def test_selection_expr_stream_hist_invert_axes(self):
# Create SelectionExpr on element
hist = Histogram(([1, 2, 3, 4, 5], [1, 5, 2, 3,
7])).opts(invert_axes=True)
expr_stream = SelectionExpr(hist)
# Check stream properties
self.assertEqual(len(expr_stream.input_streams), 1)
self.assertIsInstance(expr_stream.input_streams[0], SelectionXY)
self.assertIsNone(expr_stream.bbox)
self.assertIsNone(expr_stream.selection_expr)
# Simulate interactive update by triggering source stream.
# Select second and forth bar.
expr_stream.input_streams[0].event(bounds=(2.5, 1.5, 6, 4.6))
self.assertEqual(repr(expr_stream.selection_expr),
repr((dim('x') >= 1.5) & (dim('x') <= 4.6)))
self.assertEqual(expr_stream.bbox, {'x': (1.5, 4.6)})
# Select third, forth, and fifth bar. Make sure there is special
# handling when last bar is selected to include values exactly on the
# upper edge in the selection
expr_stream.input_streams[0].event(bounds=(-10, 2.5, 10, 8))
self.assertEqual(repr(expr_stream.selection_expr),
repr((dim('x') >= 2.5) & (dim('x') <= 8)))
self.assertEqual(expr_stream.bbox, {'x': (2.5, 8)})
def test_selection_expr_stream_hist_invert_xaxis_yaxis(self):
# Create SelectionExpr on element
hist = Histogram(([1, 2, 3, 4, 5], [1, 5, 2, 3, 7])).opts(
invert_xaxis=True,
invert_yaxis=True,
)
expr_stream = SelectionExpr(hist)
# Check stream properties
self.assertEqual(len(expr_stream._source_streams), 1)
self.assertIsInstance(expr_stream._source_streams[0], SelectionXY)
self.assertIsNone(expr_stream.bbox)
self.assertIsNone(expr_stream.selection_expr)
# Simulate interactive update by triggering source stream.
# Select second and forth bar.
expr_stream._source_streams[0].event(bounds=(4.6, 6, 1.5, 2.5))
self.assertEqual(repr(expr_stream.selection_expr),
repr(dim('x').digitize(hist.edges).isin([2, 4])))
self.assertEqual(expr_stream.bbox, {'x': (1.5, 4.5)})
# Select third, forth, and fifth bar. Make sure there is special
# handling when last bar is selected to include values exactly on the
# upper edge in the selection
expr_stream._source_streams[0].event(bounds=(8, 10, 2.5, -10))
self.assertEqual(
repr(expr_stream.selection_expr),
repr((dim('x').digitize(hist.edges).isin([3, 4, 5]))
| (dim('x') == hist.edges[-1])))
self.assertEqual(expr_stream.bbox, {'x': (2.5, 5.5)})
def test_histogram_padding_square_positive(self):
points = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1)
plot = bokeh_renderer.get_plot(points)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, 0.19999999999999996)
self.assertEqual(x_range.end, 3.8)
self.assertEqual(y_range.start, 0)
self.assertEqual(y_range.end, 3.2)
def test_histogram_padding_nonsquare(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1, width=600)
plot = bokeh_renderer.get_plot(histogram)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, 0.35)
self.assertEqual(x_range.end, 3.65)
self.assertEqual(y_range.start, 0)
self.assertEqual(y_range.end, 3.2)
def test_histogram_line_width_op(self):
histogram = Histogram([(0, 0, 1), (0, 1, 4), (0, 2, 8)],
vdims=['y', 'line_width']).options(line_width='line_width')
plot = bokeh_renderer.get_plot(histogram)
cds = plot.handles['cds']
glyph = plot.handles['glyph']
self.assertEqual(cds.data['line_width'], np.array([1, 4, 8]))
self.assertEqual(glyph.line_width, {'field': 'line_width'})
def test_histogram_alpha_op(self):
histogram = Histogram([(0, 0, 0), (0, 1, 0.2), (0, 2, 0.7)],
vdims=['y', 'alpha']).options(alpha='alpha')
plot = bokeh_renderer.get_plot(histogram)
cds = plot.handles['cds']
glyph = plot.handles['glyph']
