def plotImage(self, image, plot=None):
'''Plots a tiff image.
| image -- Image object
| plot -- plot instance to be updated
| if None, a plot instance will be created
Returns the plot instance.
'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image.data)
plot = Plot(pd, default_origin = "bottom left", padding=0)
plot.bgcolor = 'white'
plot.fixed_preferred_size = (100, 100)
plot.x_axis.visible = False
plot.y_axis.visible = False
self.imageplot = plot
imgPlot = plot.img_plot("imagedata", colormap=self.cmap,
name='image')[0]
self.imgPlot = imgPlot
self.appendImageTools(imgPlot)
else:
plot.data.set_data('imagedata', image.data)
plot.aspect_ratio = float(image.data.shape[1]) / image.data.shape[0]
plot.invalidate_and_redraw()
return plot
def plot1DCut(self, image, plot=None):
'''Plots a 1D cut of the image.
Currently, the 1D cut is a plot of mean intensity vs column #.
| image -- Image object
| plot -- plot instance to be updated
| if None, a plot instance will be created
Returns the plot instance.
'''
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 10, 0, 5))
plot.plot(('x', 'y'), name='1D Cut', type='line', color='blue')
plot.value_axis.title = '1D Cut'
plot.x_axis.visible = False
plot.bgcolor = "white"
plot.fixed_preferred_size = (100, 30)
plot.value_range.low = 'auto'
plot.value_range.high = 'auto'
plot.index_range.low = 'auto'
plot.index_range.high = 'auto'
self.append1DCutTools(plot)
else:
index = range(image.data.shape[1])
values = image.data.mean(axis=0)
self.setData(values, index, plot)
plot.invalidate_and_redraw()
return plot
def _signals_plot_default(self):
print('_signals_plot_default')
"""Create the Plot instance."""
plot = Plot()
plot.add(self.signals_renderer)
x_axis = PlotAxis(component=plot,
mapper=self.signals_renderer.index_mapper,
orientation='bottom')
# y_axis = PlotAxis(component=plot,
# mapper=self.signals_renderer.value_mapper,
# orientation='left')
plot.overlays.extend([x_axis])
plot.origin_axis_visible = False
plot.padding_top = 0
plot.padding_left = 0
plot.padding_right = 0
plot.padding_bottom = 50
plot.border_visible = False
plot.bgcolor = "white"
plot.use_downsampling = True
return plot
def plotHistogram(self, image, plot=None):
'''Plots a histogram.
| image -- Image object
| plot -- plot instance to be updated
| if None, a plot instance will be created
Returns the plot instance.
'''
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 10, 0, 0))
plot.plot(('x', 'y'), name='Histogram', type='bar', bar_width=1.0)
plot.line_color = 'black'
plot.bgcolor = "white"
plot.fixed_preferred_size = (100, 30)
add_default_grids(plot)
plot.value_range.low = 'auto'
plot.value_range.high = 'auto'
plot.index_range.low = 'auto'
plot.index_range.high = 'auto'
plot.value_axis.title = "Histogram"
self.appendHistogramTools(plot)
else:
data = np.histogram(image.data, bins=10000)
index = np.delete(data[1], data[1].size-1)
values = data[0]
self.setData(values, index, plot)
plot.invalidate_and_redraw()
return plot
def _create_plot_component():
# Create some RGBA image data
image = zeros((200, 400, 4), dtype=uint8)
image[:, 0:40, 0] += 255 # Vertical red stripe
image[0:25, :, 1] += 255 # Horizontal green stripe; also yellow square
image[-80:, -160:, 2] += 255 # Blue square
image[:, :, 3] = 255
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd, default_origin="top left")
plot.x_axis.orientation = "top"
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.bgcolor = "white"
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
plot.overlays.append(
ZoomTool(component=plot, tool_mode="box", always_on=False))
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(
ImageInspectorOverlay(component=img_plot, image_inspector=imgtool))
return plot
