def add_all_components(self, cnmf_data_dict: dict, input_params_dict: dict):
"""
Add all components from a CNMF(E) output. Arguments correspond to CNMF(E) outputs
:param cnmf_data_dict: CNMF results data directly from the HDF5 file
:param input_params_dict: dict of input params, from the batch manager
:param calc_raw_min_max: Calculate raw min & max for each ROI
:return:
"""
if not hasattr(self, 'roi_list'):
self.create_roi_list()
self.cnmf_data_dict = cnmf_data_dict
# self.cnmf_obj = load_CNMF(self.cnmf_data_dict)
self.cnmA = self.cnmf_data_dict['estimates']['A']
self.cnmb = self.cnmf_data_dict['estimates']['b']
self.cnm_f = self.cnmf_data_dict['estimates']['f']
self.cnmC = self.cnmf_data_dict['estimates']['C']
self.cnmYrA = self.cnmf_data_dict['estimates']['YrA']
self.dims = self.cnmf_data_dict['dims']
self.cnmS = self.cnmf_data_dict['estimates']['S']
self.cnm_dfof = self.cnmf_data_dict['estimates']['F_dff']
# components are already filtered from the output file
self.idx_components = np.arange(self.cnmC.shape[0])
self.orig_idx_components = deepcopy(self.idx_components)
self.input_params_dict = input_params_dict
# spatial components
contours = caiman_get_contours(self.cnmA[:, self.idx_components], self.dims, thr=0.9)
temporal_components = self.cnmC
self.input_params_dict = self.input_params_dict
num_components = len(temporal_components)
self.ui.radioButton_curve_data.setChecked(True)
for ix in range(num_components):
self.vi.viewer.status_bar_label.showMessage('Please wait, adding component #: '
+ str(ix) + ' / ' + str(num_components))
curve_data = temporal_components[ix]
contour = contours[ix]
roi = VolCNMF(curve_plot_item=self.get_plot_item(),
view_box=self.vi.viewer.getView(),
cnmf_idx=self.idx_components[ix],
curve_data=curve_data,
contour=contour,
dfof_data=self.cnm_dfof[ix] if (self.cnm_dfof is not None) else None,
spike_data=self.cnmS[ix])
self.roi_list.append(roi)
self.vi.workEnv_changed("ROIs imported")
self.roi_list.reindex_colormap(random_shuffle=True)
self.vi.viewer.status_bar_label.showMessage('Finished adding all components!')
def add_all_components(self,
cnmf_data_dict,
input_params_dict,
calc_raw_min_max=False):
"""
Add all components from a CNMF(E) output. Arguments correspond to CNMF(E) outputs
:param cnmf_data_dict: CNMF results data directly from the HDF5 file
:param input_params_dict: dict of input params, from the batch manager
:param calc_raw_min_max: Calculate raw min & max for each ROI
:return:
"""
if not hasattr(self, 'roi_list'):
self.create_roi_list()
self.cnmf_data_dict = cnmf_data_dict
self.cnmA = self.cnmf_data_dict['estimates']['A']
self.cnmb = self.cnmf_data_dict['estimates']['b']
self.cnm_f = self.cnmf_data_dict['estimates']['f']
self.cnmC = self.cnmf_data_dict['estimates']['C']
self.cnmS = self.cnmf_data_dict['estimates']['S']
self.cnm_dfof = self.cnmf_data_dict['estimates']['F_dff']
self.cnmYrA = self.cnmf_data_dict['estimates']['YrA']
self.dims = self.cnmf_data_dict['dims']
self.idx_components = cnmf_data_dict['estimates']['idx_components']
if self.idx_components is None:
self.idx_components = np.arange(self.cnmC.shape[0])
self.orig_idx_components = deepcopy(self.idx_components)
self.input_params_dict = input_params_dict
# spatial components
contours = caiman_get_contours(self.cnmA[:, self.idx_components],
