feat(letsplot): implement scatter-connected-temporal

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

@@ -1,23 +1,58 @@
-""" pyplots.ai
+""" anyplot.ai
 scatter-connected-temporal: Connected Scatter Plot with Temporal Path
-Library: letsplot 4.9.0 | Python 3.14.3
-Quality: 90/100 | Created: 2026-03-13
+Library: letsplot 4.10.1 | Python 3.13.13
+Quality: 88/100 | Updated: 2026-06-09
 """
 
+import os
+
 import numpy as np
 import pandas as pd
-from lets_plot import *  # noqa: F403
-from lets_plot.export import ggsave as export_ggsave
+from lets_plot import (
+    LetsPlot,
+    aes,
+    arrow,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_path,
+    geom_point,
+    geom_segment,
+    geom_text,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    layer_tooltips,
+    scale_color_gradient,
+    scale_fill_gradient,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
 
+LetsPlot.setup_html()
 
-LetsPlot.setup_html()  # noqa: F405
+# Theme tokens — Imprint palette, theme-adaptive chrome
+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"
+GRID = "rgba(26,26,23,0.12)" if THEME == "light" else "rgba(240,239,232,0.12)"
 
-# Data — Unemployment rate vs inflation rate (Phillips curve), 1990-2023
+# Imprint sequential colormap: brand green (early) → blue (recent)
+SEQ_LOW = "#009E73"  # Imprint position 1 — start of temporal path
+SEQ_HIGH = "#4467A3"  # Imprint position 3 — end of temporal path
+
+# Data — Phillips curve dynamics, unemployment vs inflation 1990–2023
 np.random.seed(42)
 years = np.arange(1990, 2024)
 n = len(years)
 
-# Simulate realistic Phillips curve dynamics with regime shifts
 unemployment = np.concatenate(
     [
         np.linspace(5.6, 4.0, 10) + np.random.randn(10) * 0.3,  # 1990s decline
@@ -47,14 +82,10 @@
     }
 )
 
-# Color gradient: map time index to a normalized value for color encoding
-df["time_norm"] = df["time_idx"] / (n - 1)
-
-# Label only key years — reduced set to avoid crowding in dense areas
+# Annotate key economic turning points
 key_years = {1990, 2000, 2007, 2009, 2020, 2023}
 df_labels = df[df["year"].isin(key_years)].copy()
 
-# Per-label offset to prevent overlap and clipping
 nudge_map = {
     1990: (0.35, 0.7),
     2000: (0.35, -0.7),
@@ -64,96 +95,90 @@
     2023: (0.35, 0.7),
 }
 
-# Highlight start and end points with larger markers
 df_endpoints = df[df["year"].isin([1990, 2023])].copy()
 df_labels["label_x"] = df_labels.apply(lambda r: r["unemployment"] + nudge_map.get(r["year"], (0, 0))[0], axis=1)
 df_labels["label_y"] = df_labels.apply(lambda r: r["inflation"] + nudge_map.get(r["year"], (0, 0))[1], axis=1)
 
-# Arrow segment at end of path to show time direction
+# Direction arrow at terminal segment of the path
 last = df.iloc[-1]
 prev = df.iloc[-2]
-
 arrow_df = pd.DataFrame(
     {"x": [prev["unemployment"]], "y": [prev["inflation"]], "xend": [last["unemployment"]], "yend": [last["inflation"]]}
 )
 
