def run(self, results):
par = self.getValueOfParameter("parameter")
i = int(self.getValueOfParameter("iteration number"))
title = self.getValueOfParameter("title")
if(par==""):
return False
if(i >= results.__len__()):
return False
dialogform = Dialog(QApplication.activeWindow())
fig = Figure((5.0, 4.0), dpi=100)
ax = SubplotZero(fig, 1, 1, 1)
fig.add_subplot(ax)
for n in ["top", "right"]:
ax.axis[n].set_visible(False)
for n in ["bottom", "left"]:
ax.axis[n].set_visible(True)
x = results[i].getResults(par)
if(not(x.__len__())):
return False
ax.boxplot(x, notch=0, sym='+', vert=1, whis=1.5)
ax.set_title(title)
dialogform.showFigure(fig)
return True
def run(self, results):
par = self.getValueOfParameter("parameter")
i = int(self.getValueOfParameter("iteration number"))
title = self.getValueOfParameter("title")
if(par==""):
return False
if(i >= results.__len__()):
return False
dialogform = Dialog(QApplication.activeWindow())
fig = Figure((5.0, 4.0), dpi=100)
ax = SubplotZero(fig, 1, 1, 1)
fig.add_subplot(ax)
for n in ["top", "right"]:
ax.axis[n].set_visible(False)
for n in ["bottom", "left"]:
ax.axis[n].set_visible(True)
x = results[i].getResults(par)
if(not(x.__len__())):
return False
ax.hist(x, 100, normed=1)
ax.set_xlabel('Error')
ax.set_ylabel('Probability')
ax.grid(True)
ax.set_title(title)
dialogform.showFigure(fig)
return True
def run(self, results):
par1 = self.getValueOfParameter("parameter 1")
par2 = self.getValueOfParameter("parameter 2")
i = int(self.getValueOfParameter("iteration number"))
title = self.getValueOfParameter("title")
if(par1==""):
return False
if(par2==""):
return False
if(i >= results.__len__()):
return False
dialogform = Dialog(QApplication.activeWindow())
fig = Figure((5.0, 4.0), dpi=100)
ax = SubplotZero(fig, 1, 1, 1)
fig.add_subplot(ax)
for n in ["top", "right"]:
ax.axis[n].set_visible(False)
for n in ["bottom", "left"]:
ax.axis[n].set_visible(True)
y1 = results[i].getResults(par1)
y2 = results[i].getResults(par2)
if(not(y1.__len__())):
return False
if(not(y2.__len__())):
return False
ax.plot(range(0,y1.__len__()),y1,color='r')
ax.plot(range(0,y2.__len__()),y2,color='b')
ax.set_title(title)
leg = ax.legend((par1, par2),
'upper center', shadow=True)
frame = leg.get_frame()
frame.set_facecolor('0.80') # set the frame face color to light gray
# matplotlib.text.Text instances
for t in leg.get_texts():
t.set_fontsize('small') # the legend text fontsize
# matplotlib.lines.Line2D instances
for l in leg.get_lines():
l.set_linewidth(1.5) # the legend line width
dialogform.showFigure(fig)
return True
def run(self, results):
par1 = self.getValueOfParameter("parameter 1")
par2 = self.getValueOfParameter("parameter 2")
i = int(self.getValueOfParameter("iteration number"))
title = self.getValueOfParameter("title")
if(par1==""):
return False
if(par2==""):
return False
if(i >= results.__len__()):
return False
dialogform = Dialog(QApplication.activeWindow())
fig = Figure((5.0, 4.0), dpi=100)
ax = SubplotZero(fig, 1, 1, 1)
fig.add_subplot(ax)
for n in ["top", "right"]:
ax.axis[n].set_visible(False)
for n in ["bottom", "left"]:
ax.axis[n].set_visible(True)
x = results[i].getResults(par1)
y = results[i].getResults(par2)
if(not(x.__len__())):
return False
if(not(y.__len__())):
return False
ax.plot(x,y,'.')