self.dims)
# if dfof:
# temporal_components = cnmC
# else:
# temporal_components = cnmC[idx_components]
self.input_params_dict = self.input_params_dict
num_components = len(self.cnmC)
if calc_raw_min_max:
img = self.vi.viewer.workEnv.imgdata.seq.T
self.ui.radioButton_curve_data.setChecked(True)
for ix in range(num_components):
self.vi.viewer.status_bar_label.showMessage(
'Please wait, adding component #: ' + str(ix) + ' / ' +
str(num_components))
curve_data = self.cnmC[ix]
contour = contours[ix]
if calc_raw_min_max:
# Get a binary mask
mask = self.cnmA[:, self.idx_components[ix]].toarray().reshape(
self.dims, order='F') > 0
# mask3d = np.array((mask,) * curve_data.shape[0])
max_ix = curve_data.argmax()
min_ix = curve_data.argmin()
array_at_max = img[max_ix, :, :].copy()
array_at_max = array_at_max[mask]
array_at_min = img[min_ix, :, :].copy()
array_at_min = array_at_min[mask]
raw_min_max = self.get_raw_min_max(array_at_max=array_at_max,
array_at_min=array_at_min)
else:
raw_min_max = None
roi = CNMFROI(curve_plot_item=self.get_plot_item(),
view_box=self.vi.viewer.getView(),
cnmf_idx=self.idx_components[ix],
curve_data=curve_data,
contour=contour,
raw_min_max=raw_min_max,
dfof_data=self.cnm_dfof[ix] if
(self.cnm_dfof is not None) else None,
spike_data=self.cnmS[ix])
self.roi_list.append(roi)
if calc_raw_min_max:
del img
self.roi_list.reindex_colormap()
self.vi.viewer.status_bar_label.showMessage(
'Finished adding all components!')
def restore_from_states(self, states: dict):
if 'metadata' in states.keys():
self.metadata = states['metadata']
if not hasattr(self, 'roi_list'):
self.create_roi_list()
self.cnmf_data_dicts = states['cnmf_data_dicts']
# for state in states['states']:
# roi = VolMultiCNMFROI.from_state(
# self.get_plot_item(),
# self.vi.viewer.getView(),
# state
# )
#
# self.roi_list.append(roi)
self.input_params_dict = states['input_params_cnmf']
self.num_zlevels = states['num_zlevels']
self.cnmA = states['cnmf_output']['cnmA']
self.cnmb = states['cnmf_output']['cnmb']
self.cnmC = states['cnmf_output']['cnmC']
self.cnm_f = states['cnmf_output']['cnm_f']
self.cnmYrA = states['cnmf_output']['cnmYrA']
self.idx_components = states['cnmf_output']['idx_components']
self.orig_idx_components = states['cnmf_output']['orig_idx_components']
self.dims = states['cnmf_output']['dims']
if not hasattr(self, 'roi_list'):
self.create_roi_list()
for zcenter in range(self.num_zlevels):
print(f"Loading z-level {zcenter}")
contours = caiman_get_contours(
self.cnmA[zcenter][:, self.idx_components[zcenter]],
self.dims[zcenter],
# swap_dim=True
)
num_components = len(self.idx_components[zcenter])
self.ui.radioButton_curve_data.setChecked(True)
roi_ixs = []
roi_xy = []
for ix in range(len(contours)):
coors = contours[ix]['coordinates']
coors = coors[~np.isnan(coors).any(axis=1)]
roi_xy += [coors]
roi_ixs += [ix] * coors.shape[0]
roi_xy = np.vstack(roi_xy)
roi_ixs = np.vstack(roi_ixs)
self.roi_xys.append(roi_xy)
self.roi_ixs.append(roi_ixs)
cm = matplotlib_color_map.get_cmap('hsv')
cm._init()
lut = (cm._lut * 255).view(np.ndarray)
cm_ixs = np.linspace(0, 210, np.unique(roi_ixs).size + 1, dtype=int)
roi_crs = []
for roi_ix, cm_ix in zip(np.unique(roi_ixs), cm_ixs):
c = lut[cm_ix]
roi_crs.append(
np.array([c] * roi_ixs[roi_ixs == roi_ix].size) # color for each spot
