def sa():
import topo
from topo.command.analysis import save_plotgroup
from param import normalize_path
import contrib.jacommands
import contrib.surround_analysis_new_cleaned
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand, FeatureCurveCommand
reload(contrib.surround_analysis_new_cleaned)
s = normalize_path.prefix
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
from topo.analysis.featureresponses import FeatureResponses, PatternPresenter, FeatureMaps
PatternPresenter.duration = 4.0
normalize_path.prefix = s
SinusoidalMeasureResponseCommand.scale = __main__.__dict__.get(
"analysis_scale", 1.0)
SinusoidalMeasureResponseCommand.frequencies = [2.4]
if __main__.__dict__.get("Max", False):
from topo.misc.distribution import DSF_MaxValue
preference_fn = DSF_MaxValue(value_scale=(0., 1. / numpy.pi),
selectivity_scale=(0., 17.0))
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1Complex"], preference_fn=preference_fn)()
else:
save_plotgroup("Orientation Preference and Complexity")
contrib.surround_analysis_new_cleaned.surround_analysis(
"V1Complex").run_lhi_informed_analysis(
max_curves=__main__.__dict__.get("max_curves", 20),
center_size=__main__.__dict__.get("center_size", 20),
index=__main__.__dict__.get("index", 0))
def __call__(self,times=None,**kwargs):
# if analysis_group('b'):
# return None
if self.pickle:
or_pkl = open(param.normalize_path('ormap_{time}.pkl'.format(time=topo.sim.time())),'wb')
pkl_data = {}
for f in self.frequencies:
save_plotgroup("Orientation Preference",use_cached_results=False,saver_params={'filename_suffix':'_{0}'.format(f)},
pre_plot_hooks=[measure_sine_pref.instance(frequencies=[f],num_phase=self.num_phase,num_orientation=self.num_orientation,
preference_fn=DSF_WeightedAverage(value_scale=(0., 1./math.pi)))])
if self.pickle:
for sheet in self.sheets:
im = topo.sim[sheet].sheet_views['OrientationPreference'].view()[0][0:-1,0:-1]
try:
polar_or = image.hue_to_polar(im)
pwdata = analysisJL.polarmap_contours(polar_or)
pws = analysisJL.identify_pinwheels(*pwdata)
pwbitsofinformation = type('pwdata', (), dict(zip(['recontours', 'imcontours', 'intersections', 'pinwheels'] , pwdata+(pws,) )))
pw_results = analysisJL.pinwheel_analysis(im, len(pws), ignore_DC=False)
pkl_data['OR_Analysis_{freq}_{sheet}'.format(freq = f,sheet=sheet)] = pw_results
except:
print "OR Pinwheel Analysis failed"
or_sel = topo.sim[sheet].sheet_views['OrientationSelectivity'].view()[0]
pkl_data['OrientationPreference_{freq}_{sheet}'.format(freq = f,sheet=sheet)] = im
pkl_data['OrientationSelectivity_{freq}_{sheet}'.format(freq = f,sheet=sheet)] = or_sel
if self.pickle:
pickle.dump(pkl_data,or_pkl)
def __call__(self,times=None,**kwargs):
if analysis_group('b'):
return None
save_plotgroup("Position Preference",use_cached_results=False,
pre_plot_hooks=[measure_position_pref.instance(divisions=self.divisions,
x_range=self.x_range,
y_range=self.y_range,
size=self.size,scale=self.scale)])
def test_orientation_preference_saving(self):
save_plotgroup('Orientation Preference')
self.exists("testplotfilesaver_000000.00_B_Orientation_Preference.png")
self.exists(
"testplotfilesaver_000000.00_B_Orientation_PreferenceAndSelectivity.png"
)
self.exists(
"testplotfilesaver_000000.00_B_Orientation_Selectivity.png")
self.exists("testplotfilesaver_000000.00__Color_Key.png")
def collect_activity_statistics():
contrib.jacommands.activity_history = numpy.concatenate((contrib.jacommands.activity_history, topo.sim["V1"].activity.flatten()), axis=1)
if(int(topo.sim.time()) == 10000):
pylab.figure()
pylab.hist(contrib.jacommands.activity_history, (numpy.arange(20.0) / 20.0))
pylab.savefig(str(topo.sim.time()) + 'activity_histogram.png')
# measure_or_tuning_fullfield()
# cyclic_tuning_curve_batch(filename="OrientationTC:V1:[0,0]",sheet=topo.sim["V1"],coords=[(0,0)],x_axis="orientation")
save_plotgroup('Activity')
def projection_plotgroup(times=None,**kwargs):
if analysis_group('b'):
return None
from topo.command.analysis import save_plotgroup
projections = [('V1PV','PVLGNOnAfferent'),('V1PV','PVLGNOffAfferent'),('V1Exc','LGNOnAfferent'),('V1Exc','LGNOffAfferent'),('V1Exc','LateralExcitatory'),('V1Exc','V1PV2V1Exc'),('V1SOM','V1Exc2V1SOM'),('V1Exc','V1PV2V1Exc'),('V1','LGNOnAfferent'),('V1','LGNOffAfferent'),('V1PV','V1Exc2V1PV'),('V1','LateralInhibitory')]
for proj in projections:
try:
save_plotgroup("Projection",projection=topo.sim[proj[0]].projections(proj[1]))
except:
print "{0} does not exist".format(proj)
def plotting_and_saving_analysis_fn(prefix=""):
"""For use with memuse_batch() to test snapshot and plotting memory usage."""
import topo
from topo.command import save_snapshot
from topo.command.analysis import measure_sine_pref,save_plotgroup
print "%sMemuse at time %s: %s" % (prefix,topo.sim.timestr(),allsizes_mb())
measure_sine_pref()
print "%sAfter measure_sine_pref: %s" % (prefix,allsizes_mb())
save_plotgroup("Orientation Preference")
print "%sAfter save_plotgroup: %s" % (prefix,allsizes_mb())
save_snapshot("/tmp/tmp.typ")
print "%sAfter save_snapshot: %s" % (prefix,allsizes_mb())
def t_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
print 'Called complex_analysis_function'
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand, FeatureCurveCommand
import contrib.jacommands
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
print 'Build a list of all sheets worth measuring'
f = lambda x: hasattr(x, 'measure_maps') and x.measure_maps
measured_sheets = filter(f, topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
print 'Set potentially reasonable defaults; not necessarily useful'
topo.command.analysis.coordinate = (0.0, 0.0)
if input_sheets:
topo.command.analysis.input_sheet_name = input_sheets[0].name
if measured_sheets:
topo.command.analysis.sheet_name = measured_sheets[0].name
FeatureCurveCommand.curve_parameters = [{
"contrast": 30
}, {
"contrast": 50
}, {
"contrast": 70
}, {
"contrast": 90
}]
import numpy
# reset treshold and desable noise before measuring maps
#m = numpy.mean(topo.sim["V1Simple"].output_fns[2].t)
#topo.sim["V1Simple"].output_fns[2].t*=0
#topo.sim["V1Simple"].output_fns[2].t+=m
#sc = topo.sim["V1Simple"].output_fns[1].generator.scale
#topo.sim["V1Simple"].output_fns[1].generator.scale=0.0
#save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Orientation Preference")
def v2_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
from param import normalize_path
topo.sim["V1Simple"].measure_maps = True
topo.sim["V1Complex"].measure_maps = True
topo.sim["V2"].measure_maps = True
topo.sim["V2"].in_connections[0].strength = 4
save_plotgroup("Orientation Preference and Complexity")
# Plot all projections for all measured_sheets
measured_sheets = [
s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s, 'measure_maps') and s.measure_maps
]
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection", projection=p)
save_plotgroup("Activity")
# topo.sim["V1Simple"].measure_maps = False
# topo.sim["V1Complex"].measure_maps = False
save_plotgroup("Corner OR Preference")
from topo.command import save_snapshot
def v2_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet.basic import GeneratorSheet
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
from param import normalize_path
topo.sim["V1Simple"].measure_maps = True
topo.sim["V1Complex"].measure_maps = True
topo.sim["V2"].measure_maps = True
topo.sim["V2"].in_connections[0].strength=4
save_plotgroup("Orientation Preference and Complexity")
# Plot all projections for all measured_sheets
measured_sheets = [s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s,'measure_maps') and s.measure_maps]
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
save_plotgroup("Activity")
# topo.sim["V1Simple"].measure_maps = False
# topo.sim["V1Complex"].measure_maps = False
save_plotgroup("Corner OR Preference")
from topo.command.basic import save_snapshot
def plotting_and_saving_analysis_fn(prefix=""):
"""For use with memuse_batch() to test snapshot and plotting memory usage."""
