def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
# Create some line plots of some of the data
plot1 = Plot(pd, title="Line Plot", padding=50, border_visible=True)
plot1.legend.visible = True
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd,
range2d=plot1.range2d,
title="Scatter plot",
padding=50,
border_visible=True)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
# Create a container and add our plots
container = HPlotContainer()
container.add(plot1)
container.add(plot2)
return container
def _create_data(self, *names):
numpoints = self.numpoints
plotdata = ArrayPlotData(times=create_dates(numpoints))
for name in names:
plotdata.set_data(
name, cumprod(random.lognormal(0.0, 0.04, size=numpoints)))
self.plotdata = plotdata
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
# Create some line plots of some of the data
plot1 = Plot(pd, padding=50)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Add the scrollbar
hscrollbar = PlotScrollBar(component=plot1,
axis="index",
resizable="h",
height=15)
plot1.padding_top = 0
hscrollbar.force_data_update()
# Create a container and add our plots
container = VPlotContainer()
container.add(plot1)
container.add(hscrollbar)
return container
def createWindow(widget):
''' Example on creating a new plot window in the
main window MDI-Area
'''
import plotWidget
from PySide import QtGui
from numpy import linspace
from scipy.special import jn
from chaco.api import ArrayPlotData, Plot
window = widget.createNewWindow()
container = plotWidget.plotContainer(window)
plotWidget = plotWidget.PlotWidget(container)
container.setPlotWidget(plotWidget)
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
plot = Plot(pd,
title=None,
padding_left=60,
padding_right=5,
padding_top=5,
padding_bottom=30,
border_visible=True)
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plotWidget.setPlot(plot)
layout = QtGui.QBoxLayout(QtGui.QBoxLayout.TopToBottom)
layout.addWidget(container)
window.setLayout(layout)
window.show()
def _ts_data_changed(self):
""" Dataset has changed: update the plot.
ENH: add the possibility to pass a dict to ArrayPlotData.
"""
print "data changed: updating the plot..."
arr_data = ArrayPlotData()
for k, v in self.ts_data.items():
arr_data.set_data(k, v)
self.ts_plot = ToolbarPlot(arr_data)
for i, k in enumerate([k for k in self.ts_data.keys()
if k != "index"]):
self.ts_plot.plot(("index", k),
name=k,
color=colors[i % len(colors)])
if self.index_is_dates:
# Index was an array of datetime: overwrite the x axis
self.ts_plot.x_axis = None
x_axis = PlotAxis(self.ts_plot,
orientation="bottom",
tick_generator=ScalesTickGenerator(
scale=CalendarScaleSystem()))
self.ts_plot.overlays.append(x_axis)
self.ts_plot.x_grid.tick_generator = x_axis.tick_generator
if self.data_file:
self.ts_plot.title = "Time series visualization from %s" % self.data_file
else:
self.ts_plot.title = "Time series visualization"
attach_tools(self.ts_plot)
def _create_plot_component():
# Create a GridContainer to hold all of our plots
container = GridContainer(padding=20, fill_padding=True,
bgcolor="lightgray", use_backbuffer=True,
shape=(3,3), spacing=(12,12))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index = x)
# Plot some bessel functions and add the plots to our container
for i in range(9):
pd.set_data("y" + str(i), jn(i,x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]), line_width=2.0,
bgcolor = "white", border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 10
# Set each plot's aspect ratio based on its position in the
# 3x3 grid of plots.
n,m = divmod(i, 3)
plot.aspect_ratio = float(n+1) / (m+1)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container
container.add(plot)
return container
def plotImage(self, image, plot=None):
'''Plots a tiff image.
| image -- Image object
| plot -- plot instance to be updated
| if None, a plot instance will be created
Returns the plot instance.
