def draw_colorbar(self):
scatplot=self.scatplot
cmap_renderer = scatplot.plots["my_plot"][0]
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="range")
cmap_renderer.overlays.append(selection)
if self.thresh is not None:
cmap_renderer.color_data.metadata['selections']=self.thresh
cmap_renderer.color_data.metadata_changed={'selections':self.thresh}
# Create the colorbar, handing in the appropriate range and colormap
colormap=scatplot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=DataRange1D(low = 0.0,
high = 1.0)),
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar_selection=RangeSelection(component=colorbar)
colorbar.tools.append(colorbar_selection)
ovr=colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray",
metadata_name='selections'))
#ipshell('colorbar, colorbar_selection and ovr available:')
self.cbar_selection=colorbar_selection
self.cmap_renderer=cmap_renderer
colorbar.plot = cmap_renderer
colorbar.padding_top = scatplot.padding_top
colorbar.padding_bottom = scatplot.padding_bottom
self.colorbar=colorbar
return colorbar
def _create_colorbar(self):
cmap = self.plot.color_mapper
self._colorbar = ColorBar(index_mapper=LinearMapper(range=cmap.range),
color_mapper=cmap,
orientation='v',
resizable='v',
width=30,
padding=30)
def __init__(self, **kwargs):
super(VariableMeshPannerView, self).__init__(**kwargs)
# Create the plot
self.add_trait("field", DelegatesTo("vm_plot"))
plot = self.vm_plot.plot
img_plot = self.vm_plot.img_plot
if self.use_tools:
plot.tools.append(PanTool(img_plot))
zoom = ZoomTool(component=img_plot,
tool_mode="box",
always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
img_plot.overlays.append(overlay)
image_value_range = DataRange1D(self.vm_plot.fid)
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=img_plot,
padding_right=40,
resizable='v',
width=30)
self.colorbar.tools.append(
PanTool(self.colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(self.colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
self.colorbar.overlays.append(zoom_overlay)
# create a range selection for the colorbar
range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(img_plot)
self.full_container = HPlotContainer(padding=30)
self.container = OverlayPlotContainer(padding=0)
self.full_container.add(self.colorbar)
self.full_container.add(self.container)
self.container.add(self.vm_plot.plot)
def draw_colorbar(self):
scatplot = self.scatplot
cmap_renderer = scatplot.plots["my_plot"][0]
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="range")
cmap_renderer.overlays.append(selection)
if self.thresh is not None:
cmap_renderer.color_data.metadata['selections'] = self.thresh
cmap_renderer.color_data.metadata_changed = {
'selections': self.thresh
}
# Create the colorbar, handing in the appropriate range and colormap
colormap = scatplot.color_mapper
colorbar = ColorBar(
index_mapper=LinearMapper(range=DataRange1D(low=0.0, high=1.0)),
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar_selection = RangeSelection(component=colorbar)
colorbar.tools.append(colorbar_selection)
ovr = colorbar.overlays.append(
RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray",
metadata_name='selections'))
#ipshell('colorbar, colorbar_selection and ovr available:')
self.cbar_selection = colorbar_selection
self.cmap_renderer = cmap_renderer
colorbar.plot = cmap_renderer
colorbar.padding_top = scatplot.padding_top
colorbar.padding_bottom = scatplot.padding_bottom
self.colorbar = colorbar
return colorbar
def _create_plot_component(self):
# Create a plot data object and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.data[self.data_name])
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot(
"imagedata",
name="my_plot",
#xbounds=(0,10),
#ybounds=(0,10),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = "Matrix"
self.tplot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
return container
def _create_plot_component():
# Create a scalar field to colormap# Create a scalar field to colormap
xbounds = (-2*pi, 2*pi, 600)
ybounds = (-1.5*pi, 1.5*pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs,ys)
z = jn(2, x)*y*x
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
plot.img_plot("imagedata",
name="my_plot",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=jet)
# Tweak some of the plot properties
plot.title = "Selectable Image Plot"
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
colormap = my_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a range selection for the colorbar
range_selection = RangeSelection(component=colorbar)
colorbar.tools.append(range_selection)
colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
#my_plot.set_value_selection((-1.3, 6.9))
return container
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
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=gist_earth,
marker = "square",
fill_alpha = 0.5,
marker_size = 8,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Pan/Zoom Color Bar"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Add pan and zoom to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(plot)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.plot = plot
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Add pan and zoom tools to the colorbar
colorbar.tools.append(PanTool(colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(colorbar, axis="index", tool_mode="range",
always_on=True, drag_button="right")
colorbar.overlays.append(zoom_overlay)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot, colorbar, use_backbuffer=True, bgcolor="lightgray")
return container
def __init__(self,xdata=None,ydata=None,weights=None,model=None,
include_models=None,exclude_models=None,fittype=None,**traits):
"""
:param xdata: the first dimension of the data to be fit
:type xdata: array-like
:param ydata: the second dimension of the data to be fit
:type ydata: array-like
:param weights:
The weights to apply to the data. Statistically interpreted as inverse
errors (*not* inverse variance). May be any of the following forms:
* None for equal weights
* an array of points that must match `ydata`
* a 2-sequence of arrays (xierr,yierr) such that xierr matches the
`xdata` and yierr matches `ydata`
* a function called as f(params) that returns an array of weights
that match one of the above two conditions
:param model: the initial model to use to fit this data
:type model:
None, string, or :class:`pymodelfit.core.FunctionModel1D`
instance.