import topo
from topo.command import save_snapshot
from topo.command.analysis import measure_sine_pref, save_plotgroup
print "%sMemuse at time %s: %s" % (prefix, topo.sim.timestr(),
allsizes_mb())
measure_sine_pref()
print "%sAfter measure_sine_pref: %s" % (prefix, allsizes_mb())
save_plotgroup("Orientation Preference")
print "%sAfter save_plotgroup: %s" % (prefix, allsizes_mb())
save_snapshot("/tmp/tmp.typ")
print "%sAfter save_snapshot: %s" % (prefix, allsizes_mb())
def t_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
print 'Called complex_analysis_function'
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
import contrib.jacommands
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
print 'Build a list of all sheets worth measuring'
f = lambda x: hasattr(x,'measure_maps') and x.measure_maps
measured_sheets = filter(f,topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
print 'Set potentially reasonable defaults; not necessarily useful'
topo.command.analysis.coordinate=(0.0,0.0)
if input_sheets: topo.command.analysis.input_sheet_name=input_sheets[0].name
if measured_sheets: topo.command.analysis.sheet_name=measured_sheets[0].name
FeatureCurveCommand.curve_parameters=[{"contrast":30},{"contrast":50},{"contrast":70},{"contrast":90}]
import numpy
# reset treshold and desable noise before measuring maps
#m = numpy.mean(topo.sim["V1Simple"].output_fns[2].t)
#topo.sim["V1Simple"].output_fns[2].t*=0
#topo.sim["V1Simple"].output_fns[2].t+=m
#sc = topo.sim["V1Simple"].output_fns[1].generator.scale
#topo.sim["V1Simple"].output_fns[1].generator.scale=0.0
#save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Orientation Preference")
def default_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
# CEBALERT: why are these imports here rather than at the top?
import topo
from topo.command.analysis import save_plotgroup
# Save all plotgroups listed in default_analysis_plotgroups
for pg in default_analysis_plotgroups:
save_plotgroup(pg,use_cached_results=True)
# Plot projections from each measured map
measured_sheets = [s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s,'measure_maps') and s.measure_maps]
for s in measured_sheets:
for p in s.in_connections:
save_plotgroup("Projection",projection=p)
# Test response to a standardized pattern
from topo.pattern import Gaussian
from math import pi
pattern_present(inputs=Gaussian(orientation=pi/4,aspect_ratio=4.7))
save_plotgroup("Activity",saver_params={"filename_suffix":"_45d"})
def default_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
# CEBALERT: why are these imports here rather than at the top?
import topo
from topo.command.analysis import save_plotgroup
# Save all plotgroups listed in default_analysis_plotgroups
for pg in default_analysis_plotgroups:
save_plotgroup(pg, use_cached_results=True)
# Plot projections from each measured map
measured_sheets = [
s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s, 'measure_maps') and s.measure_maps
]
for s in measured_sheets:
for p in s.in_connections:
save_plotgroup("Projection", projection=p)
# Test response to a standardized pattern
from topo.pattern import Gaussian
from math import pi
pattern_present(inputs=Gaussian(orientation=pi / 4, aspect_ratio=4.7))
save_plotgroup("Activity", saver_params={"filename_suffix": "_45d"})
def analyse(self, steps=[], ns=10, offset_x=0, offset_y=0):
save_plotgroup("Orientation Preference and Complexity")
# save_plotgroup("Position Preference")
for (x, y) in steps:
xindex = self.center_r + offset_x + x
yindex = self.center_c + offset_y + y
xcoor, ycoor = self.sheet.matrixidx2sheet(xindex, yindex)
c = topo.command.pylabplot.measure_size_response.instance(
sheet=self.sheet, num_phase=8, num_sizes=ns, max_size=3.0, coords=[(xcoor, ycoor)]
)
c.duraton = 4.0
c(coords=[(xcoor, ycoor)], frequencies=[__main__.__dict__.get("FREQ", 2.4)])
self.data_dict[(xindex, yindex)] = {}
self.data_dict[(xindex, yindex)]["ST"] = self.calculate_RF_sizes(xindex, yindex)
self.plot_size_tunning(xindex, yindex)
self.data_dict[(xindex, yindex)]["OCT"] = self.perform_orientation_contrast_analysis(
self.data_dict[(xindex, yindex)]["ST"], xcoor, ycoor, xindex, yindex
)
self.plot_orientation_contrast_tuning(xindex, yindex)
self.plot_orientation_contrast_tuning_abs(xindex, yindex)
# f = open(normalize_path("dict.dat"),'wb')
# import pickle
# pickle.dump(self.data_dict,f)
# f.close()
self.plot_histograms_of_measures()
lhi = compute_local_homogeneity_index(self.sheet.sheet_views["OrientationPreference"].view()[0] * pi, 0.5)
self.plot_map_feature_to_surround_modulation_feature_correlations(lhi, "Local Homogeneity Index")
self.plot_map_feature_to_surround_modulation_feature_correlations(
self.sheet.sheet_views["OrientationSelectivity"].view()[0], "OrientationSelectivity"
)
self.plot_map_feature_to_surround_modulation_feature_correlations(
self.sheet.sheet_views["OrientationPreference"].view()[0] * numpy.pi, "OrientationPreference"
)
def sa():
import topo
from topo.command.analysis import save_plotgroup
from param import normalize_path
import contrib.jacommands
import contrib.surround_analysis_new_cleaned
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
reload(contrib.surround_analysis_new_cleaned)
s = normalize_path.prefix
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
from topo.analysis.featureresponses import FeatureResponses , PatternPresenter, FeatureMaps
PatternPresenter.duration=4.0
normalize_path.prefix = s
SinusoidalMeasureResponseCommand.scale=__main__.__dict__.get("analysis_scale",1.0)
SinusoidalMeasureResponseCommand.frequencies=[2.4]
if __main__.__dict__.get("Max",False):
from topo.misc.distribution import DSF_MaxValue
preference_fn=DSF_MaxValue(value_scale=(0., 1./numpy.pi),selectivity_scale=(0.,17.0))
topo.command.pylabplot.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Complex"],preference_fn=preference_fn)()
else:
save_plotgroup("Orientation Preference and Complexity")
contrib.surround_analysis_new_cleaned.surround_analysis("V1Complex").run_lhi_informed_analysis(max_curves=__main__.__dict__.get("max_curves",20),center_size=__main__.__dict__.get("center_size",20),index=__main__.__dict__.get("index",0))
def push_pull_analysis_function():
print 'Push pull complex_analysis_function'
import topo
import numpy
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
import contrib.jacommands
from contrib.push_pull.CCLISSOM_push_pull_extra import check_RF_corrleation_vs_connection_weights_correlation
from param import normalize_path
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
SinusoidalMeasureResponseCommand.frequencies=[2.4]
SinusoidalMeasureResponseCommand.scale=__main__.__dict__.get("analysis_scale",1.0)
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
print 'Build a list of all sheets worth measuring'
f = lambda x: hasattr(x,'measure_maps') and x.measure_maps
measured_sheets = filter(f,topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
print 'Set potentially reasonable defaults; not necessarily useful'
topo.command.analysis.coordinate=(0.0,0.0)
if input_sheets: topo.command.analysis.input_sheet_name=input_sheets[0].name
if measured_sheets: topo.command.analysis.sheet_name=measured_sheets[0].name
FeatureCurveCommand.curve_parameters=[{"contrast":30},{"contrast":50},{"contrast":70},{"contrast":90}]
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity",normalize="Individually")
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p,density=3.0)
print 'Starting push pull analysis'
#analyse_push_pull_connectivity()
check_RF_corrleation_vs_connection_weights_correlation()
print 'Finished push pull analysis'
return
if(float(topo.sim.time()) >= 10005.0):
print 'Measuring orientations'
SinusoidalMeasureResponseCommand.frequencies=[2.4]
topo.command.pylabplot.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Simple"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1Simple"],coords=[(0,0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,-0.1)])()
