代表功率谱的图上的倍数的阵列，绘图，拟合高斯分布

Question

这是我完成的代码，该代码读取一个数据文本文件，该文件的开头有一些元数据。 然后2列数据代表左列的Angle 2 theta和右列的Radiation count。 我的代码在元数据中查找wavelenght的值，并将其存储在变量中以备后用，然后重置为文件的开头，跳过元数据并将所需的数据集读入数组。 辐射计数对数与衍射角的对数图为图。 此图上有几个峰，我希望能够分别访问每个峰，计算并存储参数，例如半峰全宽和曲线下面积，以供以后访问。 搜索称为数据的主数组，进行扫描以创建较小的数组以存储我需要计算每个单独峰的参数的其他数据，这是一个很好的方法吗？ 这是下面的代码，其后是我在文本文件中使用的数据。

import matplotlib.pyplot as plt
import numpy as np
import math
figure = plt.figure()
count = 0
with open("chan.txt", "r") as metapart:
    for line in metapart:
        if "&END" in line:
            break
        count = count + 1
    print count
    print metapart.next()
    metapart.seek(0)
    data = np.genfromtxt(metapart, skiprows=11)
    print data
metapart.close()
x_data = data[:,0]
y_init = data[:,1]
y_data = []
for i in y_init:
    if i > 0:
        y_data.append(math.log10(i))
    else:
        y_data.append(i)
print y_data
plt.plot(x_data,y_data, 'r')
plt.show()

