def loadTTreeTestChannel(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
test_chan = channelcollection.TestChannel2()
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan, 50., -23.)
self.greenstree.fitLeakCurrent(v_eq, 10.)
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def __init__(self):
channel_names = ['L', 'K_ir', 'K_m35']
# reduced trees
full_tree, red_tree, self.full_locs, self.red_locs = data.reduceMorphology(
)
sim_tree = red_tree.__copy__(new_tree=NeuronSimTree())
# measured data
self.v_dat = data.DataContainer(with_zd=True)
# get file name
file_name = utils.getFileName(channel_names, True, suffix='_predef')
print(file_name)
# load hall of fame
with open(file_name, 'rb') as file:
hall_of_fame = pickle.load(file)
# get the original simtree with the potassium params
self.model_evaluator = optimizer.ModelEvaluator(
sim_tree,
self.v_dat,
self.red_locs[0],
self.red_locs[1],
channel_names=channel_names,
mode='evaluate')
self.model_evaluator.setParameterValues(hall_of_fame[0])
self._createHcnTees()
self._createGreensTrees()
def optimizeModel(channel_names=None, zd=False, suffix=''):
"""
Optimizes the morphology equipped with channels in `channel_names` to
recordings with or without ZD
Parameters
----------
channel_names: list of str
Choose channel names from from {'L', 'K_m', 'K_m35', 'K_ir', 'h_HAY', 'h_u'}
zd: bool
True for data with ZD, false for data without ZD
"""
global MAX_ITER, N_OFFSPRING
if channel_names is None:
channel_names = ['L', 'K_ir', 'K_m35', 'h_u']
file_name = utils.getFileName(channel_names, zd, suffix=suffix)
full_tree, red_tree, full_locs, red_locs = data.reduceMorphology()
sim_tree = red_tree.__copy__(new_tree=NeuronSimTree())
# measured data
v_dat = data.DataContainer(with_zd=zd)
model_evaluator = ModelEvaluator(sim_tree,
v_dat,
red_locs[0],
red_locs[1],
channel_names=channel_names)
final_pop, hall_of_fame, logs, hist = optimize(model_evaluator)
# save hall of fame
file = open(file_name, 'wb')
pickle.dump(hall_of_fame, file)
file.close()
def loadTTreeActive(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.1
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
h_chan = channelcollection.h()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(h_chan, 50., -43.)
self.greenstree.fitLeakCurrent(v_eq, 10.)
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=10.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def createPointNeurons(self, v_eq=-75.):
self.v_eq = v_eq
self.dt = .025
gh, eh = 50., -43.
h_chan = channelcollection.h()
self.greens_tree = GreensTree(
file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc'))
self.greens_tree.setPhysiology(1., 100. / 1e6)
self.greens_tree.addCurrent(h_chan, gh, eh)
self.greens_tree.fitLeakCurrent(v_eq, 10.)
self.greens_tree.setEEq(v_eq)
self.greens_tree_pas = self.greens_tree.__copy__(new_tree=GreensTree())
self.greens_tree_pas.asPassiveMembrane()
self.sim_tree = self.greens_tree.__copy__(new_tree=NeuronSimTree())
# set the impedances
self.greens_tree_pas.setCompTree()
self.freqs = np.array([0.])
self.greens_tree_pas.setImpedance(self.freqs)
# create sov tree
self.sov_tree = self.greens_tree_pas.__copy__(new_tree=SOVTree())
self.sov_tree.calcSOVEquations(maxspace_freq=50.)
