def plot(self, interp='nearest', normed=True, title='RevCorrWindow', scale=2.0):
"""Plots the RFs as bitmaps in a window. normed = 'global'|True|False"""
rfs = [] # list of receptive fields to pass to ReceptiveFieldFrame object
if normed == 'global': # normalize across all timepoints for all neurons
vmin = min([ sta.rf.min() for sta in self.stas ]) # global min
vmax = max([ sta.rf.max() for sta in self.stas ]) # global max
for ni, sta in enumerate(self.stas):
# create a copy to manipulate for display purposes, (nt, width, height):
rf = sta.rf.copy()
if normed: # either 'global' or True
if normed == True: # normalize across the timepoints for this Neuron
vmin, vmax = rf.min(), rf.max()
norm = mpl.colors.normalize(vmin=vmin, vmax=vmax, clip=True)
rf = norm(rf) # normalize the rf the same way across all timepoints
else: # don't normalize across timepoints, leave each one to autoscale
for ti in range(self.nt):
norm = mpl.colors.normalize(vmin=None, vmax=None, clip=True)
rf[ti] = norm(rf[ti]) # normalize the rf separately at each timepoint
rf *= 255 # scale up to 8 bit values
rf = rf.round().astype(np.uint8) # downcast from float to uint8
rfs.append(rf)
nids = [ neuron.id for neuron in self.neurons ]
win = RevCorrWindow(title=title, rfs=rfs, nids=nids, ts=self.ts, scale=scale)
win.show()
return win # necessary in IPython
def plot(self, interp='nearest', normed=True, title='RevCorrWindow', scale=2.0):
"""Plots the spatiotemporal RF as bitmaps in a wx.Frame"""
# create a copy to manipulate for display purposes, (nt, width, height):
rf = self.rf.copy()
if normed: # normalize across the timepoints for this RevCorr
norm = mpl.colors.normalize(vmin=rf.min(), vmax=rf.max(), clip=True)
rf = norm(rf) # normalize the rf the same way across all timepoints
else: # don't normalize across timepoints, leave each one to autoscale
for ti in range(self.nt):
norm = mpl.colors.normalize(vmin=None, vmax=None, clip=True)
rf[ti] = norm(rf[ti]) # normalize the rf separately at each timepoint
rf *= 255 # scale up to 8 bit values
rf = rf.round().astype(np.uint8) # downcast from float to uint8
win = RevCorrWindow(title=title, rfs=[rf], nids=[self.neuron.id],
ts=self.ts, scale=scale)
win.show()
return win # necessary in IPython
def plot(self, normed=True, title='RevCorrWindow', scale=2.0):
"""Plot the spatiotemporal RF as bitmaps in a custom (non MPL) Qt window"""
# create a copy to manipulate for display purposes, (nt, width, height):
rf = self.rf.copy()
if normed: # normalize across the timepoints for this RevCorr
norm = mpl.colors.normalize(vmin=rf.min(),
vmax=rf.max(),
clip=True)
rf = norm(
rf) # normalize the rf the same way across all timepoints
else: # don't normalize across timepoints, leave each one to autoscale
for ti in range(self.nt):
norm = mpl.colors.normalize(vmin=None, vmax=None, clip=True)
rf[ti] = norm(
rf[ti]) # normalize the rf separately at each timepoint
rf *= 255 # scale up to 8 bit values
rf = rf.round().astype(np.uint8) # downcast from float to uint8
win = RevCorrWindow(title=title,
rfs=[rf],
nids=[self.neuron.id],
ts=self.ts,
scale=scale)
win.show()
return win # necessary in IPython
def plot(self,
normed=True,
title='RevCorrWindow',
scale=2.0,
MPL=False,
margins=True):
"""Plots the RFs as bitmaps in a window.
normed = True|False|'global'
MPL = True|False|'axes'"""
rfs = [
] # list of receptive fields to pass to ReceptiveFieldFrame object
if normed == 'global': # normalize across all timepoints for all neurons
vmin = min([sta.rf.min() for sta in self.stas]) # global min
vmax = max([sta.rf.max() for sta in self.stas]) # global max
for sta in (self.stas):
if sta is None:
rf = None
else:
# create a copy to manipulate for display purposes, (nt, width, height):
rf = sta.rf.copy()
if normed: # either 'global' or True
if normed == True: # normalize across the timepoints for this Neuron
vmin, vmax = rf.min(), rf.max()
norm = mpl.colors.Normalize(vmin=vmin,
vmax=vmax,
clip=True)
rf = norm(
rf
) # normalize the rf the same way across all timepoints
else: # don't normalize across timepoints, leave each one to autoscale
for ti in range(self.nt):
norm = mpl.colors.Normalize(vmin=None,
vmax=None,
clip=True)
rf[ti] = norm(
rf[ti]
) # normalize the rf separately at each timepoint
rf *= 255 # scale up to 8 bit values
rf = rf.round().astype(
np.uint8) # downcast from float to uint8
rfs.append(rf)
if MPL == True:
core.mplrevcorr(title=title,
rfs=rfs,
nids=self.nids,
ts=self.ts,
scale=scale,
dpi=MPL,
margins=margins)
elif MPL == 'axes':
core.mplrevcorraxes(title=title,
rfs=rfs,
nids=self.nids,
ts=self.ts,
scale=scale)
else:
win = RevCorrWindow(title=title,
rfs=rfs,
nids=self.nids,
ts=self.ts,
scale=scale)
win.show()
return win # necessary in IPython