self.assertEqual(cds.data['alpha'], np.array([0, 0.2, 0.7]))
self.assertEqual(glyph.fill_alpha, {'field': 'alpha'})
def test_histogram_padding_square_negative(self):
points = Histogram([(1, -2), (2, -1), (3, -3)]).options(padding=0.1)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.19999999999999996)
self.assertEqual(x_range[1], 3.8)
self.assertEqual(y_range[0], -3.2)
self.assertEqual(y_range[1], 0)
def test_histogram_padding_nonsquare(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1, aspect=2)
plot = mpl_renderer.get_plot(histogram)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.35)
self.assertEqual(x_range[1], 3.65)
self.assertEqual(y_range[0], 0)
self.assertEqual(y_range[1], 3.2)
def test_histogram_color_op(self):
histogram = Histogram([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims=['y', 'color']).options(color='color')
plot = mpl_renderer.get_plot(histogram)
artist = plot.handles['artist']
children = artist.get_children()
for c, w in zip(children, ['#000000', '#FF0000', '#00FF00']):
self.assertEqual(c.get_facecolor(), tuple(c/255. for c in hex2rgb(w))+(1,))
def test_histogram_padding_logx(self):
histogram = Histogram([(1, 1), (2, 2), (3,3)]).options(padding=0.1, logx=True)
plot = mpl_renderer.get_plot(histogram)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.41158562699652224)
self.assertEqual(x_range[1], 4.2518491541367327)
self.assertEqual(y_range[0], 0)
self.assertEqual(y_range[1], 3.2)
def test_histogram_line_width_op(self):
histogram = Histogram([(0, 0, 1), (0, 1, 4), (0, 2, 8)],
vdims=['y', 'line_width']).options(linewidth='line_width')
plot = mpl_renderer.get_plot(histogram)
artist = plot.handles['artist']
children = artist.get_children()
for c, w in zip(children, np.array([1, 4, 8])):
self.assertEqual(c.get_linewidth(), w)
def test_histogram_padding_logy(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1, logy=True)
plot = mpl_renderer.get_plot(histogram)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.19999999999999996)
self.assertEqual(x_range[1], 3.8)
self.assertEqual(y_range[0], 1)
self.assertEqual(y_range[1], 3.3483695221017129)
def test_histogram_padding_logx(self):
histogram = Histogram([(1, 1), (2, 2), (3,3)]).options(padding=0.1, logx=True)
plot = bokeh_renderer.get_plot(histogram)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, 0.41158562699652224)
self.assertEqual(x_range.end, 4.2518491541367327)
self.assertEqual(y_range.start, 0)
self.assertEqual(y_range.end, 3.2)
def test_histogram_color_op(self):
histogram = Histogram([(0, 0, '#000'), (0, 1, '#F00'), (0, 2, '#0F0')],
vdims=['y', 'color']).options(color='color')
plot = bokeh_renderer.get_plot(histogram)
cds = plot.handles['cds']
glyph = plot.handles['glyph']
self.assertEqual(cds.data['color'], np.array(['#000', '#F00', '#0F0']))
self.assertEqual(glyph.fill_color, {'field': 'color'})
self.assertEqual(glyph.line_color, 'black')
def test_histogram_padding_logy(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1,
logy=True)
plot = bokeh_renderer.get_plot(histogram)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, 0.19999999999999996)
self.assertEqual(x_range.end, 3.8)
self.assertEqual(y_range.start, 0.033483695221017122)
self.assertEqual(y_range.end, 3.3483695221017129)
def test_histogram_padding_logy(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1, logy=True)
plot = mpl_renderer.get_plot(histogram)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.19999999999999996)
self.assertEqual(x_range[1], 3.8)
