def _do_plot_boilerplate(kwargs, image=False):
""" Used by various plotting functions. Checks/handles hold state,
returns a Plot object for the plotting function to use.
"""
if kwargs.has_key("hold"):
hold(kwargs["hold"])
del kwargs["hold"]
# Check for an active window; if none, open one.
if len(session.windows) == 0:
if image:
win = session.new_window(is_image=True)
activate(win)
else:
figure()
cont = session.active_window.get_container()
if not cont:
cont = Plot(session.data)
session.active_window.set_container(cont)
existing_tools = [type(t) for t in (cont.tools + cont.overlays)]
if not PanTool in existing_tools:
cont.tools.append(PanTool(cont))
if not ZoomTool in existing_tools:
cont.overlays.append(ZoomTool(cont, tool_mode="box", always_on=True, drag_button="right"))
if not session.hold:
cont.delplot(*cont.plots.keys())
return cont
class ImagePlot(HasTraits):
#plot = Instance(Plot)
# traits_view = View(
# Group(Item('container',
# editor=ComponentEditor(),
# show_label=False)),
# width=500, height=500,
# buttons=NoButtons,
# resizable=True, title="QTLab Analysis Plot")
# Item('plot', editor=ComponentEditor(), show_label=False),
# width=500, height=500, resizable=True, title="QTLab Analysis Plot")
# def __init__(self, title, xtitle, x, ytitle, y, z):
# super(ImagePlot, self).__init__()
# self.create_plot(title, xtitle, x, ytitle, y, z)
def _create_window(self, title, xtitle, x, ytitle, y, z):
'''
- Left-drag pans the plot.
- Mousewheel up and down zooms the plot in and out.
- Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular
region to zoom. If you use a sequence of zoom boxes, pressing alt-left-arrow
and alt-right-arrow moves you forwards and backwards through the "zoom
history".
'''
self._plotname = title
# Create window
self.data = ArrayPlotData()
self.plot = Plot(self.data)
self.update_plot(x, y, z)
self.plot.title = title
self.plot.x_axis.title = xtitle
self.plot.y_axis.title = ytitle
cmap_renderer = self.plot.plots[self._plotname][0]
# Create colorbar
self._create_colorbar()
self._colorbar.plot = cmap_renderer
self._colorbar.padding_top = self.plot.padding_top
self._colorbar.padding_bottom = self.plot.padding_bottom
# Add some tools
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(ZoomTool(component=self.plot, tool_mode="box", always_on=False))
# selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35, selection_type="mask")
# cmap_renderer.overlays.append(selection)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(self.plot)
container.add(self._colorbar)
self.container = container
# Return a window containing our plot container
return Window(self, -1, component=container)
def update_plot(self, x, y, z):
self.data.set_data('x', x)
self.data.set_data('y', y)
self.data.set_data('z', z)
if self.plot.plots.has_key(self._plotname):
self.plot.delplot(self._plotname)
# determine correct bounds
xstep = (x.max() - x.min())/(len(x)-1)
ystep = (y.max() - y.min())/(len(y)-1)
x0, x1 = x.min() - xstep/2, x.max() + xstep/2
y0, y1 = y.min() - ystep/2, y.max() + ystep/2
self.plot.img_plot('z',
name = self._plotname,
xbounds = (x0, x1),
ybounds = (y0, y1),
colormap = jet)
# self.plot.plot(("x", "y", "z"),
# type = "img_plot",
# name = self._plotname,
# color_mapper = jet,
# marker = "square",
# fill_alpha = 0.5,
# marker_size = 6,
# outline_color = "black",
# border_visible = True,
# bgcolor = "white")
def _create_colorbar(self):
cmap = self.plot.color_mapper
self._colorbar = ColorBar(index_mapper=LinearMapper(range=cmap.range),
color_mapper=cmap,
orientation='v',
resizable='v',
width=30,
padding=30)
class FiberView( HasTraits ):
timer = Instance( Timer )
# model = FiberModel(options)
plot_data = Instance( ArrayPlotData )
plot = Instance( Plot )
start_stop = Button()
exit = Button()
# Default TraitsUI view
traits_view = View(
Item('plot', editor=ComponentEditor(), show_label=False),
# Items
HGroup( spring,
Item( "start_stop", show_label = False ),
Item( "exit", show_label = False ), spring),
HGroup( spring ),
# GUI window
resizable = True,
width = 1000,
height = 700,
kind = 'live' )
def __init__(self, options, **kwtraits):
super( FiberView, self).__init__(**kwtraits )
self.model = FiberModel(options)
# debugging
self.debug = options.debug
self.model.debug = options.debug
# timing parameters
self.max_packets = options.max_packets
self.hertz = options.hertz
# extend options to model
self.model.max_packets = options.max_packets
self.model.preallocate_arrays()
self.model.num_analog_channels = options.num_analog_channels
# generate traits plot
self.plot_data = ArrayPlotData( x = self.model._tdata, y = self.model._ydata )
self.plot = Plot( self.plot_data )
renderer = self.plot.plot(("x", "y"), type="line", name='old', color="green")[0]
# self.plot.delplot('old')
# recording flags
self.model.recording = False
self.model.trialEnded = True
print 'Viewer initialized.'
