diff --git a/standalone_scripts/5kHz.py b/standalone_scripts/5kHz.py
index 16ae7f4..66f0968 100755
--- a/standalone_scripts/5kHz.py
+++ b/standalone_scripts/5kHz.py
@@ -3,20 +3,20 @@
 import numpy as np
 
 run = eval(sys.argv[1])
-ds = DataSource(exp='rixx45619',run=run)
+ds = DataSource(exp="rixx45619", run=run)
 myrun = next(ds.runs())
-det = myrun.Detector('epixhr')
-timing = myrun.Detector('timing')
+det = myrun.Detector("epixhr")
+timing = myrun.Detector("timing")
 
-print('pedestal means, 7 modes:',det.calibconst['pedestals'][0].mean(axis=(1,2,3)))
-print('pedestal maxes, 7 modes:',det.calibconst['pedestals'][0].max(axis=(1,2,3)))
+print("pedestal means, 7 modes:", det.calibconst["pedestals"][0].mean(axis=(1, 2, 3)))
+print("pedestal maxes, 7 modes:", det.calibconst["pedestals"][0].max(axis=(1, 2, 3)))
 
 fl = []
 n = 0
 n20 = []
 
 
-desiredCode = {'120Hz':272, '4kHz':273, '5kHz':274}
+desiredCode = {"120Hz": 272, "4kHz": 273, "5kHz": 274}
 
 lastEventWas120Hz = False
 nFoo = 0
@@ -26,35 +26,35 @@
     if lastEventWas120Hz:
         ## do something
         pass
-    if not allcodes[desiredCode['120Hz']]:
-##        print("found", allcodes, "not", desiredCode)
-##        print("no 120Hz")
+    if not allcodes[desiredCode["120Hz"]]:
+        ##        print("found", allcodes, "not", desiredCode)
+        ##        print("no 120Hz")
         mean = det.raw.raw(evt).mean()
-        print("mean for event %d after 120Hz %0.3f" %(nFoo, mean))
+        print("mean for event %d after 120Hz %0.3f" % (nFoo, mean))
 
-        np.save("not120_%d.npy" %(nFoo), det.raw.raw(evt))
+        np.save("not120_%d.npy" % (nFoo), det.raw.raw(evt))
         if lastEventWas120Hz:
-            print("mean for event after 120Hz %0.3f" %(mean))
-            np.save("lastEventWas120Hz_%d.npy" %(n120s-1), det.raw.raw(evt))
+            print("mean for event after 120Hz %0.3f" % (mean))
+            np.save("lastEventWas120Hz_%d.npy" % (n120s - 1), det.raw.raw(evt))
         nFoo += 1
         lastEventWas120Hz = False
         continue
     else:
-        print("found 120Hz after %d, %0.3f" %(nFoo, mean))
+        print("found 120Hz after %d, %0.3f" % (nFoo, mean))
         mean = det.raw.raw(evt).mean()
-        np.save("thisEventWas120Hz_%d.npy" %(n120s), det.raw.raw(evt))
-        print("mean now %0.3f" %(mean))
+        np.save("thisEventWas120Hz_%d.npy" % (n120s), det.raw.raw(evt))
+        print("mean now %0.3f" % (mean))
         nFoo = 0
         n120s += 1
         lastEventWas120Hz = True
-##136 means NEH got the beam
+    ##136 means NEH got the beam
 
     rawFrames = det.raw.raw(evt)
     frames = det.raw.calib(evt)
-    n20.append((frames>20).sum())
+    n20.append((frames > 20).sum())
     fl.append(frames)
     n += 1
-    if n%1000 == 0:
+    if n % 1000 == 0:
         print(n)
 fl = np.array(fl)
 print(n)
diff --git a/standalone_scripts/DamageCounter.py b/standalone_scripts/DamageCounter.py
index b85deaf..54fb43f 100644
--- a/standalone_scripts/DamageCounter.py
+++ b/standalone_scripts/DamageCounter.py
@@ -1,47 +1,49 @@
 from psana import DataSource
 import os
 import numpy as np
-os.environ['PS_SMD_N_EVENTS'] = '50'
-os.environ['PS_SRV_NODES']='1'
- 
-ds = DataSource(exp='rixc00121', run=32, intg_det='epixhr')
-smd = ds.smalldata(filename='ts.h5')
+
+os.environ["PS_SMD_N_EVENTS"] = "50"
+os.environ["PS_SRV_NODES"] = "1"
+
+ds = DataSource(exp="rixc00121", run=32, intg_det="epixhr")
+smd = ds.smalldata(filename="ts.h5")
 myrun = next(ds.runs())
-epix = myrun.Detector('epixhr')
-timeObj = myrun.Detector('timing')
-nevt=0
+epix = myrun.Detector("epixhr")
+timeObj = myrun.Detector("timing")
+nevt = 0
 nBeam = 0
-for nevt,evt in enumerate(myrun.events()):
+for nevt, evt in enumerate(myrun.events()):
     if evt is None:
         continue
     ec = timeObj.raw.eventcodes(evt)
     if not ec[281]:
         continue
     nBeam += 1
-    
+
     frames = epix.raw.raw(evt)
     if frames is None:
         continue
     smd.event(evt, ts=evt.datetime().timestamp(), nBeam=nBeam, nevt=nevt)
-    
+
 ##if smd.summary:
 ##    smd.save_summary(step_av) # this method accepts either a dictionary or kwargs
 smd.done()
 
 import matplotlib.pyplot as plt
 import h5py
+
 a = h5py.File("ts.h5")
-mona_ts = a['timestamp'][()]
-ts = a['ts'][()]
-print(False in (ts ==mona_ts))
-counter=np.arange(10000)
+mona_ts = a["timestamp"][()]
+ts = a["ts"][()]
+print(False in (ts == mona_ts))
+counter = np.arange(10000)
 ##plt.plot(counter[:400], ts[:400])
 ##plt.xlabel("entry")
 ##plt.ylabel("timestamp")
 ##plt.show()
 
 
-nBeam = a['nBeam'][()]
+nBeam = a["nBeam"][()]
 print("nBeam max:", nBeam.max())
-nevt = a['nevt'][()]
+nevt = a["nevt"][()]
 print("nevt max:", nevt.max())
diff --git a/standalone_scripts/MapCompEnOn.py b/standalone_scripts/MapCompEnOn.py
index b060950..fb5b626 100644
--- a/standalone_scripts/MapCompEnOn.py
+++ b/standalone_scripts/MapCompEnOn.py
@@ -8,7 +8,7 @@
 import os
 import math
 
-if len(sys.argv) != 3 or sys.argv[1] != '-f':
+if len(sys.argv) != 3 or sys.argv[1] != "-f":
     print("Usage: python MapCompEnOn.py -f filename")
     sys.exit(1)
 
