def countPointsInRegions(points, labeledImage = DefaultLabeledImageFile, intensities = None, intensityRow = 0, level= None, allIds = False, sort = True, returnIds = True, returnCounts = False, collapse = None):
global Label;
points = io.readPoints(points);
intensities = io.readPoints(intensities);
pointLabels = labelPoints(points, labeledImage, level = level, collapse = collapse);
if intensities is None:
ll, cc = numpy.unique(pointLabels, return_counts = True);
cci = None;
else:
if intensities.ndim > 1:
intensities = intensities[:,intensityRow];
ll, ii, cc = numpy.unique(pointLabels, return_counts = True, return_inverse = True);
cci = numpy.zeros(ll.shape);
for i in range(ii.shape[0]):
cci[ii[i]] += intensities[i];
if allIds:
lla = numpy.setdiff1d(Label.ids, ll);
ll = numpy.hstack((ll, lla));
cc = numpy.hstack((cc, numpy.zeros(lla.shape, dtype = cc.dtype)));
if not cci is None:
cci = numpy.hstack((cci, numpy.zeros(lla.shape, dtype = cc.dtype)));
#cc = numpy.vstack((ll,cc)).T;
if sort:
ii = numpy.argsort(ll);
cc = cc[ii];
ll = ll[ii];
if not cci is None:
cci = cci[ii];
if returnIds:
if cci is None:
return ll, cc
else:
if returnCounts:
return ll, cc, cci;
else:
return ll, cci
else:
if cci is None:
return cc;
else:
if returnCounts:
return cc, cci;
else:
return cci;
def thresholdPoints(points, intensities, threshold = 0, row = 0):
"""Threshold points by intensities"""
points, intensities = io.readPoints((points, intensities));
if not isinstance(threshold, tuple):
threshold = (threshold, all);
if not isinstance(row, tuple):
row = (row, row);
if intensities.ndim > 1:
i = intensities[:,row[0]];
else:
i = intensities;
iids = numpy.ones(i.shape, dtype = 'bool');
if not threshold[0] is all:
iids = numpy.logical_and(iids, i >= threshold[0]);
if intensities.ndim > 1:
i = intensities[:,row[1]];
if not threshold[1] is all:
iids = numpy.logical_and(iids, i <= threshold[1]);
return (points[iids, ...], intensities[iids, ...]);
def writePoints(filename, points, indices = True):
"""Write points as elastix/transformix point file
Arguments:
filename (str): file name of the elastix point file.
points (array or str): source of the points.
indices (bool): write as pixel indices or physical coordiantes
Returns:
str : file name of the elastix point file
"""
points = io.readPoints(points);
#points = points[:,[1,0,2]]; # points in ClearMap (y,x,z) -> permute to (x,y,z)
with open(filename, 'w') as pointfile:
if indices:
pointfile.write('index\n')
else:
pointfile.write('point\n')
pointfile.write(str(points.shape[0]) + '\n');
numpy.savetxt(pointfile, points, delimiter = ' ', newline = '\n', fmt = '%.5e')
pointfile.close();
return filename;
def thresholdPoints(points, intensities, threshold=0, row=0):
"""Threshold points by intensities"""
points, intensities = io.readPoints((points, intensities))
if not isinstance(threshold, tuple):
threshold = (threshold, all)
if not isinstance(row, tuple):
row = (row, row)
if intensities.ndim > 1:
i = intensities[:, row[0]]
else:
i = intensities
iids = numpy.ones(i.shape, dtype='bool')
if not threshold[0] is all:
iids = numpy.logical_and(iids, i >= threshold[0])
if intensities.ndim > 1:
i = intensities[:, row[1]]
if not threshold[1] is all:
iids = numpy.logical_and(iids, i <= threshold[1])
print('After Threshold, Num Points=' + str(points[iids, ...].shape[0]))
return (points[iids, ...], intensities[iids, ...])
