feat(pygal): implement scatter-connected-temporal

plots/scatter-connected-temporal/implementations/python/pygal.py

@@ -1,109 +1,124 @@
-""" pyplots.ai
+""" anyplot.ai
 scatter-connected-temporal: Connected Scatter Plot with Temporal Path
-Library: pygal 3.1.0 | Python 3.14.3
-Quality: 87/100 | Created: 2026-03-13
+Library: pygal 3.1.0 | Python 3.13.13
+Quality: 84/100 | Updated: 2026-06-09
 """
 
+import os
+import re
+import sys
+
+
+# Remove script's own directory from sys.path so the real pygal package is found first
+_here = os.path.dirname(os.path.abspath(__file__))
+if _here in sys.path:
+    sys.path.remove(_here)
+
 import cairosvg
 import numpy as np
 import pygal
 from pygal.style import Style
 
 
-# Data — Life expectancy vs GDP per capita for a fictional country (1990-2023)
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
+
+# Imprint palette — amber semantic anchor for key events
+ANYPLOT_AMBER = "#DDCC77"
+
+# Data — Life expectancy vs GDP per capita for a developing country (1990–2023)
 np.random.seed(42)
 years = list(range(1990, 2024))
 n_years = len(years)
 
-# GDP per capita starts ~8000, grows with occasional dips (recessions)
 gdp_base = 8000
 gdp_growth = np.cumsum(np.random.normal(450, 300, n_years))
-gdp_growth[8:10] -= 1500  # 1998-1999 recession dip
-gdp_growth[18:20] -= 2000  # 2008-2009 financial crisis
-gdp_growth[30:32] -= 800  # 2020-2021 pandemic dip
+gdp_growth[8:10] -= 1500  # 1998–1999 recession
+gdp_growth[18:20] -= 2000  # 2008–2009 financial crisis
+gdp_growth[30:32] -= 800  # 2020–2021 pandemic
 gdp_per_capita = gdp_base + gdp_growth
 gdp_per_capita = np.maximum(gdp_per_capita, 5000)
 
-# Life expectancy starts ~68, rises gradually with dips during crises
 le_base = 68.0
 le_growth = np.cumsum(np.random.normal(0.25, 0.12, n_years))
-le_growth[18:20] -= 0.4  # Financial crisis stress
-le_growth[30:32] -= 1.2  # Pandemic drop
+le_growth[18:20] -= 0.4
+le_growth[30:32] -= 1.2
 life_expectancy = le_base + le_growth
 life_expectancy = np.clip(life_expectancy, 64, 82)
 
-# Define temporal eras for color gradient segments (light → dark blue)
-# Darkened lightest shades for better contrast against light background
+# Imprint imprint_seq gradient (#009E73 → #4467A3) for temporal progression
 eras = [
-    ("1990-1997", 0, 8, "#4da6c9"),
-    ("1998-2003", 8, 14, "#3d8fb5"),
-    ("2004-2009", 14, 20, "#2e78a0"),
-    ("2010-2015", 20, 26, "#22618a"),
-    ("2016-2019", 26, 30, "#1a4d70"),
-    ("2020-2023", 30, 34, "#0e2f44"),
+    ("1990–1997", 0, 8, "#009E73"),
+    ("1998–2003", 8, 14, "#0E937D"),
+    ("2004–2009", 14, 20, "#1B8886"),
+    ("2010–2015", 20, 26, "#297D90"),
+    ("2016–2019", 26, 30, "#367299"),
+    ("2020–2023", 30, 34, "#4467A3"),
 ]
 
-# Select key years to annotate
 annotate_years = {1998, 2005, 2008, 2015, 2020}
 
-# Style — refined with subtle grid and cohesive blue palette
+# Title scaled for 81-char length: round(66 × 67/81) = 55
+title = "Life Expectancy vs GDP · scatter-connected-temporal · python · pygal · anyplot.ai"
+title_font_size = max(44, round(66 * 67 / len(title)))
+
 font = "DejaVu Sans, Helvetica, Arial, sans-serif"
 custom_style = Style(
-    background="white",
-    plot_background="#f4f7fa",
-    foreground="#3a3a3a",
-    foreground_strong="#2a2a2a",
-    foreground_subtle="#d8d8d8",
-    guide_stroke_color="#e0e0e0",
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_MUTED,
+    guide_stroke_color=INK_MUTED,
     guide_stroke_dasharray="3,5",
-    colors=("#4da6c9", "#3d8fb5", "#2e78a0", "#22618a", "#1a4d70", "#0e2f44", "#e8a838", "#c0392b", "#1a3a5c"),
+    colors=("#009E73", "#0E937D", "#1B8886", "#297D90", "#367299", "#4467A3", ANYPLOT_AMBER, "#AE3030", "#4467A3"),
     font_family=font,
     title_font_family=font,
-    title_font_size=52,
-    label_font_size=40,
-    major_label_font_size=36,
-    legend_font_size=34,
-    legend_font_family=font,
-    value_font_size=32,
-    value_label_font_size=36,
-    tooltip_font_size=26,
+    title_font_size=title_font_size,
+    label_font_size=56,
+    major_label_font_size=44,
+    legend_font_size=44,
+    value_font_size=36,
+    value_label_font_size=44,
+    tooltip_font_size=36,
     tooltip_font_family=font,
     opacity=0.92,
     opacity_hover=1.0,
     stroke_opacity=0.9,
     stroke_opacity_hover=1.0,
 )
 
