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cortical_stimulation_visualization.py
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import pylab
import numpy
import pickle
import math
import mozaik.storage.queries as queries
import matplotlib.gridspec as gridspec
from mozaik.visualization.plotting import Plotting
from mozaik.visualization.helper_functions import *
from parameters import ParameterSet
from mozaik.storage.queries import *
from mozaik.analysis.analysis import *
from mozaik.controller import Global
from mozaik.visualization.plotting import (Plotting, GSynPlot, RasterPlot, PerNeuronAnalogSignalScatterPlot,
VmPlot, ConductanceSignalListPlot, ScatterPlot,
AnalogSignalListPlot, OverviewPlot, PerNeuronValueScatterPlot, PlotTuningCurve, PerNeuronValuePlot, CorticalColumnRasterPlot)
from mozaik.visualization.simple_plot import *
from mozaik.visualization.plot_constructors import LinePlot, PerStimulusPlot, PerStimulusADSPlot, ADSGridPlot
from mozaik.tools.circ_stat import circular_dist
import mozaik.visualization.helper_functions as phf
from mozaik.controller import Global
logger = mozaik.getMozaikLogger()
class ResponseOverview(Plotting):
required_parameters = ParameterSet({
'sheet_name': str, # the name of the sheet for which to plot
'neuron': int,
})
def subplot(self, subplotspec):
dsv = queries.param_filter_query(
self.datastore, sheet_name=self.parameters.sheet_name)
return PerStimulusPlot(dsv, function=self._ploter, title_style="Clever").make_line_plot(subplotspec)
def _ploter(self, dsv, subplotspec):
d = []
gs = gridspec.GridSpecFromSubplotSpec(
1, 7, subplot_spec=subplotspec, wspace=0.7)
params = {}
params['x_label'] = None
params['x_tick_labels'] = None
params['x_tick_style'] = 'Custom'
params['y_label'] = 'trial'
scale = 0.08
#lams = self.datastore.full_datastore.get_analysis_result(identifier='PerNeuronValue',analysis_algorithm ='NeuronAnnotationsToPerNeuronValues', value_name = 'Light activation magnitude(V1_Exc_L2/3,'+str(scale) + ',0.0,0.0,1.0,10)', sheet_name = 'V1_Exc_L2/3')
for z in self.datastore.full_datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues', sheet_name='V1_Exc_L2/3'):
print z.value_name
lams = self.datastore.full_datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues',
value_name='Light activation magnitude(V1_Exc_L2/3,'+str(scale) + ',0.0,0.5,10)', sheet_name='V1_Exc_L2/3')
fontsize = 15
if len(lams) == 1:
lams = lams[0]
spike_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
dsv1 = param_filter_query(self.datastore, value_name=['Firing rate'], st_name='InternalStimulus', sheet_name='V1_Exc_L2/3',
st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_orientation=0, st_stimulating_signal_parameters_scale=scale)
axis = pylab.subplot(gs[0, 6])
pylab.plot(lams.get_value_by_id(list(spike_ids)), numpy.array(
dsv1.get_analysis_result()[0].get_value_by_id(list(spike_ids)))*2, 'o', markersize=2)
pylab.xlabel(r'photon flux $(ph/cm^2/s)$', fontsize=fontsize)
# pylab.yticks([0,50,100],fontsize=fontsize)
pylab.xticks(
[0, 5e15], [0.0, r'$5 \times 10^{15}$'], fontsize=fontsize)
pylab.xlim(0, 5e15)
pylab.ylabel('spikes/s', fontsize=fontsize)
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
else:
axis = pylab.subplot(gs[0, 6])
pylab.plot([0, 1], [0, 1], 'o', markersize=2)
pylab.xlabel(r'photon flux $(ph/cm^2/s)$', fontsize=fontsize)
pylab.yticks([0, 50, 100], fontsize=fontsize)
pylab.xticks(
[0, 5e15], [0.0, r'$5 \times 10^{15}$'], fontsize=fontsize)
pylab.xlim(0, 5e15)
pylab.ylabel('(spikes/s)', fontsize=fontsize)
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
d.extend([("Spike_plot", RasterPlot(dsv,
ParameterSet({'sheet_name': self.parameters.sheet_name,
'trial_averaged_histogram': False, 'spontaneous': False,
'neurons': [self.parameters.neuron]})
), gs[0, 0:2], {'title': None}),
("Conductance_plot", GSynPlot(dsv,
ParameterSet({'sheet_name': self.parameters.sheet_name, 'spontaneous': False,
'neuron': self.parameters.neuron})
), gs[0, 2:4], {'title': None}),
("Vm_plot", VmPlot(dsv,
ParameterSet({'sheet_name': self.parameters.sheet_name, 'spontaneous': False,
'neuron': self.parameters.neuron})
), gs[0, 4:6], {'title': None})
])
return d
class LightStimulationOverview(Plotting):
required_parameters = ParameterSet({})
def subplot(self, subplotspec):
plots = {}
scale = 0.08
scales = [0.005, 0.01, 0.04, 0.08, 0.16, 0.32][::-1]
gs = gridspec.GridSpecFromSubplotSpec(
23, 60, subplot_spec=subplotspec, hspace=1.0, wspace=100.0)
analog_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
x = self.datastore.get_neuron_postions()['V1_Exc_L2/3'][0]
y = self.datastore.get_neuron_postions()['V1_Exc_L2/3'][1]
depth = self.datastore.get_neuron_postions()['V1_Exc_L2/3'][2]
ors = numpy.array(l23_exc_or.ids)[numpy.nonzero(numpy.array(
[circular_dist(o, 0, numpy.pi) for o in l23_exc_or.values]) < 0.15)[0]]
close_spikes = numpy.array([a for a in ors if (a in spike_ids)])
close_spikes_sheet_indexes = self.datastore.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=close_spikes)
close_spikes = close_spikes[numpy.sqrt(
x[close_spikes_sheet_indexes]**2 + y[close_spikes_sheet_indexes]**2) < 1.5]
depth_of_close_spikes = depth[self.datastore.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=close_spikes)]
depth_of_close_spikes, close_spikes = zip(
*sorted(zip(depth_of_close_spikes, close_spikes), reverse=True))
close_analogs = list(set(close_spikes) & set(analog_ids))
depth_of_all_spikes = depth[self.datastore.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=spike_ids)]
lams = self.datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues',