def complex_analysis_function():
"""
Analysis command for run_batch; Saves maps of orientation preference and analyzes complex cells
using Modulation Ratio, Circular Variance and Orientation Bandwidth. Additionally, it saves projection
plots.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
save_plotgroup("Orientation Preference, Modulation Ratio, Circular Variance and Orientation Bandwidth")
save_plotgroup("Activity")
# Plot all projections for all measured_sheets
measured_sheets = [s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s,'measure_maps') and s.measure_maps]
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
def rf_analysis():
import topo
import pylab
import topo.analysis.vision
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet.basic import GeneratorSheet
from topo.command.analysis import measure_or_tuning_fullfield, measure_or_pref
from topo.command.pylabplots import cyclic_tuning_curve
from param import normalize_path
if(float(topo.sim.time()) <=20010):
save_plotgroup("Orientation Preference")
save_plotgroup("Activity")
# Plot all projections for all measured_sheets
measured_sheets = [s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s,'measure_maps') and s.measure_maps]
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
prefix="WithGC"
measure_or_tuning_fullfield()
s=topo.sim["V1"]
cyclic_tuning_curve(filename_suffix=prefix,filename="OrientationTC:V1:[0,0]",sheet=s,coords=[(0,0)],x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,filename="OrientationTC:V1:[0.1,0.1]",sheet=s,coords=[(0.1,0.1)],x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,filename="OrientationTC:V1:[-0.1,-0.1]",sheet=s,coords=[(-0.1,-0.1)],x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,filename="OrientationTC:V1:[0.1,-0.1]",sheet=s,coords=[(0.1,-0.1)],x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,filename="OrientationTC:V1:[-0.1,0.1]",sheet=s,coords=[(-0.1,0.1)],x_axis="orientation")
else:
topo.command.basic.activity_history = numpy.concatenate((contrib.jacommands.activity_history,topo.sim["V1"].activity.flatten()),axis=1)
if(float(topo.sim.time()) == 20000):
topo.sim["V1"].plastic=False
contrib.jacommands.homeostatic_analysis_function()
if(float(topo.sim.time()) == 20001):
pylab.figure()
def test_cfprojection_saving(self):
save_plotgroup("Projection", projection=self.sim["B"].projections("Afferent"))
self.exists("testplotfilesaver_000000.00_B_Afferent.png")
def test_cf_saving(self):
save_plotgroup("Connection Fields", sheet=self.sim["B"])
self.exists("testplotfilesaver_000000.00_Afferent_(from_A).png")
def test_orientation_preference_saving(self):
save_plotgroup("Orientation Preference")
self.exists("testplotfilesaver_000000.00_B_Orientation_Preference.png")
self.exists("testplotfilesaver_000000.00_B_Orientation_PreferenceAndSelectivity.png")
self.exists("testplotfilesaver_000000.00_B_Orientation_Selectivity.png")
self.exists("testplotfilesaver_000000.00__Color_Key.png")
def test_activity_saving(self):
save_plotgroup("Activity")
self.exists("testplotfilesaver_000000.00_A_Activity.png")
self.exists("testplotfilesaver_000000.00_B_Activity.png")
def figure4():
dirr = '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE'
rhos = {}
X = []
Y = []
qual = []
if False:
for a in os.listdir(dirr):
b = os.path.join(dirr,a);
if not stat.S_ISDIR(os.stat(b).st_mode):
continue
# load result file
f = open(os.path.join(b,'results.pickle'))
d = pickle.load(f)
# lets find out the pinwheel density
X.append(abs(d['exc_inh_strength']))
Y.append(abs(d['exc_short_long_ratio']))
mmap = d['orprefmap'][2:-3,2:-3]
rho = pinwheel_analysis(mmap)['metadata']['rho']
metric = gamma_metric(rho,k=10.0)
sel = numpy.mean(numpy.mean(mmap))
print a, " ", str(rho) , " " , str(metric) , "SEL ", str(sel)
qual.append(metric)
print X
print Y
print qual
pylab.figure()
pylab.scatter(Y,X,s=numpy.array(qual)*50.0)
data = np.histogram2d(Y, X, bins=[len(np.unique(Y)),len(np.unique(X))], weights=qual)
pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.gca()
im = pylab.imshow(data[0],interpolation='none',cmap='gray')#,vmin=0.3)
pylab.yticks([0,len(np.unique(Y))-1],[data[1][0],data[1][-1]])
pylab.xticks([0,len(np.unique(X))-1],[data[2][0],data[2][-1]])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
pylab.colorbar(im,cax=cax)
pylab.savefig('figure4.png',dpi=600)
if True:
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=9.7_-p_exc_short_long_ratio=0.3_-p_cortex_exc_target_activity=0.003/snapshot.typ',"fig4_1_")
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=10.6_-p_exc_short_long_ratio=0.5_-p_cortex_exc_target_activity=0.003/snapshot.typ',"fig4_2_")
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=9.4_-p_exc_short_long_ratio=0.6_-p_cortex_exc_target_activity=0.003/snapshot.typ',"fig4_3_")
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=10.6_-p_exc_short_long_ratio=0.8_-p_cortex_exc_target_activity=0.003/snapshot.typ',"fig4_4_")
if False:
fname= '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=9.7_-p_exc_short_long_ratio=0.3_-p_cortex_exc_target_activity=0.003'
f = open(fname+'/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][0:-1,0:-1],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/or_map1.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][0:-1,0:-1]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/fft_map1.png', pad_inches=0)
load_snapshot(fname+'/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[2],density=6.0,saver_params={'filename_prefix' : '1'})
fname= '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=10.6_-p_exc_short_long_ratio=0.5_-p_cortex_exc_target_activity=0.003'
f = open(fname+'/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][0:-1,0:-1],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/or_map2.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][0:-1,0:-1]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/fft_map2.png', pad_inches=0)
load_snapshot(fname+'/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[2],density=6.0,saver_params={'filename_prefix' : '2'})
fname= '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=9.4_-p_exc_short_long_ratio=0.6_-p_cortex_exc_target_activity=0.003'
f = open(fname+'/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][0:-1,0:-1],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/or_map3.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][0:-1,0:-1]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/fft_map3.png', pad_inches=0)
load_snapshot(fname+'/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[2],density=6.0,saver_params={'filename_prefix' : '3'})
fname= '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_EI_II_LONGE/a-p_exc_strength=9_-p_exc_inh_strength=10.6_-p_exc_short_long_ratio=0.8_-p_cortex_exc_target_activity=0.003'
f = open(fname+'/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][0:-1,0:-1],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/or_map4.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][0:-1,0:-1]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure3/generated_data/fft_map4.png', pad_inches=0)
load_snapshot(fname+'/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[2],density=6.0,saver_params={'filename_prefix' : '4'})
def activity_plotgroup(times=None, **kwargs):
if analysis_group('a'):
return None
from topo.command.analysis import save_plotgroup
save_plotgroup("Activity")
def rf_analysis():
import topo
import pylab
import topo.analysis.vision
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.command.analysis import measure_or_tuning_fullfield, measure_or_pref
from topo.command.pylabplot import cyclic_tuning_curve
from param import normalize_path
if (float(topo.sim.time()) <= 20010):
save_plotgroup("Orientation Preference")
save_plotgroup("Activity")
# Plot all projections for all measured_sheets
measured_sheets = [
s for s in topo.sim.objects(ProjectionSheet).values()