def plotImage(self, image, title, plot):
'''plot one image
image: 2d ndarray or ssp matrix
title: string, plot title
plot: plot instance to be update, if None, a plot instance will be created
return: plot instance'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image)
plot = Plot(pd, default_origin = "bottom left")
plot.title = title
plot.bgcolor = 'white'
if not title == 'Total Intensity':
plot.x_axis.visible = False
plot.y_axis.visible = False
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
# TODO: mess with color maps on else block
else:
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
self._appendTools(imgPlot, title)
else:
plot.data.set_data('imagedata', image)
plot.title = title
plot.aspect_ratio = float(image.shape[1]) / image.shape[0]
plot.invalidate_draw()
return plot
def plotImage(self, image, plot=None):
'''plot one image
image: Image object
plot: plot instance to be update, if None, a plot instance will be created
return: plot instance'''
if plot == None:
pd = ArrayPlotData()
pd.set_data('imagedata', image.data)
plot = Plot(pd, default_origin = "bottom left", padding=0)
#plot.title = image.name
plot.bgcolor = 'white'
plot.fixed_preferred_size = (100, 100)
plot.x_axis.visible = False
plot.y_axis.visible = False
self.imageplot = plot
# TODO: mess with color maps on else block
imgPlot = plot.img_plot("imagedata", colormap=jet, name='image')[0]
self.imgPlot = imgPlot
self._appendImageTools(imgPlot)
#plot.overlays.append(MyLineDrawer(plot))
else:
plot.data.set_data('imagedata', image.data)
imgPlot = plot.plots['image'][0]
#plot.title = image.name
plot.aspect_ratio = float(image.data.shape[1]) / image.data.shape[0]
plot.invalidate_draw()
return plot
def plotRRMap(self, ydata, title, plot=None):
'''Plots an RR map.
| ydata -- y-data to be plotted
| title -- RR type, to be displayed on y-axis
| plot -- plot instance to be updated
| if None, a plot instance will be created
Returns the plot instance.
'''
if plot == None:
pd = ArrayPlotData()
plot = Plot(pd, padding=(79, 5, 0, 0))
self.setData(ydata, None, plot)
plot.plot(('x', 'y'), name='rrplot', type="scatter", color='green',
marker="circle", marker_size=6)
plot.value_axis.title = title
plot.bgcolor = 'white'
plot.aspect_ratio = 2.5
plot.fixed_preferred_size = (100, 50)
plot.y_axis.tick_label_formatter = lambda val:('%.2E'%val)
plot.x_axis.visible = False
hgrid, vgrid = add_default_grids(plot)
self.appendRRTools(plot)
else:
self.setData(ydata, None, plot)
plot.invalidate_and_redraw()
return plot
def plotHistogram(self, image, plot=None):
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 10, 0, 0))
plot.plot(('x', 'y'), name='Histogram', type='bar', bar_width=5.0, color='auto')
#plot.title = 'Histogram'
plot.line_color = 'black'
plot.bgcolor = "white"
plot.fixed_preferred_size = (100, 30)
add_default_grids(plot)
plot.value_axis.title = "Histogram"
self._appendHistogramTools(plot)
'''
plot.overlays.append(PlotAxis(plot, orientation='left'))
plot.overlays.append(PlotAxis(plot, orientation='bottom'))
'''
else:
data = np.histogram(image.data, bins=10000)
index = np.delete(data[1], data[1].size-1)
values = data[0]
plot.index_range.low= np.min(index)
plot.index_range.high = np.max(index)
plot.value_range.low = 0
plot.value_range.high = np.max(values)
plot.data.set_data('x', index)
plot.data.set_data('y', values)
plot.request_redraw()
return plot
def _create_plot_component():
# Create some RGBA image data
image = zeros((200,400,4), dtype=uint8)
image[:,0:40,0] += 255 # Vertical red stripe
image[0:25,:,1] += 255 # Horizontal green stripe; also yellow square
image[-80:,-160:,2] += 255 # Blue square
image[:,:,3] = 255
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd, default_origin="top left")
