def extract(fpath):
fdir = os.path.split(fpath)[0]
fname = os.path.splitext(os.path.split(fpath)[1])[0]
sima_mc_path = os.path.join(fdir, fname + '_mc.sima')
if not os.path.exists(sima_mc_path):
raise Exception(
'Data not motion corrected yet; can\'t extract ROI data')
rois = ROIList.load(
os.path.join(fdir, fname + '_RoiSet.zip'),
fmt='ImageJ') # load ROIs as sima polygon objects (list)
dataset = sima.ImagingDataset.load(os.path.join(
fdir, fname + '_mc.sima')) # reload motion-corrected dataset
dataset.add_ROIs(rois, 'from_ImageJ')
print('Extracting roi signals from %s' % fdir)
signals = dataset.extract(rois)
extracted_signals = np.asarray(
signals['raw']) # turn signals list into an np array
np.save(os.path.join(fdir, fname + '_extractedsignals.npy'),
extracted_signals)
print('Done with extracting roi signals')
def getRoi():
ds_path = request.form.get('path')
label = request.form.get('label')
roi_id = request.form.get('id')
dataset = ImagingDataset.load(ds_path)
convertedRois = {}
try:
rois = ROIList.load(os.path.join(dataset.savedir, 'rois.pkl'),
label=label)
except:
return jsonify({})
for i, roi in enumerate(rois):
if roi.id == roi_id:
break
roi_points = []
try:
for i in xrange(roi.im_shape[0]):
roi_points.append([])
except:
for i in xrange(np.max(np.array(roi.coords)[:, :, 2])):
roi_points.append([])
for poly in roi.polygons:
coords = np.array(poly.exterior.coords)
if np.all(coords[-1] == coords[0]):
coords = coords[:-1]
plane = int(coords[0, -1])
coords = coords[:, :2].astype(int).tolist()
roi_points[plane].append(coords)
return jsonify({roi.id: {'label': roi.label, 'points': roi_points}})
def test_extract_roi(self):
# make sure number of loaded ROIs (from zip and extracted signals) are correct
rois = ROIList.load(
os.path.join(self.fdir, self.fname + '_RoiSet.zip'),
fmt='ImageJ') # load ROIs as sima polygon objects (list)
assert self.sig.shape[1] == 11 # CZ manually drew 11 ROIs
assert len(rois) == 11
def extract_rois((full_path_to_file, individualframename)):
# Create SIMA Sequence & ImagingDataset objects from image file(s) or
# motion correction if action == 'extract', assume that motion
# correction was done on EC2 previously
if action == 'both':
try:
motion_correction((full_path_to_file, individualframename))
except Exception as e:
print('Motion correction failed')
print e
logging.Exception('Motion correction failed')
filename = os.path.splitext(os.path.basename(full_path_to_file))[0]
dataset = sima.ImagingDataset.load(filename + '_mc.sima')
# Obtain ROIs
if to_segment:
logging.info("Segmenting images for %s..." % filename)
# Automated segmentation
# Define segmentation method and post processing.
segment_approach = sima.segment.PlaneNormalizedCuts()
segment_approach.append(sima.segment.SparseROIsFromMasks())
segment_approach.append(sima.segment.SmoothROIBoundaries())
segment_approach.append(sima.segment.MergeOverlapping(threshold=0.5))
# Apply segmentation to dataset
rois = dataset.segment(segment_approach)
logging.info("Done segmenting images for %s" % filename)
print("Done segmenting images for %s" % filename)
else:
logging.info("Importing ROIs from ImageJ for %s..." % filename)
print("Importing ROIs from ImageJ for %s..." % filename)
# Load ROIs from ImageJ
rois = ROIList.load(filename + '_mc_' + roi_filename, fmt='ImageJ')
dataset.add_ROIs(rois, 'from_ImageJ')
logging.info("Done importing ROIs for %s" % filename)
print("Done importing ROIs for %s" % filename)
# Extract signals from ROIs into numpy file
signals = dataset.extract(rois)
extracted_signals = np.asarray(signals['raw'])
np.save(filename + '_extractedsignals', extracted_signals)
logging.info("Done extracting signals")
print("Done extracting signals")
def extract_rois((full_path_to_file, individualframename)):
# Create SIMA Sequence & ImagingDataset objects from image file(s) or
# motion correction if action == 'extract', assume that motion
# correction was done on EC2 previously
if action == 'both':
try:
motion_correction((full_path_to_file, individualframename))
except Exception as e:
print('Motion correction failed')
print e
logging.Exception('Motion correction failed')
filename = os.path.splitext(os.path.basename(full_path_to_file))[0]
dataset = sima.ImagingDataset.load(filename + '_mc.sima')
# Obtain ROIs
if to_segment:
logging.info("Segmenting images for %s..." % filename)
