def __init__(self, data=None, **kwtraits):
if 'origin' in kwtraits:
self.default_origin = kwtraits.pop('origin')
if "title" in kwtraits:
title = kwtraits.pop("title")
else:
title = None
super(Plot, self).__init__(**kwtraits)
if data is not None:
if isinstance(data, AbstractPlotData):
self.data = data
elif type(data) in (ndarray, tuple, list):
self.data = ArrayPlotData(data)
else:
raise ValueError, "Don't know how to create PlotData for data" \
"of type " + str(type(data))
if not self._title:
self._title = PlotLabel(font="swiss 16", visible=False,
overlay_position="top", component=self)
if title is not None:
self.title = title
if not self.legend:
self.legend = Legend(visible=False, align="ur", error_icon="blank",
padding=10, component=self)
# ensure that we only get displayed once by new_window()
self._plot_ui_info = None
return
class Plot(DataView):
""" Represents a correlated set of data, renderers, and axes in a single
screen region.
A Plot can reference an arbitrary amount of data and can have an
unlimited number of renderers on it, but it has a single X-axis and a
single Y-axis for all of its associated data. Therefore, there is a single
range in X and Y, although there can be many different data series. A Plot
also has a single set of grids and a single background layer for all of its
renderers. It cannot be split horizontally or vertically; to do so,
create a VPlotContainer or HPlotContainer and put the Plots inside those.
Plots can be overlaid as well; be sure to set the **bgcolor** of the
overlaying plots to "none" or "transparent".
A Plot consists of composable sub-plots. Each of these is created
or destroyed using the plot() or delplot() methods. Every time that
new data is used to drive these sub-plots, it is added to the Plot's
list of data and data sources. Data sources are reused whenever
possible; in order to have the same actual array drive two de-coupled
data sources, create those data sources before handing them to the Plot.
"""
#------------------------------------------------------------------------
# Data-related traits
#------------------------------------------------------------------------
# The PlotData instance that drives this plot.
data = Instance(AbstractPlotData)
# Mapping of data names from self.data to their respective datasources.
datasources = Dict(Str, Instance(AbstractDataSource))
#------------------------------------------------------------------------
# General plotting traits
#------------------------------------------------------------------------
# Mapping of plot names to *lists* of plot renderers.
plots = Dict(Str, List)
# The default index to use when adding new subplots.
default_index = Instance(AbstractDataSource)
# Optional mapper for the color axis. Not instantiated until first use;
# destroyed if no color plots are on the plot.
color_mapper = Instance(AbstractColormap)
# List of colors to cycle through when auto-coloring is requested. Picked
# and ordered to be red-green color-blind friendly, though should not
# be an issue for blue-yellow.
auto_colors = List(["green", "lightgreen", "blue", "lightblue", "red",
"pink", "darkgray", "silver"])
# index into auto_colors list
_auto_color_idx = Int(-1)
_auto_edge_color_idx = Int(-1)
_auto_face_color_idx = Int(-1)
# Mapping of renderer type string to renderer class
# This can be overriden to customize what renderer type the Plot
# will instantiate for its various plotting methods.
renderer_map = Dict(dict(line = LinePlot,
bar = BarPlot,
scatter = ScatterPlot,
polygon = PolygonPlot,
filled_line = FilledLinePlot,
cmap_scatter = ColormappedScatterPlot,
img_plot = ImagePlot,
cmap_img_plot = CMapImagePlot,
contour_line_plot = ContourLinePlot,
contour_poly_plot = ContourPolyPlot,
candle = CandlePlot,
quiver = QuiverPlot,))
#------------------------------------------------------------------------
# Annotations and decorations
#------------------------------------------------------------------------
# The title of the plot.
title = Property()
# The font to use for the title.
title_font = Property()
# Convenience attribute for title.overlay_position; can be "top",
# "bottom", "left", or "right".
title_position = Property()
# Use delegates to expose the other PlotLabel attributes of the plot title
title_text = Delegate("_title", prefix="text", modify=True)
title_color = Delegate("_title", prefix="color", modify=True)
title_angle = Delegate("_title", prefix="angle", modify=True)
