def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = arange(low, high+0.001, (high-low)/numpoints)
# Plot some bessel functions
plots = {}
broadcaster = BroadcasterTool()
for i in range(4):
y = jn(i, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
add_default_grids(plot)
add_default_axes(plot)
# Create a pan tool and give it a reference to the plot it should
# manipulate, but don't attach it to the plot. Instead, attach it to
# the broadcaster.
pan = PanTool(plot)
broadcaster.tools.append(pan)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# Add an axis on the right-hand side that corresponds to the second plot.
# Note that it uses plot.value_mapper instead of plot0.value_mapper.
plot1 = plots["Bessel j_1"]
axis = PlotAxis(plot1, orientation="right")
plot1.underlays.append(axis)
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
container.overlays.append(legend)
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(PlotLabel("Bessel functions",
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def _tt1_fired(self):
self.addele()
self.plotdata.del_data("x")
self.plotdata.del_data("y")
self.plotdata.del_data("xrt")
self.plotdata.del_data("xx")
self.plotdata.del_data("yy")
self.plotdata.del_data("tt")
self.plotdata.set_data("x",self.x)
self.plotdata.set_data("y",self.y)
self.plotdata.set_data("xrt",self.xrt)
self.plotdata.set_data("xx",xx)
self.plotdata.set_data("yy",yy)
self.plotdata.set_data("tt",self.ttt)
plot1 = Plot(self.plotdata)
plot1.plot(("x", "y"),type="line",color="blue",name='1')
plot1.plot(("x", "y"),type="scatter",color="blue", marker = 'circle', marker_size = 2,name='1')
plot1.plot(("x", "xrt"),type="line",color="red",name='2')
plot1.plot(("x", "xrt"),type="scatter",color="red", marker = 'circle', marker_size = 2,name='2')
plot2 = Plot(self.plotdata)
plot2.plot(("xx", "yy"),type="line",color="blue")
plot2.plot(("xx","yy"),type="scatter",color="blue")
plot3 = Plot(self.plotdata)
plot3.plot(("x", "tt"),type="line",color="blue")
plot3.plot(("x","tt"),type="scatter",color="blue")
container = HPlotContainer(plot1,plot2,plot3)
self.plot= container
legend= Legend(padding=10, align="ur")
legend.plots = plot1.plots
plot1.overlays.append(legend)
def _create_plot_component_vertical(signals=Array, use_downsampling=False):
# container = HPlotContainer(resizable = "hv", bgcolor="lightgray",
# fill_padding=True, padding = 10)
container = VPlotContainer(resizable="hv",
bgcolor="lightgray",
fill_padding=True,
padding=50)
nSignal, nSample = np.shape(signals)
time = arange(nSample)
value_range = None
plots = {}
for i in range(nSignal):
plot = create_line_plot(
(time, signals[i]),
color=tuple(COLOR_PALETTE[i % len(COLOR_PALETTE)]),
width=1.0,
# orientation="v")
orientation="h")
plot.origin_axis_visible = True
# plot.origin = "top left"
plot.padding_left = 10
plot.padding_right = 10
plot.border_visible = False
plot.bgcolor = "white"
if value_range is None:
value_range = plot.value_mapper.range
else:
plot.value_range = value_range
value_range.add(plot.value)
container.add(plot)
plots["Corr fun %d" % i] = plot
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
legend.plots = plots
# container.padding_top = 50
container.overlays.append(
PlotLabel("Correlation function",
component=container,
font="swiss 16",
overlay_position="top"))
# selection_overlay = RangeSelectionOverlay(component=plot)
# plot.tools.append(RangeSelection(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
# plot.overlays.append(selection_overlay)
plot.overlays.append(zoom)
return container
def _select_angle_names_changed(self):
for _myos_in in range(2):
self.stream_Angle[_myos_in] = np.zeros(self.Angle_length)
self.plot_Angle = Plot(self.data_Angle)
self.plot_Angle.auto_colors = ['blue','red']