'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image.data)
plot = Plot(pd, default_origin = "bottom left", padding=0)
plot.bgcolor = 'white'
plot.fixed_preferred_size = (100, 100)
plot.x_axis.visible = False
plot.y_axis.visible = False
self.imageplot = plot
imgPlot = plot.img_plot("imagedata", colormap=self.cmap,
name='image')[0]
self.imgPlot = imgPlot
self.appendImageTools(imgPlot)
else:
plot.data.set_data('imagedata', image.data)
plot.aspect_ratio = float(image.data.shape[1]) / image.data.shape[0]
plot.invalidate_and_redraw()
return plot
class DataChooser(HasTraits):
plot = Instance(Plot)
data_name = Enum("jn0", "jn1", "jn2")
traits_view = View(Item('data_name', label="Y data"),
Item('plot', editor=ComponentEditor(), show_label=False),
width=800, height=600, resizable=True,
title="Data Chooser")
def __init__(self):
x = linspace(-5, 10, 100)
self.data = {"jn0": jn(0, x),
"jn1": jn(1, x),
"jn2": jn(2, x)}
# Create the data and the PlotData object
self.plotdata = ArrayPlotData(x=x, y=self.data["jn0"])
# Create a Plot and associate it with the PlotData
plot = Plot(self.plotdata)
# Create a line plot in the Plot
plot.plot(("x", "y"), type="line", color="blue")
self.plot = plot
def _data_name_changed(self, old, new):
self.plotdata.set_data("y", self.data[self.data_name])
def _fuel_cycle_plot_component(x, y, x_name, y_name):
# Create some data
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="line",
marker="circle",
index_sort="ascending",
color="red",
marker_size=3,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Fuel Cycle Plot"
plot.line_width = 0.5
plot.padding = 100
plot.x_axis.title = x_name
plot.x_axis.title_font = "Roman 16"
plot.x_axis.tick_label_font = "Roman 12"
plot.y_axis.title = y_name
plot.y_axis.title_font = "Roman 16"
plot.y_axis.tick_label_font = "Roman 12"
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def __init__(self, index, data_series, **kw):
super(MyPlot, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('data_series', data_series)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'data_series'))
def _create_plot_component():
# Use n_gon to compute center locations for our polygons
points = n_gon(center=(0,0), r=3, nsides=4)
# Choose some colors for our polygons
colors = {3:0xaabbcc, 4:'orange', 5:'yellow', 6:'lightgreen'}
# Create a PlotData object to store the polygon data
pd = ArrayPlotData()
# Create a Polygon Plot to draw the regular polygons
polyplot = Plot(pd)
# Store path data for each polygon, and plot
nsides = 3
for p in points:
npoints = n_gon(center=p, r=2, nsides=nsides)
nxarray, nyarray = transpose(npoints)
pd.set_data("x" + str(nsides), nxarray)
pd.set_data("y" + str(nsides), nyarray)
plot = polyplot.plot(("x"+str(nsides), "y"+str(nsides)), type="polygon",
face_color=colors[nsides], hittest_type="poly")[0]
plot.tools.append(DataspaceMoveTool(plot, drag_button="left"))
nsides = nsides + 1
# Tweak some of the plot properties
polyplot.padding = 50
polyplot.title = "Polygon Plot"
polyplot.x_axis.mapper.range.set(low=-10, high=10)
polyplot.y_axis.mapper.range.set(low=-10, high=10)
return polyplot
def _create_data(self, names):
plotdata = ArrayPlotData(times = np.squeeze(dateArray))
i = 0
for name in names:
plotdata.set_data(name, data[:,i])
i += 1
self.plotdata = plotdata
def _plot_default(self):
# Create starting points for the vectors.
numpts = self.numpts
x = sort(random(numpts))
y = random(numpts)
# Create vectors.
vectorlen = self.vectorlen
vectors = array((random(numpts)*vectorlen, random(numpts)*vectorlen)).T
data = ArrayPlotData()
data.set_data('index', x)
data.set_data('value', y)
data.set_data('vectors', vectors)
quiverplot = Plot(data)
quiverplot.quiverplot(('index', 'value', 'vectors'))
# Attach some tools to the plot
quiverplot.tools.append(PanTool(quiverplot, constrain_key="shift"))
zoom = ZoomTool(quiverplot)
quiverplot.overlays.append(zoom)
container = OverlayPlotContainer(quiverplot, padding=50)
return container
def _create_plot_component():
# Create a GridContainer to hold all of our plots: 2 rows, 3 columns:
container = GridContainer(padding=40,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True,
shape=(2, 3),
spacing=(20, 20))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index=x)
# Plot some bessel functions and add the plots to our container
for i in range(6):
pd.set_data("y" + str(i), jn(i, x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]),
line_width=2.0,
bgcolor="white",
border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 0
plot.padding_top = 30
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container (
container.add(plot)
# Set the upper-left plot to only be resizable vertically, and to have a
# fixed horizontal width. This also constrains the width of the first column.
ul_plot = container.components[0]
ul_plot.set(resizable="v", width=200)
ul_plot.overlays.append(
PlotLabel("Not horizontally resizable", component=ul_plot))