# should we wait for a ttl input to start?
if options.ttl_start:
self.model.ttl_start = True
self.ttl_received = False
# initialize FIO0 for TTL input
self.FIO0_DIR_REGISTER = 6100
self.FIO0_STATE_REGISTER = 6000
self.model.labjack.writeRegister(self.FIO0_DIR_REGISTER, 0) # Set FIO0 low
# initialize output array
self.out_arr = None
# keep track of number of runs
self.run_number = 0
self.timer = Timer(self.model.dt, self.time_update) # update every 1 ms
def run( self ):
self._plot_update()
self.model._get_current_data()
def time_update( self ):
""" Callback that gets called on a timer to get the next data point over the labjack """
# print "time_update"
# print "self.model.ttl_start", self.model.ttl_start
# print "self.ttl_received", self.ttl_received
# print "self.model.recording", self.model.recording
if self.model.ttl_start and not self.ttl_received:
ttl = self.check_for_ttl()
if ttl:
self.ttl_received = True
# self.model.recording = True
# self.model.trialEnded = False
self._start_stop_fired()
if self.model.recording and not self.model.ttl_start:
self.run()
elif self.model.ttl_start:
if self.model.recording and self.ttl_received:
self.run()
# elif self.model.recording:
# self._start_stop_fired()
else:
if self.debug:
pass #print "--timer tic--"
pass
def check_for_ttl(self):
start_ttl = self.model.labjack.readRegister(self.FIO0_STATE_REGISTER)
# print "start_ttl: ", start_ttl
return start_ttl
def clean_time_series(self, time_series, blip_thresh = 10.0):
""" Removes blips, NAs, etc. """
blip_idxs = time_series > blip_thresh
time_series[blip_idxs] = np.median(time_series)
return time_series
def _plot_update( self ):
num_display_points = 100*25 # For 1000 Hz
if self.model.master_index > num_display_points:
disp_begin = self.model.master_index - num_display_points
disp_end = self.model.master_index
print 'disp_begin', disp_begin
print 'disp_end', disp_end
ydata = self.clean_time_series( np.array(self.model._ydata[disp_begin:disp_end]) )
xdata = np.array(self.model._tdata[disp_begin:disp_end])
self.plot_data.set_data("y", ydata)
self.plot_data.set_data("x", xdata)
else:
self.plot_data.set_data( "y", self.clean_time_series( self.model._ydata[0:self.model.master_index] ))
self.plot_data.set_data( "x", self.model._tdata[0:self.model.master_index] )
self.plot = Plot( self.plot_data )
# self.plot.delplot('old')
the_plot = self.plot.plot(("x", "y"), type="line", name = 'old', color="green")[0]
self.plot.request_redraw()
# Note: These should be moved to a proper handler (ModelViewController)
def _start_stop_fired( self ):
self.model.start_time = time.time()
self.model.recording = not self.model.recording
self.model.trialEnded = not self.model.trialEnded
if self.model.trialEnded:
self.save()
def _exit_fired(self):
print "Closing connection to LabJack..."
self.ttl_start = False
self.recording = False
self.model.sdr.running = False
self.model.recording = False
# self.model.labjack.streamStop()
self.model.labjack.close()
print "connection closed. Safe to close..."