plot.x_axis.orientation = "top"
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.bgcolor = "white"
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
plot.overlays.append(ZoomTool(component=plot,
tool_mode="box", always_on=False))
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool))
return plot
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.bgcolor = "lightgray"
plot.outer_position = [30, 30]
plot.tools.append(MoveTool(component=plot, drag_button="right"))
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(
rwi,
renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class=tvtk.InteractorStyleTrackballCamera,
bgcolor="transparent",
event_passthrough=True, )
mlab.show()
return window, render_window
def _TracePlot_default(self):
plot = Plot(self.TraceData, width=500, height=500, resizable='hv')
plot.plot(('t', 'y8'),
type='line',
line_style='solid',
color=0xFFFFFF,
line_width=2,
render_style='connectedpoints',
name='ch0 & ch1')
plot.bgcolor = scheme['background']
plot.value_range.low = 0.0
plot.x_grid = None
plot.y_grid = None
return plot
def _create_line_plot(self):
line_data = ArrayPlotData(frequency=np.array((0.,1.)), counts=np.array((0.,0.)), fit=np.array((0.,0.)))
line_plot = Plot(line_data, padding=8, padding_left=64, padding_bottom=32)
line_plot.plot(('frequency','counts'), style='line', color=scheme['data 1'], line_width=2)
line_plot.bgcolor = scheme['background']
line_plot.x_grid = None
line_plot.y_grid = None
line_plot.index_axis.title = 'Frequency [MHz]'
line_plot.value_axis.title = 'Fluorescence counts'
line_plot.value_range.low = 0.0
line_plot.tools.append(PanTool(line_plot))
line_plot.overlays.append(SimpleZoom(line_plot, enable_wheel=False))
self.line_data = line_data
self.line_plot = line_plot
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.bgcolor = "lightgray"
plot.outer_position = [30,30]
plot.tools.append(MoveTool(component=plot,drag_button="right"))
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(rwi, renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()
return window, render_window
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)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Tweak some of the plot properties
plot1.title = "Inset Plot"
plot1.padding = 50
# 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)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, padding=50)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
plot2.resizable = ""
plot2.bounds = [250, 250]
plot2.position = [550, 150]
plot2.bgcolor = "white"
plot2.border_visible = True
plot2.unified_draw = True
plot2.tools.append(PanTool(plot2))
plot2.tools.append(MoveTool(plot2, drag_button="right"))
zoom = ZoomTool(component=plot2, tool_mode="box", always_on=False)
plot2.overlays.append(zoom)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(plot1)
container.add(plot2)
return container
def plotRRMap(self, rr, rrchoice, plot=None):
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 5, 0, 0))
self._setData(rr, plot)
plot.plot(('x', 'y'), name='rrplot', type="scatter", color='green',
marker="circle", marker_size=6)
#plot.title = 'rrplot'
plot.value_axis.title = rrchoice
#plot.y_axis.visible = False
plot.bgcolor = 'white'
plot.aspect_ratio = 2.5
plot.fixed_preferred_size = (100, 50)
#left, bottom = add_default_axes(plot)
hgrid, vgrid = add_default_grids(plot)
self._appendCMapTools(plot)
else:
self._setData(rr, plot)
plot.request_redraw()
return plot
def _create_plot_component(self):
# Create the plot
pd = self.pd
plot = Plot(pd, default_origin="top left", orientation="h")
plot.x_axis.orientation = "top"
plot.padding = 20
#plot.y_axis.orientation = "top"
# Tweak some of the plot properties
#plot.bgcolor = "white"
plot.bgcolor = bg_color