这是文本文件中的数据：

 &SRS
<MetaDataAtStart>
multiple=True
Wavelength (Angstrom)=0.97587
mode=assessment
background=False
issid=py11n2g
noisy=False
</MetaDataAtStart>
&END
Two Theta(deg)  Counts(sec^-1)
10.0    0.0
10.1    0.0
10.2    0.0
10.3    0.0
10.4    0.0
10.5    0.0
10.6    0.0
10.7    0.0
10.8    0.0
10.9    0.0
11.0    0.0
11.1    0.0
11.2    0.0
11.3    0.0
11.4    0.0
11.5    0.0
11.6    0.0
11.7    0.0
11.8    0.0
11.9    0.0
12.0    0.0
12.1    0.0
12.2    0.0
12.3    0.0
12.4    0.0
12.5    0.0
12.6    0.0
12.7    0.0
12.8    0.0
12.9    0.0
13.0    0.0
13.1    0.0
13.2    0.0
13.3    0.0
13.4    0.0
13.5    0.0
13.6    0.0
13.7    0.0
13.8    0.0
13.9    0.0
14.0    0.0
14.1    0.0
14.2    0.0
14.3    0.0
14.4    0.0
14.5    0.0
14.6    0.0
14.7    0.0
14.8    0.0
14.9    0.0
15.0    0.0
15.1    0.0
15.2    0.0
15.3    0.0
15.4    0.0
15.5    0.0
15.6    0.0
15.7    0.0
15.8    0.0
15.9    0.0
16.0    0.0
16.1    0.0
16.2    0.0
16.3    0.0
16.4    0.0
16.5    0.0
16.6    0.0
16.7    0.0
16.8    0.0
16.9    0.0
17.0    0.0
17.1    0.0
17.2    0.0
17.3    0.0
17.4    0.0
17.5    0.0
17.6    0.0
17.7    0.0
17.8    0.0
17.9    0.0
18.0    0.0
18.1    0.0
18.2    0.0
18.3    0.0
18.4    0.0
18.5    0.0
18.6    0.0
18.7    0.0
18.8    0.0
18.9    0.0
19.0    0.0
19.1    0.0
19.2    0.0
19.3    0.0
19.4    0.0
19.5    0.0
19.6    0.0
19.7    0.0
19.8    0.0
19.9    0.0
20.0    0.0
20.1    0.0
20.2    0.0
20.3    0.0
20.4    0.0
20.5    0.0
20.6    0.0
20.7    0.0
20.8    0.0
20.9    0.0
21.0    0.0
21.1    0.0
21.2    0.0
21.3    0.0
21.4    0.0
21.5    0.0
21.6    0.0
21.7    0.0
21.8    0.0
21.9    0.0
22.0    0.0
22.1    0.0
22.2    0.0
22.3    0.0
22.4    0.0
22.5    0.0
22.6    0.0
22.7    0.0
22.8    0.0
22.9    0.0
23.0    0.0
23.1    0.0
23.2    0.0
23.3    0.0
23.4    0.0
23.5    0.0
23.6    0.0
23.7    2.0
23.8    8.0
23.9    30.0
24.0    86.0
24.1    205.0
24.2    400.0
24.3    642.0
24.4    848.0
24.5    922.0
24.6    823.0
24.7    605.0
24.8    365.0
24.9    181.0
25.0    74.0
25.1    25.0
25.2    7.0
25.3    2.0
25.4    0.0
25.5    0.0
25.6    0.0
25.7    0.0
25.8    0.0
25.9    0.0
26.0    0.0
26.1    0.0
26.2    0.0
26.3    0.0
26.4    0.0
26.5    0.0
26.6    0.0
26.7    0.0
26.8    0.0
26.9    0.0
27.0    0.0
27.1    0.0
27.2    0.0
27.3    0.0
27.4    0.0
27.5    1.0
27.6    3.0
27.7    10.0
27.8    33.0
27.9    88.0
28.0    193.0
28.1    347.0
28.2    515.0
28.3    630.0
28.4    636.0
28.5    529.0
28.6    363.0
28.7    206.0
28.8    96.0
28.9    37.0
29.0    12.0
29.1    3.0
29.2    1.0
29.3    0.0
29.4    0.0
29.5    0.0
29.6    0.0
29.7    0.0
29.8    0.0
29.9    0.0
30.0    0.0
30.1    0.0
30.2    0.0
30.3    0.0
30.4    0.0
30.5    0.0
30.6    0.0
30.7    0.0
30.8    0.0
30.9    0.0
31.0    0.0
31.1    0.0
31.2    0.0
31.3    0.0
31.4    0.0
31.5    0.0
31.6    0.0
31.7    0.0
31.8    0.0
31.9    0.0
32.0    0.0
32.1    0.0
32.2    0.0
32.3    0.0
32.4    0.0
32.5    0.0
32.6    0.0
32.7    0.0
32.8    0.0
32.9    0.0
33.0    0.0
33.1    0.0
33.2    0.0
33.3    0.0
33.4    0.0
33.5    0.0
33.6    0.0
33.7    0.0
33.8    0.0
33.9    0.0
34.0    0.0
34.1    0.0
34.2    0.0
34.3    0.0
34.4    0.0
34.5    0.0
34.6    0.0
34.7    0.0
34.8    0.0
34.9    0.0
35.0    0.0
35.1    0.0
35.2    0.0
35.3    0.0
35.4    0.0
35.5    0.0
35.6    0.0
35.7    0.0
35.8    0.0
35.9    0.0
36.0    0.0
36.1    0.0
36.2    0.0
36.3    0.0
36.4    0.0
36.5    0.0
36.6    0.0
36.7    0.0
36.8    0.0
36.9    0.0
37.0    0.0
37.1    0.0
37.2    0.0
37.3    0.0
37.4    0.0
37.5    0.0
37.6    0.0
37.7    0.0
37.8    0.0
37.9    0.0
38.0    0.0
38.1    0.0
38.2    0.0
38.3    0.0
38.4    0.0
38.5    0.0
38.6    0.0
38.7    0.0
38.8    0.0
38.9    0.0
39.0    0.0
39.1    0.0
39.2    0.0
39.3    0.0
39.4    0.0
39.5    0.0
39.6    0.0
39.7    1.0