@@ -17,7 +17,7 @@
 base_filename = os.path.splitext(os.path.basename(file_name))[0]
 
 # Open the HDF5 file
-with h5py.File(file_name, 'r') as file:
+with h5py.File(file_name, "r") as file:
     # Define the name of the group containing the datasets
     group_name = "slice"  # Change this to the actual group name
     dataset_names = list(file[group_name].keys())
@@ -36,82 +36,85 @@
 
 # Access and plot a specific slice from the combined data (e.g., the first slice)
 slice_to_display = combined_data[2, :, :]
-#plt.imshow(slice_to_display, cmap='viridis')  # Adjust the colormap as needed
-#plt.colorbar()  # Add a colorbar for reference
-#plt.show()
+# plt.imshow(slice_to_display, cmap='viridis')  # Adjust the colormap as needed
+# plt.colorbar()  # Add a colorbar for reference
+# plt.show()
 
 
 timeVec = [float(x) * 1 for x in dataset_names]
 
 
+rowsNum = combined_data.shape[1]
+colsNum = combined_data.shape[2]
 
-rowsNum=combined_data.shape[1]
-colsNum=combined_data.shape[2]
 
+X1 = np.zeros((rowsNum, colsNum))
+Y1 = np.zeros((rowsNum, colsNum))
+X2 = np.zeros((rowsNum, colsNum))
+Y2 = np.zeros((rowsNum, colsNum))
+X3 = np.zeros((rowsNum, colsNum))
+Y3 = np.zeros((rowsNum, colsNum))
 
-X1=np.zeros((rowsNum, colsNum))
-Y1=np.zeros((rowsNum, colsNum))
-X2=np.zeros((rowsNum, colsNum))
-Y2=np.zeros((rowsNum, colsNum))
-X3=np.zeros((rowsNum, colsNum))
-Y3=np.zeros((rowsNum, colsNum))
+Xinterp = np.linspace(min(timeVec), max(timeVec), 5000)
 
-Xinterp=np.linspace(min(timeVec),max(timeVec),5000)
-                           
-AvAll=np.mean(combined_data,axis=(1,2))
+AvAll = np.mean(combined_data, axis=(1, 2))
 
-#for ir in range(0,rowsNum):
+# for ir in range(0,rowsNum):
 #    for ic in range(0,colsNum):
-minir=0
-maxir=rowsNum
-minic=0
-maxic=colsNum
-for ir in range(minir,maxir):
-    for ic in range(minic,maxic):
-       # print('row :',ir)
-       # print('col :',ic)
-        
-#for ir in range(10,11):
-#    for ic in range(30,31):
-        timelineToDisplay=combined_data[:,ir,ic]
-        f = interp1d(timeVec, timelineToDisplay, kind='linear')
+minir = 0
+maxir = rowsNum
+minic = 0
+maxic = colsNum
+for ir in range(minir, maxir):
+    for ic in range(minic, maxic):
+        # print('row :',ir)
+        # print('col :',ic)
+
+        # for ir in range(10,11):
+        #    for ic in range(30,31):
+        timelineToDisplay = combined_data[:, ir, ic]
+        f = interp1d(timeVec, timelineToDisplay, kind="linear")
         y_new = f(Xinterp)
-        y_new_2ndpart = f(Xinterp[Xinterp>(np.mean(timeVec))])                
-        thediff=np.diff(y_new)
-        thediff_2ndpart=np.diff(y_new_2ndpart)
+        y_new_2ndpart = f(Xinterp[Xinterp > (np.mean(timeVec))])
+        thediff = np.diff(y_new)
+        thediff_2ndpart = np.diff(y_new_2ndpart)
         window_size = 150
         polynomial_order = 1
 
         # Apply Savitzky-Golay filtering
         smoothed_data = savgol_filter(thediff, window_size, polynomial_order)
-        differencialfiltered=np.array(smoothed_data)
-        
+        differencialfiltered = np.array(smoothed_data)
+
         smoothed_data_2ndpart = savgol_filter(thediff_2ndpart, window_size, polynomial_order)
-        differencialfiltered_2ndpart=np.array(smoothed_data_2ndpart)
-        X=Xinterp[0:-1]
-        Y=smoothed_data
-        X1[ir,ic]=Xinterp[np.argmax(smoothed_data)]
-        Y1[ir,ic]=y_new[np.argmax(smoothed_data)]
-        X2[ir,ic]=Xinterp[np.argmin(smoothed_data)]
-        Y2[ir,ic]=y_new[np.argmin(smoothed_data)]
-        X3[ir,ic]=min(X[Y==min(Y[len(Y)//2:-1])])
-        Y3[ir,ic]=y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])]
+        differencialfiltered_2ndpart = np.array(smoothed_data_2ndpart)
+        X = Xinterp[0:-1]
+        Y = smoothed_data
+        X1[ir, ic] = Xinterp[np.argmax(smoothed_data)]
+        Y1[ir, ic] = y_new[np.argmax(smoothed_data)]
+        X2[ir, ic] = Xinterp[np.argmin(smoothed_data)]
+        Y2[ir, ic] = y_new[np.argmin(smoothed_data)]
+        X3[ir, ic] = min(X[Y == min(Y[len(Y) // 2 : -1])])
+        Y3[ir, ic] = y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])]
 
         if False:
-            plt.subplot(3,1,1)
-            plt.plot(Xinterp,y_new,'k')        
-            plt.plot(Xinterp[np.argmin(smoothed_data)],y_new[np.argmin(smoothed_data)],'r*')
-            plt.plot(Xinterp[np.argmax(smoothed_data)],y_new[np.argmax(smoothed_data)],'b*')
-            plt.plot(min(X[Y==min(Y[len(Y)//2:-1])]),y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])],'y*')
-            
-            plt.subplot(3,1,2)
-            plt.hist(y_new,100)
-            plt.subplot(3,1,3)
-        
-            plt.plot(thediff,'b.')
-            plt.plot(smoothed_data,'r')
+            plt.subplot(3, 1, 1)
+            plt.plot(Xinterp, y_new, "k")
+            plt.plot(Xinterp[np.argmin(smoothed_data)], y_new[np.argmin(smoothed_data)], "r*")
+            plt.plot(Xinterp[np.argmax(smoothed_data)], y_new[np.argmax(smoothed_data)], "b*")
+            plt.plot(
+                min(X[Y == min(Y[len(Y) // 2 : -1])]),
+                y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])],
+                "y*",
+            )
+
+            plt.subplot(3, 1, 2)
+            plt.hist(y_new, 100)
+            plt.subplot(3, 1, 3)
+
+            plt.plot(thediff, "b.")
+            plt.plot(smoothed_data, "r")
             plt.show()
-        
+
 #        print(X1[ir,ic])
 #        print(X2[ir,ic])
 #        print(X3[ir,ic])
@@ -119,96 +122,97 @@
 #        print(Y2[ir,ic])
 #        print(Y3[ir,ic])
 #        print('%%%%%%%%%%%%%%%%%%%%%%')
-        
+
 fig = plt.figure()
-#fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(4, 12))  # Adjust figsize as needed
+# fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(4, 12))  # Adjust figsize as needed
 plt.suptitle("Click on figure to see the timeline of a pixel", fontsize=12)
 plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9)
-plt.subplot(3,1,1)
-#plt.imshow(X3,vmin=X3.min(),vmax=X3.max())
-plt.imshow(X3,vmin=(np.mean(X3)-1*(np.std(X3))),vmax=(np.mean(X3)+1*(np.std(X3))))
-plt.title('Onset transfer function')
+plt.subplot(3, 1, 1)
+# plt.imshow(X3,vmin=X3.min(),vmax=X3.max())
+plt.imshow(X3, vmin=(np.mean(X3) - 1 * (np.std(X3))), vmax=(np.mean(X3) + 1 * (np.std(X3))))
+plt.title("Onset transfer function")
 plt.colorbar()
-#plt.tight_layout()
+# plt.tight_layout()
 
