def plot_easy_inference():
dg = io.getGraph()
pos = gd.getpos(dg)
f = myplots.fignum(4, (8,8))
ax = f.add_subplot(111)
ax.set_title('putative worm chip network')
gd.easy_draw(dg, pos)
f.savefig(myplots.figpath('worm_chip_graph.pdf'))
def load(plots = defplots,
reset = False):
kwargs = dict(reset = reset)
edge_set = get_edge_set()
g = get_graph(**mem.sr(kwargs))
pos = get_pos(**mem.sr(kwargs))
trips = set([])
for k1 in g:
for k2 in g[k1].keys():
for k3 in g[k1].keys():
if g[k2].has_key(k3):
trips.add((k2,k3,k1))
tripoints = dict([((e[0],e[1]),pos[e[2]]) for e in trips])
if plots.get('basic_structure', False):
f = myplots.fignum(1)
ax = f.add_subplot(111)
gd.easy_draw(g, pos)
f.savefig(myplots.figpath('basic_structure_edges={0}'.format(edge_set)))
if plots.get('feed_forward', True):
gd.overlay(g,pos,g.edges(),
tripoints = tripoints,
alphas = dict([(e,.1) for e in g.edges()]))
f.savefig(myplots.figpath('feed_forward_edges={0}'.format(edge_set)))
if plots.get('degrees' , False):
make_degree_plots_0();
maxflow = nx.algorithms.ford_fulkerson(g, 'AVAL','PVPL','weight')
imaps = get_array_imaps()
nnames = imaps['nnames']
node_stats = dict([(k,{}) for k in nnames])
for k,v in node_stats.iteritems():
v['out_degree'] = len([e for e in g.edges() if e[0] == k])
v['in_degree'] = len([e for e in g.edges() if e[1] == k])
f = myplots.fignum(3, (12,6))
outs = [v['out_degree'] for k, v in node_stats.iteritems()]
ins =[v['in_degree'] for k , v in node_stats.iteritems()]
raw_data= array([outs,ins]).T
make_data_transform(raw_data)
data = transform_data(raw_data)
kd = make_kdtree(data)
k = 5
nn = compute_nns(kd, k)
knn= nn['nn']
knn_dists = nn['dists']
dists = compute_dists(data)
mean_dists = np.mean(knn_dists[:,1:],1)
mean_colors =sqrt(mean_dists[:,newaxis] * [1/np.max(mean_dists), 0,0])
ax = f.add_subplot(121)
ax.scatter(data[:,0],data[:,1],s = 15,
facecolor = mean_colors,
edgecolor = 'none'
)
ax.set_xlabel('scaled out degree')
ax.set_ylabel('scaled in degree')
ax2 = f.add_subplot(122)
ax2.imshow(dists,
interpolation = 'nearest',
aspect = 'auto')
ax2.set_title('distance matrix for scaled degrees')
f.savefig(myplots.figpath('distances_{0}'.format(edge_set)))
return g
