def write_frames(self, nframes):
if nframes >= 1e6:
raise Exception(
"Cannot handle movies with 1 million frames or more.")
self.frame_directory = tempfile.mkdtemp(
prefix='tmp_NeuroTools_visualization_')
time_label = self.fig.text(0.01,
0.01,
"t = 0 ms",
horizontalalignment='left')
for i in range(int(nframes)):
for panel, obj in self.panels:
assert self.frame_duration == obj.frame_duration, "%s != %s" % (
self.frame_duration, obj.frame_duration)
panel.lines = []
panel.images = []
plot = getattr(panel, obj.plot_function)
plot(*obj.next_frame(), **obj.kwargs)
if obj.plot_function == "imshow" and i == 0:
pos = panel.get_position()
try:
l, b, w, h = pos # older versions of Matplotlib
except TypeError:
l, b, w, h = pos.bounds # newer versions return a Bbox object
cb_panel = self.fig.add_axes([l + w, b, 0.05 * w, h])
self.fig.colorbar(panel.images[0], cb_panel)
time_label.set_text("t = %g ms" % (i * self.frame_duration, ))
self.canvas.print_figure(
os.path.join(self.frame_directory, "frame%06d.png" % i))
progress_bar(float(i) / nframes)
def runTest(self):
import time
print '\nINFO: Testing progress bar...'
for i in range(100):
plotting.progress_bar(i/100.)
time.sleep(.01)
print '\n'
def runTest(self):
import time
print '\nINFO: Testing progress bar...'
for i in range(100):
plotting.progress_bar(i / 100.)
time.sleep(.01)
print '\n'
def write_frames(self, nframes):
if nframes >= 1e6:
raise Exception("Cannot handle movies with 1 million frames or more.")
self.frame_directory = tempfile.mkdtemp(prefix='tmp_NeuroTools_visualization_')
time_label = self.fig.text(0.01, 0.01, "t = 0 ms", horizontalalignment='left')
for i in range(int(nframes)):
for panel,obj in self.panels:
assert self.frame_duration == obj.frame_duration, "%s != %s" % (self.frame_duration, obj.frame_duration)
panel.lines = []; panel.images = []
plot = getattr(panel, obj.plot_function)
plot(*obj.next_frame(), **obj.kwargs)
if obj.plot_function == "imshow" and i==0:
pos = panel.get_position()
try:
l,b,w,h = pos # older versions of Matplotlib
except TypeError:
l,b,w,h = pos.bounds # newer versions return a Bbox object
cb_panel = self.fig.add_axes([l+w, b, 0.05*w, h])
self.fig.colorbar(panel.images[0], cb_panel)
time_label.set_text("t = %g ms" % (i*self.frame_duration,))
self.canvas.print_figure(os.path.join(self.frame_directory, "frame%06d.png" % i))
progress_bar(float(i)/nframes)
def _calculate_input_currents(self, visual_space, duration):
"""
Calculate the input currents for all cells.
"""
assert isinstance(visual_space, VisualSpace)
if duration is None:
duration = visual_space.get_maximum_duration()
P_rf = self.parameters.receptive_field
rf_function = eval(P_rf.func)
rf_ON = SpatioTemporalReceptiveField(rf_function,
P_rf.func_params,
P_rf.width, P_rf.height, P_rf.duration)
rf_OFF = SpatioTemporalReceptiveField(lambda x,y,t,p: -1.0*rf_function(x,y,t,p),
P_rf.func_params,
P_rf.width, P_rf.height, P_rf.duration)
dx = dy = P_rf.spatial_resolution
dt = P_rf.temporal_resolution
for rf in rf_ON, rf_OFF:
rf.quantize(dx, dy, dt)
rf = {'X_ON': rf_ON, 'X_OFF': rf_OFF}
# create population of CellWithReceptiveFields, setting the receptive
# field centres based on the size/location of self
logger.debug("Creating population of `CellWithReceptiveField`s")
input_cells = {}
effective_visual_field_width, effective_visual_field_height = self.parameters.size
#x_values = numpy.linspace(-effective_visual_field_width/2.0, effective_visual_field_width/2.0, self.shape[0])
#y_values = numpy.linspace(-effective_visual_field_height/2.0, effective_visual_field_height/2.0, self.shape[1])
for rf_type in self.rf_types:
input_cells[rf_type] = []
for i in xrange(0,len(self.sheets[rf_type].pop.positions[0])):
cell = CellWithReceptiveField(self.sheets[rf_type].pop.positions[0][i], self.sheets[rf_type].pop.positions[1][i], rf[rf_type], self.parameters.gain)
cell.initialize(visual_space.background_luminance, duration)
input_cells[rf_type].append(cell)
logger.debug("Processing frames")
t = 0
retinal_input = []
while t < duration:
for rf_type in self.rf_types:
for cell in input_cells[rf_type]:
cell.view(visual_space)
t = visual_space.update()
visual_region = VisualRegion(location_x=0,location_y=0, size_x=P_rf.width,size_y=P_rf.height)
im = visual_space.view(visual_region, pixel_size=rf_ON.spatial_resolution)
retinal_input.append(im)
progress_bar(t/duration)
input_currents = {}
for rf_type in self.rf_types:
input_currents[rf_type] = [cell.response_current() for cell in input_cells[rf_type]]
cell0_currents = input_currents[rf_type][0]
#logger.debug("Input current values for %s cell #0: %s" % (rf_type, cell0_currents['amplitudes']))
#visual_logging.debug(cell0_currents['amplitudes'], cell0_currents['times'],
# "Time (ms)", "Current (nA)", "Input current values for %s cell #0" % rf_type)
for i in xrange(0,1):
a = [cell.response_current()['amplitudes'][i] for cell in input_cells[rf_type]]
return (input_currents,retinal_input)