if hasattr(s, 'measure_maps') and s.measure_maps
]
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection", projection=p)
prefix = "WithGC"
measure_or_tuning_fullfield()
s = topo.sim["V1"]
cyclic_tuning_curve(filename_suffix=prefix,
filename="OrientationTC:V1:[0,0]",
sheet=s,
coords=[(0, 0)],
x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,
filename="OrientationTC:V1:[0.1,0.1]",
sheet=s,
coords=[(0.1, 0.1)],
x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,
filename="OrientationTC:V1:[-0.1,-0.1]",
sheet=s,
coords=[(-0.1, -0.1)],
x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,
filename="OrientationTC:V1:[0.1,-0.1]",
sheet=s,
coords=[(0.1, -0.1)],
x_axis="orientation")
cyclic_tuning_curve(filename_suffix=prefix,
filename="OrientationTC:V1:[-0.1,0.1]",
sheet=s,
coords=[(-0.1, 0.1)],
x_axis="orientation")
else:
topo.command.activity_history = numpy.concatenate(
(contrib.jacommands.activity_history,
topo.sim["V1"].activity.flatten()),
axis=1)
if (float(topo.sim.time()) == 20000):
topo.sim["V1"].plastic = False
contrib.jacommands.homeostatic_analysis_function()
if (float(topo.sim.time()) == 20001):
pylab.figure()
def push_pull_analysis_function():
print 'Push pull complex_analysis_function'
import topo
import numpy
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand, FeatureCurveCommand
import contrib.jacommands
from contrib.push_pull.CCLISSOM_push_pull_extra import check_RF_corrleation_vs_connection_weights_correlation
from param import normalize_path
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
SinusoidalMeasureResponseCommand.frequencies = [2.4]
SinusoidalMeasureResponseCommand.scale = __main__.__dict__.get(
"analysis_scale", 2.3)
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
print 'Build a list of all sheets worth measuring'
f = lambda x: hasattr(x, 'measure_maps') and x.measure_maps
measured_sheets = filter(f, topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
print 'Set potentially reasonable defaults; not necessarily useful'
topo.command.analysis.coordinate = (0.0, 0.0)
if input_sheets:
topo.command.analysis.input_sheet_name = input_sheets[0].name
if measured_sheets:
topo.command.analysis.sheet_name = measured_sheets[0].name
FeatureCurveCommand.curve_parameters = [{
"contrast": 30
}, {
"contrast": 50
}, {
"contrast": 70
}, {
"contrast": 90
}]
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity", normalize="Individually")
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection", projection=p, density=3.0)
print 'Starting push pull analysis'
#analyse_push_pull_connectivity()
check_RF_corrleation_vs_connection_weights_correlation()
print 'Finished push pull analysis'
return
if (float(topo.sim.time()) >= 10005.0):
print 'Measuring orientations'
SinusoidalMeasureResponseCommand.frequencies = [2.4]
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1Simple"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1Simple"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,-0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.1, -0.1)])()
def complex_surround_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand, FeatureCurveCommand
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.command import save_snapshot
from param import normalize_path
import contrib.cc_lesi.connection_analysis
import contrib.jacommands
import contrib.surround_analysis_new_cleaned
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
SinusoidalMeasureResponseCommand.frequencies = [2.4]
SinusoidalMeasureResponseCommand.scale = __main__.__dict__.get(
"analysis_scale", 2.3)
from topo.analysis.featureresponses import PatternPresenter
PatternPresenter.duration = 4.0
import topo.command.pylabplot
reload(topo.command.pylabplot)
# Build a list of all sheets worth measuring
f = lambda x: hasattr(x, 'measure_maps') and x.measure_maps
measured_sheets = filter(f, topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
# Set potentially reasonable defaults; not necessarily useful
topo.command.analysis.coordinate = (0.0, 0.0)
if input_sheets:
topo.command.analysis.input_sheet_name = input_sheets[0].name
if measured_sheets:
topo.command.analysis.sheet_name = measured_sheets[0].name
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity", normalize='Individually')
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection", projection=p)
contrib.cc_lesi.connection_analysis.Analyse_connectivity()
if (float(topo.sim.time()) > 6020.0):
if __main__.__dict__.get("save", False):
save_snapshot(normalize_path('snapshot.typ'))
#contrib.surround_analysis.run_dynamics_analysis(0.0,0.0,0.7,__main__.__dict__.get("analysis_scale",0.3))
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1Complex"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1Complex"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,-0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0.1]",
sheet=topo.sim["V1Complex"],
coords=[(-0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,-0.1]",
sheet=topo.sim["V1Complex"],
coords=[(-0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,0.2]",
sheet=topo.sim["V1Complex"],
coords=[(0.2, 0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,-0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,-0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.42)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,-0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.42)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.0)])()
#contrib.surround_analysis_new_cleaned.surround_analysis("V1Complex").run_analysis_with_step_grid(4,4,max_curves=__main__.__dict__.get("max_curves",20))
contrib.surround_analysis_new_cleaned.surround_analysis(
"V1Complex").run_lhi_informed_analysis(
max_curves=__main__.__dict__.get("max_curves", 26),
center_size=__main__.__dict__.get("center_size", 20))
def test_cf_saving(self):
save_plotgroup("Connection Fields", sheet=self.sim['B'])
self.exists("testplotfilesaver_000000.00_Afferent_(from_A).png")
def OR_plotgroup(times=None, **kwargs):
from topo.command.analysis import save_plotgroup
save_plotgroup("Orientation Preference",use_cached_results=True)
def complex_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet.basic import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
import contrib.jacommands
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
# Build a list of all sheets worth measuring
f = lambda x: hasattr(x,'measure_maps') and x.measure_maps
measured_sheets = filter(f,topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
# Set potentially reasonable defaults; not necessarily useful
topo.command.analysis.coordinate=(0.0,0.0)
if input_sheets: topo.command.analysis.input_sheet_name=input_sheets[0].name
if measured_sheets: topo.command.analysis.sheet_name=measured_sheets[0].name
FeatureCurveCommand.curve_parameters=[{"contrast":30},{"contrast":50},{"contrast":70},{"contrast":90}]
import numpy
# reset treshold and desable noise before measuring maps
#m = numpy.mean(topo.sim["V1Simple"].output_fns[2].t)
#topo.sim["V1Simple"].output_fns[2].t*=0
#topo.sim["V1Simple"].output_fns[2].t+=m
#sc = topo.sim["V1Simple"].output_fns[1].generator.scale
#topo.sim["V1Simple"].output_fns[1].generator.scale=0.0
a = topo.sim["V1Complex"].in_connections[0].strength
SinusoidalMeasureResponseCommand.scale=__main__.__dict__.get("analysis_scale",0.35)
if((float(topo.sim.time()) >= 5003.0) and (float(topo.sim.time()) < 5004.0)):
topo.sim["V1Complex"].in_connections[0].strength=0
SinusoidalMeasureResponseCommand.frequencies=[3.0]
if((float(topo.sim.time()) >= 5005.0) and (float(topo.sim.time()) < 5006.0)):
SinusoidalMeasureResponseCommand.frequencies=[3.0]
if((float(topo.sim.time()) >= 5006.0) and (float(topo.sim.time()) < 5007.0)):
topo.sim["V1Complex"].in_connections[0].strength=0
SinusoidalMeasureResponseCommand.frequencies=[2.4]
if((float(topo.sim.time()) >= 5007.0) and (float(topo.sim.time()) < 5008.0)):
SinusoidalMeasureResponseCommand.frequencies=[2.4]