# Set the bottom center plot to have a fixed width and height.
# This also constrains the height of the bottom row and the width of
# the middle column.
cplot = container.components[4]
cplot.set(resizable="", bounds=[400, 400])
cplot.overlays.append(PlotLabel("Not resizable", component=cplot))
container.padding_top = 50
container.overlays.append(
PlotLabel(
'Resize the window - some plots resize, others cannot '
'(see source code)',
component=container,
font="swiss 16",
overlay_position="top"))
return container
class DataChooser(HasTraits):
plot = Instance(Plot)
data_name = Enum("jn0", "jn1", "jn2")
traits_view = View(Item("data_name", label="Y data"),
Item("plot", editor=ComponentEditor(),
show_label=False),
width=800,
height=600,
resizable=True,
title="Dynamic Plot Content")
def __init__(self):
super(DataChooser, self).__init__()
x = linspace(-5, 10, 100)
#jn is the Bessel function
self.data = {"jn0": jn(0, x), "jn1": jn(1, x), "jn2": jn(2, x)}
self.plotdata = ArrayPlotData(x=x, y=self.data["jn0"])
plot = Plot(self.plotdata)
plot.plot(("x", "y"), type="line", color="blue")
self.plot = plot
def _data_name_changed(self):
self.plotdata.set_data("y", self.data[self.data_name])
class DataChooser(HasTraits):
plot = Instance(Plot)
data_name = Enum("jn0", "jn1", "jn2")
traits_view = View(Item('data_name', label="Y data"),
Item('plot', editor=ComponentEditor(),
show_label=False),
width=800,
height=600,
resizable=True,
title="Data Chooser")
def __init__(self):
x = linspace(-5, 10, 100)
self.data = {"jn0": jn(0, x), "jn1": jn(1, x), "jn2": jn(2, x)}
# Create the data and the PlotData object
self.plotdata = ArrayPlotData(x=x, y=self.data["jn0"])
# Create a Plot and associate it with the PlotData
plot = Plot(self.plotdata)
# Create a line plot in the Plot
plot.plot(("x", "y"), type="line", color="blue")
self.plot = plot
def _data_name_changed(self, old, new):
self.plotdata.set_data("y", self.data[self.data_name])
def plotImage(self, image, plot=None):
'''plot one image
image: Image object
plot: plot instance to be update, if None, a plot instance will be created
return: plot instance'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image.data)
plot = Plot(pd, default_origin = "bottom left", padding=0)
#plot.title = image.name
plot.bgcolor = 'white'
plot.fixed_preferred_size = (100, 100)
plot.x_axis.visible = False
plot.y_axis.visible = False
self.imageplot = plot
# TODO: mess with color maps on else block
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
self.imgPlot = imgPlot
self._appendImageTools(imgPlot)
#plot.overlays.append(MyLineDrawer(plot))
else:
plot.data.set_data('imagedata', image.data)
imgPlot = plot.plots['image'][0]
#plot.title = image.name
plot.aspect_ratio = float(image.data.shape[1]) / image.data.shape[0]
plot.invalidate_draw()
return plot
def _create_plot_component():
# Create some data
numpts = 500
x1 = random(numpts)
y1 = random(numpts)
x2 = x1 + standard_normal(numpts) * 0.05
y2 = y1 + standard_normal(numpts) * 0.05
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", column_stack([x1, x2]).reshape(-1))
pd.set_data("value", column_stack([y1, y2]).reshape(-1))
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="segment",
color="forestgreen",
line_width=2,
line_style='dash',
alpha=0.7,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Segment Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(self):
selected = Event()
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot1 = Plot(pd, title="Line Plot", padding=50, border_visible=True)
plot1.legend.visible = True
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
self.zoom = BetterSelectingZoom(component=plot1, tool_mode="box", always_on=False, selection_completed = selected)
plot1.overlays.append(self.zoom)
container = HPlotContainer()
container.add(plot1)
self.zoom.on_trait_change(self.selection_changed, 'ratio')
return container
class DataChooser(HasTraits):
plot = Instance(Plot)
data_name = Enum("jn0", "jn1", "jn2")
# default view for a traits class
traits_view = View(
Item('data_name', label="Y data"),
Item('plot', editor=ComponentEditor(), show_label=False),
width=800, height=600, resizable=True,
title="Data Chooser")
def _plot_default(self):
x = linspace(-5, 10, 100)
self.data = {"jn0": jn(0, x),
"jn1": jn(1, x),
"jn2": jn(2, x)}
self.plotdata = ArrayPlotData(x = x, y = self.data["jn0"])
plot = Plot(self.plotdata)
plot.plot(("x", "y"), type="line", color="blue")
return plot
# by default Enum trait displayed as a drop down menu
def _data_name_changed(self):
self.plotdata.set_data("y", self.data[self.data_name])
def test_highlight_on_log_plot(self):
# test for bug: the highlight tool raises an exception when used on
# a loglog plot
x = np.linspace(1, 15, 200)
plotdata = ArrayPlotData()
plotdata.set_data("x", x)
plotdata.set_data("y", x * x)
plot = Plot(plotdata)
plot.plot(("x", "y"), index_scale='log', value_scale='log')
# necessary for the machinery involved in _find_curve
plot.datasources["x"].sort_order = 'ascending'
plot.datasources["y"].sort_order = 'ascending'
# add the highlight tool
htool = HighlightTool(plot, threshold=20.)
