def basic_save_load_snapshot(self):
"""
Very basic test to check the activity matrix of a GeneratorSheet
comes back ok, and that class attributes are pickled.
"""
assert topo.sim.name == SIM_NAME
topo.sim["R"] = GeneratorSheet(input_generator=Gaussian(), nominal_density=2)
topo.sim.run(1)
R_act = copy.deepcopy(topo.sim["R"].activity)
Line.x = 12.0
topo.sim.startup_commands.append("z=99")
save_snapshot(SNAPSHOT_NAME)
Line.x = 9.0
exec "z=88" in __main__.__dict__
topo.sim["R"].set_input_generator(Line())
topo.sim.run(1)
load_snapshot(resolve_path(SNAPSHOT_NAME, search_paths=[normalize_path.prefix]))
# CEBALERT: should also test that unpickling order is correct
# (i.e. startup_commands, class attributes, simulation)
assert_array_equal(R_act, topo.sim["R"].activity)
self.assertEqual(Line.x, 12.0)
self.assertEqual(__main__.__dict__["z"], 99)
def a():
dirr = '/home/jan/projects/topographica/GCAL_EI/'
rhos = {}
X = []
Y = []
qual = []
if True:
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)
f.close()
load_snapshot(os.path.join(b,'snapshot.typ'))
f = open(os.path.join(b,'results.pickle'),'w')
if topo.sim["V1"].projections()["EtoELong"].strength != 0:
d['exc_short_long_ratio'] = 1 - 1 / (topo.sim["V1"].projections()["EtoE"].strength / topo.sim["V1"].projections()["EtoELong"].strength+1)
else:
d['exc_short_long_ratio'] = 1.0
print d['exc_short_long_ratio']
pickle.dump(d,f)
def basic_save_load_snapshot(self):
"""
Very basic test to check the activity matrix of a GeneratorSheet
comes back ok, and that class attributes are pickled.
"""
assert topo.sim.name == SIM_NAME
topo.sim['R'] = GeneratorSheet(input_generator=Gaussian(),
nominal_density=2)
topo.sim.run(1)
R_act = copy.deepcopy(topo.sim['R'].activity)
Line.x = 12.0
topo.sim.startup_commands.append("z=99")
save_snapshot(SNAPSHOT_NAME)
Line.x = 9.0
exec "z=88" in __main__.__dict__
topo.sim['R'].set_input_generator(Line())
topo.sim.run(1)
load_snapshot(
resolve_path(SNAPSHOT_NAME, search_paths=[normalize_path.prefix]))
# CEBALERT: should also test that unpickling order is correct
# (i.e. startup_commands, class attributes, simulation)
assert_array_equal(R_act, topo.sim['R'].activity)
self.assertEqual(Line.x, 12.0)
self.assertEqual(__main__.__dict__['z'], 99)
def compare_with_and_without_snapshot_LoadSnapshot(script="models/lissom.ty"):
data_filename = os.path.split(script)[1] + "_PICKLETEST"
snapshot_filename = os.path.split(script)[1] + "_PICKLETEST.typ_"
locn = resolve_path(os.path.join('tests', data_filename))
print "Loading pickle from %s" % locn
try:
data = pickle.load(open(locn, "rb"))
except IOError:
print "\nData file '" + data_filename + "' could not be opened; run _A() first"
raise
# retrieve parameters used when script was run
run_for = data['run_for']
break_at = data['break_at']
look_at = data['look_at']
# # CEBALERT: shouldn't need to re-list - should be able to read from data!
# cortex_density=data['cortex_density']
# lgn_density=data['lgn_density']
# retina_density=data['retina_density']
# dims=data['dims']
# dataset=data['dataset']
from topo.command import load_snapshot
locn = resolve_path(os.path.join('tests', snapshot_filename))
print "Loading snapshot at %s" % locn
try:
load_snapshot(locn)
except IOError:
print "\nPickle file '" + snapshot_filename + "' could not be opened; run _B() first."