if((float(topo.sim.time()) >= 10002.0) and (float(topo.sim.time()) < 10003.0)):
topo.sim["V1Complex"].in_connections[0].strength=0
SinusoidalMeasureResponseCommand.frequencies=[2.4]
if((float(topo.sim.time()) >= 10003.0) and (float(topo.sim.time()) < 10004.0)):
topo.sim["V1Complex"].in_connections[0].strength=0
SinusoidalMeasureResponseCommand.frequencies=[3.0]
if((float(topo.sim.time()) >= 10004.0) and (float(topo.sim.time()) < 10005.0)):
SinusoidalMeasureResponseCommand.frequencies=[2.4]
if((float(topo.sim.time()) >= 10005.0) and (float(topo.sim.time()) < 10006.0)):
SinusoidalMeasureResponseCommand.frequencies=[3.0]
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity")
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
if(float(topo.sim.time()) >= 10005.0):
print 'Measuring orientations'
SinusoidalMeasureResponseCommand.frequencies=[2.4]
topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Complex"])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1Complex"],coords=[(0,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.0)])()
topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Simple"])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0]",sheet=topo.sim["V1Simple"],coords=[(0,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,-0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,0.1]",sheet=topo.sim["V1Simple"],coords=[(-0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,-0.1]",sheet=topo.sim["V1Simple"],coords=[(-0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.2,0.2]",sheet=topo.sim["V1Simple"],coords=[(0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.2,-0.2]",sheet=topo.sim["V1Simple"],coords=[(0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.2,0.2]",sheet=topo.sim["V1Simple"],coords=[(-0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.2,-0.2]",sheet=topo.sim["V1Simple"],coords=[(-0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0.1]",sheet=topo.sim["V1Simple"],coords=[(0.0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,-0.1]",sheet=topo.sim["V1Simple"],coords=[(0.0,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,0]",sheet=topo.sim["V1Simple"],coords=[(-0.1,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,0]",sheet=topo.sim["V1Simple"],coords=[(0.1,-0.0)])()
#topo.sim["V1Simple"].output_fns[1].generator.scale=sc
topo.sim["V1Complex"].in_connections[0].strength = a
def complex_surround_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
from topo.base.projection import ProjectionSheet
from topo.sheet.basic import GeneratorSheet
import contrib.jacommands
import contrib.surround_analysis
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
SinusoidalMeasureResponseCommand.frequencies=[3.0]
SinusoidalMeasureResponseCommand.scale=__main__.__dict__.get("analysis_scale",0.3)
from topo.analysis.featureresponses import PatternPresenter
PatternPresenter.duration=2.0
import topo.command.pylabplots
reload(topo.command.pylabplots)
# Build a list of all sheets worth measuring
f = lambda x: hasattr(x,'measure_maps') and x.measure_maps
measured_sheets = filter(f,topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
# Set potentially reasonable defaults; not necessarily useful
topo.command.analysis.coordinate=(0.0,0.0)
if input_sheets: topo.command.analysis.input_sheet_name=input_sheets[0].name
if measured_sheets: topo.command.analysis.sheet_name=measured_sheets[0].name
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity",normalize=True)
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
if(float(topo.sim.time()) > 6020.0):
contrib.surround_analysis.run_dynamics_analysis(0.0,0.0,0.7,__main__.__dict__.get("analysis_scale",0.3))
topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Complex"])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1Complex"],coords=[(0,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,-0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,0.24)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.24)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.42)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,-0.24)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.24)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.42)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.3)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.0)])()
#topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Simple"])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0]",sheet=topo.sim["V1Simple"],coords=[(0,0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,-0.1]",sheet=topo.sim["V1Simple"],coords=[(0.1,-0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,0.1]",sheet=topo.sim["V1Simple"],coords=[(-0.1,0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,-0.1]",sheet=topo.sim["V1Simple"],coords=[(-0.1,-0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.2,0.2]",sheet=topo.sim["V1Simple"],coords=[(0.2,0.2)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.2,-0.2]",sheet=topo.sim["V1Simple"],coords=[(0.2,-0.2)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.2,0.2]",sheet=topo.sim["V1Simple"],coords=[(-0.2,0.2)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.2,-0.2]",sheet=topo.sim["V1Simple"],coords=[(-0.2,-0.2)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0.1]",sheet=topo.sim["V1Simple"],coords=[(0.0,0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,-0.1]",sheet=topo.sim["V1Simple"],coords=[(0.0,-0.1)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.1,0]",sheet=topo.sim["V1Simple"],coords=[(-0.1,0.0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.1,0]",sheet=topo.sim["V1Simple"],coords=[(0.1,-0.0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.3,0.3]",sheet=topo.sim["V1Simple"],coords=[(0.3,0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.3,-0.3]",sheet=topo.sim["V1Simple"],coords=[(0.3,-0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.3,0.3]",sheet=topo.sim["V1Simple"],coords=[(-0.3,0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.3,-0.3]",sheet=topo.sim["V1Simple"],coords=[(-0.3,-0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.24,0.24]",sheet=topo.sim["V1Simple"],coords=[(0.24,0.24)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.24,-0.24]",sheet=topo.sim["V1Simple"],coords=[(0.24,-0.24)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.24,0.24]",sheet=topo.sim["V1Simple"],coords=[(-0.24,0.42)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.24,-0.24]",sheet=topo.sim["V1Simple"],coords=[(-0.24,-0.24)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0.24]",sheet=topo.sim["V1Simple"],coords=[(0.0,0.24)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,-0.24]",sheet=topo.sim["V1Simple"],coords=[(0.0,-0.42)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.24,0]",sheet=topo.sim["V1Simple"],coords=[(-0.24,0.0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.24,0]",sheet=topo.sim["V1Simple"],coords=[(0.24,-0.0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,0.3]",sheet=topo.sim["V1Simple"],coords=[(0.0,0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0,-0.3]",sheet=topo.sim["V1Simple"],coords=[(0.0,-0.3)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[-0.3,0]",sheet=topo.sim["V1Simple"],coords=[(-0.3,0.0)])()
#topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="SimpleORTC[0.3,0]",sheet=topo.sim["V1Simple"],coords=[(0.3,-0.0)])()
contrib.surround_analysis.surround_analysis("V1Complex").analyse([(0,0),(5,0),(-5,0),(0,5),(0,-5),(5,5),(5,-5),(-5,5),(-5,-5),(8,0),(-8,0),(0,8),(0,-8),(8,8),(8,-8),(-8,8),(-8,-8)],15,5)
def activity_plotgroup():
from topo.command.analysis import save_plotgroup
save_plotgroup("Activity")
def test_activity_saving(self):
save_plotgroup('Activity')
self.exists("testplotfilesaver_000000.00_A_Activity.png")
self.exists("testplotfilesaver_000000.00_B_Activity.png")
def activity_plotgroup(times=None, **kwargs):