def writePoints(filename, points, indices=True):
"""Write points as elastix/transformix point file
Arguments:
filename (str): file name of the elastix point file.
points (array or str): source of the points.
indices (bool): write as pixel indices or physical coordiantes
Returns:
str : file name of the elastix point file
"""
points = io.readPoints(points)
#points = points[:,[1,0,2]]; # points in ClearMap (y,x,z) -> permute to (x,y,z)
with open(filename, 'w') as pointfile:
if indices:
pointfile.write('index\n')
else:
pointfile.write('point\n')
pointfile.write(str(points.shape[0]) + '\n')
numpy.savetxt(pointfile,
points,
delimiter=' ',
newline='\n',
fmt='%.5e')
pointfile.close()
return filename
def overlayPoints(dataSource,
pointSource,
sink=None,
pointColor=[1, 0, 0],
x=all,
y=all,
z=all):
"""Overlay points on 3D data and return as color image
Arguments:
dataSouce (str or array): volumetric image data
pointSource (str or array): point data to be overlayed on the image data
pointColor (array): RGB color for the overlayed points
x, y, z (all or tuple): sub-range specification
Returns:
(str or array): image overlayed with points
See Also:
:func:`overlayLabel`
"""
data = io.readData(dataSource, x=x, y=y, z=z)
points = io.readPoints(pointSource, x=x, y=y, z=z, shift=True)
#print data.shape
if not pointColor is None:
dmax = data.max()
dmin = data.min()
if dmin == dmax:
dmax = dmin + 1
cimage = numpy.repeat((data - dmin) / (dmax - dmin), 3)
cimage = cimage.reshape(data.shape + (3, ))
if data.ndim == 2:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], :] = pointColor
elif data.ndim == 3:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], p[2], :] = pointColor
else:
raise RuntimeError(
'overlayPoints: data dimension %d not suported' % data.ndim)
else:
cimage = vox.voxelize(points, data.shape, voxelizationMethod='Pixel')
cimage = cimage.astype(data.dtype) * data.max()
data.shape = data.shape + (1, )
cimage.shape = cimage.shape + (1, )
cimage = numpy.concatenate((data, cimage), axis=3)
#print cimage.shape
return io.writeData(sink, cimage)
def readPointsGroup(filenames, **args):
"""Turn a list of filenames for points into a numpy stack"""
#check if stack already:
if isinstance(filenames, numpy.ndarray):
return filenames
#read the individual files
group = []
for f in filenames:
data = io.readPoints(f, **args)
#data = numpy.reshape(data, (1,) + data.shape);
group.append(data)
return group
def readPointsGroup(filenames, **args):
"""Turn a list of filenames for points into a numpy stack"""
#check if stack already:
if isinstance(filenames, numpy.ndarray):
return filenames;
#read the individual files
group = [];
for f in filenames:
data = io.readPoints(f, **args);
#data = numpy.reshape(data, (1,) + data.shape);
group.append(data);
return group
def voxelize(points, dataSize = None, sink = None, voxelizeParameter = None, method = 'Spherical', size = (5,5,5), weights = None):
"""Converts a list of points into an volumetric image array
Arguments:
points (array): point data array
dataSize (tuple): size of final image
sink (str, array or None): the location to write or return the resulting voxelization image, if None return array
voxelizeParameter (dict):
========== ==================== ===========================================================
Name Type Descritption
========== ==================== ===========================================================
*method* (str or None) method for voxelization: 'Spherical', 'Rectangular' or 'Pixel'
*size* (tuple) size parameter for the voxelization
*weights* (array or None) weights for each point, None is uniform weights
========== ==================== ===========================================================
Returns:
(array): volumetric data of smeared out points
"""
if dataSize is None:
dataSize = tuple(int(math.ceil(points[:,i].max())) for i in range(points.shape[1]));
elif isinstance(dataSize, basestring):
dataSize = io.dataSize(dataSize);
points = io.readPoints(points);
if method.lower() == 'spherical':
if weights is None:
data = vox.voxelizeSphere(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeSphereWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'rectangular':
if weights is None:
data = vox.voxelizeRectangle(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeRectangleWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'pixel':