# The PlotLabel object that contains the title.
_title = Instance(PlotLabel)
# The legend on the plot.
legend = Instance(Legend)
# Convenience attribute for legend.align; can be "ur", "ul", "ll", "lr".
legend_alignment = Property
#------------------------------------------------------------------------
# Public methods
#------------------------------------------------------------------------
def __init__(self, data=None, **kwtraits):
if 'origin' in kwtraits:
self.default_origin = kwtraits.pop('origin')
if "title" in kwtraits:
title = kwtraits.pop("title")
else:
title = None
super(Plot, self).__init__(**kwtraits)
if data is not None:
if isinstance(data, AbstractPlotData):
self.data = data
elif type(data) in (ndarray, tuple, list):
self.data = ArrayPlotData(data)
else:
raise ValueError, "Don't know how to create PlotData for data" \
"of type " + str(type(data))
if not self._title:
self._title = PlotLabel(font="swiss 16", visible=False,
overlay_position="top", component=self)
if title is not None:
self.title = title
if not self.legend:
self.legend = Legend(visible=False, align="ur", error_icon="blank",
padding=10, component=self)
# ensure that we only get displayed once by new_window()
self._plot_ui_info = None
return
def add_xy_plot(self, index_name, value_name, renderer_factory, name=None,
origin=None, **kwds):
""" Add a BaseXYPlot renderer subclass to this Plot.
Parameters
----------
index_name : str
The name of the index datasource.
value_name : str
The name of the value datasource.
renderer_factory : callable
The callable that creates the renderer.
name : string (optional)
The name of the plot. If None, then a default one is created
(usually "plotNNN").
origin : string (optional)
Which corner the origin of this plot should occupy:
"bottom left", "top left", "bottom right", "top right"
**kwds :
Additional keywords to pass to the factory.
"""
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
index = self._get_or_create_datasource(index_name)
self.index_range.add(index)
value = self._get_or_create_datasource(value_name)
self.value_range.add(value)
if self.index_scale == "linear":
imap = LinearMapper(range=self.index_range)
else:
imap = LogMapper(range=self.index_range)
if self.value_scale == "linear":
vmap = LinearMapper(range=self.value_range)
else:
vmap = LogMapper(range=self.value_range)
renderer = renderer_factory(
index = index,
value = value,
index_mapper = imap,
value_mapper = vmap,
orientation = self.orientation,
origin = origin,
**kwds
)
self.add(renderer)
self.plots[name] = [renderer]
self.invalidate_and_redraw()
return self.plots[name]
def plot(self, data, type="line", name=None, index_scale="linear",
value_scale="linear", origin=None, **styles):
""" Adds a new sub-plot using the given data and plot style.
Parameters
----------
data : string, tuple(string), list(string)
The data to be plotted. The type of plot and the number of
arguments determines how the arguments are interpreted:
one item: (line/scatter)
The data is treated as the value and self.default_index is
used as the index. If **default_index** does not exist, one is
created from arange(len(*data*))
two or more items: (line/scatter)
Interpreted as (index, value1, value2, ...). Each index,value
pair forms a new plot of the type specified.
two items: (cmap_scatter)
Interpreted as (value, color_values). Uses **default_index**.
three or more items: (cmap_scatter)
Interpreted as (index, val1, color_val1, val2, color_val2, ...)
type : comma-delimited string of "line", "scatter", "cmap_scatter"
The types of plots to add.
name : string
The name of the plot. If None, then a default one is created
(usually "plotNNN").
index_scale : string
The type of scale to use for the index axis. If not "linear", then
a log scale is used.
value_scale : string
The type of scale to use for the value axis. If not "linear", then
a log scale is used.
origin : string
Which corner the origin of this plot should occupy:
"bottom left", "top left", "bottom right", "top right"
styles : series of keyword arguments
attributes and values that apply to one or more of the
plot types requested, e.g.,'line_color' or 'line_width'.
