def show_results(result):
"""
visualizes the result of the parameter search.
Parameters:
result - list of result dictionaries.
"""
import numpy
rates = numpy.sort([r['source_rate'] for r in result])
weights = numpy.sort([r['weight'] for r in result])
neuron_rates = numpy.zeros((len(rates), len(weights)))
for r_i in range(len(rates)):
for w_i in range(len(weights)):
neuron_rates[r_i, w_i] = [r['neuron_rate'] for r in result
if (r['source_rate'] == rates[r_i])
and (r['weight'] == weights[w_i])][0]
import NeuroTools.plotting as plotting
pylab = plotting.get_display(True)
pylab.rcParams.update(plotting.pylab_params())
subplot = pylab.imshow(neuron_rates,
interpolation = 'nearest',
origin = 'lower')
plotting.set_labels(subplot.get_axes(),
xlabel = 'rate',
ylabel = 'weight')
pylab.colorbar()
# could add fancy xticks and yticks here
import tempfile, os
(fd, figfilename) = tempfile.mkstemp(prefix = 'parameter_search_result',
suffix = '.png',
dir = os.getcwd())
pylab.gcf().savefig(figfilename)
def show_results(result):
"""
visualizes the result of the parameter search.
Parameters:
result - list of result dictionaries.
"""
import numpy
t_smooth = 100. # ms. integration time to show fiber activity
snrs = numpy.sort([r['snr'] for r in result])
neuron_rates = numpy.zeros(len(snr))
for snr_i in range(len(snrs)):
neuron_rates[r_i] = [
r['neuron_rate'] for r in result
if (r['source_rate'] == snrs[snr_i])
][0]
import NeuroTools.plotting as plotting
pylab = plotting.get_display(True)
pylab.rcParams.update(plotting.pylab_params())
print rates, neuron_rates
subplot = pylab.imshow(neuron_rates,
interpolation='nearest',
origin='lower')
plotting.set_labels(subplot.get_axes(), xlabel='rate', ylabel='weight')
pylab.colorbar()
# could add fancy xticks and yticks here
import tempfile, os
(fd, figfilename) = tempfile.mkstemp(prefix='parameter_search_result',
suffix='.png',
dir=os.getcwd())
pylab.gcf().savefig(figfilename)
############# MAKING FIGURE ############################
from NeuroTools.plotting import pylab_params
from numpy import zeros, where, arange
pylab.close("all")
pylab.ioff() # pylab.ion() #
""" Figure
Prints to a figure the mean firing rate
* in (x,y) accross time during the stimulation and
* in t accross positions within the center
for the output (ON and OFF) and for the different parameter values.
"""
pylab.rcParams.update(pylab_params(fig_width_pt=497.9)) # , text_fontsize=8))
pylab.figure(num=1, dpi=300, facecolor="w", edgecolor="k")
x = params["position"][0]
y = params["position"][1]
# Lmargin, Rmargin, dmargin, umargin = 0.05, 0.15, 0.05, 0.05
# pylab.axes([Lmargin, dmargin , 1.0 - Rmargin- Lmargin,1.0-umargin-dmargin]) # [left, bottom, width, height]
# pylab.subplot(131)
pylab.axes([0.1, 0.33, 0.3 / 1.61, 0.3]) # [left, bottom, width, height]
pylab.scatter(x, y, c=params["amplitude"], faceted=False) # , edgecolors='none'
pylab.title("Input", fontsize="small")
pylab.axis("equal")
pylab.subplot(232)
pylab.plot(lower_edges[:-1], temporal_ON)
pylab.title("time course (ROI) ", fontsize="small")
# pylab.title('time course ON',fontsize = 'small')
############# MAKING FIGURE ############################
from NeuroTools.plotting import pylab_params
from numpy import zeros, where, arange
pylab.close('all')
pylab.ioff() #pylab.ion() #
""" Figure
Prints to a figure the mean firing rate
* in (x,y) accross time during the stimulation and
* in t accross positions within the center
for the output (ON and OFF) and for the different parameter values.
"""
pylab.rcParams.update(pylab_params(fig_width_pt=497.9)) #, text_fontsize=8))
pylab.figure(num=1, dpi=300, facecolor='w', edgecolor='k')
x = params['position'][0]
y = params['position'][1]
#Lmargin, Rmargin, dmargin, umargin = 0.05, 0.15, 0.05, 0.05
#pylab.axes([Lmargin, dmargin , 1.0 - Rmargin- Lmargin,1.0-umargin-dmargin]) # [left, bottom, width, height]
#pylab.subplot(131)
pylab.axes([0.1, 0.33, .3 / 1.61, .3]) # [left, bottom, width, height]
pylab.scatter(x, y, c=params['amplitude'], faceted=False) #, edgecolors='none'
pylab.title('Input', fontsize='small')
pylab.axis('equal')
pylab.subplot(232)
pylab.plot(lower_edges[:-1], temporal_ON)
pylab.title('time course (ROI) ', fontsize='small')
#pylab.title('time course ON',fontsize = 'small')