camel-cased routines in radiomics modules, updated documentation

cerr/plan_container.py

@@ -32,7 +32,8 @@ def get_empty_list():
 class PlanC:
     """Plan Container for imaging metadata. Each Attribute of this class holds metadata for a particular type
     of imaging object. Additional attributes can be added to this class without breaking the functionality
-    fom other attributes.
+    from other attributes.
+
     Attributes:
         header (cerr.dataclasses.header.Header): Header storing information about date of creation, version etc.
         scan (List[cerr.dataclasses.scan.Scan]): List of pyCERR's scan objects for modalities such as CT, MR, PT or derived types.

cerr/radiomics/first_order.py

@@ -11,7 +11,7 @@
 import cerr.plan_container as pc
 from cerr.utils.statistics import quantile, prctile
 
-def radiomics_first_order_stats(planC, structNum, offsetForEnergy=0, binWidth=None, binNum=None):
+def stats(planC, structNum, offsetForEnergy=0, binWidth=None, binNum=None):
     """
 
     This routine calculates 1st order statistical features from the input image and segmentation based on

cerr/radiomics/ibsi1.py

@@ -99,17 +99,17 @@ def calcRadiomicsForImgType(volToEval, maskBoundingBox3M, morphMask3M, gridS, pa
 
     # Shape  features
     if 'shape' in paramS['featureClass'] and paramS['featureClass']['shape']["featureList"] != {}:
-        from cerr.radiomics.shape import compute_shape_features
-        featDict['shape'] = compute_shape_features(morphMask3M,gridS['xValsV'],gridS['yValsV'],gridS['zValsV'])
+        from cerr.radiomics.shape import calcShapeFeatures
+        featDict['shape'] = calcShapeFeatures(morphMask3M, gridS['xValsV'], gridS['yValsV'], gridS['zValsV'])
 
     # Assign nan values outside the mask, so that min/max within the mask are used for quantization
     volToEval[~maskBoundingBox3M] = np.nan
 
     # Texture-based scalar features
     if 'texture' in paramS['settings']:
         # Quantization
-        quantized3M = preprocess.imquantize_cerr(volToEval, num_level=textureBinNum,\
-                                                 xmin=minClipIntensity, xmax=maxClipIntensity, binwidth=textureBinWidth)
+        quantized3M = preprocess.imquantize(volToEval, num_level=textureBinNum, \
+                                            xmin=minClipIntensity, xmax=maxClipIntensity, binwidth=textureBinWidth)
         nL = quantized3M.max()
 
         if any(name in ['glcm','glrlm','glszm'] for name in paramS['featureClass'].keys()):
@@ -122,8 +122,8 @@ def calcRadiomicsForImgType(volToEval, maskBoundingBox3M, morphMask3M, gridS, pa
     if 'firstOrder' in paramS['featureClass'] and paramS['featureClass']['firstOrder']["featureList"] != {}:
         voxelVol = np.prod(gridS["PixelSpacingV"]) * 1000 # units of mm
         scanV = volToEval[maskBoundingBox3M]
-        featDict['firstOrder'] = first_order.radiomics_first_order_stats(scanV, voxelVol,
-                                        firstOrderOffsetEnergy, firstOrderEntropyBinWidth, firstOrderEntropyBinNum)
+        featDict['firstOrder'] = first_order.stats(scanV, voxelVol,
+                                                   firstOrderOffsetEnergy, firstOrderEntropyBinWidth, firstOrderEntropyBinNum)
 
     # GLCM
     if 'glcm' in paramS['featureClass'] and paramS['featureClass']['glcm']["featureList"] != {}:

cerr/radiomics/preprocess.py

@@ -15,7 +15,7 @@
 # from scipy.ndimage import generate_binary_structure
 # from scipy.interpolate import RegularGridInterpolator
 
-def imquantize_cerr(x, num_level=None, xmin=None, xmax=None, binwidth=None):
+def imquantize(x, num_level=None, xmin=None, xmax=None, binwidth=None):
     """
 
     Function to quantize an image based on the number of bins (num_level) or the bin width (bin_width).

cerr/radiomics/run_length.py

@@ -1,5 +1,5 @@
 """
-This module contains routines for claculation of Run Length texture features
+This module contains routines for calculation of Run Length texture features
 """
 
 import numpy as np
@@ -102,89 +102,6 @@ def calcRLM(quantizedM, offsetsM, nL, rlmType=1):
     return rlmOut
 