for _pic in self.select_angle_names:
self.plot_Angle.plot(('x',)+(_pic,),name = _pic,color = 'auto')
self.plot_Angle.title='Multiple Angle'
_legend = Legend(padding=10,align="ur")
_legend.plots = self.plot_Angle.plots
self.plot_Angle.overlays.append(_legend)
def gen_line_plot(series_one, series_two, y_axis_name=''):
"""
Parameters
----------
series_one : nd array
series_two : nd array
"""
size = min(series_one.shape[0],
series_two.shape[0])
idx = ArrayDataSource(arange(size))
series_one_data = ArrayDataSource(series_one[:size])
series_two_data = ArrayDataSource(series_two[:size])
y_range = DataRange1D(series_one_data)
y_range.tight_bounds = False
y_range.margin = 50
x_mapper = LinearMapper(range=DataRange1D(idx))
y_mapper = LinearMapper(range=y_range)
series_one_plot = LinePlot(index=idx,
value=series_one_data, index_mapper=x_mapper,
value_mapper=y_mapper, color='blue')
series_two_plot = LinePlot(index=idx,
value=series_two_data, index_mapper=x_mapper,
value_mapper=y_mapper, color='red')
container = OverlayPlotContainer(bgcolor='white',
padding=25, fill_padding=False, border_visible=True)
y_axis = PlotAxis(mapper=y_mapper, component=container,
orientation='left')
x_axis = PlotAxis(mapper=x_mapper, component=container,
orientation='bottom')
x_axis.title = 'Time'
y_axis.title = y_axis_name
legend = Legend(component=container, padding=10, align='ur')
legend.plots = {
'Predicted': series_one_plot,
'Actual': series_two_plot,
}
container.add(series_one_plot)
container.add(series_two_plot)
container.overlays.append(y_axis)
container.overlays.append(legend)
return container
def _select_angle_names_changed(self):
for _myos_in in range(2):
self.stream_Angle[_myos_in] = deque(np.zeros(self.Angle_length))
self.plot_Angle = Plot(self.data_Angle)
self.plot_Angle.auto_colors = ['blue', 'red']
for _pic in self.select_angle_names:
self.plot_Angle.plot(('x', ) + (_pic, ), name=_pic, color='auto')
self.plot_Angle.title = 'Multiple Angle'
_legend = Legend(padding=10, align="ur")
_legend.plots = self.plot_Angle.plots
self.plot_Angle.overlays.append(_legend)
def _select_emg_names_changed(self):
for chanel in range(8):
self.stream_emg[chanel] = np.zeros(self.sEMG_length)
self.plot_EMG = Plot(self.data_EMG)
self.plot_EMG.auto_colors = ['green', 'lightgreen','blue',
'lightblue', 'red', 'pink', 'darkgray', 'silver']
for pic in self.select_emg_names:
self.plot_EMG.plot(('x',)+(pic,),name = pic,color = 'auto')
self.plot_EMG.title='Multiple sEMG'
legend = Legend(padding=10,align="ur")
legend.plots = self.plot_EMG.plots
self.plot_EMG.overlays.append(legend)
def _create_plot_component_vertical(signals=Array,
use_downsampling=False):
# container = HPlotContainer(resizable = "hv", bgcolor="lightgray",
# fill_padding=True, padding = 10)
container = VPlotContainer(resizable="hv", bgcolor="lightgray",
fill_padding=True, padding=50)
nSignal, nSample = np.shape(signals)
time = arange(nSample)
value_range = None
plots = {}
for i in range(nSignal):
plot = create_line_plot((time, signals[i]),
color=tuple(COLOR_PALETTE[i % len(COLOR_PALETTE)]),
width=1.0,
# orientation="v")
orientation="h")
plot.origin_axis_visible = True
# plot.origin = "top left"
plot.padding_left = 10
plot.padding_right = 10
plot.border_visible = False
plot.bgcolor = "white"
if value_range is None:
value_range = plot.value_mapper.range
else:
plot.value_range = value_range
value_range.add(plot.value)
container.add(plot)
plots["Corr fun %d" % i] = plot
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
legend.plots = plots
# container.padding_top = 50
container.overlays.append(PlotLabel("Correlation function",
component=container,
font="swiss 16",
overlay_position="top"))