raise
assert topo.sim.time() == break_at
assert_array_equal(data[topo.sim.time()],
topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d" % topo.sim.time())
print "Match at %s after loading snapshot" % topo.sim.time()
topo.sim.run(run_for - break_at)
assert_array_equal(data[topo.sim.time()],
topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d" % topo.sim.time())
print "Match at %s after running loaded snapshot" % topo.sim.time()
def compare_with_and_without_snapshot_LoadSnapshot(script="models/lissom.ty"):
data_filename=os.path.split(script)[1]+"_PICKLETEST"
snapshot_filename=os.path.split(script)[1]+"_PICKLETEST.typ_"
locn = resolve_path(os.path.join('tests',data_filename))
print "Loading pickle from %s"%locn
try:
data = pickle.load(open(locn,"rb"))
except IOError:
print "\nData file '"+data_filename+"' could not be opened; run _A() first"
raise
# retrieve parameters used when script was run
run_for=data['run_for']
break_at=data['break_at']
look_at=data['look_at']
# # CEBALERT: shouldn't need to re-list - should be able to read from data!
# cortex_density=data['cortex_density']
# lgn_density=data['lgn_density']
# retina_density=data['retina_density']
# dims=data['dims']
# dataset=data['dataset']
from topo.command import load_snapshot
locn = resolve_path(os.path.join('tests',snapshot_filename))
print "Loading snapshot at %s"%locn
try:
load_snapshot(locn)
except IOError:
print "\nPickle file '"+snapshot_filename+"' could not be opened; run _B() first."
raise
assert topo.sim.time()==break_at
assert_array_equal(data[topo.sim.time()],topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d"%topo.sim.time())
print "Match at %s after loading snapshot"%topo.sim.time()
topo.sim.run(run_for-break_at)
assert_array_equal(data[topo.sim.time()],topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d"%topo.sim.time())
print "Match at %s after running loaded snapshot"%topo.sim.time()
def loadModelSnapshot(saveSnapshotTime,
LGNRingNo=5, V1RingNo=5, basename='snapshot'):
# LGNRingNo and V1RingNo are paramters that change the model structure
typPath = '../models/snapshots/'
loadSnapshot = "%s-LGN%s-V1%s-T%s.typ" % (basename, str(LGNRingNo), str(V1RingNo), str(saveSnapshotTime))
if saveSnapshotTime is not None:
fullTypPath = typPath+loadSnapshot
snapshotExists = os.path.exists(fullTypPath)
# Load snapshot and return
if snapshotExists:
print "Loading snapshot %s" % loadSnapshot
load_snapshot(typPath+loadSnapshot)
print ("Snapshot %s loaded." % loadSnapshot)
return False
else:
# saveSnapshotFlag. True indicates a snapshot is needed as it is desired but doesn't exist.
return True
else:
return False
def process_distributed_sm_measurement(directory):
from topo.command import load_snapshot
import cPickle as pickle
data_dict = {}
load_snapshot('snapshot.typ')
for a in os.listdir(directory):
b = os.path.join(directory,a);
b = os.path.join(b,'dict.dat');
f = open(b,'r')
d = pickle.load(f)
data_dict.update(d)
sa = surround_analysis()
for (x,y) in data_dict.keys():
sa.plot_size_tunning(x,y)
sa.plot_orientation_contrast_tuning_abs(x,y)
self.plot_histograms_of_measures()
def plot_ortcs(snapshot_path,name_prefix):
def disable_top_right_axis(ax):
for loc, spine in ax.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none') # don't draw spine
for tick in ax.yaxis.get_major_ticks():
tick.tick2On = False
for tick in ax.xaxis.get_major_ticks():
tick.tick2On = False