plot.tools.append(htool)
# we create a view of the plot so that the screen bounds are set
pv = PlotViewer(plot=plot)
pv.edit_traits()
# this should not raise an exception
event = MockEvent(x=170., y=60.)
htool._find_curve(plot.plots['plot0'], event)
def __init__(self, index, series_a, series_b, series_c, **kw):
super(PlotExample, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('series_a', series_a)
plot_data.set_data('series_b', series_b)
plot_data.set_data('series_c', series_c)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'series_a'),
type='bar',
bar_width=0.8,
color='auto')
self.plot.plot(('index', 'series_b'),
type='bar',
bar_width=0.8,
color='auto')
self.plot.plot(('index', 'series_c'),
type='bar',
bar_width=0.8,
color='auto')
# set the plot's value range to 0, otherwise it may pad too much
self.plot.value_range.low = 0
# replace the index values with some nicer labels
label_axis = LabelAxis(self.plot,
orientation='bottom',
title='Months',
positions=range(1, 10),
labels=['jan', 'feb', 'march', 'april', 'may'],
small_haxis_style=True)
self.plot.underlays.remove(self.plot.index_axis)
self.plot.index_axis = label_axis
self.plot.underlays.append(label_axis)
def __init__(self):
# The delegates views don't work unless we caller the superclass __init__
super(CursorTest, self).__init__()
container = HPlotContainer(padding=0, spacing=20)
self.plot = container
# a subcontainer for the first plot.
# I'm not sure why this is required. Without it, the layout doesn't work right.
subcontainer = OverlayPlotContainer(padding=40)
container.add(subcontainer)
# make some data
index = numpy.linspace(-10, 10, 512)
value = numpy.sin(index)
# create a LinePlot instance and add it to the subcontainer
line = create_line_plot([index, value], add_grid=True, add_axis=True, index_sort="ascending", orientation="h")
subcontainer.add(line)