39.8    5.0
39.9    18.0
40.0    53.0
40.1    125.0
40.2    249.0
40.3    415.0
40.4    575.0
40.5    667.0
40.6    645.0
40.7    521.0
40.8    352.0
40.9    198.0
41.0    93.0
41.1    37.0
41.2    12.0
41.3    3.0
41.4    1.0
41.5    0.0
41.6    0.0
41.7    0.0
41.8    0.0
41.9    0.0
42.0    0.0
42.1    0.0
42.2    0.0
42.3    0.0
42.4    0.0
42.5    0.0
42.6    0.0
42.7    0.0
42.8    0.0
42.9    0.0
43.0    0.0
43.1    0.0
43.2    0.0
43.3    0.0
43.4    0.0
43.5    0.0
43.6    0.0
43.7    0.0
43.8    0.0
43.9    0.0
44.0    0.0
44.1    0.0
44.2    0.0
44.3    0.0
44.4    0.0
44.5    0.0
44.6    0.0
44.7    0.0
44.8    0.0
44.9    0.0
45.0    0.0
45.1    0.0
45.2    0.0
45.3    0.0
45.4    0.0
45.5    0.0
45.6    0.0
45.7    0.0
45.8    0.0
45.9    0.0
46.0    0.0
46.1    0.0
46.2    0.0
46.3    0.0
46.4    0.0
46.5    0.0
46.6    0.0
46.7    0.0
46.8    0.0
46.9    0.0
47.0    1.0
47.1    5.0
47.2    18.0
47.3    58.0
47.4    155.0
47.5    351.0
47.6    670.0
47.7    1079.0
47.8    1463.0
47.9    1672.0
48.0    1610.0
48.1    1307.0
48.2    894.0
48.3    515.0
48.4    250.0
48.5    103.0
48.6    35.0
48.7    10.0
48.8    3.0
48.9    1.0
49.0    0.0
49.1    0.0
49.2    0.0
49.3    1.0
49.4    2.0
49.5    8.0
49.6    25.0
49.7    63.0
49.8    135.0
49.9    244.0
50.0    374.0
50.1    483.0
50.2    528.0
50.3    488.0
50.4    382.0
50.5    252.0
50.6    141.0
50.7    66.0
50.8    26.0
50.9    9.0
51.0    3.0
51.1    1.0
51.2    0.0
51.3    0.0
51.4    0.0
51.5    0.0
51.6    0.0
51.7    0.0
51.8    0.0
51.9    0.0
52.0    0.0
52.1    0.0
52.2    0.0
52.3    0.0
52.4    0.0
52.5    0.0
52.6    0.0
52.7    0.0
52.8    0.0
52.9    0.0
53.0    0.0
53.1    0.0
53.2    0.0
53.3    0.0
53.4    0.0
53.5    0.0
53.6    0.0
53.7    0.0
53.8    0.0
53.9    0.0
54.0    0.0
54.1    0.0
54.2    0.0
54.3    0.0
54.4    0.0
54.5    0.0
54.6    0.0
54.7    0.0
54.8    0.0
54.9    0.0
55.0    0.0
55.1    0.0
55.2    0.0
55.3    0.0
55.4    0.0
55.5    0.0
55.6    0.0
55.7    0.0
55.8    0.0
55.9    0.0
56.0    0.0
56.1    0.0
56.2    0.0
56.3    0.0
56.4    0.0
56.5    0.0
56.6    0.0
56.7    0.0
56.8    0.0
56.9    0.0
57.0    0.0
57.1    0.0
57.2    0.0
57.3    0.0
57.4    0.0
57.5    0.0
57.6    0.0
57.7    0.0
57.8    1.0
57.9    3.0
58.0    10.0
58.1    27.0
58.2    60.0
58.3    113.0
58.4    184.0
58.5    256.0
58.6    305.0
58.7    310.0
58.8    270.0
58.9    202.0
59.0    129.0
59.1    70.0
59.2    33.0
59.3    13.0
59.4    4.0
59.5    1.0
59.6    0.0
59.7    0.0
59.8    0.0
59.9    0.0
60.0    0.0
60.1    0.0
60.2    0.0
60.3    0.0
60.4    0.0
60.5    0.0
60.6    0.0
60.7    0.0
60.8    0.0
60.9    0.0
61.0    0.0
61.1    0.0
61.2    0.0
61.3    0.0
61.4    0.0
61.5    0.0
61.6    0.0
61.7    0.0
61.8    0.0
61.9    0.0
62.0    0.0
62.1    0.0
62.2    0.0
62.3    0.0
62.4    0.0
62.5    0.0
62.6    0.0
62.7    0.0
62.8    0.0
62.9    0.0
63.0    0.0
63.1    0.0
63.2    0.0
63.3    0.0
63.4    0.0
63.5    0.0
63.6    0.0
63.7    0.0
63.8    0.0
63.9    0.0
64.0    0.0
64.1    0.0
64.2    0.0
64.3    0.0
64.4    0.0
64.5    0.0
64.6    0.0
64.7    0.0
64.8    0.0
64.9    0.0
65.0    0.0
65.1    0.0
65.2    0.0
65.3    0.0
65.4    0.0
65.5    0.0
65.6    0.0
65.7    0.0
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66.0    0.0
66.1    0.0
66.2    0.0
66.3    0.0
66.4    0.0
66.5    0.0
66.6    0.0
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67.0    0.0
67.1    0.0
67.2    0.0
67.3    0.0
67.4    0.0
67.5    0.0
67.6    0.0
67.7    0.0
67.8    0.0
67.9    0.0
68.0    0.0
68.1    0.0
68.2    0.0
68.3    0.0
68.4    0.0
68.5    0.0
68.6    0.0
68.7    0.0
68.8    0.0
68.9    0.0
69.0    0.0
69.1    0.0
69.2    0.0
69.3    0.0
69.4    0.0
69.5    0.0
69.6    0.0
69.7    0.0
69.8    0.0
69.9    0.0
70.0    0.0
70.1    0.0
70.2    0.0
70.3    0.0