# Attach some tools to the plot
# plot.tools.append(PanTool(plot,constrain_key="shift", drag_button = 'right'))
printer = DataPrinter(component=plot, process=self.process_selection)
plot.tools.append(printer)
plot.overlays.append(
ZoomTool(component=plot, tool_mode="box", always_on=False))
return plot
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
'''if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0 '''
a = batch.type
b = batch.number
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i], y1[i]))
plot0 = create_scatter_plot((x1, y1),
marker=tmarker,
color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
plot0.alpha = 1
else:
plot0.fill_alpha = 0.2
plot0.edge_alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0,
vtitle='PCA Indices',
htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0,
tool_mode="box",
always_on=False,
maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0,
drag_button="right",
maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
if batch.isSelected:
Container.add(plot0)
index = index + 1
self.RightPlot = Container
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding = 50, fill_padding = True, bgcolor = "lightgray", use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i],y1[i]))
plot0 = create_scatter_plot((x1,y1), marker = tmarker, color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
else:
plot0.alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0, vtitle='PCA Indices', htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0, tool_mode="box", always_on=False, maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0, drag_button="right", maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
Container.add(plot0)
index = index +1
self.RightPlot = 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 _plot(self, x, y, z, scale):
pd = ArrayPlotData()
pd.set_data("imagedata", z)
plot = Plot(pd, padding_left=60, fill_padding=True)
plot.bgcolor = "white"
cmap = fix(jet, (0, z.max()))
origin = "bottom left" # origin = 'top left' # to flip y-axis
plot.img_plot(
"imagedata",
name="surface2d",
xbounds=(np.min(x), np.max(x)),
ybounds=(1.0, y[-1, -1]),
colormap=cmap,
hide_grids=True,
interpolation="nearest",
origin=origin,
)
plot.default_origin = origin
plot.x_axis.title = u"Angle (2\u0398)"
tick_font = settings.tick_font
plot.x_axis.title_font = settings.axis_title_font
plot.y_axis.title_font = settings.axis_title_font
plot.x_axis.tick_label_font = tick_font
plot.y_axis.tick_label_font = tick_font
plot.y_axis.title = "Dataset"
plot.y_axis.tick_interval = 1.0
actual_plot = plot.plots["surface2d"][0]
self.plot_zoom_tool = ClickUndoZoomTool(
plot,
tool_mode="box",
always_on=True,
pointer="cross",
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
x_min_zoom_factor=-np.inf,
y_min_zoom_factor=-np.inf,
)
plot.overlays.append(self.plot_zoom_tool)
plot.tools.append(TraitsTool(plot))
# Add a color bar
colormap = actual_plot.color_mapper
colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=actual_plot,
orientation="v",
resizable="v",
width=30,
padding=40,
padding_top=50,
fill_padding=True,
)
colorbar._axis.title_font = settings.axis_title_font
colorbar._axis.tick_label_font = settings.tick_font
# Add pan and zoom tools to the colorbar
self.colorbar_zoom_tool = ClickUndoZoomTool(
colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
)
pan_tool = PanToolWithHistory(
colorbar,
history_tool=self.colorbar_zoom_tool,
constrain_direction="y",
constrain=True,
drag_button=settings.pan_button,
)
colorbar.tools.append(pan_tool)
colorbar.overlays.append(self.colorbar_zoom_tool)
# Add a label to the top of the color bar
colorbar_label = PlotLabel(
u"Intensity\n{:^9}".format("(" + get_value_scale_label(scale) + ")"),