self.assertEqual(y_range[0], 0.03348369522101712)
self.assertEqual(y_range[1], 3.3483695221017129)
self.log_handler.assertContains('WARNING', 'Logarithmic axis range encountered value less than')
def test_histogram_padding_logy(self):
histogram = Histogram([(1, 2), (2, 1), (3, 3)]).options(padding=0.1, logy=True)
plot = bokeh_renderer.get_plot(histogram)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, 0.19999999999999996)
self.assertEqual(x_range.end, 3.8)
self.assertEqual(y_range.start, 0.033483695221017122)
self.assertEqual(y_range.end, 3.3483695221017129)
self.log_handler.assertContains('WARNING', 'Logarithmic axis range encountered value less than')
def test_histogram_line_color_op(self):
histogram = Histogram([(0, 0, '#000'), (0, 1, '#F00'), (0, 2, '#0F0')],
vdims=['y', 'color']).options(edgecolor='color')
plot = mpl_renderer.get_plot(histogram)
artist = plot.handles['artist']
children = artist.get_children()
self.assertEqual(children[0].get_edgecolor(), (0, 0, 0, 1))
self.assertEqual(children[1].get_edgecolor(), (1, 0, 0, 1))
self.assertEqual(children[2].get_edgecolor(), (0, 1, 0, 1))
def test_histogram_padding_datetime_nonsquare(self):
histogram = Histogram([(np.datetime64('2016-04-0%d' % i, 'ns'), i) for i in range(1, 4)]).options(
padding=0.1, aspect=2
)
plot = mpl_renderer.get_plot(histogram)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 16891.35)
self.assertEqual(x_range[1], 16894.65)
self.assertEqual(y_range[0], 0)
self.assertEqual(y_range[1], 3.2)
def test_histogram_plot_styling(self):
props = {
'color': 'orange',
'line_width': 7,
'line_color': 'green',
}
hist = Histogram((self.edges, self.frequencies)).opts(**props)
state = self._get_plot_state(hist)
marker = state['data'][0]['marker']
self.assert_property_values(marker, props)
def test_op_ndoverlay_value(self):
colors = ['blue', 'red']
overlay = NdOverlay(
{
color: Histogram(np.arange(i + 2))
for i, color in enumerate(colors)
}, 'Color').options('Histogram', fill_color='Color')
plot = bokeh_renderer.get_plot(overlay)
for subplot, color in zip(plot.subplots.values(), colors):
self.assertEqual(subplot.handles['glyph'].fill_color, color)
def test_histogram_padding_datetime_nonsquare(self):
histogram = Histogram([(np.datetime64('2016-04-0%d' % i, 'ns'), i) for i in range(1, 4)]).options(
padding=0.1, width=600
)
plot = bokeh_renderer.get_plot(histogram)
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
self.assertEqual(x_range.start, np.datetime64('2016-03-31T08:24:00.000000000'))
self.assertEqual(x_range.end, np.datetime64('2016-04-03T15:36:00.000000000'))
self.assertEqual(y_range.start, 0)
self.assertEqual(y_range.end, 3.2)
def test_op_ndoverlay_value(self):
colors = ['blue', 'red']
overlay = NdOverlay({color: Histogram(np.arange(i+2))
for i, color in enumerate(colors)}, 'Color').options(
'Histogram', facecolor='Color'
)
plot = mpl_renderer.get_plot(overlay)
colors = [(0, 0, 1, 1), (1, 0, 0, 1)]
for subplot, color in zip(plot.subplots.values(), colors):
children = subplot.handles['artist'].get_children()
for c in children:
self.assertEqual(c.get_facecolor(), color)
def test_histogram_categorical_color_op(self):
histogram = Histogram([(0, 0, 'A'), (0, 1, 'B'), (0, 2, 'C')],
vdims=['y', 'color']).options(color='color')
plot = bokeh_renderer.get_plot(histogram)
cds = plot.handles['cds']
glyph = plot.handles['glyph']
cmapper = plot.handles['color_color_mapper']
self.assertTrue(cmapper, CategoricalColorMapper)
self.assertEqual(cmapper.factors, ['A', 'B', 'C'])
self.assertEqual(cds.data['color'], np.array(['A', 'B', 'C']))
