""" Renders some contoured and colormapped images of a scalar value field. - Left-drag pans the plot. - Mousewheel up and down zooms the plot in and out. - Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular region to zoom. If you use a sequence of zoom boxes, pressing alt-left-arrow and alt-right-arrow moves you forwards and backwards through the "zoom history". """ # Major library imports from numpy import cos, linspace, log, meshgrid, pi, sin from enthought.enable.example_support import DemoFrame, demo_main # Enthought library imports from enthought.enable.api import Component, ComponentEditor, Window from enthought.traits.api import HasTraits, Instance from enthought.traits.ui.api import Item, Group, View # Chaco imports from enthought.chaco.api import ArrayPlotData, ColorBar, gmt_drywet, \ HPlotContainer, LinearMapper, Plot from enthought.chaco.tools.api import PanTool, ZoomTool #=============================================================================== # # Create the Chaco plot. #=============================================================================== def _create_plot_component(): # Create a scalar field to colormap x_extents = (-2*pi, 2*pi) y_extents = (-1.5*pi, 1.5*pi) xs = linspace(-2*pi, 2*pi, 200) ys = linspace(-1.5*pi, 1.5*pi, 100) x, y = meshgrid(xs,ys) zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \ sin(((x+1)**2 + y**2)/4) # Create a plot data obect and give it this data pd = ArrayPlotData() pd.set_data("imagedata", zs) # Create the left plot, a colormap and simple contours lplot = Plot(pd) lplot.img_plot("imagedata", name="cm_plot", xbounds=x_extents, ybounds=y_extents, colormap=gmt_drywet) lplot.contour_plot("imagedata", type="line", xbounds=x_extents, ybounds=y_extents) # Tweak some of the plot properties lplot.title = "Colormap and contours" lplot.padding = 20 lplot.bg_color = "white" lplot.fill_padding = True # Add some tools to the plot zoom = ZoomTool(lplot, tool_mode="box", always_on=False) lplot.overlays.append(zoom) lplot.tools.append(PanTool(lplot, constrain_key="shift")) # Right now, some of the tools are a little invasive, and we need the # actual CMapImage object to give to them cm_plot = lplot.plots["cm_plot"][0] # Create the colorbar, handing in the appropriate range and colormap colormap = cm_plot.color_mapper colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range), color_mapper=colormap, plot=cm_plot, orientation='v', resizable='v', width=30, padding=20) colorbar.padding_top = lplot.padding_top colorbar.padding_bottom = lplot.padding_bottom # Create the left plot, contours of varying color and width rplot = Plot(pd, range2d=lplot.range2d) rplot.contour_plot("imagedata", type="line", xbounds=x_extents, ybounds=y_extents, bgcolor="black", levels=15, styles="solid", widths=list(linspace(4.0, 0.1, 15)), colors=gmt_drywet) # Add some tools to the plot zoom = ZoomTool(rplot, tool_mode="box", always_on=False) rplot.overlays.append(zoom) rplot.tools.append(PanTool(rplot, constrain_key="shift")) # Tweak some of the plot properties rplot.title = "Varying contour lines" rplot.padding = 20 rplot.bg_color = "white" rplot.fill_padding = True # Create a container and add our plots container = HPlotContainer(padding=40, fill_padding=True, bgcolor = "white", use_backbuffer=True) container.add(colorbar) container.add(lplot) container.add(rplot) return container #=============================================================================== # Attributes to use for the plot view. size=(950,650) title="Some contour plots" bg_color="lightgray" #=============================================================================== # # Demo class that is used by the demo.py application. #=============================================================================== class Demo(HasTraits): plot = Instance(Component) traits_view = View( Group( Item('plot', editor=ComponentEditor(size=size, bgcolor=bg_color), show_label=False), orientation = "vertical"), resizable=True, title=title ) def _plot_default(self): return _create_plot_component() demo = Demo() #=============================================================================== # Stand-alone frame to display the plot. #=============================================================================== class PlotFrame(DemoFrame): def _create_window(self): # Return a window containing our plot return Window(self, -1, component=_create_plot_component(), bg_color=bg_color) if __name__ == "__main__": demo_main(PlotFrame, size=size, title=title) # EOF