Renamed routines to use camel-case

cerr/dcm_export/iod_helper.py

@@ -16,7 +16,7 @@
 from random import randint
 
 
-def get_file_meta(dataType) -> FileMetaDataset:
+def getFileMeta(dataType) -> FileMetaDataset:
     file_meta = FileMetaDataset()
     file_meta.FileMetaInformationGroupLength = 202
     file_meta.FileMetaInformationVersion = b"\x00\x01"
@@ -42,7 +42,7 @@ def get_file_meta(dataType) -> FileMetaDataset:
     return file_meta
 
 
-def add_equipment_tags(ds: FileDataset, equipDict):
+def addEquipmentTags(ds: FileDataset, equipDict):
     dt = datetime.now()
     ds.Manufacturer = "pyCERR"
     ds.ManufacturerModelName = "pyCERR"
@@ -53,7 +53,7 @@ def add_equipment_tags(ds: FileDataset, equipDict):
 
     return ds
 
-def add_study_tags(ds: FileDataset, studyDict):
+def addStudyTags(ds: FileDataset, studyDict):
     dt = datetime.now()
     ds.StudyDate = studyDict["StudyDate"]
     ds.StudyTime = studyDict["StudyTime"]
@@ -63,7 +63,7 @@ def add_study_tags(ds: FileDataset, studyDict):
 
     return ds
 
-def add_series_tags(ds: FileDataset, seriesDict):
+def addSeriesTags(ds: FileDataset, seriesDict):
     dt = datetime.now()
     ds.Modality = seriesDict['Modality']
     ds.SeriesDate = dt.strftime("%Y%m%d")
@@ -74,7 +74,7 @@ def add_series_tags(ds: FileDataset, seriesDict):
 
     return ds
 
-def add_patient_tags(ds: FileDataset, patDict):
+def addPatientTags(ds: FileDataset, patDict):
     ds.PatientName = patDict["PatientName"]
     ds.PatientID = patDict["PatientID"]
     ds.PatientBirthDate = patDict["PatientBirthDate"]
@@ -84,7 +84,7 @@ def add_patient_tags(ds: FileDataset, patDict):
     ds.PatientWeight = patDict["PatientWeight"]
     return ds
 
-def add_content_tags(ds: FileDataset, contentDict):
+def addContentTags(ds: FileDataset, contentDict):
     dt = datetime.now()
     ds.ContentCreatorName = ''
     ds.ContentDate = dt.strftime("%Y%m%d")
@@ -95,7 +95,7 @@ def add_content_tags(ds: FileDataset, contentDict):
     return ds
 
 
-def add_sop_common_tags(ds: FileDataset):
+def addSOPCommonTags(ds: FileDataset):
     dt = datetime.now()
     ds.SpecificCharacterSet = "ISO_IR 192"  # "ISO_IR 100"
     ds.InstanceCreationDate = dt.strftime("%Y%m%d")
@@ -110,11 +110,11 @@ def add_sop_common_tags(ds: FileDataset):
     return ds
 
 
-def add_general_image_tags(ds: FileDataset):
+def addGeneralImageTags(ds: FileDataset):
     # ds.InstanceNumber # 0020,0013
     pass
 
-def add_image_plane_tags(ds: FileDataset):
+def addImagePlaneTags(ds: FileDataset):
     # ds.PixelSpacing # 0028,0030 (mm)
     # ds.ImageOrientation
     # ds.ImagePosition
@@ -123,17 +123,17 @@ def add_image_plane_tags(ds: FileDataset):
     # ds.SliceThickness # optional, for scan
     pass
 
-def add_image_pixel_tags(ds: FileDataset):
+def addImagePixelTags(ds: FileDataset):
     pass
 
 
-def add_ref_FOR_tags(ds_refFOR: Sequence):
+def addRefFORTags(ds_refFOR: Sequence):
     #ds_refFOR.FrameOfReference
     #s_refFOR.RTReferencedStudySequence = Sequence()
     pass
 
 
-def add_structure_set_tags(ds: FileDataset, structureDict):
+def addStructureSetTags(ds: FileDataset, structureDict):
     # https://dicom.nema.org/dicom/2013/output/chtml/part03/sect_A.19.html
     dt = datetime.now()
     ds.StructureSetLabel = structureDict['StructureSetLabel'] # 3006,0002 Structure Set Label

cerr/dcm_export/rtstruct_iod.py

@@ -13,7 +13,7 @@
 
 org_root = '1.3.6.1.4.1.9590.100.1.2.'
 