70.4    0.0
70.5    0.0
70.6    0.0
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71.0    0.0
71.1    0.0
71.2    0.0
71.3    0.0
71.4    0.0
71.5    0.0
71.6    0.0
71.7    0.0
71.8    0.0
71.9    0.0
72.0    0.0
72.1    0.0
72.2    0.0
72.3    0.0
72.4    0.0
72.5    0.0
72.6    0.0
72.7    0.0
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72.9    0.0
73.0    0.0
73.1    0.0
73.2    0.0
73.3    0.0
73.4    0.0
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73.6    0.0
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74.0    0.0
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74.3    0.0
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76.1    0.0
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77.0    0.0
77.1    0.0
77.2    0.0
77.3    0.0
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77.6    0.0
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77.9    0.0
78.0    0.0
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78.2    0.0
78.3    0.0
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81.0    0.0
81.1    0.0
81.2    0.0
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86.3    0.0
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89.1    0.0
89.2    0.0
89.3    0.0
89.4    0.0
89.5    0.0
89.6    0.0
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90.1    0.0
90.2    0.0
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90.4    0.0
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91.0    0.0
91.1    0.0
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91.6    0.0
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92.2    0.0
92.3    0.0
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92.5    0.0
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93.0    0.0
93.1    0.0
93.2    0.0
93.3    0.0
93.4    0.0
93.5    0.0
93.6    0.0
93.7    0.0
93.8    0.0
93.9    0.0
94.0    0.0
94.1    0.0
94.2    0.0
94.3    0.0
94.4    0.0
94.5    0.0
94.6    0.0
94.7    0.0
94.8    0.0
94.9    0.0
95.0    0.0
95.1    0.0
95.2    0.0
95.3    0.0
95.4    0.0
95.5    0.0
95.6    0.0
95.7    0.0
95.8    0.0
95.9    0.0
96.0    0.0
96.1    0.0
96.2    0.0
96.3    0.0
96.4    0.0
96.5    0.0
96.6    0.0
96.7    0.0
96.8    0.0
96.9    0.0
97.0    0.0
97.1    0.0
97.2    0.0
97.3    0.0
97.4    0.0
97.5    0.0
97.6    0.0
97.7    0.0
97.8    0.0
97.9    0.0
98.0    0.0
98.1    0.0
98.2    0.0
98.3    0.0
98.4    0.0
98.5    0.0
98.6    0.0
98.7    0.0
98.8    0.0
98.9    0.0
99.0    0.0
99.1    0.0
99.2    0.0
99.3    0.0
99.4    0.0
99.5    0.0
99.6    0.0
99.7    0.0
99.8    0.0
99.9    0.0
100.0   0.0
100.1   0.0
100.2   0.0
100.3   0.0
100.4   0.0
100.5   0.0
100.6   0.0
100.7   0.0
100.8   0.0
100.9   0.0
101.0   0.0
101.1   0.0
101.2   0.0
101.3   0.0
101.4   0.0
101.5   0.0
101.6   0.0
101.7   0.0
101.8   0.0
101.9   0.0
102.0   0.0
102.1   0.0
102.2   0.0
102.3   0.0
102.4   0.0
102.5   0.0
102.6   0.0
102.7   0.0
102.8   0.0
102.9   0.0
103.0   0.0
103.1   0.0
103.2   0.0
103.3   0.0
103.4   0.0
103.5   0.0
103.6   0.0
103.7   0.0
103.8   0.0
103.9   0.0
104.0   0.0
104.1   0.0
104.2   0.0
104.3   0.0
104.4   0.0
104.5   0.0
104.6   0.0
104.7   0.0
104.8   0.0
104.9   0.0
105.0   0.0
105.1   0.0
105.2   0.0
105.3   0.0
105.4   0.0
105.5   0.0
105.6   0.0
105.7   0.0
105.8   0.0
105.9   0.0
106.0   0.0
106.1   0.0
106.2   0.0
106.3   0.0
106.4   0.0
106.5   0.0
106.6   0.0
106.7   0.0
106.8   0.0
106.9   0.0
107.0   0.0
107.1   0.0
107.2   0.0
107.3   0.0
107.4   0.0
107.5   0.0
107.6   0.0
107.7   0.0
107.8   0.0
107.9   0.0
108.0   0.0
108.1   0.0
108.2   0.0
108.3   0.0
108.4   0.0
108.5   0.0
108.6   0.0
108.7   0.0
108.8   0.0
108.9   0.0
109.0   0.0
109.1   0.0
109.2   0.0
109.3   0.0
109.4   0.0
109.5   0.0
109.6   0.0
109.7   0.0
109.8   0.0
109.9   0.0
110.0   0.0