#raise SystemExit
#sys.exit()
def save( self ):
print "Saving!"
# Finally, construct and save full data array
print "Saving acquired data..."
for i in xrange( len( self.model.data ) ):
new_array = 1
block = self.model.data[i]
for k in self.model.result.keys():
print k
if k != 0:
if new_array == 1:
array = np.array(block[k])
array.shape = (len(array),1)
new_array = 0
else:
new_data = np.array(block[k])
new_data.shape = (len(block[k]),1)
# if new_data is short by one, fill in with last entry
if new_data.shape[0] < array.shape[0]:
new_data = np.append( new_data, new_data[-1] )
new_data.shape = (new_data.shape[0], 1)
print "Appended point to new_data, shape now:",new_data.shape
if new_data.shape[0] > array.shape[0]:
new_data = new_data[:-1]
print "Removed point from new_data, shape now:",new_data.shape
print "array shape, new_data shape:", array.shape, new_data.shape
array = np.hstack((array, new_data ))
if i == 0:
self.out_arr = array
print "Array shape:", self.out_arr.shape
else:
self.out_arr = np.vstack( (self.out_arr, array) )
print "Array shape:", self.out_arr.shape
date = time.localtime()
outfile = self.model.savepath + self.model.filename
outfile += str(date[0]) + '_' + str(date[1]) + '_' + str(date[2]) + '_'
outfile += str(date[3]) + ':' + str(date[4]) + '_run_number_' + str(self.run_number)
np.savez(outfile, data=self.out_arr, time_stamps=self.model._tdata)
# time_outfile = outfile + '_t'
# np.savez(time_outfile, self.model._tdata)
print "Saved ", outfile
# Plot the data collected this last run
self.plot_last_data()
# clean up
self.reset_variables()
def reset_variables(self):
self.out_arr = None
self.ttl_received = False
self.run_number += 1
self.model.recording = False
self.trialEnded = True
self.plot.delplot('old')
def plot_last_data(self):
import pylab as pl
if self.out_arr.shape[1] == 4:
pl.figure()
pl.subplot(411)
pl.plot(self.out_arr[:,0])
pl.subplot(412)
pl.plot(self.out_arr[:,1])
pl.subplot(413)
pl.plot(self.out_arr[:,2])
pl.subplot(414)
pl.plot(self.out_arr[:,3])
pl.show()
class ImagePlot(HasTraits):
#plot = Instance(Plot)
# traits_view = View(
# Group(Item('container',
# editor=ComponentEditor(),
# show_label=False)),
# width=500, height=500,
# buttons=NoButtons,
# resizable=True, title="QTLab Analysis Plot")
# Item('plot', editor=ComponentEditor(), show_label=False),
# width=500, height=500, resizable=True, title="QTLab Analysis Plot")
# def __init__(self, title, xtitle, x, ytitle, y, z):
# super(ImagePlot, self).__init__()
# self.create_plot(title, xtitle, x, ytitle, y, z)
def _create_window(self, title, xtitle, x, ytitle, y, z):
'''
- Left-drag pans the plot.
- Mousewheel up and down zooms the plot in and out.
- Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular
region to zoom. If you use a sequence of zoom boxes, pressing alt-left-arrow
and alt-right-arrow moves you forwards and backwards through the "zoom
history".
