def combineSections(animalPath, section, outTableName):
alph = ['a','b','c','d','e','f'];
for z in range(len(alph)):
fName = section+alph[z];
resultDir = os.path.join(animalPath,'Results',fName);
tableFN = os.path.join(resultDir,'ResultsTable.csv');
if z == 0:
table= pd.read_csv(tableFN,names = ['id','counts','name','subname']);
else:
temp_table = pd.read_csv(tableFN,names = ['id','counts','name','subname']);
table = pd.DataFrame.append(table,temp_table);
#Table generation
totTable = table.groupby(['id','name'],as_index=False).sum();
totTableFN = os.path.join(animalPath,outTableName);
io.writeTable(totTableFN,totTable.values);
print('success!')
labeledImage=annoImg_fn,
level=None,
collapse=None)
#defField = deformationField(sink = [], transformParameterFile = transformParameterFN, transformDirectory = None, resultDirectory = transformResultDir)
#defDistance = deformationDistance(defField, sink = None, scale = None)
#print 'Mean Deformation distance= %d' %defDistance.mean()
ids, counts = countPointsInRegions(points,
labeledImage=annoImg_fn,
intensities=None,
collapse=None)
#Table generation
table = numpy.zeros(ids.shape,
dtype=[('id', 'int64'), ('counts', 'f8'),
('name', 'a256')])
table['id'] = ids
table['counts'] = counts
table['name'] = labelToName(ids)
io.writeTable(tableFN, table)
beforeAlign = plt.fredOverlayPoints(auto_R_fn,
pointsRe,
pointColor=[200, 0, 0])
io.writeData(os.path.join(resultDir, 'BeforeAlign.tif'), beforeAlign)
afterAlign = plt.fredOverlayPoints(refImg_fn,
points,
pointColor=[200, 0, 0])
io.writeData(os.path.join(resultDir, 'AfterAlign.tif'), afterAlign)
def output_analysis(
threshold=(20, 900), row=(3, 3), check_cell_detection=False, **params):
"""Wrapper for analysis:
Inputs
-------------------
Thresholding: the threshold parameter is either intensity or size in voxel, depending on the chosen "row"
Row:
row = (0,0) : peak intensity from the raw data
row = (1,1) : peak intensity from the DoG filtered data
row = (2,2) : peak intensity from the background subtracted data
row = (3,3) : voxel size from the watershed
Check Cell detection: (For the testing phase only, remove when running on the full size dataset)
"""
dct = pth_update(set_parameters_for_clearmap(**params))
points, intensities = io.readPoints(
dct["ImageProcessingParameter"]["sink"])
#Thresholding: the threshold parameter is either intensity or size in voxel, depending on the chosen "row"
#row = (0,0) : peak intensity from the raw data
#row = (1,1) : peak intensity from the DoG filtered data
#row = (2,2) : peak intensity from the background subtracted data
#row = (3,3) : voxel size from the watershed
points, intensities = thresholdPoints(points,
intensities,
threshold=threshold,
row=row)
#points, intensities = thresholdPoints(points, intensities, threshold = (20, 900), row = (2,2));
io.writePoints(dct["FilteredCellsFile"], (points, intensities))
## Check Cell detection (For the testing phase only, remove when running on the full size dataset)
#######################
# if check_cell_detection:
# import ClearMap.Visualization.Plot as plt
# pointSource= os.path.join(BaseDirectory, FilteredCellsFile[0]);
# data = plt.overlayPoints(cFosFile, pointSource, pointColor = None, **cFosFileRange);
# io.writeData(os.path.join(BaseDirectory, "cells_check.tif"), data);
# Transform point coordinates
#############################
points = io.readPoints(
dct["CorrectionResamplingPointsParameter"]["pointSource"])
points = resamplePoints(**dct["CorrectionResamplingPointsParameter"])
points = transformPoints(
points,
transformDirectory=dct["CorrectionAlignmentParameter"]
["resultDirectory"],
indices=False,
resultDirectory=None)