#plot middle
xm = range(math.floor(min(ax.axis())),math.floor(max(ax.axis())+1),1)
ax.plot(xm,xm)
ax.set_xlabel(par1)
ax.set_ylabel(par2)
ax.set_title(title)
dialogform.showFigure(fig)
return True
def run(self, results):
par1 = self.getValueOfParameter("parameter 1")
par2 = self.getValueOfParameter("parameter 2")
title = self.getValueOfParameter("title")
if(par1==""):
return False
if(par2==""):
return False
x = pycalimero.doublevector();
y = pycalimero.doublevector();
for i in results:
x.append(i.getResults(par1)[0])
y.append(i.getResults(par2)[0])
dialogform = Dialog(QApplication.activeWindow())
fig = Figure((5.0, 4.0), dpi=100)
ax = SubplotZero(fig, 1, 1, 1)
fig.add_subplot(ax)
for n in ["top", "right"]:
ax.axis[n].set_visible(False)
for n in ["bottom", "left"]:
ax.axis[n].set_visible(True)
if(not(x.__len__())):
return False
if(not(y.__len__())):
return False
ax.plot (x, y, '.')
ax.set_title(title)
dialogform.showFigure(fig)
return True
def main(path, name):
from numpy import linspace, loadtxt
d = SimulatedData(path)
psth = d.spike_time.psth()
from mpl_toolkits.axes_grid.axislines import SubplotZero
import matplotlib.pyplot as plt
f1 = plt.figure(figsize=[6,8])
ax = SubplotZero(f1, 411)
f1.add_subplot(ax)
psth.plot_raster(ax)
ax = SubplotZero(f1, 412)
f1.add_subplot(ax)
psth.plot_rate(ax, smoothed=True)
ax = SubplotZero(f1, 413)
f1.add_subplot(ax)
dat = loadtxt(d.path['ML response'])
t = linspace(0, 5000, dat.size)
ax.plot(t, dat, 'k')
for direction in ["left", "right", "top", "bottom"]:
ax.axis[direction].set_visible(False)
logging.info(str(dir(ax.axis["bottom"])))
# ax.axis["bottom"].major_ticklabels=[]
ax.set_title("ML")
ax = SubplotZero(f1, 414)
f1.add_subplot(ax)
dat = loadtxt(d.path['HHLS response'])
t = linspace(0, 5000, dat.size)
ax.plot(t, dat, 'k')
for direction in ["left", "right", "top"]:
ax.axis[direction].set_visible(False)
ax.axis["bottom"].set_label("Time (ms)")
ax.set_title("HHLS")
f1.subplots_adjust(hspace=0.47, top=0.95, bottom=0.05)
f2 = plt.figure(figsize=[4,4])
ax = SubplotZero(f2, 111)
f2.add_subplot(ax)
mf = psth.hist_mean_rate(ax, bins=linspace(0,8,20))
ax.set_title({"highvar": "High variance", "lowvar": "Low variance"}[name])
print "Mean firing rate =", mf.mean(), "Hz", "(", mf.std(),")"
plt.show()
ax1 = fig.add_subplot(gs[:6, 1], aspect='equal') # dipole moment ill.
ax1.axis('off')
ax1.set_title('extracellular potential')
ax2 = fig.add_subplot(gs[:6, 2], aspect='equal') # dipole moment ill.
ax2.axis('off')
ax2.set_title('magnetic field')