-plt.subplot(3,1,2)
-plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
-plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
-plt.title('Onset CompEnOn')
+plt.subplot(3, 1, 2)
+plt.imshow(X2, vmin=X2.min(), vmax=X2.max())
+plt.imshow(X2, vmin=(np.mean(X2) - 1 * (np.std(X2))), vmax=(np.mean(X2) + 1 * (np.std(X2))))
+plt.title("Onset CompEnOn")
 plt.colorbar()
-#plt.tight_layout()
-
-plt.subplot(3,1,3)
-#plt.imshow(X3[minir:maxir,minic:maxic]-X1[minir:maxir,minic:maxic])
-X4=X3-X1
-#plt.imshow(X3-X1,vmin=(X3-X1).min(),vmax=(X3-X1).max())
-plt.imshow(X4,vmin=(np.mean(X4)-1*(np.std(X4))),vmax=(np.mean(X4)+1*(np.std(X4))))
-plt.title('Length transfer function')
+# plt.tight_layout()
+
+plt.subplot(3, 1, 3)
+# plt.imshow(X3[minir:maxir,minic:maxic]-X1[minir:maxir,minic:maxic])
+X4 = X3 - X1
+# plt.imshow(X3-X1,vmin=(X3-X1).min(),vmax=(X3-X1).max())
+plt.imshow(X4, vmin=(np.mean(X4) - 1 * (np.std(X4))), vmax=(np.mean(X4) + 1 * (np.std(X4))))
+plt.title("Length transfer function")
 plt.colorbar()
-#plt.tight_layout()
-
-#fig = plt.figure()
-#plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
-#plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
-#plt.title('Onset CompEnOn')
-#plt.colorbar()
-#plt.tight_layout()
+# plt.tight_layout()
 
+# fig = plt.figure()
+# plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
+# plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
+# plt.title('Onset CompEnOn')
+# plt.colorbar()
+# plt.tight_layout()
 
 
 # Save the plotted image to a PNG file with the filename
 save_path = "/sdf/data/lcls/ds/rix/rixx1003721/results/scan/{}_plot.png".format(base_filename)
 plt.savefig(save_path)
 
+
 def onclick(event):
     ix, iy = event.xdata, event.ydata
-    colsel= math.trunc(ix)
-    rowsel=math.trunc(iy)
-    #print(f'x = {ix}, y = {iy}')
+    colsel = math.trunc(ix)
+    rowsel = math.trunc(iy)
+    # print(f'x = {ix}, y = {iy}')
 
-    timelineToDisplay=combined_data[:,rowsel,colsel]
-    f = interp1d(timeVec, timelineToDisplay, kind='linear')
+    timelineToDisplay = combined_data[:, rowsel, colsel]
+    f = interp1d(timeVec, timelineToDisplay, kind="linear")
     y_new = f(Xinterp)
-    y_new_2ndpart = f(Xinterp[Xinterp>(np.mean(timeVec))])                
-    thediff=np.diff(y_new)
-    thediff_2ndpart=np.diff(y_new_2ndpart)
+    y_new_2ndpart = f(Xinterp[Xinterp > (np.mean(timeVec))])
+    thediff = np.diff(y_new)
+    thediff_2ndpart = np.diff(y_new_2ndpart)
     window_size = 150
     polynomial_order = 1
 
-        # Apply Savitzky-Golay filtering
+    # Apply Savitzky-Golay filtering
     smoothed_data = savgol_filter(thediff, window_size, polynomial_order)
-    differencialfiltered=np.array(smoothed_data)
-        
+    differencialfiltered = np.array(smoothed_data)
+
     smoothed_data_2ndpart = savgol_filter(thediff_2ndpart, window_size, polynomial_order)
-    differencialfiltered_2ndpart=np.array(smoothed_data_2ndpart)
-    X=Xinterp[0:-1]
-    Y=smoothed_data
-    X1[ir,ic]=Xinterp[np.argmax(smoothed_data)]
-    Y1[ir,ic]=y_new[np.argmax(smoothed_data)]
-    X2[ir,ic]=Xinterp[np.argmin(smoothed_data)]
-    Y2[ir,ic]=y_new[np.argmin(smoothed_data)]
-    X3[ir,ic]=min(X[Y==min(Y[len(Y)//2:-1])])
-    Y3[ir,ic]=y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])]
-#    plt.figure(10)
-#    plt.close(10)
-#    plt.subplots_adjust(left=0.2, bottom=0.2, right=1.0, top=1.0)
+    differencialfiltered_2ndpart = np.array(smoothed_data_2ndpart)
+    X = Xinterp[0:-1]
+    Y = smoothed_data
+    X1[ir, ic] = Xinterp[np.argmax(smoothed_data)]
+    Y1[ir, ic] = y_new[np.argmax(smoothed_data)]
+    X2[ir, ic] = Xinterp[np.argmin(smoothed_data)]
+    Y2[ir, ic] = y_new[np.argmin(smoothed_data)]
+    X3[ir, ic] = min(X[Y == min(Y[len(Y) // 2 : -1])])
+    Y3[ir, ic] = y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])]
+    #    plt.figure(10)
+    #    plt.close(10)
+    #    plt.subplots_adjust(left=0.2, bottom=0.2, right=1.0, top=1.0)
     plt.figure(10)
     plt.ion()
     plt.clf()
     plt.show(block=False)
-    plt.plot(Xinterp,y_new,'k')        
-    plt.plot(Xinterp[np.argmin(smoothed_data)],y_new[np.argmin(smoothed_data)],'r*')
-    plt.plot(Xinterp[np.argmax(smoothed_data)],y_new[np.argmax(smoothed_data)],'b*')
-    plt.plot(min(X[Y==min(Y[len(Y)//2:-1])]),y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])],'y*')
-    plt.title(f'Pixel:{colsel},{rowsel}')
+    plt.plot(Xinterp, y_new, "k")
+    plt.plot(Xinterp[np.argmin(smoothed_data)], y_new[np.argmin(smoothed_data)], "r*")
+    plt.plot(Xinterp[np.argmax(smoothed_data)], y_new[np.argmax(smoothed_data)], "b*")
+    plt.plot(
+        min(X[Y == min(Y[len(Y) // 2 : -1])]),
+        y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])],
+        "y*",
+    )
+    plt.title(f"Pixel:{colsel},{rowsel}")
     plt.show()
-    #print('Do you get there????')
-cid = fig.canvas.mpl_connect('button_press_event', onclick)
+    # print('Do you get there????')
 
