feat(bokeh): implement scatter-basic

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

@@ -1,22 +1,35 @@
-""" anyplot.ai
+"""anyplot.ai
 scatter-basic: Basic Scatter Plot
-Library: bokeh 3.9.0 | Python 3.14.4
-Quality: 90/100 | Updated: 2026-04-23
+Library: bokeh 3.9.1 | Python 3.13.14
+Quality: 87/100 | Updated: 2026-06-25
 """
 
 import os
+import sys
+import time
+from pathlib import Path
+
+
+# Remove script directory from sys.path so 'import bokeh' finds the installed
+# package rather than this file (bokeh.py would shadow itself otherwise).
+_script_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path = [p for p in sys.path if os.path.abspath(p) != _script_dir]
 
 import numpy as np
-from bokeh.io import export_png, output_file, save
+from bokeh.io import output_file, save
 from bokeh.models import ColumnDataSource, HoverTool
 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"
-BRAND = "#009E73"
+BRAND = "#009E73"  # Imprint palette position 1 — ALWAYS first series
 
 # Data — study hours vs exam scores, moderate positive correlation
 np.random.seed(42)
@@ -27,41 +40,61 @@
 
 source = ColumnDataSource(data={"study_hours": study_hours, "exam_scores": exam_scores})
 
-# Plot
+# Plot — 3200×1800 canonical landscape canvas
+W, H = 3200, 1800
+title = "scatter-basic · python · bokeh · anyplot.ai"
+
 p = figure(
-    width=4800,
-    height=2700,
-    title="scatter-basic · bokeh · anyplot.ai",
+    width=W,
+    height=H,
+    title=title,
     x_axis_label="Study Hours per Day",
     y_axis_label="Exam Score (%)",
     toolbar_location=None,
     x_range=(0, 10.5),
     y_range=(10, 104),
+    min_border_bottom=160,
+    min_border_left=180,
+    min_border_top=110,
+    min_border_right=50,
 )
 
 scatter_renderer = p.scatter(
-    x="study_hours", y="exam_scores", source=source, size=34, color=BRAND, alpha=0.7, line_color=PAGE_BG, line_width=1.2
+    x="study_hours",
+    y="exam_scores",
+    source=source,
+    size=28,
+    color=BRAND,
+    alpha=0.7,
+    line_color=INK_SOFT,
+    line_width=1.2,
 )
 
+# Trend line — linear regression to show positive correlation
+coeffs = np.polyfit(study_hours, exam_scores, 1)
+x_trend = np.array([0.0, 10.5])
+y_trend = np.polyval(coeffs, x_trend)
+p.line(x=x_trend, y=y_trend, line_color=INK_SOFT, line_width=4, line_alpha=0.5, line_dash="dashed")
+
 # HoverTool — Bokeh's distinctive interactive feature (HTML only; PNG stays clean)
 hover = HoverTool(
     renderers=[scatter_renderer],
     tooltips=[("Study Hours", "@study_hours{0.1} hrs"), ("Exam Score", "@exam_scores{0.0}%")],
 )
 p.add_tools(hover)
 
-# Typography — sized for 4800×2700 canvas
-p.title.text_font_size = "42pt"
+# Typography — bokeh.md canonical values for 3200×1800 canvas
+p.title.text_font_size = "50pt"
 p.title.text_font_style = "bold"
 p.title.text_color = INK
 p.title.align = "center"
 
-p.xaxis.axis_label_text_font_size = "32pt"
-p.yaxis.axis_label_text_font_size = "32pt"
+p.xaxis.axis_label_text_font_size = "42pt"
+p.yaxis.axis_label_text_font_size = "42pt"
 p.xaxis.axis_label_text_font_style = "normal"
 p.yaxis.axis_label_text_font_style = "normal"
-p.xaxis.major_label_text_font_size = "24pt"
-p.yaxis.major_label_text_font_size = "24pt"
+p.xaxis.major_label_text_font_size = "34pt"
+p.yaxis.major_label_text_font_size = "34pt"
 p.xaxis.axis_label_standoff = 28
 p.yaxis.axis_label_standoff = 28
 
@@ -81,18 +114,34 @@
 p.xaxis.minor_tick_line_color = None
 p.yaxis.minor_tick_line_color = None
 
-# Clean L-frame: keep left+bottom axis lines only (handled above via axis_line_color)
+# Grid — both axes for scatter (style guide), subtle weight
 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
-p.xgrid.grid_line_width = 2
-p.ygrid.grid_line_width = 2
+p.xgrid.grid_line_width = 1
+p.ygrid.grid_line_width = 1
 
 p.xaxis.ticker.desired_num_ticks = 10
 p.yaxis.ticker.desired_num_ticks = 8
 
-# Save
-export_png(p, filename=f"plot-{THEME}.png")
-output_file(f"plot-{THEME}.html", title="scatter-basic · bokeh · anyplot.ai")
+# Save — HTML first (interactive catalog artifact), then PNG via Selenium
+output_file(f"plot-{THEME}.html", title="scatter-basic · python · bokeh · anyplot.ai")
 save(p)
+
+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()