Examples
--------
::
plot("my_data", type="line", name="myplot", color=lightblue)
plot(("x-data", "y-data"), type="scatter")
plot(("x", "y1", "y2", "y3"))
Returns
-------
[renderers] -> list of renderers created in response to this call to plot()
"""
if len(data) == 0:
return
if isinstance(data, basestring):
data = (data,)
self.index_scale = index_scale
self.value_scale = value_scale
# TODO: support lists of plot types
plot_type = type
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
if plot_type in ("line", "scatter", "polygon", "bar", "filled_line"):
# Tie data to the index range
if len(data) == 1:
if self.default_index is None:
# Create the default index based on the length of the first
# data series
value = self._get_or_create_datasource(data[0])
self.default_index = ArrayDataSource(arange(len(value.get_data())),
sort_order="none")
self.index_range.add(self.default_index)
index = self.default_index
else:
index = self._get_or_create_datasource(data[0])
if self.default_index is None:
self.default_index = index
self.index_range.add(index)
data = data[1:]
# Tie data to the value_range and create the renderer for each data
new_plots = []
simple_plot_types = ("line", "scatter")
for value_name in data:
value = self._get_or_create_datasource(value_name)
self.value_range.add(value)
if plot_type in simple_plot_types:
cls = self.renderer_map[plot_type]
# handle auto-coloring request
if styles.get("color") == "auto":
self._auto_color_idx = \
(self._auto_color_idx + 1) % len(self.auto_colors)
styles["color"] = self.auto_colors[self._auto_color_idx]
elif plot_type in ("polygon", "filled_line"):
cls = self.renderer_map[plot_type]
# handle auto-coloring request
if styles.get("edge_color") == "auto":
self._auto_edge_color_idx = \
(self._auto_edge_color_idx + 1) % len(self.auto_colors)
styles["edge_color"] = self.auto_colors[self._auto_edge_color_idx]
if styles.get("face_color") == "auto":
self._auto_face_color_idx = \
(self._auto_face_color_idx + 1) % len(self.auto_colors)
styles["face_color"] = self.auto_colors[self._auto_face_color_idx]
elif plot_type == 'bar':
cls = self.renderer_map[plot_type]
# handle auto-coloring request
if styles.get("color") == "auto":
self._auto_color_idx = \
(self._auto_color_idx + 1) % len(self.auto_colors)
styles["fill_color"] = self.auto_colors[self._auto_color_idx]
else:
raise ValueError("Unhandled plot type: " + plot_type)
if self.index_scale == "linear":
imap = LinearMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
else:
imap = LogMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
if self.value_scale == "linear":
vmap = LinearMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
else:
vmap = LogMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
plot = cls(index=index,
value=value,
index_mapper=imap,
value_mapper=vmap,
orientation=self.orientation,
origin = origin,
**styles)
self.add(plot)
new_plots.append(plot)
if plot_type == 'bar':
# For bar plots, compute the ranges from the data to make the
# plot look clean.
def custom_index_func(data_low, data_high, margin, tight_bounds):
""" Compute custom bounds of the plot along index (in
data space).
"""
bar_width = styles.get('bar_width', cls().bar_width)
plot_low = data_low - bar_width
plot_high = data_high + bar_width
return plot_low, plot_high
if self.index_range.bounds_func is None:
self.index_range.bounds_func = custom_index_func
def custom_value_func(data_low, data_high, margin, tight_bounds):
""" Compute custom bounds of the plot along value (in
data space).
"""
plot_low = data_low - (data_high-data_low)*0.1
plot_high = data_high + (data_high-data_low)*0.1
return plot_low, plot_high
if self.value_range.bounds_func is None:
self.value_range.bounds_func = custom_value_func
self.index_range.tight_bounds = False
self.value_range.tight_bounds = False
self.index_range.refresh()
self.value_range.refresh()
self.plots[name] = new_plots
elif plot_type == "cmap_scatter":
if len(data) != 3:
raise ValueError("Colormapped scatter plots require (index, value, color) data")
else:
index = self._get_or_create_datasource(data[0])
if self.default_index is None:
self.default_index = index
self.index_range.add(index)
value = self._get_or_create_datasource(data[1])
self.value_range.add(value)
color = self._get_or_create_datasource(data[2])
if not styles.has_key("color_mapper"):
raise ValueError("Scalar 2D data requires a color_mapper.")
