import numpy as np
from ..strategy import ConnectionStrategy
[docs]class ConnectomePFPurkinje(ConnectionStrategy):
"""
Legacy implementation for the connections between parallel fibers and purkinje cells.
"""
[docs] def validate(self):
pass
def connect(self):
# Gather information for the legacy code block below.
granule_cell_type = self.from_cell_types[0]
purkinje_cell_type = self.to_cell_types[0]
granules = self.scaffold.cells_by_type[granule_cell_type.name]
purkinjes = self.scaffold.cells_by_type[purkinje_cell_type.name]
first_granule = int(granules[0, 0])
purkinje_extension_x = purkinje_cell_type.placement.extension_x
def connectome_pf_pc(first_granule, granules, purkinjes, x_pc):
pf_pc = np.zeros((0, 2))
# for all Purkinje cells: calculate and choose which parallel fibers fall into the area of PC dendritic tree (then delete them from successive computations, since 1 parallel fiber is connected to a maximum of PCs)
for i in purkinjes:
# which parallel fibers fall into the x range of values?
bool_matrix = (granules[:, 2]).__ge__(i[2] - x_pc / 2.0) & (
granules[:, 2]
).__le__(
i[2] + x_pc / 2.0
) # CAMBIARE IN new_granules SE VINCOLO SU 30 pfs
good_pf = np.where(bool_matrix)[
0
] # finds indexes of parallel fibers that, on the selected axis, satisfy the condition
# construction of the output matrix: the first column has the GrC id, while the second column has the PC id
matrix = np.zeros((len(good_pf), 2))
matrix[:, 1] = i[0]
matrix[:, 0] = good_pf + first_granule
pf_pc = np.vstack((pf_pc, matrix))
return pf_pc
result = connectome_pf_pc(
first_granule, granules, purkinjes, purkinje_extension_x
)
self.scaffold.connect_cells(self, result)