value_name='Light activation magnitude(V1_Exc_L2/3,'+str(scale) + ',0.0,0.5,10)', sheet_name='V1_Exc_L2/3')[0]
lamsChR = self.datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues',
value_name='Light activation magnitude ChR('+str(scale) + ',0.0_0.5_10)', sheet_name='V1_Exc_L2/3')[0]
dsv = param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, st_stimulating_signal_parameters_scale=scale, st_trial=0)
plots['ExampleRaster'] = (RasterPlot(dsv, ParameterSet({'sheet_name': 'V1_Exc_L2/3', 'neurons': list(
close_spikes[:100]), 'trial_averaged_histogram': True, 'spontaneous': False})), gs[:, 19:34], {})
fontsize = 15
axis = pylab.subplot(gs[:10, 0:16])
for sc in scales:
segs = param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', sheet_name='V1_Exc_L2/3',
st_stimulating_signal_parameters_orientation=0, st_stimulating_signal_parameters_scale=sc, st_trial=0).get_segments()
assert len(segs) == 1
pylab.plot(segs[0].get_vm(close_analogs[0]))
phf.disable_top_right_axis(axis)
pylab.xlim(0, 1000)
pylab.ylim(-80, -40)
pylab.yticks([-80, -60, -40], fontsize=fontsize)
pylab.xticks([0, 200, 800, 1000], [
0, 0.2, 0.8, 1.0], fontsize=fontsize)
pylab.ylabel('Vm (mV)', fontsize=fontsize)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
axis = pylab.subplot(gs[13:, 0:16])
ids = self.datastore.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=close_analogs)
for sc in scales:
f = open(Global.root_directory +
'/mixed_signalsV1_Exc_L2_3_'+str(sc)+'_0.0_0.5_10.pickle')
mixed_signals = pickle.load(f)
pylab.plot(mixed_signals[ids[0]], label=(
'%.3f' % sc) + '$e^{16} (ph/cm^2/s)$')
pylab.legend(loc='upper right', ncol=2)
phf.disable_top_right_axis(axis)
pylab.xlim(0, 1000)
pylab.xticks([0, 200, 800, 1000], [0, 0.2, 0.8, 1.0])
pylab.xlabel('time (s)', fontsize=fontsize)
pylab.yticks([0, 0.15, 0.3], fontsize=fontsize)
pylab.ylim(-0.02, 0.4)
pylab.ylabel('inward current (nA)', fontsize=fontsize)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
dsv = param_filter_query(self.datastore, value_name=['Firing rate'], st_name='InternalStimulus', sheet_name='V1_Exc_L2/3',
st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_orientation=0, st_stimulating_signal_parameters_scale=scale)
axis = pylab.subplot(gs[:10, 37:47])
pylab.plot(lams.get_value_by_id(list(spike_ids)), numpy.array(
dsv.get_analysis_result()[0].get_value_by_id(list(spike_ids)))*2, 'o')
pylab.xlabel(r'photon flux $(ph/cm^2/s)$', fontsize=fontsize)
pylab.yticks([0, 50, 100], fontsize=fontsize)
pylab.xticks([0, 1e16], [0.0, r'$10^{16}$'], fontsize=fontsize)
pylab.xlim(0, 1.0e16)
pylab.ylabel('firing rate (spk/s)', fontsize=fontsize)
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
axis = pylab.subplot(gs[13:, 37:47])
pylab.plot(lams.get_value_by_id(list(spike_ids)),
lamsChR.get_value_by_id(list(spike_ids)), 'o')
pylab.xlabel(r'photon flux $(ph/cm^2/s)$', fontsize=fontsize)
pylab.xticks([0, 1e16], [0.0, r'$10^{16}$'], fontsize=fontsize)
pylab.xlim(0, 1.0e16)
pylab.yticks([0, 0.05, 0.1], fontsize=fontsize)
pylab.ylabel('inward current (nA)', fontsize=fontsize)
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
axis = pylab.subplot(gs[:10, 50:60])
pylab.plot(depth_of_close_spikes, numpy.array(
dsv.get_analysis_result()[0].get_value_by_id(list(close_spikes)))*2, 'o')
pylab.xticks([100, 250, 400], [100, 250, 400])
pylab.xlabel(r'depth ($\mu m$)', fontsize=fontsize)
pylab.yticks([0, 50, 100], fontsize=fontsize)
pylab.ylabel('firing rate (spk/s)', fontsize=fontsize)
pylab.xlim(100, 400)
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
axis = pylab.subplot(gs[13:, 50:60])
pylab.scatter(l23_exc_or.values-numpy.pi/2, lams.values, s=3)
pylab.xticks([-numpy.pi/2, 0, numpy.pi/2],
["-$\\frac{\\pi}{2}$", "0", "$\\frac{\\pi}{2}$"])
pylab.xlabel('orientation', fontsize=fontsize)
pylab.ylabel(r'photon flux $(ph/cm^2/s)$', fontsize=fontsize)
pylab.yticks([0, 1e16], [0.0, r'$10^{16}$'], fontsize=fontsize)
pylab.ylim(0, 1.0e16)
pylab.xlim([-numpy.pi/2, numpy.pi/2])
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
return plots
class OrientationTuningSummaryFiringRates(Plotting):
required_parameters = ParameterSet({
'cortical_stimulation': bool,
'scale': bool
})
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(6, 29, subplot_spec=subplotspec,
hspace=1.0, wspace=5.0)
spike_ids = sorted(numpy.random.permutation(queries.param_filter_query(
self.datastore, sheet_name='V1_Exc_L2/3').get_segments()[0].get_stored_spike_train_ids()))
scales = [0.01, 0.07, 0.14]
contrasts = [3, 5, 10, 100]
fontsize = 15
if self.parameters.cortical_stimulation:
if self.parameters.scale:
base = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_scale=scales[1], value_name=[
'stimulating_signal_parameters_orientation baseline of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
mmax = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_scale=scales[1], value_name=[
'stimulating_signal_parameters_orientation max of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
else:
base = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_contrast=contrasts[-1], value_name=[
'stimulating_signal_parameters_orientation baseline of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
mmax = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_contrast=contrasts[-1], value_name=[
'stimulating_signal_parameters_orientation max of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
else:
base = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name=['FullfieldDriftingSinusoidalGratingA'], st_contrast=contrasts[-1], value_name=[
'orientation baseline of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
