def plot_iv_curve(self, ax=None):
# pylint: disable=E1103
title = '%s: IV Curve' % (self.cell_description or None)
if not ax:
f = QuantitiesFigure()
f.suptitle(title)
ax = f.add_subplot(1, 1, 1)
ax.set_xlabel('Injected Current')
ax.set_ylabel('SteadyStateVoltage')
V_in_mV = [
self.get_iv_point_steaddy_state(c).rescale('mV').magnitude
for c in self.currents
]
v = np.array(V_in_mV) * pq.mV
i = factorise_units_from_list(self.currents)
low_v = V_in_mV < self.v_regressor_limit if self.v_regressor_limit else range(
len(V_in_mV))
print 'i[low_v]', i[low_v]
print 'v[low_v]', v[low_v]
ax.plot(
i[low_v],
v[low_v],
)
ax.plot(
i[np.logical_not(low_v)],
v[np.logical_not(low_v)],
)
ax.plot(
i[np.logical_not(low_v)],
v[np.logical_not(low_v)],
)
# Plot the regressor:
i_units = unit('1:pA').units
v_units = unit('1:mV').units
iv = np.vstack(
(i.rescale(i_units).magnitude, v.rescale(v_units).magnitude)).T
if not len(iv[low_v, 0]):
return
(a_s, b_s, r, tt, stderr) = stats.linregress(iv[low_v, 0], iv[low_v,
1])
input_resistance = (a_s * (v_units / i_units)).rescale('MOhm')
reversal_potential = b_s * v_units
self.input_resistance = input_resistance
self.reversal_potential = reversal_potential
ax.plot(
i,
i * input_resistance + reversal_potential,
label="Fit: [V(mV) = %2.3f * I(pA) + %2.3f]" % (a_s, b_s) +
" \n[Input Resistance: %2.2fMOhm Reversal Potential: %2.2f mV" %
(input_resistance, reversal_potential))
ax.legend()
PM.save_figure(figname=title)
def plot_state_curve_summary(cls, alphabeta_chl, state, figsize):
fig = QuantitiesFigure(figsize=figsize)
fig.suptitle("AlphaBeta Channel - %s : %s"%(alphabeta_chl.name, state))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
cls.plot_alpha_beta_curves(ax1, ax2, alphabeta_chl, state)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
cls.plot_inf_tau_curves(ax3, ax4, alphabeta_chl, state)
return fig
def create_figure(self):
self.fig = QuantitiesFigure(**self.fig_kwargs)
# Add a title to the plot:
if self.figtitle:
self.fig.suptitle(self.figtitle)
# Work out what traces are on what graphs:
plotspec_to_traces = dict([(plot_spec, [
tr for tr in self.all_trace_objs
if plot_spec.addtrace_predicate(tr)
]) for plot_spec in self.plot_specs])
if self.linkage:
self.linkage.process(plotspec_to_traces)
n_plots = len(self.plot_specs)
for (i, plot_spec) in enumerate(self.plot_specs):
# Create the axis:
ax = self.fig.add_subplot(n_plots, 1, i + 1)
ax.set_xunit(pq.millisecond)
ax.set_xmargin(0.05)
ax.set_ymargin(0.05)
ax.set_xaxis_maxnlocator(self.nxticks)
# Leave the plotting to the tag-plot object
plot_spec.plot(
ax=ax,
all_traces=self.all_trace_objs,
all_eventsets=self.all_event_set_objs,
time_range=self.timerange,
linkage=self.linkage,
#plot_xaxis_details=plot_xaxis_details,
show_xlabel=self.show_xlabel,
show_xticklabels=self.show_xticklabels,
show_xticklabels_with_units=self.show_xticklabels_with_units,
show_xaxis_position=self.show_xaxis_position,
is_top_plot=(i == 0),
is_bottom_plot=(i == n_plots - 1),
xticks=self.xticks)
# Save the Axis:
self.subaxes.append(ax)
if self.mpl_tight_bounds:
import pylab
try:
pylab.tight_layout()
except AttributeError:
pass # This is version specfic
except ValueError:
pass # Top can't be less than bottom
def test_trace_method_traceout(src_trace, method_name, method_functor):
f = QuantitiesFigure(figsize=(6, 4))
f.suptitle('Testing Method: %s' % method_name)
ax1 = f.add_subplot(211)
ax2 = f.add_subplot(212)
ax1.plotTrace(src_trace, label='Original')
for (conv_type, conv_functor) in conversions:
tr_new = conv_functor(src_trace)
if not mf.TraceMethodCtrl.has_method(conv_type, method_name):
continue
ax2.plotTrace(method_functor(tr_new),
label='%s:%s' % (conv_type.__name__, method_name))
ax1.legend()
ax2.legend()
return mrd.Section('Test: %s' % method_name,
mrd.Image(f.fig, auto_adjust=False))
def PlotGHKMaxCurrentFlow(cls, calciumAlphaBetaBetaChannel, figsize):
V = StdLimits.get_default_voltage_array().rescale("mV")
# Plot the
fig = QuantitiesFigure(figsize=figsize)
ax1 = fig.add_subplot(221,
xUnit="mV",
yUnit="pA/cm2",
xlabel="Voltage",
ylabel="")
#Plot the 'I_ca' as defined in Biophysics of Computations:
