def convert_pst2tif(self,paths):
""" batch convert .lsm files to tiffs """
for path in paths:
io.pst2tiff(path)
tifpaths = [os.path.splitext(path)[0]+'.tif' for path in paths]
return tifpaths
def convert_pst2tif(self, paths):
""" batch convert .lsm files to tiffs """
for path in paths:
io.pst2tiff(path)
tifpaths = [os.path.splitext(path)[0] + '.tif' for path in paths]
return tifpaths
def write_extraction_mask(self):
""" write both the .roi file and the tif pages
ROI file format specification: each row a ROI,
first col: kw for roi type
if circle: label, layer, pos x, pos y, diameter
if poly: label, layer, pos x_1, pos_y1, ... pos x_n, pos y_n
"""
outpath = self.SaveFileDialog(
title='saving ROIs',
default_dir=self.Main.Options.general['cwd'],
extension='*.roi')
outpath = self.append_extension(outpath, '.roi')
fh = open(outpath, 'w')
# iterate over ROIs
for i, ROI in enumerate(self.Main.ROIs.ROI_list):
label = str(ROI.label)
if type(ROI) == myCircleROI:
# pos = sp.array([ROI.pos().x(),ROI.pos().y()])
# pos = self.get_ROI_position(ROI)
x = str(sp.around(ROI.center[0], decimals=2))
y = str(sp.around(ROI.center[1], decimals=2))
d = str(sp.around(ROI.diameter, decimals=2))
fh.write('\t'.join(['circle', label, x, y, d, '\n']))
if type(ROI) == myPolyLineROI:
fh.write('\t'.join(['polygon', label]))
fh.write('\t')
handle_pos = [tup[1] for tup in ROI.getSceneHandlePositions()]
pos_mapped = [ROI.ViewBox.mapToView(pos) for pos in handle_pos]
for pos in (pos_mapped):
x = pos.x()
y = pos.y()
fh.write('\t'.join([
str(sp.around(x, decimals=2)),
str(sp.around(y, decimals=2))
]))
fh.write('\t')
fh.write('\n')
fh.close()
print "saved ROIs in .roi format to", outpath
# outpath = os.path.splitext(outpath)[0] + '_mask.tif'
# outpath = self.MainWindow.SaveFileDialog(title='saving ROIs',defaultdir=self.path,extension='.tif')
# extraction mask
self.Main.Processing.calc_extraction_mask()
io.save_tstack(self.Main.Data.extraction_mask.astype('uint16'),
os.path.splitext(outpath)[0] + '_mask.tif')
print "saved ROIs in .tif format to", outpath
pass
def write_extraction_mask(self):
""" write both the .roi file and the tif pages
ROI file format specification: each row a ROI,
first col: kw for roi type
if circle: label, layer, pos x, pos y, diameter
if poly: label, layer, pos x_1, pos_y1, ... pos x_n, pos y_n
"""
outpath = self.SaveFileDialog(title='saving ROIs',default_dir = self.Main.Options.general['cwd'], extension='*.roi')
outpath = self.append_extension(outpath, '.roi')
fh = open(outpath,'w')
# iterate over ROIs
for i,ROI in enumerate(self.Main.ROIs.ROI_list):
label = str(ROI.label)
if type(ROI) == myCircleROI:
# pos = sp.array([ROI.pos().x(),ROI.pos().y()])
# pos = self.get_ROI_position(ROI)
x = str(sp.around(ROI.center[0],decimals=2))
y = str(sp.around(ROI.center[1],decimals=2))
d = str(sp.around(ROI.diameter,decimals=2))
fh.write('\t'.join(['circle',label,x,y,d,'\n']))
if type(ROI) == myPolyLineROI:
fh.write('\t'.join(['polygon',label]))
fh.write('\t')
handle_pos = [tup[1] for tup in ROI.getSceneHandlePositions()]
pos_mapped = [ROI.ViewBox.mapToView(pos) for pos in handle_pos]
for pos in (pos_mapped):
x = pos.x()
y = pos.y()
fh.write('\t'.join([str(sp.around(x,decimals=2)),str(sp.around(y,decimals=2))]))
fh.write('\t')
fh.write('\n')
fh.close()
print("saved ROIs in .roi format to", outpath)
# outpath = os.path.splitext(outpath)[0] + '_mask.tif'
# outpath = self.MainWindow.SaveFileDialog(title='saving ROIs',defaultdir=self.path,extension='.tif')
# extraction mask
self.Main.Processing.calc_extraction_mask()
io.save_tstack(self.Main.Data.extraction_mask.astype('uint16'),os.path.splitext(outpath)[0] + '_mask.tif')
print("saved ROIs in .tif format to", outpath)
pass
def load_tif(self,paths):
