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Renders some contoured and colormapped images of a scalar value field.
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- Left-drag pans the plot.
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- Mousewheel up and down zooms the plot in and out.
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- Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular
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region to zoom. If you use a sequence of zoom boxes, pressing alt-left-arrow and alt-right-arrow moves you forwards and backwards through the "zoom
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# Major library imports
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from numpy import cos, linspace, log, meshgrid, pi, sin
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# Enthought library imports
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from enable.api import Component, ComponentEditor
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from traits.api import HasTraits, Instance
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from traitsui.api import Item, Group, View
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from chaco.api import ArrayPlotData, ColorBar, gmt_drywet, \
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HPlotContainer, LinearMapper, Plot
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from chaco.tools.api import PanTool, ZoomTool
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#===============================================================================
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# # Create the Chaco plot.
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#===============================================================================
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def _create_plot_component():
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# Create a scalar field to colormap
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x_extents = (-2*pi, 2*pi)
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y_extents = (-1.5*pi, 1.5*pi)
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xs = linspace(-2*pi, 2*pi, 200)
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ys = linspace(-1.5*pi, 1.5*pi, 100)
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x, y = meshgrid(xs,ys)
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zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \
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sin(((x+1)**2 + y**2)/4)
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# Create a plot data obect and give it this data
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pd.set_data("imagedata", zs)
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# Create the left plot, a colormap and simple contours
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lplot.img_plot("imagedata",
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lplot.contour_plot("imagedata",
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# Tweak some of the plot properties
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lplot.title = "Colormap and contours"
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lplot.bg_color = "white"
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lplot.fill_padding = True
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# Add some tools to the plot
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zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
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lplot.overlays.append(zoom)
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lplot.tools.append(PanTool(lplot, constrain_key="shift"))
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# Right now, some of the tools are a little invasive, and we need the
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# actual CMapImage object to give to them
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cm_plot = lplot.plots["cm_plot"][0]
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# Create the colorbar, handing in the appropriate range and colormap
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colormap = cm_plot.color_mapper
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colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
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color_mapper=colormap,
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colorbar.padding_top = lplot.padding_top
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colorbar.padding_bottom = lplot.padding_bottom
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# Create the left plot, contours of varying color and width
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rplot = Plot(pd, range2d=lplot.range2d)
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rplot.contour_plot("imagedata",
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widths=list(linspace(4.0, 0.1, 15)),
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# Add some tools to the plot
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zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
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rplot.overlays.append(zoom)
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rplot.tools.append(PanTool(rplot, constrain_key="shift"))
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# Tweak some of the plot properties
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rplot.title = "Varying contour lines"
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rplot.bg_color = "white"
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rplot.fill_padding = True
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# Create a container and add our plots
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container = HPlotContainer(padding=40, fill_padding=True,
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bgcolor = "white", use_backbuffer=True)
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container.add(colorbar)
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#===============================================================================
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# Attributes to use for the plot view.
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title="Some contour plots"
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#===============================================================================
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# # Demo class that is used by the demo.py application.
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#===============================================================================
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class Demo(HasTraits):
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plot = Instance(Component)
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Item('plot', editor=ComponentEditor(size=size,
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orientation = "vertical"),
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resizable=True, title=title
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def _plot_default(self):
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return _create_plot_component()
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if __name__ == "__main__":
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demo.configure_traits()
added
removed
examples/demo/basic/contour_cmap_plot.py