# selection_overlay = RangeSelectionOverlay(component=plot)
# plot.tools.append(RangeSelection(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
# plot.overlays.append(selection_overlay)
plot.overlays.append(zoom)
return container
def _select_emg_names_changed(self):
for chanel in range(8):
self.stream_emg[chanel] = deque(np.zeros(self.sEMG_length))
self.plot_EMG = Plot(self.data_EMG)
self.plot_EMG.auto_colors = [
'green', 'lightgreen', 'blue', 'lightblue', 'red', 'pink',
'darkgray', 'silver'
]
for pic in self.select_emg_names:
self.plot_EMG.plot(('x', ) + (pic, ), name=pic, color='auto')
self.plot_EMG.title = 'Multiple sEMG'
legend = Legend(padding=10, align="ur")
legend.plots = self.plot_EMG.plots
self.plot_EMG.overlays.append(legend)
def _plots_default(self):
plotdata = self.get_data_sets()
plots = Plot(plotdata)
plotsDict = {}
# plot background sonar image and impound lines
xbounds = self.model.survey_rng_m
ybounds = self.model.depth_m
plots.img_plot("sonarimg", colormap=jet,
xbounds=xbounds, ybounds=ybounds)
ip = plots.plot(('impound1_X','impound1_Y'), type='line', marker='square')
plotsDict['Impoundment line'] = ip
plots.x_axis.title = 'Distance along survey line (m)'
plots.y_axis.title = 'Depth (m)'
# add core samples as scatter with separate y-axis
corex = plotdata.get_data('coreX')
corey = plotdata.get_data('coreY')
scatter = create_scatter_plot((corex,corey), marker='diamond',
color='red' )
scatter.index_range = plots.index_range
axis = PlotAxis(scatter, orientation='right')
axis.title = 'Core sample dist from survey line (m)'
scatter.underlays.append(axis)
plots.add(scatter)
# create vertical line for indicating selected core sample position
vals1 = [0 for x in corey]
vline = create_line_plot((corey,vals1), color='blue', orientation='v')
vline.value_range = scatter.index_range
plots.add(vline)
# Add Legend
legend = Legend(component=plots, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.plots = plotsDict
plots.overlays.append(legend)
# Add tools
scatter.tools.append(PickTool(scatter))
plots.tools.append(TraceTool(plots))
plots.tools.append(PanTool(plots))
plots.tools.append(ZoomTool(plots))
return plots
def __init__(self):
super(calc, self).__init__()
self.addele()
self.plotdata = ArrayPlotData(x = self.x, y = self.y, xrt=self.xrt, xx=xx, yy=yy,tt=self.ttt)
plot1 = Plot(self.plotdata)
plot1.plot(("x", "y"),type="line",color="blue",name='1')
plot1.plot(("x", "y"),type="scatter",color="blue", marker = 'circle', marker_size = 2,name='1')
plot1.plot(("x", "xrt"),type="line",color="red",name='2')
plot1.plot(("x", "xrt"),type="scatter",color="red", marker = 'circle', marker_size = 2,name='2')
plot2 = Plot(self.plotdata)
plot2.plot(("xx", "yy"),type="line",color="blue")
plot2.plot(("xx","yy"),type="scatter",color="blue")
plot3 = Plot(self.plotdata)
plot3.plot(("x", "tt"),type="line",color="blue")
plot3.plot(("x","tt"),type="scatter",color="blue")
container = HPlotContainer(plot1,plot2,plot3)
self.plot= container
legend= Legend(padding=10, align="ur")
legend.plots = plot1.plots
plot1.overlays.append(legend)
def plotParsedData(self):
self.plotdata = ArrayPlotData(x = self.data["ts"], wrdlt = self.data["wrdlt"])
self.plotA = Plot(self.plotdata)
self.plotAA = self.plotA.plot(("x", "wrdlt"), type="line", color=(0,0.99,0), spacing=0, padding=0, alpha=0.7, use_downsampling=True, line_style = "dash") #render_style='connectedhold'
# cache default axes limits
self.Arng = [ self.plotA.x_axis.mapper.range.low, self.plotA.x_axis.mapper.range.high,
self.plotA.y_axis.mapper.range.low, self.plotA.y_axis.mapper.range.high]
self.plotA.x_axis.tick_label_position="inside"
self.plotA.y_axis.tick_label_position="inside"
self.container = VPlotContainer(self.plotA, spacing=0, padding=0, bgcolor="lightgray", use_backbuffer = True)
self.plotA.spacing = 0 # set child padding after container set!