'''
self._plotname = title
# Create window
self.data = ArrayPlotData()
self.plot = Plot(self.data)
self.update_plot(x, y, z)
self.plot.title = title
self.plot.x_axis.title = xtitle
self.plot.y_axis.title = ytitle
cmap_renderer = self.plot.plots[self._plotname][0]
# Create colorbar
self._create_colorbar()
self._colorbar.plot = cmap_renderer
self._colorbar.padding_top = self.plot.padding_top
self._colorbar.padding_bottom = self.plot.padding_bottom
# Add some tools
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(
ZoomTool(component=self.plot, tool_mode="box", always_on=False))
# selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35, selection_type="mask")
# cmap_renderer.overlays.append(selection)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.plot)
container.add(self._colorbar)
self.container = container
# Return a window containing our plot container
return Window(self, -1, component=container)
def update_plot(self, x, y, z):
self.data.set_data('x', x)
self.data.set_data('y', y)
self.data.set_data('z', z)
if self.plot.plots.has_key(self._plotname):
self.plot.delplot(self._plotname)
# determine correct bounds
xstep = (x.max() - x.min()) / (len(x) - 1)
ystep = (y.max() - y.min()) / (len(y) - 1)
x0, x1 = x.min() - xstep / 2, x.max() + xstep / 2
y0, y1 = y.min() - ystep / 2, y.max() + ystep / 2
self.plot.img_plot('z',
name=self._plotname,
xbounds=(x0, x1),
ybounds=(y0, y1),
colormap=jet)
# self.plot.plot(("x", "y", "z"),
# type = "img_plot",
# name = self._plotname,
# color_mapper = jet,
# marker = "square",
# fill_alpha = 0.5,
# marker_size = 6,
# outline_color = "black",
# border_visible = True,
# bgcolor = "white")
def _create_colorbar(self):
cmap = self.plot.color_mapper
self._colorbar = ColorBar(index_mapper=LinearMapper(range=cmap.range),
color_mapper=cmap,
orientation='v',
resizable='v',
width=30,
padding=30)
class FiberView( HasTraits ):
timer = Instance( Timer )
# model = FiberModel(options)
plot_data = Instance( ArrayPlotData )
plot = Instance( Plot )
start_stop = Button()
exit = Button()
# Default TraitsUI view
traits_view = View(
Item('plot', editor=ComponentEditor(), show_label=False),
# Items
HGroup( spring,
Item( "start_stop", show_label = False ),
Item( "exit", show_label = False ), spring),
HGroup( spring ),
# GUI window
resizable = True,
width = 1000,
height = 700,
kind = 'live' )
def __init__(self, options, **kwtraits):
super( FiberView, self).__init__(**kwtraits )
self.model = FiberModel(options)
# debugging
self.debug = options.debug
self.model.debug = options.debug
# timing parameters
self.max_packets = options.max_packets
self.hertz = options.hertz
# extend options to model
self.model.max_packets = options.max_packets
self.model.preallocate_arrays()
self.model.num_analog_channels = options.num_analog_channels
# generate traits plot
self.plot_data = ArrayPlotData( x = self.model._tdata, y = self.model._ydata )
self.plot = Plot( self.plot_data )
renderer = self.plot.plot(("x", "y"), type="line", name='old', color="green")[0]
# self.plot.delplot('old')
# recording flags
self.model.recording = False
self.model.trialEnded = True
print 'Viewer initialized.'
# should we wait for a ttl input to start?
if options.ttl_start:
self.model.ttl_start = True
self.ttl_received = False
# initialize FIO0 for TTL input
self.FIO0_DIR_REGISTER = 6100
self.FIO0_STATE_REGISTER = 6000
self.model.labjack.writeRegister(self.FIO0_DIR_REGISTER, 0) # Set FIO0 low
# initialize output array
self.out_arr = None
# keep track of number of runs
self.run_number = 0
self.timer = Timer(self.model.dt, self.time_update) # update every 1 ms
def run( self ):
self._plot_update()
self.model._get_current_data()
def time_update( self ):
""" Callback that gets called on a timer to get the next data point over the labjack """
# print "time_update"
# print "self.model.ttl_start", self.model.ttl_start
# print "self.ttl_received", self.ttl_received
# print "self.model.recording", self.model.recording
if self.model.ttl_start and not self.ttl_received:
ttl = self.check_for_ttl()
if ttl:
self.ttl_received = True
# self.model.recording = True
# self.model.trialEnded = False
self._start_stop_fired()
