Update autoreduce/reduce_HB2C.py

autoreduce/reduce_HB2C.py

@@ -4,16 +4,16 @@ import sys
 import h5py
 
 filename = sys.argv[1]
-output_file=os.path.split(filename)[-1].replace('.nxs.h5','')
+output_file = os.path.split(filename)[-1].replace('.nxs.h5', '')
 outdir = sys.argv[2]
 
-powder=False
+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=False
+            powder = False
 
 if powder:
 
@@ -22,7 +22,7 @@ if powder:
 
     data = LoadWAND(filename)
     runNumber = data.getRunNumber()
-    cal = LoadWAND(IPTS=7776,RunNumbers=29780)
+    cal = LoadWAND(IPTS=7776, RunNumbers=101567)
     WANDPowderReduction(InputWorkspace=data,
                         CalibrationWorkspace=cal,
                         Target='Theta',
@@ -32,39 +32,39 @@ if powder:
     from postprocessing.publish_plot import publish_plot
     request = publish_plot('HB2C', runNumber, files={'file': div})
 
-else: # Single Crystal
+else:  # Single Crystal
 
     import matplotlib as mpl
-    mpl.use( "agg" )
+    mpl.use("agg")
     import matplotlib.pyplot as plt
-    from matplotlib.colors import LogNorm
     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]
+        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]
         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 += 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])
+        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
-    f, (ax1, ax2) = plt.subplots(2, figsize=(8,4))
-    ax1.set_title(u'{}, {}, s2={:.2f}, duration={:.1f}s'.format(title,output_file,offset,duration))
-    ax1.plot(np.linspace(offset,120+offset,960),bc.sum(1)[::-1])
-    ax1.set_xlim(offset,120+offset)
+    f, (ax1, ax2) = plt.subplots(2, figsize=(8, 4))
+    ax1.set_title(u'{}, {}, s2={:.2f}, duration={:.1f}s'.format(title, output_file, offset, duration))
+    ax1.plot(np.linspace(offset, 120+offset, 960), bc.sum(1)[::-1])
+    ax1.set_xlim(offset, 120+offset)
     plt.setp(ax1.get_xticklabels(), visible=False)
-    im = ax2.imshow(bc.T[::-1,::-1], cmap='viridis',aspect=1/7.5,extent=(offset,120+offset,0,128))
+    im = ax2.imshow(bc.T[::-1, ::-1], cmap='viridis', aspect=1/7.5, extent=(offset, 120+offset, 0, 128))
     ax2.set_xlabel(u'2theta')
-    ax2.set_xlim(offset,120+offset)
-    ax2.set_ylim(0,128)
+    ax2.set_xlim(offset, 120+offset)
+    ax2.set_ylim(0, 128)
     ax2.get_yaxis().set_visible(False)
     cb = f.colorbar(im)
     ax2.set_aspect('auto')