self.assertEqual(glyph.fill_color, {'field': 'color', 'transform': cmapper})
self.assertEqual(glyph.line_color, 'black')
def test_histogram_plot(self):
hist = Histogram((self.edges, self.frequencies))
state = self._get_plot_state(hist)
np.testing.assert_equal(state['data'][0]['x'], self.edges)
np.testing.assert_equal(state['data'][0]['y'], self.frequencies)
self.assertEqual(state['data'][0]['type'], 'bar')
self.assertEqual(state['data'][0]['orientation'], 'v')
self.assertEqual(state['data'][0]['width'], 1)
self.assertEqual(state['layout']['xaxis']['range'], [-3.5, 2.5])
self.assertEqual(state['layout']['xaxis']['title']['text'], 'x')
self.assertEqual(state['layout']['yaxis']['range'], [0, 5])
self.assertEqual(state['layout']['yaxis']['title']['text'],
'Frequency')
def test_histogram_linear_color_op(self):
histogram = Histogram([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
vdims=['y', 'color']).options(color='color')
plot = bokeh_renderer.get_plot(histogram)
cds = plot.handles['cds']
glyph = plot.handles['glyph']
cmapper = plot.handles['color_color_mapper']
self.assertTrue(cmapper, LinearColorMapper)
self.assertEqual(cmapper.low, 0)
self.assertEqual(cmapper.high, 2)
self.assertEqual(cds.data['color'], np.array([0, 1, 2]))
self.assertEqual(glyph.fill_color, {'field': 'color', 'transform': cmapper})
self.assertEqual(glyph.line_color, 'black')
def cb(aname):
x = np.linspace(0, 1, 10)
y = np.random.randn(10)
curve = Curve((x, y), group=aname)
hist = Histogram(y)
return (curve + hist).opts(shared_axes=False)
def setUp(self):
self.values = np.arange(10)
self.edges = np.arange(11)
self.dataset1d = Histogram((self.edges, self.values))
class Binned1DTest(ComparisonTestCase):
def setUp(self):
self.values = np.arange(10)
self.edges = np.arange(11)
self.dataset1d = Histogram((self.edges, self.values))
def test_slice_all(self):
sliced = self.dataset1d[:]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_exclusive_upper(self):
"Exclusive upper boundary semantics for bin centers"
sliced = self.dataset1d[:6.5]
self.assertEqual(sliced.values, np.arange(6))
self.assertEqual(sliced.edges, np.arange(7))
def test_slice_exclusive_upper_exceeded(self):
"Slightly above the boundary in the previous test"
sliced = self.dataset1d[:6.55]
self.assertEqual(sliced.values, np.arange(7))
self.assertEqual(sliced.edges, np.arange(8))
def test_slice_inclusive_lower(self):
"Inclusive lower boundary semantics for bin centers"
sliced = self.dataset1d[3.5:]
self.assertEqual(sliced.values, np.arange(3, 10))
self.assertEqual(sliced.edges, np.arange(3, 11))
def test_slice_inclusive_lower_undershot(self):
"Inclusive lower boundary semantics for bin centers"
sliced = self.dataset1d[3.45:]
self.assertEqual(sliced.values, np.arange(3, 10))
self.assertEqual(sliced.edges, np.arange(3, 11))
def test_slice_bounded(self):
sliced = self.dataset1d[3.5:6.5]
self.assertEqual(sliced.values, np.arange(3, 6))
self.assertEqual(sliced.edges, np.arange(3, 7))
def test_slice_lower_out_of_bounds(self):
sliced = self.dataset1d[-3:]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_upper_out_of_bounds(self):
sliced = self.dataset1d[:12]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_both_out_of_bounds(self):
sliced = self.dataset1d[-3:13]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_scalar_index(self):
self.assertEqual(self.dataset1d[4.5], 4)
self.assertEqual(self.dataset1d[3.7], 3)
self.assertEqual(self.dataset1d[9.9], 9)
def test_scalar_index_boundary(self):
"""
Scalar at boundary indexes next bin.