:param include_models:
With `exclude_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param exclude_models:
With `include_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param fittype:
The fitting technique for the initial fit (see
:class:`pymodelfit.core.FunctionModel`).
:type fittype: string
kwargs are passed in as any additional traits to apply to the
application.
"""
self.modelpanel = View(Label('empty'),kind='subpanel',title='model editor')
self.tmodel = TraitedModel(model)
if model is not None and fittype is not None:
self.tmodel.model.fittype = fittype
if xdata is None or ydata is None:
if not hasattr(self.tmodel.model,'data') or self.tmodel.model.data is None:
raise ValueError('data not provided and no data in model')
if xdata is None:
xdata = self.tmodel.model.data[0]
if ydata is None:
ydata = self.tmodel.model.data[1]
if weights is None:
weights = self.tmodel.model.data[2]
self.on_trait_change(self._paramsChanged,'tmodel.paramchange')
self.modelselector = NewModelSelector(include_models,exclude_models)
self.data = [xdata,ydata]
if weights is None:
self.weights = np.ones_like(xdata)
self.weighttype = 'equal'
else:
self.weights = np.array(weights,copy=True)
self.savews = True
weights1d = self.weights
while len(weights1d.shape)>1:
weights1d = np.sum(weights1d**2,axis=0)
pd = ArrayPlotData(xdata=self.data[0],ydata=self.data[1],weights=weights1d)
self.plot = plot = Plot(pd,resizable='hv')
self.scatter = plot.plot(('xdata','ydata','weights'),name='data',
color_mapper=_cmapblack if self.weights0rem else _cmap,
type='cmap_scatter', marker='circle')[0]
self.errorplots = None
if not isinstance(model,FunctionModel1D):
self.fitmodel = True
self.updatemodelplot = False #force plot update - generates xmod and ymod
plot.plot(('xmod','ymod'),name='model',type='line',line_style='dash',color='black',line_width=2)
del plot.x_mapper.range.sources[-1] #remove the line plot from the x_mapper source so only the data is tied to the scaling
self.on_trait_change(self._rangeChanged,'plot.index_mapper.range.updated')
self.pantool = PanTool(plot,drag_button='left')
plot.tools.append(self.pantool)
self.zoomtool = ZoomTool(plot)
self.zoomtool.prev_state_key = KeySpec('a')
self.zoomtool.next_state_key = KeySpec('s')
plot.overlays.append(self.zoomtool)
self.scattertool = None
self.scatter.overlays.append(ScatterInspectorOverlay(self.scatter,
hover_color = "black",
selection_color="black",
selection_outline_color="red",
selection_line_width=2))
self.colorbar = colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper.range,
plot=plot,
orientation='v',
resizable='v',
width = 30,
padding = 5)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
colorbar._axis.title = 'Weights'
self.plotcontainer = container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
super(FitGui,self).__init__(**traits)
self.on_trait_change(self._scale_change,'plot.value_scale,plot.index_scale')
if weights is not None and len(weights)==2:
self.weightsChanged() #update error bars
def create_plot_component(self):
self.plot_data = ArrayPlotData()
self.plot_data.set_data("imagedata", self.model.intens_yz)
self.plot_data.set_data('vert_image',self.model.intens_xz)
self.plot_data.set_data('beam_section', self.model.intens_xy)
# Create a contour polygon plot of the data
plot = Plot(self.plot_data)
plot.title = 'Horizontal plane'
plot.padding=5
plot.padding_top = 22
plot.padding_left=25
plot.padding_bottom=20
my_plot = plot.img_plot("imagedata", name='my_plot', # xbounds=x,#ybounds=y,
colormap=jet, hide_grids=True)[0]