data = voxelizePixel(points, dataSize, weights);
else:
raise RuntimeError('voxelize: mode: %s not supported!' % method);
return io.writeData(sink, data);
def voxelize(points, dataSize = None, sink = None, voxelizeParameter = None, method = 'Spherical', size = (5,5,5), weights = None):
"""Converts a list of points into an volumetric image array
Arguments:
points (array): point data array
dataSize (tuple): size of final image
sink (str, array or None): the location to write or return the resulting voxelization image, if None return array
voxelizeParameter (dict):
========== ==================== ===========================================================
Name Type Descritption
========== ==================== ===========================================================
*method* (str or None) method for voxelization: 'Spherical', 'Rectangular' or 'Pixel'
*size* (tuple) size parameter for the voxelization
*weights* (array or None) weights for each point, None is uniform weights
========== ==================== ===========================================================
Returns:
(array): volumetric data of smeared out points
"""
if dataSize is None:
dataSize = tuple(int(math.ceil(points[:,i].max())) for i in range(points.shape[1]));
elif isinstance(dataSize, basestring):
dataSize = io.dataSize(dataSize);
points = io.readPoints(points);
if method.lower() == 'spherical':
if weights is None:
data = vox.voxelizeSphere(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeSphereWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'rectangular':
if weights is None:
data = vox.voxelizeRectangle(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeRectangleWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'pixel':
data = voxelizePixel(points, dataSize, weights);
else:
raise RuntimeError('voxelize: mode: %s not supported!' % method);
return io.writeData(sink, data);
def flatfieldFromLine(line, xsize):
"""Creates a 2d flat field image from a 1d line of estimated intensities
Arguments:
lines (array): array of intensities along y axis
xsize (int): size of image in x dimension
Returns:
array: full 2d flat field
"""
line = io.readPoints(line)
flatfield = numpy.zeros((xsize, line.size))
for i in range(xsize):
flatfield[i, :] = line
return flatfield
def flatfieldFromLine(line, xsize):
"""Creates a 2d flat field image from a 1d line of estimated intensities
Arguments:
lines (array): array of intensities along y axis
xsize (int): size of image in x dimension
Returns:
array: full 2d flat field
"""
line = io.readPoints(line);
flatfield = numpy.zeros((xsize, line.size));
for i in range(xsize):
flatfield[i,:] = line;
return flatfield;
def overlayPoints(dataSource, pointSource, sink = None, pointColor = [1,0,0], x = all, y = all, z = all):
"""Overlay points on 3D data and return as color image
Arguments:
dataSouce (str or array): volumetric image data
pointSource (str or array): point data to be overlayed on the image data
pointColor (array): RGB color for the overlayed points
x, y, z (all or tuple): sub-range specification
Returns:
(str or array): image overlayed with points
See Also:
:func:`overlayLabel`
"""
data = io.readData(dataSource, x = x, y = y, z = z);
points = io.readPoints(pointSource, x = x, y = y, z = z, shift = True);
#print data.shape
if not pointColor is None:
dmax = data.max(); dmin = data.min();
if dmin == dmax:
dmax = dmin + 1;
cimage = numpy.repeat( (data - dmin) / (dmax - dmin), 3);
cimage = cimage.reshape(data.shape + (3,));
if data.ndim == 2:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], :] = pointColor;
elif data.ndim == 3:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], p[2], :] = pointColor;
else:
raise RuntimeError('overlayPoints: data dimension %d not suported' % data.ndim);
else:
cimage = vox.voxelize(points, data.shape, method = 'Pixel');
cimage = cimage.astype(data.dtype) * data.max();
data.shape = data.shape + (1,);
cimage.shape = cimage.shape + (1,);
cimage = numpy.concatenate((data, cimage), axis = 3);
#print cimage.shape
return io.writeData(sink, cimage);
def transformPoints(source,
sink=None,
transformParameterFile=None,
transformDirectory=None,
indices=True,
resultDirectory=None,
tmpFile=None):
"""Transform coordinates math:`x` via elastix estimated transformation to :math:`T(x)`
Note the transformation is from the fixed image coorindates to the moving image coordiantes.