 
+cid = fig.canvas.mpl_connect("button_press_event", onclick)
 
 
 plt.show()
-
-
-
diff --git a/standalone_scripts/MapCompEnOnThreshold.py b/standalone_scripts/MapCompEnOnThreshold.py
index 0d7068f..e0a8bcd 100644
--- a/standalone_scripts/MapCompEnOnThreshold.py
+++ b/standalone_scripts/MapCompEnOnThreshold.py
@@ -8,7 +8,7 @@
 import os
 import math
 
-if len(sys.argv) != 3 or sys.argv[1] != '-f':
+if len(sys.argv) != 3 or sys.argv[1] != "-f":
     print("Usage: python MapCompEnOn.py -f filename")
     sys.exit(1)
 
@@ -17,7 +17,7 @@
 base_filename = os.path.splitext(os.path.basename(file_name))[0]
 
 # Open the HDF5 file
-with h5py.File(file_name, 'r') as file:
+with h5py.File(file_name, "r") as file:
     # Define the name of the group containing the datasets
     group_name = "slice"  # Change this to the actual group name
     dataset_names = list(file[group_name].keys())
@@ -36,77 +36,76 @@
 
 # Access and plot a specific slice from the combined data (e.g., the first slice)
 slice_to_display = combined_data[2, :, :]
-#plt.imshow(slice_to_display, cmap='viridis')  # Adjust the colormap as needed
-#plt.colorbar()  # Add a colorbar for reference
-#plt.show()
+# plt.imshow(slice_to_display, cmap='viridis')  # Adjust the colormap as needed
+# plt.colorbar()  # Add a colorbar for reference
+# plt.show()
 
 
 timeVec = [float(x) * 1 for x in dataset_names]
 
 
+rowsNum = combined_data.shape[1]
+colsNum = combined_data.shape[2]
 
-rowsNum=combined_data.shape[1]
-colsNum=combined_data.shape[2]
 
+X1 = np.zeros((rowsNum, colsNum))
+Y1 = np.zeros((rowsNum, colsNum))
+X2 = np.zeros((rowsNum, colsNum))
+Y2 = np.zeros((rowsNum, colsNum))
+X3 = np.zeros((rowsNum, colsNum))
+Y3 = np.zeros((rowsNum, colsNum))
 
-X1=np.zeros((rowsNum, colsNum))
-Y1=np.zeros((rowsNum, colsNum))
-X2=np.zeros((rowsNum, colsNum))
-Y2=np.zeros((rowsNum, colsNum))
-X3=np.zeros((rowsNum, colsNum))
-Y3=np.zeros((rowsNum, colsNum))
+Xinterp = np.linspace(min(timeVec), max(timeVec), 5000)
 
-Xinterp=np.linspace(min(timeVec),max(timeVec),5000)
-                           
-AvAll=np.mean(combined_data,axis=(1,2))
+AvAll = np.mean(combined_data, axis=(1, 2))
 
-#for ir in range(0,rowsNum):
+# for ir in range(0,rowsNum):
 #    for ic in range(0,colsNum):
-minir=0
-maxir=rowsNum
-minic=0
-maxic=colsNum
-for ir in range(minir,maxir):
-    for ic in range(minic,maxic):
-       # print('row :',ir)
-       # print('col :',ic)
-        
-#for ir in range(10,11):
-#    for ic in range(30,31):
-        timelineToDisplay=combined_data[:,ir,ic]
-        f = interp1d(timeVec, timelineToDisplay, kind='linear')
+minir = 0
+maxir = rowsNum
+minic = 0
+maxic = colsNum
+for ir in range(minir, maxir):
+    for ic in range(minic, maxic):
+        # print('row :',ir)
+        # print('col :',ic)
+
+        # for ir in range(10,11):
+        #    for ic in range(30,31):
+        timelineToDisplay = combined_data[:, ir, ic]
+        f = interp1d(timeVec, timelineToDisplay, kind="linear")
         y_new = f(Xinterp)
-        y_new_2ndpart = f(Xinterp[Xinterp>(np.mean(timeVec))])                
-        thediff=np.diff(y_new)
-        thediff_2ndpart=np.diff(y_new_2ndpart)
+        y_new_2ndpart = f(Xinterp[Xinterp > (np.mean(timeVec))])
+        thediff = np.diff(y_new)
+        thediff_2ndpart = np.diff(y_new_2ndpart)
         window_size = 150
         polynomial_order = 1
 
         # Apply Savitzky-Golay filtering
         smoothed_data = savgol_filter(thediff, window_size, polynomial_order)
-        differencialfiltered=np.array(smoothed_data)
-        
+        differencialfiltered = np.array(smoothed_data)
+
         smoothed_data_2ndpart = savgol_filter(thediff_2ndpart, window_size, polynomial_order)
-        differencialfiltered_2ndpart=np.array(smoothed_data_2ndpart)
-        X=Xinterp[0:-1]
-        Y=smoothed_data
-        xx=Xinterp
-        yy=y_new
-        X1[ir,ic]=xx[np.argmax(smoothed_data)]
-        Y1[ir,ic]=yy[np.argmax(smoothed_data)]
-        X2[ir,ic]=xx[np.argmin(smoothed_data)]
-        Y2[ir,ic]=yy[np.argmin(smoothed_data)]
-        X3[ir,ic]=min(xx[yy<(np.min(yy)+(np.mean(yy)-np.min(yy))/2)])
-        Y3[ir,ic]=yy[xx==min(xx[yy<(np.min(yy)+(np.mean(yy)-np.min(yy))/2)])]
+        differencialfiltered_2ndpart = np.array(smoothed_data_2ndpart)
+        X = Xinterp[0:-1]
+        Y = smoothed_data
+        xx = Xinterp
+        yy = y_new
+        X1[ir, ic] = xx[np.argmax(smoothed_data)]
+        Y1[ir, ic] = yy[np.argmax(smoothed_data)]
+        X2[ir, ic] = xx[np.argmin(smoothed_data)]
+        Y2[ir, ic] = yy[np.argmin(smoothed_data)]
+        X3[ir, ic] = min(xx[yy < (np.min(yy) + (np.mean(yy) - np.min(yy)) / 2)])
+        Y3[ir, ic] = yy[xx == min(xx[yy < (np.min(yy) + (np.mean(yy) - np.min(yy)) / 2)])]
 
