feat(matplotlib): implement ecdf-basic

plots/ecdf-basic/implementations/python/matplotlib.py

@@ -1,7 +1,7 @@
 """ anyplot.ai
 ecdf-basic: Basic ECDF Plot
-Library: matplotlib 3.10.9 | Python 3.14.4
-Quality: 89/100 | Updated: 2026-04-24
+Library: matplotlib 3.11.0 | Python 3.13.14
+Quality: 91/100 | Updated: 2026-06-25
 """
 
 import os
@@ -10,52 +10,65 @@
 import numpy as np
 
 
-# Theme
 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"
 INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
-BRAND = "#009E73"
+BRAND = "#009E73"  # Imprint palette position 1
 
-# Data: API response times (ms) — log-normal distribution with a long tail
-np.random.seed(42)
-response_times_ms = np.random.lognormal(mean=4.6, sigma=0.55, size=250)
+# Data: IoT sensor temperature readings (°C) — bimodal mix of offices and server rooms
+np.random.seed(7)
+office = np.random.normal(loc=22.0, scale=1.8, size=160)
+servers = np.random.normal(loc=18.5, scale=0.9, size=90)
+temps = np.concatenate([office, servers])
+t_min = temps.min()
 
-# Plot
-fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
+# Canvas — 3200 × 1800 px landscape (figsize × dpi, no bbox_inches='tight')
+fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-ax.ecdf(response_times_ms, color=BRAND, linewidth=3.5)
+# Subtle fill under the ECDF curve for visual weight
+sorted_temps = np.sort(temps)
+ecdf_y = np.arange(1, len(sorted_temps) + 1) / len(sorted_temps)
+ax.fill_between(sorted_temps, 0, ecdf_y, step="post", alpha=0.10, color=BRAND)
 
-# Percentile reference lines (p50, p95, p99) — common SRE reading
-percentiles = [50, 95, 99]
-percentile_values = np.percentile(response_times_ms, percentiles)
-for p, v in zip(percentiles, percentile_values, strict=True):
-    ax.axhline(y=p / 100, color=INK_SOFT, linestyle=":", linewidth=1.5, alpha=0.6)
-    ax.axvline(x=v, ymax=p / 100, color=INK_SOFT, linestyle=":", linewidth=1.5, alpha=0.6)
+# ECDF step function (drawn on top of fill)
+ax.ecdf(temps, color=BRAND, linewidth=2.5)
+
+# Bimodal mode markers — highlight the two sensor populations
+for mode_temp, label in [(18.5, "Server\nrooms"), (22.0, "Offices")]:
+    ax.axvline(mode_temp, color=INK_MUTED, linestyle="--", linewidth=0.8, alpha=0.45)
+    ax.text(mode_temp + 0.2, 0.05, label, fontsize=8, color=INK_MUTED, va="bottom", ha="left")
+
+# Quartile reference guides (Q1, Q2, Q3)
+quartiles = [25, 50, 75]
+q_values = np.percentile(temps, quartiles)
+for q, v in zip(quartiles, q_values, strict=True):
+    yf = q / 100
+    ax.plot([t_min - 0.3, v], [yf, yf], color=INK_MUTED, linestyle=":", linewidth=0.8, alpha=0.6)
+    ax.plot([v, v], [0, yf], color=INK_MUTED, linestyle=":", linewidth=0.8, alpha=0.6)
     ax.annotate(
-        f"p{p}  {v:.0f} ms",
-        xy=(v, p / 100),
-        xytext=(10, -18 if p == 99 else 8),
-        textcoords="offset points",
-        fontsize=14,
-        color=INK_SOFT,
+        f"Q{q // 25}  {v:.1f}°C", xy=(v, yf), xytext=(5, 5), textcoords="offset points", fontsize=9, color=INK_MUTED
     )
 
-# Style
-ax.set_xlabel("Response Time (ms)", fontsize=20, color=INK)
-ax.set_ylabel("Cumulative Proportion of Requests", fontsize=20, color=INK)
-ax.set_title("API Response Times · ecdf-basic · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
-ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
+# Chrome
+title = "Sensor Temperatures · ecdf-basic · python · matplotlib · anyplot.ai"
+title_fs = max(8, round(12 * 67 / len(title))) if len(title) > 67 else 12
+ax.set_title(title, fontsize=title_fs, fontweight="medium", color=INK)
+ax.set_xlabel("Temperature (°C)", fontsize=10, color=INK)
+ax.set_ylabel("Cumulative Proportion", fontsize=10, color=INK)
+ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT, labelcolor=INK_SOFT)
 ax.set_ylim(0, 1.02)
-ax.set_xlim(left=0)
+ax.set_xlim(left=t_min - 0.5)
+
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 for s in ("left", "bottom"):
     ax.spines[s].set_color(INK_SOFT)
-ax.grid(True, alpha=0.10, linewidth=0.8, color=INK)
 
-plt.tight_layout()
-plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
+
+fig.subplots_adjust(left=0.09, right=0.97, top=0.92, bottom=0.12)
+plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)