)
roi_crs = np.vstack(roi_crs)
self.roi_crs.append(roi_crs)
xy_coors = self.roi_xys[-1]
brushes = list(map(pg.mkBrush, roi_crs))
pens = list(map(pg.mkPen, roi_crs))
sp = pg.ScatterPlotItem(
xy_coors[:, 0],
xy_coors[:, 1],
symbol='s',
size=1,
pxMode=True,
brush=brushes,
pen=pens
)
self.vi.viewer.getView().addItem(sp)
sp.hide()
self.roi_sps.append(sp)
for ix in tqdm(range(len(self.idx_components[zcenter]))):
self.vi.viewer.status_bar_label.showMessage(
f"Please wait, adding component {ix} / {num_components} "
f"on zlevel {zcenter} / {self.num_zlevels - 1}"
)
curve_data = self.cnmC[zcenter][self.idx_components[zcenter][ix]]
contour = contours[ix]
cnmf_idx = self.idx_components[zcenter][ix]
roi = VolMultiCNMFROI(
curve_plot_item=self.get_plot_item(),
view_box=self.vi.viewer.getView(),
cnmf_idx=cnmf_idx,
curve_data=curve_data,
contour=contour,
zcenter=zcenter,
zlevel=self.vi.viewer.current_zlevel,
roi_ix=ix,
scatter_plot=sp,
parent_manager=self,
)
roi_state = list(
filter(
lambda r: r['cnmf_idx'] == cnmf_idx,
states['states'][zcenter]
)
)[0]
for k in roi_state['tags'].keys():
roi.set_tag(k, roi_state['tags'][k])
roi.dfof_data = roi_state['dfof_data']
roi.spike_data = roi_state['spike_data']
self.roi_list.append(roi)
self.roi_list.list_widget.addItems(
list(map(str, range(len(self.roi_list))))
)
self.vi.workEnv_changed("ROIs imported")
# self.roi_list.reindex_colormap(random_shuffle=True)
self.roi_sps[self.vi.viewer.current_zlevel].show()
self.vi.viewer.status_bar_label.showMessage('Finished adding all components!')
def add_all_components(
self,
cnmf_data_dicts: List[dict],
input_params_dict: dict,
):
self.input_params_dict = input_params_dict
if not hasattr(self, 'roi_list'):
self.create_roi_list()
self.cnmf_data_dicts = cnmf_data_dicts
self.num_zlevels = len(self.cnmf_data_dicts)
for zcenter, cnmf_data_dict in enumerate(self.cnmf_data_dicts):
self.cnmA.append(cnmf_data_dict['estimates']['A'])
self.cnmb.append(cnmf_data_dict['estimates']['b'])
self.cnm_f.append(cnmf_data_dict['estimates']['f'])
self.cnmC.append(cnmf_data_dict['estimates']['C'])
self.cnmS.append(cnmf_data_dict['estimates']['S'])
self.cnm_dfof.append(cnmf_data_dict['estimates']['F_dff'])
self.cnmYrA.append(cnmf_data_dict['estimates']['YrA'])
self.dims.append(cnmf_data_dict['dims'])
self.idx_components.append(cnmf_data_dict['estimates']['idx_components'])
if self.idx_components[-1] is None:
self.idx_components[-1] = np.arange(self.cnmC[-1].shape[0])
self.orig_idx_components.append(
deepcopy(
self.idx_components[-1]
)
)
contours = caiman_get_contours(
self.cnmA[-1][:, self.idx_components[-1]],
self.dims[-1],
# swap_dim=True
)
num_components = len(self.idx_components[-1])
self.ui.radioButton_curve_data.setChecked(True)
roi_ixs = []
roi_xy = []
for ix in range(len(contours)):
coors = contours[ix]['coordinates']
coors = coors[~np.isnan(coors).any(axis=1)]
roi_xy += [coors]
roi_ixs += [ix] * coors.shape[0]
roi_xy = np.vstack(roi_xy)
roi_ixs = np.vstack(roi_ixs)
self.roi_xys.append(roi_xy)
self.roi_ixs.append(roi_ixs)
cm = matplotlib_color_map.get_cmap('hsv')
cm._init()
lut = (cm._lut * 255).view(np.ndarray)
cm_ixs = np.linspace(0, 210, np.unique(roi_ixs).size + 1, dtype=int)
roi_crs = []
for roi_ix, cm_ix in zip(np.unique(roi_ixs), cm_ixs):
c = lut[cm_ix]
