making it python 3 compatible

bin/Notebooks/helloRadiomics.ipynb

@@ -24,6 +24,7 @@
    },
    "outputs": [],
    "source": [
+    "from __future__ import print_function, unicode_literals, division, absolute_import\n",
     "import sys\n",
     "import os\n",
     "import logging\n",
@@ -96,9 +97,9 @@
     "maskName = os.path.join(dataDir, testCase + '_label.nrrd')\n",
     "\n",
     "if not os.path.exists(imageName):\n",
-    "  print 'Error: problem finding input image', imageName\n",
+    "  print('Error: problem finding input image', imageName)\n",
     "if not os.path.exists(maskName):\n",
-    "  print 'Error: problem finding input labelmap', maskName"
+    "  print('Error: problem finding input labelmap', maskName)"
    ]
   },
   {
@@ -152,7 +153,7 @@
      "output_type": "stream",
      "text": [
       "Enabled input images:\n",
-      "\toriginal\n"
+      "\tOriginal\n"
      ]
     }
    ],
@@ -172,9 +173,9 @@
     "\n",
     "# extractor.enableInputImages(wavelet={}, log={'sigma':[3.0]})\n",
     "\n",
-    "print \"Enabled input images:\"\n",
+    "print(\"Enabled input images:\")\n",
     "for imageType in extractor.inputImages.keys():\n",
-    "    print '\\t' + imageType"
+    "    print('\\t' + imageType)"
    ]
   },
   {
@@ -246,6 +247,7 @@
       "\n",
       "    :math:`entropy = -\\displaystyle\\sum^{N_l}_{i=1}{p(i)\\log_2\\big(p(i)+\\epsilon\\big)}`\n",
       "\n",
+      "    Here, :math:`\\epsilon` is an arbitrarily small positive number (:math:`\\approx 2.2\\times10^{-16}`).\n",
       "    Entropy specifies the uncertainty/randomness in the\n",
       "    image values. It measures the average amount of\n",
       "    information required to encode the image values.\n",
@@ -391,13 +393,13 @@
     }
    ],
    "source": [
-    "print \"Active features:\"\n",
-    "for cls, features in extractor.enabledFeatures.iteritems():\n",
+    "print(\"Active features:\")\n",
+    "for cls, features in extractor.enabledFeatures.items():\n",
     "  if len(features) == 0:\n",
     "    features = extractor.getFeatureNames(cls)\n",
     "  for f in features:\n",
-    "    print f\n",
-    "    print eval('extractor.featureClasses[\"%s\"].get%sFeatureValue.__doc__' % (cls, f))"
+    "    print(f)\n",
+    "    print(eval('extractor.featureClasses[\"%s\"].get%sFeatureValue.__doc__' % (cls, f)))"
    ]
   },
   {
@@ -421,62 +423,63 @@
       "Calculating features\n",
       "\t\tComputing firstorder\n",
       "Computed general_info_BoundingBox: (162; 84; 11; 47; 70; 7)\n",
-      "Computed general_info_GeneralSettings: {'verbose': True; 'binWidth': 25; 'label': 1; 'interpolator': 'sitkBSpline'; 'resampledPixelSpacing': None; 'padDistance': 5}\n",
+      "Computed general_info_GeneralSettings: {'verbose': True; 'label': 1; 'resampledPixelSpacing': None; 'binWidth': 25; 'interpolator': 'sitkBSpline'; 'padDistance': 5}\n",
       "Computed general_info_ImageHash: 5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566\n",
       "Computed general_info_ImageSpacing: (0.7812499999999999; 0.7812499999999999; 6.499999999999998)\n",
-      "Computed general_info_InputImages: {'original': {}}\n",
+      "Computed general_info_InputImages: {'Original': {}}\n",
       "Computed general_info_MaskHash: 9dc2c3137b31fd872997d92c9a92d5178126d9d3\n",
-      "Computed general_info_Version: v1.0.post11.dev0+g610dffc\n",
+      "Computed general_info_Version: v1.0.1.post6.dev0+g5b1e8bb\n",
       "Computed general_info_VolumeNum: 2\n",
       "Computed general_info_VoxelNum: 4137\n",
-      "Computed original_firstorder_InterquartileRange: 253.0\n",
-      "Computed original_firstorder_Skewness: 0.275650859086\n",
-      "Computed original_firstorder_Uniformity: 0.0451569635559\n",
-      "Computed original_firstorder_MeanAbsoluteDeviation: 133.447261953\n",
-      "Computed original_firstorder_Energy: 33122817481.0\n",
+      "Computed original_firstorder_Kurtosis: 2.18077293939\n",
+      "Computed original_firstorder_StandardDeviation: 156.611235894\n",
+      "Computed original_firstorder_10Percentile: 632.0\n",
+      "Computed original_firstorder_90Percentile: 1044.4\n",
+      "Computed original_firstorder_Minimum: 468.0\n",
       "Computed original_firstorder_RobustMeanAbsoluteDeviation: 103.00138343\n",
-      "Computed original_firstorder_Median: 812.0\n",
-      "Computed original_firstorder_TotalEnergy: 131407662126.0\n",
+      "Computed original_firstorder_Range: 798.0\n",
       "Computed original_firstorder_Maximum: 1266.0\n",
+      "Computed original_firstorder_Uniformity: 0.0451569635559\n",
+      "Computed original_firstorder_Mean: 825.235436307\n",
+      "Computed original_firstorder_TotalEnergy: 131407662126.0\n",
+      "Computed original_firstorder_MeanAbsoluteDeviation: 133.447261953\n",
       "Computed original_firstorder_RootMeanSquared: 2829.57282108\n",
-      "Computed original_firstorder_90Percentile: 1044.4\n",
-      "Computed original_firstorder_Minimum: 468.0\n",
+      "Computed original_firstorder_InterquartileRange: 253.0\n",
       "Computed original_firstorder_Entropy: 4.6019355539\n",
-      "Computed original_firstorder_StandardDeviation: 156.611235894\n",
-      "Computed original_firstorder_Range: 798.0\n",
       "Computed original_firstorder_Variance: 24527.0792084\n",
-      "Computed original_firstorder_10Percentile: 632.0\n",
-      "Computed original_firstorder_Kurtosis: 2.18077293939\n",
-      "Computed original_firstorder_Mean: 825.235436307\n"
+      "Computed original_firstorder_Energy: 33122817481.0\n",
+      "Computed original_firstorder_Skewness: 0.275650859086\n",
+      "Computed original_firstorder_Median: 812.0\n"
      ]
     }
    ],
    "source": [
-    "print \"Calculating features\"\n",
+    "print(\"Calculating features\")\n",
     "featureVector = extractor.execute(imageName, maskName)\n",
     "\n",
     "for featureName in featureVector.keys():\n",
-    "  print \"Computed %s: %s\" % (featureName, featureVector[featureName])"
+    "  print(\"Computed %s: %s\" % (featureName, featureVector[featureName]))"
    ]
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