 
-##### ======= Matlab implementation that uses np.roll (circshift)
-def calcRLM_old(quantizedM, offsetsM, nL, rlmType=1):
-    if rlmType != 1:
-        rlmOut = []
-
-    pad_width = [(1, 1)] * quantizedM.ndim
-    quantizedM = np.pad(quantizedM, pad_width, mode='constant', constant_values=0)
-
-    numRowsPad, numColsPad, numSlcsPad = 1, 1, 1
-    q = np.pad(quantizedM, ((numRowsPad, numRowsPad), (numColsPad, numColsPad), (numSlcsPad, numSlcsPad)),
-               mode='constant', constant_values=0)
-    q[np.isnan(q)] = 0
-    q = q.astype(np.uint16)
-
-    numOffsets = offsetsM.shape[0]
-    maxRunLen = int(np.ceil(np.max(quantizedM.shape)*2))
-    #maxRunLen = 1000
-    print('Max Run Length = ' + str(maxRunLen))
-
-    rlmM = np.zeros((nL, maxRunLen))
-    if rlmType == 2:
-        rlmOut = [np.zeros((nL, maxRunLen)) for i in range(numOffsets)]
-
-    #lenV = np.zeros(np.prod(q.shape), dtype = np.uint16)
-
-    siz = q.shape
-
-    for off in range(numOffsets):
-        if rlmType == 2:
-            rlmM = rlmOut[off]
-
-        offset = offsetsM[off]
-
-        for level in range(1, nL + 1):
-            #t = time.time()
-            prevM = (q == level).astype(int)
-            #diffM = (q == level).astype(int) - (np.roll(q, offset, axis=(0, 1, 2)) == level).astype(int)
-            diffM = prevM - np.roll(prevM, offset, axis=(0, 1, 2))
-            startM = diffM == 1
-            startIndV = np.where(startM)
-
-            #prevM = (q == level).astype(np.uint16)
-            prevM = prevM.astype(np.uint16)
-            convergedM = ~startM
-
-            #elapsed = time.time() - t
-            #print('Level start = ' + str(level) + '. Time = ' + str(elapsed))
-            #t = time.time()
-
-            #lenV *= 0
-            #start = 0
-            while not np.all(convergedM):
-                nextM = np.roll(prevM, -offset, axis=(0, 1, 2))
-                addM = prevM + nextM
-                newConvergedM = (addM == prevM)
-                toUpdateM = ~convergedM & newConvergedM
-                prevM = nextM
-                prevM[startIndV] = addM[startIndV]
-                lenV = addM[toUpdateM]
-                # if len(lenV) > 0:
-                #     countsV = np.bincount(lenV)
-                #     numCounts = len(countsV) - 1
-                #     rlmM[level - 1, :numCounts] = rlmM[level - 1, :numCounts] + countsV[1:]
-                if len(lenV) > 0:
-                    rlmM[level - 1, :] = rlmM[level - 1, :] + np.bincount(lenV, minlength=maxRunLen+1)[1:]
-                #stop = start + len(addM[toUpdateM])
-                #lenV[start:stop] = addM[toUpdateM]
-                #start = stop
-                convergedM = convergedM | newConvergedM
-            #rlmM[level - 1, :] = np.bincount(lenV[:stop], minlength=maxRunLen+1)[1:]
-            #elapsed = time.time() - t
-            #print('Level end = ' + str(level) + '. Time = ' + str(elapsed))
-
-        if rlmType == 2:
-            #rlmOut.append(rlmM)
-            rlmOut[off] = rlmM
-
-    if rlmType == 1:
-        rlmOut = rlmM
-
-    return rlmOut
-
-
 def rlmToScalarFeatures(rlmM, numVoxels):
     """
 

cerr/radiomics/shape.py

@@ -33,7 +33,7 @@ def trimeshSurfaceArea(v,f):
 
     return area
 
-def vector_norm3d(v):
+def vectorNorm3d(v):
     return np.linalg.norm(v, axis=1)
 
 def eig(a):
@@ -50,7 +50,7 @@ def calcMaxDistBetweenPts(ptsM, distType):
         distType (str or Callable): Type of distance. E.g. 'euclidean' as supported by scipy.spatial.distance.cdist
 
     Returns:
-        float: Maximim distance between the input points
+        float: Maximum distance between the input points
     """
 
     dmax = 0
@@ -75,7 +75,7 @@ def calcMaxDistBetweenPts(ptsM, distType):
 
     return dmax
 
-def compute_shape_features(mask3M, xValsV, yValsV, zValsV):
+def calcShapeFeatures(mask3M, xValsV, yValsV, zValsV):
     """Routine to calculate shape features for the inout mask and grid
 
     Args:

docs/cerr.rst

@@ -1,38 +1,15 @@
 cerr package
 ============
 
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   cerr.contour
-   cerr.dataclasses
-   cerr.datasets
-   cerr.dcm_export
-   cerr.radiomics
-   cerr.registration
-   cerr.roe
-   cerr.utils
-
 Submodules
 ----------
 
-cerr.dvh module
----------------
-
-.. automodule:: cerr.dvh
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 cerr.plan\_container module
 ---------------------------
 
 .. automodule:: cerr.plan_container
    :members:
-   :undoc-members:
+   :exclude-members: json_serialize
    :show-inheritance:
 
 cerr.viewer module
@@ -43,6 +20,31 @@ cerr.viewer module
    :undoc-members:
    :show-inheritance:
 
+cerr.dvh module
+---------------
+
+.. automodule:: cerr.dvh
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   cerr.contour
+   cerr.dataclasses
+   cerr.datasets
+   cerr.dcm_export
+   cerr.radiomics
+   cerr.registration
+   cerr.roe
+   cerr.utils
+
+
 Module contents
 ---------------