self.plotA.padding = 0
self.plotA.tools.append(PanTool(self.plotA))
self.plotA.tools.append(ZoomTool(self.plotA))
self.plotA.overlays.append(BetterSelectingZoom(self.plotA))
legend = Legend(component=self.plotA, padding=1, align="ur")
# to hide plots, make LegendHighlighter scale line to 0 on selection
legend.tools.append(LegendHighlighter(legend, line_scale=0.0))
self.plots = {}
self.plots["wrdlt"] = self.plotAA
legend.plots = self.plots
self.plotA.overlays.append(legend)
def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
now = time()
timex = linspace(now, now+7*24*3600, numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i%2 == 1:
plot = create_line_plot((timex,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot((timex,y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
left, bottom = add_default_axes(plot)
left.tick_generator = ScalesTickGenerator()
bottom.tick_generator = ScalesTickGenerator(scale=CalendarScaleSystem())
add_default_grids(plot, tick_gen=bottom.tick_generator)
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i==0:
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(PlotLabel("Bessel functions",
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
fprefix = './test_data/CEN184/'
filename1 = fprefix + 'THL_2012-03-21_18-40-42_000.dat'
filename2 = fprefix + 'THL_2012-03-21_18-44-42_000.dat'
fprefix = './test_data/CEN111/'
filename3 = fprefix + 'THL_2011-07-09_15-02-54_000.dat'
ioreader = HekaIO(filename3)
#read a block
blo = ioreader.read_block(group = 2)
#protocol stuff
f = open(filename1)
head = BundleHeader(f)
head.load(f)
bi = head.oBundleItems[2]
pgf = PGFFile(f,bi)
value_mapper = None
index_mapper = None
plots = {}
firstplot = True
for seg in blo.segments:
#prococol building
for a_sig in seg.analogsignals:
x = array(a_sig.times)
y = array(a_sig)
ch = int(a_sig.annotations['trSourceChannel'])
plot = create_line_plot((x,y), width=0.5,color=tuple(COLOR_PALETTE[ch]))
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
#code for protocols
print "###########################"
st_rec = pgf.tree['children'][a_sig.annotations['pgf_index']]['contents']
se_rec = pgf.tree['children'][a_sig.annotations['pgf_index']]['children'][0]['children'][1]['contents']
for key in ['stMark','stDataStartSegment','stDataStartTime']:
print key+ ' ' +str(st_rec.__dict__[key])
for key in ['seVoltageIncMode','seDuration','seDurationIncMode','seVoltage',]:
print key+' '+str(se_rec.__dict__[key])
for key in ['pgf_index','trTraceCount','trAdcChannel','trSourceChannel','swStimCount']:
print "%s:%s"%(key,a_sig.annotations[key])
#se_index = int(seg.annotations['seSeriesCount']) -1
#sw_index = int(a_sig.annotations['swSweepCount']) -1
#st_index = int(a_sig.annotations['swStimCount']) -1
#print pgf.tree['children'][se_index]['children'][st_index]['children'][1]['contents'].seVoltage
if not firstplot:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
else:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
add_default_grids(plot)
add_default_axes(plot)
plot.index_range.tight_bounds = False
plot.index_range.refresh()
plot.value_range.tight_bounds = False
plot.value_range.refresh()
plot.tools.append(PanTool(plot))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(plot, drag_button="right")
plot.tools.append(dragzoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
#print a_sig.annotations
plot.overlays.append(legend)
firstplot = False
container.add(plot)
plots["sweep %s"%a_sig.annotations['trLabel'][:4]] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(PlotLabel(blo.annotations['grLabel'],
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def __init__(self, **traits):
Ice.Application.__init__(self)
HasTraits.__init__(self,**traits)
plots = {}
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
self.jtrig = 0
# Plot all telescopes
self.LSTdefault = ArrayPlotData()
self.LSTdefault.set_data('xtel', LSTelX)
self.LSTdefault.set_data('ytel', LSTelY)
self.plotLST = Plot(self.LSTdefault)
self.rLST = self.plotLST.plot(('xtel', 'ytel'), type = "scatter", color = 'black', name = 'LST', marker = 'circle')
self.rLST[0].marker_size = 6
self.plotLST.range2d.x_range.high = np.max(TelX + 100.)
self.plotLST.range2d.x_range.low = np.min(TelX - 100.)
self.plotLST.range2d.y_range.high = np.max(TelY + 100.)
self.plotLST.range2d.y_range.low = np.min(TelY - 100.)
