def __call__(self,fullmatrix,simple_sheet_name=None,complex_sheet_name=None,bins=frange(0,2.0,0.1,inclusive=True),**params):
p=ParamOverrides(self,params)
from topo.analysis.vision import complexity
if (topo.sim.objects().has_key(simple_sheet_name) and topo.sim.objects().has_key(complex_sheet_name)):
v1s = complexity(fullmatrix[topo.sim[simple_sheet_name]]).flatten()
v1c = complexity(fullmatrix[topo.sim[complex_sheet_name]]).flatten()
#double the number of complex cells to reflect large width of layer 2/3
v1c = numpy.concatenate((array(v1c),array(v1c)),axis=1)
pylab.figure()
n = pylab.subplot(311)
pylab.hist(v1s,bins)
pylab.axis([0,2.0,0,4100])
n.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(3))
n = pylab.subplot(312)
pylab.hist(v1c,bins)
pylab.axis([0,2.0,0,4100])
n.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(3))
n = pylab.subplot(313)
pylab.hist(numpy.concatenate((array(v1s),array(v1c)),axis=1),bins)
pylab.axis([0,2.0,0,4100])
n.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(3))
self._generate_figure(p)
def test_all_directions(x=0,y=0,scale=0.6):
results=KeyedList()
## g=pattern.Gaussian(x=x,y=y,aspect_ratio=4.66667,
## size=0.088388,scale=1.0)
g = pattern.SineGrating(frequency=2.4,phase=0.0,orientation=0,scale=scale)
inputs = {}
for j in range(4):
inputs['Retina%s'%j] = pattern.Sweeper(generator=copy.deepcopy(g),
speed=2.0/24.0)
for i in frange(0.0, 2.0, 2.0/18.0, inclusive=True):
orientation = i*pi+pi/2
for j in range(4):
s = inputs['Retina%s'%j]
s.step = j
s.orientation = orientation
pattern_present(inputs=inputs,duration=1.0,
plastic=False,
overwrite_previous=True,
apply_output_fn=True)
if hasattr(topo,'guimain'):
topo.guimain.refresh_activity_windows()
results[i]=decode_feature(topo.sim['V1'],
preference_map="DirectionPreference")
return results
def test_all_orientations(x=0,y=0):
results=KeyedList()
for i in frange(0.0, 1.0, 1.0/36.0, inclusive=True):
input = pattern.Gaussian(x=x, y=y, orientation=i*pi,
size=0.088388, aspect_ratio=4.66667, scale=1.0)
pattern_present(inputs={'Retina' : input}, duration=1.0,
plastic=False,
overwrite_previous=True,
apply_output_fn=True)
if hasattr(topo,'guimain'):
topo.guimain.refresh_activity_windows()
results[i]=decode_feature(topo.sim['V1'], preference_map="OrientationPreference")
return results
def __call__(self, view_name, axis, xlabel='', sheets_to_plot=[], quantiles=[0.25,0.5,0.75],**params):
"""
Parameter sheets_to_plot can be any list of valid sheet names.
They must have view_name view present to obtain valid plot.
"""
p=ParamOverrides(self,params)
if len(sheets_to_plot) > 8:
self.warning( "Too many sheets to plot. Plotting first 8 of them." )
sheets_to_plot=sheets_to_plot[:8]
# list of values across all cortical sheets
list_total=[]
subplot_base_number=100*(len(sheets_to_plot)+1)+10
num_plotted_sheets=0
x_min,x_max,dx=axis
pylab.figure()
for name in sheets_to_plot:
# get the sheet object
try:
sheet=topo.sim.objects()[name]
except KeyError:
self.warning( "Name '"+name+"' is not present in Topographica objects. Skipping.")
continue
# get the histogrammed view
try:
view=sheet.sheet_views[view_name].view()[0]
except KeyError:
self.warning( "View '"+view_name+"' is not present in '"+name+"' views. Skipping.")
continue
num_plotted_sheets += 1
# get linear list of values
list_local = [ element for row in view for element in row ]
list_total += list_local
# plot it
pylab.subplot(subplot_base_number+num_plotted_sheets)
pylab.title(name,fontsize=10)
pylab.ylabel('Number Of Cells')
hist = pylab.hist(list_local,frange(x_min,x_max,dx)) #,inclusive=True))
# Compute 1000 ceil of bin with highest number of cells
# the plot is nicer this way
upper_bound = ceil(float(hist[0].max())/500)*500
pylab.axis([x_min,x_max,0,upper_bound])
# plot the quantiles right below
list_local.sort()
list_length=len(list_local)
for quantile in quantiles:
quantile_value=list_local[int(list_length*quantile)]
pylab.plot([quantile_value]*2,[upper_bound,0],'r' if quantile == 0.5 else 'g')
# if there would be only one sheet, sum of all sheets would be redundant
if num_plotted_sheets > 1:
num_plotted_sheets += 1
pylab.subplot(subplot_base_number + num_plotted_sheets)
pylab.title('Sum of All sheets',fontsize=10)
pylab.ylabel('Number Of Cells')
pylab.xlabel(xlabel)
hist = pylab.hist(list_total,frange(x_min,x_max,dx)) # ,inclusive=True))
# Compute 1000 ceil of bin with highest number of cells
# the plot is nicer this way
upper_bound = ceil(float(hist[0].max())/1000)*1000
pylab.axis([x_min,x_max,0,upper_bound])
list_total.sort()
list_length=len(list_total)
for quantile in quantiles:
quantile_value=list_total[int(list_length*quantile)]
pylab.plot([quantile_value]*2,[upper_bound,0],'r' if quantile == 0.5 else 'g')
self._generate_figure(p)
