Update autoreduce/reduce_HB2C.py autoreduce/reduce_HB2C2.py

autoreduce/reduce_HB2C.py

@@ -2,6 +2,7 @@
 import os
 import sys
 import h5py
+from postprocessing.publish_plot import publish_plot
 
 filename = sys.argv[1]
 output_file = os.path.split(filename)[-1].replace('.nxs.h5', '')
@@ -13,7 +14,7 @@ with h5py.File(filename, 'r') as f:
     if '/entry/DASlogs/HB2C:CS:ITEMS:Nature' in f:
         nature = f['/entry/DASlogs/HB2C:CS:ITEMS:Nature/value'].value[0][0]
         if nature == 'Powder':
-            powder = False
+            powder = True
 
 if powder:
 
@@ -29,7 +30,6 @@ if powder:
                         NumberBins=1000,
                         OutputWorkspace='reduced')
     div = SavePlot1D('reduced', OutputType='plotly')
-    from postprocessing.publish_plot import publish_plot
     request = publish_plot('HB2C', runNumber, files={'file': div})
 
 else:  # Single Crystal

autoreduce/reduce_HB2C2.py

+#!/usr/bin/env python2
+import os
+import sys
+import h5py
+try:
+    from postprocessing.publish_plot import publish_plot, plot_heatmap
+except ImportError:
+    from finddata import publish_plot
+
+filename = sys.argv[1]
+output_file = os.path.split(filename)[-1].replace('.nxs.h5', '')
+outdir = sys.argv[2]
+
+powder = False
+
+with h5py.File(filename, 'r') as f:
+    if '/entry/DASlogs/HB2C:CS:ITEMS:Nature' in f:
+        nature = f['/entry/DASlogs/HB2C:CS:ITEMS:Nature/value'].value[0][0]
+        if nature == 'Powder':
+            powder = True
+
+if powder:
+
+    sys.path.append("/opt/mantidnightly/bin")
+    from mantid.simpleapi import LoadWAND, WANDPowderReduction, SavePlot1D
+
+    data = LoadWAND(filename)
+    runNumber = data.getRunNumber()
+    cal = LoadWAND(IPTS=7776, RunNumbers=101567)
+    WANDPowderReduction(InputWorkspace=data,
+                        CalibrationWorkspace=cal,
+                        Target='Theta',
+                        NumberBins=1000,
+                        OutputWorkspace='reduced')
+    div = SavePlot1D('reduced', OutputType='plotly')
+    request = publish_plot('HB2C', runNumber, files={'file': div})
+
+else:  # Single Crystal
+
+    from plotly.offline import plot
+    import plotly.graph_objs as go
+    import numpy as np
+    with h5py.File(filename, 'r') as f:
+        offset = f['/entry/DASlogs/HB2C:Mot:s2.RBV/average_value'].value[0]
+        title = f['/entry/title'].value[0]
+        mon = f['/entry/monitor1/total_counts'].value[0]
+        duration = f['/entry/duration'].value[0]
+        run_number = f['/entry/run_number'].value[0]
+        bc = np.zeros((512*480*8))
+        for b in range(8):
+            bc += np.bincount(f['/entry/bank'+str(b+1)+'_events/event_id'].value,
+                              minlength=512*480*8)
+        bc = bc.reshape((480*8, 512))
+        bc = (bc[::4, ::4] + bc[1::4, ::4] + bc[2::4, ::4] + bc[3::4, ::4]
+              + bc[::4, 1::4] + bc[1::4, 1::4] + bc[2::4, 1::4] + bc[3::4, 1::4]
+              + bc[::4, 2::4] + bc[1::4, 2::4] + bc[2::4, 2::4] + bc[3::4, 2::4]
+              + bc[::4, 3::4] + bc[1::4, 3::4] + bc[2::4, 3::4] + bc[3::4, 3::4])
+
+    vanadium = np.load('/HFIR/HB2C/shared/autoreduce/vanadium_101567.npy')
+    vanadium_mon = 163519902
+    bc = bc / vanadium * vanadium_mon / mon
+
+    plot_heatmap(run_number,
+                 np.linspace(120+offset,offset,960), np.arange(0,128), bc.T,
+                 x_title=u'2theta', instrument='HB2C')