         if True:
-            plt.plot(xx,yy,'k')        
-            plt.plot(np.mean(xx),np.mean(yy)+(np.max(yy)-np.mean(yy))/2,'r*')
-            #plt.plot(np.mean(xx),np.min(yy)+(np.mean(yy)-np.min(yy))/2,'g*')
-            plt.plot(X3[ir,ic],Y3[ir,ic],'g*')
+            plt.plot(xx, yy, "k")
+            plt.plot(np.mean(xx), np.mean(yy) + (np.max(yy) - np.mean(yy)) / 2, "r*")
+            # plt.plot(np.mean(xx),np.min(yy)+(np.mean(yy)-np.min(yy))/2,'g*')
+            plt.plot(X3[ir, ic], Y3[ir, ic], "g*")
             plt.show()
-            
-        
+
+
 #        print(X1[ir,ic])
 #        print(X2[ir,ic])
 #        print(X3[ir,ic])
@@ -114,96 +113,97 @@
 #        print(Y2[ir,ic])
 #        print(Y3[ir,ic])
 #        print('%%%%%%%%%%%%%%%%%%%%%%')
-        
+
 fig = plt.figure()
-#fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(4, 12))  # Adjust figsize as needed
+# fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(4, 12))  # Adjust figsize as needed
 plt.suptitle("Click on figure to see the timeline of a pixel", fontsize=12)
 plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9)
-plt.subplot(3,1,1)
-#plt.imshow(X3,vmin=X3.min(),vmax=X3.max())
-plt.imshow(X3,vmin=(np.mean(X3)-1*(np.std(X3))),vmax=(np.mean(X3)+1*(np.std(X3))))
-plt.title('Onset transfer function')
+plt.subplot(3, 1, 1)
+# plt.imshow(X3,vmin=X3.min(),vmax=X3.max())
+plt.imshow(X3, vmin=(np.mean(X3) - 1 * (np.std(X3))), vmax=(np.mean(X3) + 1 * (np.std(X3))))
+plt.title("Onset transfer function")
 plt.colorbar()
-#plt.tight_layout()
+# plt.tight_layout()
 
-plt.subplot(3,1,2)
-plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
-plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
-plt.title('Onset CompEnOn')
+plt.subplot(3, 1, 2)
+plt.imshow(X2, vmin=X2.min(), vmax=X2.max())
+plt.imshow(X2, vmin=(np.mean(X2) - 1 * (np.std(X2))), vmax=(np.mean(X2) + 1 * (np.std(X2))))
+plt.title("Onset CompEnOn")
 plt.colorbar()
-#plt.tight_layout()
-
-plt.subplot(3,1,3)
-#plt.imshow(X3[minir:maxir,minic:maxic]-X1[minir:maxir,minic:maxic])
-X4=X3-X1
-#plt.imshow(X3-X1,vmin=(X3-X1).min(),vmax=(X3-X1).max())
-plt.imshow(X4,vmin=(np.mean(X4)-1*(np.std(X4))),vmax=(np.mean(X4)+1*(np.std(X4))))
-plt.title('Length transfer function')
+# plt.tight_layout()
+
+plt.subplot(3, 1, 3)
+# plt.imshow(X3[minir:maxir,minic:maxic]-X1[minir:maxir,minic:maxic])
+X4 = X3 - X1
+# plt.imshow(X3-X1,vmin=(X3-X1).min(),vmax=(X3-X1).max())
+plt.imshow(X4, vmin=(np.mean(X4) - 1 * (np.std(X4))), vmax=(np.mean(X4) + 1 * (np.std(X4))))
+plt.title("Length transfer function")
 plt.colorbar()
-#plt.tight_layout()
-
-#fig = plt.figure()
-#plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
-#plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
-#plt.title('Onset CompEnOn')
-#plt.colorbar()
-#plt.tight_layout()
+# plt.tight_layout()
 
+# fig = plt.figure()
+# plt.imshow(X2,vmin=X2.min(),vmax=X2.max())
+# plt.imshow(X2,vmin=(np.mean(X2)-1*(np.std(X2))),vmax=(np.mean(X2)+1*(np.std(X2))))
+# plt.title('Onset CompEnOn')
+# plt.colorbar()
+# plt.tight_layout()
 
 
 # Save the plotted image to a PNG file with the filename
 save_path = "/sdf/data/lcls/ds/rix/rixx1003721/results/scan/{}_plot.png".format(base_filename)
 plt.savefig(save_path)
 
+
 def onclick(event):
     ix, iy = event.xdata, event.ydata
-    colsel= math.trunc(ix)
-    rowsel=math.trunc(iy)
-    #print(f'x = {ix}, y = {iy}')
+    colsel = math.trunc(ix)
+    rowsel = math.trunc(iy)
+    # print(f'x = {ix}, y = {iy}')
 
-    timelineToDisplay=combined_data[:,rowsel,colsel]
-    f = interp1d(timeVec, timelineToDisplay, kind='linear')
+    timelineToDisplay = combined_data[:, rowsel, colsel]
+    f = interp1d(timeVec, timelineToDisplay, kind="linear")
     y_new = f(Xinterp)
-    y_new_2ndpart = f(Xinterp[Xinterp>(np.mean(timeVec))])                
-    thediff=np.diff(y_new)
-    thediff_2ndpart=np.diff(y_new_2ndpart)
+    y_new_2ndpart = f(Xinterp[Xinterp > (np.mean(timeVec))])
+    thediff = np.diff(y_new)
+    thediff_2ndpart = np.diff(y_new_2ndpart)
     window_size = 150
     polynomial_order = 1
 
