def _ch_plot_default(self): x = 0.26 y = 0. h_cross_x = np.array([self.x_low, self.x_high]) h_cross_y = np.array([y, y]) v_cross_x = np.array([x, x]) v_cross_y = np.array([self.y_low, self.y_high]) cross_hair_data = ArrayPlotData(h_cross_x=h_cross_x, h_cross_y=h_cross_y, v_cross_x=v_cross_x, v_cross_y=v_cross_y) ch_plot = Plot(cross_hair_data) ch_plot.plot(("h_cross_x", "h_cross_y"), type="line", color="green",) ch_plot.plot(("v_cross_x", "v_cross_y"), type="line", color="green",) while len(ch_plot.underlays) > 0: ch_plot.underlays.pop(0) ch_plot.range2d = self.img_plot.range2d cross_hair_tool = CrossHairs(ch_plot) ch_plot.tools.append(cross_hair_tool) self.crosshair = cross_hair_tool self.on_trait_change(self.draw_cross_hairs, "crosshair.selected_x, crosshair.selected_y") self.on_trait_change(self.render_julia, "crosshair.selected_x, crosshair.selected_y") return ch_plot
def _container_default(self): # Create the data and the PlotData object x = linspace(-14, 14, 100) y = sin(x) * x**3 plotdata = ArrayPlotData(x=x, y=y) # Create the scatter plot scatter = Plot(plotdata) scatter.plot(("x", "y"), type="scatter", color="blue") # Create the line plot, rotated and vertically oriented line = Plot(plotdata, orientation="v", default_origin="top left") line.plot(("x", "y"), type="line", color="blue") # Add pan/zoom so we can see they are connected scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) # Set the two plots' ranges to be the same scatter.range2d = line.range2d # Create a horizontal container and put the two plots inside it return HPlotContainer(scatter, line)
def __init__(self): super(ConnectedRange, self).__init__() x = linspace(-14, 14, 100) y = sin(x) * x ** 3 plotdata = ArrayPlotData(x=x, y=y) scatter = Plot(plotdata) scatter.plot(("x", "y"), type="scatter", color="blue") line = Plot(plotdata) #line = Plot(plotdata, orientation="v", default_origin="top left") line.plot(("x", "y"), type="line", color="red") self.container = HPlotContainer(scatter, line) scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) #Axis link options, try them out #scatter.value_range = line.value_range # Link y-axis only #scatter.index_range = line.index_range # Link x-axis only scatter.range2d = line.range2d # Link both axes
def __init__(self): # Create the data and the PlotData object x = linspace(-14, 14, 100) y = sin(x) * x**3 plotdata = ArrayPlotData(x=x, y=y) # Create the scatter plot scatter = Plot(plotdata) scatter.plot(("x", "y"), type="scatter", color="blue") # Create the line plot line = Plot(plotdata) line.plot(("x", "y"), type="line", color="blue") # Create a horizontal container and put the two plots inside it self.container = HPlotContainer(scatter, line) # Add pan/zoom so we can see they are connected scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) # Set the two plots' ranges to be the same scatter.range2d = line.range2d
def __init__(self): # Create the data and the PlotData object x = linspace(-14, 14, 100) y = sin(x) * x ** 3 plotdata = ArrayPlotData(x=x, y=y) # Create the scatter plot scatter = Plot(plotdata) scatter.plot(("x", "y"), type="scatter", color="blue") # Create the line plot, rotated and vertically oriented line = Plot(plotdata, orientation="v", default_origin="top left") line.plot(("x", "y"), type="line", color="blue") # Create a horizontal container and put the two plots inside it self.container = HPlotContainer(scatter, line) # Add pan/zoom so we can see they are connected scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) # Set the two plots' ranges to be the same scatter.range2d = line.range2d
