Python models.LinearColorMapper方法代码示例

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def visualize_sentences(vecs, sentences, palette="Viridis256", filename="/notebooks/embedding/sentences.png",
                        use_notebook=False):
    tsne = TSNE(n_components=2)
    tsne_results = tsne.fit_transform(vecs)
    df = pd.DataFrame(columns=['x', 'y', 'sentence'])
    df['x'], df['y'], df['sentence'] = tsne_results[:, 0], tsne_results[:, 1], sentences
    source = ColumnDataSource(ColumnDataSource.from_df(df))
    labels = LabelSet(x="x", y="y", text="sentence", y_offset=8,
                      text_font_size="12pt", text_color="#555555",
                      source=source, text_align='center')
    color_mapper = LinearColorMapper(palette=palette, low=min(tsne_results[:, 1]), high=max(tsne_results[:, 1]))
    plot = figure(plot_width=900, plot_height=900)
    plot.scatter("x", "y", size=12, source=source, color={'field': 'y', 'transform': color_mapper}, line_color=None, fill_alpha=0.8)
    plot.add_layout(labels)
    if use_notebook:
        output_notebook()
        show(plot)
    else:
        export_png(plot, filename)
        print("save @ " + filename) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def visualize_words(words, vecs, palette="Viridis256", filename="/notebooks/embedding/words.png",
                    use_notebook=False):
    tsne = TSNE(n_components=2)
    tsne_results = tsne.fit_transform(vecs)
    df = pd.DataFrame(columns=['x', 'y', 'word'])
    df['x'], df['y'], df['word'] = tsne_results[:, 0], tsne_results[:, 1], list(words)
    source = ColumnDataSource(ColumnDataSource.from_df(df))
    labels = LabelSet(x="x", y="y", text="word", y_offset=8,
                      text_font_size="15pt", text_color="#555555",
                      source=source, text_align='center')
    color_mapper = LinearColorMapper(palette=palette, low=min(tsne_results[:, 1]), high=max(tsne_results[:, 1]))
    plot = figure(plot_width=900, plot_height=900)
    plot.scatter("x", "y", size=12, source=source, color={'field': 'y', 'transform': color_mapper}, line_color=None,
                 fill_alpha=0.8)
    plot.add_layout(labels)
    if use_notebook:
        output_notebook()
        show(plot)
    else:
        export_png(plot, filename)
        print("save @ " + filename) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def visualize_between_sentences(sentences, vec_list, palette="Viridis256",
                                filename="/notebooks/embedding/between-sentences.png",
                                use_notebook=False):
    df_list, score_list = [], []
    for sent1_idx, sentence1 in enumerate(sentences):
        for sent2_idx, sentence2 in enumerate(sentences):
            vec1, vec2 = vec_list[sent1_idx], vec_list[sent2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': sentence1, 'y': sentence2, 'similarity': score[0][0]})
                score_list.append(score[0][0])
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=np.max(score_list), high=np.min(score_list))
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=sentences, y_range=list(reversed(sentences)),
                x_axis_location="above", plot_width=900, plot_height=900,
                toolbar_location='below', tools=TOOLS,
                tooltips=[('sentences', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
            source=df,
            fill_color={'field': 'similarity', 'transform': color_mapper},
            line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                        color_mapper=color_mapper, major_label_text_font_size="7pt",
                        label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def visualize_between_words(words, vecs, palette="Viridis256", filename="/notebooks/embedding/between-words.png",
                            use_notebook=False):
    df_list = []
    for word1_idx, word1 in enumerate(words):
        for word2_idx, word2 in enumerate(words):
            vec1 = vecs[word1_idx]
            vec2 = vecs[word2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': word1, 'y': word2, 'similarity': score[0][0]})
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=1, high=0)
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=list(words), y_range=list(reversed(list(words))),
               x_axis_location="above", plot_width=900, plot_height=900,
               toolbar_location='below', tools=TOOLS,
               tooltips=[('words', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
           source=df,
           fill_color={'field': 'similarity', 'transform': color_mapper},
           line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                         color_mapper=color_mapper, major_label_text_font_size="7pt",
                         label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_spikes_linear_color_op(self):
        spikes = Spikes([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
