tools/RAW_dat_parser.py at master · lixx11/tools · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
"""
Usage:
    RAW_dat_parser.py <dat_files>... --ref=<ref_dat> [options]

Options:
    -h --help                           Show this screen.
    --ref=<ref_dat>                     Reference RAW dat file.
    --qmin=<qmin>                       qmin to calculate scaling ratio [default: 0].
    --qmax=<qmax>                       qmax to calculate scaling ratio [default: 1E10].
    --diff-mode=<diff_mode>             Plot difference in absolute scale or relative scale [default: relative].
    --smooth=<smooth>                   Smooth diff-curves or not [default: True].
    --window-length=<window_length>     Window length parameter for smoothing [default: 7].
    --poly-order=<poly_order>           The order of the polynomial used to fit the samples [default: 3].
"""

import numpy as np
from docopt import docopt
import matplotlib.pyplot as plt
from scipy.stats import linregress
from scipy.signal import savgol_filter


def load_RAW_dat(filepath):
    f = open(filepath, 'r')
    qs = []  # q
    Is = []  # intensity
    Es = []  # error
    while True:
        line = f.readline()
        if line:
            if line[:10] == '### HEADER':
                # print('processing HEADER section')
                pass
            elif line[:8] == '### DATA':
                # print('processing DATA section')
                for i in range(3):
                    f.readline()
                while True:
                    line = f.readline()
                    data = line.split()
                    if len(data) == 3:
                        qs.append(float(data[0]))
                        Is.append(float(data[1]))
                        Es.append(float(data[2]))
                    else:
                        break
        else:
            break
    qs = np.asarray(qs)
    Is = np.asarray(Is)
    Es = np.asarray(Es)
    return qs, Is, Es


if __name__ == '__main__':
    # add signal to enable CTRL-C
    import signal
    signal.signal(signal.SIGINT, signal.SIG_DFL)
    argv = docopt(__doc__)
    print argv
    ref_dat = argv['--ref']
    raw_dats = argv['<dat_files>']
    qmin = float(argv['--qmin'])
    qmax = float(argv['--qmax'])
    diff_mode = argv['--diff-mode']
    smooth = argv['--smooth']
    window_length = int(argv['--window-length'])
    poly_order = int(argv['--poly-order'])
    ref_qs, ref_Is, ref_Es = load_RAW_dat(ref_dat)
    qmin_id = np.argmin(np.abs(ref_qs - qmin))
    qmax_id = np.argmin(np.abs(ref_qs - qmax))
    data_list = []
    for raw_dat in raw_dats:
        filename = raw_dat.split('/')[-1]
        qs, Is, Es = load_RAW_dat(raw_dat)
        data_dict = {}
        data_dict['filename'] = filename
        data_dict['qs'] = qs
        data_dict['Is'] = Is
        data_dict['Es'] = Es
        data_dict['abs_diff'] = Is - ref_Is
        data_dict['rel_diff'] = (Is - ref_Is) / ref_Is
        scaling_factor =  ref_Is[qmin_id:qmax_id].sum() / Is[qmin_id:qmax_id].sum()
        data_dict['scaling_Is'] = scaling_factor * Is
        data_dict['abs_scaling_diff'] = scaling_factor * Is - ref_Is
        data_dict['rel_scaling_diff'] = (scaling_factor * Is - ref_Is) / ref_Is
        data_list.append(data_dict)
        print('scaling factor for %s: %.3f' % (raw_dat, scaling_factor))

    # making plots
    fig1 = plt.figure()
    plt.subplot(221)
    for data in data_list:
        plt.semilogy(data['qs'], data['Is'], label=data['filename'])
    plt.legend()
    plt.title('SAXS profiles before scaling')

    plt.subplot(222)
    for data in data_list:
        plt.semilogy(data['qs'], data['scaling_Is'], label=data['filename'])
    plt.legend()
    plt.title('SAXS profiles after scaling')

    plt.subplot(223)
    for data in data_list:
        x = data['qs']
        if diff_mode == 'absolute':
            if smooth == 'False':
                y = data['abs_diff']
            elif smooth == 'True':
                y = savgol_filter(data['abs_diff'], window_length, poly_order)
            else:
                print('Wrong smooth flag! True of False')
        elif diff_mode == 'relative':
            if smooth == 'False':
                y = data['abs_diff']
            elif smooth == 'True':
                y = savgol_filter(data['rel_diff'], window_length, poly_order)
            else:
                print('Wrong smooth flag! True of False')
            plt.plot(x, y, label=data['filename'])
    plt.legend()
    plt.title('%s difference before scaling' % diff_mode)

    plt.subplot(224)
    for data in data_list:
        x = data['qs']
        if diff_mode == 'absolute':
            if smooth == 'False':
                y = data['abs_scaling_diff']
            elif smooth == 'True':
                y = savgol_filter(data['abs_scaling_diff'], window_length, poly_order)
            else:
                print('Wrong smooth flag! True of False')
        elif diff_mode == 'relative':
            if smooth == 'False':
                y = data['rel_scaling_diff']
            elif smooth == 'True':
                y = savgol_filter(data['rel_scaling_diff'], window_length, poly_order)
            else:
                print('Wrong smooth flag! True of False')
            plt.plot(x, y, label=data['filename'])
    plt.legend()
    plt.title('%s difference after scaling' % diff_mode)

    plt.show()