roi_crs.append(
np.array([c] * roi_ixs[roi_ixs == roi_ix].size) # color for each spot
)
roi_crs = np.vstack(roi_crs)
self.roi_crs.append(roi_crs)
xy_coors = self.roi_xys[-1]
brushes = list(map(pg.mkBrush, roi_crs))
pens = list(map(pg.mkPen, roi_crs))
sp = pg.ScatterPlotItem(
xy_coors[:, 0],
xy_coors[:, 1],
symbol='s',
size=1,
pxMode=True,
brush=brushes,
pen=pens
)
self.vi.viewer.getView().addItem(sp)
sp.hide()
self.roi_sps.append(sp)
for ix in range(num_components):
self.vi.viewer.status_bar_label.showMessage(
f"Please wait, adding component {ix} / {num_components} "
f"on zlevel {zcenter} / {self.num_zlevels - 1}"
)
curve_data = self.cnmC[-1][ix]
contour = contours[ix]
if self.cnm_dfof[-1] is not None:
dfof_data = self.cnm_dfof[-1][ix]
else:
dfof_data = None
roi = VolMultiCNMFROI(
curve_plot_item=self.get_plot_item(),
view_box=self.vi.viewer.getView(),
cnmf_idx=self.idx_components[-1][ix],
curve_data=curve_data,
contour=contour,
spike_data=self.cnmS[-1][ix],
dfof_data=dfof_data,
zcenter=zcenter,
zlevel=self.vi.viewer.current_zlevel,
roi_ix=ix,
scatter_plot=sp,
parent_manager=self,
)
self.roi_list.append(roi, add_to_list_widget=False)
self.roi_list.list_widget.addItems(
list(map(str, range(len(self.roi_list))))
)
self.vi.workEnv_changed("ROIs imported")
# self.roi_list.reindex_colormap(random_shuffle=True)
self.roi_sps[self.vi.viewer.current_zlevel].show()
self.vi.viewer.status_bar_label.showMessage('Finished adding all components!')
def add_all_components(self,
cnmA,
cnmb,
cnmC,
cnm_f,
cnmYrA,
idx_components,
dims,
input_params_dict,
dfof=False,
calc_raw_min_max=False):
"""Add all components from a CNMF(E) output. Arguments correspond to CNMF(E) outputs"""
if not hasattr(self, 'roi_list'):
self.create_roi_list()
self.cnmA = cnmA
self.cnmb = cnmb
self.cnmC = cnmC
self.cnm_f = cnm_f
self.cnmYrA = cnmYrA
self.idx_components = idx_components
self.orig_idx_components = deepcopy(idx_components)
self.input_params_dict = input_params_dict
# spatial components
contours = caiman_get_contours(cnmA[:, idx_components], dims)
if dfof:
temporal_components = cnmC
else:
temporal_components = cnmC[idx_components]
self.input_params_dict = self.input_params_dict
num_components = len(temporal_components)
if calc_raw_min_max:
img = self.vi.viewer.workEnv.imgdata.seq.T
for ix in range(num_components):
self.vi.viewer.status_bar_label.showMessage(
'Please wait, adding component #: ' + str(ix) + ' / ' +
str(num_components))
curve_data = temporal_components[ix]
contour = contours[ix]
if calc_raw_min_max:
# Get a binary mask
mask = self.cnmA[:, idx_components[ix]].toarray().reshape(
dims, order='F') > 0
# mask3d = np.array((mask,) * curve_data.shape[0])
max_ix = curve_data.argmax()
min_ix = curve_data.argmin()
array_at_max = img[max_ix, :, :].copy()
array_at_max = array_at_max[mask]
array_at_min = img[min_ix, :, :].copy()
array_at_min = array_at_min[mask]
raw_min_max = self.get_raw_min_max(array_at_max=array_at_max,
array_at_min=array_at_min)
else:
raw_min_max = None
roi = CNMFROI(self.get_plot_item(),
self.vi.viewer.getView(),
idx_components[ix],
curve_data,
contour,
raw_min_max=raw_min_max)
self.roi_list.append(roi)
if calc_raw_min_max:
del img
self.roi_list.reindex_colormap()
self.vi.viewer.status_bar_label.showMessage(
'Finished adding all components!')