bin/addClassToBaseline.py

@@ -34,17 +34,17 @@ def main():
       if len(testCases) > 0: break
 
   if len(testCases) == 0:
-    print ("No baselinefiles containing testcases found, exiting...")
+    print("No baselinefiles containing testcases found, exiting...")
     exit(-1)
 
   newClasses = [cls for cls in featureClasses if
                 not os.path.exists(os.path.join(baselineDir, 'baseline_%s.csv' % (cls)))]
 
   if len(newClasses) == 0:
-    print "No new classes to add, exiting..."
+    print("No new classes to add, exiting...")
     exit(0)
 
-  print "Adding new classes: ", newClasses
+  print("Adding new classes: ", newClasses)
 
   newBaseline = {}
 
@@ -59,21 +59,21 @@ def main():
   for cls in newClasses:
     newBaseline[cls] = {}
 
-  print "Computing new baseline"
+  print("Computing new baseline")
   for testCase in testCases:
-    print "\tCalculating test case", testCase
+    print("\tCalculating test case", testCase)
     imagePath = os.path.join(dataDir, testCase + '_image.nrrd')
     maskPath = os.path.join(dataDir, testCase + '_label.nrrd')
     image, mask = extractor.loadImage(imagePath, maskPath)
     if image is None or mask is None:
-      print "Error during loading of image/mask, testcase:", testCase
+      print("Error during loading of image/mask, testcase:", testCase)
       continue  # testImage or mask not found / error during loading
 
     provenance = extractor.getProvenance(imagePath, maskPath, mask)
 
     image, mask, bb = imageoperations.cropToTumorMask(image, mask)
     for cls in newClasses:
-      print "\t\tCalculating class", cls
+      print("\t\tCalculating class", cls)
       newBaseline[cls][testCase] = collections.OrderedDict()
       newBaseline[cls][testCase]["Patient ID"] = testCase
       newBaseline[cls][testCase].update(provenance)
@@ -82,7 +82,7 @@ def main():
       featureClass.calculateFeatures()
       newBaseline[cls][testCase].update(featureClass.featureValues)
 
