import datetime as dt import numpy as np from bokeh.models import CategoricalColorMapper, LinearColorMapper from holoviews.core import NdOverlay from holoviews.element import Spikes from holoviews.plotting.bokeh.util import property_to_dict from ..utils import ParamLogStream from .test_plot import TestBokehPlot, bokeh_renderer class TestSpikesPlot(TestBokehPlot): def test_spikes_colormapping(self): spikes = Spikes(np.random.rand(20, 2), vdims=['Intensity']) color_spikes = spikes.opts(color_index=1) self._test_colormapping(color_spikes, 1) def test_empty_spikes_plot(self): spikes = Spikes([], vdims=['Intensity']) plot = bokeh_renderer.get_plot(spikes) source = plot.handles['source'] self.assertEqual(len(source.data['x']), 0) self.assertEqual(len(source.data['y0']), 0) self.assertEqual(len(source.data['y1']), 0) def test_batched_spike_plot(self): overlay = NdOverlay({ i: Spikes([i], kdims=['Time']).opts( position=0.1*i, spike_length=0.1, show_legend=False) for i in range(10) }) plot = bokeh_renderer.get_plot(overlay) extents = plot.get_extents(overlay, {}) self.assertEqual(extents, (0, 0, 9, 1)) def test_spikes_padding_square(self): spikes = Spikes([1, 2, 3]).opts(padding=0.1) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0.8) self.assertEqual(x_range.end, 3.2) def test_spikes_padding_square_heights(self): spikes = Spikes([(1, 1), (2, 2), (3, 3)], vdims=['Height']).opts(padding=0.1) plot = bokeh_renderer.get_plot(spikes) x_range, y_range = plot.handles['x_range'], plot.handles['y_range'] self.assertEqual(x_range.start, 0.8) self.assertEqual(x_range.end, 3.2) self.assertEqual(y_range.start, 0) self.assertEqual(y_range.end, 3.2) def test_spikes_padding_hard_xrange(self): spikes = Spikes([1, 2, 3]).redim.range(x=(0, 3)).opts(padding=0.1) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0) self.assertEqual(x_range.end, 3) def test_spikes_padding_soft_xrange(self): spikes = Spikes([1, 2, 3]).redim.soft_range(x=(0, 3)).opts(padding=0.1) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0) self.assertEqual(x_range.end, 3) def test_spikes_padding_unequal(self): spikes = Spikes([1, 2, 3]).opts(padding=(0.05, 0.1)) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0.9) self.assertEqual(x_range.end, 3.1) def test_spikes_padding_nonsquare(self): spikes = Spikes([1, 2, 3]).opts(padding=0.1, width=600) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0.9) self.assertEqual(x_range.end, 3.1) def test_spikes_padding_logx(self): spikes = Spikes([(1, 1), (2, 2), (3,3)]).opts(padding=0.1, logx=True) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, 0.89595845984076228) self.assertEqual(x_range.end, 3.3483695221017129) def test_spikes_padding_datetime_square(self): spikes = Spikes([np.datetime64('2016-04-0%d' % i) for i in range(1, 4)]).opts( padding=0.1 ) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, np.datetime64('2016-03-31T19:12:00.000000000')) self.assertEqual(x_range.end, np.datetime64('2016-04-03T04:48:00.000000000')) def test_spikes_padding_datetime_square_heights(self): spikes = Spikes([(np.datetime64('2016-04-0%d' % i), i) for i in range(1, 4)], vdims=['Height']).opts( padding=0.1 ) plot = bokeh_renderer.get_plot(spikes) x_range, y_range = plot.handles['x_range'], plot.handles['y_range'] self.assertEqual(x_range.start, np.datetime64('2016-03-31T19:12:00.000000000')) self.assertEqual(x_range.end, np.datetime64('2016-04-03T04:48:00.000000000')) self.assertEqual(y_range.start, 0) self.assertEqual(y_range.end, 3.2) def test_spikes_padding_datetime_nonsquare(self): spikes = Spikes([np.datetime64('2016-04-0%d' % i) for i in range(1, 4)]).opts( padding=0.1, width=600 ) plot = bokeh_renderer.get_plot(spikes) x_range = plot.handles['x_range'] self.assertEqual(x_range.start, np.datetime64('2016-03-31T21:36:00.000000000')) self.assertEqual(x_range.end, np.datetime64('2016-04-03T02:24:00.000000000')) def test_spikes_datetime_vdim_hover(self): points = Spikes([(0, 1, dt.datetime(2017, 1, 1))], vdims=['value', 'date']).opts(tools=['hover']) plot = bokeh_renderer.get_plot(points) cds = plot.handles['cds'] self.assertEqual(cds.data['date'].astype('datetime64'), np.array([1483228800000000000])) hover = plot.handles['hover'] self.assertEqual(hover.tooltips, [('x', '@{x}'), ('value', '@{value}'), ('date', '@{date}{%F %T}')]) self.assertEqual(hover.formatters, {'@{date}': "datetime"}) def test_spikes_datetime_kdim_hover(self): points = Spikes([(dt.datetime(2017, 1, 1), 1)], 'x', 