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
"""
ccg = copy.deepcopy(self.cell_class_groups)
if self.cell_groups is None:
self.cell_classes = []
for group in self.params.children()[1:]:
if group.value() is True:
self.cell_classes.extend(ccg[group.name()])
self.cell_classes = self.layer_call(self.cell_classes)
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
(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