import numpy as np from bokeh.models import ColumnDataSource, HoverTool from bokeh.palettes import Cividis256 as Pallete from bokeh.plotting import Figure, figure from bokeh.transform import factor_cmap def draw_interactive_scatter_plot( texts: np.ndarray, xs: np.ndarray, ys: np.ndarray, values: np.ndarray, labels: np.ndarray, text_column: str, label_column: str ) -> Figure: # Smooth down values for coloring, by taking the entropy = log10(perplexity) and multiply it by 10000 values = ((np.log10(values)) * 10000).round().astype(int) # Normalize values to range between 0-255, to assign a color for each value max_value = values.max() min_value = values.min() if max_value - min_value == 0: values_color = np.ones(len(values)) else: values_color = ((values - min_value) / (max_value - min_value) * 255).round().astype(int) values_color_sorted = sorted(values_color) values_list = values.astype(str).tolist() values_sorted = sorted(values_list) labels_list = labels.astype(str).tolist() source = ColumnDataSource(data=dict(x=xs, y=ys, text=texts, label=values_list, original_label=labels_list)) hover = HoverTool(tooltips=[(text_column, "@text{safe}"), (label_column, "@original_label")]) p = figure(plot_width=800, plot_height=800, tools=[hover]) p.circle("x", "y", size=10, source=source, fill_color=factor_cmap("label", palette=[Pallete[id_] for id_ in values_color_sorted], factors=values_sorted)) p.axis.visible = False p.xgrid.grid_line_color = None p.ygrid.grid_line_color = None p.toolbar.logo = None return p