'y').opts(tools=['hover']) plot = bokeh_renderer.get_plot(points) cds = plot.handles['cds'] self.assertEqual(cds.data['x'].astype('datetime64'), np.array([1483228800000000000])) self.assertEqual(cds.data['y0'], np.array([0])) self.assertEqual(cds.data['y1'], np.array([1])) hover = plot.handles['hover'] self.assertEqual(hover.tooltips, [('x', '@{x}{%F %T}'), ('y', '@{y}')]) self.assertEqual(hover.formatters, {'@{x}': "datetime"}) def test_spikes_datetime_kdim_hover_spike_length_override(self): points = Spikes([(dt.datetime(2017, 1, 1), 1)], 'x', 'y').opts( tools=['hover'], spike_length=0.75) plot = bokeh_renderer.get_plot(points) cds = plot.handles['cds'] self.assertEqual(cds.data['x'].astype('datetime64'), np.array([1483228800000000000])) self.assertEqual(cds.data['y0'], np.array([0])) self.assertEqual(cds.data['y1'], np.array([0.75])) hover = plot.handles['hover'] self.assertEqual(hover.tooltips, [('x', '@{x}{%F %T}'), ('y', '@{y}')]) self.assertEqual(hover.formatters, {'@{x}': "datetime"}) ########################### # Styling mapping # ########################### def test_spikes_color_op(self): spikes = Spikes([(0, 0, '#000'), (0, 1, '#F00'), (0, 2, '#0F0')], vdims=['y', 'color']).opts(color='color') plot = bokeh_renderer.get_plot(spikes) cds = plot.handles['cds'] glyph = plot.handles['glyph'] self.assertEqual(cds.data['color'], np.array(['#000', '#F00', '#0F0'])) self.assertEqual(property_to_dict(glyph.line_color), {'field': 'color'}) def test_spikes_linear_color_op(self): spikes = Spikes([(0, 0, 0), (0, 1, 1), (0, 2, 2)], vdims=['y', 'color']).opts(color='color') plot = bokeh_renderer.get_plot(spikes) 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(property_to_dict(glyph.line_color), {'field': 'color', 'transform': cmapper}) def test_spikes_categorical_color_op(self): spikes = Spikes([(0, 0, 'A'), (0, 1, 'B'), (0, 2, 'C')], vdims=['y', 'color']).opts(color='color') plot = bokeh_renderer.get_plot(spikes) 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(property_to_dict(glyph.line_color), {'field': 'color', 'transform': cmapper}) def test_spikes_line_color_op(self): spikes = Spikes([(0, 0, '#000'), (0, 1, '#F00'), (0, 2, '#0F0')], vdims=['y', 'color']).opts(line_color='color') plot = bokeh_renderer.get_plot(spikes) cds = plot.handles['cds'] glyph = plot.handles['glyph'] self.assertEqual(cds.data['line_color'], np.array(['#000', '#F00', '#0F0'])) self.assertEqual(property_to_dict(glyph.line_color), {'field': 'line_color'}) def test_spikes_alpha_op(self): spikes = Spikes([(0, 0, 0), (0, 1, 0.2), (0, 2, 0.7)], vdims=['y', 'alpha']).opts(alpha='alpha') plot = bokeh_renderer.get_plot(spikes) cds = plot.handles['cds'] glyph = plot.handles['glyph'] self.assertEqual(cds.data['alpha'], np.array([0, 0.2, 0.7])) self.assertEqual(property_to_dict(glyph.line_alpha), {'field': 'alpha'}) def test_spikes_line_alpha_op(self): spikes = Spikes([(0, 0, 0), (0, 1, 0.2), (0, 2, 0.7)], vdims=['y', 'alpha']).opts(line_alpha='alpha') plot = bokeh_renderer.get_plot(spikes) cds = plot.handles['cds'] glyph = plot.handles['glyph'] self.assertEqual(cds.data['line_alpha'], np.array([0, 0.2, 0.7])) self.assertEqual(property_to_dict(glyph.line_alpha), {'field': 'line_alpha'}) def test_spikes_line_width_op(self): spikes = Spikes([(0, 0, 1), (0, 1, 4), (0, 2, 8)], vdims=['y', 'line_width']).opts(line_width='line_width') plot = bokeh_renderer.get_plot(spikes) cds = plot.handles['cds'] glyph = plot.handles['glyph'] self.assertEqual(cds.data['line_width'], np.array([1, 4, 8])) self.assertEqual(property_to_dict(glyph.line_width), {'field': 'line_width'}) def test_op_ndoverlay_value(self): colors = ['blue', 'red'] overlay = NdOverlay({color: Spikes(np.arange(i+2)) for i, color in enumerate(colors)}, 'Color').opts('Spikes', color='Color') plot = bokeh_renderer.get_plot(overlay) for subplot, color in zip(plot.subplots.values(), colors): self.assertEqual(subplot.handles['glyph'].line_color, color) def test_spikes_color_index_color_clash(self): spikes = Spikes([(0, 0, 0), (0, 1, 1), (0, 2, 2)], vdims=['y', 'color']).opts(color='color', color_index='color') with ParamLogStream() as log: bokeh_renderer.get_plot(spikes) log_msg = log.stream.read() warning = ( "The `color_index` parameter is deprecated in favor of color style mapping, e.g. " "`color=dim('color')` or `line_color=dim('color')`\nCannot declare style mapping " "for 'color' option and declare a color_index; ignoring the color_index.\n" ) self.assertEqual(log_msg, warning)