z_inp = self.greens_tree_pas.calcZF((1, .5), (1, .5))[0]
alphas, gammas = self.sov_tree.getSOVMatrices(locarg=[(1., .5)])
# create NET
node_0 = NETNode(0, [0], [0], z_kernel=(alphas, gammas[:, 0]**2))
net_py = NET()
net_py.setRoot(node_0)
# check if correct
assert np.abs(gammas[0, 0]**2 / np.abs(alphas[0]) - z_inp) < 1e-10
assert np.abs(node_0.z_bar - z_inp) < 1e-10
# to initialize neuron tree
self.sim_tree.initModel(dt=self.dt)
# add ion channel to NET simulator
a_soma = 4. * np.pi * (self.sim_tree[1].R * 1e-4)**2
self.cnet = netsim.NETSim(net_py, v_eq=self.v_eq)
hchan = channelcollection.h()
self.cnet.addChannel(hchan, 0, gh * a_soma, eh)
# add the synapse
# to neuron tree
self.sim_tree.addDoubleExpSynapse((1, .5), .2, 3., 0.)
self.sim_tree.setSpikeTrain(0, 0.001, [5.])
# to net sim
self.cnet.addSynapse(0, {
'tau_r': .2,
'tau_d': 3.,
'e_r': 0.
},
g_max=0.001)
self.cnet.setSpikeTimes(0, [5. + self.dt])
def loadTTreeTestChannelSoma(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
test_chan = channelcollection.TestChannel2()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan,
50.,
23.,
node_arg=[self.greenstree[1]])
self.greenstree.fitLeakCurrent(v_eq, 10.)
# for node in self.greenstree:
# print node.getGTot(channel_storage=self.greenstree.channel_storage)
# print node.currents
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def runCalciumCoinc(recompute_ctree=False,
recompute_biophys=False,
axdict=None,
pshow=True):
global D2S_CASPIKE, D2S_APIC
global CA_LOC
lss_ = ['-', '-.', '--']
css_ = [colours[3], colours[0], colours[1]]
lws_ = [.8, 1.2, 1.6]
# create the full model
phys_tree = getL5Pyramid()
sim_tree = phys_tree.__copy__(new_tree=NeuronSimTree())
# compartmentfitter object
cfit = CompartmentFitter(phys_tree, name='bac_firing', path='data/')
# single branch initiation zone
branch = sim_tree.pathToRoot(sim_tree[236])[::-1]
locs_sb = sim_tree.distributeLocsOnNodes(D2S_CASPIKE,
node_arg=branch,
name='single branch')
# abpical trunk locations
apic = sim_tree.pathToRoot(sim_tree[221])[::-1]
locs_apic = sim_tree.distributeLocsOnNodes(D2S_APIC,
node_arg=apic,
name='apic connection')
# store set of locations
fit_locs = [(1, .5)] + locs_apic + locs_sb
sim_tree.storeLocs(fit_locs, name='ca coinc')
# PSP input location index
ca_ind = sim_tree.getNearestLocinds([CA_LOC], name='ca coinc')[0]
# obtain the simplified tree
ctree, clocs = getCTree(cfit,
fit_locs,
'data/ctree_bac_firing',
recompute_biophys=recompute_biophys,
recompute_ctree=recompute_ctree)
# print(ctree)
print('--- ctree nodes currents')
print('\n'.join([str(n.currents) for n in ctree]))
reslist, creslist_sb, creslist_sb_ = [], [], []
locindslist_sb, locindslist_apic_sb = [], []
if axdict is None:
pl.figure('inp')
axes_input = [pl.subplot(131), pl.subplot(132), pl.subplot(133)]
pl.figure('V trace')
axes_trace = [pl.subplot(131), pl.subplot(132), pl.subplot(133)]
pl.figure('morph')
axes_morph = [pl.subplot(121), pl.subplot(122)]
else:
axes_input = axdict['inp']
axes_trace = axdict['trace']
axes_morph = axdict['morph']
pshow = False
for jj, stim in enumerate(['current', 'psp', 'coinc']):
print('--- sim full ---')
rec_locs = sim_tree.getLocs('ca coinc')
# runn the simulation
res = runCaCoinc(sim_tree,
rec_locs,
ca_ind,
0,
stim_type=stim,
rec_kwargs=dict(record_from_syns=True,
record_from_iclamps=True))
print('---- sim reduced ----')
rec_locs = clocs
# run the simulation of the reduced tree
csim_tree = createReducedNeuronModel(ctree)
cres = runCaCoinc(csim_tree,
rec_locs,
ca_ind,
0,
stim_type=stim,
rec_kwargs=dict(record_from_syns=True,
record_from_iclamps=True))
id_offset = 1.
