feat(bokeh): implement streamgraph-basic

plots/streamgraph-basic/implementations/python/bokeh.py

@@ -1,27 +1,42 @@
-""" pyplots.ai
+""" anyplot.ai
 streamgraph-basic: Basic Stream Graph
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 88/100 | Updated: 2026-05-05
 """
 
+import os
+import time
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
-from bokeh.io import export_png, output_file, save
-from bokeh.models import ColumnDataSource, Legend
+from bokeh.io import output_file, save
+from bokeh.models import ColumnDataSource, HoverTool, Legend
 from bokeh.plotting import figure
+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"
 
+# Okabe-Ito palette — first series always #009E73
+COLORS = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
 
 # Data: monthly streaming hours by music genre over two years
 np.random.seed(42)
 
 months = pd.date_range(start="2022-01-01", periods=24, freq="ME")
 categories = ["Pop", "Rock", "Hip-Hop", "Electronic", "Jazz", "Classical"]
 
-# Generate streaming data for each genre with natural variations
 n_points = len(months)
 base = np.linspace(0, 4 * np.pi, n_points)
 
-data = {
+raw = {
     "Pop": 45 + 18 * np.sin(base) + np.random.randn(n_points) * 3,
     "Rock": 38 + 12 * np.sin(base + 0.8) + np.random.randn(n_points) * 2.5,
     "Hip-Hop": 35 + 22 * np.sin(base + 1.6) + np.random.randn(n_points) * 4,
@@ -30,24 +45,18 @@
     "Classical": 14 + 6 * np.sin(base + 4.0) + np.random.randn(n_points) * 1.5,
 }
 
-# Ensure all values are positive
 for cat in categories:
-    data[cat] = np.maximum(data[cat], 5)
+    raw[cat] = np.maximum(raw[cat], 5)
 
-# Convert to DataFrame
-df = pd.DataFrame(data)
-df["month"] = months
+df = pd.DataFrame(raw)
 
-# Calculate stacked values with symmetric baseline (centered around 0)
+# Symmetric baseline — center the stack around zero
 values = df[categories].values
-cumulative = np.cumsum(values, axis=1)
-total = cumulative[:, -1]
-
-# Center the baseline: offset so the middle is at 0
+total = values.sum(axis=1)
 baseline_offset = total / 2
+
 y_bottom = np.zeros_like(values)
 y_top = np.zeros_like(values)
-
 for i in range(len(categories)):
     if i == 0:
         y_bottom[:, i] = -baseline_offset
@@ -56,86 +65,108 @@
         y_bottom[:, i] = y_top[:, i - 1]
         y_top[:, i] = y_bottom[:, i] + values[:, i]
 
-# Create smooth interpolated curves for flowing appearance using numpy
+# Smooth interpolation for flowing curves
 x_numeric = np.arange(n_points)
 n_smooth = n_points * 10
 x_smooth = np.linspace(0, n_points - 1, n_smooth)
 
-# Convert smooth indices back to datetime
-date_min = months.min()
-date_max = months.max()
-months_smooth = pd.date_range(start=date_min, end=date_max, periods=n_smooth)
-
+months_smooth = pd.date_range(start=months.min(), end=months.max(), periods=n_smooth)
 y_bottom_smooth = np.zeros((n_smooth, len(categories)))
 y_top_smooth = np.zeros((n_smooth, len(categories)))
 
-# Use polynomial interpolation for smooth curves
 for i in range(len(categories)):
-    # Fit polynomial and interpolate for smooth curves
-    poly_bottom = np.polyfit(x_numeric, y_bottom[:, i], deg=min(10, n_points - 1))
-    poly_top = np.polyfit(x_numeric, y_top[:, i], deg=min(10, n_points - 1))
-    y_bottom_smooth[:, i] = np.polyval(poly_bottom, x_smooth)
-    y_top_smooth[:, i] = np.polyval(poly_top, x_smooth)
+    deg = min(10, n_points - 1)
+    y_bottom_smooth[:, i] = np.polyval(np.polyfit(x_numeric, y_bottom[:, i], deg), x_smooth)
+    y_top_smooth[:, i] = np.polyval(np.polyfit(x_numeric, y_top[:, i], deg), x_smooth)
 
