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+#!/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()
+