colormap = my_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=25,
padding=0)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
colorbar.padding_left = 35
colorbar.padding_right = 30
colorbar._axis.tick_label_formatter = lambda x: ('%.0e'%x)
rplot = Plot(self.plot_data)
rplot.title = 'Vertical plane'
rplot.padding=plot.padding
rplot.padding_top = plot.padding_top
rplot.padding_left = plot.padding_left
rplot.padding_bottom = plot.padding_bottom
my_plot = rplot.img_plot('vert_image',name='vert_plot',colormap=jet)[0]
colormap = my_plot.color_mapper
colorbar_rplot = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=25,
padding=0)
colorbar_rplot.padding_top = plot.padding_top
colorbar_rplot.padding_bottom = plot.padding_bottom
colorbar_rplot.padding_right = 20
colorbar_rplot.padding_left = 35
colorbar_rplot._axis.tick_label_formatter = lambda x: ('%.0e'%x)
sec_plot = Plot(self.plot_data)
sec_plot.title = 'Beam section'
sec_plot.padding=plot.padding
sec_plot.padding_top = plot.padding_top
sec_plot.padding_left = plot.padding_left
sec_plot.padding_bottom = plot.padding_bottom
my_plot = sec_plot.img_plot('beam_section',name='beam_section_plot',colormap=jet)[0]
colormap = my_plot.color_mapper
colorbar_sec_plot = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=25,
padding=0)
colorbar_sec_plot.padding_top = plot.padding_top
colorbar_sec_plot.padding_bottom = plot.padding_bottom
colorbar_sec_plot.padding_right = 20
colorbar_sec_plot.padding_left = 35
colorbar_sec_plot._axis.tick_label_formatter = lambda x: ('%.0e'%x)
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.add(rplot)
container.add(colorbar_rplot)
container.add(sec_plot)
container.add(colorbar_sec_plot)
self.plot_container = container
def _create_plot_component(file_name):
# Create a scalar field to colormap
z = load(file_name)
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
plot.bgcolor = 'gray'
img_plot = plot.img_plot("imagedata",
name='my_plot',
colormap=jet,
hide_grids=True)[0]
#cont_plot=plot.contour_plot('imagedata', type='line', name='countour')
# Tweak some of the plot properties
#plot.title = file_name
plot.padding = 40
plot.padding_right=20
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
#plot.tools.append(TraitsTool(plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
img_plot.overlays.append(zoom)
#plot.y_axis.tick_label_formatter = lambda x: "%.3e" % x
#plot.x_axis.tick_label_formatter = lambda x: "%.3e" % x
#plot.tools.append(SaveTool(plot))
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
my_plot = img_plot#plot.plots["my_plot"][0]
colormap = my_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=25,
padding=0)
colorbar.origin = 'bottom left'
#colorbar._axis.tick_label_formatter = lambda x: '%.0e'%(x*10e6) + u' [\u00b5' + 'Watt]'
colorbar._axis.tick_label_formatter = lambda x: ('%.0e'%(x*1e6))
colorbar._axis.orientation = 'right'
colorbar._axis.title = u'Intensity [\u00b5W]'
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
colorbar.padding_right = 100
# create a range selection for the colorbar
range_selection = RangeSelection(component=colorbar)
colorbar.tools.append(range_selection)
colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.5,
fill_color="lightgray"))
range_selection.listeners.append(my_plot)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(ImageInspectorOverlay(component=img_plot, image_inspector=imgtool))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "white"
container.tools.append(SaveTool(container))
container.tools.append(TraitsTool(container))
#my_plot.set_value_selection((-1.3, 6.9))
return container