rough psi-scan working example

examples/tomopackTo3DguessExperimental-psiScan-check.py

+"""
+EA and BM  sooo early in the morning of 2021-06-08 
+
+Now that you've run tomopackTo3DguessExperimental-psiScan.py you should have nice 3D positions in psi-Scan.npy
+
+Let's make sure they're OK and then try to use the sample to calibrate the attenuation curve
+"""
+import radioSphere
+import numpy
+import tifffile
+import matplotlib.pyplot as plt
+
+pos3D = numpy.load("psi-Scan.npy")
+radiusMM  = 1
+
+rootDir = "./data/2021-05-19-EPFL/"
+
+SDD = 403.784
+
+SOD = 36.3206
+
+zoomPosCentre = -22.90
+
+
+IO_for_radio = tifffile.imread(f"{rootDir}/01-holder-AVG16/Proj/00025.tif")
+radio = tifffile.imread(       f"{rootDir}/02-sample-AVG16/Proj/00025.tif") / IO_for_radio
+logRadio = -numpy.log(radio)
+
+
+projMM = radioSphere.projectSphereMM(pos3D, numpy.ones(pos3D.shape[0]), sourceDetectorDistMM=SDD, pixelSizeMM=0.139*4, detectorResolution=[524, 428])
+
+
+plt.subplot(1, 2, 1)
+plt.imshow(logRadio/logRadio.max())
+plt.title("Does this measurement...")
+
+plt.subplot(1, 2, 2)
+plt.imshow(projMM/projMM.max())
+plt.title("look like this identification? (if not Ctrl+C)")
+
+plt.show()
+
+#tifffile.imsave("psi-Scan.tif", im.astype('<f4'))
+
+"""
+Now try to use sample for calibration
+"""
+
+plt.plot(logRadio.ravel(), projMM.ravel(), 'x')
+plt.xlabel("-log(I/I0)")
+plt.ylabel("mm")
+plt.show()
+
+

examples/tomopackTo3DguessExperimental-psiScan.py

@@ -19,7 +19,7 @@ import glob
 
 radioCrop = 0
 
-nSphere   = 25
+nSphere   = 26
 
 radiusMM  = 1
 

tools/detectSpheres.py

@@ -403,38 +403,37 @@ def psiSeriesScanTo3DPositions(radio,      # not (necessarily) MM
         #tifffile.imsave(cachePsiFile, psiXseries.astype('<f4'))
         #print("done.")
 
-    L_x  = 20 # TODO: SCALING IN X DIRECTION SHOULD BE A FUNCTION OF THE CONE ANGLE
-    L_yz =  2 # TODO: THIS SHOULD BE A FUNCTION OF THE PIXELS PER RADIUS
+    #L_x  = 20 # TODO: SCALING IN X DIRECTION SHOULD BE A FUNCTION OF THE CONE ANGLE
+    #L_yz =  2 # TODO: THIS SHOULD BE A FUNCTION OF THE PIXELS PER RADIUS
 
-    struct = psiXseries[(psiXseries.shape[0])//2 -  L_x:(psiXseries.shape[0])//2 + L_x  + 1,
-                        (psiXseries.shape[1])//2 - L_yz:(psiXseries.shape[1])//2 + L_yz + 1,
-                        (psiXseries.shape[2])//2 - L_yz:(psiXseries.shape[2])//2 + L_yz + 1]
+    #struct = psiXseries[(psiXseries.shape[0])//2 -  L_x:(psiXseries.shape[0])//2 + L_x  + 1,
+                        #(psiXseries.shape[1])//2 - L_yz:(psiXseries.shape[1])//2 + L_yz + 1,
+                        #(psiXseries.shape[2])//2 - L_yz:(psiXseries.shape[2])//2 + L_yz + 1]
 
-    fXconvolvedSeries = scipy.ndimage.convolve(fXseries,struct/struct.sum())
-    #if useCache and not loadedCache:
-        #tifffile.imsave(f'{cacheFile[:-4]}_struct.tif', struct.astype('<f4'))
-        #tifffile.imsave(f'{cacheFile[:-4]}_fXconvolvedSeries.tif', fXconvolvedSeries.astype('<f4'))
+    #fXconvolvedSeries = scipy.ndimage.convolve(fXseries,struct/struct.sum())
+    ##if useCache and not loadedCache:
+        ##tifffile.imsave(f'{cacheFile[:-4]}_struct.tif', struct.astype('<f4'))
+        ##tifffile.imsave(f'{cacheFile[:-4]}_fXconvolvedSeries.tif', fXconvolvedSeries.astype('<f4'))
 
 
-    binaryPeaks = fXconvolvedSeries > massThreshold
+    #binaryPeaks = fXconvolvedSeries > massThreshold
 
     zoomLevel = sourceDetectorDistMM/((CORxPositions[0] + CORxPositions[-1])/2)
     CORxDelta = numpy.abs(CORxPositions[0]-CORxPositions[1])
     # Look in a volume of +/- half a radius in all directions for the highest value (+/- 1 radius keeps overlapping and causing issues, half a radius doesn't overlap particles, but still contains one clean peak)
-    fXconvolvedMaximumFiltered = scipy.ndimage.maximum_filter(fXconvolvedSeries,
-                                                                size=(numpy.int(numpy.floor(radiusMM/CORxDelta)),
+    fXseriesMaximumFiltered = scipy.ndimage.maximum_filter(fXseries,
+                                                              size=(3*numpy.int(numpy.floor(radiusMM/CORxDelta)),
                                                                     numpy.int(numpy.floor(radiusMM/pixelSizeMM*zoomLevel)),
-                                                                    numpy.int(numpy.floor(radiusMM/pixelSizeMM*zoomLevel))
-                                                                    )
-                                                                )
-    allPeaks = fXconvolvedSeries == fXconvolvedMaximumFiltered
-    masses = allPeaks*fXconvolvedSeries
+                                                                    numpy.int(numpy.floor(radiusMM/pixelSizeMM*zoomLevel)))
+                                                              )
+    allPeaks = fXseries == fXseriesMaximumFiltered
+    masses = allPeaks*fXseries
+
     if verbose:
         tifffile.imsave(cacheFile[:-4] + '_masses.tif', masses.astype('<f4'))
         tifffile.imsave(cacheFile[:-4] + '_peaks.tif', allPeaks.astype('<f4'))
-        tifffile.imsave(cacheFile[:-4] + '_masses.tif', masses.astype('<f4'))
         tifffile.imsave(cacheFile[:-4] + '_fXseries.tif', fXseries.astype('<f4'))
-        tifffile.imsave(cacheFile[:-4] + '_fXconvolvedMaximumFiltered.tif', fXconvolvedMaximumFiltered.astype('<f4'))
+        tifffile.imsave(cacheFile[:-4] + '_fXseriesMaximumFiltered.tif', fXseriesMaximumFiltered.astype('<f4'))
 
     if scanFixedNumber:
         # get the indices of all of the peaks, from highest to lowest