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
def view0(modules,
data_src = 'bdtnp',
net_src = 'fRN',
max_rank = 4,
module_type = 'doubles'):
'''
A routine to view the sign of interaction coefficients for
a given transcription factor split per-cluster and per-module
size.
Designed to be run on the output of view_output.modules()
'''
#COMPUTE BULK STATISTICS FOR EACH TF
bd_data = nio.getBDTNP()
genes = bd_data.keys()
tfs =sorted(set(it.chain(*[k for k in modules.keys()])))
tf_net = nx.Graph()
tf_net.add_nodes_from(tfs)
tf_edges = it.chain(*[[(e0, e1)
for e0 in term
for e1 in term
if e0 != e1]
for term in modules.keys()])
tf_net.add_edges_from(tf_edges)
pos = nx.graphviz_layout(tf_net)
fig = myplots.fignum(1, (8,8))
tfnodes = tf_net.nodes()
tfnames = tfnodes
all_coefs = \
list(it.chain(*[t['coefs'] for t in modules.values()]))
cstd = std(all_coefs)
def colorfun(coefs):
coef = median(coefs)
arr = array([1.,0.,0.]) if coef < 0\
else array([0.,0.,1.])
return arr*min([1, abs(coef/cstd*2)])
def widthfun(coefs, maxwid):
return max([1,5.* len(coefs) /maxwid])
for tf in tfs:
fig.clf()
ax = fig.add_subplot(111)
ecols,ewids, estyles, ealphas =\
[{} for i in range(4)]
edges = []
tf_doublet_terms = [(k, v)
for k, v in modules.iteritems()
if tf in k and len(set(k)) == 2 ]
tf_triplet_terms = [(k, v)
for k, v in modules.iteritems()
if tf in k and len(set(k)) == 3 ]
isdub = dict([(k,0.) for k in tfnames])
istrip =dict([(k,0.) for k in tfnames])
coflens = [len(e[1]['coefs'])
for e in tf_triplet_terms + tf_doublet_terms]
max_l = max(coflens)
for tt,v in sorted(tf_triplet_terms,
key = lambda x: len(x[1]['genes']))[::-1][:max_rank]:
partners =tuple( [t for t in set(tt) if not t==tf])
for p in partners: istrip[p] = 1 #istrip[[tfnodes.index(p) for p in partners]] = 1
edge = partners
ecols[edge] = colorfun(modules[tt]['coefs'])
ewids[edge] = widthfun(modules[tt]['coefs'],max_l)
ealphas[edge] = 1 if module_type in ['triples','all'] \
else .1
estyles[edge] = 'dotted'
edges.append(edge)
for td,v in sorted(tf_doublet_terms,
key = lambda x: len(x[1]['genes']))[::-1][:max_rank]:
partners = tuple([t for t in set(td) if not t==tf])
for p in partners: isdub[p] = 1
edge = (tuple([tf] + list(partners)))
ecols[edge] = colorfun(modules[td]['coefs'])
ewids[edge] = widthfun(modules[td]['coefs'], max_l)
ealphas[edge] = 1 if module_type in ['doubles','all'] \
else .1
estyles[edge] = 'solid'
edges.append(edge)
tf_graph = nx.DiGraph()
tf_graph.add_nodes_from(tfnodes)
tf_graph.add_edges_from(edges)
ckw = dict([ (k, dict(color = ecols[k], #array([1,0,0])*isdub[k] +\
# array([0,0,1])*istrip[k],
alpha = ealphas[k],
linestyle = estyles[k],
linewidth = ewids[k],
arrowstyle = '-'))
for k in tf_graph.edges()])
circlepoints = dict([ (k, dict(facecolor ='white', #array([1,0,0])*isdub[k] +\
# array([0,0,1])*istrip[k],
alpha = round(ealphas[k],2),
edgecolor = ecols[k],
linestyle = estyles[k],
linewidth = 3,))
for k in tf_graph.edges()])
ax.set_title('Top modules for TF: {0}'.format(tf))
myplots.padded_limits(ax, *zip(*pos.values()))
nodes = tf_graph.nodes()
gd.draw(tf_graph,pos,edges,
scatter_nodes =tf_graph.nodes(),
skw = {'s':[200 if n == tf else 2
for n in nodes],
'facecolor':[colorfun(modules[tuple([n])]['coefs'])
if n == tf
else 'black'
for n in nodes],
'edgecolor':'black',
'linewidth':2,
'alpha':1},#[1 if n == tf else .1 for n in nodes]},
ckw = {})
colors,alphas,tripoints = [{} for i in range(3)]
for e in edges:
colors[e] = 'black'
alphas[e] = .2
tripoints[e] = array(pos[tf])
plot_tri = False
plot_circ = True
if plot_tri:
gd.overlay(tf_graph, pos, edges,
tripoints = tripoints,
colors = colors,
alphas = alphas)
if plot_circ:
gd.overlay(tf_graph, pos, edges,
circlepoints =circlepoints)
ax2 = fig.add_axes([.05,.05,.2,.2])
ax2.set_xticks([])
ax2.set_yticks([])
coefs = modules[tuple([tf])]['coefs']
l = len(coefs)
sx = sy = ceil(sqrt(l))
xs = mod(arange(l), sx)
ys = floor( arange(l) / sx)
cs = [colorfun([c/2]) for c in sorted(coefs)]
ss = 100
ax2.scatter(xs, ys, s = ss, color = cs)
fig.savefig(figtemplate.format('tf_{0}_net_rank{1}_{2}'.\
format(tf,max_rank,module_type)))
