def make_sf_mesh(n=5, sf_params=None, mesh_params=None):
'''
For default values of sf_params, sf_mesh see flow_all.
'''
cxn_rule = mesh_params['cxn_rule']
if cxn_rule == 'random_matrix':
block_odds = reshape(random.rand(square(n)), (n, n)) * .1
block_counts = [sf_params['n'] for i in range(n)]
elif cxn_rule == 'mouse_brain':
centers, volumes = aba.brain_regions(n)
dists = sum(square(centers),1)[:,newaxis] \
+ sum(square(centers),1)[newaxis,:] \
- 2 * dot(centers,centers.T)
ranks = argsort(argsort(dists, 1), 1)
block_odds = 1.0 / square((ranks + 1))
for i in range(len(block_odds)):
block_odds[i, i] = 0
block_counts = array(
[v * sqrt(sf_params['n'] / mean(volumes)) for v in volumes], int)
else:
raise Exception('connection rule {0} not understood'.format(cxn_rule))
sf_graphs = []
for i in range(n):
sfp = {}
sfp.update(sf_params)
sfp['n'] = block_counts[i]
g = make_sf(**sfp)
sf_graphs.append(g)
ofs = 0
gbig = nx.DiGraph()
communities = []
for i, g in enumerate(sf_graphs):
nodes = [n + ofs for n in g.nodes()]
edges = [(e[0] + ofs, e[1] + ofs, g.get_edge_data(e[0],
e[1])['weight'])
for e in g.edges()]
communities.append(nodes)
gbig.add_nodes_from(nodes)
gbig.add_weighted_edges_from(edges)
ofs += len(g)
for i, ci in enumerate(communities):
for j, cj in enumerate(communities):
in_degs = gbig.in_degree(ci)
for node_name in ci:
indeg = in_degs[node_name]
out_idxs = np.unique(
array(
np.floor(
random.rand(indeg * block_odds[i, j]) * len(cj)),
int))
out_nodes = [cj[oidx] for oidx in out_idxs]
out_edges = [(node_name, onode, edge_weight_fun())
for onode in out_nodes]
gbig.add_weighted_edges_from(out_edges)
return gbig, communities
def make_sf_mesh(n =5,
sf_params = None,
mesh_params = None):
'''
For default values of sf_params, sf_mesh see flow_all.
'''
cxn_rule =mesh_params['cxn_rule']
if cxn_rule == 'random_matrix':
block_odds = reshape(random.rand(square(n)), (n,n))*.1
block_counts = [sf_params['n'] for i in range(n)]
elif cxn_rule == 'mouse_brain':
centers,volumes = aba.brain_regions(n)
dists = sum(square(centers),1)[:,newaxis] \
+ sum(square(centers),1)[newaxis,:] \
- 2 * dot(centers,centers.T)
ranks = argsort(argsort(dists,1),1)
block_odds = 1.0 / square((ranks +1))
for i in range(len(block_odds)): block_odds[i,i] = 0
block_counts = array([v * sqrt(sf_params['n']/ mean(volumes) )
for v in volumes],int)
else:
raise Exception('connection rule {0} not understood'.format(cxn_rule))
sf_graphs = []
for i in range(n):
sfp ={}
sfp.update(sf_params)
sfp['n'] = block_counts[i]
g = make_sf(**sfp)
sf_graphs.append(g)
ofs= 0
gbig = nx.DiGraph()
communities = []
for i,g in enumerate(sf_graphs):
nodes = [n + ofs for n in g.nodes()]
edges = [(e[0] + ofs, e[1] + ofs , g.get_edge_data(e[0],e[1])['weight'])
for e in g.edges()]
communities.append(nodes)
gbig.add_nodes_from(nodes)
gbig.add_weighted_edges_from(edges)
ofs += len(g)
for i,ci in enumerate(communities):
for j,cj in enumerate(communities):
in_degs = gbig.in_degree(ci)
for node_name in ci:
indeg = in_degs[node_name]
out_idxs = np.unique(array(
np.floor(random.rand(
indeg*block_odds[i,j])*len(cj))
,int))
out_nodes = [cj[oidx] for oidx in out_idxs]
out_edges = [(node_name, onode, edge_weight_fun())
for onode in out_nodes ]
gbig.add_weighted_edges_from(out_edges)
return gbig, communities
def show_brain_flows(g,ngs,fgs, communities):
centers,volumes, voxels = aba.brain_regions(len(communities),
return_voxels = True)
p0 = {}
n_communities = {}
for i, c in enumerate(communities):
nv = len(voxels[i])
for j, n in enumerate(c):
p0[n] = voxels[i][int(floor(random.rand()*nv))][:2] + random.rand()*.4
n_communities[n] = i
comm_ct = mycolors.getct(len(n_communities))
nodelist = g.nodes()
ckw = dict([(k,dict(facecolor = 'gray',
alpha = 1,
linewidth = .5,
arrowstyle = '-|>',
edgecolor = 'black',
color = comm_ct[n_communities[k[0]]],
shrinkA = 0,
shrinkB = 0))
for k in g.edges() ])
skw =dict(facecolor = 'none',
edgecolor = [comm_ct[n_communities[n]]
for n in nodelist],
s = 1)
gd.draw(g,p0,g.edges()[::10],
scatter_nodes = nodelist,
ckw = ckw,
skw = skw,
ckalpha = .8,
cktype = 'simple')
return
colors = mycolors.getct(len(fgs))
for i,fg in enumerate(fgs):
edges = fg.edges()
weights = [fg[e[0]][e[1]]['weight'] for e in edges]
ckw = dict([(k, dict(color = colors[i],
linewidth = weights[j]/3))
for j, k in enumerate(edges)
])
gd.draw(fg,
p0,
edges,
ckw = ckw,
scatter_nodes = [],
cktype = 'simple',
ckalpha = .25,
)
def show_brain_flows(g, ngs, fgs, communities):
centers, volumes, voxels = aba.brain_regions(len(communities),
return_voxels=True)
p0 = {}
n_communities = {}
for i, c in enumerate(communities):
nv = len(voxels[i])
for j, n in enumerate(c):
p0[n] = voxels[i][int(floor(
random.rand() * nv))][:2] + random.rand() * .4
n_communities[n] = i
comm_ct = mycolors.getct(len(n_communities))
nodelist = g.nodes()
ckw = dict([(k,
dict(facecolor='gray',
alpha=1,
linewidth=.5,
arrowstyle='-|>',
edgecolor='black',
color=comm_ct[n_communities[k[0]]],
shrinkA=0,
shrinkB=0)) for k in g.edges()])
skw = dict(facecolor='none',
edgecolor=[comm_ct[n_communities[n]] for n in nodelist],
s=1)
gd.draw(g,
p0,
g.edges()[::10],
scatter_nodes=nodelist,
ckw=ckw,
skw=skw,
ckalpha=.8,
cktype='simple')
return
colors = mycolors.getct(len(fgs))
for i, fg in enumerate(fgs):
edges = fg.edges()
weights = [fg[e[0]][e[1]]['weight'] for e in edges]
ckw = dict([(k, dict(color=colors[i], linewidth=weights[j] / 3))
for j, k in enumerate(edges)])
gd.draw(
fg,
p0,
edges,
ckw=ckw,
scatter_nodes=[],
cktype='simple',
ckalpha=.25,
)