self.plotLST.title = "Triggered telescopes"
container.add(self.plotLST)
self.MSTdefault = ArrayPlotData()
self.MSTdefault.set_data('xtel', MSTelX)
self.MSTdefault.set_data('ytel', MSTelY)
self.plotMST = Plot(self.MSTdefault)
self.rMST = self.plotMST.plot(('xtel', 'ytel'), type = "scatter", color = 'black', name = 'MST', marker = 'circle')
self.rMST[0].marker_size = 4
self.plotMST.range2d = self.plotLST.range2d
container.add(self.plotMST)
self.SSTdefault = ArrayPlotData()
self.SSTdefault.set_data('xtel', SSTelX)
self.SSTdefault.set_data('ytel', SSTelY)
self.plotSST = Plot(self.SSTdefault)
self.rSST = self.plotSST.plot(('xtel', 'ytel'), type = "scatter", color = 'black', name = 'MST', marker = 'circle')
self.rSST[0].marker_size = 1
self.plotSST.range2d = self.plotLST.range2d
container.add(self.plotSST)
self.plotLST.tools.append(PanTool(self.plotLST))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(self.plotLST, tool_mode="box", always_on=False)
self.plotLST.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(self.plotLST, drag_button="right")
self.plotLST.tools.append(dragzoom)
# Add a legend in the upper right corner, and make it relocatable
plots["LST"] = self.rLST
plots["MST"] = self.rMST
plots["SST"] = self.rSST
legend = Legend(component=self.plotLST, padding=10, align="ur", line_spacing = 7, font = 'modern 14')
legend.tools.append(LegendTool(legend, drag_button="right"))
self.plotLST.overlays.append(legend)
legend.plots = plots
# Triggered telescopes
self.LSTdata = ArrayPlotData()
self.LSTdata.set_data('xtel', LSTelX)
self.LSTdata.set_data('ytel', LSTelY)
self.plotLSTtrig = Plot(self.LSTdata)
self.rLSTdata = self.plotLSTtrig.plot(('xtel', 'ytel'), type = "scatter", marker = 'circle', marker_size = 6)
self.plotLSTtrig.range2d = self.plotLST.range2d
container.add(self.plotLSTtrig)
self.MSTdata = ArrayPlotData()
self.MSTdata.set_data('xtel', MSTelX)
self.MSTdata.set_data('ytel', MSTelY)
self.plotMSTtrig = Plot(self.MSTdata)
self.rMSTdata = self.plotMSTtrig.plot(('xtel', 'ytel'), type = "scatter", marker = 'circle', marker_size = 4)
self.plotMSTtrig.range2d = self.plotMST.range2d
container.add(self.plotMSTtrig)
self.SSTdata = ArrayPlotData()
self.SSTdata.set_data('xtel', SSTelX)
self.SSTdata.set_data('ytel', SSTelY)
self.plotSSTtrig = Plot(self.SSTdata)
self.rSSTdata = self.plotSSTtrig.plot(('xtel', 'ytel'), type = "scatter", marker = 'circle', marker_size = 1)
self.plotSSTtrig.range2d = self.plotSST.range2d
container.add(self.plotSSTtrig)
# Set the list of plots on the legend
#legend.plots = plots
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
#draw_png(home_dir + '/Projects/visRTA/Plots/visTrigTel'+str(counter)+'.png', _create_plot_component(self))
#filename = home_dir + '/Projects/visRTA/Plots/visTrigTel'+str(counter)+'.png'
#gc = PlotGraphicsContext(size, dpi=72.0)
#gc.render_component(container)
#gc.save(filename)
self.plot = container
print "end init"
def _create_plot_component():
container = OverlayPlotContainer(padding=60,
fill_padding=True,
use_backbuffer=True,
border_visible=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = arange(low, high + 0.001, (high - low) / numpoints)
# Plot some bessel functions
plots = {}
broadcaster = BroadcasterTool()
for i in range(4):
y = jn(i, x)
plot = create_line_plot((x, y),
color=tuple(COLOR_PALETTE[i]),
width=2.0)
if i == 0:
add_default_grids(plot)
left_axis, _ = add_default_axes(plot)
left_axis.title = "Bessel j0, j2, j3"
elif i != 1:
# Map correctly j2 and j3 on the first plot's axis:
plot0 = plots["Bessel j_0"]
plot.index_mapper = plot0.index_mapper
plot.value_mapper = plot0.value_mapper
plot0.value_mapper.range.add(plot.value)
# Create a pan/zoom tool and give it a reference to the plot it should
# manipulate, but don't attach it to the plot. Instead, attach it to
# the broadcaster. Do it only for each independent set of axis_mappers:
if i in [0, 1]:
pan = PanTool(component=plot)
broadcaster.tools.append(pan)
zoom = ZoomTool(component=plot)
broadcaster.tools.append(zoom)
container.add(plot)
plots["Bessel j_%d" % i] = plot
# Add an axis on the right-hand side that corresponds to the second plot.
# Note that it uses plot.value_mapper instead of plot0.value_mapper.
plot1 = plots["Bessel j_1"]
axis = PlotAxis(plot1, orientation="right")
plot1.underlays.append(axis)
axis.title = "Bessel j1"
# Add the broadcast tool to one of the renderers: adding it to the
# container instead breaks the box mode of the ZoomTool:
plot0 = plots["Bessel j_0"]
plot0.tools.append(broadcaster)
# Create a legend, with tools to move it around and highlight renderers:
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
legend.tools.append(LegendHighlighter(legend))
container.overlays.append(legend)
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(
PlotLabel("Bessel functions",
component=container,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def build_plot(self):
print 'Building plot...'
fitrange = self.fitrange # Just for convenience
onearray = Array
onearray = sp.ones(self.indata.shape[0])
minuses = onearray * (-1.)
