from __future__ import print_function, division
import moose
from matplotlib import pyplot
import numpy as np
from moose_nerp.prototypes.iso_scaling import iso_scaling
from moose_nerp.prototypes import tables
try:
_GRAPHS
except NameError:
_GRAPHS = {}
def _get_graph(name, figsize=None):
try:
f = _GRAPHS[name]
except KeyError:
f = _GRAPHS[name] = pyplot.figure(figsize=figsize)
f.canvas.set_window_title(name)
else:
f.clear()
f.canvas.draw() # this is here to make it easier to see what changed
return f
def graphs(model, vmtab, plotcurr, simtime, currtab=[],curlabl="",catab=[],plastab=[], compartments=None):
for typenum,neurtype in enumerate(model.neurontypes()):
f = _get_graph('{} voltage&calcium'.format(neurtype), figsize=(6,6))
axes = f.add_subplot(211) if len(catab) else f.gca()
for oid in vmtab[typenum]:#tables.find_tables(neurtype,'Vm'):
compnum = oid.msgOut[0].e2.name
print('in graphs', compnum)
if compartments is None or int(compnum) in compartments:
t = np.linspace(0, simtime, len(oid.vector))
axes.plot(t, oid.vector, label=compnum)
axes.set_ylabel('Vm {}'.format(neurtype))
axes.legend(fontsize=8, loc='best')
axes.set_title('voltage vs. time')
if len(catab):
axes = f.add_subplot(212)
if len(catab) > typenum:
for oid in catab[typenum]:
neurnum=oid.name.split('_')[-1].split('[')[0]
t = np.linspace(0, simtime, len(oid.vector))
axes.plot(t, oid.vector*1e3, label=neurnum)
axes.set_ylabel('calcium, uM')
axes.set_xlabel('Time (sec)')
axes.legend(fontsize=8, loc='best')
axes.set_title('calcium vs. time')
f.tight_layout()
f.canvas.draw()
if len(plastab):
fig,axes =pyplot.subplots(len(plastab)+1, 1,sharex=True)
fig.suptitle('Plasticity')
for item_num,item in enumerate(plastab):
for plasnum,plastype in enumerate(['plas','cum','syn']):
if plastype=='plas':
title='wt change'
scaling=1000
else:
title=plastype
scaling=1
neurnum=item[plastype].name.split(plastype)[-1]
t = np.linspace(0, simtime, len(item[plastype].vector))
axes[plasnum].plot(t, scaling*item[plastype].vector, label=neurnum)
axes[plasnum].set_ylabel(str(scaling)+'*'+title)
axes[plasnum].legend(loc='best', fontsize=8)
axes[plasnum].set_xlabel('time')
fig.tight_layout()
fig.canvas.draw()
if plotcurr:
f = _get_graph('D1/D2 currents', figsize=(6,12))
numplots=len(model.Channels)
for plotnum, channame in enumerate(sorted(model.Channels)):
try:
axes = f.add_subplot(numplots, 1, plotnum + 1)
toplot = [tab.vector / (model.ghKluge if 'chanCa' in tab.path else 1)
for tab in currtab[neurtype][channame]]
scaling = iso_scaling(*toplot)
for vec in toplot:
t = np.linspace(0, simtime, len(vec))
axes.plot(t, vec / scaling.divisor)
labelstring=u'{}, {}{}'.format(channame, scaling.unit, curlabl)
axes.set_ylabel(labelstring)
if plotnum == 1:
axes.set_title('current vs. time')
except:
print("no channel", channame)
f.subplots_adjust(left=0.16, bottom=0.05, right=0.95, top=0.95, hspace=0.26)
f.canvas.draw()
def SingleGraphSet(traces, currents, simtime, title='Voltage'):
t = np.linspace(0, simtime, len(traces[0]))
f=pyplot.figure()
f.canvas.set_window_title(title)
axes=f.add_subplot(1,1,1)
for i in range(len(traces)):
axes.plot(t,traces[i],label=currents[i])
axes.set_ylabel('Vm, volts') # FIXME
axes.set_xlabel('Time, sec')
axes.legend(fontsize=8,loc='best')
f.canvas.draw()
##
def CurrentGraphSet(value,g, keys, simtime):
#
f = pyplot.figure()
f.canvas.set_window_title('Conductance')
axes = f.add_subplot(1, 1, 1)
#
if isinstance(value,dict):
for key in keys:
t = np.linspace(0, simtime, len(value[key]))
axes.plot(t, value[key], label=g[key])
else:
for i in range(len(value)):
axes.plot(t,value[i],label=g[i])
axes.set_ylabel('gk, volts')
axes.set_xlabel('Time, sec')
axes.legend(fontsize=10,loc='best')
f.canvas.draw()