chl = calciumAlphaBetaBetaChannel
nmp = (chl.CaZ * chl.F) / (chl.R * chl.T)
nmpV = (nmp * V).rescale("")
iCa = chl.permeability * nmpV * chl.F * (
chl.intracellular_concentration - chl.extracellular_concentration *
np.exp(-1.0 * nmpV)) / (1.0 - np.exp(-1.0 * nmpV))
iCa.rescale("pA/cm2")
ax1.plot(V, iCa)
def plot_traces(self, ax=None):
title = '%s: (Voltage Responses to Current Injections)' \
% self.cell_description
if not ax:
f = QuantitiesFigure()
f.suptitle(title)
ax = f.add_subplot(1, 1, 1)
ax.set_xlabel('Time')
ax.set_ylabel('Voltage')
# Plot the traces
for i_inj in self.currents:
ax.plotTrace(self.get_trace(i_inj), label='i_inj: %s' % i_inj)
# Add the regions:
ax.axvspan(self.tSteaddyStateStart,
self.tSteaddyStateStop,
facecolor='g',
alpha=0.25)
ax.legend()
from mreorg.scriptplots import PM
PM.save_figure(figname=title)
def compareNeuroMLChl(xmlFile):
model, chl_type = os.path.splitext(xmlFile)[0].split("/")[-2:]
print model, chl_type
op_dir = LocMgr.ensure_dir_exists(Join(html_output_dir, model, chl_type))
op_html = Join(op_dir, "index.html")
c = ComparisonResult(xmlfile=xmlFile, op_file = op_html, same_chl=True, exception=None)
try:
# Make the NeuroUnits channel:
chl_neuro = NeuroML_Via_NeuroUnits_ChannelNEURON(xml_filename=xmlFile, )
c.chl_neurounits = chl_neuro
op_pdf_file = Join(op_dir, 'Op1.pdf')
#WriteToPDF(eqnset = chl_neuro.eqnset, filename = op_pdf_file)
c.chl_neurounits_pdf = op_pdf_file
# Make the NeuroML channel:
xsl_file = "/home/michael/srcs/neuroml/CommandLineUtils/ChannelMLConverter/ChannelML_v1.8.1_NEURONmod.xsl"
chl_xsl = NeuroML_Via_XSL_ChannelNEURON(xml_filename=xmlFile, xsl_filename=xsl_file, )
c.chl_xsl = chl_xsl
c.chl_xsl_hoc = []
chl_neuro_res = simulate_chl_all(chl_neuro)
chl_xsl_res = simulate_chl_all(chl_xsl)
c.chl_neurounit_hoc = []
for i, (rN, rX) in enumerate(zip(chl_neuro_res, chl_xsl_res)):
c.chl_neurounit_hoc.append(rN.hocfilename )
c.chl_xsl_hoc.append(rX.hocfilename )
tN = rN.get_trace("CurrentClamp").convert_to_fixed(dt=unit("1.01:ms"))
tX = rX.get_trace("CurrentClamp").convert_to_fixed(dt=unit("1.01:ms"))
# Compare current traces:
tN._data[np.fabs(tN.time_pts_ms - 0) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 0) <0.05] *=0
tN._data[np.fabs(tN.time_pts_ms - 200) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 200) <0.05] *=0
tN._data[np.fabs(tN.time_pts_ms - 700) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 700) <0.05] *=0
print "TR1"
f = QuantitiesFigure()
ax1 = f.add_subplot(4, 1, 1)
ax2 = f.add_subplot(4, 1, 2)
ax3 = f.add_subplot(4, 1, 3)
ax4 = f.add_subplot(4, 1, 4)
ax1.plotTrace(tN, color='b')
ax1.plotTrace(tX, color='g', linewidth=20, alpha=0.2)
ax2.plotTrace(tN.window((200, 250)*pq.ms), color='b')
ax2.plotTrace(tX.window((200, 250)*pq.ms), color='g', linewidth=20, alpha=0.2)
num = (tN-tX)
denom = (tN+tX)
diff = num/denom
ax3.plotTrace(diff, color='r')
ax4.plotTrace(rN.get_trace('SomaVoltage'), color='m')
ax4.plotTrace(rX.get_trace('SomaVoltage'), color='m', linewidth=20, alpha=0.2)
if num.max()[1] > unit("0.1:pA"):
c.same_chl = False
out_im = Join(op_dir, "out_im%03d" % i)
pylab.savefig(out_im+".png")
pylab.savefig(out_im+".pdf")
c.output_image_files.append(out_im)
pylab.close()
c.finished_ok=True
except NeuroUnitsImportNeuroMLNotImplementedException, e:
print 'Exception caught:', e
s = StringIO.StringIO()
traceback.print_exc(file=s)
c.exception_long=s.getvalue()
c.exception="%s (%s)"%(str(e), str(type(e)))
c.same_chl = False
c.finished_ok=False
import morphforge.stdimports as mf
from morphforge.traces.generation import TraceStringParser
import quantities as pq
from mhlibs.quantities_plot import QuantitiesFigure
t = """{d:pA} FLAT(1) FOR 100ms THEN RAMPTO(50) UNTIL 130ms THEN FLAT(0) THEN AT 150ms FLAT(120) FOR 20ms THEN FLAT(0) UNTIL 180ms"""
tr = TraceStringParser.Parse(t)
trFix = tr.convert_to_fixed(dt=01. * pq.ms)
print 't1:', tr.integrate()
print 't2:', trFix.integrate()
f = QuantitiesFigure()
ax1 = f.add_subplot(211)
ax2 = f.add_subplot(212)
trFixSquared = trFix**2
print trFixSquared.integrate()
print(tr**2).integrate()
ax1.plotTrace(trFix)
ax2.plotTrace(trFixSquared)
import pylab
pylab.show()