                              vdims=['y', 'color']).options(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(glyph.line_color, {'field': 'color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_errorbars_linear_color_op(self):
        errorbars = ErrorBars([(0, 0, 0.1, 0.2, 0), (0, 1, 0.2, 0.4, 1), (0, 2, 0.6, 1.2, 2)],
                              vdims=['y', 'perr', 'nerr', 'color']).options(color='color')
        plot = bokeh_renderer.get_plot(errorbars)
        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.line_color, {'field': 'color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_box_whisker_linear_color_op(self):
        a = np.repeat(np.arange(5), 5)
        b = np.repeat(np.arange(5), 5)
        box = BoxWhisker((a, b, np.arange(25)), ['a', 'b'], 'd').options(box_color='b')
        plot = bokeh_renderer.get_plot(box)
        source = plot.handles['vbar_1_source']
        cmapper = plot.handles['box_color_color_mapper']
        glyph = plot.handles['vbar_1_glyph']
        self.assertEqual(source.data['box_color'], np.arange(5))
        self.assertTrue(cmapper, LinearColorMapper)
        self.assertEqual(cmapper.low, 0)
        self.assertEqual(cmapper.high, 4)
        self.assertEqual(glyph.fill_color, {'field': 'box_color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_vectorfield_linear_color_op(self):
        vectorfield = VectorField([(0, 0, 0, 1, 0), (0, 1, 0, 1, 1), (0, 2, 0, 1, 2)],
                                  vdims=['A', 'M', 'color']).options(color='color')
        plot = bokeh_renderer.get_plot(vectorfield)
        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, 0, 1, 2, 0, 1, 2]))
        self.assertEqual(glyph.line_color, {'field': 'color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_violin_linear_color_op(self):
        a = np.repeat(np.arange(5), 5)
        b = np.repeat(np.arange(5), 5)
        violin = Violin((a, b, np.arange(25)), ['a', 'b'], 'd').options(violin_color='b')
        plot = bokeh_renderer.get_plot(violin)
        source = plot.handles['patches_1_source']
        cmapper = plot.handles['violin_color_color_mapper']
        glyph = plot.handles['patches_1_glyph']
        self.assertEqual(source.data['violin_color'], np.arange(5))
        self.assertTrue(cmapper, LinearColorMapper)
        self.assertEqual(cmapper.low, 0)
        self.assertEqual(cmapper.high, 4)
        self.assertEqual(glyph.fill_color, {'field': 'violin_color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_point_linear_color_op(self):
        points = Points([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
                        vdims='color').options(color='color')
        plot = bokeh_renderer.get_plot(points)
        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, {'field': 'color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_label_linear_color_op(self):
        labels = Labels([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
                        vdims='color').options(text_color='color')
        plot = bokeh_renderer.get_plot(labels)
        cds = plot.handles['cds']
        glyph = plot.handles['glyph']
        cmapper = plot.handles['text_color_color_mapper']
        self.assertTrue(cmapper, LinearColorMapper)
        self.assertEqual(cmapper.low, 0)
        self.assertEqual(cmapper.high, 2)
        self.assertEqual(cds.data['text_color'], np.array([0, 1, 2]))
        self.assertEqual(glyph.text_color, {'field': 'text_color', 'transform': cmapper}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_bars_linear_color_op(self):
        bars = Bars([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
                              vdims=['y', 'color']).options(color='color')
        plot = bokeh_renderer.get_plot(bars)
        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') 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
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') 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def test_polygons_linear_color_op(self):
        polygons = Polygons([
            {('x', 'y'): [(0, 0), (0, 1), (1, 0)], 'color': 7},
            {('x', 'y'): [(1, 0), (1, 1), (0, 1)], 'color': 3}
        ], vdims='color').options(color='color')
        plot = bokeh_renderer.get_plot(polygons)
        cds = plot.handles['source']
        glyph = plot.handles['glyph']
        cmapper = plot.handles['color_color_mapper']
        self.assertEqual(glyph.line_color, 'black')
        self.assertEqual(glyph.fill_color, {'field': 'color', 'transform': cmapper})
        self.assertEqual(cds.data['color'], np.array([7, 3]))
        self.assertIsInstance(cmapper, LinearColorMapper)
        self.assertEqual(cmapper.low, 3)
        self.assertEqual(cmapper.high, 7) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import LinearColorMapper [as 别名]
def _vtklut2bkcmap(self, lut, name):
        table = lut.GetTable()
        low, high = lut.GetTableRange()
        rgba_arr =  np.frombuffer(memoryview(table),  dtype=np.uint8).reshape((-1, 4))
        palette = [self._rgb2hex(*rgb) for rgb in rgba_arr[:,:3]]
        return LinearColorMapper(low=low, high=high, name=name, palette=palette)