def _container_default(self): x = linspace(-10, 10, 100) y = sin(x) * x plotdata = ArrayPlotData(x=x, y=y) scatter = Plot(plotdata) scatter.plot(('x', 'y'), type='scatter', color='blue') line = Plot(plotdata) line.plot(('x', 'y'), type='line', color='green') container = HPlotContainer(scatter, line) scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) # Chaco has the concept of data range to express bounds in data space # Standard 2D plots all have 2D ranges on them # Here two plots now share same range object, and will change together # In response to changes to the data space bounds. scatter.range2d = line.range2d return container
def _container_default(self): # Create the data and the PlotData object x = linspace(-14, 14, 100) y = sin(x) * x**3 plotdata = ArrayPlotData(x = x, y = y) # Create the scatter plot scatter = Plot(plotdata) scatter.plot(("x", "y"), type="scatter", color="blue") # Create the line plot line = Plot(plotdata) line.plot(("x", "y"), type="line", color="blue") # Add pan/zoom so we can see they are connected scatter.tools.append(PanTool(scatter)) scatter.tools.append(ZoomTool(scatter)) line.tools.append(PanTool(line)) line.tools.append(ZoomTool(line)) # Set the two plots' ranges to be the same scatter.range2d = line.range2d # Create a horizontal container and put the two plots inside it return HPlotContainer(scatter, line)
def _render_scatter_overlay(base_plot, array_plot_data, marker="circle", fill_alpha=0.5, marker_size=6, tools=[]): #if 'index' not in array_plot_data.arrays: #return base_plot, None scatter_plot = Plot(array_plot_data, aspect_ratio = base_plot.aspect_ratio, default_origin="top left") if "color" in array_plot_data.arrays: scatter_plot.plot(("index", "value", "color"), type="cmap_scatter", name="scatter_plot", color_mapper=jet, marker = marker, fill_alpha = fill_alpha, marker_size = marker_size, ) scatter_renderer = scatter_plot.plots['scatter_plot'][0] colorbar = _create_colorbar(scatter_plot.color_mapper, tools=tools) colorbar.plot = scatter_renderer #colorbar.padding_top = scatter_renderer.padding_top #colorbar.padding_bottom = scatter_renderer.padding_bottom if 'colorbar' in tools: # this part is for making the colormapped points fade when they # are not selected by the threshold. # The renderer is the actual class that does the rendering - # the Plot class calls this other class. When we say # plots['scatter_plot'] # we are getting the named plot that we created above. # The extra [0] on the end is because we get a list - in case # there is more than one plot named "scatter_plot" selection = ColormappedSelectionOverlay(scatter_renderer, fade_alpha=0.35, selection_type="range") scatter_renderer.overlays.append(selection) else: colorbar = None if 'inspector' in tools: # Attach the inspector and its overlay scatter_renderer = scatter_plot.plots['scatter_plot'][0] #scatter_plot.tools.append(PeakSelectionTool(scatter_renderer)) selection = ColormappedSelectionOverlay(scatter_renderer, fade_alpha=0.35, selection_type="mask") scatter_plot.overlays.append(selection) scatter_plot.x_grid.visible = False scatter_plot.y_grid.visible = False scatter_plot.range2d = base_plot.range2d return scatter_plot, colorbar
def _render_quiver_overlay(base_plot, array_plot_data, line_color="white", line_width=1.0, arrow_size=5): if 'vectors' not in array_plot_data.arrays: return base_plot quiverplot = Plot(array_plot_data, aspect_ratio = base_plot.aspect_ratio, default_origin="top left") quiverplot.quiverplot(("index", "value", "vectors"), name="quiver_plot", line_color=line_color, line_width=line_width, arrow_size=arrow_size) quiverplot.x_grid.visible = False quiverplot.y_grid.visible = False quiverplot.range2d = base_plot.range2d return quiverplot
def render_scatplot(self): peakdata=ArrayPlotData() peakdata.set_data("index",self.peaks[self.img_idx][:,0]) peakdata.set_data("value",self.peaks[self.img_idx][:,1]) peakdata.set_data("color",self.peaks[self.img_idx][:,2]) scatplot=Plot(peakdata,aspect_ratio=self.img_plot.aspect_ratio,default_origin="top left") scatplot.plot(("index", "value", "color"), type="cmap_scatter", name="my_plot", color_mapper=jet(DataRange1D(low = 0.0, high = 1.0)), marker = "circle", fill_alpha = 0.5, marker_size = 6, ) scatplot.x_grid.visible = False scatplot.y_grid.visible = False scatplot.range2d=self.img_plot.range2d self.scatplot=scatplot self.peakdata=peakdata return scatplot