colormap = styles.pop("color_mapper", None)
if self.color_mapper is not None and self.color_mapper.range is not None:
color_range = self.color_mapper.range
else:
color_range = DataRange1D()
if isinstance(colormap, AbstractColormap):
self.color_mapper = colormap
if colormap.range is None:
color_range.add(color)
colormap.range = color_range
elif callable(colormap):
color_range.add(color)
self.color_mapper = colormap(color_range)
else:
raise ValueError("Unexpected colormap %r in plot()." % colormap)
if self.index_scale == "linear":
imap = LinearMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
else:
imap = LogMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
if self.value_scale == "linear":
vmap = LinearMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
else:
vmap = LogMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
cls = self.renderer_map["cmap_scatter"]
plot = cls(index=index,
index_mapper=imap,
value=value,
value_mapper=vmap,
color_data=color,
color_mapper=self.color_mapper,
orientation=self.orientation,
origin=origin,
**styles)
self.add(plot)
self.plots[name] = [plot]
else:
raise ValueError("Unknown plot type: " + plot_type)
return self.plots[name]
def img_plot(self, data, name=None, colormap=None,
xbounds=None, ybounds=None, origin=None, hide_grids=True, **styles):
""" Adds image plots to this Plot object.
If *data* has shape (N, M, 3) or (N, M, 4), then it is treated as RGB or
RGBA (respectively) and *colormap* is ignored.
If *data* is an array of floating-point data, then a colormap can
be provided via the *colormap* argument, or the default of 'Spectral'
will be used.
*Data* should be in row-major order, so that xbounds corresponds to
*data*'s second axis, and ybounds corresponds to the first axis.
Parameters
----------
data : string
The name of the data array in self.plot_data
name : string
The name of the plot; if omitted, then a name is generated.
xbounds, ybounds : string, tuple, or ndarray
Bounds where this image resides. Bound may be: a) names of
data in the plot data; b) tuples of (low, high) in data space,
c) 1D arrays of values representing the pixel boundaries (must
be 1 element larger than underlying data), or
d) 2D arrays as obtained from a meshgrid operation
origin : string
Which corner the origin of this plot should occupy:
"bottom left", "top left", "bottom right", "top right"
hide_grids : bool, default True
Whether or not to automatically hide the grid lines on the plot
styles : series of keyword arguments
Attributes and values that apply to one or more of the
plot types requested, e.g.,'line_color' or 'line_width'.
"""
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
value = self._get_or_create_datasource(data)
array_data = value.get_data()
if len(array_data.shape) == 3:
if array_data.shape[2] not in (3,4):
raise ValueError("Image plots require color depth of 3 or 4.")
cls = self.renderer_map["img_plot"]
kwargs = dict(**styles)
else:
if colormap is None:
if self.color_mapper is None:
colormap = Spectral(DataRange1D(value))
else:
colormap = self.color_mapper
elif isinstance(colormap, AbstractColormap):
if colormap.range is None:
colormap.range = DataRange1D(value)
else:
colormap = colormap(DataRange1D(value))
self.color_mapper = colormap
cls = self.renderer_map["cmap_img_plot"]
kwargs = dict(value_mapper=colormap, **styles)
return self._create_2d_plot(cls, name, origin, xbounds, ybounds, value,
hide_grids, **kwargs)
def contour_plot(self, data, type="line", name=None, poly_cmap=None,
xbounds=None, ybounds=None, origin=None, hide_grids=True, **styles):
""" Adds contour plots to this Plot object.
Parameters
----------
data : string
The name of the data array in self.plot_data, which must be
floating point data.
type : comma-delimited string of "line", "poly"
The type of contour plot to add. If the value is "poly"
and no colormap is provided via the *poly_cmap* argument, then
a default colormap of 'Spectral' is used.
name : string
The name of the plot; if omitted, then a name is generated.
poly_cmap : string
The name of the color-map function to call (in
chaco.default_colormaps) or an AbstractColormap instance
to use for contour poly plots (ignored for contour line plots)
xbounds, ybounds : string, tuple, or ndarray
Bounds where this image resides. Bound may be: a) names of
data in the plot data; b) tuples of (low, high) in data space,
c) 1D arrays of values representing the pixel boundaries (must
be 1 element larger than underlying data), or
d) 2D arrays as obtained from a meshgrid operation
origin : string
Which corner the origin of this plot should occupy:
"bottom left", "top left", "bottom right", "top right"
hide_grids : bool, default True
Whether or not to automatically hide the grid lines on the plot
styles : series of keyword arguments
Attributes and values that apply to one or more of the
plot types requested, e.g.,'line_color' or 'line_width'.