Arguments:
source (str): source of the points
sink (str or None): sink for transformed points
transformParameterFile (str or None): parameter file for the primary transformation, if None, the file is determined from the transformDirectory.
transformDirectory (str or None): result directory of elastix alignment, if None the transformParameterFile has to be given.
indices (bool): if True use points as pixel coordinates otherwise spatial coordinates.
resultDirectory (str or None): elastic result directory
tmpFile (str or None): file name for the elastix point file.
Returns:
array or str: array or file name of transformed points
"""
global TransformixBinary
checkElastixInitialized()
global ElastixSettings
if tmpFile == None:
tmpFile = os.path.join(tempfile.gettempdir(), 'elastix_input.txt')
# write text file
if isinstance(source, basestring):
#check if we have elastix signature
with open(source) as f:
line = f.readline()
f.close()
if line[:5] == 'point' or line[:5] != 'index':
txtfile = source
else:
points = io.readPoints(source)
#points = points[:,[1,0,2]];
txtfile = tmpFile
writePoints(txtfile, points)
elif isinstance(source, numpy.ndarray):
txtfile = tmpFile
#points = source[:,[1,0,2]];
writePoints(txtfile, source)
else:
raise RuntimeError('transformPoints: source not string or array!')
if resultDirectory == None:
outdirname = os.path.join(tempfile.gettempdir(), 'elastix_output')
else:
outdirname = resultDirectory
if not os.path.exists(outdirname):
os.makedirs(outdirname)
if transformParameterFile == None:
if transformDirectory == None:
RuntimeError(
'neither alignment directory and transformation parameter file specified!'
)
transformparameterdir = transformDirectory
transformparameterfile = getTransformParameterFile(
transformparameterdir)
else:
transformparameterdir = os.path.split(transformParameterFile)
transformparameterdir = transformparameterdir[0]
transformparameterfile = transformParameterFile
#transform
#make path in parameterfiles absolute
setPathTransformParameterFiles(transformparameterdir)
#run transformix
cmd = TransformixBinary + ' -threads 8 -def ' + txtfile + ' -out ' + outdirname + ' -tp ' + transformparameterfile
res = os.system(cmd)
if res != 0:
raise RuntimeError('failed executing ' + cmd)
#read data / file
if sink == []:
return io.path.join(outdirname, 'outputpoints.txt')
else:
#read coordinates
transpoints = parseElastixOutputPoints(os.path.join(
outdirname, 'outputpoints.txt'),
indices=indices)
#correct x,y,z to y,x,z
#transpoints = transpoints[:,[1,0,2]];
#cleanup
for f in os.listdir(outdirname):
os.remove(os.path.join(outdirname, f))
os.rmdir(outdirname)
return io.writePoints(sink, transpoints)
def flatfieldLineFromRegression(data,
sink=None,
method='polynomial',
reverse=None,
verbose=False):
"""Create flat field line fit from a list of positions and intensities
The fit is either to be assumed to be a Gaussian:
.. math:
I(x) = a \\exp^{- (x- x_0)^2 / (2 \\sigma)) + b"
or follows a order 6 radial polynomial
.. math:
I(x) = a + b (x- x_0)^2 + c (x- x_0)^4 + d (x- x_0)^6
Arguments:
data (array): intensity data as vector of intensities or (n,2) dim array of positions d=0 and intensities measurements d=1:-1
sink (str or None): destination to write the result of the fit
method (str): method to fit intensity data, 'Gaussian' or 'Polynomial'
reverse (bool): reverse the line fit after fitting