vd_offset = 7.2
vlim = (-80., 20.)
ilim = (-.1, 2.2)
# input current
ax = axes_input[jj]
ax.plot(res['t'], -res['i_clamp'][0], c='r', lw=lwidth)
ax.plot(res['t'], res['i_syn'][0] + id_offset, c='b', lw=lwidth)
ax.set_yticks([0., id_offset])
if jj == 1 or jj == 2:
drawScaleBars(ax, ylabel=' nA', b_offset=0)
else:
drawScaleBars(ax)
if jj == 2:
ax.set_yticklabels([r'Soma', r'Dend'])
ax.set_ylim(ilim)
# somatic trace
ax = axes_trace[jj]
ax.set_xticks([0., 50.])
ax.plot(res['t'], res['v_m'][0], c='DarkGrey', lw=lwidth)
ax.plot(cres['t'], cres['v_m'][0], c=cll[0], lw=1.6 * lwidth, ls='--')
# dendritic trace
ax.plot(res['t'],
res['v_m'][ca_ind] + vd_offset,
c='DarkGrey',
lw=lwidth)
ax.plot(cres['t'],
cres['v_m'][ca_ind] + vd_offset,
c=cll[1],
lw=1.6 * lwidth,
ls='--')
ax.set_yticks([cres['v_m'][0][0], cres['v_m'][ca_ind][0] + vd_offset])
if jj == 1 or jj == 2:
drawScaleBars(ax, xlabel=' ms', ylabel=' mV', b_offset=15)
# drawScaleBars(ax, xlabel=' ms', b_offset=25)
else:
drawScaleBars(ax)
if jj == 2:
ax.set_yticklabels([r'Soma', r'Dend'])
ax.set_ylim(vlim)
print('iv')
plocs = sim_tree.getLocs('ca coinc')
markers = [{'marker': 's', 'mfc': cfl[0], 'mec': 'k', 'ms': markersize/1.1}] + \
[{'marker': 's', 'mfc': cfl[1], 'mec': 'k', 'ms': markersize/1.1} for _ in locs_apic + locs_sb]
markers[ca_ind]['marker'] = 'v'
plotargs = {'lw': lwidth / 1.3, 'c': 'DarkGrey'}
sim_tree.plot2DMorphology(axes_morph[0],
use_radius=False,
plotargs=plotargs,
marklocs=plocs,
locargs=markers,
lims_margin=0.01)
# compartment tree dendrogram
labelargs = {
0: {
'marker': 's',
'mfc': cfl[0],
'mec': 'k',
'ms': markersize * 1.2
}
}
labelargs.update({
ii: {
'marker': 's',
'mfc': cfl[1],
'mec': 'k',
'ms': markersize * 1.2
}
for ii in range(1, len(plocs))
})
ctree.plotDendrogram(axes_morph[1],
plotargs={
'c': 'k',
'lw': lwidth
},
labelargs=labelargs)
pl.show()
def basalAPBackProp(recompute_ctree=False, recompute_biophys=False, axes=None, pshow=True):
global STIM_PARAMS, D2S_BASAL, SLOCS
global CMAP_MORPH
rc, rb = recompute_ctree, recompute_biophys
if axes is None:
pl.figure(figsize=(7,5))
ax1, ax2, ax4, ax5 = pl.subplot(221), pl.subplot(223), pl.subplot(222), pl.subplot(224)
divider = make_axes_locatable(ax1)
ax3 = divider.append_axes("top", "30%", pad="10%")
ax4, ax5 = myAx(ax4), myAx(ax5)
pl.figure(figsize=(5,5))
