def calc(self, var='ori', fixed=None, force=False):
"""Calculate the actual tuning curve"""
if not force and self.var == var:
return # calc was already run with desired var, so results should be fine
if fixed != None:
fixedsweepis = []
for fixedvar, fixedvals in fixed.items():
vals = self.experiment.sweeptable.data[fixedvar]
if fixedvar == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
sweepis = []
for fixedval in toiter(fixedvals):
sweepis.append(np.where(vals == fixedval)[0])
sweepis = np.concatenate(sweepis)
fixedsweepis.append(sweepis)
# intersect all fixedvar arrays in fixedsweepis:
fixedsweepis = core.intersect1d(fixedsweepis)
#print(fixedsweepis)
# get values for var at all unique sweep indices:
try:
vals = self.experiment.sweeptable.data[var]
except AttributeError:
# something different about ptc15, new bug?. Also, sweeptable values are in a list
# instead of an array like for post ptc15. Should be converted to an array
# somewhere, so it need not be done here? Also, ptc15 experiments are missing
# .s and .d attribs, whose contents seems to be found in .oldparams
vals = np.asarray(self.experiment.sweeptable[var])
if var == 'ori': # correct for orientation offset by adding it to ori vals
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
try:
vals += self.experiment.s.orioff # static parameter
except AttributeError: # for ptc15, should be fixed:
vals += self.experiment.oldparams['orioff'] # static parameter
vals %= maxori
x = np.unique(vals) # x axis
y = np.zeros(len(x), dtype=int) # spike counts for each variable value
for vali, val in enumerate(x):
sweepis = np.where(vals == val)[0]
if fixed != None:
sweepis = np.intersect1d(sweepis,
fixedsweepis,
assume_unique=True)
print(sweepis)
for sweepi in sweepis:
y[vali] += self.counts[sweepi].sum()
self.x, self.y = x, y
self.peak = x[y.argmax()]
def calc(self, var='ori', fixed=None, force=False):
"""Calculate the actual tuning curve"""
if not force and self.var == var:
return # calc was already run with desired var, so results should be fine
if fixed != None:
fixedsweepis = []
for fixedvar, fixedvals in fixed.items():
vals = self.experiment.sweeptable.data[fixedvar]
if fixedvar == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
sweepis = []
for fixedval in toiter(fixedvals):
sweepis.append(np.where(vals == fixedval)[0])
sweepis = np.concatenate(sweepis)
fixedsweepis.append(sweepis)
# intersect all fixedvar arrays in fixedsweepis:
fixedsweepis = core.intersect1d(fixedsweepis)
#print(fixedsweepis)
# get values for var at all unique sweep indices:
try:
vals = self.experiment.sweeptable.data[var]
except AttributeError:
# something different about ptc15, new bug?. Also, sweeptable values are in a list
# instead of an array like for post ptc15. Should be converted to an array
# somewhere, so it need not be done here? Also, ptc15 experiments are missing
# .s and .d attribs, whose contents seems to be found in .oldparams
vals = np.asarray(self.experiment.sweeptable[var])
if var == 'ori': # correct for orientation offset by adding it to ori vals
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
try:
vals += self.experiment.s.orioff # static parameter
except AttributeError: # for ptc15, should be fixed:
vals += self.experiment.oldparams['orioff'] # static parameter
vals %= maxori
x = np.unique(vals) # x axis
y = np.zeros(len(x), dtype=int) # spike counts for each variable value
for vali, val in enumerate(x):
sweepis = np.where(vals == val)[0]
if fixed != None:
sweepis = np.intersect1d(sweepis, fixedsweepis, assume_unique=True)
print(sweepis)
for sweepi in sweepis:
y[vali] += self.counts[sweepi].sum()
self.x, self.y = x, y
self.peak = x[y.argmax()]
def sta(self, neurons=None, **kwargs):
"""Return an STAs RevCorrs object"""
if neurons == None:
# no Neurons were passed, use all the Neurons from the default Sort for this
# experiment's Recording
keyvals = self.r.n.items() # get key val pairs in a list of tuples
keyvals.sort() # make sure they're sorted by key
neurons = []
for key, val in keyvals:
neurons.append(val)
else:
try:
# assume neurons is a Neuron id or list of Neuron ids, get the associated
# Neuron objects from the default Sort for this experiment's Recording
neurons = [ self.r.n[ni] for ni in toiter(neurons) ]
except KeyError: # neurons is probably a list of Neuron objects
pass
staso = STAs(neurons=neurons, experiment=self, **kwargs) # init a new STAs object
staso.calc()
return staso
def plot(self, var='ori', fixed=None):
"""var: string name of variable you want to plot a tuning curve for
fixed: dict with keys containing names of vars to keep fixed when building tuning
curve, and values containing each var's value(s) to fix at
Ex: r71.n[1].tune().plot('phase0', fixed={'ori':138, 'sfreqCycDeg':[0.4, 0.8]})
"""
if not self.done:
self.calc(tdelay=self.tdelay)
if fixed != None:
fixedsweepis = []
for fixedvar, fixedvals in fixed.items():
vals = self.experiment.sweeptable.data[fixedvar]
if fixedvar == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
sweepis = []
for fixedval in toiter(fixedvals):
sweepis.append(np.where(vals == fixedval)[0])
sweepis = np.concatenate(sweepis)
fixedsweepis.append(sweepis)
# intersect all fixedvar arrays in fixedsweepis:
fixedsweepis = core.intersect1d(fixedsweepis)
#print(fixedsweepis)
# get values for var at all unique sweep indices:
vals = self.experiment.sweeptable.data[var]
if var == 'ori': # correct for orientation offset by adding
if (vals > 180).any():
maxori = 360
else:
maxori = 180
vals = vals.copy() # don't modify the sweeptable!
vals += self.experiment.s.orioff # static parameter
vals %= maxori
x = np.unique(vals) # x axis
y = np.zeros(len(x), dtype=int) # spike counts for each variable value
for vali, val in enumerate(x):
sweepis = np.where(vals == val)[0]
if fixed != None:
sweepis = np.intersect1d(sweepis, fixedsweepis, assume_unique=True)
print sweepis
for sweepi in sweepis:
y[vali] += self.counts[sweepi].sum()
# create a new figure:
f = pl.figure()
a = f.add_subplot(111)
a.plot(x, y, 'k.-')
a.set_xlabel(var)
a.set_ylabel('spike count')
titlestr = lastcmd()
titlestr += ' nid%d' % self.neuron.id
a.set_title(titlestr)
f.canvas.window().setWindowTitle(titlestr)
a.text(0.99, 0.99, 'peak=(%s, %s)' % (x[y.argmax()], y.max()),
transform=a.transAxes,
horizontalalignment='right',
verticalalignment='top')
f.tight_layout(pad=0.3) # crop figure to contents
self.f = f
self.x, self.y = x, y
return self
