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plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=(0, 10),
ybounds=(0, 5),
colormap=jet)[0]
img_plot.index_mapper.aspect_ratio = 0.5
img_plot.index_mapper.maintain_aspect_ratio = True
print('here')
# 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
write_metadata=True,
is_listener=True))
left_plot.overlays.append(ZoomTool(left_plot, tool_mode="range"))
left_plot.tools.append(PanTool(left_plot))
# Create the right plot
right_plot = Plot(plotdata)
right_plot.index_range = left_plot.index_range
right_plot.orientation = "v"
right_plot.x_axis.title = "j1(x)"
right_plot.y_axis.title = "X"
renderer2 = right_plot.plot(("x","y2"), type="line", color="red", width=2.0)[0]
renderer2.index = renderer.index
renderer2.overlays.append(LineInspector(renderer2, write_metadata=True, is_listener=True))
renderer2.overlays.append(LineInspector(renderer2, axis="value", is_listener=True))
right_plot.overlays.append(ZoomTool(right_plot, tool_mode="range"))
right_plot.tools.append(PanTool(right_plot))
container = HPlotContainer(background="lightgray")
container.add(left_plot)
container.add(right_plot)
return container
def main():
# 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
plot = Plot(pd, bgcolor="none", padding=30, border_visible=True,
overlay_border=True, use_backbuffer=False)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
plot.plot(("index", "y3"), name="j_3", color="auto")
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(600,500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [200,200]
plot.padding = 25
plot.outer_position = [30,30]
plot.tools.append(MoveTool(component=plot,drag_button="right"))
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
# 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"
def chaco_gen(self):
# set the diagonal of the adjmat to lower threshold rather than 0
# otherwise the color scheme is a mess for non-sparse matrices
#z_adjmat = (self.adj_thresdiag-np.mean(self.adj_thresdiag))/np.std(self.adj_thresdiag)
#l_adjmat = np.log(self.adj_thresdiag/(1-self.adj_thresdiag))
#print np.where(np.isnan(l_adjmat))
#l_adjmat[np.where(np.isnan(l_adjmat))]=0
#print np.where(np.isnan(l_adjmat))
self.conn_mat = Plot(ArrayPlotData(imagedata=self.adj_thresdiag))
#centerpoint=np.mean(self.adj_thresdiag)/2+np.max(self.adj_thresdiag)/4+\
# np.min(self.adj_thresdiag)/4
cm=ColorMapper.from_palette_array(self.cmap_connmat_pl(xrange(256)))
self.conn_mat.img_plot("imagedata",name='conmatplot',colormap=cm)
self.conn_mat.tools.append(ZoomTool(self.conn_mat))
self.conn_mat.tools.append(ConnmatPanClickTool(self,self.conn_mat))
self.xa=color_axis.ColorfulAxis(self.conn_mat,self.node_colors,'x')
self.ya=color_axis.ColorfulAxis(self.conn_mat,self.node_colors,'y')
self.conn_mat.underlays=[self.xa,self.ya]
# Give me a cursor
cursor = CursorTool(line, drag_button="left", color='red', show_value_line=False)
# and set it's initial position (in data-space units)
cursor.current_position = BUt, _k_BU(BU_F_, k_F_, BUt)
# This is a rendered component so it goes in the overlays list
line.overlays.append(cursor)
# Link it up
cursor.on_trait_change(update_positions, 'current_position')
# Some other standard tools
line.tools.append(PanTool(line, drag_button="right"))
line.overlays.append(ZoomTool(line))
plot.overlays.append(PlotLabel('Burnup-Criticality Estimator',
component=plot,
font = "Roman 20",
overlay_position="top")
)
return plot
# 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 _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
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