aboutsummaryrefslogtreecommitdiff
path: root/scripts/detector-shift
blob: ce3bf591b73b828a812c92e49339aa71a1e288c2 (plain)
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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Determine mean detector shift based on prediction refinement results
#
# Copyright © 2015-2018 Deutsches Elektronen-Synchrotron DESY,
#                       a research centre of the Helmholtz Association.
#
# Author:
#    2015-2018 Thomas White <taw@physics.org>
#    2016      Mamoru Suzuki <mamoru.suzuki@protein.osaka-u.ac.jp>
#    2018      Chun Hong Yoon
#

import sys
import os
import re
import numpy as np
import matplotlib.pyplot as plt

if sys.argv[1] == "-":
    f = sys.stdin
else:
    f = open(sys.argv[1], 'r')

if len(sys.argv) > 2:
    geom = sys.argv[2]
    have_geom = 1
else:
    have_geom = 0

# Determine the mean shifts
x_shifts = []
y_shifts = []
z_shifts = []

prog1 = re.compile("^predict_refine/det_shift\sx\s=\s([0-9\.\-]+)\sy\s=\s([0-9\.\-]+)\smm$")
prog2 = re.compile("^predict_refine/clen_shift\s=\s([0-9\.\-]+)\smm$")

while True:

    fline = f.readline()
    if not fline:
        break

    match = prog1.match(fline)
    if match:
        xshift = float(match.group(1))
        yshift = float(match.group(2))
        x_shifts.append(xshift)
        y_shifts.append(yshift)

    match = prog2.match(fline)
    if match:
        zshift = float(match.group(1))
        z_shifts.append(zshift)

f.close()

mean_x = sum(x_shifts) / len(x_shifts)
mean_y = sum(y_shifts) / len(y_shifts)
print('Mean shifts: dx = {:.2} mm,  dy = {:.2} mm'.format(mean_x,mean_y))
print('Shifts will be applied to geometry file when you close the graph window')
print('Click anywhere on the graph to override the detector shift')

def plotNewCentre(x, y):
    circle1 = plt.Circle((x,y),.1,color='r',fill=False)
    fig.gca().add_artist(circle1)
    plt.plot(x, y, 'b8', color='m')
    plt.grid(True)

def onclick(event):
    print('New shifts: dx = {:.2} mm,  dy = {:.2} mm'.format(event.xdata, event.ydata))
    print('Shifts will be applied to geometry file when you close the graph window')
    mean_x = event.xdata
    mean_y = event.ydata
    plotNewCentre(mean_x, mean_y)

nbins = 200
H, xedges, yedges = np.histogram2d(x_shifts,y_shifts,bins=nbins)
H = np.rot90(H)
H = np.flipud(H)
Hmasked = np.ma.masked_where(H==0,H)

# Plot 2D histogram using pcolor
plt.ion()
fig2 = plt.figure()
cid = fig2.canvas.mpl_connect('button_press_event', onclick)
plt.pcolormesh(xedges,yedges,Hmasked)
plt.title('Detector shifts according to prediction refinement')
plt.xlabel('x shift / mm')
plt.ylabel('y shift / mm')
plt.plot(0, 0, 'bH', color='c')
fig = plt.gcf()
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
plotNewCentre(mean_x, mean_y)
plt.show(block=True)

# Apply shifts to geometry
if have_geom:

    out = os.path.splitext(geom)[0]+'-predrefine.geom'
    print('Applying corrections to {}, output filename {}'.format(geom,out))
    g = open(geom, 'r')
    h = open(out, 'w')
    panel_resolutions = {}

    prog1 = re.compile("^\s*res\s+=\s+([0-9\.]+)\s")
    prog2 = re.compile("^\s*(.*)\/res\s+=\s+([0-9\.]+)\s")
    prog3 = re.compile("^\s*(.*)\/corner_x\s+=\s+([0-9\.\-]+)\s")
    prog4 = re.compile("^\s*(.*)\/corner_y\s+=\s+([0-9\.\-]+)\s")
    default_res = 0
    while True:

        fline = g.readline()
        if not fline:
            break

        match = prog1.match(fline)
        if match:
            default_res = float(match.group(1))
            h.write(fline)
            continue

        match = prog2.match(fline)
        if match:
            panel = match.group(1)
            panel_res = float(match.group(2))
            default_res =  panel_res
            panel_resolutions[panel] = panel_res
            h.write(fline)
            continue

        match = prog3.match(fline)
        if match:
            panel = match.group(1)
            panel_cnx = float(match.group(2))
            if panel in panel_resolutions:
                res = panel_resolutions[panel]
            else:
                res = default_res
                print('Using default resolution ({} px/m) for panel {}'.format(res, panel))
            h.write('%s/corner_x = %f\n' % (panel,panel_cnx+(mean_x*res*1e-3)))
            continue

        match = prog4.match(fline)
        if match:
            panel = match.group(1)
            panel_cny = float(match.group(2))
            if panel in panel_resolutions:
                res = panel_resolutions[panel]
            else:
                res = default_res
                print('Using default resolution ({} px/m) for panel {}'.format(res, panel))
            h.write('%s/corner_y = %f\n' % (panel,panel_cny+(mean_y*res*1e-3)))
            continue

        h.write(fline)

    g.close()
    h.close()