from topo.command.analysis import save_plotgroup
save_plotgroup("Activity")
def figure2():
dirr = '/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE'
rhos = {}
X = []
Y = []
qual = []
if False:
for a in os.listdir(dirr):
b = os.path.join(dirr,a);
if not stat.S_ISDIR(os.stat(b).st_mode):
continue
# load result file
f = open(os.path.join(b,'results.pickle'))
d = pickle.load(f)
# lets find out the pinwheel density
X.append(d['lat_strength_ratio'])
Y.append(d['exc_inh_ratio'])
#mmap = d['orprefmap'][25:-24,25:-24]
mmap = d['orprefmap'][2:-3,2:-3]
rho = pinwheel_analysis(mmap)['metadata']['rho']
metric = gamma_metric(rho,k=10.0)
sel = numpy.mean(numpy.mean(mmap))
print a, " ", str(rho) , " " , str(metric) , "SEL ", str(sel)
qual.append(metric)
data = np.histogram2d(Y, X, bins=[len(np.unique(Y)),len(np.unique(X))], weights=qual)
pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
im = pylab.imshow(data[0],interpolation='none',cmap='gray')#,vmin=0.3)
pylab.colorbar(im,fraction=0.046, pad=0.04)
pylab.yticks([0,len(np.unique(Y))-1],[data[1][0],data[1][-1]])
pylab.xticks([0,len(np.unique(X))-1],[data[2][0],data[2][-1]])
pylab.savefig('figure2.png',dpi=600)
if True:
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.2_exc_inh_ratio=0.85/snapshot.typ',"fig2_1_")
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.6_exc_inh_ratio=0.8/snapshot.typ',"fig2_2_")
plot_ortcs('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.8_exc_inh_ratio=0.65/snapshot.typ',"fig2_3_")
if False:
f = open('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.2_exc_inh_ratio=0.85/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][61:-60,61:-60],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/or_map3.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][61:-60,61:-60]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/fft_map3.png', pad_inches=0)
load_snapshot('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.2_exc_inh_ratio=0.85/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[0],density=1.5,saver_params={'filename_prefix' : '3'})
f = open('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.6_exc_inh_ratio=0.8/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][61:-60,61:-60],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/or_map2.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][61:-60,61:-60]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/fft_map2.png', pad_inches=0)
load_snapshot('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.6_exc_inh_ratio=0.8/snapshot.typ')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[0],density=1.5,saver_params={'filename_prefix' : '2'})
f = open('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.8_exc_inh_ratio=0.65/results.pickle')
d = pickle.load(f)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(d['orprefmap'][61:-60,61:-60],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/or_map1.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(d['orprefmap'][61:-60,61:-60]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/fft_map1.png', pad_inches=0)
load_snapshot('/home/jan/Doc/Papers/fast_inh_paper/DATA/GCAL_SHORTRANGE/lat_strength_ratio=2.8_exc_inh_ratio=0.65/results.pickle')
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[0],density=1.5,saver_params={'filename_prefix' : '1'})
if False:
load_snapshot('/home/jan/projects/topographica/GCAL_EI/a-p_exc_strength=3.3_-p_inh_strength=2.805/snapshot.typ')
mmap = topo.sim["V1"].sheet_views["OrientationPreference"].view()[0]
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(mmap[61:-60,61:-60],interpolation='none',cmap='hsv',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/or_map4.png', pad_inches=0)
fig = pylab.figure(dpi=600,facecolor='w',figsize=(5,5))
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(power_spectrum(mmap[61:-60,61:-60]),interpolation='none',cmap='gray',aspect='normal')
pylab.savefig('/home/jan/Doc/Papers/fast_inh_paper/SVG/Figure1/generated_data/fft_map4.png', pad_inches=0)
save_plotgroup("Projection",projection=topo.sim["V1"].projections().values()[0],density=1.5,saver_params={'filename_prefix' : '4'})
def test_cfprojection_saving(self):
save_plotgroup('Projection',
projection=self.sim['B'].projections('Afferent'))
self.exists("testplotfilesaver_000000.00_B_Afferent.png")
def complex_surround_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
import topo
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand,FeatureCurveCommand
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.command import save_snapshot
from param import normalize_path
import contrib.jacommands
import contrib.surround_analysis_new_cleaned
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
SinusoidalMeasureResponseCommand.frequencies=[2.4]
SinusoidalMeasureResponseCommand.scale=__main__.__dict__.get("analysis_scale",0.3)
from topo.analysis.featureresponses import PatternPresenter
PatternPresenter.duration=4.0
import topo.command.pylabplot
reload(topo.command.pylabplot)
# Build a list of all sheets worth measuring
f = lambda x: hasattr(x,'measure_maps') and x.measure_maps
measured_sheets = filter(f,topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
# Set potentially reasonable defaults; not necessarily useful
topo.command.analysis.coordinate=(0.0,0.0)
if input_sheets: topo.command.analysis.input_sheet_name=input_sheets[0].name
if measured_sheets: topo.command.analysis.sheet_name=measured_sheets[0].name
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity",normalize='Individually')
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection",projection=p)
if(float(topo.sim.time()) > 6020.0):
if __main__.__dict__.get("save",False):
save_snapshot(normalize_path('snapshot.typ'))
#contrib.surround_analysis.run_dynamics_analysis(0.0,0.0,0.7,__main__.__dict__.get("analysis_scale",0.3))
#topo.command.pylabplot.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1Complex"])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1Complex"],coords=[(0,0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,-0.1]",sheet=topo.sim["V1Complex"],coords=[(-0.1,-0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(0.2,-0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1Complex"],coords=[(-0.2,-0.2)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.1)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1Complex"],coords=[(-0.1,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1Complex"],coords=[(0.1,-0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,-0.3]",sheet=topo.sim["V1Complex"],coords=[(-0.3,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.42)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,-0.24]",sheet=topo.sim["V1Complex"],coords=[(-0.24,-0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.24)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.24]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.42)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.24,0]",sheet=topo.sim["V1Complex"],coords=[(-0.24,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.24,0]",sheet=topo.sim["V1Complex"],coords=[(0.24,-0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.3]",sheet=topo.sim["V1Complex"],coords=[(0.0,-0.3)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.3,0]",sheet=topo.sim["V1Complex"],coords=[(-0.3,0.0)])()
#topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.3,0]",sheet=topo.sim["V1Complex"],coords=[(0.3,-0.0)])()
#contrib.surround_analysis_new_cleaned.surround_analysis("V1Complex").run_analysis_with_step_grid(4,4,max_curves=__main__.__dict__.get("max_curves",20))
contrib.surround_analysis_new_cleaned.surround_analysis("V1Complex").run_lhi_informed_analysis(max_curves=__main__.__dict__.get("max_curves",20),center_size=__main__.__dict__.get("center_size",20))
def complex_analysis_function():
"""
Basic example of an analysis command for run_batch; users are
likely to need something similar but highly customized.