mmax = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name=['FullfieldDriftingSinusoidalGratingA'], st_contrast=contrasts[-1], value_name=[
'orientation max of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
#responsive_spike_ids = numpy.array(spike_ids)[numpy.array(base)+numpy.array(mmax) > 0.0]
responsive_spike_ids = spike_ids
if self.parameters.cortical_stimulation:
if self.parameters.scale:
dsv = queries.param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
value_name='Firing rate', analysis_algorithm=['TrialAveragedFiringRate'], st_stimulating_signal_parameters_scale=scales)
else:
dsv = queries.param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
value_name='Firing rate', analysis_algorithm=['TrialAveragedFiringRate'], st_stimulating_signal_parameters_contrast=contrasts)
plots['ORTCMean'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'stimulating_signal_parameters_orientation', 'neurons': list(
responsive_spike_ids), 'sheet_name': "V1_Exc_L2/3", 'centered': True, 'mean': True, 'pool': False, 'polar': False})), gs[:, :6], {'x_label': 'orientation', 'y_lim': (0, None), 'title': None, 'y_label': 'firing rate (sp/s)'})
plots['ORTC1'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'stimulating_signal_parameters_orientation', 'neurons': list(responsive_spike_ids[0:3]), 'sheet_name': "V1_Exc_L2/3",
'centered': True, 'mean': False, 'pool': False, 'polar': False})), gs[0:3, 6:15], {'y_lim': (0, None), 'title': None, 'left_border': None, 'x_label': None, 'y_axis': False, 'x_axis': False})
plots['ORTC2'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'stimulating_signal_parameters_orientation', 'neurons': list(
responsive_spike_ids[3:6]), 'sheet_name': "V1_Exc_L2/3", 'centered': True, 'mean': False, 'pool': False, 'polar': False})), gs[3:, 6:15], {'y_lim': (0, None), 'title': None, 'left_border': None, 'y_axis': None, 'x_axis': False})
else:
dsv = queries.param_filter_query(self.datastore, st_name='FullfieldDriftingSinusoidalGratingA',
value_name='Firing rate', analysis_algorithm=['TrialAveragedFiringRate'])
plots['ORTCMean'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'orientation', 'neurons': list(
responsive_spike_ids), 'sheet_name': "V1_Exc_L2/3", 'centered': True, 'mean': True, 'pool': False, 'polar': False})), gs[:, :6], {'y_lim': (0, None), 'title': None, 'y_label': 'firing rate (sp/s)'})
plots['ORTC1'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'orientation', 'neurons': list(responsive_spike_ids[0:3]), 'sheet_name': "V1_Exc_L2/3", 'centered': True,
'mean': False, 'pool': False, 'polar': False})), gs[0:3, 6:15], {'y_lim': (0, None), 'title': None, 'left_border': None, 'x_label': None, 'y_axis': False, 'x_axis': False})
plots['ORTC2'] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'orientation', 'neurons': list(responsive_spike_ids[3:6]), 'sheet_name': "V1_Exc_L2/3", 'centered': True,
'mean': False, 'pool': False, 'polar': False})), gs[3:, 6:15], {'y_lim': (0, None), 'title': None, 'left_border': None, 'y_axis': None, 'x_axis': False})
if self.parameters.cortical_stimulation:
if self.parameters.scale:
low_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_scale=scales[1]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
high_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_scale=scales[2]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
else:
low_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=contrasts[0]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
high_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=contrasts[3]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
else:
low_c = queries.param_filter_query(self.datastore, value_name=['orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_contrast=contrasts[0]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
high_c = queries.param_filter_query(self.datastore, value_name=['orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_contrast=contrasts[3]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
axis = pylab.subplot(gs[:, 16:22])
pylab.scatter(low_c, high_c, color='k')
pylab.plot([0, 40], [0, 40], color='#AAAAAA')
pylab.xlim(0, 40)
pylab.xticks([0, 20, 40])
pylab.xlabel('HWHH low contrast', fontsize=fontsize)
pylab.ylim(0, 40)
pylab.yticks([0, 20, 40])
pylab.ylabel('HWHH high contrast', fontsize=fontsize)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
if self.parameters.cortical_stimulation:
if self.parameters.scale:
dsv = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_scale=scales[-1])
else:
dsv = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=contrasts[-1])
else:
dsv = queries.param_filter_query(self.datastore, value_name=[
'orientation HWHH of Firing rate'], sheet_name=['V1_Exc_L2/3'], st_contrast=[100])
y = dsv.get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
axis = pylab.subplot(gs[:, 23:])
pylab.hist(y, color='k', bins=numpy.arange(0, 50, 3))
pylab.xlim(0, 50)
pylab.xticks([0, 25, 50])
pylab.ylim(0, 300)
pylab.yticks([0, 150, 300])
pylab.ylabel('# neurons', fontsize=fontsize)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
pylab.xlabel('HWHH high contrast', fontsize=fontsize)
return plots
class StatisticsOverview(Plotting):
required_parameters = ParameterSet({
'cortical_stimulation': bool,
'type': int,
'window_length': float,
})
def calculate_cc(self, sheet_name, neurons):
if self.parameters.type == 0:
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(
self.datastore, st_name='FullfieldDriftingSinusoidalGratingA', st_contrast=100).get_stimuli()]))
dsv1 = queries.param_filter_query(self.datastore, st_name='FullfieldDriftingSinusoidalGratingA', st_contrast=100,
sheet_name=sheet_name, analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList')
