added comments describing what the code is doing

xpdtools/calib3.py

@@ -16,9 +16,16 @@
 #take a horizontal cut out from the center of the ring to the end of the image
 #row is constant and column index goes from col index of center to end
 
+#findrings() takes a tiff file as an input and returns the image indices of the center points and points on rings 0,1,2,5
 def findrings(file_name):
 
+	#convert tiff file to numpy array
 	image=tf.imread(file_name)
+
+	#define a class that creates a 1D array of data from the image from either a vertical or horizontal slice (slyce) through
+	# the image. v=vertical slice, h=horizontal slice
+	#for v slice, index gives the col index in tiff image
+	#for h slice, index hives row index in tiff image
 	class Slyce():
 		def __init__(self,direction,index,image):
 			self.direction=direction
@@ -29,8 +36,14 @@ def __init__(self,direction,index,image):
 			if direction=='h':
 				self.data=list(image[index,:])
 
+	#findringcenter() takes the image (as numpy array), a threshold, and d as inputs and returns the indices of the center of
+	#the inner ring
+	#thres is used to define a threshold that determines if a point that is identified as a maximum is significant enough to be on the center ring
+	#d defines a distance around the center of the image that determines where slices are taken in the image to find the center ring
+	#the larger d is, the farther away from the center of the image the slices will be taken
 	def findringcenter(image,thres=0.2,d=20):
 
+		#findcenter() takes image numpy array as an input and finds the indices of the center of the image (not of the rings)
 		def findcenter(image):
 			s=image.shape
 			# number of rows divided by 2
@@ -41,6 +54,7 @@ def findcenter(image):
 
 		r,c=findcenter(image)
 
+		#take 10 slices within a range of 'd' away from the cetner of the image  and put them into a list
 		slyce1=Slyce('v',c-d,image)
 		slyce2=Slyce('v',c+d,image)
 		slyce3=Slyce('v',c,image)
@@ -54,15 +68,19 @@ def findcenter(image):
 		slyce10=Slyce('h',r-d/2,image)
 		slyces=[slyce1, slyce2, slyce3, slyce4, slyce5, slyce6, slyce7, slyce8, slyce9, slyce10]
 
+		#find threshold takes thres and determines a threshold value from the maximum pixel value in the tiff image
 		def findthreshold(image,thres):
 			return thres*np.max(image)
 
 		thresh=findthreshold(image,thres)
 
+		# clickpoints takes a slyce and the calculated threshold as inputs and returns the index (in a 1-d array) of 1 or two points
+		#that are on the INNER ring
 		def clickpoints(slyce,thresh):
 			p1=np.argmax(slyce.data)
 			p2=np.argmax(slyce.data[:p1]+slyce.data[p1+1:])
 
+			#if statement to determine if max points are large enough to actually be on center ring
 			if slyce.data[p2]>=thresh:
 				slyce.pixels.append(p1)
 				slyce.pixels.append(p2)
@@ -81,16 +99,21 @@ def clickpoints(slyce,thresh):
 				for pixel in slyce.pixels:
 					points2click.append([slyce.index,pixel])
 
-
+		#finds distance between two points
 		def finddistance(a,b):
 			dif1=a[1]-b[1]
 			dif2=a[0]-b[0]
 			d_sq=np.abs(dif1**2+dif2**2)
 			return np.sqrt(d_sq)
 
-
+		#coords keeps track of the coordinates that are tested to find the center of the inner ring
 		coords=[]
+		#spread keeps track of the range of point distances from the tested point to the points that have been identified on the
+		#center ring
 		spread=[]
+		#the center of the ring is determined by testing a range of points around the center of the image and calculating the distances
+		#between that point and the points that have been identified on the inner ring through the clickpoints() function
+		#the point that has the smallest range of distances is identified as the center
 		for row in range (int(r-3*d),int(r+3*d)):
 			for col in range (int(c-3*d),int(c+3*d)):
 				pointdist=[]
@@ -104,6 +127,8 @@ def finddistance(a,b):
 
 
 	center_pt=findringcenter(image)
+
+	#create horizontal slyces from the center of the inner ring (but use 5 and find the median value to avoid issues with hot pixels)
 	cs1=Slyce('h',center_pt[0]-2,image)
 	cs2=Slyce('h',center_pt[0]-1,image)
 	cs3=Slyce('h',center_pt[0],image)
@@ -118,17 +143,19 @@ def finddistance(a,b):
 	value4=cs4.data
 	value5=cs5.data
 	value_cs=np.zeros(np.shape(value1)[0])
+
+	#create list of median values in horizontal center slyce to avoid hot pixel values
 	for i, blank in enumerate(value_cs):
 		value_cs[i]=np.median([value1[i],value2[i],value3[i],value4[i],value5[i]])
 
-
+	#take half of center slyce from the center of the inner ring out
 	halfcs=list(value_cs[(center_pt[1]):])
 
 	#find derivative of values on the slyce
 	dx=1
 	deriv=list(np.gradient(halfcs,dx))
 
-	#creates a function that finds the indices of a list where the derivative changes sign
+	#creates a function that finds the indices of a list where the derivative changes sign (these are points where peaks could occur)
 	def zerocross(lines):
 		z=[]
 		for i in range(1,len(lines)-1):
@@ -143,6 +170,8 @@ def zerocross(lines):
 	clickpts=[]
 	firstpoint=[center_pt[0],rings[0]+center_pt[1]]
 
+	#tests a series of conditions to determine if there is a peak at a certain index in 1D array
+	#tests to make sure that peak is real and not noise/small fluctuation
 	if (halfcs[rings[0]]>0.7*np.max(halfcs)) or (halfcs[rings[0]-1]>0.7*np.max(halfcs)) or (halfcs[rings[0]+1]>0.7*np.max(halfcs)):
 		clickpts.append(rings[0])
 		clickpts.append(rings[1])
@@ -155,7 +184,7 @@ def zerocross(lines):
 		clickpts.append(rings[6])
 
 
-
+	#find indices of points that shoudl be "clicked" in tiff image array
 	points_image=[]
 	for clickpt in clickpts:
 		points_image.append([center_pt[0],clickpt+center_pt[1]])