verbose (bool): print and plot information for the fit
Returns:
array: fitted intensities on points
"""
data = io.readPoints(data)
# split data
if len(data.shape) == 1:
x = numpy.arange(0, data.shape[0])
y = data
elif len(data.shape) == 2:
x = data[:, 0]
y = data[:, 1:-1]
else:
raise RuntimeError(
'flatfieldLineFromRegression: input data not a line or array of x,i data'
)
#calculate mean of the intensity measurements
ym = numpy.mean(y, axis=1)
if verbose:
plt.figure()
for i in range(1, data.shape[1]):
plt.plot(x, data[:, i])
plt.plot(x, ym, 'k')
if method.lower() == 'polynomial':
## fit r^6
mean = sum(ym * x) / sum(ym)
def f(x, m, a, b, c, d):
return a + b * (x - m)**2 + c * (x - m)**4 + d * (x - m)**6
popt, pcov = curve_fit(f, x, ym, p0=(mean, 1, 1, 1, .1))
m = popt[0]
a = popt[1]
b = popt[2]
c = popt[3]
d = popt[4]
if verbose:
print "polynomial fit: %f + %f (x- %f)^2 + %f (x- %f)^4 + %f (x- %f)^6" % (
a, b, m, c, m, d, m)
def fopt(x):
return f(x, m=m, a=a, b=b, c=c, d=d)
flt = map(fopt, range(0, int(x[-1])))
else:
## Gaussian fit
mean = sum(ym * x) / sum(ym)
sigma = sum(ym * (x - mean)**2) / (sum(ym))
def f(x, a, m, s, b):
return a * numpy.exp(-(x - m)**2 / 2 / s) + b
popt, pcov = curve_fit(f, x, ym, p0=(1000, mean, sigma, 400))
a = popt[0]
m = popt[1]
s = popt[2]
b = popt[3]
if verbose:
print "Gaussian fit: %f exp(- (x- %f)^2 / (2 %f)) + %f" % (a, m, s,
b)
def fopt(x):
return f(x, a=a, m=m, s=s, b=b)
if reverse:
flt.reverse()
if verbose:
plt.plot(x, flt)
plt.title('flatfieldLineFromRegression')
return io.writePoints(sink, flt)
################
detectCells(**ImageProcessingParameter)
#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=(20, 900),
row=(3, 3))
io.writePoints(FilteredCellsFile, (points, intensities))
# Transform point coordinates
#############################
points = io.readPoints(CorrectionResamplingPointsParameter["pointSource"])
points = resamplePoints(**CorrectionResamplingPointsParameter)
points = transformPoints(
points,
transformDirectory=CorrectionAlignmentParameter["resultDirectory"],
indices=False,
resultDirectory=None)
CorrectionResamplingPointsInverseParameter["pointSource"] = points
points = resamplePointsInverse(**CorrectionResamplingPointsInverseParameter)
RegistrationResamplingPointParameter["pointSource"] = points
points = resamplePoints(**RegistrationResamplingPointParameter)
points = transformPoints(
points,
transformDirectory=RegistrationAlignmentParameter["resultDirectory"],
indices=False,
resultDirectory=None)
def transformPoints(source, sink = None, transformParameterFile = None, transformDirectory = None, indices = True, resultDirectory = None, tmpFile = None):
"""Transform coordinates math:`x` via elastix estimated transformation to :math:`T(x)`
Note the transformation is from the fixed image coorindates to the moving image coordiantes.
Arguments:
source (str): source of the points
sink (str or None): sink for transformed points
transformParameterFile (str or None): parameter file for the primary transformation, if None, the file is determined from the transformDirectory.
transformDirectory (str or None): result directory of elastix alignment, if None the transformParameterFile has to be given.
indices (bool): if True use points as pixel coordinates otherwise spatial coordinates.
resultDirectory (str or None): elastic result directory
tmpFile (str or None): file name for the elastix point file.