"""
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
value = self._get_or_create_datasource(data)
if value.value_depth != 1:
raise ValueError("Contour plots require 2D scalar field")
if type == "line":
cls = self.renderer_map["contour_line_plot"]
kwargs = dict(**styles)
# if colors is given as a factory func, use it to make a
# concrete colormapper. Better way to do this?
if "colors" in kwargs:
cmap = kwargs["colors"]
if isinstance(cmap, FunctionType):
kwargs["colors"] = cmap(DataRange1D(value))
elif getattr(cmap, 'range', 'dummy') is None:
cmap.range = DataRange1D(value)
elif type == "poly":
if poly_cmap is None:
poly_cmap = Spectral(DataRange1D(value))
elif isinstance(poly_cmap, FunctionType):
poly_cmap = poly_cmap(DataRange1D(value))
elif getattr(poly_cmap, 'range', 'dummy') is None:
poly_cmap.range = DataRange1D(value)
cls = self.renderer_map["contour_poly_plot"]
kwargs = dict(color_mapper=poly_cmap, **styles)
else:
raise ValueError("Unhandled contour plot type: " + type)
return self._create_2d_plot(cls, name, origin, xbounds, ybounds, value,
hide_grids, **kwargs)
def _process_2d_bounds(self, bounds, array_data, axis):
"""Transform an arbitrary bounds definition into a linspace.
Process all the ways the user could have defined the x- or y-bounds
of a 2d plot and return a linspace between the lower and upper
range of the bounds.
Parameters
----------
bounds : any
User bounds definition
array_data : 2D array
The 2D plot data
axis : int
The axis along which the bounds are to be set
"""
num_ticks = array_data.shape[axis] + 1
if bounds is None:
return arange(num_ticks)
if type(bounds) is tuple:
# create a linspace with the bounds limits
return linspace(bounds[0], bounds[1], num_ticks)
if type(bounds) is ndarray and len(bounds.shape) == 1:
# bounds is 1D, but of the wrong size
if len(bounds) != num_ticks:
msg = ("1D bounds of an image plot needs to have 1 more "
"element than its corresponding data shape, because "
"they represent the locations of pixel boundaries.")
raise ValueError(msg)
else:
return linspace(bounds[0], bounds[-1], num_ticks)
if type(bounds) is ndarray and len(bounds.shape) == 2:
# bounds is 2D, assumed to be a meshgrid
# This is triggered when doing something like
# >>> xbounds, ybounds = meshgrid(...)
# >>> z = f(xbounds, ybounds)
if bounds.shape != array_data.shape:
msg = ("2D bounds of an image plot needs to have the same "
"shape as the underlying data, because "
"they are assumed to be generated from meshgrids.")
raise ValueError(msg)
else:
if axis == 0: bounds = bounds[:,0]
else: bounds = bounds[0,:]
interval = bounds[1] - bounds[0]
return linspace(bounds[0], bounds[-1]+interval, num_ticks)
raise ValueError("bounds must be None, a tuple, an array, "
"or a PlotData name")
def _create_2d_plot(self, cls, name, origin, xbounds, ybounds, value_ds,
hide_grids, **kwargs):
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
array_data = value_ds.get_data()
# process bounds to get linspaces
if isinstance(xbounds, basestring):
xbounds = self._get_or_create_datasource(xbounds).get_data()
xs = self._process_2d_bounds(xbounds, array_data, 1)
if isinstance(ybounds, basestring):
ybounds = self._get_or_create_datasource(ybounds).get_data()
ys = self._process_2d_bounds(ybounds, array_data, 0)
# Create the index and add its datasources to the appropriate ranges
index = GridDataSource(xs, ys, sort_order=('ascending', 'ascending'))
self.range2d.add(index)
mapper = GridMapper(range=self.range2d,
stretch_data_x=self.x_mapper.stretch_data,
stretch_data_y=self.y_mapper.stretch_data)
plot = cls(index=index,
value=value_ds,
index_mapper=mapper,
orientation=self.orientation,
origin=origin,
**kwargs)
if hide_grids:
self.x_grid.visible = False
self.y_grid.visible = False
self.add(plot)
self.plots[name] = [plot]
return self.plots[name]
def candle_plot(self, data, name=None, value_scale="linear", origin=None,
**styles):
""" Adds a new sub-plot using the given data and plot style.
