def get_errorbar_plot():
x = np.linspace(1., 5., 10)
y = 3.2 * x**2 + 4.0
y_with_noise = (y[None, :] + scipy.stats.norm(loc=0, scale=2.8).rvs(
(10, 1)))
means = y_with_noise.mean(0)
stds = y_with_noise.std(0)
x, y = get_data_sources(x=x, y=means)
low = ArrayDataSource(means - stds)
high = ArrayDataSource(means + stds)
x_range = DataRange1D(low=0, high=6)
y_range = DataRange1D(tight_bounds=False)
y_range.add(y, low, high)
errorbar_plot = ErrorBarPlot(index=x,
value=y,
index_mapper=LinearMapper(range=x_range),
value_mapper=LinearMapper(range=y_range),
value_low=low,
value_high=high,
endcap_size=11.,
**PLOT_DEFAULTS)
add_axes(errorbar_plot, x_label='Test values', y_label='Measured')
return errorbar_plot
def get_candle_plot():
means = np.array([0.2, 0.8, 0.5])
stds = np.array([1.0, 0.3, 0.5])
data = scipy.stats.norm(loc=means, scale=stds).rvs((100, 3))
x = ArrayDataSource(np.arange(3))
center = ArrayDataSource(data.mean(0))
bar_min = ArrayDataSource(data.mean(0) - data.std(0))
bar_max = ArrayDataSource(data.mean(0) + data.std(0))
stem_min = ArrayDataSource(data.min(0))
stem_max = ArrayDataSource(data.max(0))
x_range = DataRange1D(low=-1, high=3)
y_range = DataRange1D(tight_bounds=False)
y_range.add(center, bar_min, bar_max, stem_min, stem_max)
candle_plot = CandlePlot(index=x,
index_mapper=LinearMapper(range=x_range),
value_mapper=LinearMapper(range=y_range),
center_values=center,
bar_min=bar_min,
bar_max=bar_max,
min_values=stem_min,
max_values=stem_max,
center_color='yellow',
bar_color='orange',
**PLOT_DEFAULTS)
add_axes(candle_plot, x_label='Items', y_label='Values')
return candle_plot
def _init_components(self):
# Since this is called after the HasTraits constructor, we have to make
# sure that we don't blow away any components that the caller may have
# already set.
if self.range2d is None:
self.range2d = DataRange2D()
if self.index_mapper is None:
if self.index_scale == "linear":
imap = LinearMapper(range=self.range2d.x_range)
else:
imap = LogMapper(range=self.range2d.x_range)
self.index_mapper = imap
if self.value_mapper is None:
if self.value_scale == "linear":
vmap = LinearMapper(range=self.range2d.y_range)
else:
vmap = LogMapper(range=self.range2d.y_range)
self.value_mapper = vmap
if self.x_ticks is None:
self.x_ticks = ScalesTickGenerator(
scale=self._make_scale(self.index_scale))
if self.y_ticks is None:
self.y_ticks = ScalesTickGenerator(
scale=self._make_scale(self.value_scale))
if self.x_grid is None:
self.x_grid = PlotGrid(mapper=self.x_mapper,
orientation="vertical",
line_color="lightgray",
line_style="dot",
component=self,
tick_generator=self.x_ticks)
if self.y_grid is None:
self.y_grid = PlotGrid(mapper=self.y_mapper,
orientation="horizontal",
line_color="lightgray",
line_style="dot",
component=self,
tick_generator=self.y_ticks)
if self.x_axis is None:
self.x_axis = PlotAxis(mapper=self.x_mapper,
orientation="bottom",
component=self,
tick_generator=self.x_ticks)
if self.y_axis is None:
self.y_axis = PlotAxis(mapper=self.y_mapper,
orientation="left",
component=self,
tick_generator=self.y_ticks)
def __init__(self,
xs,
ys,
index_bounds=None,
value_bounds=None,
*args,
**kw):
index = ArrayDataSource(xs)
value = ArrayDataSource(ys)
if index_bounds is not None:
index_range = DataRange1D(low=index_bounds[0],
high=index_bounds[1])
else:
index_range = DataRange1D()
index_range.add(index)
index_mapper = LinearMapper(range=index_range)
index_range.tight_bounds = False
if value_bounds is not None:
value_range = DataRange1D(low=value_bounds[0],
high=value_bounds[1])
else:
value_range = DataRange1D()
value_range.add(value)
value_mapper = LinearMapper(range=value_range)
value_range.tight_bounds = False
self.index = index
self.value = value
self.index_mapper = index_mapper
self.value_mapper = value_mapper
# self.color = "red"
# self.line_width = 1.0
# self.line_style = "solid"
super(BaseInset, self).__init__(*args, **kw)
tick_label_font = 'Helvetica 8'
left = PlotAxis(orientation='left',
mapper=value_mapper,
bgcolor=self.bgcolor,
tick_label_font=tick_label_font)
bottom = PlotAxis(orientation='bottom',
mapper=index_mapper,
bgcolor=self.bgcolor,
tick_label_font=tick_label_font)
self.underlays.append(left)
self.underlays.append(bottom)
def __init__(self):
# Create some data
x = np.random.random(N_POINTS)
y = np.random.random(N_POINTS)