# here's our first cursor.
csr = CursorTool(line, drag_button="left", color="blue")
self.cursor1 = csr
# and set it's initial position (in data-space units)
csr.current_position = 0.0, 0.0
# this is a rendered component so it goes in the overlays list
line.overlays.append(csr)
# some other standard tools
line.tools.append(PanTool(line, drag_button="right"))
line.overlays.append(ZoomTool(line))
# make some 2D data for a colourmap plot
xy_range = (-5, 5)
x = numpy.linspace(xy_range[0], xy_range[1], 100)
y = numpy.linspace(xy_range[0], xy_range[1], 100)
X, Y = numpy.meshgrid(x, y)
Z = numpy.sin(X) * numpy.arctan2(Y, X)
# easiest way to get a CMapImagePlot is to use the Plot class
ds = ArrayPlotData()
ds.set_data("img", Z)
img = Plot(ds, padding=40)
cmapImgPlot = img.img_plot("img", xbounds=xy_range, ybounds=xy_range, colormap=jet)[0]
container.add(img)
# now make another cursor
csr2 = CursorTool(cmapImgPlot, drag_button="left", color="white", line_width=2.0)
self.cursor2 = csr2
csr2.current_position = 1.0, 1.5
cmapImgPlot.overlays.append(csr2)
# add some standard tools. Note, I'm assigning the PanTool to the
# right mouse-button to avoid conflicting with the cursors
cmapImgPlot.tools.append(PanTool(cmapImgPlot, drag_button="right"))
cmapImgPlot.overlays.append(ZoomTool(cmapImgPlot))
def _create_plot_component():
# Create some data
numpts = 5000
x = sort(random(numpts))
y = random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():# Create a scalar field to colormap
xbounds = (-2*pi, 2*pi, 600)
ybounds = (-1.5*pi, 1.5*pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs,ys)
z = sin(x)*y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds = xbounds[:2],
ybounds = ybounds[:2],
colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "My First Image Plot"
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
img_plot.overlays.append(overlay)
return plot
def _create_plot_component():
# Create some x-y data series (with NaNs) to plot
x = linspace(-5.0, 15.0, 500)
x[75:125] = nan
x[200:250] = nan
x[300:330] = nan
pd = ArrayPlotData(index = x)
pd.set_data("value1", jn(0, x))
pd.set_data("value2", jn(1, x))
# Create some line and scatter plots of the data
plot = Plot(pd)
plot.plot(("index", "value1"), name="j_0(x)", color="red", width=2.0)
plot.plot(("index", "value2"), type="scatter", marker_size=1,
name="j_1(x)", color="green")
# Tweak some of the plot properties
plot.title = "Plots with NaNs"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def plotImage(self, image, title, plot):
'''plot one image
image: 2d ndarray or ssp matrix
title: string, plot title
plot: plot instance to be update, if None, a plot instance will be created
return: plot instance'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image)
plot = Plot(pd, default_origin = "bottom left")
plot.title = title
plot.bgcolor = 'white'
if not title == 'Total Intensity':
plot.x_axis.visible = False
plot.y_axis.visible = False
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
# TODO: mess with color maps on else block
else:
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
self._appendTools(imgPlot, title)
else:
plot.data.set_data('imagedata', image)
plot.title = title
plot.aspect_ratio = float(image.shape[1]) / image.shape[0]
plot.invalidate_draw()
return plot
def __init__(self, index, data_series, **kw):
super(MyPlot, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('data_series', data_series)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'data_series'))
def _create_data(self, names):
plotdata = ArrayPlotData(times=np.squeeze(dateArray))
i = 0
for name in names:
plotdata.set_data(name, data[:, i])
i += 1
self.plotdata = plotdata
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
marker="circle",
index_sort="ascending",
color=(1.0, 0.0, 0.74, 0.4),
marker_size=marker_size,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
class Viewer1D(Viewer):
image = Array
result = Array
def _reconstruction_default(self):
rows, cols = self.image.shape[:2]
self.plot_data = ArrayPlotData(original=self.image[0],
reconstruction=self.result[0])
aspect = cols / float(rows)
old = Plot(self.plot_data)
old.plot('original', )
old.title = 'Old'
self.new = Plot(self.plot_data)
self.new.plot('reconstruction')
self.new.title = 'New'
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result[0])
self.new.request_redraw()
def _create_plot_component():
# Create some data
numpts = 5000
x = sort(random(numpts))
y = random(numpts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
marker="circle",
index_sort="ascending",
color="orange",
marker_size=3,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(): # Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=viridis)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def _create_plot_component(): # Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata", xbounds=xbounds[:2], ybounds=ybounds[:2], colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection, component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def _plot_default(self):
# Create starting points for the vectors.
numpts = self.numpts
x = sort(random(numpts))
y = random(numpts)
# Create vectors.
vectorlen = self.vectorlen
vectors = array(
(random(numpts) * vectorlen, random(numpts) * vectorlen)).T
data = ArrayPlotData()
data.set_data('index', x)
data.set_data('value', y)
data.set_data('vectors', vectors)
quiverplot = Plot(data)
quiverplot.quiverplot(('index', 'value', 'vectors'))
# Attach some tools to the plot
quiverplot.tools.append(PanTool(quiverplot, constrain_key="shift"))
zoom = ZoomTool(quiverplot)
quiverplot.overlays.append(zoom)
container = OverlayPlotContainer(quiverplot, padding=50)
return container
def updateplots(self):
x = np.sort(np.random.random(100))
y = np.random.random(100)
color = np.exp(-(x ** 2 + y ** 2)) # np.random.random(100)
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create some line plots of some of the data
plot = Plot(pd)