-def get_dcm_tag_vals(structNumV, planC, seriesOpts = {}):
+def getDcmTagVals(structNumV, planC, seriesOpts = {}):
 
     assocScanNum = scn.getScanNumFromUID(planC.structure[structNumV[0]].assocScanUID,planC)
     pat_tags = {"PatientName": planC.scan[assocScanNum].scanInfo[0].patientName,
@@ -69,7 +69,7 @@ def get_dcm_tag_vals(structNumV, planC, seriesOpts = {}):
 
     return pat_tags, study_tags, series_tags, equiqmt_tags, content_tags, struct_set_tags
 
-def get_ref_FOR_seq(structNumV, planC):
+def getRefFORSeq(structNumV, planC):
     # 3006,0010 Referenced Frame of Reference Sequence
         # 0020,0052  Frame of Reference UID
         # 3006,0012  RT Referenced Study Sequence
@@ -104,7 +104,7 @@ def get_ref_FOR_seq(structNumV, planC):
     return refFORSeq
 
 
-def get_struct_set_roi_seq(structNumV, planC):
+def getStructSetROISeq(structNumV, planC):
     # 3006,0020 Structure Set ROI Sequence
         # 3006,0022  ROI Number
         # 3006,0024  Referenced Frame of Reference UID
@@ -132,7 +132,7 @@ def get_struct_set_roi_seq(structNumV, planC):
 def convertCerrToDcmCoords(pointsM,planC):
     return pointsM
 
-def get_roi_contour_seq(structNumV, planC):
+def getROIContourSeq(structNumV, planC):
     # 3006, 0039   ROI Contour Sequence
         #3006,0084   Referenced ROI Number
         #3006,002A   ROI Display Color
@@ -190,7 +190,7 @@ def get_roi_contour_seq(structNumV, planC):
 
     return roiContourSeq
 
-def get_roi_observ_seq(structNumV, planC):
+def getROIObservSeq(structNumV, planC):
     #3006,0080 RT Observation Sequence
         # 3006,0082  Observation Number
         # 3006,0084  Referenced ROI Number
@@ -235,36 +235,36 @@ def create(structNumV, filePath, planC, seriesOpts = {}):
 
     # Get related UIDs for structNumV
     pat_tags, study_tags, series_tags, equiqmt_tags, content_tags, struct_set_tags = \
-        get_dcm_tag_vals(structNumV, planC, seriesOpts)
+        getDcmTagVals(structNumV, planC, seriesOpts)
 
     # Initialize RTSTRUCT series
     # ds = create_reg_dataset(base_series_data, filePath)
-    file_meta = iod_helper.get_file_meta('RTSTRUCT')
+    file_meta = iod_helper.getFileMeta('RTSTRUCT')
     ds = FileDataset(filePath, {}, file_meta=file_meta, preamble=b"\0" * 128)
 
-    ds = iod_helper.add_sop_common_tags(ds)
+    ds = iod_helper.addSOPCommonTags(ds)
 
     # Add Patient tags
-    ds = iod_helper.add_patient_tags(ds, pat_tags)
+    ds = iod_helper.addPatientTags(ds, pat_tags)
 
     # Add Study tags
-    ds = iod_helper.add_study_tags(ds, study_tags)
+    ds = iod_helper.addStudyTags(ds, study_tags)
 
     # Add Series tags
-    ds = iod_helper.add_series_tags(ds, series_tags)
+    ds = iod_helper.addSeriesTags(ds, series_tags)
 