component=colorbar,
font=settings.axis_title_font,
)
colorbar.overlays.append(colorbar_label)
colorbar.tools.append(TraitsTool(colorbar))
# Add the plot and colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
return container
def _plot(self, x, y, z, scale):
pd = ArrayPlotData()
pd.set_data("imagedata", z)
plot = Plot(pd, padding_left=60, fill_padding=True)
plot.bgcolor = 'white'
cmap = fix(jet, (0, z.max()))
origin = 'bottom left' # origin = 'top left' # to flip y-axis
plot.img_plot("imagedata", name="surface2d",
xbounds=(np.min(x), np.max(x)),
ybounds=(1.0, y[-1,-1]),
colormap=cmap, hide_grids=True, interpolation='nearest',
origin=origin,
)
plot.default_origin = origin
plot.x_axis.title = u'Angle (2\u0398)'
tick_font = settings.tick_font
plot.x_axis.title_font = settings.axis_title_font
plot.y_axis.title_font = settings.axis_title_font
plot.x_axis.tick_label_font = tick_font
plot.y_axis.tick_label_font = tick_font
plot.y_axis.title = "Dataset"
plot.y_axis.tick_interval = 1.0
actual_plot = plot.plots["surface2d"][0]
self.plot_zoom_tool = ClickUndoZoomTool(
plot, tool_mode="box", always_on=True, pointer="cross",
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
x_min_zoom_factor=-np.inf, y_min_zoom_factor=-np.inf,
)
plot.overlays.append(self.plot_zoom_tool)
plot.tools.append(TraitsTool(plot))
# Add a color bar
colormap = actual_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=actual_plot,
orientation='v',
resizable='v',
width=30,
padding=40,
padding_top=50,
fill_padding=True)
colorbar._axis.title_font = settings.axis_title_font
colorbar._axis.tick_label_font = settings.tick_font
# Add pan and zoom tools to the colorbar
self.colorbar_zoom_tool = ClickUndoZoomTool(colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button=settings.zoom_button,
undo_button=settings.undo_button)
pan_tool = PanToolWithHistory(colorbar,
history_tool=self.colorbar_zoom_tool,
constrain_direction="y", constrain=True,
drag_button=settings.pan_button)
colorbar.tools.append(pan_tool)
colorbar.overlays.append(self.colorbar_zoom_tool)
# Add a label to the top of the color bar
colorbar_label = PlotLabel(
u'Intensity\n{:^9}'.format('(' + get_value_scale_label(scale) + ')'),
component=colorbar,
font=settings.axis_title_font,
)
colorbar.overlays.append(colorbar_label)
colorbar.tools.append(TraitsTool(colorbar))
# Add the plot and colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
return container
def _plot(self, x, y, z, scale):
pd = ArrayPlotData()
# pd.set_data("imagedata", z)
plot = Plot(pd, padding_left=60, fill_padding=True)
plot.bgcolor = 'white'
cmap = fix(jet, (0, z.max()))
plot.default_origin = 'bottom left' # origin = 'top left' # to flip y-axis
fid = FunctionImageData(func=self._prepare_data_for_window, data_range=plot.range2d)
pd.set_data("imagedata", fid)
self.img_plot = plot.img_plot("imagedata", name="surface2d",
xbounds=(np.min(x), np.max(x)),
ybounds=(1.0, y[-1,-1]),
colormap=cmap, hide_grids=True, interpolation='nearest'
# origin=origin,
)[0]
#plot.default_origin = origin
plot.x_axis = MyPlotAxis(component=plot, orientation='bottom')
plot.y_axis = MyPlotAxis(component=plot, orientation='left')
plot.x_axis.title = u'Angle (2\u0398)'
tick_font = settings.tick_font
plot.x_axis.title_font = settings.axis_title_font
plot.y_axis.title_font = settings.axis_title_font
plot.x_axis.tick_label_font = tick_font
plot.y_axis.tick_label_font = tick_font
plot.y_axis.title = "Dataset"
# if <10 datasets we want to reduce down the tickmarks to multiples
if len(y)<10:
plot.y_axis.tick_interval = 1.0
else:
plot.y_axis.tick_interval = len(y)/10
actual_plot = plot.plots["surface2d"][0]
self.plot_zoom_tool = ClickUndoZoomTool(
plot, always_on=True, pointer="cross",