-        # Apply Savitzky-Golay filtering
+    # Apply Savitzky-Golay filtering
     smoothed_data = savgol_filter(thediff, window_size, polynomial_order)
-    differencialfiltered=np.array(smoothed_data)
-        
+    differencialfiltered = np.array(smoothed_data)
+
     smoothed_data_2ndpart = savgol_filter(thediff_2ndpart, window_size, polynomial_order)
-    differencialfiltered_2ndpart=np.array(smoothed_data_2ndpart)
-    X=Xinterp[0:-1]
-    Y=smoothed_data
-    X1[ir,ic]=Xinterp[np.argmax(smoothed_data)]
-    Y1[ir,ic]=y_new[np.argmax(smoothed_data)]
-    X2[ir,ic]=Xinterp[np.argmin(smoothed_data)]
-    Y2[ir,ic]=y_new[np.argmin(smoothed_data)]
-    X3[ir,ic]=min(X[Y==min(Y[len(Y)//2:-1])])
-    Y3[ir,ic]=y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])]
-#    plt.figure(10)
-#    plt.close(10)
-#    plt.subplots_adjust(left=0.2, bottom=0.2, right=1.0, top=1.0)
+    differencialfiltered_2ndpart = np.array(smoothed_data_2ndpart)
+    X = Xinterp[0:-1]
+    Y = smoothed_data
+    X1[ir, ic] = Xinterp[np.argmax(smoothed_data)]
+    Y1[ir, ic] = y_new[np.argmax(smoothed_data)]
+    X2[ir, ic] = Xinterp[np.argmin(smoothed_data)]
+    Y2[ir, ic] = y_new[np.argmin(smoothed_data)]
+    X3[ir, ic] = min(X[Y == min(Y[len(Y) // 2 : -1])])
+    Y3[ir, ic] = y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])]
+    #    plt.figure(10)
+    #    plt.close(10)
+    #    plt.subplots_adjust(left=0.2, bottom=0.2, right=1.0, top=1.0)
     plt.figure(10)
     plt.ion()
     plt.clf()
     plt.show(block=False)
-    plt.plot(Xinterp,y_new,'k')        
-    plt.plot(Xinterp[np.argmin(smoothed_data)],y_new[np.argmin(smoothed_data)],'r*')
-    plt.plot(Xinterp[np.argmax(smoothed_data)],y_new[np.argmax(smoothed_data)],'b*')
-    plt.plot(min(X[Y==min(Y[len(Y)//2:-1])]),y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data)//2):-1])],'y*')
-    plt.title(f'Pixel:{colsel},{rowsel}')
+    plt.plot(Xinterp, y_new, "k")
+    plt.plot(Xinterp[np.argmin(smoothed_data)], y_new[np.argmin(smoothed_data)], "r*")
+    plt.plot(Xinterp[np.argmax(smoothed_data)], y_new[np.argmax(smoothed_data)], "b*")
+    plt.plot(
+        min(X[Y == min(Y[len(Y) // 2 : -1])]),
+        y_new_2ndpart[np.argmin(smoothed_data[(len(smoothed_data) // 2) : -1])],
+        "y*",
+    )
+    plt.title(f"Pixel:{colsel},{rowsel}")
     plt.show()
-    #print('Do you get there????')
-cid = fig.canvas.mpl_connect('button_press_event', onclick)
+    # print('Do you get there????')
 
 
+cid = fig.canvas.mpl_connect("button_press_event", onclick)
 
 
 plt.show()
-
-
-
diff --git a/standalone_scripts/OpenXav.py b/standalone_scripts/OpenXav.py
index 27a3d38..20617d0 100644
--- a/standalone_scripts/OpenXav.py
+++ b/standalone_scripts/OpenXav.py
@@ -1,15 +1,14 @@
 import matplotlib.pyplot as plt
 import h5py
 
-file_name = '../scan/TimeScanParallel_c0_r286_n1.h5'  # Update with your file name
+file_name = "../scan/TimeScanParallel_c0_r286_n1.h5"  # Update with your file name
 
-with h5py.File(file_name, 'r') as file:
-    dataset = file['dataset_name']  # Replace 'dataset_name' with the actual dataset name
+with h5py.File(file_name, "r") as file:
+    dataset = file["dataset_name"]  # Replace 'dataset_name' with the actual dataset name
     data = dataset[:]  # Load the dataset into a NumPy array
 
 
 # Assuming 'data' is a 2D image
-plt.imshow(data, cmap='viridis')  # Adjust cmap and other parameters as needed
+plt.imshow(data, cmap="viridis")  # Adjust cmap and other parameters as needed
 plt.colorbar()
 plt.show()
-    
diff --git a/standalone_scripts/cfg_utils.py b/standalone_scripts/cfg_utils.py
index 52c914f..f8b4355 100644
--- a/standalone_scripts/cfg_utils.py
+++ b/standalone_scripts/cfg_utils.py
@@ -1,32 +1,32 @@
 import numpy
 
-def dumpvars(prefix,c):
-    for key,val in vars(c).items():
-        name = prefix+'.'+key
-        if (isinstance(val,int) or
-            isinstance(val,float) or
-            isinstance(val,str) or
-            isinstance(val,list)):
-            print('{:} {:}'.format(name,val))
-        elif isinstance(val,numpy.ndarray):
-            print('{:}[{:}] {:}'.format(name,val.shape,val))
-        elif hasattr(val,'value'):
-            print('{:} {:}'.format(name,val.names[val.value]))
+
+def dumpvars(prefix, c):
+    for key, val in vars(c).items():
+        name = prefix + "." + key
+        if isinstance(val, int) or isinstance(val, float) or isinstance(val, str) or isinstance(val, list):
+            print("{:} {:}".format(name, val))
+        elif isinstance(val, numpy.ndarray):
+            print("{:}[{:}] {:}".format(name, val.shape, val))
+        elif hasattr(val, "value"):
+            print("{:} {:}".format(name, val.names[val.value]))
         else:
             try:
-                dumpvars(name,val)
+                dumpvars(name, val)
             except:
-                print('Error dumping {:} {:}'.format(name,type(val)))
+                print("Error dumping {:} {:}".format(name, type(val)))
+
+
+def dump_seg(seg, cfg):
+    print("-- segment {:} --".format(seg))
+    dumpvars("config", cfg.config)
 
-def dump_seg(seg,cfg):
-    print('-- segment {:} --'.format(seg))
-    dumpvars('config',cfg.config)
 
-def dump_det_config(det,name):
+def dump_det_config(det, name):
     for config in det._configs:
         if not name in config.__dict__:
-            print('Skipping config {:}'.format(config.__dict__))
+            print("Skipping config {:}".format(config.__dict__))
             continue
-        scfg = getattr(config,name)
-        for seg,segcfg in scfg.items():
-            dump_seg(seg,segcfg)
+        scfg = getattr(config, name)
+        for seg, segcfg in scfg.items():
+            dump_seg(seg, segcfg)
diff --git a/standalone_scripts/compareConfigs.py b/standalone_scripts/compareConfigs.py
index 7940474..db3d42d 100644
--- a/standalone_scripts/compareConfigs.py
+++ b/standalone_scripts/compareConfigs.py
@@ -1,27 +1,30 @@
 import sys
 
