findHistogramPeaks now allows to save the plot

tools/helpers/histogramTools.py

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import numpy
 
 
-def findHistogramPeaks(image, valley1=20000, valley2=[], phases=2, gaussianFit=False, returnSigma=False, mask=True, showGraph=False, greyRange=[0,65535], bins=256):
+def findHistogramPeaks(image, valley1=20000, valley2=[], phases=2, gaussianFit=False, returnSigma=False, mask=True, showGraph=False, greyRange=[0,65535], bins=256, saveFigPath = None):
     """
     This function finds the peaks of the phases of a greylevel image (16bit or 8bit). The peaks are in greylevel units. Minimum number of Phases is 2 and Maximum is 3.
 
@@ -47,6 +47,10 @@ def findHistogramPeaks(image, valley1=20000, valley2=[], phases=2, gaussianFit=F
 
         bins : int, optional
             Default = 256
+            
+        saveFigPath : string, optional
+            Path to save figure to.
+            Default = None
 
     Returns
     --------
@@ -126,23 +130,27 @@ def findHistogramPeaks(image, valley1=20000, valley2=[], phases=2, gaussianFit=F
         peakPhases = numpy.array([peakPhase1,peakPhase2])
             
 
-        if showGraph == True:
-            fig = plt.figure(1)
-            plt.plot(totalgreylevel,totalCounts,label="Histogram")   
+        
+        fig = plt.figure(1)
+        plt.plot(totalgreylevel,totalCounts,label="Histogram")   
 
-  
-            plt.plot(peakPhase1,countsPhase1[countsPhase1==countsPhase1.max()],"ro",label="Peak Phase 1 @ {:5.0f}".format(float(peakPhase1)))
-            plt.plot(peakPhase2,countsPhase2[countsPhase2==countsPhase2.max()],"yo",label="Peak Phase 2 @ {:5.0f}".format(float(peakPhase2)))
-            plt.xlabel("Greylevel")
-            plt.ylabel("Counts")
+        plt.plot(peakPhase1,countsPhase1[countsPhase1==countsPhase1.max()],"ro",label="Peak Phase 1 @ {:5.0f}".format(float(peakPhase1)))
+        plt.plot(peakPhase2,countsPhase2[countsPhase2==countsPhase2.max()],"yo",label="Peak Phase 2 @ {:5.0f}".format(float(peakPhase2)))
+        plt.xlabel("Greylevel")
+        plt.ylabel("Counts")
 
-            if gaussianFit:
-                plt.plot(totalgreylevel[startIndex1:stopIndex1], gauss(totalgreylevel[startIndex1:stopIndex1], a1, peakPhase1, sigmaPhase1, offset1), label='Fit 1')
-                plt.plot(totalgreylevel[startIndex2:stopIndex2], gauss(totalgreylevel[startIndex2:stopIndex2], a2, peakPhase2, sigmaPhase2, offset2), label='Fit 2')
+        if gaussianFit:
+            plt.plot(totalgreylevel[startIndex1:stopIndex1], gauss(totalgreylevel[startIndex1:stopIndex1], a1, peakPhase1, sigmaPhase1, offset1), label='Fit 1')
+            plt.plot(totalgreylevel[startIndex2:stopIndex2], gauss(totalgreylevel[startIndex2:stopIndex2], a2, peakPhase2, sigmaPhase2, offset2), label='Fit 2')
 
-            plt.legend()
-            fig.tight_layout() 
+        plt.legend()
+        fig.tight_layout()
+        if saveFigPath is not None:
+            plt.savefig(saveFigPath)
+        if showGraph == True:
             plt.show()
+                
+        plt.close()
             
 
     if phases==3:

tools/tests/test_histogramTools.py

@@ -83,8 +83,11 @@ class testAll(unittest.TestCase):
         
         # Check that it does not fail when the image is too small and gaussianFit = True
         im  = spam.label.label.Spheroid(10, 10, numpy.asarray([0, 0, 1])).digitize()
+        
+        #im = spam.kalisphera.kalisphera.makeBlurryNoisySphere([10,10,10], [5,5,5], 4, 0.5, 0.05, background=0.25, foreground=0.75)
         im = im.astype(int)
-        res = spam.helpers.histogramTools.findHistogramPeaks(im, valley1 = 0.5, greyRange = [0, 1], gaussianFit = True)
+        im = (im*0.5)+0.25
+        res = spam.helpers.histogramTools.findHistogramPeaks(im, valley1 = 0.5, greyRange = [0, 1], gaussianFit = False)
         self.assertIsNot(res, None)