     # Add Equipment tags
-    ds = iod_helper.add_equipment_tags(ds, equiqmt_tags)
+    ds = iod_helper.addEquipmentTags(ds, equiqmt_tags)
 
     # Add Content tags
-    ds = iod_helper.add_content_tags(ds, content_tags)
+    ds = iod_helper.addContentTags(ds, content_tags)
 
     # Add Structure Set tags
-    ds = iod_helper.add_structure_set_tags(ds, struct_set_tags)
-    ds.ReferencedFrameOfReferenceSequence = get_ref_FOR_seq(structNumV, planC)
-    ds.StructureSetROISequence = get_struct_set_roi_seq(structNumV, planC)
-    ds.ROIContourSequence = get_roi_contour_seq(structNumV, planC)
-    ds.RTROIObservationsSequence = get_roi_observ_seq(structNumV, planC)
+    ds = iod_helper.addStructureSetTags(ds, struct_set_tags)
+    ds.ReferencedFrameOfReferenceSequence = getRefFORSeq(structNumV, planC)
+    ds.StructureSetROISequence = getStructSetROISeq(structNumV, planC)
+    ds.ROIContourSequence = getROIContourSeq(structNumV, planC)
+    ds.RTROIObservationsSequence = getROIObservSeq(structNumV, planC)
 
     print("Writing RTSTRUCT file ...", filePath)
     ds.save_as(filePath)

cerr/plan_container.py

@@ -390,7 +390,7 @@ def loadH5Strucutre(structGrp, planC):
     return planC
 
 
-def load_dcm_dir(dcm_dir, opts={}, initplanC=''):
+def loadDcmDir(dcm_dir, opts={}, initplanC=''):
     """This routine imports metadata from DICOM directory and sub-directories into an instance of PlanC.
 
     Args:
@@ -410,7 +410,7 @@ def load_dcm_dir(dcm_dir, opts={}, initplanC=''):
         raise FileNotFoundError(dcm_dir + 'is not a valid directory path')
     # pc.PlanC is the container to hold various dicom objects
     # Parse dcm_dir an extract a map of CT, RTSTRUCT, RTDOSE etc files to pass to populate_planC_field routine
-    df_img = parse_dcm_dir(dcm_dir)
+    df_img = parseDcmHeader(dcm_dir)
     #pt_groups = df_img.groupby(by=["PatientName","PatientID","Modality"])
     # Ignore fileName column from grouping
     if not isinstance(initplanC, PlanC):
@@ -431,19 +431,19 @@ def load_dcm_dir(dcm_dir, opts={}, initplanC=''):
         modality = group_content.iloc[0,4]
         if modality in ["CT","PT", "MR"]:
             # populate scan attributes
-            scan_meta = populate_planC_field('scan', files, opts)
+            scan_meta = populatePlanCField('scan', files, opts)
             planC.scan.extend(scan_meta)
         elif modality in ["RTSTRUCT", "SEG"]:
             # populate structure attributes
-            struct_meta = populate_planC_field('structure', files)
+            struct_meta = populatePlanCField('structure', files)
             planC.structure.extend(struct_meta)
         elif modality == "RTPLAN":
             # populate beams attributes
-            beams_meta = populate_planC_field('beams', files)
+            beams_meta = populatePlanCField('beams', files)
             planC.beams.extend(beams_meta)
         elif modality == "RTDOSE":
             # populate dose attributes
-            dose_meta = populate_planC_field('dose', files)
+            dose_meta = populatePlanCField('dose', files)
             planC.dose.extend(dose_meta)
         else:
             print(d["Modality"][0]+ " not supported")
@@ -468,7 +468,7 @@ def load_dcm_dir(dcm_dir, opts={}, initplanC=''):
     #with open(save_file, 'wb') as pickle_file:
     #    pickle.dump(planC, pickle_file)
 
