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Merge branch 'dev_jd' into dev_pg
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* dev_jd:
  Add missing file
  Visualise interareal connectivity for 30 regions
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@pgleeson
pgleeson committed Jul 1, 2019
2 parents 2c62bd7 + b912ba9 commit e7ddae9
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Showing 2 changed files with 41 additions and 13 deletions.
24 changes: 11 additions & 13 deletions NeuroML2/GenerateNeuroMLlite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,16 +160,6 @@ def internal_connections(pops, W, pre_pop, post_pop):

nlayers = 4 #(L2/3E, L5/6, L2/3I, L5/6I)

# If a FF or a FB connection the opposite connection cannot be specified. Therefore, check if the user did not
# specifiy both a FF and FB connection between two regions.
# extract values for the lower diagonal matrix
conn_tril = conn[np.tril_indices(n_areas, -1)]
conn_triu = conn[np.triu_indices(n_areas, 1)]
# if any(conn_tril + conn_triu >= 2):
# raise ValueError('Both a FF and FB connection was specified for one of the areas. '
# 'Please check your connectivity matrix')


# Define connections to self areas (diagonal entries)
interlayer_conn_diag = np.array([[wee, wei, i_l2e_l5e, 0],
[wie, wii, 0, 0],
Expand Down Expand Up @@ -820,7 +810,9 @@ def internal_connections(pops, W, pre_pop, post_pop):
net_id='Interareal'


if '-3rois' not in sys.argv and '-4rois' not in sys.argv:
if '-3rois' not in sys.argv and \
'-4rois' not in sys.argv and \
'-30rois' not in sys.argv:
# Background current simulation.
# Note: For testing porpose, only the rest simulation is performed if the flag '-analysis' is not
# passed
Expand Down Expand Up @@ -931,6 +923,7 @@ def internal_connections(pops, W, pre_pop, post_pop):
#Load the array with the ordered rank
import pandas as pd

ranking = pd.read_csv('../Python/interareal/areas_ranking.txt')
if '-3rois' in sys.argv:
areas = ['V1', 'V4', 'MT']
# Create the FB and FF connectiviy between the areas.
Expand All @@ -948,10 +941,15 @@ def internal_connections(pops, W, pre_pop, post_pop):
[1, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]])


if '-30rois' in sys.argv:
areas = list(ranking['region'])
conn = pd.read_csv('../Python/interareal/fln_conn.txt', sep='\t', header=None)
# Transfrom dataframe into numpy array
conn = np.array(conn)

net_id='Interareal_%d' %len(areas)

ranking = pd.read_csv('../Python/interareal/areas_ranking.txt')
# Check if the selected regions are present in the file that describe the ranking
assert(len(areas) == sum(ranking['region'].isin(areas)))
# sort the regions by the areas in the ranking
Expand Down
30 changes: 30 additions & 0 deletions Python/interareal/fln_conn.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,30 @@
0 1 1 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0
1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0
1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0
1 1 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1
1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0
0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0
1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1
0 1 1 1 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1
0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0
0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1
0 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1
0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1
1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
0 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0
0 1 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 0 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 0
0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1
0 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1
0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 1
0 0 0 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1
0 1 0 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1
0 0 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 0 1 1
0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1
0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0

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