oor = queries.param_filter_query(self.datastore, identifier='PerNeuronValue',
value_name='orientation preference', sheet_name=sheet_name, st_contrast=100).get_analysis_result()
else:
orr = list(set([MozaikParametrized.idd(s).direct_stimulation_parameters.stimulating_signal_parameters.orientation.value for s in queries.param_filter_query(
self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_contrast=100).get_stimuli()]))
dsv1 = queries.param_filter_query(self.datastore, st_stimulating_signal_parameters_contrast=100,
sheet_name=sheet_name, analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList')
oor = queries.param_filter_query(self.datastore, identifier='PerNeuronValue', value_name='stimulating_signal_parameters_orientation preference',
sheet_name=sheet_name, st_stimulating_signal_parameters_contrast=100).get_analysis_result()
print oor
vm_gr_asls = []
psth_gr_asls = []
if True:
for neuron_idd in neurons:
col = orr[numpy.argmin(
[circular_dist(o, oor[0].get_value_by_id(neuron_idd), numpy.pi) for o in orr])]
if self.parameters.type == 0:
dsv = queries.param_filter_query(
dsv1, y_axis_name='trial-trial cross-correlation of Vm (no AP)', st_orientation=col, ads_unique=True)
else:
dsv = queries.param_filter_query(dsv1, y_axis_name='trial-trial cross-correlation of Vm (no AP)',
st_stimulating_signal_parameters_orientation=col, ads_unique=True)
vm_gr_asls.append(dsv.get_analysis_result()[
0].get_asl_by_id(neuron_idd))
if self.parameters.type == 0:
dsv = queries.param_filter_query(
dsv1, y_axis_name='trial-trial cross-correlation of psth (bin=10.0)', st_orientation=col, ads_unique=True)
else:
dsv = queries.param_filter_query(dsv1, y_axis_name='trial-trial cross-correlation of psth (bin=10.0)',
st_stimulating_signal_parameters_orientation=col, ads_unique=True)
psth_gr_asls.append(dsv.get_analysis_result()[
0].get_asl_by_id(neuron_idd))
vm_cc_gr = numpy.mean(numpy.array(vm_gr_asls), axis=0)
psth_cc_gr = numpy.mean(numpy.array(psth_gr_asls), axis=0)
return vm_cc_gr, psth_cc_gr
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(
2, 3, subplot_spec=subplotspec, hspace=0.25, wspace=0.3)
fontsize = 15
analog_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
spike_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
x = self.datastore.get_neuron_postions()['V1_Exc_L2/3'][0]
y = self.datastore.get_neuron_postions()['V1_Exc_L2/3'][1]
ors = numpy.array(l23_exc_or.ids)[numpy.nonzero(numpy.array(
[circular_dist(o, 0, numpy.pi) for o in l23_exc_or.values]) < 0.15)[0]]
close_spikes = numpy.array([a for a in ors if (a in spike_ids)])
close_spikes_sheet_indexes = self.datastore.get_sheet_indexes(
sheet_name='V1_Exc_L2/3', neuron_ids=close_spikes)
close_spikes = close_spikes[numpy.sqrt(
x[close_spikes_sheet_indexes]**2 + y[close_spikes_sheet_indexes]**2) < 1.5]
close_analogs = list(set(close_spikes) & set(analog_ids))
if self.parameters.cortical_stimulation:
dsv_pref = queries.param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, st_stimulating_signal_parameters_contrast=100)
dsv_nonpref = queries.param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=1.57079632679, st_stimulating_signal_parameters_contrast=100)
else:
dsv_pref = queries.param_filter_query(
self.datastore, st_name='FullfieldDriftingSinusoidalGratingA', st_orientation=0, st_contrast=100)
dsv_nonpref = queries.param_filter_query(
self.datastore, st_name='FullfieldDriftingSinusoidalGratingA', st_orientation=numpy.pi/2, st_contrast=100)
def autolabel(rects, offset=0.16):
# attach some text labels
for rect in rects:
pylab.gca().text(rect.get_x()+0.043, rect.get_y() + rect.get_height() + abs(pylab.gca().get_ylim()[0] - pylab.gca().get_ylim()[1])*offset,
'%.3f' % float(rect.get_height()), ha='center', va='bottom', fontsize=13, rotation=90)
color_cycle = {
'Bl': (0, 0, 0),
'Or': (.9, .6, 0),
'SB': (.35, .7, .9),
'bG': (0, .6, .5),
'Ye': (.95, .9, .25),
'Bu': (0, .45, .7),
'Ve': (.8, .4, 0),
'rP': (.8, .6, .7),
}
colors = [color_cycle[c] for c in sorted(color_cycle.keys())]
mean_CC_pref = numpy.mean(param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(Correlation coefficient(psth (bin=10.0)))', ads_unique=True).get_analysis_result()[0].get_value_by_ids(close_analogs, close_analogs)[numpy.triu_indices(len(close_analogs), 1)])
mean_CC_nonpref = numpy.mean(param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(Correlation coefficient(psth (bin=10.0)))', ads_unique=True).get_analysis_result()[0].get_value_by_ids(close_analogs, close_analogs)[numpy.triu_indices(len(close_analogs), 1)])
std_CC_pref = numpy.std(param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue', value_name='Trial-to-trial-mean(Correlation coefficient(psth (bin=10.0)))',
ads_unique=True).get_analysis_result()[0].get_value_by_ids(close_analogs, close_analogs)[numpy.triu_indices(len(close_analogs), 1)])
std_CC_nonpref = numpy.std(param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(Correlation coefficient(psth (bin=10.0)))', ads_unique=True).get_analysis_result()[0].get_value_by_ids(close_analogs, close_analogs)[numpy.triu_indices(len(close_analogs), 1)])
pylab.subplot(gs[0, 0])
r1 = pylab.bar(numpy.array([0.2, 0.6])+self.parameters.type*0.1, [mean_CC_pref, mean_CC_nonpref], width=0.08,
color=colors[self.parameters.type], yerr=[std_CC_pref, std_CC_nonpref], error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2))
pylab.xlim(0, 1.0)
pylab.ylim(0, 0.4)
pylab.ylabel('Corr. Coef.', fontsize=fontsize)
pylab.yticks([0, 0.4])
pylab.xticks([0.3, 0.7], ['preferred', 'orthogonal'])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
autolabel(r1, offset=0.3)