(exclusive upper boundary for current bin)
"""
self.assertEqual(self.dataset1d[4], 4)
self.assertEqual(self.dataset1d[5], 5)
def test_scalar_lowest_index(self):
self.assertEqual(self.dataset1d[0], 0)
def test_scalar_lowest_index_out_of_bounds(self):
with self.assertRaises(IndexError):
self.dataset1d[-1]
def test_scalar_highest_index_out_of_bounds(self):
with self.assertRaises(IndexError):
self.dataset1d[10]
def test_groupby_kdim(self):
grouped = self.dataset1d.groupby('x', group_type=Dataset)
holomap = HoloMap({self.edges[i:i+2].mean(): Dataset([(i,)], vdims=['Frequency'])
for i in range(10)}, kdims=['x'])
self.assertEqual(grouped, holomap)
class Binned1DTest(ComparisonTestCase):
def setUp(self):
self.values = np.arange(10)
self.edges = np.arange(11)
self.dataset1d = Histogram((self.edges, self.values))
def test_slice_all(self):
sliced = self.dataset1d[:]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_exclusive_upper(self):
"Exclusive upper boundary semantics for bin centers"
sliced = self.dataset1d[:6.5]
self.assertEqual(sliced.values, np.arange(6))
self.assertEqual(sliced.edges, np.arange(7))
def test_slice_exclusive_upper_exceeded(self):
"Slightly above the boundary in the previous test"
sliced = self.dataset1d[:6.55]
self.assertEqual(sliced.values, np.arange(7))
self.assertEqual(sliced.edges, np.arange(8))
def test_slice_inclusive_lower(self):
"Inclusive lower boundary semantics for bin centers"
sliced = self.dataset1d[3.5:]
self.assertEqual(sliced.values, np.arange(3, 10))
self.assertEqual(sliced.edges, np.arange(3, 11))
def test_slice_inclusive_lower_undershot(self):
"Inclusive lower boundary semantics for bin centers"
sliced = self.dataset1d[3.45:]
self.assertEqual(sliced.values, np.arange(3, 10))
self.assertEqual(sliced.edges, np.arange(3, 11))
def test_slice_bounded(self):
sliced = self.dataset1d[3.5:6.5]
self.assertEqual(sliced.values, np.arange(3, 6))
self.assertEqual(sliced.edges, np.arange(3, 7))
def test_slice_lower_out_of_bounds(self):
sliced = self.dataset1d[-3:]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_upper_out_of_bounds(self):
sliced = self.dataset1d[:12]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_slice_both_out_of_bounds(self):
sliced = self.dataset1d[-3:13]
self.assertEqual(sliced.values, self.values)
self.assertEqual(sliced.edges, self.edges)
def test_scalar_index(self):
self.assertEqual(self.dataset1d[4.5], 4)
self.assertEqual(self.dataset1d[3.7], 3)
self.assertEqual(self.dataset1d[9.9], 9)
def test_scalar_index_boundary(self):
"""
Scalar at boundary indexes next bin.
(exclusive upper boundary for current bin)
"""
self.assertEqual(self.dataset1d[4], 4)
self.assertEqual(self.dataset1d[5], 5)
def test_scalar_lowest_index(self):
self.assertEqual(self.dataset1d[0], 0)
def test_scalar_lowest_index_out_of_bounds(self):
with self.assertRaises(IndexError):
self.dataset1d[-1]
def test_scalar_highest_index_out_of_bounds(self):
with self.assertRaises(IndexError):
self.dataset1d[10]
def test_groupby_kdim(self):
grouped = self.dataset1d.groupby('x', group_type=Dataset)
holomap = HoloMap({self.edges[i:i+2].mean(): Dataset([(i,)], vdims=['Frequency'])
for i in range(10)}, kdims=['x'])
self.assertEqual(grouped, holomap)
def setUp(self):
self.values = np.arange(10)
self.edges = np.arange(11)
self.dataset1d = Histogram((self.edges, self.values))
def test_histogram_alpha_op(self):
histogram = Histogram([(0, 0, 0), (0, 1, 0.2), (0, 2, 0.7)],
vdims=['y', 'alpha']).options(alpha='alpha')
with self.assertRaises(Exception):
mpl_renderer.get_plot(histogram)