dct["CorrectionResamplingPointsInverseParameter"]["pointSource"] = points
points = resamplePointsInverse(
**dct["CorrectionResamplingPointsInverseParameter"])
dct["RegistrationResamplingPointParameter"]["pointSource"] = points
points = resamplePoints(**dct["RegistrationResamplingPointParameter"])
points = transformPoints(
points,
transformDirectory=dct["RegistrationAlignmentParameter"]
["resultDirectory"],
indices=False,
resultDirectory=None)
io.writePoints(dct["TransformedCellsFile"], points)
# Heat map generation
#####################
points = io.readPoints(dct["TransformedCellsFile"])
intensities = io.readPoints(dct["FilteredCellsFile"][1])
#Without weigths:
vox = voxelize(points, dct["AtlasFile"], **dct["voxelizeParameter"])
if not isinstance(vox, str):
io.writeData(os.path.join(dct["OutputDirectory"], "cells_heatmap.tif"),
vox.astype("int32"))
#With weigths from the intensity file (here raw intensity):
dct["voxelizeParameter"]["weights"] = intensities[:, 0].astype(float)
vox = voxelize(points, dct["AtlasFile"], **dct["voxelizeParameter"])
if not isinstance(vox, str):
io.writeData(
os.path.join(dct["OutputDirectory"], "cells_heatmap_weighted.tif"),
vox.astype("int32"))
#Table generation:
##################
#With integrated weigths from the intensity file (here raw intensity):
try:
ids, counts = countPointsInRegions(points,
labeledImage=dct["AnnotationFile"],
intensities=intensities,
intensityRow=0)
table = numpy.zeros(ids.shape,
dtype=[("id", "int64"), ("counts", "f8"),
("name", "a256")])
table["id"] = ids
table["counts"] = counts
table["name"] = labelToName(ids)
io.writeTable(
os.path.join(dct["OutputDirectory"],
"Annotated_counts_intensities.csv"), table)
#Without weigths (pure cell number):
ids, counts = countPointsInRegions(points,
labeledImage=dct["AnnotationFile"],
intensities=None)
table = numpy.zeros(ids.shape,
dtype=[("id", "int64"), ("counts", "f8"),
("name", "a256")])
table["id"] = ids
table["counts"] = counts
table["name"] = labelToName(ids)
io.writeTable(
os.path.join(dct["OutputDirectory"], "Annotated_counts.csv"),
table)
except:
print("Table not generated.\n")
print("Analysis Completed")
return
for z in range(len(listAnimals)):
if z==0:
tab = pd.read_csv(listAnimals[z],names = ['id','name','counts'],usecols = ['id','name','counts']);
colNames = ['id','name',listAnimals[z]]
else:
tempTab = pd.read_csv(listAnimals[z],names = ['id','name','counts','subname']);
tab = pd.merge(tab,tempTab[['id','counts']], how = 'outer', on = 'id')
colNames.append(listAnimals[z])
tab.columns = colNames
return tab
def getSectionCSV(animalPath, sections):
#This function takes an input of section name ('052417-1') and creates a list of relavent CSV filse
out = [];
for i in range(len(sections)):
out.append(os.path.join(animalPath,sections[i]));
return out
combineSections(animalPath,section,outTableName)
sections = ['052417-1.csv','052417-2.csv','052417-3.csv','052417-4.csv','052417-5.csv','052417-6.csv','052417-7.csv','052417-8.csv']
sections =getSectionCSV(animalPath,sections)
#
#listAnimals = ['/media/sf_Fred_Data/OdorInduction/cfosOdor060717/060717-1_2MT.csv','/media/sf_Fred_Data/OdorInduction/cfosOdor060717/060717-2_2MT.csv',\
# '/media/sf_Fred_Data/OdorInduction/cfosOdor060717/060717-3_2MT.csv','/media/sf_Fred_Data/OdorInduction/cfosOdor060717/060717-5_2MT.csv','/media/sf_Fred_Data/OdorInduction/cfosOdor060717/060717-6_2MT.csv']
outTableName = 'CombinedTables.csv';
a = combineAnimals(sections);
totTableFN = os.path.join(animalPath,outTableName);
io.writeTable(totTableFN,a.values);
def output_analysis_helper(threshold=(20, 900), row=(3, 3), **params):
'''
Function to change elastix result directory before running 'step 6' i.e. point transformix to atlas.