"""
print 'Called complex_analysis_function'
import topo
from topo.command.analysis import save_plotgroup
from topo.base.projection import ProjectionSheet
from topo.sheet import GeneratorSheet
from topo.analysis.featureresponses import SinusoidalMeasureResponseCommand, FeatureCurveCommand
import contrib.jacommands
exec "from topo.analysis.vision import analyze_complexity" in __main__.__dict__
print 'Analysing'
import matplotlib
matplotlib.rc('xtick', labelsize=17)
matplotlib.rc('ytick', labelsize=17)
print 'Build a list of all sheets worth measuring'
f = lambda x: hasattr(x, 'measure_maps') and x.measure_maps
measured_sheets = filter(f, topo.sim.objects(ProjectionSheet).values())
input_sheets = topo.sim.objects(GeneratorSheet).values()
print 'Set potentially reasonable defaults; not necessarily useful'
topo.command.analysis.coordinate = (0.0, 0.0)
if input_sheets:
topo.command.analysis.input_sheet_name = input_sheets[0].name
if measured_sheets:
topo.command.analysis.sheet_name = measured_sheets[0].name
FeatureCurveCommand.curve_parameters = [{
"contrast": 30
}, {
"contrast": 50
}, {
"contrast": 70
}, {
"contrast": 90
}]
import numpy
# reset treshold and desable noise before measuring maps
#m = numpy.mean(topo.sim["V1Simple"].output_fns[2].t)
#topo.sim["V1Simple"].output_fns[2].t*=0
#topo.sim["V1Simple"].output_fns[2].t+=m
#sc = topo.sim["V1Simple"].output_fns[1].generator.scale
#topo.sim["V1Simple"].output_fns[1].generator.scale=0.0
a = topo.sim["V1Complex"].in_connections[0].strength
SinusoidalMeasureResponseCommand.scale = __main__.__dict__.get(
"analysis_scale", 2.3)
MeasureResponseCommand.scale = __main__.__dict__.get("analysis_scale", 2.3)
#if((float(topo.sim.time()) >= 5003.0) and (float(topo.sim.time()) < 5004.0)):
# topo.sim["V1Complex"].in_connections[0].strength=0
# SinusoidalMeasureResponseCommand.frequencies=[3.0]
# if((float(topo.sim.time()) >= 5005.0) and (float(topo.sim.time()) < 5006.0)):
# SinusoidalMeasureResponseCommand.frequencies=[3.0]
# if((float(topo.sim.time()) >= 5006.0) and (float(topo.sim.time()) < 5007.0)):
# topo.sim["V1Complex"].in_connections[0].strength=0
# SinusoidalMeasureResponseCommand.frequencies=[2.4]
# if((float(topo.sim.time()) >= 5007.0) and (float(topo.sim.time()) < 5008.0)):
# SinusoidalMeasureResponseCommand.frequencies=[2.4]
# if((float(topo.sim.time()) >= 10002.0) and (float(topo.sim.time()) < 10003.0)):
# topo.sim["V1Complex"].in_connections[0].strength=0
# SinusoidalMeasureResponseCommand.frequencies=[2.4]
# if((float(topo.sim.time()) >= 10003.0) and (float(topo.sim.time()) < 10004.0)):
# topo.sim["V1Complex"].in_connections[0].strength=0
# SinusoidalMeasureResponseCommand.frequencies=[3.0]
# if((float(topo.sim.time()) >= 10004.0) and (float(topo.sim.time()) < 10005.0)):
# SinusoidalMeasureResponseCommand.frequencies=[2.4]
# if((float(topo.sim.time()) >= 10005.0) and (float(topo.sim.time()) < 10006.0)):
# SinusoidalMeasureResponseCommand.frequencies=[3.0]
save_plotgroup("Orientation Preference and Complexity")
save_plotgroup("Activity")
# Plot all projections for all measured_sheets
for s in measured_sheets:
for p in s.projections().values():
save_plotgroup("Projection", projection=p)
if (float(topo.sim.time()) >= 10005.0):
print 'Measuring orientations'
SinusoidalMeasureResponseCommand.frequencies = [2.4]
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1Complex"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1Complex"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,-0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0.1]",
sheet=topo.sim["V1Complex"],
coords=[(-0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,-0.1]",
sheet=topo.sim["V1Complex"],
coords=[(-0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,0.2]",
sheet=topo.sim["V1Complex"],
coords=[(0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,-0.2]",
sheet=topo.sim["V1Complex"],
coords=[(0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,0.2]",
sheet=topo.sim["V1Complex"],
coords=[(-0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,-0.2]",
sheet=topo.sim["V1Complex"],
coords=[(-0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,-0.1]",
sheet=topo.sim["V1Complex"],
coords=[(0.0, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0]",
sheet=topo.sim["V1Complex"],
coords=[(-0.1, 0.0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0]",
sheet=topo.sim["V1Complex"],
coords=[(0.1, -0.0)])()
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1Simple"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0,0]",
sheet=topo.sim["V1Simple"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0.1,0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0.1,-0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[-0.1,0.1]",
sheet=topo.sim["V1Simple"],
coords=[(-0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[-0.1,-0.1]",
sheet=topo.sim["V1Simple"],
coords=[(-0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0.2,0.2]",
sheet=topo.sim["V1Simple"],
coords=[(0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0.2,-0.2]",
sheet=topo.sim["V1Simple"],
coords=[(0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[-0.2,0.2]",
sheet=topo.sim["V1Simple"],
coords=[(-0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[-0.2,-0.2]",
sheet=topo.sim["V1Simple"],
coords=[(-0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0,0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0,-0.1]",
sheet=topo.sim["V1Simple"],
coords=[(0.0, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[-0.1,0]",
sheet=topo.sim["V1Simple"],
coords=[(-0.1, 0.0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="SimpleORTC[0.1,0]",
sheet=topo.sim["V1Simple"],
coords=[(0.1, -0.0)])()
#topo.sim["V1Simple"].output_fns[1].generator.scale=sc
topo.sim["V1Complex"].in_connections[0].strength = a
def gc_homeo_af():
import contrib.jsldefs
import topo.command.pylabplots
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import FeatureResponses , PatternPresenter, FeatureMaps
#FeatureResponses.repetitions=10
FeatureMaps.selectivity_multiplier=20
PatternPresenter.duration=0.2
PatternPresenter.apply_output_fns=False
import topo.command.pylabplots
