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#!/usr/bin/env python
#
# Determine mean detector shift based on prediction refinement results
#
# Copyright (c) 2015-2016 Deutsches Elektronen-Synchrotron DESY,
# a research centre of the Helmholtz Association.
#
# Author:
# 2015-2016 Thomas White <taw@physics.org>
# 2016 Marmoru Suzuki <mamoru.suzuki@protein.osaka-u.ac.jp>
#
import sys
import os
import re
import numpy as np
import matplotlib.pyplot as plt
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 = %.2f mm, dy = %.2f mm' % (mean_x,mean_y)
# Apply shifts to geometry
if have_geom:
out = os.path.splitext(geom)[0]+'-predrefine.geom'
print 'Applying corrections to %s, output filename %s' % (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 (%f px/m) for panel %s' % (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 (%f px/m) for panel %s' % (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()
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
fig2 = plt.figure()
plt.pcolormesh(xedges,yedges,Hmasked)
plt.title('Detector shifts according to prediction refinement')
plt.xlabel('x shift / mm')
plt.ylabel('y shift / mm')
circle1 = plt.Circle((mean_x,mean_y),.1,color='r',fill=False)
fig = plt.gcf()
fig.gca().add_artist(circle1)
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
plt.plot(0, 0, 'bH', color='c')
plt.plot(mean_x, mean_y, 'b8', color='m')
plt.grid(True)
plt.show()
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