gs = GridSpec(2,2)
ax_morph, ax_red1, ax_red2 = pl.subplot(gs[:,0]), pl.subplot(gs[1,0]), pl.subplot(gs[1,1])
else:
ax1, ax2, ax3 = axes['trace']
ax4, ax5 = axes['amp-delay']
ax_morph, ax_red1, ax_red2 = axes['morph']
pshow = False
# create the full model
phys_tree = getL23PyramidNaK()
sim_tree = phys_tree.__copy__(new_tree=NeuronSimTree())
# distribute locations to measure backAPs on branches
leafs_basal = [node for node in sim_tree.leafs if node.swc_type == 3]
branches = [sim_tree.pathToRoot(leaf)[::-1] for leaf in leafs_basal]
locslist = [sim_tree.distributeLocsOnNodes(D2S_BASAL, node_arg=branch) for branch in branches]
branchlist = [b for ii, b in enumerate(branches) if len(locslist[ii]) == 3]
locs = [locs for locs in locslist if len(locs) == 3][1]
# do back prop sims
amp_diffs_3loc, delay_diffs_3loc = np.zeros(3), np.zeros(3)
amp_diffs_1loc, delay_diffs_1loc = np.zeros(3), np.zeros(3)
amp_diffs_biop, delay_diffs_biop = np.zeros(3), np.zeros(3)
# compartmentfitter object
cfit = CompartmentFitter(phys_tree, name='basal_bAP', path='data/')
# create reduced tree
ctree, clocs = getCTree(cfit, [SLOCS[0]] + locs, 'data/ctree_basal_bAP_3loc',
recompute_ctree=rc, recompute_biophys=rb)
csimtree = createReducedNeuronModel(ctree)
print(ctree)
# run the simulation of he full tree
res = runSim(sim_tree, locs, SLOCS[0], stim_params=STIM_PARAMS)
calcAmpDelayWidth(res)
amp_diffs_biop[:] = res['amp'][1:]
delay_diffs_biop[:] = res['delay'][1:]
# run the simulation of the reduced tree
cres = runSim(csimtree, clocs[1:], clocs[0], stim_params=STIM_PARAMS)
calcAmpDelayWidth(cres)
amp_diffs_3loc[:] = cres['amp'][1:]
delay_diffs_3loc[:] = cres['delay'][1:]
# reduced models with one single dendritic site
creslist = []
for jj, loc in enumerate(locs):
# create reduced tree with all 1 single dendritic site locs
ctree, clocs = getCTree(cfit, [SLOCS[0]] + [loc], 'data/ctree_basal_bAP_1loc%d'%jj,
recompute_ctree=rc, recompute_biophys=False)
csimtree = createReducedNeuronModel(ctree)
print(ctree)
# run the simulation of the reduced tree
cres_ss = runSim(csimtree, [clocs[1]], clocs[0], stim_params=STIM_PARAMS)
calcAmpDelayWidth(cres_ss)
creslist.append(cres_ss)
amp_diffs_1loc[jj] = cres_ss['amp'][1]
delay_diffs_1loc[jj] = cres_ss['delay'][1]
ylim = (-90., 60.)
x_range = np.array([-3.,14])
xlim = (0., 12.)