-c0 = open(sys.argv[1], 'r')
-c1 = open(sys.argv[2], 'r')
+c0 = open(sys.argv[1], "r")
+c1 = open(sys.argv[2], "r")
 
 lines0 = c0.readlines()
 lines1 = c1.readlines()
 
 for l0 in lines0:
-    if not l0.startswith('config'): continue
-    if l0.startswith('step 1'): break
+    if not l0.startswith("config"):
+        continue
+    if l0.startswith("step 1"):
+        break
     try:
         r0, v0 = l0.split()
     except:
-##        print(l0)
+        ##        print(l0)
         continue
 
     for l1 in lines1:
-        if not l1.startswith('config'): continue
-        if l1.startswith('step 1'): break
+        if not l1.startswith("config"):
+            continue
+        if l1.startswith("step 1"):
+            break
         try:
             r1, v1 = l1.split()
         except:
             continue
         if r1 == r0 and v1 != v0:
-            print("%s: %s -> %s" %(r0, v0, v1))
-
+            print("%s: %s -> %s" % (r0, v0, v1))
diff --git a/standalone_scripts/dumbBandPlotter.py b/standalone_scripts/dumbBandPlotter.py
index bd39bc9..feb1e37 100644
--- a/standalone_scripts/dumbBandPlotter.py
+++ b/standalone_scripts/dumbBandPlotter.py
@@ -7,10 +7,9 @@
 col = eval(sys.argv[3])
 
 a = np.load(dataFile)
-plt.plot(np.arange(a.shape[0]), a[:,row, col],'.', ms=1)
+plt.plot(np.arange(a.shape[0]), a[:, row, col], ".", ms=1)
 plt.xlabel("event")
 plt.ylabel("Raw")
-title = "pixel %d,%d in %s" %(row, col, dataFile)
+title = "pixel %d,%d in %s" % (row, col, dataFile)
 plt.title(title)
-plt.savefig(title.replace(" ","_") + ".png")
-
+plt.savefig(title.replace(" ", "_") + ".png")
diff --git a/standalone_scripts/dumpPVs.py b/standalone_scripts/dumpPVs.py
index 90c413f..3a04800 100644
--- a/standalone_scripts/dumpPVs.py
+++ b/standalone_scripts/dumpPVs.py
@@ -4,10 +4,10 @@
 
 exp = sys.argv[1]
 run = eval(sys.argv[2])
-pvList = sys.argv[3].split(',')
-allNames =  pvList.join('_')
+pvList = sys.argv[3].split(",")
+allNames = pvList.join("_")
 
-detList = [DataSource(exp=exp,run=run,intg_det=pv) for pv in pvList]
+detList = [DataSource(exp=exp, run=run, intg_det=pv) for pv in pvList]
 data = [[]] * len(pvList)
 myrun = next(ds.runs())
 while True:
@@ -17,5 +17,4 @@
     for n, ds in enumerate(dsList):
         data[n].append(det(evt))
 
-np.save("%s_r%d_PVs_%s.npy" %(exp, run, allNames), np.array(data))
-
+np.save("%s_r%d_PVs_%s.npy" % (exp, run, allNames), np.array(data))
diff --git a/standalone_scripts/nonBasicScript.py b/standalone_scripts/nonBasicScript.py
index 338d679..72438d8 100644
--- a/standalone_scripts/nonBasicScript.py
+++ b/standalone_scripts/nonBasicScript.py
@@ -7,15 +7,15 @@
 try:
     special = sys.argv[3]
 except:
-    special = ''
+    special = ""
 
-ds = DataSource(exp=exp,run=run,intg_det='epixhr')
+ds = DataSource(exp=exp, run=run, intg_det="epixhr")
 
 myrun = next(ds.runs())
-det = myrun.Detector('epixhr')
+det = myrun.Detector("epixhr")
 try:
-    digIn = myrun.Detector('ePixHR_Dig_in')
-    anaIn = myrun.Detector('ePixHR_Ana_in')
+    digIn = myrun.Detector("ePixHR_Dig_in")
+    anaIn = myrun.Detector("ePixHR_Ana_in")
 except:
     digIn = anaIn = None
 
@@ -38,26 +38,26 @@
         pass
 
     try:
-        pedestals = det.calibconst['pedestals'][0]
+        pedestals = det.calibconst["pedestals"][0]
         lowPed = pedestals[2]
         print("pedestal:", pedestals)
         print("pedestal mean, std")
         print(lowPed.mean(), lowPed.std())
-        if 'pedestal' in special:
-            np.save("pedestals_%d.npy" %(run), pedestals)
-            
+        if "pedestal" in special:
+            np.save("pedestals_%d.npy" % (run), pedestals)
+
     except:
         print("no pedestal")
 
     try:
-        gains = det.calibconst['pixel_gain'][0]
-        print(gains.mean(axis=(1,2,3)))
+        gains = det.calibconst["pixel_gain"][0]
+        print(gains.mean(axis=(1, 2, 3)))
     except:
         print("no gain: pain")
     try:
-        print('dig:', digIn(evt))
-        print('ana:', anaIn(evt))
+        print("dig:", digIn(evt))
+        print("ana:", anaIn(evt))
     except:
         print("no PV")
-        
+
     break
diff --git a/standalone_scripts/plotWave8.py b/standalone_scripts/plotWave8.py
index f889d1f..b88532d 100755
--- a/standalone_scripts/plotWave8.py
+++ b/standalone_scripts/plotWave8.py
@@ -2,19 +2,18 @@
 import sys
 import matplotlib.pyplot as plt
 
-data = np.load(sys.argv[1]) 
+data = np.load(sys.argv[1])
 if True:
 
-    
     ######plot amplitude vs time
 
     yscale = data.max()
-    sumData = data.sum(axis=1)## right axis?
+    sumData = data.sum(axis=1)  ## right axis?
 
     xavierOrder = [15, 8, 14, 13, 12, 9, 10, 11]
     xavierPosition = [1, 5, 21, 25, 8, 14, 18, 12]
-    t_s = range(data.shape[0])##right axis?
-    mksize=1
+    t_s = range(data.shape[0])  ##right axis?
+    mksize = 1
     for k in [1, 2, 3]:
         plt.figure(k)
         scale = yscale
@@ -23,37 +22,33 @@
             scale = 0.2
             label = "Normalized "
         for i in range(8):
-            plt.subplot(5,5,xavierPosition[i])
-            if k!=3:
-                plt.plot(t_s,data[:,xavierOrder[i]],'.b', markersize=mksize)
+            plt.subplot(5, 5, xavierPosition[i])
+            if k != 3:
+                plt.plot(t_s, data[:, xavierOrder[i]], ".b", markersize=mksize)
             else:
-                plt.plot(sumData,data[:,xavierOrder[i]],'.b', markersize=mksize)
-            plt.title('Diode %d' %(xavierOrder[i]))
-            plt.ylim(0,scale)
-        if k!=3:
-            plt.suptitle('%sFlux Wave8 VS time (s)' %(label))
+                plt.plot(sumData, data[:, xavierOrder[i]], ".b", markersize=mksize)
+            plt.title("Diode %d" % (xavierOrder[i]))
+            plt.ylim(0, scale)
+        if k != 3:
+            plt.suptitle("%sFlux Wave8 VS time (s)" % (label))
         else:
             plt.suptitle("Flux Wave8 vs total")
 