Returns:
array or str: array or file name of transformed points
"""
global TransformixBinary;
checkElastixInitialized();
global ElastixSettings;
if tmpFile == None:
tmpFile = os.path.join(tempfile.tempdir, 'elastix_input.txt');
# write text file
if isinstance(source, basestring):
#check if we have elastix signature
with open(source) as f:
line = f.readline();
f.close();
if line[:5] == 'point' or line[:5] != 'index':
txtfile = source;
else:
points = io.readPoints(source);
#points = points[:,[1,0,2]];
txtfile = tmpFile;
writePoints(txtfile, points);
elif isinstance(source, numpy.ndarray):
txtfile = tmpFile;
#points = source[:,[1,0,2]];
writePoints(txtfile, source);
else:
raise RuntimeError('transformPoints: source not string or array!');
if resultDirectory == None:
outdirname = os.path.join(tempfile.tempdir, 'elastix_output');
else:
outdirname = resultDirectory;
if not os.path.exists(outdirname):
os.makedirs(outdirname);
if transformParameterFile == None:
if transformDirectory == None:
RuntimeError('neither alignment directory and transformation parameter file specified!');
transformparameterdir = transformDirectory
transformparameterfile = getTransformParameterFile(transformparameterdir);
else:
transformparameterdir = os.path.split(transformParameterFile);
transformparameterdir = transformparameterdir[0];
transformparameterfile = transformParameterFile;
#transform
#make path in parameterfiles absolute
setPathTransformParameterFiles(transformparameterdir);
#run transformix
cmd = TransformixBinary + ' -def ' + txtfile + ' -out ' + outdirname + ' -tp ' + transformparameterfile;
res = os.system(cmd);
if res != 0:
raise RuntimeError('failed executing ' + cmd);
#read data / file
if sink == []:
return io.path.join(outdirname, 'outputpoints.txt')
else:
#read coordinates
transpoints = parseElastixOutputPoints(os.path.join(outdirname, 'outputpoints.txt'), indices = indices);
#correct x,y,z to y,x,z
#transpoints = transpoints[:,[1,0,2]];
#cleanup
for f in os.listdir(outdirname):
os.remove(os.path.join(outdirname, f));
os.rmdir(outdirname)
return io.writePoints(sink, transpoints);
def countPointsInRegions(
points,
labeledImage=DefaultLabeledImageFile,
intensities=None,
intensityRow=0,
level=None,
allIds=False,
sort=True,
returnIds=True,
returnCounts=False,
collapse=None,
):
global Label
points = io.readPoints(points)
intensities = io.readPoints(intensities)
pointLabels = labelPoints(points, labeledImage, level=level, collapse=collapse)
if intensities is None:
ll, cc = numpy.unique(pointLabels, return_counts=True)
cci = None
else:
if intensities.ndim > 1:
intensities = intensities[:, intensityRow]
ll, ii, cc = numpy.unique(pointLabels, return_counts=True, return_inverse=True)
cci = numpy.zeros(ll.shape)
for i in range(ii.shape[0]):
cci[ii[i]] += intensities[i]
if allIds:
lla = numpy.setdiff1d(Label.ids, ll)
ll = numpy.hstack((ll, lla))
cc = numpy.hstack((cc, numpy.zeros(lla.shape, dtype=cc.dtype)))
if not cci is None:
cci = numpy.hstack((cci, numpy.zeros(lla.shape, dtype=cc.dtype)))
# cc = numpy.vstack((ll,cc)).T;
if sort:
ii = numpy.argsort(ll)
cc = cc[ii]
ll = ll[ii]
if not cci is None:
cci = cci[ii]
if returnIds:
if cci is None:
return ll, cc
else:
if returnCounts:
return ll, cc, cci
else:
return ll, cci
else:
if cci is None:
return cc
else:
if returnCounts:
return cc, cci
else:
return cci
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