# Define index array for fit function:
self.mod_x = sp.arange(self.line_center - 50., self.line_center + 50.,
.01)
self.Model = sp.zeros(self.mod_x.shape[0])
# Establish continuum array in a way that opens for other, more
# elaborate continua.
self.contarray = sp.ones(self.mod_x.shape[0]) * \
self.Components['Contin'][0]
self.y = {}
for comp in self.CompoList:
self.y[comp] = gauss( # x, mu, sigma, amplitude
self.mod_x, self.Components[comp][0] + self.line_center,
self.Components[comp][1], self.Components[comp][2])
self.Model = self.contarray + self.y[self.select]
broca = BroadcasterTool()
# Define the part of the data to show in initial view:
plotrange = sp.where((self.x > self.line_center - 30)
& (self.x < self.line_center + 30))
# Define the y axis max value in initial view (can be panned/zoomed):
maxval = float(self.indata[fitrange].max() * 1.2)
minval = maxval / 15.
minval = abs(np.median(self.indata[fitrange])) * 1.5
maxerr = self.errs[fitrange].max() * 1.3
resmin = max(sp.absolute(self.Resids[self.fitrange]).max(), 5.) * 1.2
cenx = sp.array([self.line_center, self.line_center])
ceny = sp.array([-minval, maxval])
cenz = sp.array([-maxval, maxval])
# Gray shading of ignored ranges
rangelist = np.array(self.rangelist)
grayx = np.array(rangelist.flatten().repeat(2))
grayx = np.hstack((self.x.min(), grayx, self.x.max()))
grayy = np.ones_like(grayx) * self.indata.max() * 2.
grayy[1::4] = -grayy[1::4]
grayy[2::4] = -grayy[2::4]
grayy = np.hstack((grayy[-1], grayy[:-1]))
# Build plot of data and model
self.plotdata = ArrayPlotData(
wl=self.x,
data=self.indata,
xs=self.mod_x,
cont=self.contarray,
ones=onearray,
minus=minuses,
model=self.Model,
errors=self.errs,
ceny=ceny,
cenz=cenz,
cenx=cenx,
Residuals=self.Resids,
grayx=grayx,
grayy=grayy,
)
# Add dynamically created components to plotdata
for comp in self.CompoList:
self.plotdata.set_data(comp, self.y[comp])
olplot = GridContainer(shape=(2, 1),
padding=10,
fill_padding=True,
bgcolor='transparent',
spacing=(5, 10))
plot = Plot(self.plotdata)
plot.y_axis.title = 'Flux density'
resplot = Plot(self.plotdata, tick_visible=True, y_auto=True)
resplot.x_axis.title = u'Wavelength [Å]'
resplot.y_axis.title = u'Residuals/std. err.'
# Create initial plot: Spectrum data, default first component,
# default total line profile.
self.comprenders = []
self.datarender = plot.plot(('wl', 'data'),
color='black',
name='Data',
render_style='connectedhold')
self.contrender = plot.plot(('xs', 'cont'),
color='darkgray',
name='Cont')
self.modlrender = plot.plot(('xs', 'model'),
color='blue',
line_width=1.6,
name='Model')
self.centrender = plot.plot(('cenx', 'ceny'),
color='black',
type='line',
line_style='dot',
name='Line center',
line_width=1.)
self.rangrender = plot.plot(
('grayx', 'grayy'),
type='polygon',
face_color='lightgray',
edge_color='gray',
face_alpha=0.3,
alpha=0.3,
)
# There may be an arbitrary number of gaussian components, so:
print 'Updating model'
for comp in self.CompoList:
self.comprenders.append(
plot.plot(
('xs', comp),
type='line',
color=Paired[self.Components[comp]
[3]], # tuple(COLOR_PALETTE[self.CompNum]),
line_color=Paired[self.Components[comp][
3]], # tuple(COLOR_PALETTE[self.CompNum]),
line_style='dash',
name=comp))
# Create panel with residuals:
resplot.plot(('wl', 'Residuals'), color='black', name='Resids')
resplot.plot(('wl', 'ones'), color='green')
resplot.plot(('wl', 'minus'), color='green')
resplot.plot(('cenx', 'cenz'),
color='red',
type='line',
line_style='dot',
line_width=.5)
resplot.plot(
('grayx', 'grayy'), # Yes, that one again
type='polygon',
face_color='lightgray',
edge_color='gray',
face_alpha=0.3,
alpha=0.3,
)
plot.x_axis.visible = False
# Set ranges to change automatically when plot values change.
plot.value_range.low_setting,\
plot.value_range.high_setting = (-minval, maxval)
plot.index_range.low_setting,\
plot.index_range.high_setting = (self.line_center - 30.,
self.line_center + 30.)