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
def disable_left_axis(ax):
for loc, spine in ax.spines.iteritems():
if loc in ['left']:
spine.set_color('none') # don't draw spine
for tick in ax.yaxis.get_major_ticks():
tick.tick1On = False
def clean_figure():
import pylab
pylab.title('')
pylab.xlabel('')
pylab.ylabel('')
pylab.yticks([], [])
disable_top_right_axis(pylab.gca())
disable_left_axis(pylab.gca())
pylab.gca().get_legend().set_visible(False)
#pylab.gca().legend_.remove()
import topo.command.pylabplot
load_snapshot(snapshot_path)
topo.command.pylabplot.measure_or_tuning_fullfield.instance(sheet=topo.sim["V1"])()
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",sheet=topo.sim["V1"],coords=[(0,0)])(); clean_figure(); pylab.savefig(name_prefix+"ORTC[0,0].png")
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",sheet=topo.sim["V1"],coords=[(0.1,0.1)])(); clean_figure(); pylab.savefig(name_prefix+"ORTC[0.1,0.1].png")
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",sheet=topo.sim["V1"],coords=[(0.1,-0.1)])(); clean_figure(); pylab.savefig(name_prefix+"ORTC[0.1,-0.1].png")
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",sheet=topo.sim["V1"],coords=[(-0.1,0.1)])(); clean_figure(); pylab.savefig(name_prefix+"ORTC[-0.1,0.1].png")
topo.command.pylabplot.cyclic_tuning_curve.instance(x_axis="orientation",sheet=topo.sim["V1"],coords=[(-0.1,-0.1)])(); clean_figure();pylab.savefig(name_prefix+"ORTC[-0.1,-0.1].png")
from contrib.jacommands import LateralOrientationAnnisotropy
from topo.analysis.featureresponses import MeasureResponseCommand, FeatureMaps, SinusoidalMeasureResponseCommand, FeatureCurveCommand
FeatureMaps.num_orientation=16
MeasureResponseCommand.scale=1.0
MeasureResponseCommand.duration=4.0
SinusoidalMeasureResponseCommand.frequencies=[2.4]
FeatureCurveCommand.num_orientation=16
FeatureCurveCommand.curve_parameters=[{"contrast":15},{"contrast":50},{"contrast":90}]
from topo.command import load_snapshot
#load_snapshot('./DATA/LESI/TEST-small/CCSimple_000002.00.typ')
#load_snapshot('./DATA/LESI/TEST/CCSimple_000002.00.typ')
load_snapshot('./DATA/LESI/CCLESIGifLatest/CCSimple_010000.00.typ')
#load_snapshot('./DATA/LESI/CCLESIGif-NEW1/CCSimple_010000.00_with_map.typ')
#load_snapshot('./DATA/LESI/CCLESIGif12-Orig-LARGE_NEWEXPANDER5000/CCSimple_005002.00.typ')
from topo.command import wipe_out_activity, clear_event_queue
wipe_out_activity()
clear_event_queue()
from topo.pattern import SineGrating, Disk
class SineGratingDiskTemp(SineGrating):
mask_shape = param.Parameter(default=Disk(smoothing=0,size=1.0))
def new_set_parameters(a,b,c,d,e,f,g):
print a,b,c,d,e,f,g
topo.sim["LGNOn"].projections()["LateralGC"].strength=a
from topo.numbergen import UniformRandom, BoundedNumber, ExponentialDecay
from topo.command import pattern_present
from param import normalize_path
import numpy
from topo.analysis.featureresponses import MeasureResponseCommand, FeatureMaps, SinusoidalMeasureResponseCommand, FeatureCurveCommand
FeatureMaps.num_orientation=16
MeasureResponseCommand.scale=1.0
MeasureResponseCommand.duration=4.0
SinusoidalMeasureResponseCommand.frequencies=[2.4]
FeatureCurveCommand.num_orientation=16
FeatureCurveCommand.curve_parameters=[{"contrast":15},{"contrast":50},{"contrast":90}]
from topo.command import load_snapshot
load_snapshot('CCSimple_010005_new_or_map.00.typ')
from topo.command import wipe_out_activity, clear_event_queue
