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)
