Merge pull request #61 from aditiiyer/testing

Added documentation

cerr/radiomics/preprocess.py

@@ -290,17 +290,18 @@ def padScan(scan3M, mask3M, method, marginV, cropFlag=True):
                 'periodic' - Image is padded with periodic expansion.
                 'nearest' - Image is padded by using values from nearest neighbors.
                 'mirror' - Image is padded by mirrorig the boundary region as per the margin.
-        marginV:
-        cropFlag:
+        marginV: np.array (1D) specifying amount of padding to be applied
+                [nRows, nCols, nSlices] in voxels.
+        cropFlag: [optional, default:True] bool for flag to crop around mask bounding box
+                  when set to True.
     Returns:
-        np.ndarray(dtype=:
+        np.ndarray(dtype=:) outScan3M
+         outMask3M
+          outLimitsV
         np.ndarray(dtype=int): , maskBoundingBox3M, morphmask3M, gridS, paramS, diagS
 
     """
 
-    if cropFlag is None:
-        cropFlag = True
-
     if method.lower() == 'none':
         marginV = [0, 0, 0]
 

cerr/radiomics/textureUtils.py

@@ -9,7 +9,19 @@
 from cerr.utils.mask import compute_boundingbox
 
 def loadSettingsFromFile(settingsFile, scanNum=None, planC=None):
-    """ Load filter parameters from user-input JSON file"""
+    """
+    Function to load filter parameters from user-input JSON.
+
+    Args:
+        settingsFile: string from path to JSON file.
+        scanNum: [optional, default=None] Scan no. from which to extract additional
+                    parameters like voxel size.
+        planC: [optional, default=None] pyCERR's plan container object.
+
+    Returns:
+        paramS: dictionary of radiomics parameters parsed from JSON file.
+         filterTypes: list of texture filters specified in JSON file.
+    """
 
     # Read settings
     with open(settingsFile) as json_file:
@@ -29,12 +41,15 @@ def loadSettingsFromFile(settingsFile, scanNum=None, planC=None):
 
 def processImage(filterType, scan3M, mask3M, paramS):
     """
-    Process scan using selected filter and parameters
+    Function to process scan using selected filter and parameters
+    Args:
+        filterType: String for name of supported filter.
+        scan3M: np.ndarray for 3D scan.
+        mask3M: np.ndarray(dtype=bool) for 3D binary mask.
+        paramS: dictionary of parameters (read from JSON).
+    Returns:
+        outS: dictionary containing response maps for each filter type.
 
-    filterType : Name of supported filter
-    scan3M     : 3D scan array
-    mask3M     : 3D mask
-    paramS     : Dictionary of parameters (read from JSON)
     """
 
     filterType = filterType.strip().lower()
@@ -156,11 +171,16 @@ def processImage(filterType, scan3M, mask3M, paramS):
 
 def generateTextureMapFromPlanC(planC, scanNum, strNum, configFilePath):
     """
-    Filter image and add to planC
-    planC           : Plan container
-    scanNum         : Index of scan to be filtered
-    strNum          : Index of ROI
-    configFilePath  : Path to JSON config file with filter parameters
+    Function to filter scan and import result to planC.
+
+    Args:
+        planC: pyCERR's plan container object.
+        scanNum: int for index of scan to be filtered.
+        strNum: int for index of ROI.
+        configFilePath: string for path to JSON config file with filter parameters.
+
+    Returns:
+        planC: pyCERR's plan container object with texture map as pseudo-scan.
     """
 
     # Extract scan and mask

cerr/utils/statisticsUtils.py

@@ -4,11 +4,17 @@
 import numpy as np
 
 def quantile(x,q):
+    """
+        Function to compute specified quantile from input array.
+
+        Returns:
+            qth quantile of values in input x.
+        """
     n = len(x)
     y = np.sort(x)
     return(np.interp(q, np.linspace(1/(2*n), (2*n-1)/(2*n), n), y))
 
 def prctile(x,p):
-    """Equivalent to Matlab's prctile"""
+    """MATLAB prctile.m equivalent function to compute percentile"""
     y = x[~np.isnan(x)]
     return(quantile(y,np.array(p)/100))

cerr/utils/uid.py

@@ -2,6 +2,17 @@
 from datetime import datetime
 
 def createUID(modality):
+    """
+    Function to create a unique identifier for various pyCERR dataclass objects.
+
+    Args:
+        modality: string for pyCERR object. May be 'SCAN', 'STRUCTURE', 'DOSE',
+                  'BEAMS', 'STRUCTURESET', 'DVH', 'IVH', 'DEFORM', 'BEAM', 'CERR',
+                  'TEXTURE', 'ANNOTATION', 'SEGLABEL', 'REGISTRATION', 'IM', 'FEATURESET'.
+
+    Returns:
+        Unique identifier for object of specified class, using current date and time.
+    """
     modality = modality.upper()
     if modality == 'SCAN':
         modality = 'CT'