mean_Exc_pref = numpy.squeeze([a.magnitude for a in param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3',
y_axis_name='exc. conductance trial-to-trial mean', ads_unique=True).get_analysis_result()[0].get_asl_by_id(close_analogs)])
mean_Inh_pref = numpy.squeeze([a.magnitude for a in param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3',
y_axis_name='inh. conductance trial-to-trial mean', ads_unique=True).get_analysis_result()[0].get_asl_by_id(close_analogs)])
mean_Exc_nonpref = numpy.squeeze([a.magnitude for a in param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3',
y_axis_name='exc. conductance trial-to-trial mean', ads_unique=True).get_analysis_result()[0].get_asl_by_id(close_analogs)])
mean_Inh_nonpref = numpy.squeeze([a.magnitude for a in param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3',
y_axis_name='inh. conductance trial-to-trial mean', ads_unique=True).get_analysis_result()[0].get_asl_by_id(close_analogs)])
mean_Con_Ratio_pref = numpy.mean(numpy.mean(
mean_Exc_pref, axis=1)/numpy.mean(mean_Inh_pref, axis=1))
mean_Con_Ratio_nonpref = numpy.mean(numpy.mean(
mean_Exc_nonpref, axis=1)/numpy.mean(mean_Inh_nonpref, axis=1))
std_Con_Ratio_pref = numpy.std(numpy.mean(
mean_Exc_pref, axis=1)/numpy.mean(mean_Inh_pref, axis=1))
std_Con_Ratio_nonpref = numpy.std(numpy.mean(
mean_Exc_nonpref, axis=1)/numpy.mean(mean_Inh_nonpref, axis=1))
pylab.subplot(gs[1, 0])
r1 = pylab.bar(numpy.array([0.2, 0.6])+self.parameters.type*0.1, [mean_Con_Ratio_pref, mean_Con_Ratio_nonpref], width=0.08, yerr=[
std_Con_Ratio_pref, std_Con_Ratio_nonpref], color=colors[self.parameters.type], error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2))
pylab.xlim(0, 1.0)
pylab.ylim(0, 1.0)
pylab.ylabel('Mean Exc:Inh cond. ratio', fontsize=fontsize)
pylab.yticks([0, 1.0])
pylab.xticks([0.3, 0.7], ['preferred', 'orthogonal'])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
autolabel(r1)
mean_ToTVar_pref = numpy.mean(param_filter_query(dsv_pref, analysis_algorithm='AnalogSignal_PerNeuronMeanVar', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='PerNeuronMean(Vm (no AP) trial-to-trial variance)', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
mean_ToTVar_nonpref = numpy.mean(param_filter_query(dsv_nonpref, analysis_algorithm='AnalogSignal_PerNeuronMeanVar', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='PerNeuronMean(Vm (no AP) trial-to-trial variance)', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
std_ToTVar_pref = numpy.std(param_filter_query(dsv_pref, analysis_algorithm='AnalogSignal_PerNeuronMeanVar', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='PerNeuronMean(Vm (no AP) trial-to-trial variance)', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
std_ToTVar_nonpref = numpy.std(param_filter_query(dsv_nonpref, analysis_algorithm='AnalogSignal_PerNeuronMeanVar', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='PerNeuronMean(Vm (no AP) trial-to-trial variance)', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
pylab.subplot(gs[0, 1])
r1 = pylab.bar(numpy.array([0.2, 0.6])+self.parameters.type*0.1, [mean_ToTVar_pref, mean_ToTVar_nonpref], width=0.08, yerr=[
std_ToTVar_pref, std_ToTVar_nonpref], color=colors[self.parameters.type], error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2))
pylab.xlim(0, 1.0)
pylab.ylim(0, 25.0)
pylab.ylabel('Trial-to-trial Vm variance (mV)', fontsize=fontsize)
pylab.yticks([0, 25.0])
pylab.xticks([0.3, 0.7], ['preferred', 'orthogonal'])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
autolabel(r1)
mean_Var_pref = numpy.mean(param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(PerNeuronVar(Vm (no AP)))', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
mean_Var_nonpref = numpy.mean(param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(PerNeuronVar(Vm (no AP)))', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
std_Var_pref = numpy.std(param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(PerNeuronVar(Vm (no AP)))', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
std_Var_nonpref = numpy.std(param_filter_query(dsv_nonpref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(PerNeuronVar(Vm (no AP)))', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs))
pylab.subplot(gs[1, 1])
r1 = pylab.bar(numpy.array([0.2, 0.6])+self.parameters.type*0.1, [mean_Var_pref, mean_Var_nonpref], width=0.08, yerr=[
std_Var_pref, std_Var_nonpref], color=colors[self.parameters.type], error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2))
pylab.xlim(0, 1.0)
pylab.ylim(0, 40.0)
pylab.ylabel('Trial averaged Vm variance (mV)', fontsize=fontsize)
pylab.yticks([0, 40.0])
pylab.xticks([0.3, 0.7], ['preferred', 'orthogonal'])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
autolabel(r1)
if False:
logger.info('DSADSADFSAFSDAFSDFA ' + str(close_analogs))
vm_cc_gr_s1, psth_cc_gr_s1 = self.calculate_cc(
'V1_Exc_L2/3', close_analogs)
logger.info('DSADSADFSAFSDAFSDFA ' +
str(len(vm_cc_gr_s1)) + ' ' + str(len(psth_cc_gr_s1)))
z = int(min(self.parameters.window_length,
(len(vm_cc_gr_s1)-1)/2)/2)*2
logger.info('DSADSADFSAFSDAFSDFA ' + str(z))
bin_size = 10.0
plots["Spike_sheet_1"] = (StandardStyleLinePlot([numpy.linspace(-z, z, 2*int(z/bin_size)+1)], [psth_cc_gr_s1[int(len(psth_cc_gr_s1)/2)-int(z/bin_size):int(len(psth_cc_gr_s1)/2)+int(z/bin_size)+1]]), gs[0, 2], {'colors': [
colors[self.parameters.type]], 'x_tick_style': 'Custom', 'x_ticks': [], 'y_tick_style': 'Custom', 'y_ticks': [0, 0.2], 'y_tick_labels': [0.0, 0.1], 'linewidth': 2.0, 'y_lim': (-0.02, 0.2), 'y_label': 'spikes'})