# Automated segmentation
# Define segmentation method and post processing.
segment_approach = sima.segment.PlaneNormalizedCuts()
segment_approach.append(sima.segment.SparseROIsFromMasks())
segment_approach.append(sima.segment.SmoothROIBoundaries())
segment_approach.append(sima.segment.MergeOverlapping(threshold=0.5))
# Apply segmentation to dataset
rois = dataset.segment(segment_approach)
logging.info("Done segmenting images for %s" % filename)
print("Done segmenting images for %s" % filename)
else:
logging.info("Importing ROIs from ImageJ for %s..." % filename)
print("Importing ROIs from ImageJ for %s..." % filename)
# Load ROIs from ImageJ
rois = ROIList.load(filename + '_mc_' + roi_filename, fmt='ImageJ')
dataset.add_ROIs(rois, 'from_ImageJ')
logging.info("Done importing ROIs for %s" % filename)
print("Done importing ROIs for %s" % filename)
# Extract signals from ROIs into numpy file
signals = dataset.extract(rois)
extracted_signals = np.asarray(signals['raw'])
np.save(filename + '_extractedsignals', extracted_signals)
logging.info("Done extracting signals")
print("Done extracting signals")
def extract_dff(directory, plot=False, load_signal=False): hd, tl = os.path.split(directory) if load_signal: dff = np.load(os.path.join(hd, 'DFF')) else: dataset = sima.ImagingDataset.load(directory) rois = ROIList.load(os.path.join(hd, 'manual', 'RoiSet.zip'), fmt='ImageJ') dataset.add_ROIs(rois, 'from_ImageJ') # this immediately saves the ROIs signals = dataset.extract(rois, signal_channel='0', label='0') signals = dataset.signals(channel='0')['0'] # signals are in signals['raw'] # first list index is for each cycle # second index is for each roi within a cycle (third is signal at time t) ## convert each signal into a df/f trace k = 30 baseline_ix = np.array([[0, 90], [240, 320]]) traces = np.stack(signals['raw'], axis=1) dff = np.zeros_like(traces) nroi, ncycles, t = traces.shape # normalize the signal for i, roi in enumerate(traces): for j, cycle in enumerate(roi): cur_mins = [] for b in baseline_ix: ITI = cycle[b[0]:b[1]] sroi = movmean(ITI, 30) cur_mins.append(np.min(sroi)) bsl = np.min(cur_mins) roi_df = (cycle - bsl) / bsl dff[i,j,:] = roi_df # rois x cycles x time # smooth the dff # sroi_df = sp.signal.savgol_filter(roi_df, 29, 3) # sroi_df = sp.ndimage.filters.gaussian_filter1d(roi_df,3) # dff[i,j,:] = sroi_df # rois x cycles x time np.save(os.path.join(hd, 'manual', 'DFF'), dff) # check by plotting if plot: plt.subplots() plt.plot(traces[0,0,:], label='raw signal') plt.plot(dff[0,0,:], label='dF/F') plt.legend() plt.show()
def selectRoi():
ds_path = request.form.get('path')
label = request.form.get('label')
plane = float(request.form.get('z'))
point = Point(float(request.form.get('x')), float(request.form.get('y')))
dataset = ImagingDataset.load(ds_path)
rois = ROIList.load(os.path.join(dataset.savedir, 'rois.pkl'), label=label)
for roi in rois:
for poly in roi.polygons:
z_coord = np.array(poly.exterior.coords)[0, 2]
if z_coord == plane or plane == -1:
if poly.contains(point):
return jsonify(label=roi.label, id=roi.id)
return jsonify({'error': 'roi not found'})
def getRois():
ds_path = request.form.get("path")
label = request.form.get("label")
dataset = ImagingDataset.load(ds_path)
convertedRois = {}
rois = ROIList.load(os.path.join(dataset.savedir, "rois.pkl"), label=label)
for i, roi in enumerate(rois):
if roi.label is None:
roi.label = i
convertedRois[roi.label] = {}
for poly in roi.polygons:
coords = np.array(poly.exterior.coords)
plane = int(coords[0, -1])
# coords = list(coords[:,:2].ravel())
coords = coords[:, :2].tolist()
try:
convertedRois[roi.label][plane].append(coords)
except KeyError:
convertedRois[roi.label][plane] = [coords]
return jsonify(**convertedRois)
def getRois():
ds_path = request.form.get('path')
label = request.form.get('label')
dataset = ImagingDataset.load(ds_path)
convertedRois = {}
rois = ROIList.load(os.path.join(dataset.savedir, 'rois.pkl'), label=label)
for i, roi in enumerate(rois):
if roi.label is None:
roi.label = i
convertedRois[roi.label] = {}
for poly in roi.polygons:
coords = np.array(poly.exterior.coords)
plane = int(coords[0, -1])
#coords = list(coords[:,:2].ravel())
coords = coords[:, :2].tolist()
try:
convertedRois[roi.label][plane].append(coords)
except KeyError:
convertedRois[roi.label][plane] = [coords]
return jsonify(**convertedRois)
def ROILoadFromDir(_path, _dir):
fullRAP_roi_list = []
for i in range(0, len(_dir)):
fullRAP_roi_list.append(ROIList.load(_path + _dir[i], fmt='ImageJ'))
return fullRAP_roi_list