def _plot_default(self): distr_len = len(self.data) # PolygonPlot holding the circles of the Hinton diagram polyplot = Plot(self.plot_data) for idx in range(distr_len): p = polyplot.plot(('x%d' % idx, 'y%d' % idx), type="polygon", face_color=get_class_color(idx), edge_color='black') self._set_title(polyplot) self._remove_grid_and_axes(polyplot) # create x axis for labels axis = self._create_increment_one_axis(polyplot, 1., distr_len, 'bottom') self._add_index_axis(polyplot, axis) # create y axis for probability density #prob_axis = self._create_probability_axis(polyplot) #polyplot.value_axis = prob_axis #polyplot.underlays.append(prob_axis) # tweak some of the plot properties range2d = DataRange2D(low=(0.5, 0.), high=(distr_len + 0.5, 1.)) polyplot.range2d = range2d polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low) / (range2d.y_range.high - range2d.y_range.low)) polyplot.border_visible = False polyplot.padding = [0, 0, 25, 25] # create a container to position the plot and the colorbar side-by-side container = HPlotContainer(use_backbuffer=True, valign='center') container.add(polyplot) container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE self.decorate_plot(container, self.data) return container
def _plot_default(self): distr_len = len(self.data) # PolygonPlot holding the circles of the Hinton diagram polyplot = Plot(self.plot_data) for idx in range(distr_len): p = polyplot.plot(('x%d' % idx, 'y%d' % idx), type="polygon", face_color=get_class_color(idx), edge_color='black') self._set_title(polyplot) self._remove_grid_and_axes(polyplot) # create x axis for labels axis = self._create_increment_one_axis(polyplot, 1., distr_len, 'bottom') self._add_index_axis(polyplot, axis) # create y axis for probability density #prob_axis = self._create_probability_axis(polyplot) #polyplot.value_axis = prob_axis #polyplot.underlays.append(prob_axis) # tweak some of the plot properties range2d = DataRange2D(low=(0.5, 0.), high=(distr_len+0.5, 1.)) polyplot.range2d = range2d polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low) / (range2d.y_range.high - range2d.y_range.low)) polyplot.border_visible = False polyplot.padding = [0, 0, 25, 25] # create a container to position the plot and the colorbar side-by-side container = HPlotContainer(use_backbuffer=True, valign='center') container.add(polyplot) container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE self.decorate_plot(container, self.data) return container
def _brain_default(self): plot = Plot(self.brain_data, padding=0) plot.width = self.brain_voxels.shape[1] plot.height = self.brain_voxels.shape[0] plot.aspect_ratio = 1. plot.index_axis.visible = False plot.value_axis.visible = False renderer = plot.img_plot("axial", colormap=gray)[0] plot.color_mapper.range = DataRange1D(low=0., high=1.0) plot.bgcolor = 'pink' # Brain tools plot.tools.append(PanTool(plot, drag_button="right")) plot.tools.append(ZoomTool(plot)) imgtool = ImageInspectorTool(renderer) renderer.tools.append(imgtool) overlay = ImageInspectorOverlay(component=renderer, image_inspector=imgtool, bgcolor="white", border_visible=True) renderer.overlays.append(overlay) # Brain track cursor self.cursor = CursorTool2D(renderer, drag_button='left', color='red', line_width=2.0) #self.cursor.on_trait_change(self.update_stackedhist, 'current_index') self.cursor.current_positionyou = (0., 0.) renderer.overlays.append(self.cursor) # Brain colorbar colormap = plot.color_mapper colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range), color_mapper=colormap, plot=plot, orientation='v', resizable='v', width=20, padding=(30, 