-# Colors - Python Blue first, then harmonious colorblind-safe palette
-colors = ["#306998", "#FFD43B", "#E07A5F", "#81B29A", "#F2CC8F", "#3D405B"]
-
-# Create figure
+# Plot
 p = figure(
     width=4800,
     height=2700,
-    title="streamgraph-basic · bokeh · pyplots.ai",
+    title="streamgraph-basic · bokeh · anyplot.ai",
     x_axis_label="Time",
     y_axis_label="Streaming Hours (relative)",
     x_axis_type="datetime",
 )
 
-# Style title and axes for large canvas
+# Font sizes for 4800×2700 px canvas
 p.title.text_font_size = "32pt"
+p.title.text_color = INK
 p.xaxis.axis_label_text_font_size = "24pt"
 p.yaxis.axis_label_text_font_size = "24pt"
 p.xaxis.major_label_text_font_size = "18pt"
 p.yaxis.major_label_text_font_size = "18pt"
 
-# Create patches for each category (streamgraph layers) with smooth curves
-legend_items = []
+# Theme-adaptive chrome
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = None
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
+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 = INK_SOFT
+p.yaxis.major_tick_line_color = INK_SOFT
+p.xgrid.grid_line_color = INK
+p.ygrid.grid_line_color = INK
+p.xgrid.grid_line_alpha = 0.10
+p.ygrid.grid_line_alpha = 0.10
+
+# Draw streamgraph patches
 x_values = months_smooth.values
+legend_items = []
+hover_renderers = []
 
 for i, cat in enumerate(categories):
-    # Create polygon coordinates for smooth stacked area
-    # Go forward along top, then backward along bottom
     xs = np.concatenate([x_values, x_values[::-1]])
     ys = np.concatenate([y_top_smooth[:, i], y_bottom_smooth[:, i][::-1]])
 
-    source = ColumnDataSource(data={"x": xs, "y": ys})
-
+    source = ColumnDataSource(data={"x": xs, "y": ys, "genre": [cat] * len(xs)})
     renderer = p.patch(
-        x="x", y="y", source=source, fill_color=colors[i], fill_alpha=0.85, line_color=colors[i], line_width=2
+        x="x", y="y", source=source, fill_color=COLORS[i], fill_alpha=0.85, line_color=COLORS[i], line_width=2
     )
     legend_items.append((cat, [renderer]))
+    hover_renderers.append(renderer)
 
-# Add legend outside the plot
+# HoverTool — shows genre name on hover
+hover = HoverTool(renderers=hover_renderers, tooltips=[("Genre", "@genre")])
+p.add_tools(hover)
+
+# Legend outside the plot area
 legend = Legend(items=legend_items, location="center")
-legend.label_text_font_size = "20pt"
-legend.glyph_height = 35
-legend.glyph_width = 35
-legend.spacing = 12
+legend.label_text_font_size = "22pt"
+legend.label_text_color = INK_SOFT
+legend.glyph_height = 40
+legend.glyph_width = 40
+legend.spacing = 15
+legend.background_fill_color = ELEVATED_BG
+legend.border_line_color = INK_SOFT
 p.add_layout(legend, "right")
 
-# Grid styling - subtle
-p.grid.grid_line_alpha = 0.3
-p.grid.grid_line_dash = [6, 4]
-
-# Keep y-axis visible but subtle
-p.yaxis.visible = True
-p.outline_line_color = None
-
-# Toolbar placement
 p.toolbar_location = "above"
 
-# Save as PNG
-export_png(p, filename="plot.png")
-
-# Also save interactive HTML
-output_file("plot.html")
+# Save interactive HTML
+output_file(f"plot-{THEME}.html")
 save(p)
+
+# Screenshot with headless Chrome (export_png unavailable in this environment)
+W, H = 4800, 2700
+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()