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 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 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 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 saveSnapShot(saveSnapshotTime):
if saveSnapshotTime is not None:
print("Snapshot missing! Regenenerating over %d periods"
% int(saveSnapshotTime))
# typName ='gcal_continuous_%d.typ' % int(saveSnapshotTime)
fullTypPath = typPath+ loadSnapshot#typName
scheduleInfo(saveSnapshotTime, 10)
topo.sim.run(int(saveSnapshotTime))
save_snapshot(os.path.abspath(fullTypPath))
print "Snapshot generated. Rerun script."
sys.exit()
def compare_with_and_without_snapshot_CreateSnapshot(
script="models/lissom.ty"):
data_filename = os.path.split(script)[1] + "_PICKLETEST"
locn = resolve_path(os.path.join('tests', data_filename))
print "Loading pickle at %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']
__main__.__dict__['cortex_density'] = cortex_density
__main__.__dict__['lgn_density'] = lgn_density
__main__.__dict__['retina_density'] = retina_density
__main__.__dict__['dims'] = dims
__main__.__dict__['dataset'] = dataset
execfile(script, __main__.__dict__)
# check we have the same before any pickling
topo.sim.run(break_at)
assert_array_equal(data[topo.sim.time()],
topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d" % topo.sim.time())
from topo.command import save_snapshot
locn = normalize_path(os.path.join('tests', data_filename + '.typ_'))
print "Saving snapshot to %s" % locn
save_snapshot(locn)
def compare_with_and_without_snapshot_CreateSnapshot(script="models/lissom.ty"):
data_filename=os.path.split(script)[1]+"_PICKLETEST"
locn = resolve_path(os.path.join('tests',data_filename))
print "Loading pickle at %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']
__main__.__dict__['cortex_density']=cortex_density
__main__.__dict__['lgn_density']=lgn_density
__main__.__dict__['retina_density']=retina_density
__main__.__dict__['dims']=dims
__main__.__dict__['dataset']=dataset
execfile(script,__main__.__dict__)
# check we have the same before any pickling
topo.sim.run(break_at)
assert_array_equal(data[topo.sim.time()],topo.sim[look_at].activity,
err_msg="\nAt topo.sim.time()=%d"%topo.sim.time())
from topo.command import save_snapshot
locn = normalize_path(os.path.join('tests',data_filename+'.typ_'))
print "Saving snapshot to %s"%locn
save_snapshot(locn)
def save_snapshot(times=None, **kwargs):
if topo.sim.time() != times[-1]:
return None
from topo.command import save_snapshot
save_snapshot()
def saver_function():
from topo.command import save_snapshot
save_snapshot(normalize_path('snapshot.typ'))
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 saver_function():
from topo.command import save_snapshot
save_snapshot(normalize_path('snapshot.typ'))
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))