# Create a scatter plot and get a reference to it (separate from the
# Plot object) because we'll need it for the regression tool below.
scatterplot = plot.plot(
("index", "value", "color"),
type="cmap_scatter",
color_mapper=reverse(Spectral),
marker="square",
fill_alpha=0.9,
marker_size=6,
bgcolor=QtGui.QColor(240, 240, 240),
)[0]
# Tweak some of the plot properties
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, drag_button="right"))
plot.overlays.append(ZoomTool(plot))
# Add the regression tool and overlay. These need to be added
# directly to the scatterplot instance (and not the Plot instance).
regression = RegressionLasso(scatterplot, selection_datasource=scatterplot.index)
scatterplot.tools.append(regression)
scatterplot.overlays.append(RegressionOverlay(scatterplot, lasso_selection=regression))
self.chaco_widget1.visualization.plot = plot
self.chaco_widget2.visualization.plot = plot
def _create_plot_component():
# Create some RGBA image data
image = zeros((200,400,4), dtype=uint8)
image[:,0:40,0] += 255 # Vertical red stripe
image[0:25,:,1] += 255 # Horizontal green stripe; also yellow square
image[-80:,-160:,2] += 255 # Blue square
image[:,:,3] = 255
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd, default_origin="top left")
plot.x_axis.orientation = "top"
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.bgcolor = "white"
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
plot.overlays.append(ZoomTool(component=plot,
tool_mode="box", always_on=False))
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool))
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = randint(0, 7, numpts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=accent,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Range-selectable Data Points"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def __init__(self, index, series_a, series_b, series_c, **kw):
super(PlotExample, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('series_a', series_a)
plot_data.set_data('series_b', series_b)
plot_data.set_data('series_c', series_c)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'series_a'), type='bar', bar_width=0.8, color='auto')
self.plot.plot(('index', 'series_b'), type='bar', bar_width=0.8, color='auto')
self.plot.plot(('index', 'series_c'), type='bar', bar_width=0.8, color='auto')
# set the plot's value range to 0, otherwise it may pad too much
self.plot.value_range.low = 0
# replace the index values with some nicer labels
label_axis = LabelAxis(self.plot, orientation='bottom',
title='Months',
positions = list(range(1, 10)),
labels = ['jan', 'feb', 'march', 'april', 'may'],
small_haxis_style=True)
self.plot.underlays.remove(self.plot.index_axis)
self.plot.index_axis = label_axis
self.plot.underlays.append(label_axis)
def _create_plot_component():
# Create a scalar field to colormap
xs = linspace(0, 10, 600)
ys = linspace(0, 5, 600)
x, y = meshgrid(xs,ys)
z = exp(-(x**2+y**2)/100)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=(0, 10),
ybounds=(0, 5),
colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "My First Image Plot"
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
img_plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some RGBA image data
image = zeros((200, 400, 4), dtype=uint8)
image[:, 0:40, 0] += 255 # Vertical red stripe
image[0:25, :, 1] += 255 # Horizontal green stripe; also yellow square
image[-80:, -160:, 2] += 255 # Blue square
image[:, :, 3] = 255
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd, default_origin="top left")
plot.x_axis.orientation = "top"
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.bgcolor = "white"
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
plot.overlays.append(
ZoomTool(component=plot, tool_mode="box", always_on=False))
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(
ImageInspectorOverlay(component=img_plot, image_inspector=imgtool))
return plot
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot1 = Plot(pd, padding=50)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Add the scrollbar
hscrollbar = PlotScrollBar(component=plot1, axis="index", resizable="h",
height=15)
plot1.padding_top = 0
hscrollbar.force_data_update()
# Create a container and add our plots
container = VPlotContainer()
container.add(plot1)
container.add(hscrollbar)
return container
def createWindow(widget):
''' Example on creating a new plot window in the
main window MDI-Area
'''
import plotWidget
from PySide import QtGui
from numpy import linspace
from scipy.special import jn
from chaco.api import ArrayPlotData, Plot
window = widget.createNewWindow()