-
-
-
     plt.figure(4)
-    plt.plot(t_s, sumData,'.b', markersize=mksize)
-    plt.title('Sum of all Wave8 tiles VS event')
-#    plt.show()
+    plt.plot(t_s, sumData, ".b", markersize=mksize)
+    plt.title("Sum of all Wave8 tiles VS event")
+    #    plt.show()
 
     plt.figure(5)
     for i in range(8):
-        plt.subplot(8,1,i+1)
-        plt.hist(data[:, i+8],100)
-        plt.ylabel('Diode %d' %(i+8))
+        plt.subplot(8, 1, i + 1)
+        plt.hist(data[:, i + 8], 100)
+        plt.ylabel("Diode %d" % (i + 8))
 
     plt.figure(6)
     for i in range(8):
-        plt.subplot(8,1,i+1)
-        plt.hist(data[:, i+8],1000)
-        plt.ylabel('Diode norm should not matter %d' %(i+8))
+        plt.subplot(8, 1, i + 1)
+        plt.hist(data[:, i + 8], 1000)
+        plt.ylabel("Diode norm should not matter %d" % (i + 8))
 
     plt.show()
-
diff --git a/standalone_scripts/roiCombinationExample.py b/standalone_scripts/roiCombinationExample.py
index e1c6aa5..7f98fae 100755
--- a/standalone_scripts/roiCombinationExample.py
+++ b/standalone_scripts/roiCombinationExample.py
@@ -2,8 +2,7 @@
 import copy
 
 a = np.load("roiFromSwitched_e550_rmfxx1005021.npy")
-b = copy.copy(a)*0
+b = copy.copy(a) * 0
 b[0, 150:250, 150:250] = 1
 c = np.bitwise_and(a, b)
 np.save("maskedRoiFromSwitched_e550_rmfxx1005021.npy", c)
-
diff --git a/standalone_scripts/seabornTest.py b/standalone_scripts/seabornTest.py
index dc2d7cc..6dc7083 100644
--- a/standalone_scripts/seabornTest.py
+++ b/standalone_scripts/seabornTest.py
@@ -12,23 +12,23 @@
 if mode == 0:
     sns.regplot(x=x, y=y, x_bins=10, fit_reg=None)
     plt.show()
-elif mode==1:
+elif mode == 1:
     fig, ax = plt.subplots()
     sns.regplot(x=x, y=y, x_bins=10, fit_reg=None)
     plt.show()
-elif mode==2:
+elif mode == 2:
     fig, ax = plt.subplots()
-    sns.regplot(x=x, y=y, x_bins=10, fit_reg=None,ax=ax)
+    sns.regplot(x=x, y=y, x_bins=10, fit_reg=None, ax=ax)
     y = np.random.normal(x**2, np.abs(x) + 2)
-    sns.regplot(x=x, y=y, x_bins=10, fit_reg=None,ax=ax)
+    sns.regplot(x=x, y=y, x_bins=10, fit_reg=None, ax=ax)
     plt.show()
-elif mode==3:
+elif mode == 3:
     fig, ax = plt.subplots()
     sns.regplot(x=x, y=y, x_bins=10, ax=ax)
     y = np.random.normal(x**2, np.abs(x) + 2)
     sns.regplot(x=x, y=y, x_bins=10, ax=ax)
     plt.show()
-elif mode==4:
+elif mode == 4:
     fig, ax = plt.subplots()
     sns.regplot(x=x, y=y, x_bins=10, ax=ax)
     x = np.random.uniform(0, 2, 10000)
diff --git a/standalone_scripts/simpler_epixhr.py b/standalone_scripts/simpler_epixhr.py
index ad308a7..62fecfd 100644
--- a/standalone_scripts/simpler_epixhr.py
+++ b/standalone_scripts/simpler_epixhr.py
@@ -3,57 +3,60 @@
 import numpy as np
 
 import sys
+
 expt = sys.argv[1]
 run = eval(sys.argv[2])
 
-ds = DataSource(exp='%s' %(expt), run=run)
+ds = DataSource(exp="%s" % (expt), run=run)
 myrun = next(ds.runs())
-det = myrun.Detector('epixhr')
-
+det = myrun.Detector("epixhr")
 
 
 from psmon import publish
 import psmon.plots as plots
-from psmon.plotting import Histogram,LinePlot,Image
-publish.local=True
+from psmon.plotting import Histogram, LinePlot, Image
+
+publish.local = True
 publish.plot_opts.aspect = 1
 
 print(ds)
 
-tim = myrun.Detector('timing')
+tim = myrun.Detector("timing")
 try:
-    scan = myrun.Detector('scan')
+    scan = myrun.Detector("scan")
     print(vars(scan))
 except:
     pass
 
-def dump1(arr,title,nx,start):
-    print(f'{title} [{nx}]')
-    s = ''
-    for i in range(start,start+nx):
-        s += ' {:04x}'.format(arr[i])
-        if i%16==15:
-            s += '\n'
+
+def dump1(arr, title, nx, start):
+    print(f"{title} [{nx}]")
+    s = ""
+    for i in range(start, start + nx):
+        s += " {:04x}".format(arr[i])
+        if i % 16 == 15:
+            s += "\n"
     print(s)
-    return start+nx
+    return start + nx
+
 
 def dump2(arr):
     for i in range(arr.shape[0]):
-        s = ''
+        s = ""
         for j in range(arr.shape[1]):
-            s += ' {:04x}'.format(arr[i][j])
-            if j%16==15:
-                s += '\n'
+            s += " {:04x}".format(arr[i][j])
+            if j % 16 == 15:
+                s += "\n"
         print(s)
 
+
 ppid = 0
-detName = 'epixhr'
+detName = "epixhr"
 
-for nstep,step in enumerate(myrun.steps()):
+for nstep, step in enumerate(myrun.steps()):
 
-    for nevt,evt in enumerate(step.events()):
-        if nevt==0:
-            print(f'step {nstep}')
+    for nevt, evt in enumerate(step.events()):
+        if nevt == 0:
+            print(f"step {nstep}")
             dump_det_config(det, detName)
-            dump_det_config(det, detName+'hw')
-
+            dump_det_config(det, detName + "hw")