0, 0, 0)) colorbar.padding_top = plot.padding_top colorbar.padding_bottom = plot.padding_bottom # Noisy brain plot2 = Plot(self.brain_data, padding=0) plot2.width = self.brain_voxels.shape[1] plot2.height = self.brain_voxels.shape[0] plot2.aspect_ratio = 1. plot2.index_axis.visible = False plot2.value_axis.visible = False renderer2 = plot2.img_plot("noisy_axial", colormap=gray)[0] plot2.color_mapper.range = DataRange1D(low=0., high=1.0) plot2.bgcolor = 'pink' plot2.range2d = plot.range2d # Brain_map tools plot2.tools.append(PanTool(plot2, drag_button="right")) plot2.tools.append(ZoomTool(plot2)) imgtool2 = ImageInspectorTool(renderer2) renderer2.tools.append(imgtool2) overlay2 = ImageInspectorOverlay(component=renderer2, image_inspector=imgtool2, bgcolor="white", border_visible=True) renderer2.overlays.append(overlay2) # Brain_map track cursor self.cursor2 = CursorTool2D(renderer2, drag_button='left', color='red', line_width=2.0) #self.cursor2.on_trait_change(self.cursor2_changed, 'current_index') self.cursor2.current_position = (0., 0.) renderer2.overlays.append(self.cursor2) # Brain_map colorbar colormap2 = plot2.color_mapper colorbar2 = ColorBar(index_mapper=LinearMapper(range=colormap2.range), color_mapper=colormap2, plot=plot2, orientation='v', resizable='v', width=20, padding=(30, 0, 0, 0)) colorbar2.padding_top = plot2.padding_top colorbar2.padding_bottom = plot2.padding_bottom # Create a container to position the plot and the colorbar side-by-side container = HPlotContainer(use_backbuffer=True, padding=(0, 0, 10, 10)) container.add(plot) container.add(colorbar) container.bgcolor = "lightgray" container2 = HPlotContainer(use_backbuffer=True, padding=(0, 0, 10, 10)) container2.add(plot2) container2.add(colorbar2) container2.bgcolor = "lightgray" Hcontainer = HPlotContainer(use_backbuffer=True) Hcontainer.add(container) Hcontainer.add(container2) Hcontainer.bgcolor = "lightgray" return Hcontainer
def _create_plot(self): # count data if len(self.df) > 0: self.indexes = np.arange(len(self.df.date_time)) time_ds = ArrayDataSource(self.indexes) vol_ds = ArrayDataSource(self.df.volumn.values, sort_order="none") xmapper = LinearMapper(range=DataRange1D(time_ds)) vol_mapper = LinearMapper(range=DataRange1D(vol_ds)) #################################################################### # create volumn plot vol_plot = BarPlot(index=time_ds, value=vol_ds, index_mapper=xmapper, value_mapper=vol_mapper, line_color="transparent", fill_color="blue", bar_width=0.6, antialias=False, height=100, resizable="h", origin="bottom left", bgcolor="white", border_visible=True ) vol_plot.padding = 30 vol_plot.padding_left = 40 vol_plot.tools.append( PanTool(vol_plot, constrain=True, constrain_direction="x")) add_default_grids(vol_plot) add_default_axes(vol_plot) #print vol_plot.index_mapper.range.high #print vol_plot.value_mapper.range.low, vol_plot.value_mapper.range.high self.vol_plot = vol_plot self.container.add(vol_plot) #################################################################### ## Create price plot sorted_vals = np.vstack( (self.df.open, self.df.high, self.df.low, self.df.close)) sorted_vals.sort(0) __bool = self.df.close.values - self.df.open.values self.up_boolean = __bool >= 0 self.down_boolean = np.invert(self.up_boolean) pd = ArrayPlotData( up_index=self.indexes[self.up_boolean], up_min=sorted_vals[0][self.up_boolean], up_bar_min=sorted_vals[1][self.up_boolean], up_bar_max=sorted_vals[2][self.up_boolean], up_max=sorted_vals[3][self.up_boolean], down_index=self.indexes[self.down_boolean], down_min=sorted_vals[0][self.down_boolean], down_bar_min=sorted_vals[1][self.down_boolean], down_bar_max=sorted_vals[2][self.down_boolean], down_max=sorted_vals[3][self.down_boolean], volumn=self.df.volumn.values, index=self.indexes ) price_plot = Plot(pd) up_plot = price_plot.candle_plot( ("up_index", "up_min", "up_bar_min", "up_bar_max", "up_max"), color=color_red, bgcolor="azure", bar_line_color="black", stem_color="black", end_cap=False)[0] down_plot = price_plot.candle_plot( ("down_index", "down_min", "down_bar_min", "down_bar_max", "down_max"), color=color_green, bar_line_color="black", stem_color="black", end_cap=False)[0] price_plot.fill_padding = True price_plot.padding = 30 price_plot.padding_left = 40 price_plot.tools.append(ZoomTool(component=price_plot, tool_mode="box", zoom_factor=1.2, always_on=False)) price_plot.tools.append(PanTool(price_plot, drag_button="left")) price_plot.tools.append( XYTool(price_plot, callback=self._update_ohlc)) # get data self._add_line_tool(up_plot) self._add_line_tool(down_plot) price_plot.range2d = self._compute_range2d() price_plot.index_mapper = vol_plot.index_mapper # maper vol_plot and price_plot self.price_plot = price_plot self.container.add(price_plot)
def _create_plot(self): # count data if len(self.df) > 0: self.indexes = np.arange(len(self.df.date_time)) time_ds = ArrayDataSource(self.indexes) vol_ds = ArrayDataSource(self.df.volumn.values, sort_order="none") xmapper = LinearMapper(range=DataRange1D(time_ds)) vol_mapper = LinearMapper(range=DataRange1D(vol_ds)) #################################################################### # create volumn plot vol_plot = BarPlot( index=time_ds, value=vol_ds, index_mapper=xmapper, value_mapper=vol_mapper, line_color="transparent", fill_color="blue", bar_width=0.6, antialias=False, height=100, resizable="h", origin="bottom left", bgcolor="white", border_visible=True, ) vol_plot.padding = 30 vol_plot.padding_left = 40 vol_plot.tools.append(PanTool(vol_plot, constrain=True, constrain_direction="x")) add_default_grids(vol_plot) add_default_axes(vol_plot) # print vol_plot.index_mapper.range.high # print vol_plot.value_mapper.range.low, vol_plot.value_mapper.range.high self.vol_plot = vol_plot self.container.add(vol_plot) #################################################################### ## Create price plot sorted_vals = np.vstack((self.df.open, self.df.high, self.df.low, self.df.close)) sorted_vals.sort(0) __bool = self.df.close.values - self.df.open.values self.up_boolean = __bool >= 0 self.down_boolean = np.invert(self.up_boolean) pd = ArrayPlotData( up_index=self.indexes[self.up_boolean], up_min=sorted_vals[0][self.up_boolean], up_bar_min=sorted_vals[1][self.up_boolean], up_bar_max=sorted_vals[2][self.up_boolean], up_max=sorted_vals[3][self.up_boolean], down_index=self.indexes[self.down_boolean], down_min=sorted_vals[0][self.down_boolean], down_bar_min=sorted_vals[1][self.down_boolean], down_bar_max=sorted_vals[2][self.down_boolean], down_max=sorted_vals[3][self.down_boolean], volumn=self.df.volumn.values, index=self.indexes, ) price_plot = Plot(pd) up_plot = price_plot.candle_plot( ("up_index", "up_min", "up_bar_min", "up_bar_max", "up_max"), color=color_red, bgcolor="azure", bar_line_color="black", stem_color="black", end_cap=False, )[0] down_plot = price_plot.candle_plot( ("down_index", "down_min", "down_bar_min", "down_bar_max", "down_max"), color=color_green, bar_line_color="black", stem_color="black", end_cap=False, )[0] price_plot.fill_padding = True price_plot.padding = 30 price_plot.padding_left = 40 price_plot.tools.append(ZoomTool(component=price_plot, tool_mode="box", zoom_factor=1.2, always_on=False)) price_plot.tools.append(PanTool(price_plot, drag_button="left")) price_plot.tools.append(XYTool(price_plot, callback=self._update_ohlc)) # get data self._add_line_tool(up_plot) self._add_line_tool(down_plot) price_plot.range2d = self._compute_range2d() price_plot.index_mapper = vol_plot.index_mapper # maper vol_plot and price_plot self.price_plot = price_plot self.container.add(price_plot)