tp_full = res['t'][np.argmax(res['v_m'][0])]
tp_3comp = cres['t'][np.argmax(cres['v_m'][0])]
tp_1comp = creslist[2]['t'][np.argmax(creslist[2]['v_m'][0])]
tlim_full = tp_full + x_range
tlim_3comp = tp_3comp + x_range
tlim_1comp = tp_1comp + x_range
i0_full, i1_full = np.round(tlim_full / DT).astype(int)
i0_3comp, i1_3comp = np.round(tlim_3comp / DT).astype(int)
i0_1comp, i1_1comp = np.round(tlim_1comp / DT).astype(int)
ax1.set_ylabel(r'soma')
ax1.plot(res['t'][i0_full:i1_full] - tlim_full[0], res['v_m'][0][i0_full:i1_full],
lw=lwidth, c='DarkGrey', label=r'full')
ax1.plot(cres['t'][i0_3comp:i1_3comp] - tlim_3comp[0], cres['v_m'][0][i0_3comp:i1_3comp],
ls='--', lw=1.6*lwidth, c=colours[0], label=r'3 comp')
ax1.plot(creslist[2]['t'][i0_1comp:i1_1comp] - tlim_1comp[0], creslist[2]['v_m'][0][i0_1comp:i1_1comp],
ls='-.', lw=1.6*lwidth, c=colours[1], label=r'1 comp')
ax1.set_ylim(ylim)
# ax1.set_xlim(xlim)
drawScaleBars(ax1, b_offset=15)
myLegend(ax1, add_frame=False, loc='center left', bbox_to_anchor=[0.35, 0.55], fontsize=ticksize,
labelspacing=.8, handlelength=2., handletextpad=.2)
ax2.set_ylabel(r'dend' + '\n($d_{soma} = 150$ $\mu$m)')
ax2.plot(res['t'][i0_full:i1_full] - tlim_full[0], res['v_m'][3][i0_full:i1_full],
lw=lwidth, c='DarkGrey', label=r'full')
ax2.plot(cres['t'][i0_3comp:i1_3comp] - tlim_3comp[0], cres['v_m'][3][i0_3comp:i1_3comp],
ls='--', lw=1.6*lwidth, c=colours[0], label=r'3 comp')
ax2.plot(creslist[2]['t'][i0_1comp:i1_1comp] - tlim_1comp[0], creslist[2]['v_m'][1][i0_1comp:i1_1comp],
ls='-.', lw=1.6*lwidth, c=colours[1], label=r'1 comp')
imax = np.argmax(res['v_m'][3])
xp = res['t'][imax]
ax2.annotate(r'$v_{amp}$',
xy=(xlim[0], np.mean(ylim)), xytext=(xlim[0], np.mean(ylim)),
fontsize=ticksize, ha='center', va='center', rotation=90.)
ax2.annotate(r'$t_{delay}$',
xy=(xp, ylim[1]), xytext=(xp, ylim[1]),
fontsize=ticksize, ha='center', va='center', rotation=0.)
ax2.set_ylim(ylim)
ax2.set_xlim(xlim)
drawScaleBars(ax2, xlabel=' ms', ylabel=' mV', b_offset=15)
# myLegend(ax2, add_frame=False, ncol=2, fontsize=ticksize,
# loc='upper center', bbox_to_anchor=[.5, -.1],
# labelspacing=.6, handlelength=2., handletextpad=.2, columnspacing=.5)
ax3.plot(res['t'][i0_full:i1_full] - tlim_full[0], -res['i_clamp'][0][i0_full:i1_full],
lw=lwidth, c='r')
ax3.set_yticks([0.,3.])