color = np.exp(-(x**2 + y**2))
# Create a plot data object and give it this data
data = ArrayPlotData(index=x, value=y, color=color)
# Create the plot
plot = Plot(data)
plot.plot(("index", "value", "color"), type="cmap_scatter",
color_mapper=jet)
# Create the colorbar, handing in the appropriate range and colormap
colormap = plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot, colorbar)
self.plot = container
def get_multiline_plot():
prng = np.random.RandomState(seed=1234)
x = np.linspace(0, 10, 50)
y_data = np.column_stack([
x**2,
50 * np.sin(x),
50 * np.cos(x),
0.5 * x**2 + 2 * prng.randn(50),
0.7 * x**2 + prng.randn(50),
])
# data sources for the two axes
xs = ArrayDataSource(np.arange(50))
ys = ArrayDataSource(np.arange(y_data.shape[1]))
y_range = DataRange1D(low=-0.5, high=y_data.shape[1] - 0.5)
y_mapper = LinearMapper(range=y_range)
# data source for the multiple lines
lines_source = MultiArrayDataSource(data=y_data.T)
colors = ['blue', 'green', 'yellow', 'orange', 'red']
def color_generator(color_idx):
return color_table[colors[color_idx]]
multiline_plot = MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=DataRange1D(xs)),
value_mapper=y_mapper,
value=lines_source,
normalized_amplitude=1.0,
use_global_bounds=False,
color_func=color_generator,
**PLOT_DEFAULTS)
add_axes(multiline_plot, x_label='Days', y_label='Stock price changes')
y_grid = PlotGrid(mapper=y_mapper,
orientation="horizontal",
line_style="dot",
component=multiline_plot)
multiline_plot.overlays.append(y_grid)
return multiline_plot
def _matrix_plot_container_default(self):
matrix = np.nan_to_num(self.matrix)
width = matrix.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data("values", matrix)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, width),
ybounds=(0, width),
colormap=self._create_colormap())[0]
#### fix axes
self._remove_grid_and_axes(plot)
axis = self._create_increment_one_axis(plot, 0.5, width, 'bottom')
self._add_value_axis(plot, axis)
axis = self._create_increment_one_axis(
plot,
0.5,
width,
'left',
ticks=[str(i) for i in range(width - 1, -1, -1)])
self._add_index_axis(plot, axis)
#### tweak plot attributes
self._set_title(plot)
plot.aspect_ratio = 1.
# padding [left, right, up, down]
plot.padding = [0, 0, 25, 25]
# create the colorbar, handing in the appropriate range and colormap
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='v',
width=20,
padding=[0, 20, 0, 0])
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# 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 = 0xFFFFFF
self.decorate_plot(container, self.matrix)
return container
def get_multiline_plot():
prices = datasets.fetch_mldata('regression-datasets stock')
T, N_LINES = 70, 5
prices_data = prices['data'][:T, :N_LINES]
prices_data -= prices_data[0, :]
# data sources for the two axes
xs = ArrayDataSource(np.arange(T))
ys = ArrayDataSource(np.arange(N_LINES))
y_range = DataRange1D(low=-0.5, high=N_LINES - 0.5)
y_mapper = LinearMapper(range=y_range)
# data source for the multiple lines
lines_source = MultiArrayDataSource(data=prices_data.T)
colors = ['blue', 'green', 'yellow', 'orange', 'red']
def color_generator(color_idx):
return color_table[colors[color_idx]]
multiline_plot = MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=DataRange1D(xs)),
value_mapper=y_mapper,
value=lines_source,
normalized_amplitude=1.0,
use_global_bounds=False,
color_func=color_generator,
**PLOT_DEFAULTS)
add_axes(multiline_plot, x_label='Days', y_label='Stock price changes')
y_grid = PlotGrid(mapper=y_mapper,
orientation="horizontal",
line_style="dot",
component=multiline_plot)
multiline_plot.overlays.append(y_grid)
return multiline_plot
def _1D_mapper(source):
data_range = DataRange1D()
data_range.add(source)
return LinearMapper(range=data_range)
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits = ((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses,
'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height * nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='',
width=15,
height=250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# 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 = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(
PanTool(img_plot,
constrain=True,
constrain_direction="y",
speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container