

    x1 = np.append(x1,float(col[0])) 
    x2 = np.append(x2,float(col[1]))
    x3 = np.polyfit(x1,x2,1)
    print(x3)


 Output is:

 [-0.22867408  0.37552763]
 [-0.96186389 -0.82851367]
 [-0.95783751 -0.77027985]
 [-0.90374253 -0.79169204]
 [-0.97569702 -0.93091431]
 [-0.29008702 -0.26483726]
 [-0.27811928 -0.26755408]

     .........

 [-0.06747052 -0.0218189 ]

我的目標是使用x3的值並從原始值x1和x2中減去，我當時想將輸出另存為.txt文件，但輸出是幾個1維數組。

有誰知道如何保存x3的結果，以便能夠從原始值中減去它？ 我不確定如何編碼。 我的目標是趨勢化一組數據。

非常感謝

Answer 1

嘗試這個：

import numpy as np 
import matplotlib.pyplot as plt 
x = open(r'''C:\Users\Documents\ex.txt''')

ls = x.readlines()

x1 = np.array([])
x2 = np.array([])
x_array = np.array([])
x3_array = np.array([])

for l in ls: 

    col = l.split()
    x_array = np.append(x_array, [float(col[0]), float(col[1])])
    x1 = np.append(x1,float(col[0])) 
    x2 = np.append(x2,float(col[1]))
    x3_array = np.append(x3_array, np.polyfit(x1,x2,1))

sub_res = x_array - x3_array
print (sub_res)

Answer 2

首先關於導入數據：

data = np.genfromtxt('c:/Users/kaufnbnj/Desktop/ex.txt', names = ['x1', 'x2'])

array([(-1.6421959 ,  0.75105526), ( 0.49699835, -1.30655843),
       (-0.49023753, -0.18885532), (-1.35728437,  0.28223819),
       (-0.16098233, -1.1153938 ), ( 1.16702295,  0.56480029),
       (-2.05008496,  0.26253708), ( 0.88344929, -0.2768    ),
       ( 1.01770396, -0.04471032), ( 0.19402505, -1.39277239),
       ( 0.20956657, -0.31493724), ( 1.84033147, -1.5441091 ),
       ( 0.49387887, -0.40559356)],
      dtype=[('x1', '<f8'), ('x2', '<f8')])

訪問第一列：

data['x1']
array([-1.6421959 ,  0.49699835, -0.49023753, -1.35728437, -0.16098233,
        1.16702295, -2.05008496,  0.88344929,  1.01770396,  0.19402505,
        0.20956657,  1.84033147,  0.49387887])

然后是配件部分：

x3 = np.polyfit(data['x1'], data['x2'], 1)

請注意， polyfit返回擬合函數的系數，而不是輸入數據的采樣數組：

x3
array([-0.33019369, -0.3484815 ])

因此，您可以繪制輸入數據並擬合如下所示的圖：

x_fit = x3[0]*data['x1'] + x3[1]
plt.plot(data['x1'], data['x2'], 'o')
plt.plot(data['x1'], x_fit)

...且下降趨勢后的x2為：

x_detrend = data['x2'] - x_fit
plt.plot(data['x1'], x_detrend, 'o')