""" read tifs found at paths (list with paths) """
# reading
x,y,t = io.read_tiffstack(paths[0]).shape
n = len(paths)
self.Main.Data.raw = sp.zeros((x,y,t,n),dtype='uint16')
self.Main.Data.dFF = sp.zeros((x,y,t,n),dtype='float32')
for n,path in enumerate(paths):
print "loading dataset from " + path
print "Dataset size: " + str(os.stat(path).st_size / 1000000.0) + ' MB'
self.Main.MainWindow.statusBar().showMessage("loading dataset: " + path)
self.Main.Data.raw[:,:,:,n] = io.read_tiffstack(path)
self.Main.MainWindow.statusBar().clearMessage()
def load_tif(self, paths):
""" read tifs found at paths (list with paths) """
# reading
x, y, t = io.read_tiffstack(paths[0]).shape
n = len(paths)
self.Main.Data.raw = sp.zeros((x, y, t, n), dtype='uint16')
self.Main.Data.dFF = sp.zeros((x, y, t, n), dtype='float32')
for n, path in enumerate(paths):
print "loading dataset from " + path
print "Dataset size: " + str(
os.stat(path).st_size / 1000000.0) + ' MB'
self.Main.MainWindow.statusBar().showMessage("loading dataset: " +
path)
self.Main.Data.raw[:, :, :, n] = io.read_tiffstack(path)
self.Main.MainWindow.statusBar().clearMessage()
def load_nonparametric_ROIs(self, size_filter=None, thresh=0.5):
""" reads a *_mask.tif
size_filter can be a tuple and segments with an area smaller than
size_filter[0] or larger size_filter[1] are considered to be valid ROIs
"""
self.Main.ROIs.reset()
# get mask data
file_path = self.OpenFileDialog(
title='load nonparametric ROIs',
default_dir=self.Main.Options.general['cwd'],
extension='*.tif')[0]
masks = io.read_tiffstack(file_path)
# skimage based segmentation
masks_thresh = masks > thresh
masks_label = sp.zeros(masks_thresh.shape)
for i in range(masks_thresh.shape[2]):
masks_label[:, :, i] = sklabel(masks_thresh[:, :, i])
# split into a array of submasks
Nsubmasks = [
int(masks_label[:, :, i].max())
for i in range(masks_label.shape[2])
]
submasks = sp.zeros((masks.shape[0], masks.shape[1], sum(Nsubmasks)),
dtype='bool')
i = 0
for j in range(masks_label.shape[2]):
mask = masks_label[:, :, j]
for l in range(1, Nsubmasks[j] + 1):
submasks[mask == l, i] = True
i += 1
# for each mask, calculate a contour
contours = []
for i in range(submasks.shape[2]):
contour = find_contours(submasks[:, :, i], level=0.5)
contours.append(contour)
# add PolyLineROI
for i in range(submasks.shape[2]):
self.Main.ROIs.add_ROI(kind='nonparametric',
label=str(i),
contour=contours[i],
mask=submasks[:, :, i])
pass
def load_nonparametric_ROIs(self,size_filter=None,thresh=0.5):
""" reads a *_mask.tif
size_filter can be a tuple and segments with an area smaller than
size_filter[0] or larger size_filter[1] are considered to be valid ROIs
"""
self.Main.ROIs.reset()
# get mask data
file_path = self.OpenFileDialog(title='load nonparametric ROIs',default_dir = self.Main.Options.general['cwd'], extension='*.tif')[0]
masks = io.read_tiffstack(file_path)
# skimage based segmentation
masks_thresh = masks > thresh
masks_label = sp.zeros(masks_thresh.shape)
for i in range(masks_thresh.shape[2]):
masks_label[:,:,i] = sklabel(masks_thresh[:,:,i])
# split into a array of submasks
Nsubmasks = [int(masks_label[:,:,i].max()) for i in range(masks_label.shape[2])]
submasks = sp.zeros((masks.shape[0],masks.shape[1],sum(Nsubmasks)),dtype='bool')
i = 0
for j in range(masks_label.shape[2]):
mask = masks_label[:,:,j]
for l in range(1,Nsubmasks[j]+1):
submasks[mask == l,i] = True
i += 1
# for each mask, calculate a contour
contours = []
for i in range(submasks.shape[2]):
contour = find_contours(submasks[:,:,i],level=0.5)
contours.append(contour)
# add PolyLineROI
for i in range(submasks.shape[2]):
self.Main.ROIs.add_ROI(kind='nonparametric',label=str(i),contour=contours[i],mask=submasks[:,:,i])
pass