-def populate_planC_field(field_name, file_list, opts={}):
+def populatePlanCField(field_name, file_list, opts={}):
     if field_name == "scan":
         scan_meta = []
         scan_meta.append(scn.load_sorted_scan_info(file_list))
@@ -495,18 +495,15 @@ def populate_planC_field(field_name, file_list, opts={}):
         beams_meta = bms.load_beams(file_list)
         return beams_meta
 
-def load_planC_from_pkl(file_name=""):
+def loadPlanCFromPkl(file_name=""):
     file_name = r"C:\Users\aptea\PycharmProjects\pycerr\src\pycerr\tcga-ba-4074.mat"
     # Load planC from file
     #planC = sio.loadmat(file_name)
     with open(file_name, 'rb') as pickle_file:
         planC = pickle.load(pickle_file)
     return planC
 
-def save_scan_to_nii(scan_num, nii_file_name, planC):
-    pass
-
-def load_nii_scan(nii_file_name, imageType = "CT SCAN", direction='', initplanC=''):
+def loadNiiScan(nii_file_name, imageType ="CT SCAN", direction='', initplanC=''):
     if not isinstance(initplanC, PlanC):
         planC = PlanC(header=headr.Header())
     else:
@@ -605,7 +602,7 @@ def load_nii_scan(nii_file_name, imageType = "CT SCAN", direction='', initplanC=
     return planC
 
 
-def load_nii_structure(nii_file_name, assocScanNum, planC, labels_dict = {}):
+def loadNiiStructure(nii_file_name, assocScanNum, planC, labels_dict = {}):
     planC = structr.import_nii(nii_file_name,assocScanNum,planC,labels_dict)
     # struct_meta = structr.import_nii(nii_file_name,assocScanNum,planC,labels_dict)
     # numOrigStructs = len(planC.structure)
@@ -618,10 +615,10 @@ def load_nii_structure(nii_file_name, assocScanNum, planC, labels_dict = {}):
     return planC
 
 
-def load_nii_dose(nii_file_name, planC):
+def loadNiiDose(nii_file_name, planC):
     pass
 
-def load_nii_vf(dvf_file, baseScanNum, planC):
+def loadNiiVf(dvf_file, baseScanNum, planC):
 
     # Get image direction of the baseScanNum
     scanOrientV = planC.scan[baseScanNum].scanInfo[0].imageOrientationPatient
@@ -690,7 +687,7 @@ def load_nii_vf(dvf_file, baseScanNum, planC):
     return planC
 
 
-def import_scan_array(scan3M, xV, yV, zV, modality, assocScanNum, planC):
+def importScanArray(scan3M, xV, yV, zV, modality, assocScanNum, planC):
     org_root = '1.3.6.1.4.1.9590.100.1.2.' # to create seriesInstanceUID
     seriesInstanceUID = generate_uid(prefix=org_root)
     scan = scn.Scan()
@@ -749,12 +746,12 @@ def import_scan_array(scan3M, xV, yV, zV, modality, assocScanNum, planC):
     planC.scan.append(scan)
     return planC
 
-def import_structure_mask(mask3M, assocScanNum, structName, structNum, planC):
+def importStructureMask(mask3M, assocScanNum, structName, structNum, planC):
     planC = structr.import_structure_mask(mask3M, assocScanNum, structName, structNum, planC)
     return planC
 
 
-def parse_dcm_dir(dcm_dir):
+def parseDcmHeader(dcm_dir):
     from pydicom.misc import is_dicom
     from pydicom import dcmread
     import os

cerr/radiomics/ibsi1.py

@@ -5,7 +5,7 @@
 import numpy as np
 from cerr.radiomics import first_order, gray_level_cooccurence, run_length,\
     size_zone, neighbor_gray_level_dependence, neighbor_gray_tone
-from cerr.utils.mask import compute_boundingbox
+from cerr.utils.mask import computeBoundingBox
 from cerr.radiomics import preprocess, texture_utils
 import json
 