wipe_out_activity()
clear_event_queue()
from topo.pattern import SineGrating, Disk
class SineGratingDiskTemp(SineGrating):
mask_shape = param.Parameter(default=Disk(smoothing=0,size=1.0))
def set_parameters(a,b,c,d,e,f,g,h,i,j,k,l):
print a,b,c,d,e,f,g,h,i,j,k,l
topo.sim["V1Simple"].projections()["V1SimpleFeedbackExc1"].strength=b
topo.sim["V1Simple"].projections()["V1SimpleFeedbackInh"].strength=c
topo.sim["V1Complex"].projections()["LongEE"].strength=d
def GCALTemporal(stimulus = None,
timestep=0.5,
ringParams = ('MAX','MAX'),
saveSnapshotTime = None,
plastic=True,
LGN_TC = None,
LGNFFStrength = 2.33,
GCStrength=0.6,
GCDelay=0.01,
LGNDistanceDelay=None,
# V1 Settings
V1_TC = None,
V1FFStrength=1.5,
ExcStrength=1.7,
InhStrength=-1.4,
LateralDelay=0.01,
V1DistanceDelay=None,
HomeoFactor=1.0,
HomeoPlastic = False ):
VSDSheetFlag = True; saveSnapshotFlag = False; LOAD = None
###################################################
# Modifying sheets #
####################
modelName = 'fit-LGN-V1.py'
(LGNRingNo,V1RingNo) = ringParams
if LOAD is not None:
typPath = '../models/snapshots/'
load_snapshot(typPath+LOAD)
else:
print "Modifying model loaded in memory"
########################
# Sheets Modifications #
########################
p = gcal_vanilla.makeParams()
gcal_vanilla.makeSheets(p)
# Switch out the LISSOM sheets for continuous ones
switchAllSheets(VSDSheetFlag)
# Setup the input generator
setupRetina(stimulus, timestep)
# Connecting with original GCAL connections
gcal_vanilla.connectGCAL(p,LGNRingNo,V1RingNo)
############################
# Connection Modifications #
############################
'Alternatively, could use topo.sheet[<name>].projection(<name>)'
conns = dict([(el.name,el) for el in topo.sim.connections()])
''' AfferentToLGNOn, AfferentToLGNOff, LGNOnAfferent, LGNOffAfferent,
GCLGNOn-*, GCLGNOff-*, LateralInhibitory-*, LateralExcitatory-*
'''
#######
# LGN #
#######
# FEEDFORWARD DELAY IS ABOUT 15MS FOR BOTH STAGES
# Afferent from Retina to LGNOn/Off. Delay and strength.
setConnDelay('AfferentToLGNOn',15.0,conns)
setConnDelay('AfferentToLGNOff',15.0,conns)
setConnStrength('AfferentToLGNOn', LGNFFStrength,conns)
setConnStrength('AfferentToLGNOff', LGNFFStrength,conns)
assert GCStrength > 0
modifyLateralConnections(conns, 'GCLGNOn', GCStrength, GCDelay, LGNDistanceDelay)
modifyLateralConnections(conns, 'GCLGNOff', GCStrength, GCDelay, LGNDistanceDelay)
######
# V1 #
######
setConnDelay('LGNOnAfferent',15.0,conns)
setConnDelay('LGNOffAfferent',15.0,conns)
setConnStrength('LGNOnAfferent', V1FFStrength,conns)
setConnStrength('LGNOffAfferent' ,V1FFStrength,conns)
assert ExcStrength > 0; assert InhStrength < 0
modifyLateralConnections(conns, 'LateralExcitatory', ExcStrength, LateralDelay, V1DistanceDelay)
modifyLateralConnections(conns, 'LateralInhibitory', InhStrength, LateralDelay, V1DistanceDelay)
##########################
# Sheet output functions #
##########################
setLGNOutputFns(LGN_TC)
setV1OutputFns(V1_TC, HomeoFactor, HomeoPlastic)
###############################
# Plasticity and snapshotting #
###############################
if saveSnapshotFlag:
setSheetPlasticity(plastic=True) # No point making snapshot if plasticity if off
saveSnapShot(saveSnapshotTime)
setSheetPlasticity(plastic) # Set as desired
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 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'})
