def get_cell_groups(self, pairs):
"""Given a list of cell pairs, return a dict indicating which cells
are members of each user selected cell class.
This internally calls cell_class.classify_cells
"""
if self.cell_groups is None:
self.cell_classes = []
for group in self.params.children():
if group.value() is True:
self.cell_classes.extend(cell_class_groups[group.name()])
self.cell_classes = [CellClass(**c) for c in self.cell_classes]
self.cell_groups = classify_cells(self.cell_classes, pairs=pairs)
return self.cell_groups, self.cell_classes
def get_cell_groups(self, pairs):
"""Given a list of cell pairs, return a dict indicating which cells
are members of each user selected cell class.
This internally calls cell_class.classify_cells
"""
if self.cell_groups is None:
self.cell_classes = []
for group in self.params.children():
if group.value() is True:
self.cell_classes.extend(self.cell_class_groups[group.name()])
self.cell_classes = [CellClass(**c) for c in self.cell_classes]
self.cell_groups = classify_cells(self.cell_classes, pairs=pairs)
return self.cell_groups, self.cell_classes
def update_matrix(self):
project_names = self.win.filter_control_panel.selected_project_names()
# Select pairs (todo: age, acsf, internal, temp, etc.)
self.pairs = query_pairs(project_name=project_names, session=self.session).all()
# Group all cells by selected classes
cell_groups = classify_cells(self.cell_classes, pairs=self.pairs)
# Group pairs into (pre_class, post_class) groups
pair_groups = classify_pairs(self.pairs, cell_groups)
# analyze matrix elements
results = self.analysis_func(pair_groups)
shape = (len(cell_groups),) * 2
text = np.empty(shape, dtype=object)
fgcolor = np.empty(shape, dtype=object)
bgcolor = np.empty(shape, dtype=object)
bordercolor = np.empty(shape, dtype=object)
# call display function on every matrix element
for i,row in enumerate(cell_groups):
for j,col in enumerate(cell_groups):
output = self.display_func(row, col, results[(row, col)])
text[i, j] = output['text']
fgcolor[i, j] = output['fgcolor']
bgcolor[i, j] = output['bgcolor']
bordercolor[i, j] = output['bordercolor']
# Force cell class descriptions down to tuples of 2 items
# Kludgy, but works for now.
rows = []
for cell_class in self.cell_classes:
tup = cell_class.as_tuple
row = tup[:1]
if len(tup) > 1:
row = row + (' '.join(tup[1:]),)
rows.append(row)
self.win.matrix_widget.set_matrix_data(text=text, fgcolor=fgcolor, bgcolor=bgcolor, border_color=bordercolor,
rows=rows, cols=rows, size=50, header_color='k')
(CellClass(target_layer='2', pyramidal=True), 'L2', (247, 118, 118)),
(CellClass(target_layer='3', pyramidal=True), 'L3', (246, 197, 97)),
(CellClass(target_layer='4', pyramidal=True), 'L4', (100, 202, 103)),
(CellClass(target_layer='5', pyramidal=True), 'L5', (107, 155, 250)),
(CellClass(target_layer='6', pyramidal=True), 'L6', (153, 51, 255)),
]
session = db.Session()
max_distance = None#100e-6
# analyze connectivity <100 um for mouse
mouse_pairs = query_pairs(acsf="2mM Ca & Mg", age=(40, None), species='mouse', distance=(None, max_distance), session=session).all()
mouse_groups = classify_cells([c[0] for c in mouse_classes], pairs=mouse_pairs)
mouse_results = measure_connectivity(mouse_pairs, mouse_groups)
# analyze connectivity < 100 um for human
human_pairs = query_pairs(species='human', distance=(None, max_distance), session=session).all()
human_groups = classify_cells([c[0] for c in human_classes], pairs=human_pairs)
human_results = measure_connectivity(human_pairs, human_groups)
csv_file = open("manuscript_fig_4.csv", 'wb')
# plot connectivity < 100 um
for conn_plot, results, cell_classes, fig_letter in [
(mouse_conn_plot, mouse_results, mouse_classes, 'A'),
(human_conn_plot, human_results, human_classes, 'C')]:
# set axis labels