def get_variable_size_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:, -1]
tax = boston['data'][:, 9]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
# normalize between 0 and 10
marker_size = tax / tax.max() * 10.
scatter_plot = ScatterPlot(index=x,
value=y,
index_mapper=x_mapper,
value_mapper=y_mapper,
marker='circle',
marker_size=marker_size,
title='Size represents property-tax rate',
**PLOT_DEFAULTS)
scatter_plot.color = (0.0, 1.0, 0.3, 0.4)
add_axes(scatter_plot,
x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def test_scatter_plot(self):
x = np.linspace(0.0, 1.0, 5)
sp = ScatterPlot()
with warnings.catch_warnings(record=True) as w:
sp._cached_selected_pts = None
sp._cached_selected_pts = np.column_stack([x, x])
self.assertEqual(w, [])
def __init__(self, *args, **kw):
BaseInset.__init__(self, *args, **kw)
ScatterPlot.__init__(self)
self.border_visible = kw.get('border_visible', True)
self.marker = 'circle'
# self.color = 'red'
self.marker_size = 1.5
if not self.visible_axes:
self.x_axis.visible = False
self.y_axis.visible = False
# self.set_limits()
nsigma = 1
orientation = 'x'
line_width = 1
visible = True
ebo = ErrorBarOverlay(component=self,
orientation=orientation,
nsigma=nsigma,
line_width=line_width,
use_end_caps=False,
visible=visible)
self.overlays.append(ebo)
def __init__(self, *args, **kw):
BaseInset.__init__(self, *args, **kw)
ScatterPlot.__init__(self)
self.border_visible = kw.get('border_visible', True)
self.marker = 'circle'
# self.color = 'red'
self.marker_size = 1.5
if not self.visible_axes:
self.x_axis.visible = False
self.y_axis.visible = False
# self.set_limits()
nsigma = 1
orientation = 'x'
line_width = 1
visible = True
ebo = ErrorBarOverlay(component=self,
orientation=orientation,
nsigma=nsigma,
line_width=line_width,
use_end_caps=False,
visible=visible)
self.overlays.append(ebo)
def get_variable_size_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:,-1]
tax = boston['data'][:,9]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
# normalize between 0 and 10
marker_size = tax / tax.max() * 10.
scatter_plot = ScatterPlot(
index=x, value=y,
index_mapper=x_mapper, value_mapper=y_mapper,
marker='circle',
marker_size=marker_size,
title='Size represents property-tax rate',
**PLOT_DEFAULTS
)
scatter_plot.color = (0.0, 1.0, 0.3, 0.4)
add_axes(scatter_plot, x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def __init__(self, *args, **kw):
ScatterPlot.__init__(self)
BaseInset.__init__(self, *args, **kw)
self.border_visible = kw.get('border_visible', True)
self.marker = 'circle'
# self.color = 'red'
# self.marker_size = 1.5
if not self.visible_axes:
self.x_axis.visible = False
self.y_axis.visible = False
def __init__(self, *args, **kw):
ScatterPlot.__init__(self)
BaseInset.__init__(self, *args, **kw)
self.border_visible = kw.get('border_visible', True)
self.marker = 'circle'
# self.color = 'red'
# self.marker_size = 1.5
if not self.visible_axes:
self.x_axis.visible = False
self.y_axis.visible = False
def get_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:,-1]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
scatter_plot = ScatterPlot(
index=x, value=y,
index_mapper=x_mapper, value_mapper=y_mapper,
marker='circle',
**PLOT_DEFAULTS
)
scatter_plot.line_width = 1.0
add_axes(scatter_plot, x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