def flatfieldLineFromRegression(data, sink = None, method = 'polynomial', reverse = None, verbose = False):
"""Create flat field line fit from a list of positions and intensities
The fit is either to be assumed to be a Gaussian:
.. math:
I(x) = a \\exp^{- (x- x_0)^2 / (2 \\sigma)) + b"
or follows a order 6 radial polynomial
.. math:
I(x) = a + b (x- x_0)^2 + c (x- x_0)^4 + d (x- x_0)^6
Arguments:
data (array): intensity data as vector of intensities or (n,2) dim array of positions d=0 and intensities measurements d=1:-1
sink (str or None): destination to write the result of the fit
method (str): method to fit intensity data, 'Gaussian' or 'Polynomial'
reverse (bool): reverse the line fit after fitting
verbose (bool): print and plot information for the fit
Returns:
array: fitted intensities on points
"""
data = io.readPoints(data);
# split data
if len(data.shape) == 1:
x = numpy.arange(0, data.shape[0]);
y = data;
elif len(data.shape) == 2:
x = data[:,0]
y = data[:,1:-1];
else:
raise RuntimeError('flatfieldLineFromRegression: input data not a line or array of x,i data');
#calculate mean of the intensity measurements
ym = numpy.mean(y, axis = 1);
if verbose:
plt.figure()
for i in range(1,data.shape[1]):
plt.plot(x, data[:,i]);
plt.plot(x, ym, 'k');
if method.lower() == 'polynomial':
## fit r^6
mean = sum(ym * x)/sum(ym)
def f(x,m,a,b,c,d):
return a + b * (x-m)**2 + c * (x-m)**4 + d * (x-m)**6;
popt, pcov = curve_fit(f, x, ym, p0 = (mean, 1, 1, 1, .1));
m = popt[0]; a = popt[1]; b = popt[2];
c = popt[3]; d = popt[4];
if verbose:
print "polynomial fit: %f + %f (x- %f)^2 + %f (x- %f)^4 + %f (x- %f)^6" % (a, b, m, c, m, d, m);
def fopt(x):
return f(x, m = m, a = a, b = b, c = c, d = d);
flt = map(fopt, range(0, int(x[-1])));
else:
## Gaussian fit
mean = sum(ym * x)/sum(ym)
sigma = sum(ym * (x-mean)**2)/(sum(ym))
def f(x, a, m, s, b):
return a * numpy.exp(- (x - m)**2 / 2 / s) + b;
popt, pcov = curve_fit(f, x, ym, p0 = (1000, mean, sigma, 400));
a = popt[0]; m = popt[1]; s = popt[2]; b = popt[3];
if verbose:
print "Gaussian fit: %f exp(- (x- %f)^2 / (2 %f)) + %f" % (a, m, s, b);
def fopt(x):
return f(x, a = a, m = m, s = s, b = b);
if reverse:
flt.reverse();
if verbose:
plt.plot(x, flt);
plt.title('flatfieldLineFromRegression')
return io.writePoints(sink, flt);
#detect cells
try:
result, substack = detectCells(**dct["ImageProcessingParameter"])
if result == "ENDPROCESS": print("Jobid > # of jobs required, ending job")
except:
print("Jobid > # of jobs required, ending job")
print("\n finished step 4 - cell detection \n")
###########################################STEP 5########################################################
out = join_results_from_cluster_helper(**dct["ImageProcessingParameter"])
print("\n finished step 5 \n")
###########################################STEP 6########################################################
threshold = (500,3000); row = (2,2)
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)
#change dst to match parameters sweeped
dst = (os.path.join(brain,
"clearmap_cluster_output/cells_rBP%s_fIPmethod%s_fIPsize%s_dCSP%s.npy" % (rBP_size,
fIP_method, fIP_size, dCSP_threshold)),
os.path.join(brain,
"clearmap_cluster_output/intensities_rBP%s_fIPmethod%s_fIPsize%s_dCSP%s.npy" % (rBP_size,
fIP_method, fIP_size, dCSP_threshold)))
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