Parameters
----------
data : list(string), tuple(string)
The names of the data to be plotted in the ArrayDataSource. The
number of arguments determines how they are interpreted:
(index, bar_min, bar_max)
filled or outline-only bar extending from **bar_min** to
**bar_max**
(index, bar_min, center, bar_max)
above, plus a center line of a different color at **center**
(index, min, bar_min, bar_max, max)
bar extending from **bar_min** to **bar_max**, with thin
bars at **min** and **max** connected to the bar by a long
stem
(index, min, bar_min, center, bar_max, max)
like above, plus a center line of a different color and
configurable thickness at **center**
name : string
The name of the plot. If None, then a default one is created.
value_scale : string
The type of scale to use for the value axis. If not "linear",
then a log scale is used.
Styles
------
These are all optional keyword arguments.
bar_color : string, 3- or 4-tuple
The fill color of the bar; defaults to "auto".
bar_line_color : string, 3- or 4-tuple
The color of the rectangular box forming the bar.
stem_color : string, 3- or 4-tuple (default = bar_line_color)
The color of the stems reaching from the bar to the min and
max values.
center_color : string, 3- or 4-tuple (default = bar_line_color)
The color of the line drawn across the bar at the center values.
line_width : int (default = 1)
The thickness, in pixels, of the outline around the bar.
stem_width : int (default = line_width)
The thickness, in pixels, of the stem lines
center_width : int (default = line_width)
The width, in pixels, of the line drawn across the bar at the
center values.
end_cap : bool (default = True)
Whether or not to draw bars at the min and max extents of the
error bar.
Returns
-------
[renderers] -> list of renderers created in response to this call.
"""
if len(data) == 0:
return
self.value_scale = value_scale
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
# Create the datasources
if len(data) == 3:
index, bar_min, bar_max = map(self._get_or_create_datasource, data)
self.value_range.add(bar_min, bar_max)
center = None
min = None
max = None
elif len(data) == 4:
index, bar_min, center, bar_max = map(self._get_or_create_datasource, data)
self.value_range.add(bar_min, center, bar_max)
min = None
max = None
elif len(data) == 5:
index, min, bar_min, bar_max, max = \
map(self._get_or_create_datasource, data)
self.value_range.add(min, bar_min, bar_max, max)
center = None
elif len(data) == 6:
index, min, bar_min, center, bar_max, max = \
map(self._get_or_create_datasource, data)
self.value_range.add(min, bar_min, center, bar_max, max)
self.index_range.add(index)
if styles.get("bar_color") == "auto" or styles.get("color") == "auto":
self._auto_color_idx = \
(self._auto_color_idx + 1) % len(self.auto_colors)
styles["color"] = self.auto_colors[self._auto_color_idx]
if self.index_scale == "linear":
imap = LinearMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
else:
imap = LogMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
if self.value_scale == "linear":
vmap = LinearMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
else:
vmap = LogMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
cls = self.renderer_map["candle"]
plot = cls(index = index,
min_values = min,
bar_min = bar_min,
center_values = center,
bar_max = bar_max,
max_values = max,
index_mapper = imap,
value_mapper = vmap,
orientation = self.orientation,
origin = self.origin,
**styles)
self.add(plot)
self.plots[name] = [plot]
return [plot]
def quiverplot(self, data, name=None, origin=None,
**styles):
""" Adds a new sub-plot using the given data and plot style.
Parameters
----------
data : list(string), tuple(string)
The names of the data to be plotted in the ArrayDataSource. There
is only one combination accepted by this function:
(index, value, vectors)
index and value together determine the start coordinates of
each vector. The vectors are an Nx2
name : string
The name of the plot. If None, then a default one is created.
origin : string
Which corner the origin of this plot should occupy:
"bottom left", "top left", "bottom right", "top right"
Styles
------
These are all optional keyword arguments.
line_color : string (default = "black")
The color of the arrows
line_width : float (default = 1.0)
The thickness, in pixels, of the arrows.
arrow_size : int (default = 5)
The length, in pixels, of the arrowhead
Returns
-------
[renderers] -> list of renderers created in response to this call.