# ax3 = fig.add_subplot(gs[0, 3], aspect='equal') # spherical shell model ill.
# ax3.set_title('4-sphere volume conductor')
# ax4 = fig.add_subplot(gs[1, 3],
# aspect='equal'
# ) # MEG/EEG forward model ill.
# ax4.set_title('EEG and MEG signal detection')
ax3 = SubplotZero(fig, gs[7:, 0])
fig.add_subplot(ax3)
ax3.set_title('4-sphere volume conductor', verticalalignment='bottom')
ax4 = fig.add_subplot(gs[7:, 1]) # EEG
ax4.set_title('scalp electric potential $\phi_\mathbf{p}(\mathbf{r})$')
ax5 = fig.add_subplot(gs[7:, 2], sharey=ax4) # MEG
# ax5.set_title('scalp magnetic field')
#morphology - line sources for panels A and B
zips = []
xz = cell.get_idx_polygons()
for x, z in xz:
zips.append(zip(x, z))
for ax in [ax0]:
polycol = PolyCollection(zips,
linewidths=(0.5),
edgecolors='k',
facecolors='none',
fontsize=16, fontweight='demibold',
transform=ax.transAxes)
plotting.remove_axis_junk(ax)
t = np.arange(p_net.shape[1])*PSET.dt*PSET.decimate_q
inds = (t >= T[0]) & (t <= T[1])
ax.plot(t[inds], p_net[i, inds], 'k', lw=1)
ax.set_ylabel(ylabel)
ax.set_xticklabels([])
# panel F. Illustration of 4-sphere volume conductor model geometry
ax = SubplotZero(fig, gs[2, 1])
fig.add_subplot(ax)
ax.set_title('four-sphere volume conductor model')
for direction in ["xzero"]:
ax.axis[direction].set_visible(True)
for direction in ["left", "right", "bottom", "top"]:
ax.axis[direction].set_visible(False)
theta = np.linspace(0, np.pi, 31)
# draw some circles:
for i, r, label in zip(range(4), PSET.foursphereParams['radii'], ['brain', 'CSF', 'skull', 'scalp']):
ax.plot(np.cos(theta)*r, np.sin(theta)*r, 'C{}'.format(i), label=label + r', $r_%i=%i$ mm' % (i+1, r / 1000), clip_on=False)
# draw measurement points
ax1 = fig.add_subplot(gs[:6, 1], aspect='equal') # dipole moment ill.
ax1.axis('off')
ax1.set_title('extracellular potential')
ax2 = fig.add_subplot(gs[:6, 2], aspect='equal') # dipole moment ill.
ax2.axis('off')
ax2.set_title('magnetic field')
# ax3 = fig.add_subplot(gs[0, 3], aspect='equal') # spherical shell model ill.
# ax3.set_title('4-sphere volume conductor')
# ax4 = fig.add_subplot(gs[1, 3],
# aspect='equal'
# ) # MEG/EEG forward model ill.
# ax4.set_title('EEG and MEG signal detection')
ax3 = SubplotZero(fig, gs[7:, 0])
fig.add_subplot(ax3)
ax3.set_title('4-sphere volume conductor', verticalalignment='bottom')
ax4 = fig.add_subplot(gs[7:, 1]) # EEG
ax4.set_title('scalp electric potential $\phi_\mathbf{p}(\mathbf{r})$')
ax5 = fig.add_subplot(gs[7:, 2], sharey=ax4) # MEG
# ax5.set_title('scalp magnetic field')
#morphology - line sources for panels A and B
zips = []
xz = cell.get_idx_polygons()
for x, z in xz:
zips.append(zip(x, z))
for ax in [ax0]:
polycol = PolyCollection(zips,
linewidths=(0.5),
edgecolors='k',
facecolors='none',
verticalalignment='center',
fontsize=16,
fontweight='demibold',
transform=ax.transAxes)
plotting.remove_axis_junk(ax)
t = np.arange(p_net.shape[1]) * PSET.dt * PSET.decimate_q
inds = (t >= T[0]) & (t <= T[1])
ax.plot(t[inds], p_net[i, inds], 'k', lw=1)
ax.set_ylabel(ylabel)
ax.set_xticklabels([])
# panel F. Illustration of 4-sphere volume conductor model geometry
ax = SubplotZero(fig, gs[2, 1])
fig.add_subplot(ax)
ax.set_title('four-sphere volume conductor model')
for direction in ["xzero"]:
ax.axis[direction].set_visible(True)
for direction in ["left", "right", "bottom", "top"]:
ax.axis[direction].set_visible(False)
theta = np.linspace(0, np.pi, 31)
# draw some circles:
for i, r, label in zip(range(4), PSET.foursphereParams['radii'],
['brain', 'CSF', 'skull', 'scalp']):
ax.plot(np.cos(theta) * r,
np.sin(theta) * r,
'C{}'.format(i),