'''
dct = pth_update(set_parameters_for_clearmap(**params))
dct['RegistrationAlignmentParameter']["resultDirectory"] = os.path.join(
params["outputdirectory"],
'clearmap_cluster_output/elastix_auto_to_sim_atlas')
points, intensities = io.readPoints(
dct['ImageProcessingParameter']["sink"])
#Thresholding: the threshold parameter is either intensity or size in voxel, depending on the chosen "row"
#row = (0,0) : peak intensity from the raw data
#row = (1,1) : peak intensity from the DoG filtered data
#row = (2,2) : peak intensity from the background subtracted data
#row = (3,3) : voxel size from the watershed
points, intensities = thresholdPoints(points,
intensities,
threshold=threshold,
row=row)
#points, intensities = thresholdPoints(points, intensities, threshold = (20, 900), row = (2,2));
io.writePoints(dct['FilteredCellsFile'], (points, intensities))
# Transform point coordinates
#############################
points = io.readPoints(
dct['CorrectionResamplingPointsParameter']["pointSource"])
points = resamplePoints(**dct['CorrectionResamplingPointsParameter'])
points = transformPoints(
points,
transformDirectory=dct['CorrectionAlignmentParameter']
["resultDirectory"],
indices=False,
resultDirectory=None)
dct['CorrectionResamplingPointsInverseParameter']["pointSource"] = points
points = resamplePointsInverse(
**dct['CorrectionResamplingPointsInverseParameter'])
dct['RegistrationResamplingPointParameter']["pointSource"] = points
points = resamplePoints(**dct['RegistrationResamplingPointParameter'])
points = transformPoints(
points,
transformDirectory=dct['RegistrationAlignmentParameter']
["resultDirectory"],
indices=False,
resultDirectory=None)
io.writePoints(dct['TransformedCellsFile'], points)
# Heat map generation
#####################
points = io.readPoints(dct['TransformedCellsFile'])
intensities = io.readPoints(dct['FilteredCellsFile'][1])
#Without weigths:
vox = voxelize(points, dct['AtlasFile'], **dct['voxelizeParameter'])
if not isinstance(vox, basestring):
io.writeData(os.path.join(dct['OutputDirectory'], 'cells_heatmap.tif'),
vox.astype('int32'))
#With weigths from the intensity file (here raw intensity):
dct['voxelizeParameter']["weights"] = intensities[:, 0].astype(float)
vox = voxelize(points, dct['AtlasFile'], **dct['voxelizeParameter'])
if not isinstance(vox, basestring):
io.writeData(
os.path.join(dct['OutputDirectory'], 'cells_heatmap_weighted.tif'),
vox.astype('int32'))
#Table generation:
##################
#With integrated weigths from the intensity file (here raw intensity):
ids, counts = countPointsInRegions(points,
labeledImage=dct['AnnotationFile'],
intensities=intensities,
intensityRow=0)
table = np.zeros(ids.shape,
dtype=[('id', 'int64'), ('counts', 'f8'),
('name', 'a256')])
table["id"] = ids
table["counts"] = counts
table["name"] = labelToName(ids)
io.writeTable(
os.path.join(dct['OutputDirectory'],
'Annotated_counts_intensities.csv'), table)
#Without weigths (pure cell number):
ids, counts = countPointsInRegions(points,
labeledImage=dct['AnnotationFile'],
intensities=None)
table = np.zeros(ids.shape,
dtype=[('id', 'int64'), ('counts', 'f8'),
('name', 'a256')])
table["id"] = ids
table["counts"] = counts
table["name"] = labelToName(ids)
io.writeTable(os.path.join(dct['OutputDirectory'], 'Annotated_counts.csv'),
table)
print('Analysis Completed')
return