+title = "scatter-connected-temporal · python · letsplot · anyplot.ai"
+
 # Plot
 plot = (
-    ggplot(df, aes(x="unemployment", y="inflation"))  # noqa: F405
-    + geom_path(  # noqa: F405
-        aes(color="time_idx"),  # noqa: F405
-        size=1.8,
-        alpha=0.7,
-        tooltips="none",
-    )
-    + geom_segment(  # noqa: F405
+    ggplot(df, aes(x="unemployment", y="inflation"))
+    + geom_path(aes(color="time_idx"), size=1.5, alpha=0.75, tooltips="none")
+    + geom_segment(
         data=arrow_df,
-        mapping=aes(x="x", y="y", xend="xend", yend="yend"),  # noqa: F405
-        color="#1a3a5c",
-        size=2.5,
-        arrow=arrow(angle=25, length=12, type="closed"),  # noqa: F405
+        mapping=aes(x="x", y="y", xend="xend", yend="yend"),
+        color=SEQ_HIGH,
+        size=2.2,
+        arrow=arrow(angle=25, length=10, type="closed"),
     )
-    + geom_point(  # noqa: F405
-        aes(fill="time_idx"),  # noqa: F405
-        color="white",
-        size=7,
-        stroke=1.2,
+    + geom_point(
+        aes(fill="time_idx"),
+        color=PAGE_BG,
+        size=3.5,
+        stroke=1.0,
         shape=21,
-        alpha=0.85,
-        tooltips=layer_tooltips()  # noqa: F405
+        alpha=0.9,
+        tooltips=layer_tooltips()
         .line("Year|@year")
         .line("Unemployment|@{unemployment}{.1f}%")
         .line("Inflation|@{inflation}{.1f}%"),
     )
-    + geom_point(  # noqa: F405
+    + geom_point(
         data=df_endpoints,
-        mapping=aes(x="unemployment", y="inflation", fill="time_idx"),  # noqa: F405
-        color="#1a1a1a",
-        size=11,
-        stroke=2.0,
+        mapping=aes(x="unemployment", y="inflation", fill="time_idx"),
+        color=INK,
+        size=6.0,
+        stroke=1.8,
         shape=21,
         alpha=1.0,
     )
-    + geom_text(  # noqa: F405
+    + geom_text(
         data=df_labels,
-        mapping=aes(x="label_x", y="label_y", label="year_label"),  # noqa: F405
-        size=13,
-        color="#222222",
+        mapping=aes(x="label_x", y="label_y", label="year_label"),
+        size=6,
+        color=INK,
         family="monospace",
         fontface="bold",
     )
-    + scale_color_gradient(  # noqa: F405
-        low="#a8d5e2", high="#1a3a5c", name="Year", breaks=[0, (n - 1) / 2, n - 1], labels=["1990", "2006", "2023"]
+    + scale_color_gradient(
+        low=SEQ_LOW, high=SEQ_HIGH, name="Year", breaks=[0, (n - 1) / 2, n - 1], labels=["1990", "2006", "2023"]
     )
-    + scale_fill_gradient(  # noqa: F405
-        low="#a8d5e2", high="#1a3a5c", guide="none"
-    )
-    + scale_x_continuous(expand=[0.06, 0])  # noqa: F405
-    + scale_y_continuous(expand=[0.08, 0])  # noqa: F405
-    + labs(  # noqa: F405
+    + scale_fill_gradient(low=SEQ_LOW, high=SEQ_HIGH, guide="none")
+    + scale_x_continuous(expand=[0.06, 0])
+    + scale_y_continuous(expand=[0.08, 0])
+    + labs(
         x="Unemployment Rate (%)",
         y="Inflation Rate (%)",
-        title="scatter-connected-temporal · letsplot · pyplots.ai",
-        subtitle="Phillips Curve Dynamics — US-style unemployment vs inflation, 1990-2023",
+        title=title,
+        subtitle="Phillips Curve: unemployment vs inflation, 1990–2023",
     )
-    + ggsize(1600, 900)  # noqa: F405
-    + theme_minimal()  # noqa: F405
-    + theme(  # noqa: F405
-        axis_text=element_text(size=16, color="#555555"),  # noqa: F405
-        axis_title=element_text(size=20, color="#333333"),  # noqa: F405
-        plot_title=element_text(size=24, color="#1a1a1a", face="bold"),  # noqa: F405
-        plot_subtitle=element_text(size=16, color="#555555"),  # noqa: F405
-        legend_text=element_text(size=14),  # noqa: F405
-        legend_title=element_text(size=16, face="bold"),  # noqa: F405
-        panel_grid_major=element_line(color="#E0E0E0", size=0.3),  # noqa: F405
-        panel_grid_minor=element_blank(),  # noqa: F405
-        plot_background=element_rect(fill="#FAFBFC"),  # noqa: F405
-        plot_margin=[60, 50, 20, 20],
+    + ggsize(800, 450)
+    + theme_minimal()
+    + theme(
+        axis_text=element_text(size=10, color=INK_SOFT),
+        axis_title=element_text(size=12, color=INK),
+        plot_title=element_text(size=16, color=INK),
+        plot_subtitle=element_text(size=10, color=INK_SOFT),
+        legend_text=element_text(size=10, color=INK_SOFT),
+        legend_title=element_text(size=12, color=INK),
+        panel_grid_major=element_line(color=GRID, size=0.3),
+        panel_grid_minor=element_blank(),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG),
+        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+        panel_border=element_blank(),
+        plot_margin=[20, 20, 10, 10],
     )
 )
 
-# Save PNG (scale 3x to get 4800 x 2700 px)
-export_ggsave(plot, filename="plot.png", path=".", scale=3)
-
-# Save HTML for interactive version
-export_ggsave(plot, filename="plot.html", path=".")
+# Save — PNG at 3200×1800 (800×450 × scale=4), plus interactive HTML
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
+ggsave(plot, f"plot-{THEME}.html", path=".")