drawScaleBars(ax3, ylabel=' nA', b_offset=0)
# ax3.set_xlim(xlim)
# color the branches
cnodes = [b for branch in branches for b in branch]
if cnodes is None:
plotargs = {'lw': lwidth/1.3, 'c': 'DarkGrey'}
cs = {node.index: 0 for node in sim_tree}
else:
plotargs = {'lw': lwidth/1.3}
cinds = [n.index for n in cnodes]
cs = {node.index: 1 if node.index in cinds else 0 for node in sim_tree}
# mark example locations
plocs = [SLOCS[0]] + locs
markers = [{'marker': 's', 'c': cfl[0], 'mec': 'k', 'ms': markersize}] + \
[{'marker': 's', 'c': cfl[1], 'mec': 'k', 'ms': markersize} for _ in plocs[1:]]
# plot morphology
sim_tree.plot2DMorphology(ax_morph, use_radius=False, plotargs=plotargs,
cs=cs, cmap=CMAP_MORPH,
marklocs=plocs, locargs=markers, lims_margin=0.01)
# plot compartment tree schematic
ctree_3l = cfit.setCTree([SLOCS[0]] + locs)
ctree_3l = cfit.ctree
ctree_1l = cfit.setCTree([SLOCS[0]] + locs[0:1])
ctree_1l = cfit.ctree
labelargs = {0: {'marker': 's', 'mfc': cfl[0], 'mec': 'k', 'ms': markersize*1.2}}
labelargs.update({ii: {'marker': 's', 'mfc': cfl[1], 'mec': 'k', 'ms': markersize*1.2} for ii in range(1,len(plocs))})
ctree_3l.plotDendrogram(ax_red1, plotargs={'c':'k', 'lw': lwidth}, labelargs=labelargs)
labelargs = {0: {'marker': 's', 'mfc': cfl[0], 'mec': 'k', 'ms': markersize*1.2},
1: {'marker': 's', 'mfc': cfl[1], 'mec': 'k', 'ms': markersize*1.2}}
ctree_1l.plotDendrogram(ax_red2, plotargs={'c':'k', 'lw': lwidth}, labelargs=labelargs)
ax_red1.set_xticks([]); ax_red1.set_yticks([])
ax_red1.set_xlabel(r'$\Delta x = 50$ $\mu$m', fontsize=ticksize,rotation=60)
ax_red2.set_xticks([]); ax_red2.set_yticks([])
ax_red2.set_xlabel(r'$\Delta x = 150$ $\mu$m', fontsize=ticksize,rotation=60)
xb = np.arange(3)
bwidth = 1./4.
xtls = [r'50', r'100', r'150']
ax4, ax5 = myAx(ax4), myAx(ax5)
ax4.bar(xb-bwidth, amp_diffs_biop, width=bwidth, align='center', color='DarkGrey', edgecolor='k', label=r'full')
ax4.bar(xb, amp_diffs_3loc, width=bwidth, align='center', color=colours[0], edgecolor='k', label=r'4 comp')
ax4.bar((xb+bwidth)[-1:], amp_diffs_1loc[-1:], width=bwidth, align='center', color=colours[1], edgecolor='k', label=r'2 comp')
ax4.set_ylabel(r'$v_{amp}$ (mV)')
ax4.set_xticks(xb)
ax4.set_xticklabels([])
ax4.set_ylim(50.,110.)
ax4.set_yticks([50., 80.])
myLegend(ax4, add_frame=False, loc='lower center', bbox_to_anchor=[.5, 1.05], fontsize=ticksize,
labelspacing=.1, handlelength=1., handletextpad=.2, columnspacing=.5)
ax5.bar(xb-bwidth, delay_diffs_biop, width=bwidth, align='center', color='DarkGrey', edgecolor='k', label=r'full')
ax5.bar(xb, delay_diffs_3loc, width=bwidth, align='center', color=colours[0], edgecolor='k', label=r'4 comp')
ax5.bar((xb+bwidth)[-1:], delay_diffs_1loc[-1:], width=bwidth, align='center', color=colours[1], edgecolor='k', label=r'2 comp')
ax5.set_ylabel(r'$t_{delay}$ (ms)')
ax5.set_xticks(xb)
ax5.set_xticklabels(xtls)
ax5.set_xlabel(r'$d_{soma}$ ($\mu$m)')
ax5.set_yticks([0., 0.5])
if pshow:
pl.show()
def plotTrace(channel_names,
zd,
axes=None,
plot_xlabel=True,
plot_ylabel=True,
plot_legend=True,
units='uS/cm2',
suffix='_predef'):
ts = np.array([data.T0, data.T1, data.T2, data.T3]) - 100.