container = plotWidget.plotContainer(window)
plotWidget = plotWidget.PlotWidget(container)
container.setPlotWidget(plotWidget)
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
plot = Plot(pd, title=None, padding_left=60, padding_right=5, padding_top=5, padding_bottom=30, border_visible=True)
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plotWidget.setPlot(plot)
layout = QtGui.QBoxLayout(QtGui.QBoxLayout.TopToBottom)
layout.addWidget(container)
window.setLayout(layout)
window.show()
def _create_plot_component():
# Create some x-y data series (with NaNs) to plot
x = linspace(-5.0, 15.0, 500)
x[75:125] = nan
x[200:250] = nan
x[300:330] = nan
pd = ArrayPlotData(index = x)
pd.set_data("value1", jn(0, x))
pd.set_data("value2", jn(1, x))
# Create some line and scatter plots of the data
plot = Plot(pd)
plot.plot(("index", "value1"), name="j_0(x)", color="red", width=2.0)
plot.plot(("index", "value2"), type="scatter", marker_size=1,
name="j_1(x)", color="green")
# Tweak some of the plot properties
plot.title = "Plots with NaNs"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot1 = Plot(pd, title="Line Plot", padding=50, border_visible=True)
plot1.legend.visible = True
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, title="Scatter plot", padding=50,
border_visible=True)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
# Create a container and add our plots
container = HPlotContainer()
container.add(plot1)
container.add(plot2)
return container
def _plot_data_default(self):
"""Filling the ArrayPlotData before plotting"""
data = ArrayPlotData()
for i in range(self.polyg_nb):
data.set_data("polyg"+str(i)+"_x", x_[i])
data.set_data("polyg"+str(i)+"_y", y_[i])
return data
class Plotter():
def __init__(self, parent):
self.plotdata = ArrayPlotData(x=array([]), y=array([]))
self.window = self.create_plot(parent)
self.widget = self.window.control
def update_data(self, x, y):
self.plotdata.set_data("x", x)
self.plotdata.set_data("y", y)
def create_plot(self, parent):
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot = Plot(pd, padding=[40,10,0,40], border_visible=True)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# This Window object bridges the Enable and Qt4 worlds, and handles events
# and drawing. We can create whatever hierarchy of nested containers we
# want, as long as the top-level item gets set as the .component attribute
# of a Window.
return Window(parent, -1, component=plot)
class Viewer1D(Viewer):
image = Array
result = Array
def _reconstruction_default(self):
rows, cols = self.image.shape[:2]
self.plot_data = ArrayPlotData(original=self.image[0],
reconstruction=self.result[0])
aspect = cols/float(rows)
old = Plot(self.plot_data)
old.plot('original', )
old.title = 'Old'
self.new = Plot(self.plot_data)
self.new.plot('reconstruction')
self.new.title = 'New'
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result[0])
self.new.request_redraw()
def _plot_default(self):
# Create a GridContainer to hold all of our plots: 2 rows, 3 columns
container = GridPlotContainer(shape=(2,3),
spacing=(10,5),
valign='top',
bgcolor='lightgray')
# Create x data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index = x)
# Plot some Bessel functions and add the plots to our container
for i in range(6):
data_name = 'y{}'.format(i)
pd.set_data(data_name, jn(i,x))
plot = Plot(pd)
plot.plot(('index', data_name),
color=COLOR_PALETTE[i],
line_width=3.0)
# Set each plot's aspect based on its position in the grid
plot.set(height=((i % 3) + 1)*50,
resizable='h')
# Add to the grid container
container.add(plot)
return container
class Plotter():
def __init__(self, parent):
self.plotdata = ArrayPlotData(x=array([]), y=array([]))
self.window = self.create_plot(parent)
self.widget = self.window.control
def update_data(self, x, y):
self.plotdata.set_data("x", x)
self.plotdata.set_data("y", y)
def create_plot(self, parent):
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
# Create some line plots of some of the data
plot = Plot(pd, padding=[40, 10, 0, 40], border_visible=True)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot.plot(("index", "y3"), name="j_3", color="blue")
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# This Window object bridges the Enable and Qt4 worlds, and handles events
# and drawing. We can create whatever hierarchy of nested containers we
# want, as long as the top-level item gets set as the .component attribute
# of a Window.
return Window(parent, -1, component=plot)
def _create_plot_component(self):
""" Creates the plot component of the to be used in the FeatureScatter
instance.