"""
if name is None:
name = self._make_new_plot_name()
if origin is None:
origin = self.default_origin
index, value, vectors = map(self._get_or_create_datasource, data)
self.index_range.add(index)
self.value_range.add(value)
imap = LinearMapper(range=self.index_range,
stretch_data=self.index_mapper.stretch_data)
vmap = LinearMapper(range=self.value_range,
stretch_data=self.value_mapper.stretch_data)
cls = self.renderer_map["quiver"]
plot = cls(index = index,
value = value,
vectors = vectors,
index_mapper = imap,
value_mapper = vmap,
name = name,
origin = origin,
**styles
)
self.add(plot)
self.plots[name] = [plot]
return [plot]
def delplot(self, *names):
""" Removes the named sub-plots. """
# This process involves removing the plots, then checking the index range
# and value range for leftover datasources, and removing those if necessary.
# Remove all the renderers from us (container) and create a set of the
# datasources that we might have to remove from the ranges
deleted_sources = set()
for renderer in itertools.chain(*[self.plots.pop(name) for name in names]):
self.remove(renderer)
deleted_sources.add(renderer.index)
deleted_sources.add(renderer.value)
# Cull the candidate list of sources to remove by checking the other plots
sources_in_use = set()
for p in itertools.chain(*self.plots.values()):
sources_in_use.add(p.index)
sources_in_use.add(p.value)
unused_sources = deleted_sources - sources_in_use - set([None])
# Remove the unused sources from all ranges
for source in unused_sources:
if source.index_dimension == "scalar":
# Try both index and range, it doesn't hurt
self.index_range.remove(source)
self.value_range.remove(source)
elif source.index_dimension == "image":
self.range2d.remove(source)
else:
warnings.warn("Couldn't remove datasource from datarange.")
return
def hideplot(self, *names):
""" Convenience function to sets the named plots to be invisible. Their
renderers are not removed, and they are still in the list of plots.
"""
for renderer in itertools.chain(*[self.plots[name] for name in names]):
renderer.visible = False
return
def showplot(self, *names):
""" Convenience function to sets the named plots to be visible.
"""
for renderer in itertools.chain(*[self.plots[name] for name in names]):
renderer.visible = True
return
def new_window(self, configure=False):
"""Convenience function that creates a window containing the Plot
Don't call this if the plot is already displayed in a window.
"""
from chaco.ui.plot_window import PlotWindow
if self._plot_ui_info is None:
if configure:
self._plot_ui_info = PlotWindow(plot=self).configure_traits()
else:
self._plot_ui_info = PlotWindow(plot=self).edit_traits()
return self._plot_ui_info
#------------------------------------------------------------------------
# Private methods
#------------------------------------------------------------------------
def _make_new_plot_name(self):
""" Returns a string that is not already used as a plot title.
"""
n = len(self.plots)
plot_template = "plot%d"
while 1:
name = plot_template % n
if name not in self.plots:
break
else:
n += 1
return name
def _get_or_create_datasource(self, name):
""" Returns the data source associated with the given name, or creates
it if it doesn't exist.
"""
if name not in self.datasources:
data = self.data.get_data(name)
if type(data) in (list, tuple):
data = array(data)
if isinstance(data, ndarray):
if len(data.shape) == 1:
ds = ArrayDataSource(data, sort_order="none")
elif len(data.shape) == 2:
ds = ImageData(data=data, value_depth=1)
elif len(data.shape) == 3 and data.shape[2] in (3,4):
ds = ImageData(data=data, value_depth=int(data.shape[2]))
else:
raise ValueError("Unhandled array shape in creating new "
"plot: %s" % str(data.shape))
elif isinstance(data, AbstractDataSource):
ds = data
else:
raise ValueError("Couldn't create datasource for data of "
"type %s" % type(data))
self.datasources[name] = ds
return self.datasources[name]
#------------------------------------------------------------------------
# Event handlers
#------------------------------------------------------------------------
def _color_mapper_changed(self):
for plist in self.plots.values():
for plot in plist:
plot.color_mapper = self.color_mapper
self.invalidate_draw()
def _data_changed(self, old, new):
if old:
old.on_trait_change(self._data_update_handler, "data_changed",
remove=True)
if new:
new.on_trait_change(self._data_update_handler, "data_changed")
def _data_update_handler(self, name, event):
