def build_phase_plot(sym1, sym2):
import pylab
f = pylab.figure()
pylab.plot(res[sym1], res[sym2])
pylab.xlabel('Symbol: %s' % sym1)
pylab.ylabel('Symbol: %s' % sym2)
return mredoc.Figure(f)
def build_trace_plot(sym):
import pylab
f = pylab.figure()
pylab.plot(res['t'], res[sym])
pylab.xlabel('Time (s)')
pylab.ylabel('Symbol: %s')
return mredoc.Figure(f)
def build_comp_section(param):
fig = figures[param]
trcs = sorted( trace_comp_res[param] )
comp_tbl_header, comp_tbl_data= zip( *[ ('%s.max_ptp' %c, trace_comp_res[param][c].max_ptp) for c in trcs])
return mrd.Section('Parameters: %s' % str(param),
mrd.VerticalColTable(comp_tbl_header, [comp_tbl_data] ),
mrd.Figure( mrd.Image(fig, auto_adjust=False), caption='Comparison'),
)
def build_population_complete_dot(self):
fig_count = 0
fig_out = '/tmp/dotimages/'
import pydot
graph = pydot.Dot('graphname',
graph_type='digraph',
size='7,7',
ratio='compress',
compound='true',
splines='true',
sep='0.3')
size = '0.55'
fontsize = '6'
kwargs_general = {
'fontsize': fontsize,
'fixedsize': 'True',
'width': size,
'height': size,
'fontname': 'Helvetica'
}
cell_size = '0.15'
kwargs_cell = {
'shape': 'circle',
'fillcolor': '#80b3ffff',
'color': '#0066ffff',
'style': 'filled',
'penwidth': '1',
'width': cell_size,
'height': cell_size
}
kwargs_cc = {
'shape': 'circle',
'style': 'filled',
'width': '0.05',
}
kwargs_pop = {
'style': 'filled',
'color': 'lightgrey',
'nodesep': '100'
}
kwargs_synpop = {'shape': 'none', 'fixedsize': 'false'}
kwargs_synpop_img = {
'shape': 'square',
'labelloc': 'b',
'scale': 'false',
'fixedsize': 'true',
}
kwargs_synpop_edge = {
'penwidth': '3',
'color': 'green',
'minlen': '50'
}
# Map Simulation objects into dot objects:
obj2nodedict = {}
subgraphs = []
# Populations become subgraphs:
for population in self.sim.neuron_populations:
n = pydot.Cluster(population.pop_name,
label=population.pop_name,
**dict(kwargs_general.items() +
kwargs_pop.items()))
subgraphs.append(n)
obj2nodedict[population] = n
# Cells into Nodes
for cell in self.sim.cells:
n = pydot.Node(
cell.name,
label=cell.name if cell.population is None else '<%d>' %
cell.index_in_pop,
**dict(kwargs_general.items() + kwargs_cell.items()))
obj2nodedict[cell] = n
if cell.population:
obj2nodedict[cell.population].add_node(n)
else:
graph.add_node(n)
for sg in subgraphs:
graph.add_subgraph(sg)
del subgraphs
# Synapse Populations are turned into a node, with edges from pre and
# to the post synaptic population:
for synpopindex, synpop in enumerate(self.sim.synapse_populations):
synpopcluster = pydot.Cluster('SynpopCluster' +
synpop.synapse_pop_name)
# Create the connectivity graph:
connectivity_graph_figure = build_connectivity_graph(synpop)
fname = fig_out + '/synpop%d.png' % synpopindex
pylab.savefig(fname, transparent=True, dpi=400, bb_inches='tight')
n = pydot.Node(synpop.synapse_pop_name + 'im',
label='',
image=fname,
**dict(kwargs_general.items() +
kwargs_synpop_img.items()))
synpopcluster.add_node(n)
label = ''
label += synpop.synapse_pop_name
len_prepop = len(synpop.presynaptic_population
) if synpop.presynaptic_population else 1
pc_conn = 100. * len(synpop) / (
len_prepop * len(synpop.postsynaptic_population))
#print pc_conn
#pc_conn=50.
#label+= '\\nType: %s'% (synpop.type)
label += '\\nSynapses: %d (%d%%)' % (len(synpop), pc_conn)
#label= synpop.synapse_pop_name
n = pydot.Node(synpop.synapse_pop_name + 'cap',
label='"%s"' % label,
**dict(kwargs_general.items() +
kwargs_synpop.items()))
synpopcluster.add_node(n)
obj2nodedict[synpop] = synpopcluster
graph.add_subgraph(synpopcluster)
# Connect to pre- and post- synaptic pops:
post_pop = synpop.postsynaptic_population
e = pydot.Edge(
synpopcluster.get_name(), obj2nodedict[post_pop].get_name(),
**dict(kwargs_general.items() + kwargs_synpop_edge.items()))
graph.add_edge(e)
pre_pop = synpop.presynaptic_population
if pre_pop is not None:
e = pydot.Edge(
obj2nodedict[pre_pop].get_name(), synpopcluster.get_name(),
**dict(kwargs_general.items() +
kwargs_synpop_edge.items()))
graph.add_edge(e)
else:
print 'NONE'
for (i, synapse) in enumerate(self.sim.synapses):
if synapse.population:
continue
pre_cell = synapse.get_presynaptic_cell()
post_cell = synapse.get_postsynaptic_cell()
if not pre_cell:
pre_n = pydot.Node(name='SpikeTimes%d' % i,
shape='point',
color='lightsalmon',
style='filled',
**kwargs_general)
graph.add_node(pre_n)
else:
pre_n = obj2nodedict[pre_cell]
post_n = obj2nodedict[post_cell]
syn_name = '%s' % synapse.name
e = pydot.Edge(pre_n,
post_n,
label=syn_name,
color='red',
**kwargs_general)
graph.add_edge(e)
stims = {}
# Simulations:
for cclamp in self.sim.current_clamps:
label = '"IClamp: %s\\n %s"' % (cclamp.name,
cclamp.location_summary_dot_str)
n = pydot.Node(cclamp.name,
label=label,
**dict(kwargs_general.items() + kwargs_cc.items()))
stims[cclamp] = n
graph.add_node(n)
# Make the edge:
cell_node = obj2nodedict[cclamp.cell]
e = pydot.Edge(n, cell_node, label='', color='red') # **kwargs)
graph.add_edge(e)
## Records:
#records = {}
#for record in self.sim.recordables:
# name = record.name
# # label = '"Record: %s\\n %s"' % (name, record.location_summary_dot_str )
# label = '"Record: %s\\n"' % name # , record.location_summary_dot_str )
# n = pydot.Node(
# name,
# shape='circle',
# style='filled',
# width='0.05',
# fixedsize='True',
# label=label,
# fontsize=fontsize,
# )
# records[record] = n
# graph.add_node(n)
# # Make the edge:
# if isinstance(record, NEURONRecordableOnLocation):
# post_node = obj2nodedict[record.cell_location.cell]
# e = pydot.Edge(n, post_node, label='', color='green')
# graph.add_edge(e)
# # cell_node = obj2nodedict[record.cell_location.cell]
graph.write_raw('example_cluster2.dot')
# Put the stimulations on:
#fname = _DotSummaryUtils.save_dot(graph, prog='dot')
fname = _DotSummaryUtils.save_dot(graph, format='pdf', prog='fdp')
#fname = _DotSummaryUtils.save_dot(graph, prog='osage')
#fname = _DotSummaryUtils.save_dot(graph, prog='neato')
return mrd.Section('Diagram Overview', mrd.Figure(mrd.Image(fname)))