-  print "Writing new baseline"
+  print("Writing new baseline")
   for cls in newClasses:
     baselineFile = os.path.join(baselineDir, 'baseline_%s.csv' % (cls))
     with open(baselineFile, 'wb') as baseline:

bin/batchExamples/DatasetHierarchyReader.py

@@ -52,7 +52,7 @@ def readReconstructionsDirectory(self, studyDirectory, subfolders, create=False)
         recDirectory = os.path.join(studyDirectory, "Reconstructions")
         if not os.path.exists(recDirectory):
           os.mkdir(recDirectory)
-          print "\tCreated:", recDirectory
+          print("\tCreated:", recDirectory)
 
     return recDirectory, images
 
@@ -67,7 +67,7 @@ def readSegmentationsDirectory(self, studyDirectory, subfolders, create=False):
         segDirectory = os.path.join(studyDirectory, "Segmentations")
         if not os.path.exists(segDirectory):
           os.mkdir(segDirectory)
-          print "\tCreated:", segDirectory
+          print("\tCreated:", segDirectory)
 
     return segDirectory, labels
 
@@ -82,7 +82,7 @@ def readResourcesDirectory(self, studyDirectory, subfolders, create=False):
         resDirectory = os.path.join(studyDirectory, "Resources")
         if not os.path.exists(resDirectory):
           os.mkdir(resDirectory)
-          print "\tCreated:", resDirectory
+          print("\tCreated:", resDirectory)
 
     return resDirectory, resources
 
@@ -102,7 +102,7 @@ def findImageAndLabelPair(self, imageFilepaths, maskFilepaths, keywordSettings):
     conditions.
     """
 
-    keywordSettings = {k: [str(keyword.strip()) for keyword in v.split(',')] for (k, v) in keywordSettings.iteritems()}
+    keywordSettings = {k: [str(keyword.strip()) for keyword in v.split(',')] for (k, v) in keywordSettings.items()}
 
     matchedImages = []
     for imageFilepath in imageFilepaths:
@@ -117,14 +117,14 @@ def findImageAndLabelPair(self, imageFilepaths, maskFilepaths, keywordSettings):
         matchedMasks.append(maskFilepath)
 
     if len(matchedImages) < 1:
-      print "ERROR: No Images Matched"
+      print("ERROR: No Images Matched")
     elif len(matchedImages) > 1:
-      print "ERROR: Multiple Images Matched"
+      print("ERROR: Multiple Images Matched")
 
     if len(matchedMasks) < 1:
-      print "ERROR: No Masks Matched"
+      print("ERROR: No Masks Matched")
     elif len(matchedMasks) > 1:
-      print "ERROR: Multiple Masks Matched"
+      print("ERROR: Multiple Masks Matched")
 
     if (len(matchedImages) == 1) and (len(matchedMasks) == 1):
       return matchedImages[0], matchedMasks[0]

bin/batchExamples/GenerateInputCSV_Datasethierarchy.py

@@ -1,6 +1,7 @@
+from __future__ import print_function, unicode_literals, division, absolute_import
 import os
 import csv
-from DatasetHierarchyReader import DatasetHierarchyReader
+from .DatasetHierarchyReader import DatasetHierarchyReader
 
 
 def main():
@@ -15,12 +16,12 @@ def main():
   keywordSettings['mask'] = 'label'
   keywordSettings['maskExclusion'] = ''
 
-  print "Scanning files..."
+  print("Scanning files...")
 
   datasetReader = DatasetHierarchyReader(inputDirectory, filetype=filetype)
   datasetHierarchyDict = datasetReader.ReadDatasetHierarchy()
 
-  print "Found %s patients, writing csv" % (str(len(datasetHierarchyDict.keys())))
+  print("Found %s patients, writing csv" % (str(len(datasetHierarchyDict.keys()))))
 
   try:
     with open(outputFile, 'wb') as outFile:
@@ -42,8 +43,8 @@ def main():
             # ReaderName is not extracted using DatasetHierarchyReader, set it to 'N/A'
             cw.writerow([patientID, studyDate, 'N/A', imageFilepath, maskFilepath])
 