Answer 1

是的，这是一种合适的方法。

首先，一些代码可以帮助您入门（我调整了代码）：

import matplotlib.pyplot as plt
import numpy as np
import math
figure = plt.figure()

def Gaussian(x, Geoms):
    # Geoms is a n X 3 array:
    # Position, Amplitude,  FWHM

    #Geoms = [[2,.00001,3][1,17,.2]]

    Position = Geoms[:, 0]
    Amplitude = Geoms[:, 1]
    FWHM = Geoms[:, 2]

    y = np.zeros(x.shape)

    for i in np.arange(0, Amplitude.shape[0]):
        g = Position[i]; q = (x-g); v = FWHM[i]
        q = q*q; v = v*v
        y += (Amplitude[i] * np.exp(-q/(v*np.sqrt(2))))

    return y

count = 0
with open("chan.txt", "r") as metapart:
    for line in metapart:
        if "&END" in line:
            break
        count = count + 1
    print count
    print metapart.next()
    metapart.seek(0)
    data = np.genfromtxt(metapart, skiprows=11)
    print data
metapart.close()
x_data = data[:,0]
y_data = data[:,1]

# Build several gaussian peaks.
FWHM = 0.25
Geoms = np.array([[24.48, 922, FWHM], [28.35, 644, FWHM]])
y_fit = Gaussian(x_data, Geoms)

# I recommend keeping your data in its original form.  Only display it logarithmically if you want.
plt.semilogy(x_data,y_data, 'r', x_data, y_fit, 'b')
# And you may want to control the plot range.
plt.axis([0,100, 1,1000])
plt.show()

我添加了我不久前写的一个叫做高斯的函数，该函数会生成您的峰值。 抱歉，最好不要评论，但使用起来应该很简单。 它需要numpy输入。

其次，我删除了for循环以将数据截断为0。（如果需要，可以在以后查看numpy.clip（） ）。 我建议您将数据保留为原始格式，并仅以对数形式绘制 。 如果要进行拟合，则尤其如此，因为否则必须编写log_gaussian函数等，否则会造成混淆。

在这里，我只是手动添加了前两个高斯。 当然，您可以手动放置其余部分并调整值，直到它们在几分钟内与数据数字化所带来的不确定性一样准确为止。 如果您只做一次，那就是我要做的。 但是，如果您不止一次这样做，或者形状更复杂，我将花时间学习SciPy优化模块 。 您可能想从SciPy教程开始。 使用ScyPy Optimize的过程更长，但它会获得利息和股利以及许多股票分割的回报。

请享用！