resplot.value_range.low_setting,\
resplot.value_range.high_setting = (-resmin, resmin)
resplot.index_range.low_setting,\
resplot.index_range.high_setting = (plot.index_range.low_setting,
plot.index_range.high_setting)
#resplot.index_range = plot.index_range # Yes or no? FIXME
plot.overlays.append(
ZoomTool(plot,
tool_mode='box',
drag_button='left',
always_on=False))
resplot.overlays.append(
ZoomTool(resplot,
tool_mode='range',
drag_button='left',
always_on=False))
# List of renderers to tell the legend what to write
self.plots['Contin'] = self.contrender
self.plots['Center'] = self.centrender
self.plots['Model'] = self.modlrender
for i in sp.arange(len(self.comprenders)):
self.plots[self.CompoList[i]] = self.comprenders[i]
# Build Legend:
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
legend.plots = self.plots
plot.overlays.append(legend)
olplot.tools.append(broca)
pan = PanTool(plot)
respan = PanTool(resplot, constrain=True, constrain_direction='x')
broca.tools.append(pan)
broca.tools.append(respan)
plot.overlays.append(ZoomTool(plot, tool_mode='box', always_on=False))
olplot.add(plot)
olplot.add(resplot)
olplot.components[0].set(resizable='hv', bounds=[500, 400])
olplot.components[1].set(resizable='h', bounds=[500, 100])
self.plot = plot
self.resplot = resplot
self.plwin = olplot
self.legend = legend
self.plotrange = plotrange
def build_plot(self):
global plotdata # FIXME: Don't use global!
onearray = Array
onearray = sp.ones(self.indata.shape[0])
minuses = onearray * (-1.)
# Define index array for fit function:
self.mod_x = sp.arange(self.line_center - 50.,
self.line_center + 50., .01)
self.Model = sp.zeros(self.mod_x.shape[0])
# Establish continuum array in a way that opens for other, more
# elaborate continua.
self.contarray = sp.ones(self.mod_x.shape[0]) * \
self.Components['Contin']
self.y = {}
for comp in self.CompoList:
self.y[comp] = stats.norm.pdf(
self.mod_x,
self.Components[comp][0] + self.line_center,
self.Components[comp][1]
) * self.Components[comp][1] * sp.sqrt(2. * sp.pi) * \
self.Components[comp][2]
self.Model = self.contarray + self.y[self.select]
broca = BroadcasterTool()
# Define the part of the data to show in initial view:
plotrange = sp.where((self.x > self.line_center - 20) &
(self.x < self.line_center + 20))
# Define the y axis max value in initial view (can be panned/zoomed):
maxval = float(self.indata[plotrange].max() * 1.1)
minval = maxval / 15.
maxerr = self.errs[plotrange].max() * 1.3
resmin = max(self.Resids[plotrange].max(), 5.) * 1.2
cenx = sp.array([self.line_center, self.line_center])
ceny = sp.array([-minval, maxval])
cenz = sp.array([-maxval, maxval])
# Build plot of data and model
plotdata = ArrayPlotData(
wl=self.x,
data=self.indata,
xs=self.mod_x,
cont=self.contarray,
ones=onearray,
minus=minuses,
model=self.Model,
errors=self.errs,
ceny=ceny,
cenz=cenz,
cenx=cenx,
Residuals=self.Resids,
)
for comp in self.CompoList:
plotdata.set_data(comp, self.y[comp])
olplot = GridContainer(shape=(2, 1), padding=10,
fill_padding=True,
bgcolor='transparent',
spacing=(5, 10))
plot = Plot(plotdata)
plot.y_axis.title = 'Flux density'
resplot = Plot(plotdata, tick_visible=True, y_auto=True)
resplot.x_axis.title = u'Wavelength [Å]'
resplot.y_axis.title = u'Residuals/std. err.'