plots["Vm_sheet_1"] = (StandardStyleLinePlot([numpy.linspace(-z, z, 2*z+1)], [vm_cc_gr_s1.flatten()[int(len(vm_cc_gr_s1)/2)-z:int(len(vm_cc_gr_s1)/2)+z+1]]), gs[1, 2], {'x_label': 'time(ms)', 'colors': [colors[self.parameters.type]], 'x_tick_style': 'Custom', 'x_ticks': [
-z, 0, z], 'x_tick_labels': [-self.parameters.window_length, 0, self.parameters.window_length], 'y_tick_style': 'Custom', 'y_ticks': [-0.05, 0, 0.2], 'y_tick_labels': [-0.05, 0.0, 0.2], 'linewidth': 2.0, 'y_lim': (-0.05, 0.1), 'y_label': 'Vm'})
x = param_filter_query(dsv_pref, analysis_algorithm='TrialMean', sheet_name='V1_Exc_L2/3', identifier='PerNeuronValue',
value_name='Trial-to-trial-mean(PerNeuronVar(Vm (no AP)))', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs)
y = param_filter_query(dsv_pref, sheet_name='V1_Exc_L2/3', value_name=[
'Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs)
pylab.subplot(gs[0, 2])
pylab.plot(x, y, 'o', color=colors[self.parameters.type])
pylab.xlim(8, 22.0)
pylab.ylim(0, 20.0)
pylab.xticks([10, 15, 20])
pylab.yticks([0, 10, 20])
pylab.xlabel('Vm variance (mV)', fontsize=fontsize)
pylab.ylabel('firing rate (sp/s)', fontsize=fontsize)
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
x = param_filter_query(dsv_pref, analysis_algorithm='Analog_MeanSTDAndFanoFactor', sheet_name='V1_Exc_L2/3',
identifier='PerNeuronValue', value_name='Mean(VM)', ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs)
#x = param_filter_query(dsv_pref,analysis_algorithm='SubtractPNVfromPNVS',sheet_name='V1_Exc_L2/3',identifier='PerNeuronValue',value_name='Mean(VM)-Mean(VM)',ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs)
y = param_filter_query(dsv_pref, sheet_name='V1_Exc_L2/3', value_name=[
'Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(close_analogs)
pylab.subplot(gs[1, 2])
pylab.plot(x, y, 'o', color=colors[self.parameters.type])
pylab.xlim(-61.5, -58.5)
pylab.ylim(0, 20.0)
pylab.xticks([-61, -58.0])
pylab.yticks([0, 20, 20])
pylab.xlabel('Vm mean (mV)', fontsize=fontsize)
pylab.ylabel('firing rate (sp/s)', fontsize=fontsize)
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
phf.disable_top_right_axis(pylab.gca())
return plots
class ContrastResponse(Plotting):
required_parameters = ParameterSet({
'cortical_stimulation': int,
})
def get_vals(self, dsv, neuron, variable, mean=False):
assert queries.ads_with_equal_stimulus_type(dsv)
assert queries.equal_ads(dsv, except_params=['stimulus_id'])
pnvs = dsv.get_analysis_result()
st = [MozaikParametrized.idd(s.stimulus_id) for s in pnvs]
if mean:
tc_dict = colapse_to_dictionary(
[numpy.mean(z.get_value_by_id(neuron)) for z in pnvs], st, variable)
else:
tc_dict = colapse_to_dictionary(
[z.get_value_by_id(neuron) for z in pnvs], st, variable)
rads = tc_dict.values()[0][0]
values = tc_dict.values()[0][1]
a, b = zip(*sorted(zip(rads, values)))
return numpy.array(a), numpy.array(b)
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(
2, 2, subplot_spec=subplotspec, hspace=0.25, wspace=0.3)
scale = 0.16
fontsize = 15
spike_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(numpy.array([circular_dist(
l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids]) < 0.1)[0]]
dsv = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Firing rate')
dsv_var = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Tria-to-trial Var of Firing rate')
variable = 'stimulating_signal_parameters_scale'
def fitfunc(p, x): return p[1]*numpy.power(x,
p[0])/(numpy.power(x, p[0])+p[2])
x, y = self.get_vals(dsv, l23_exc_or_many[0], variable)
x, yvar = self.get_vals(dsv_var, l23_exc_or_many[0], variable)
x = numpy.concatenate(([0], x))
y = numpy.concatenate(([0], y))
yvar = numpy.concatenate(([0], yvar))
c50 = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton c50 of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[0])
scale = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton scaler of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[0])
exponent = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton exponent of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[0])
yfit = fitfunc([exponent, scale, c50],
numpy.arange(0, numpy.max(x), 0.01))
ax = pylab.subplot(gs[0, 0])
ax.errorbar(x, y, yerr=yvar, fmt='o', color='k')
ax.plot(numpy.arange(0, numpy.max(x), 0.01), yfit, color='gray')
disable_top_right_axis(pylab.gca())
three_tick_axis(ax.yaxis)
three_tick_axis(ax.xaxis)
pylab.ylim(ymin=0, ymax=int(math.ceil(numpy.max(y+yvar))+1)/2*2)
pylab.xlim(0, 0.33)
pylab.xticks([0, 0.33], ["0", r"$0.33 \times 10^{16}$"])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
pylab.ylabel('spikes/s', fontsize=fontsize)
remove_x_tick_labels()
x, y = self.get_vals(dsv, l23_exc_or_many[3], variable)
x, yvar = self.get_vals(dsv_var, l23_exc_or_many[3], variable)
x = numpy.concatenate(([0], x))
y = numpy.concatenate(([0], y))
yvar = numpy.concatenate(([0], yvar))
c50 = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton c50 of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[3])
scale = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton scaler of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[3])
exponent = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Naka-Rushton exponent of Firing rate').get_analysis_result()[0].get_value_by_id(l23_exc_or_many[3])
yfit = fitfunc([exponent, scale, c50],
numpy.arange(0, numpy.max(x), 0.01))
ax = pylab.subplot(gs[1, 0])
ax.errorbar(x, y, yerr=yvar, fmt='o', color='k')
ax.plot(numpy.arange(0, numpy.max(x), 0.01), yfit, color='gray')
disable_top_right_axis(pylab.gca())
three_tick_axis(ax.yaxis)
three_tick_axis(ax.xaxis)
pylab.ylim(ymin=0, ymax=int(math.ceil(numpy.max(y+yvar))+1)/2*2)
pylab.xlim(0, 0.33)