if self.model.recording and not self.model.ttl_start:
self.run()
elif self.model.ttl_start:
if self.model.recording and self.ttl_received:
self.run()
# elif self.model.recording:
# self._start_stop_fired()
else:
if self.debug:
pass #print "--timer tic--"
pass
def check_for_ttl(self):
start_ttl = self.model.labjack.readRegister(self.FIO0_STATE_REGISTER)
# print "start_ttl: ", start_ttl
return start_ttl
def clean_time_series(self, time_series, blip_thresh = 10.0):
""" Removes blips, NAs, etc. """
blip_idxs = time_series > blip_thresh
time_series[blip_idxs] = np.median(time_series)
return time_series
def _plot_update( self ):
num_display_points = 100*25 # For 1000 Hz
if self.model.master_index > num_display_points:
disp_begin = self.model.master_index - num_display_points
disp_end = self.model.master_index
# print 'disp_begin', disp_begin
# print 'disp_end', disp_end
ydata = self.clean_time_series( np.array(self.model._ydata[disp_begin:disp_end]) )
xdata = np.array(self.model._tdata[disp_begin:disp_end])
self.plot_data.set_data("y", ydata)
self.plot_data.set_data("x", xdata)
else:
self.plot_data.set_data( "y", self.clean_time_series( self.model._ydata[0:self.model.master_index] ))
self.plot_data.set_data( "x", self.model._tdata[0:self.model.master_index] )
self.plot = Plot( self.plot_data )
# self.plot.delplot('old')
the_plot = self.plot.plot(("x", "y"), type="line", name = 'old', color="green")[0]
self.plot.request_redraw()
# Note: These should be moved to a proper handler (ModelViewController)
def _start_stop_fired( self ):
self.model.start_time = time.time()
self.model.recording = not self.model.recording
self.model.trialEnded = not self.model.trialEnded
if self.model.trialEnded:
self.save()
#Quickly turn LED off to signal button push
self.model.start_LED()
def _exit_fired(self):
print "Closing connection to LabJack..."
self.ttl_start = False
self.recording = False
self.model.sdr.running = False
self.model.recording = False
# self.model.labjack.streamStop()
self.model.labjack.close()
print "connection closed. Safe to close..."
#raise SystemExit
#sys.exit()
def save( self ):
print "Saving!"
# Finally, construct and save full data array
print "Saving acquired data..."
for i in xrange( len( self.model.data ) ):
new_array = 1
block = self.model.data[i]
for k in self.model.result.keys():
print k
if k != 0:
if new_array == 1:
array = np.array(block[k])
array.shape = (len(array),1)
new_array = 0
else:
new_data = np.array(block[k])
new_data.shape = (len(block[k]),1)
# if new_data is short by one, fill in with last entry
if new_data.shape[0] < array.shape[0]:
new_data = np.append( new_data, new_data[-1] )
new_data.shape = (new_data.shape[0], 1)
print "Appended point to new_data, shape now:",new_data.shape
if new_data.shape[0] > array.shape[0]:
new_data = new_data[:-1]
print "Removed point from new_data, shape now:",new_data.shape
print "array shape, new_data shape:", array.shape, new_data.shape
array = np.hstack((array, new_data ))
if i == 0:
self.out_arr = array
print "Array shape:", self.out_arr.shape
else:
self.out_arr = np.vstack( (self.out_arr, array) )
print "Array shape:", self.out_arr.shape
date = time.localtime()
outfile = self.model.savepath + self.model.filename
outfile += str(date[0]) + '_' + str(date[1]) + '_' + str(date[2]) + '_'
outfile += str(date[3]) + '-' + str(date[4]) + '_run_number_' + str(self.run_number)
np.savez(outfile, data=self.out_arr, time_stamps=self.model._tdata)
# time_outfile = outfile + '_t'
# np.savez(time_outfile, self.model._tdata)
print "Saved ", outfile
# Plot the data collected this last run
self.plot_last_data()
# clean up
self.reset_variables()
def reset_variables(self):
self.out_arr = None
self.ttl_received = False
self.run_number += 1
self.model.recording = False
self.trialEnded = True
self.plot.delplot('old')
def plot_last_data(self):
import pylab as pl
if self.out_arr.shape[1] == 4:
pl.figure()
pl.subplot(411)
pl.plot(self.out_arr[:,0])
pl.subplot(412)
pl.plot(self.out_arr[:,1])
pl.subplot(413)
pl.plot(self.out_arr[:,2])
pl.subplot(414)
pl.plot(self.out_arr[:,3])
pl.show()
