feat(bokeh): implement heatmap-annotated

plots/heatmap-annotated/implementations/python/bokeh.py

@@ -1,15 +1,35 @@
-""" pyplots.ai
+""" anyplot.ai
 heatmap-annotated: Annotated Heatmap
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-24
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 84/100 | Updated: 2026-05-06
 """
 
+import os
+import sys
+import time
+from pathlib import Path
+
 import numpy as np
-from bokeh.io import export_png
-from bokeh.models import BasicTicker, ColorBar, ColumnDataSource, LinearColorMapper
+
+
+# Ensure we import the installed bokeh, not a local module with the same name
+sys.path.insert(0, "/home/runner/work/anyplot/anyplot/.venv/lib/python3.13/site-packages")
+
+from bokeh.io import output_file, save
+from bokeh.models import BasicTicker, ColorBar, ColumnDataSource, HoverTool, LinearColorMapper
+from bokeh.palettes import BrBG11
 from bokeh.plotting import figure
 from bokeh.transform import transform
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
 
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Data: Correlation matrix for financial metrics
 np.random.seed(42)
@@ -18,10 +38,10 @@
 
 # Generate realistic correlation matrix
 base = np.random.randn(100, n)
-base[:, 1] = base[:, 0] * 0.8 + np.random.randn(100) * 0.5  # Profit correlates with Revenue
-base[:, 5] = base[:, 1] * 0.7 + np.random.randn(100) * 0.6  # ROI correlates with Profit
-base[:, 6] = base[:, 0] * 0.6 + np.random.randn(100) * 0.7  # Market Cap correlates with Revenue
-base[:, 3] = -base[:, 5] * 0.5 + np.random.randn(100) * 0.8  # Debt negatively correlates with ROI
+base[:, 1] = base[:, 0] * 0.8 + np.random.randn(100) * 0.5
+base[:, 5] = base[:, 1] * 0.7 + np.random.randn(100) * 0.6
+base[:, 6] = base[:, 0] * 0.6 + np.random.randn(100) * 0.7
+base[:, 3] = -base[:, 5] * 0.5 + np.random.randn(100) * 0.8
 corr_matrix = np.corrcoef(base.T)
 np.fill_diagonal(corr_matrix, 1.0)
 
@@ -39,24 +59,22 @@
         val = corr_matrix[i, j]
         values.append(val)
         text_values.append(f"{val:.2f}")
-        # White text for dark cells, black text for light cells
         text_colors.append("white" if abs(val) > 0.5 else "black")
 
 source = ColumnDataSource(
     data={"x": x_coords, "y": y_coords, "value": values, "text": text_values, "text_color": text_colors}
 )
 
-# Create color mapper with diverging palette (blue-white-red)
-colors = ["#2166AC", "#4393C3", "#92C5DE", "#D1E5F0", "#F7F7F7", "#FDDBC7", "#F4A582", "#D6604D", "#B2182B"]
-mapper = LinearColorMapper(palette=colors, low=-1, high=1)
+# Create color mapper with diverging palette
+mapper = LinearColorMapper(palette=BrBG11, low=-1, high=1)
 
 # Create figure
 p = figure(
     width=3600,
     height=3600,
     x_range=variables,
     y_range=list(reversed(variables)),
-    title="heatmap-annotated · bokeh · pyplots.ai",
+    title="heatmap-annotated · bokeh · anyplot.ai",
     x_axis_location="above",
     toolbar_location=None,
 )
@@ -85,15 +103,35 @@
     text_color="text_color",
 )
 
+# Add hover tooltip for interactivity
+hover = HoverTool(
+    tooltips=[("Row Metric", "@y"), ("Column Metric", "@x"), ("Pearson Correlation", "@value{0.00}")], mode="mouse"
+)
+p.add_tools(hover)
+
 # Style the figure
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = INK_SOFT
+
 p.title.text_font_size = "32pt"
+p.title.text_color = INK
 p.title.align = "center"
-p.xaxis.axis_label = "Variable"
-p.yaxis.axis_label = "Variable"
+
+p.xaxis.axis_label = "Financial Metric"
+p.yaxis.axis_label = "Financial Metric"
 p.xaxis.axis_label_text_font_size = "24pt"
 p.yaxis.axis_label_text_font_size = "24pt"
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
 p.xaxis.major_label_text_font_size = "18pt"
 p.yaxis.major_label_text_font_size = "18pt"
+p.xaxis.major_label_text_color = INK_SOFT
+p.yaxis.major_label_text_color = INK_SOFT
+p.xaxis.axis_line_color = INK_SOFT
+p.yaxis.axis_line_color = INK_SOFT
+p.xaxis.major_tick_line_color = None
+p.yaxis.major_tick_line_color = None
 p.xaxis.major_label_orientation = 0.7
 p.axis.axis_line_color = None
 p.axis.major_tick_line_color = None
@@ -105,12 +143,33 @@
     ticker=BasicTicker(desired_num_ticks=9),
     label_standoff=12,
     major_label_text_font_size="16pt",
-    title="Correlation",
+    title="Pearson Correlation",
     title_text_font_size="20pt",
     width=40,
     location=(0, 0),
 )
 p.add_layout(color_bar, "right")
 
+
 # Save
-export_png(p, filename="plot.png")
+output_file(f"plot-{THEME}.html")
+save(p)
+
+# Screenshot with headless Chrome
+W, H = 3600, 3600
+opts = Options()
+for arg in (
+    "--headless=new",
+    "--no-sandbox",
+    "--disable-dev-shm-usage",
+    "--disable-gpu",
+    f"--window-size={W},{H}",
+    "--hide-scrollbars",
+):
+    opts.add_argument(arg)
+driver = webdriver.Chrome(options=opts)
+driver.set_window_size(W, H)
+driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
+time.sleep(3)
+driver.save_screenshot(f"plot-{THEME}.png")
+driver.quit()