reload(topo.command.pylabplots)
on = topo.sim["LGNOn"].in_connections[0].strength
off = topo.sim["LGNOff"].in_connections[0].strength
if __main__.__dict__.get("GC",False):
topo.sim["LGNOn"].in_connections[0].strength=0
topo.sim["LGNOff"].in_connections[0].strength=0
contrib.jsldefs.homeostatic_analysis_function()
topo.command.pylabplots.fftplot(topo.sim["V1"].sheet_views["OrientationPreference"].view()[0],filename="V1ORMAPFFT")
from topo.misc.filepath import normalize_path, application_path
from scipy.io import write_array
import numpy
write_array(normalize_path(str(topo.sim.time())+"orprefmap.txt"), topo.sim["V1"].sheet_views["OrientationPreference"].view()[0])
write_array(normalize_path(str(topo.sim.time())+"orselmap.txt"), topo.sim["V1"].sheet_views["OrientationSelectivity"].view()[0])
topo.sim["LGNOn"].in_connections[0].strength = on
topo.sim["LGNOff"].in_connections[0].strength = off
print float(topo.sim.time())
if(float(topo.sim.time()) > 19002.0):
#topo.sim["V1"].output_fns[2].scale=0.0
save_plotgroup("Position Preference")
PatternPresenter.duration=1.0
PatternPresenter.apply_output_fns=True
import topo.command.pylabplots
reload(topo.command.pylabplots)
topo.command.pylabplots.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1"],repetitions=10)(repetitions=10)
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0.1,0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0]",sheet=topo.sim["V1"],coords=[(0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0.1]",sheet=topo.sim["V1"],coords=[(0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,-0.1]",sheet=topo.sim["V1"],coords=[(0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0.1]",sheet=topo.sim["V1"],coords=[(-0.1,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,-0.1]",sheet=topo.sim["V1"],coords=[(-0.1,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,0.2]",sheet=topo.sim["V1"],coords=[(0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.2,-0.2]",sheet=topo.sim["V1"],coords=[(0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,0.2]",sheet=topo.sim["V1"],coords=[(-0.2,0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.2,-0.2]",sheet=topo.sim["V1"],coords=[(-0.2,-0.2)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,0.1]",sheet=topo.sim["V1"],coords=[(0.0,0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0,-0.1]",sheet=topo.sim["V1"],coords=[(0.0,-0.1)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[-0.1,0]",sheet=topo.sim["V1"],coords=[(-0.1,0.0)])()
topo.command.pylabplots.cyclic_tuning_curve.instance(x_axis="orientation",filename="ORTC[0.1,0]",sheet=topo.sim["V1"],coords=[(0.1,-0.0)])()
if(float(topo.sim.time()) > 20000.0):
topo.sim["V1"].output_fns[1].plastic=False
contrib.jacommands.measure_histogram(iterations=1000)
def gc_homeo_af():
import contrib.jsldefs
import topo.command.pylabplot
import contrib.jacommands
from topo.command.analysis import save_plotgroup
from topo.analysis.featureresponses import FeatureResponses, PatternPresenter, FeatureMaps
#FeatureResponses.repetitions=10
FeatureMaps.selectivity_multiplier = 20
PatternPresenter.duration = 0.2
PatternPresenter.apply_output_fns = False
import topo.command.pylabplot
reload(topo.command.pylabplot)
on = topo.sim["LGNOn"].in_connections[0].strength
off = topo.sim["LGNOff"].in_connections[0].strength
if __main__.__dict__.get("GC", False):
topo.sim["LGNOn"].in_connections[0].strength = 0
topo.sim["LGNOff"].in_connections[0].strength = 0
contrib.jsldefs.homeostatic_analysis_function()
topo.command.pylabplot.fftplot(
topo.sim["V1"].sheet_views["OrientationPreference"].view()[0],
filename="V1ORMAPFFT")
from topo.misc.filepath import normalize_path, application_path
from scipy.io import write_array
import numpy
write_array(normalize_path(str(topo.sim.time()) + "orprefmap.txt"),
topo.sim["V1"].sheet_views["OrientationPreference"].view()[0])
write_array(normalize_path(str(topo.sim.time()) + "orselmap.txt"),
topo.sim["V1"].sheet_views["OrientationSelectivity"].view()[0])
topo.sim["LGNOn"].in_connections[0].strength = on
topo.sim["LGNOff"].in_connections[0].strength = off
print float(topo.sim.time())
if (float(topo.sim.time()) > 19002.0):
#topo.sim["V1"].output_fns[2].scale=0.0
save_plotgroup("Position Preference")
PatternPresenter.duration = 1.0
PatternPresenter.apply_output_fns = True
import topo.command.pylabplot
reload(topo.command.pylabplot)
topo.command.pylabplot.measure_or_tuning_fullfield.instance(
sheet=topo.sim["V1"], repetitions=10)(repetitions=10)
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0.1, 0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0]",
sheet=topo.sim["V1"],
coords=[(0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0.1]",
sheet=topo.sim["V1"],
coords=[(0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,-0.1]",
sheet=topo.sim["V1"],
coords=[(0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0.1]",
sheet=topo.sim["V1"],
coords=[(-0.1, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,-0.1]",
sheet=topo.sim["V1"],
coords=[(-0.1, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,0.2]",
sheet=topo.sim["V1"],
coords=[(0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.2,-0.2]",
sheet=topo.sim["V1"],
coords=[(0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,0.2]",
sheet=topo.sim["V1"],
coords=[(-0.2, 0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.2,-0.2]",
sheet=topo.sim["V1"],
coords=[(-0.2, -0.2)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,0.1]",
sheet=topo.sim["V1"],
coords=[(0.0, 0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0,-0.1]",
sheet=topo.sim["V1"],
coords=[(0.0, -0.1)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[-0.1,0]",
sheet=topo.sim["V1"],
coords=[(-0.1, 0.0)])()
topo.command.pylabplot.cyclic_tuning_curve.instance(
x_axis="orientation",
filename="ORTC[0.1,0]",
sheet=topo.sim["V1"],
coords=[(0.1, -0.0)])()
if (float(topo.sim.time()) > 20000.0):
topo.sim["V1"].output_fns[1].plastic = False
contrib.jacommands.measure_histogram(iterations=1000)
def test_cfprojection_saving(self):
save_plotgroup('Projection',
projection=self.sim['B'].projections('Afferent'))
self.exists("testplotfilesaver_000000.00_B_Afferent.png")