pcolor = 'Orange' if zd else 'Blue'
if units == 'uS/cm2':
unit_conv = 1
elif units == 'pS/um2':
unit_conv = 0.01
else:
print('Unit type not defined, reverting to uS/cm2')
unit_conv = 1
# reduced trees
full_tree, red_tree, full_locs, red_locs = data.reduceMorphology()
sim_tree = red_tree.__copy__(new_tree=NeuronSimTree())
# measured data
v_dat = data.DataContainer(with_zd=zd)
# get file name
file_name = utils.getFileName(channel_names, zd, suffix=suffix)
# model
model_evaluator = optimizer.ModelEvaluator(sim_tree,
v_dat,
red_locs[0],
red_locs[1],
channel_names=channel_names)
# load hall of fame
file = open(file_name, 'rb')
hall_of_fame = pickle.load(file)
file.close()
# run the best fit model
model_evaluator.setParameterValues(hall_of_fame[0])
print("\n--> running model for parameter values: \n%s" %
model_evaluator.toStrParameterValues())
res = model_evaluator.runSim()
if axes is None:
pl.figure('v opt', figsize=(16, 4))
axes = [
pl.subplot(161),
pl.subplot(162),
pl.subplot(163),
pl.subplot(164),
pl.subplot(165),
pl.subplot(166)
]
pshow = True
else:
pshow = False
i0s = [int(tt / data.DT) for tt in ts]
i1s = [int((tt + 1000. - data.DT) / data.DT) for tt in ts]
t_plot = np.arange(0., 1000. - 3 * data.DT / 2., data.DT)
pchanstr = r'' + ', '.join([PSTRINGS[c_name] for c_name in channel_names])
ax = axes[0]
# ax.set_title(pchanstr)
ax.axison = False
ax.text(0.,
0.,
pchanstr,
horizontalalignment='center',
verticalalignment='center',
rotation=45.,
fontsize=labelsize)
ax.set_xlim((0., 1.))
ax.set_ylim((-1., 1.))
for ii in range(4):
ax = noFrameAx(axes[ii + 1])
i0, i1 = i0s[ii], i1s[ii]
# plot traces
ax.plot(t_plot,
v_dat.v_s[i0:i1],
c='k',
lw=lwidth,
label=r'$Exp_{soma}$')
ax.plot(t_plot,
v_dat.v_d[i0:i1],
c=pcolor,
lw=lwidth,
label=r'$Exp_{dend}$')
ax.plot(t_plot,
res['v_m'][0][i0:i1],
c='k',
lw=2 * lwidth,
alpha=.4,
label=r'$Sim_{soma}$')
ax.plot(t_plot,
res['v_m'][1][i0:i1],
c=pcolor,
lw=2 * lwidth,
alpha=.4,
label=r'$Sim_{soma}$')
if zd:
ax.set_ylim((-110., -50.))
else:
ax.set_ylim((-95., -50.))
if ii == 0:
if not zd:
ax.set_yticks([-65., -70.])
ax.set_yticklabels(['-65 mV', '-70 mV'])
else:
ax.set_yticks([-75., -80.])
ax.set_yticklabels(['-75 mV', '-80 mV'])
if ii == 3 and plot_legend:
myLegend(ax)
# draw x scalebar
ypos = ax.get_ylim()[0]
xpos = ax.get_xlim()[-1] - 40.
sblen = 400.
ax.plot([xpos - sblen, xpos], [ypos, ypos], 'k-', lw=2. * lwidth)
ax.annotate(r'%.0f ms' % sblen,
xy=(xpos - sblen / 2., ypos),
xytext=(xpos - sblen / 2., ypos - 4.),
size=labelsize,
rotation=0,
ha='center',
va='center')
# draw y scalebar
ypos = ax.get_ylim()[0] + 5.
xpos = ax.get_xlim()[-1]
sblen = 10.