-  except Exception, e:
-    print e
+  except Exception as e:
+    print(e)
 
 
 if __name__ == '__main__':

bin/batchExamples/GenerateInputCSV_Filename.py

@@ -1,3 +1,4 @@
+from __future__ import print_function, unicode_literals, division, absolute_import
 import os
 import csv
 
@@ -32,22 +33,22 @@ def main():
   outputFile = DATA_ROOT_PATH + r"/Included/FileList.csv"
   filetype = ".nrrd"
 
-  print "Scanning files..."
+  print("Scanning files...")
 
   datasetHierarchyDict = scanpatients(inputDirectory, filetype)
 
-  print "Found %s patients, writing csv" % (len(datasetHierarchyDict.keys()))
+  print("Found %s patients, writing csv" % (len(datasetHierarchyDict.keys())))
 
   try:
     with open(outputFile, 'wb') as outFile:
       cw = csv.writer(outFile, lineterminator='\n')
 
-      for patient, Studies in sorted(datasetHierarchyDict.iteritems(), key=lambda t: t[0]):
-        for Study, im_fileList in sorted(Studies['reconstructions'].iteritems(), key=lambda t: t[0]):
+      for patient, Studies in sorted(datasetHierarchyDict.items(), key=lambda t: t[0]):
+        for Study, im_fileList in sorted(Studies['reconstructions'].items(), key=lambda t: t[0]):
           for i_idx, im_file in enumerate(im_fileList):
 
             if Studies['segmentations'].has_key(Study):
-              for Reader, seg_fileList in sorted(Studies['segmentations'][Study].iteritems(), key=lambda t: t[0]):
+              for Reader, seg_fileList in sorted(Studies['segmentations'][Study].items(), key=lambda t: t[0]):
                 for s_idx, seg_file in enumerate(sorted(seg_fileList)):
 
                   i_name = Study
@@ -57,8 +58,8 @@ def main():
                   if s_idx > 0: s_name += " (%s)" % (str(s_idx + 1))
 
                   cw.writerow([patient, i_name, s_name, im_file, seg_file])
-  except Exception, e:
-    print e
+  except Exception as e:
+    print(e)
 
 
 def scanpatients(f, filetype):
@@ -74,9 +75,9 @@ def scanpatients(f, filetype):
           outputDict[PtNo] = {'reconstructions': {}}
           outputDict[PtNo]['segmentations'] = {}
 
-        for SqKey, SqVal in SqDic.iteritems():
+        for SqKey, SqVal in SqDic.items():
           if ("ROI_" + SqVal) in fname:
-            for ReaderKey, ReaderVal in LabelDic.iteritems():
+            for ReaderKey, ReaderVal in LabelDic.items():
               if (ReaderKey + '_') in fname:
                 if not outputDict[PtNo]['segmentations'].has_key(SqVal):
                   outputDict[PtNo]['segmentations'][SqVal] = {}

bin/batchExamples/batchprocessing.py

@@ -33,7 +33,7 @@ def main():
   radiomics.logger.handlers[0].setLevel(logging.WARNING)
 
   logging.info('Loading CSV')
-  print "Loading CSV"
+  print("Loading CSV")
 
   flists = []
   try:
@@ -43,8 +43,8 @@ def main():
   except Exception:
     logging.error('CSV READ FAILED:\n%s', traceback.format_exc())
 
-  print "Loading Done"
-  print ("Patients: " + str(len(flists)))
+  print("Loading Done")
+  print("Patients: " + str(len(flists)))
 
   kwargs = {}
   kwargs['binWidth'] = 25
@@ -60,7 +60,7 @@ def main():
   # extractor.enableInputImages(wavelet= {'level': 2})
   for idx, entry in enumerate(flists, start=1):
 
-    print "(%d/%d) Processing Patient: %s, Study: %s, Reader: %s" % (idx, len(flists), entry[0], entry[1], entry[2])
+    print("(%d/%d) Processing Patient: %s, Study: %s, Reader: %s" % (idx, len(flists), entry[0], entry[1], entry[2]))
     logging.info("(%d/%d) Processing Patient: %s, Study: %s, Reader: %s", idx, len(flists), entry[0], entry[1],
                  entry[2])