-# Axis ranges
 x_min = float(np.floor(gdp_per_capita.min() / 1000) * 1000)
 x_max = float(np.ceil(gdp_per_capita.max() / 1000) * 1000)
 y_min = float(np.floor(life_expectancy.min()))
 y_max = float(np.ceil(life_expectancy.max()) + 1)
 
-# Create XY chart with native print_labels for year annotations
 chart = pygal.XY(
-    width=4800,
-    height=2700,
+    width=3200,
+    height=1800,
     style=custom_style,
-    title="Life Expectancy vs GDP · scatter-connected-temporal · pygal · pyplots.ai",
+    title=title,
     x_title="GDP per Capita (USD)",
     y_title="Life Expectancy (years)",
     show_legend=True,
     legend_at_bottom=True,
     legend_at_bottom_columns=3,
-    legend_box_size=24,
+    legend_box_size=28,
     stroke=True,
-    dots_size=10,
+    dots_size=12,
     show_x_guides=True,
     show_y_guides=True,
-    x_value_formatter=lambda x: f"${x:,.0f}",
+    x_value_formatter=lambda x: f"${x / 1000:.0f}k",
     value_formatter=lambda y: f"{y:.1f} yrs",
     print_labels=True,
     print_values=False,
-    margin_bottom=120,
-    margin_left=70,
-    margin_right=70,
-    margin_top=55,
+    margin_bottom=130,
+    margin_left=80,
+    margin_right=80,
+    margin_top=60,
     range=(y_min, y_max),
     xrange=(x_min, x_max),
     x_labels_major_count=7,
@@ -113,8 +128,7 @@
     show_y_labels=True,
 )
 
-# Add temporal path as gradient-colored era segments
-# Each segment overlaps by 1 point for visual continuity
+# Temporal path as Imprint imprint_seq gradient era segments
 for era_name, start, end, color in eras:
     end_idx = min(end + 1, n_years)
     segment_points = [
@@ -125,34 +139,53 @@
         segment_points,
         stroke=True,
         show_dots=True,
-        dots_size=10,
-        stroke_style={"width": 6, "linecap": "round", "linejoin": "round"},
+        dots_size=12,
+        stroke_style={"width": 5, "linecap": "round", "linejoin": "round"},
     )
 
-# Highlight annotated key years with larger amber dots and native year labels
+# Key years highlighted with amber dots and year labels
 annotated_points = []
 for yr in sorted(annotate_years):
     i = yr - 1990
     annotated_points.append(
-        {"value": (float(gdp_per_capita[i]), float(life_expectancy[i])), "label": str(yr), "color": "#e8a838"}
+        {"value": (float(gdp_per_capita[i]), float(life_expectancy[i])), "label": str(yr), "color": ANYPLOT_AMBER}
     )
-chart.add("Key years", annotated_points, stroke=False, dots_size=16)
+chart.add("Key years", annotated_points, stroke=False, dots_size=20)
 
-# Highlight start and end with distinct large markers
+# Start and end markers
 chart.add(
     f"Start ({years[0]})",
-    [{"value": (float(gdp_per_capita[0]), float(life_expectancy[0])), "label": "\u25b6 1990", "color": "#c0392b"}],
+    [{"value": (float(gdp_per_capita[0]), float(life_expectancy[0])), "label": "▶ 1990", "color": "#AE3030"}],
     stroke=False,
-    dots_size=22,
+    dots_size=26,
 )
 chart.add(
     f"End ({years[-1]})",
-    [{"value": (float(gdp_per_capita[-1]), float(life_expectancy[-1])), "label": "\u25cf 2023", "color": "#1a3a5c"}],
+    [{"value": (float(gdp_per_capita[-1]), float(life_expectancy[-1])), "label": "● 2023", "color": "#4467A3"}],
     stroke=False,
-    dots_size=22,
+    dots_size=26,
 )
 
-# Save PNG via cairosvg and HTML
+# Patch label text colors for dark-theme legibility before PNG conversion
+# pygal's print_labels text color does not adapt to the dark background via the foreground Style token
+_label_texts = {str(yr) for yr in sorted(annotate_years)} | {"▶ 1990", "● 2023"}
+
+
+def _patch_label_colors(svg_str, labels, fill_color):
+    def _fix(m):
+        tag_attrs, content = m.group(1), m.group(2)
+        if not any(lbl in content for lbl in labels):
+            return m.group(0)
+        if "fill=" in tag_attrs:
+            tag_attrs = re.sub(r'\bfill="[^"]*"', f'fill="{fill_color}"', tag_attrs)
+        else:
+            tag_attrs += f' fill="{fill_color}"'
+        return f"<text{tag_attrs}>{content}</text>"
+
+    return re.sub(r"<text([^>]*)>(.*?)</text>", _fix, svg_str, flags=re.DOTALL)
+
+
 svg_data = chart.render()
-cairosvg.svg2png(bytestring=svg_data, write_to="plot.png")
-chart.render_to_file("plot.html")
+svg_str = _patch_label_colors(svg_data.decode("utf-8"), _label_texts, INK)
+cairosvg.svg2png(bytestring=svg_str.encode("utf-8"), write_to=f"plot-{THEME}.png")
+chart.render_to_file(f"plot-{THEME}.html")