ax.plot([xpos, xpos], [ypos, ypos + sblen], 'k-', lw=2. * lwidth)
ax.annotate(r'%.0f mV' % sblen,
xy=(xpos, ypos + sblen / 2.),
xytext=(xpos + 100., ypos + sblen / 2.),
size=labelsize,
rotation=90,
ha='center',
va='center')
ax = myAx(axes[5])
print('>>> parameters')
for jj, pvals in enumerate(hall_of_fame):
model_evaluator.setParameterValues(pvals)
ps = model_evaluator.getParameterValuesAsDict()
print(model_evaluator.toStrParameterValues())
d2s = np.linspace(0., 400., int(1e3))
for ii, c_name in enumerate(model_evaluator.channel_names):
func = utils.expDistr(ps['d_' + c_name],
ps['g0_' + c_name],
g_max=model_evaluator.g_max[c_name])
if jj == 0:
ax.plot(d2s,
func(d2s) * unit_conv,
c=PCOLORS[c_name],
lw=lwidth,
label=PSTRINGS[c_name])
else:
ax.plot(d2s,
func(d2s) * unit_conv,
c=PCOLORS[c_name],
lw=3. * lwidth,
alpha=.07)
if plot_legend:
myLegend(ax)
if plot_xlabel:
ax.set_xlabel(r'Distance ($\mu$m)', fontsize=labelsize)
else:
ax.set_xticklabels([])
if plot_ylabel:
if units == 'pS/um2':
ax.set_ylabel(r'Conductance (pS/$\mu$m$^2$)', fontsize=labelsize)
else:
ax.set_ylabel(r'Conductance ($\mu$S/cm$^2$)', fontsize=labelsize)
if zd:
ax.set_ylim((0., 2000. * unit_conv))
ax.set_yticks([0., 1000. * unit_conv])
else:
ax.set_ylim((0., 6000. * unit_conv))
ax.set_yticks([0., 3000. * unit_conv])
if pshow:
pl.tight_layout()
pl.show()
def plotErrors(channel_names_list,
zd_list,
ax=None,
qs=[.01, .5, .99],
suffix='_predef'):
global PSTRINGS
full_tree, red_tree, full_locs, red_locs = data.reduceMorphology()
sim_tree = red_tree.__copy__(new_tree=NeuronSimTree())
v_qmins, v_meds, v_qmaxs = [], [], []
ticklabels = []
for channel_names, zd in zip(channel_names_list, zd_list):
# load hall of fame
file_name = utils.getFileName(channel_names, zd, suffix=suffix)
file = open(file_name, 'rb')
hall_of_fame = pickle.load(file)
file.close()
# measured data
v_dat = data.DataContainer(with_zd=zd)
# model
model_evaluator = optimizer.ModelEvaluator(sim_tree,
v_dat,
red_locs[0],
red_locs[1],
channel_names=channel_names)
# compute the errors
v_errs = calcError(model_evaluator, hall_of_fame, v_dat)
# compute quantiles
v_qmin, v_med, v_qmax = np.quantile(v_errs, qs)
v_qmins.append(v_qmin)
v_meds.append(v_med)
v_qmaxs.append(v_qmax)
# tick label for this configuration
tl = r'' + ', '.join([PSTRINGS[c_name] for c_name in channel_names])
ticklabels.append(tl)
v_meds = np.array(v_meds)
v_errs = np.abs(np.array([v_qmins, v_qmaxs]) - v_meds[None, :])
inds_h = np.where(np.logical_not(np.array(zd_list)))[0]
inds_zd = np.where(np.array(zd_list))[0]
n_h = len(inds_h)
n_zd = len(inds_zd)
ticklabels = [ticklabels[ii] for ii in inds_h]
print(v_errs)
if ax is None:
pl.figure('v errors')
ax = pl.gca()
pshow = True
else:
pshow = False
if n_h > 0:
ax.bar(np.arange(n_h),
v_meds[inds_h],
width=.3,
align='edge',
yerr=v_errs[:, inds_h],
color='Blue')
if n_zd > 0:
ax.bar(np.arange(n_zd),
v_meds[inds_zd],
width=-.3,
align='edge',
yerr=v_errs[:, inds_zd],
color='Orange')
ax.set_xticks(np.arange(np.max([n_zd, n_h])))
ax.set_xticklabels(ticklabels, rotation=60., fontsize=labelsize)
ax.set_ylabel(r'$V_{error}$ (mV)')
if pshow:
pl.show()