"""
x = np.zeros(len(self.data))
y = np.zeros(len(self.data))
c = np.zeros(len(self.data))
for i, (coord, count) in enumerate(self.data.items()):
x[i], y[i] = coord
c[i] = count
c = np.log2(c)
pd = ArrayPlotData()
pd.set_data("x", x)
pd.set_data("y", y)
pd.set_data("color", c)
cm = Map(DataRange1D(low=-c.max() / 2, high=c.max()))
plot = Plot(pd)
plot.plot(("x", "y", "color"),
type="cmap_scatter",
name="my_plot",
marker="dot",
index_sort="ascending",
color_mapper=cm,
marker_size=2,
bgcolor=0xF7F7F7,
)
plot.title = "Scatter Plot With Lasso Selection"
plot.line_width = 1
plot.padding = 50
my_plot = plot.plots["my_plot"][0]
my_plot.data = self.data
my_plot.out_file = self.out_file
my_plot.label = self.label
lasso_selection = FeatureLasso(component=my_plot,
selection_datasource=my_plot.index,
drag_button="left")
my_plot.tools.append(lasso_selection)
my_plot.tools.append(BetterZoom(my_plot, zoom_factor=1.2))
my_plot.tools.append(PanTool(my_plot, drag_button="right"))
my_plot.tools.append(ScatterInspector(my_plot))
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=my_plot,
selection_fill_color=0xEF8A62)
my_plot.overlays.append(lasso_overlay)
my_plot.overlays.append(ScatterInspectorOverlay(my_plot,
hover_marker_size=4))
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(
low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper=imapper,
value_mapper=vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some data
npts = 100
x = sort(random(npts))
y = random(npts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("value2", y * 2)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="slategray",
marker_size=6,
bgcolor="white")
plot.plot(("index", "value2"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="red",
marker_size=6,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot With Selection"
plot.line_width = 1
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
my_plot.tools.append(
ScatterInspector(my_plot,
selection_mode="toggle",
persistent_hover=False))
my_plot.overlays.append(
ScatterInspectorOverlay(my_plot,
hover_color="transparent",
hover_marker_size=10,
hover_outline_color="purple",
hover_line_width=2,
selection_marker_size=8,
selection_color="lawngreen"))
my_plot.tools.append(PanTool(my_plot))
my_plot.overlays.append(ZoomTool(my_plot, drag_button="right"))
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=jet,
marker = "square",
fill_alpha = 0.5,
marker_size = 6,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper = imapper,
value_mapper = vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
class DemoWindow(HasTraits):
""" DemoWindow class contains the relevant information and functions for a single camera window: image, zoom, pan
important members:
_plot_data - contains image data to display (used by update_image)
_plot - instance of Plot class to use with _plot_data
"""
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
name = Str
view = View(Item(name='_plot', editor=ComponentEditor(), show_label=False))
def __init__(self):
""" Initialization of plot system
"""
padd = 25
self._plot_data = ArrayPlotData()
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self.right_p_x0, self.right_p_y0, self.right_p_x1, self.right_p_y1, self._quiverplots = [], [], [], [], []
def attach_tools(self):
""" attach_tools(self) contains the relevant tools: clicker, pan, zoom
"""
pan = PanTool(self._plot, drag_button='middle')
zoom_tool = ZoomTool(self._plot, tool_mode="box", always_on=False)
# zoom_tool = BetterZoom(component=self._plot, tool_mode="box", always_on=False)
zoom_tool.max_zoom_out_factor = 1.0 # Disable "bird view" zoom out
self._img_plot.overlays.append(zoom_tool)
self._img_plot.tools.append(pan)
def update_image(self, image, is_float):
""" update_image - displays/updates image in the curren camera window
parameters:
image - image data
is_float - if true, displays an image as float array,
else displays as byte array (B&W or gray)
example usage:
update_image(image,is_float=False)
"""
if is_float:
# self._plot_data.set_data('imagedata',image.astype(np.float))
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
# self._plot_data.set_data('imagedata',image.astype(np.byte))
self._plot_data.set_data('imagedata', image)
if not hasattr(
self,
'_img_plot'): # make a new plot if there is nothing to update
print "inside 1"
# self._img_plot=Instance(ImagePlot)
self._img_plot = self._plot.img_plot('imagedata', colormap=gray)[0]
def _plot_data_default(self):
plot_data = ArrayPlotData()
plot_data.set_data('distance', np.zeros(50))
plot_data.set_data('potentiometer', np.zeros(50))
plot_data.set_data('switch', np.zeros(50))
plot_data.set_data('acc_x', np.zeros(50))
plot_data.set_data('acc_y', np.zeros(50))
plot_data.set_data('acc_z', np.zeros(50))
return plot_data