tool_mode="range",
axis="index",
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
x_min_zoom_factor=-np.inf, y_min_zoom_factor=-np.inf,
)
plot.overlays.append(self.plot_zoom_tool)
plot.tools.append(TraitsTool(plot))
# Add a color bar
colormap = actual_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=actual_plot,
orientation='v',
resizable='v',
width=30,
padding=40,
padding_top=50,
fill_padding=True)
colorbar._axis.title_font = settings.axis_title_font
colorbar._axis.tick_label_font = settings.tick_font
# Add pan and zoom tools to the colorbar
self.colorbar_zoom_tool = ClickUndoZoomTool(colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button=settings.zoom_button,
undo_button=settings.undo_button)
pan_tool = PanToolWithHistory(colorbar,
history_tool=self.colorbar_zoom_tool,
constrain_direction="y", constrain=True,
drag_button=settings.pan_button)
colorbar.tools.append(pan_tool)
colorbar.overlays.append(self.colorbar_zoom_tool)
# Add a label to the top of the color bar
colorbar_label = PlotLabel(
u'Intensity\n{:^9}'.format('(' + get_value_scale_label(scale) + ')'),
component=colorbar,
font=settings.axis_title_font,
)
colorbar.overlays.append(colorbar_label)
colorbar.tools.append(TraitsTool(colorbar))
# Add the plot and colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
self.twod_plot = plot
return container
def __init__(self, **kw):
super(MLabChacoPlot, self).__init__(**kw)
self.prices = get_data()
x = self.prices['Date']
pd = ArrayPlotData(index = x)
pd.set_data("y", self.prices["Crude Supply"])
# 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", "y"), name="Crude Price", color=(.3, .3, .8, .8))
#plot.tools.append(PanTool(plot))
plot.tools.append(PanTool(plot, constrain=True, drag_button="right",
constrain_direction="x"))
range_plt = plot.plots['Crude Price'][0]
range_selection = RangeSelection(range_plt, left_button_selects=True)
range_selection.on_trait_change(self.update_interval, 'selection')
range_plt.tools.append(range_selection)
range_plt.overlays.append(RangeSelectionOverlay(range_plt))
zoom = ZoomTool(component=plot, tool_mode="range", always_on=False,
axis="index", max_zoom_out_factor=1.0,)
plot.overlays.append(zoom)
# Set the plot's bottom axis to use the Scales ticking system
scale_sys = CalendarScaleSystem(fill_ratio=0.4,
default_numlabels=5,
default_numticks=10,)
tick_gen = ScalesTickGenerator(scale=scale_sys)
bottom_axis = ScalesPlotAxis(plot, orientation="bottom",
tick_generator=tick_gen,
label_color="white",
line_color="white")
# Hack to remove default axis - FIXME: how do I *replace* an axis?
del(plot.underlays[-2])
plot.overlays.append(bottom_axis)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(700,500))
self.m = mlab.points3d(self.prices['Gasoline Supply'], self.prices['Jet Fuel Supply'], self.prices['Distillate Supply'], self.prices['Crude Supply'])
# Add another glyph module to render the full set of points
g2 = Glyph()
g2.glyph.glyph_source.glyph_source.glyph_type = "circle"
g2.glyph.glyph_source.glyph_source.filled = True
g2.actor.property.opacity = 0.75
self.m.module_manager.source.add_module(g2)
# Set a bunch of properties on the scene to make things look right
self.m.module_manager.scalar_lut_manager.lut_mode = 'PuBuGn'
self.m.glyph.mask_points.random_mode = False
self.m.glyph.mask_points.on_ratio = 1
self.m.scene.isometric_view()
self.m.scene.background = (.9, 0.95, 1.0)
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [600,120]
plot.padding = 25
plot.bgcolor = "white"
plot.outer_position = [30,30]
plot.tools.append(MoveTool(component=plot,drag_button="right"))
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(rwi, renderer,
component=container,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()