# event should be a dict with keys "added", "removed", and "changed",
# per the comments in AbstractPlotData.
if "removed" in event:
for name in event["removed"]:
del self.datasources[name]
if "added" in event:
for name in event["added"]:
self._get_or_create_datasource(name)
if "changed" in event:
for name in event["changed"]:
if name in self.datasources:
source = self.datasources[name]
source.set_data(self.data.get_data(name))
def _plots_items_changed(self, event):
if self.legend:
self.legend.plots = self.plots
def _index_scale_changed(self, old, new):
if old is None: return
if new == old: return
if not self.range2d: return
if self.index_scale == "linear":
imap = LinearMapper(range=self.index_range,
screen_bounds=self.index_mapper.screen_bounds,
stretch_data=self.index_mapper.stretch_data)
else:
imap = LogMapper(range=self.index_range,
screen_bounds=self.index_mapper.screen_bounds,
stretch_data=self.index_mapper.stretch_data)
self.index_mapper = imap
for key in self.plots:
for plot in self.plots[key]:
if not isinstance(plot, BaseXYPlot):
raise ValueError("log scale only supported on XY plots")
if self.index_scale == "linear":
imap = LinearMapper(range=plot.index_range,
screen_bounds=plot.index_mapper.screen_bounds,
stretch_data=self.index_mapper.stretch_data)
else:
imap = LogMapper(range=plot.index_range,
screen_bounds=plot.index_mapper.screen_bounds,
stretch_data=self.index_mapper.stretch_data)
plot.index_mapper = imap
def _value_scale_changed(self, old, new):
if old is None: return
if new == old: return
if not self.range2d: return
if self.value_scale == "linear":
vmap = LinearMapper(range=self.value_range,
screen_bounds=self.value_mapper.screen_bounds,
stretch_data=self.value_mapper.stretch_data)
else:
vmap = LogMapper(range=self.value_range,
screen_bounds=self.value_mapper.screen_bounds,
stretch_data=self.value_mapper.stretch_data)
self.value_mapper = vmap
for key in self.plots:
for plot in self.plots[key]:
if not isinstance(plot, BaseXYPlot):
raise ValueError("log scale only supported on XY plots")
if self.value_scale == "linear":
vmap = LinearMapper(range=plot.value_range,
screen_bounds=plot.value_mapper.screen_bounds,
stretch_data=self.value_mapper.stretch_data)
else:
vmap = LogMapper(range=plot.value_range,
screen_bounds=plot.value_mapper.screen_bounds,
stretch_data=self.value_mapper.stretch_data)
plot.value_mapper = vmap
def __title_changed(self, old, new):
self._overlay_change_helper(old, new)
def _legend_changed(self, old, new):
self._overlay_change_helper(old, new)
if new:
new.plots = self.plots
def _handle_range_changed(self, name, old, new):
""" Overrides the DataView default behavior.
Primarily changes how the list of renderers is looked up.
"""
mapper = getattr(self, name+"_mapper")
if mapper.range == old:
mapper.range = new
if old is not None:
for datasource in old.sources[:]:
old.remove(datasource)
if new is not None:
new.add(datasource)
range_name = name + "_range"
for renderer in itertools.chain(*self.plots.values()):
if hasattr(renderer, range_name):
setattr(renderer, range_name, new)
#------------------------------------------------------------------------
# Property getters and setters
#------------------------------------------------------------------------
def _set_legend_alignment(self, align):
if self.legend:
self.legend.align = align
def _get_legend_alignment(self):
if self.legend:
return self.legend.align
else:
return None
def _set_title(self, text):
self._title.text = text
if text.strip() != "":
self._title.visible = True
else:
self._title.visible = False
def _get_title(self):
return self._title.text
def _set_title_position(self, pos):
if self._title is not None:
self._title.overlay_position = pos
def _get_title_position(self):
if self._title is not None:
return self._title.overlay_position
else:
return None
def _set_title_font(self, font):
old_font = self._title.font
self._title.font = font
self.trait_property_changed("title_font", old_font, font)
def _get_title_font(self):
return self._title.font