pylab.xticks([0, 0.33], ["0", r"$0.33 \times 10^{16}$"])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
pylab.xlabel('scale', fontsize=fontsize)
pylab.ylabel('spikes/s', fontsize=fontsize)
x, y = self.get_vals(dsv, l23_exc_or_many, variable, mean=True)
x, yvar = self.get_vals(dsv_var, l23_exc_or_many, variable)
x = numpy.concatenate(([0], x))
y = numpy.concatenate(([0], y))
c50 = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Mean Naka-Rushton c50 of Firing rate').get_analysis_result()[0].value
scale = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Mean Naka-Rushton scaler of Firing rate').get_analysis_result()[0].value
exponent = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name='stimulating_signal_parameters_scale Mean Naka-Rushton exponent of Firing rate').get_analysis_result()[0].value
yfit = fitfunc([exponent, scale, c50],
numpy.arange(0, numpy.max(x), 0.01))
ax = pylab.subplot(gs[:, 1])
ax.plot(x, y, 'ok')
ax.errorbar(x, y, yerr=yvar, fmt='o', color='k')
ax.plot(numpy.arange(0, numpy.max(x), 0.01), yfit, color='gray')
disable_top_right_axis(pylab.gca())
three_tick_axis(ax.yaxis)
three_tick_axis(ax.xaxis)
pylab.ylim(ymin=0, ymax=int(math.ceil(numpy.max(y))+1)/2*2)
pylab.xlim(0, 0.33)
pylab.xticks([0, 0.33], ["0", r"$0.33 \times 10^{16}$"])
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(fontsize)
pylab.xlabel('scale', fontsize=fontsize)
pylab.ylabel('spikes/s', fontsize=fontsize)
return plots
class ContrastResponseTransformed(Plotting):
required_parameters = ParameterSet({
'cortical_stimulation': int,
'type': int,
})
def get_vals(self, dsv, neuron, variable, mean=False):
assert queries.ads_with_equal_stimulus_type(dsv)
assert queries.equal_ads(dsv, except_params=['stimulus_id'])
pnvs = dsv.get_analysis_result()
st = [MozaikParametrized.idd(s.stimulus_id) for s in pnvs]
if mean:
tc_dict = colapse_to_dictionary(
[numpy.mean(z.get_value_by_id(neuron)) for z in pnvs], st, variable)
else:
tc_dict = colapse_to_dictionary(
[z.get_value_by_id(neuron) for z in pnvs], st, variable)
rads = tc_dict.values()[0][0]
values = tc_dict.values()[0][1]
a, b = zip(*sorted(zip(rads, values)))
return numpy.array(a), numpy.array(b)
def subplot(self, subplotspec):
color_cycle = {
'Bl': (0, 0, 0),
'Or': (.9, .6, 0),
'SB': (.35, .7, .9),
'bG': (0, .6, .5),
'Ye': (.95, .9, .25),
'Bu': (0, .45, .7),
'Ve': (.8, .4, 0),
'rP': (.8, .6, .7),
}
colors = [color_cycle[c] for c in sorted(color_cycle.keys())]
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(
1, 1, subplot_spec=subplotspec, hspace=0.25, wspace=0.3)
spike_ids = param_filter_query(
self.datastore, sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(numpy.array([circular_dist(
l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids]) < 0.1)[0]]
if self.parameters.cortical_stimulation:
dsv = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Firing rate')
dsv_var = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray',
st_stimulating_signal_parameters_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Tria-to-trial Var of Firing rate')
variable = 'stimulating_signal_parameters_contrast'
else:
dsv = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='FullfieldDriftingSinusoidalGratingA',
st_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Firing rate')
dsv_var = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='FullfieldDriftingSinusoidalGratingA',
st_orientation=0, analysis_algorithm='TrialAveragedFiringRate', value_name='Tria-to-trial Var of Firing rate')
variable = 'contrast'
fontsize = 15
def fitfunc(p, x): return p[1]*numpy.power(x,
p[0])/(numpy.power(x, p[0])+p[2])
x, y = self.get_vals(dsv, l23_exc_or_many, variable, mean=True)
c50 = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name=variable + ' Mean Naka-Rushton c50 of Firing rate').get_analysis_result()[0].value
scale = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name=variable + ' Mean Naka-Rushton scaler of Firing rate').get_analysis_result()[0].value
exponent = param_filter_query(self.datastore, analysis_algorithm='NakaRushtonTuningCurveFit',
value_name=variable + ' Mean Naka-Rushton exponent of Firing rate').get_analysis_result()[0].value
yfit = fitfunc([exponent, scale, c50], numpy.arange(0, 100, 1))
ax = pylab.subplot(gs[0, 0])
ax.plot(x, y, 'o', color=colors[self.parameters.type])
ax.plot(numpy.arange(0, 100, 1), yfit,
color=colors[self.parameters.type])
disable_top_right_axis(pylab.gca())
three_tick_axis(ax.yaxis)
three_tick_axis(ax.xaxis)
pylab.ylim(0, 10)
pylab.xlim(0, 100)
for label in pylab.gca().get_xticklabels() + pylab.gca().get_yticklabels():
label.set_fontsize(19)
pylab.ylabel('spikes/s', fontsize=fontsize)
# pylab.xlabel('contrast',fontsize=fontsize)
remove_x_tick_labels()
return plots
class OrientationTuningSharpness(Plotting):
required_parameters = ParameterSet({
})
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(4, 6, subplot_spec=subplotspec,
hspace=0.5, wspace=0.1)
spike_ids = sorted(numpy.random.permutation(queries.param_filter_query(
self.datastore, sheet_name='V1_Exc_L2/3').get_segments()[0].get_stored_spike_train_ids()))
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(numpy.array([circular_dist(
l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids]) < 0.1)[0]]
contrasts = [3, 100]
sharpness = [0.1, 0.3, 0.5, 1.0, 2.0, 3.0]
fontsize = 15
for i in range(0, 6):
#lamsChR = self.datastore.get_analysis_result(identifier='PerNeuronValue',analysis_algorithm ='NeuronAnnotationsToPerNeuronValues', value_name = 'Light activation magnitude ChR(V1_Exc_L2/3,0.00616172489758,0.0,'+ str(sharpness[i]) +',10)', sheet_name = 'V1_Exc_L2/3')[0]
queries.param_filter_query(self.datastore, identifier='PerNeuronValue',