# Create initial plot: Spectrum data, default first component,
# default total line profile.
self.comprenders = []
self.datarender = plot.plot(('wl', 'data'), color='black',
name='Data',
render_style='connectedhold')
self.contrender = plot.plot(('xs', 'cont'), color='darkgray',
name='Cont')
self.modlrender = plot.plot(('xs', 'model'), color='blue',
line_width=1.6, name='Model')
self.centrender = plot.plot(('cenx', 'ceny'),
color='black',
type='line',
line_style='dot',
name='Line center',
line_width=1.)
# There may be an arbitrary number of gaussian components, so:
for comp in self.CompoList:
self.comprenders.append(
plot.plot(('xs', comp),
type='line',
#color='auto',
color=tuple(COLOR_PALETTE[self.CompNum]),
line_style='dash',
name=comp)
)
# Create panel with residuals:
resplot.plot(('wl', 'Residuals'), color='black', name='Resids')
resplot.plot(('wl', 'ones'), color='green')
resplot.plot(('wl', 'minus'), color='green')
resplot.plot(('cenx', 'cenz'), color='red',
type='line',
line_style='dot',
line_width=.5)
plot.x_axis.visible = False
# Set ranges to change automatically when plot values change.
plot.value_range.low_setting,\
plot.value_range.high_setting = (-minval, maxval)
plot.index_range.low_setting,\
plot.index_range.high_setting = (self.line_center - 20.,
self.line_center + 20.)
resplot.value_range.low_setting,\
resplot.value_range.high_setting = (-resmin, resmin)
resplot.index_range.low_setting,\
resplot.index_range.high_setting = (plot.index_range.low_setting,
plot.index_range.high_setting)
#resplot.index_range = plot.index_range # Yes or no? FIXME
plot.overlays.append(ZoomTool(plot, tool_mode='box',
drag_button='left',
always_on=False))
resplot.overlays.append(ZoomTool(resplot, tool_mode='range',
drag_button='left',
always_on=False))
# List of renderers to tell the legend what to write
self.plots['Contin'] = self.contrender
self.plots['Center'] = self.centrender
self.plots['Model'] = self.modlrender
for i in sp.arange(len(self.comprenders)):
self.plots[self.CompoList[i]] = self.comprenders[i]
# Build Legend:
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
legend.plots = self.plots
plot.overlays.append(legend)
olplot.tools.append(broca)
pan = PanTool(plot)
respan = PanTool(resplot, constrain=True, constrain_direction='x')
broca.tools.append(pan)
broca.tools.append(respan)
plot.overlays.append(ZoomTool(plot, tool_mode='box',
always_on=False))
olplot.add(plot)
olplot.add(resplot)
olplot.components[0].set(resizable='hv', bounds=[500, 400])
olplot.components[1].set(resizable='h', bounds=[500, 100])
self.plot = plot
self.resplot = resplot
self.plwin = olplot
self.legend = legend
self.plotrange = plotrange
def _create_plot_component():
container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = arange(low, high + 0.001, (high - low) / numpoints)
# Plot some bessel functions
plots = {}
broadcaster = BroadcasterTool()
for i in range(4):
y = jn(i, x)
plot = create_line_plot((x, y),
color=tuple(COLOR_PALETTE[i]),
width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
add_default_grids(plot)
add_default_axes(plot)
# Create a pan tool and give it a reference to the plot it should
# manipulate, but don't attach it to the plot. Instead, attach it to
# the broadcaster.
pan = PanTool(plot)
broadcaster.tools.append(pan)
container.add(plot)
plots["Bessel j_%d" % i] = plot
# Add an axis on the right-hand side that corresponds to the second plot.
# Note that it uses plot.value_mapper instead of plot0.value_mapper.
plot1 = plots["Bessel j_1"]
axis = PlotAxis(plot1, orientation="right")
plot1.underlays.append(axis)
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
container.overlays.append(legend)
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(
PlotLabel("Bessel functions",
component=container,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def _create_plot_component():
container = OverlayPlotContainer(
padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
now = time()
timex = linspace(now, now + 7 * 24 * 3600, numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i % 2 == 1:
plot = create_line_plot(
(timex, y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot(
(timex, y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
left, bottom = add_default_axes(plot)
left.tick_generator = ScalesTickGenerator()
bottom.tick_generator = ScalesTickGenerator(
scale=CalendarScaleSystem())
add_default_grids(plot, tick_gen=bottom.tick_generator)
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i == 0:
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d" % i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(
PlotLabel(
"Bessel functions",
component=container,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