@@ -166,7 +166,7 @@ def computeScalarFeatures(scanNum, structNum, settingsFile, planC):
     # Pre-process Image
     (processedScan3M, processedMask3M, morphMask3M, gridS, radiomicsSettingS, diagS) = \
        preprocess.preProcessForRadiomics(scanNum, structNum, radiomicsSettingS, planC)
-    minr,maxr,minc,maxc,mins,maxs,__ = compute_boundingbox(processedMask3M)
+    minr,maxr,minc,maxc,mins,maxs,__ = computeBoundingBox(processedMask3M)
     voxSizeV = gridS["PixelSpacingV"]
 
     ############################################

cerr/radiomics/preprocess.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 import cerr.contour.rasterseg as rs
-from cerr.utils.mask import compute_boundingbox
+from cerr.utils.mask import computeBoundingBox
 import SimpleITK as sitk
 
 # from scipy.interpolate import interpn
@@ -313,7 +313,7 @@ def padScan(scan3M, mask3M, method, marginV, cropFlag=True):
     mask3M = mask3M.copy()
 
     if cropFlag:
-        minr, maxr, minc, maxc, mins, maxs, __ = compute_boundingbox(mask3M)
+        minr, maxr, minc, maxc, mins, maxs, __ = computeBoundingBox(mask3M)
         croppedScan3M = scan3M[minr:maxr+1, minc:maxc+1, mins:maxs+1]
         croppedMask3M = mask3M[minr:maxr+1, minc:maxc+1, mins:maxs+1]
         minr = minr - marginV[0]

cerr/radiomics/shape.py

@@ -8,7 +8,7 @@
 from cerr.utils.mask import getSurfacePoints
 from scipy.spatial import distance
 from skimage import measure
-from cerr.utils.mask import compute_boundingbox
+from cerr.utils.mask import computeBoundingBox
 
 def trimeshSurfaceArea(v,f):
     """Routine to calculate surface area from vertices and faces of triangular mesh
@@ -101,7 +101,7 @@ def calcShapeFeatures(mask3M, xValsV, yValsV, zValsV):
     volume = voxel_volume * np.sum(maskForShape3M)
 
     # Fill holes
-    rmin,rmax,cmin,cmax,smin,smax,_ = compute_boundingbox(maskForShape3M, 0, 1)
+    rmin,rmax,cmin,cmax,smin,smax,_ = computeBoundingBox(maskForShape3M, 0, 1)
     #struct3D = np.ones((3,3,3))
     #maskForShape3M = ndimage.binary_fill_holes(maskForShape3M[rmin:rmax+1,cmin:cmax+1,smin:smax+1])
     maskForShape3M = maskForShape3M[rmin:rmax+1,cmin:cmax+1,smin:smax+1]

cerr/radiomics/texture_filters.py

@@ -10,7 +10,7 @@
 from scipy.signal import convolve
 from scipy.ndimage import rotate
 from cerr.radiomics.preprocess import padScan
-from cerr.utils.mask import compute_boundingbox
+from cerr.utils.mask import computeBoundingBox
 
 
 def meanFilter(scan3M, kernelSize, absFlag=False):
@@ -710,7 +710,7 @@ def energyFilter(tex3M, mask3M, texPadFlag, texPadSizeV, texPadMethod,\
     if not texPadFlag:
         valOrigPadV = [0,0,0,0,0,0]
     elif texPadMethod.lower=='expand':
-        minr, maxr, minc, maxc, mins, maxs, __ = compute_boundingbox(mask3M)
+        minr, maxr, minc, maxc, mins, maxs, __ = computeBoundingBox(mask3M)
         valOrigPadV = [np.min(texPadSizeV[0], minr), np.min(texPadSizeV[0], origSizeV[0]-maxr),\
                    np.min(texPadSizeV[1], minc), np.min(texPadSizeV[1], origSizeV[1]-maxc),\
                    np.min(texPadSizeV[2], mins), np.min(texPadSizeV[2], origSizeV[2]-maxs)]