analysis_algorithm='NeuronAnnotationsToPerNeuronValues', sheet_name='V1_Exc_L2/3').print_content(full_ADS=True)
lams = self.datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues',
value_name='Light activation ma13gnitude(V1_Exc_L2/3,0.0061617248975781415,0.0,' + str(sharpness[i]) + ',10)', sheet_name='V1_Exc_L2/3')[0]
axis = pylab.subplot(gs[0, i])
pylab.scatter((l23_exc_or.values+numpy.pi/2) %
numpy.pi-numpy.pi/2, lams.values, s=3)
pylab.xticks([-numpy.pi/2, 0, numpy.pi/2],
["-$\\frac{\\pi}{2}$", "0", "$\\frac{\\pi}{2}$"])
if i == 0:
pylab.ylabel(r'$photons/cm^2/s$', fontsize=fontsize)
pylab.xlabel('orientation', fontsize=fontsize)
pylab.yticks(
[0, 5e14], [0.0, r'$5 {\times} 10^{14}$'], fontsize=fontsize)
else:
phf.disable_left_axis(axis)
pylab.yticks([])
pylab.ylim(0, 5.0e14)
pylab.xlim([-numpy.pi/2, numpy.pi/2])
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
pylab.title('$\\sigma = ' +
str(sharpness[i]) + '$', fontsize=fontsize)
base = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_sharpness=sharpness[
i], st_stimulating_signal_parameters_contrast=100, value_name=['stimulating_signal_parameters_orientation baseline of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
mmax = queries.param_filter_query(self.datastore, sheet_name='V1_Exc_L2/3', st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', st_stimulating_signal_parameters_sharpness=sharpness[
i], st_stimulating_signal_parameters_contrast=100, value_name=['stimulating_signal_parameters_orientation max of Firing rate'], ads_unique=True).get_analysis_result()[0].get_value_by_id(spike_ids)
responsive_spike_ids = numpy.array(
spike_ids)[numpy.array(base)+numpy.array(mmax) > 1.0]
dsv = queries.param_filter_query(self.datastore, st_name='InternalStimulus', st_direct_stimulation_name='LocalStimulatorArray', value_name='Firing rate', analysis_algorithm=[
'TrialAveragedFiringRate'], st_stimulating_signal_parameters_contrast=contrasts, st_stimulating_signal_parameters_sharpness=sharpness[i])
if i == 0:
plots['ORTCMean'+str(i)] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'stimulating_signal_parameters_orientation', 'neurons': list(
responsive_spike_ids), 'sheet_name': "V1_Exc_L2/3", 'centered': True, 'mean': True, 'pool': False, 'polar': False})), gs[1, i], {'x_label': 'orientation', 'y_lim': (0, 10), 'y_label': 'firing rate (sp/s)', 'title': None})
else:
plots['ORTCMean'+str(i)] = (PlotTuningCurve(dsv, ParameterSet({'parameter_name': 'stimulating_signal_parameters_orientation', 'neurons': list(responsive_spike_ids), 'sheet_name': "V1_Exc_L2/3", 'centered': True,
'mean': True, 'pool': False, 'polar': False})), gs[1, i], {'x_label': 'orientation', 'y_lim': (0, 10), 'title': None, 'x_label': None, 'left_border': False, 'y_axis': False, 'title': None})
low_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=contrasts[0], st_stimulating_signal_parameters_sharpness=sharpness[i]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
high_c = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=contrasts[1], st_stimulating_signal_parameters_sharpness=sharpness[i]).get_analysis_result()[0].get_value_by_id(responsive_spike_ids)
if True:
axis = pylab.subplot(gs[3, i])
logger.info(low_c)
logger.info(high_c)
pylab.plot([0, 60], [0, 60], color='#AAAAAA')
pylab.plot(low_c, high_c, 'ok')
pylab.xlim(0, 60)
pylab.ylim(0, 60)
if i == 0:
pylab.xlabel('HWHH low cont.', fontsize=fontsize)
pylab.ylabel('HWHH high cont.', fontsize=fontsize)
pylab.yticks([0, 30, 60])
else:
phf.disable_left_axis(axis)
pylab.yticks([])
pylab.xticks([0, 30, 60])
phf.disable_top_right_axis(axis)
for label in axis.get_xticklabels() + axis.get_yticklabels():
label.set_fontsize(fontsize)
dsv = queries.param_filter_query(self.datastore, value_name=['stimulating_signal_parameters_orientation HWHH of Firing rate'], sheet_name=[
'V1_Exc_L2/3'], st_stimulating_signal_parameters_contrast=100, st_stimulating_signal_parameters_sharpness=sharpness[i])
if i == 0:
plots['HWHHHistogramExc'+str(i)] = (PerNeuronValuePlot(dsv, ParameterSet({'cortical_view': False})), gs[2, i], {'mark_mean': True, 'x_lim': (
0.0, 50.0), 'y_lim': (0.0, 400.0), 'title': None, 'y_label': '# neurons', 'x_label': 'HWHH', 'mark_value': 27})
else:
plots['HWHHHistogramExc'+str(i)] = (PerNeuronValuePlot(dsv, ParameterSet({'cortical_view': False})), gs[2, i], {'mark_mean': True, 'x_lim': (
0.0, 50.0), 'y_lim': (0.0, 400.0), 'x_label': None, 'title': None, 'y_label': '# neurons', 'left_border': False, 'y_axis': False, 'mark_value': 27})
return plots
class OrientationTuningLEE_size(Plotting):
required_parameters = ParameterSet({
})
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(4, 5, subplot_spec=subplotspec,
hspace=0.5, wspace=0.1)
spike_ids = sorted(numpy.random.permutation(queries.param_filter_query(
self.datastore, sheet_name='V1_Exc_L2/3').get_segments()[0].get_stored_spike_train_ids()))
l23_exc_or = self.datastore.get_analysis_result(
identifier='PerNeuronValue', value_name='LGNAfferentOrientation', sheet_name='V1_Exc_L2/3')[0]
l23_exc_or_many = numpy.array(spike_ids)[numpy.nonzero(numpy.array([circular_dist(
l23_exc_or.get_value_by_id(i), 0, numpy.pi) for i in spike_ids]) < 0.1)[0]]
contrasts = [3, 100]
sizes = [10, 20, 50, 100, 300]
fontsize = 15
for i in range(0, 5):
if True:
lams = self.datastore.get_analysis_result(identifier='PerNeuronValue', analysis_algorithm='NeuronAnnotationsToPerNeuronValues',
value_name='Light activation magnitude(V1_Exc_L2/3,0.00616172489758,0.0,0.5,' + str(sizes[i]) + ')', sheet_name='V1_Exc_L2/3')[0]
axis = pylab.subplot(gs[0, i])
pylab.scatter((l23_exc_or.values+numpy.pi/2) %