feat(letsplot): implement slope-basic

plots/slope-basic/implementations/python/letsplot.py

@@ -1,14 +1,18 @@
-""" pyplots.ai
+""" anyplot.ai
 slope-basic: Basic Slope Chart (Slopegraph)
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 85/100 | Updated: 2026-04-30
 """
 
+import os
+
 import pandas as pd
 from lets_plot import (
     LetsPlot,
     aes,
     element_blank,
+    element_line,
+    element_rect,
     element_text,
     geom_point,
     geom_segment,
@@ -17,110 +21,154 @@
     ggsave,
     ggsize,
     labs,
+    layer_tooltips,
     scale_color_manual,
     scale_x_continuous,
+    scale_y_continuous,
     theme,
     theme_minimal,
 )
 
 
 LetsPlot.setup_html()
 
-# Data - Sales figures for 10 products comparing Q1 vs Q4
-# Values chosen to minimize label overlap while showing variety
+# 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"
+GRID_COLOR = "#C8C7BF" if THEME == "light" else "#333330"
+
+# Okabe-Ito: green = increase, orange = decrease
+COLOR_INCREASE = "#009E73"
+COLOR_DECREASE = "#D55E00"
+
+# Consumer electronics quarterly sales ($K): Q1 vs Q4
 data = {
     "entity": [
-        "Product A",
-        "Product B",
-        "Product C",
-        "Product D",
-        "Product E",
-        "Product F",
-        "Product G",
-        "Product H",
-        "Product I",
-        "Product J",
+        "Laptops",
+        "Smartphones",
+        "Smart TVs",
+        "Monitors",
+        "Headphones",
+        "Cameras",
+        "Streaming Devices",
+        "Speakers",
+        "Tablets",
+        "Gaming Consoles",
     ],
     "Q1": [80, 210, 120, 155, 140, 195, 55, 175, 105, 90],
     "Q4": [130, 175, 160, 120, 185, 215, 95, 140, 150, 60],
 }
 df = pd.DataFrame(data)
-
-# Calculate change direction for color coding
 df["change"] = df["Q4"] - df["Q1"]
 df["direction"] = df["change"].apply(lambda x: "Increase" if x > 0 else "Decrease")
+df["abs_change"] = df["change"].abs()
 
-# Prepare data for segment plot
-df_long = pd.DataFrame(
-    {
-        "entity": df["entity"].tolist() * 2,
-        "period": ["Q1"] * 10 + ["Q4"] * 10,
-        "value": df["Q1"].tolist() + df["Q4"].tolist(),
-        "x": [0] * 10 + [1] * 10,
-    }
-)
+# Top 3 movers by absolute change — receive visual emphasis
+top_movers = set(df.nlargest(3, "abs_change")["entity"])
 
-# Create segment data for lines
+# Segment data
 df_segments = pd.DataFrame(
     {
-        "entity": df["entity"],
+        "entity": df["entity"].values,
         "x_start": [0] * 10,
         "x_end": [1] * 10,
-        "y_start": df["Q1"],
-        "y_end": df["Q4"],
-        "direction": df["direction"],
+        "y_start": df["Q1"].values,
+        "y_end": df["Q4"].values,
+        "direction": df["direction"].values,
+        "change": df["change"].values,
+    }
+)
+seg_normal = df_segments[~df_segments["entity"].isin(top_movers)].reset_index(drop=True)
+seg_top = df_segments[df_segments["entity"].isin(top_movers)].reset_index(drop=True)
+
+# Points (both endpoints)
+df_long = pd.DataFrame(
+    {"entity": df["entity"].tolist() * 2, "value": df["Q1"].tolist() + df["Q4"].tolist(), "x": [0] * 10 + [1] * 10}
+).merge(df[["entity", "direction"]], on="entity")
+
+# Endpoint labels: entity name + value for legibility in crowded regions
+df_left = pd.DataFrame(
+    {
+        "entity": df["entity"].values,
+        "value": df["Q1"].values,
+        "x": [0] * 10,
+        "label": [f"{e} (${v}K)" for e, v in zip(df["entity"], df["Q1"], strict=False)],
+    }
+)
+df_right = pd.DataFrame(
+    {
+        "entity": df["entity"].values,
+        "value": df["Q4"].values,
+        "x": [1] * 10,
+        "label": [f"{e} (${v}K)" for e, v in zip(df["entity"], df["Q4"], strict=False)],
     }
 )
 
-# Merge direction info for points
-df_long = df_long.merge(df[["entity", "direction"]], on="entity")
+# Custom tooltip format — letsplot-specific interactive feature
+_tooltip_normal = (
+    layer_tooltips()
+    .title("@entity")
+    .line("Q1 Sales|$@{y_start}K")
+    .line("Q4 Sales|$@{y_end}K")
+    .line("Change|@{change}K")
+)
+_tooltip_top = (
+    layer_tooltips()
+    .title("@entity  ★ Top mover")
+    .line("Q1 Sales|$@{y_start}K")
+    .line("Q4 Sales|$@{y_end}K")
+    .line("Change|@{change}K")
+)
 
-# Plot
 plot = (
     ggplot()
+    # Background lines: dimmed to let top movers stand out
     + geom_segment(
-        data=df_segments,
+        data=seg_normal,
         mapping=aes(x="x_start", y="y_start", xend="x_end", yend="y_end", color="direction"),
-        size=2.5,
-        alpha=0.85,
-    )
-    + geom_point(data=df_long, mapping=aes(x="x", y="value", color="direction"), size=7)
-    # Labels on left side (Q1)
-    + geom_text(
-        data=df_long[df_long["x"] == 0],
-        mapping=aes(x="x", y="value", label="entity"),
-        hjust=1.15,
-        size=13,
-        color="#333333",
+        size=1.8,
+        alpha=0.40,
+        tooltips=_tooltip_normal,
     )
-    # Labels on right side (Q4)
-    + geom_text(
-        data=df_long[df_long["x"] == 1],
-        mapping=aes(x="x", y="value", label="entity"),
-        hjust=-0.15,
-        size=13,
-        color="#333333",
+    # Top-mover lines: bold, fully opaque — emphasises biggest Q1→Q4 changes
+    + geom_segment(
+        data=seg_top,
+        mapping=aes(x="x_start", y="y_start", xend="x_end", yend="y_end", color="direction"),
+        size=3.5,
+        alpha=1.0,
+        tooltips=_tooltip_top,
     )
-    + scale_color_manual(values={"Increase": "#306998", "Decrease": "#DC2626"})
-    + scale_x_continuous(breaks=[0, 1], labels=["Q1 Sales ($K)", "Q4 Sales ($K)"], limits=[-0.6, 1.6])
-    + labs(title="slope-basic · letsplot · pyplots.ai", x="", y="Sales ($K)", color="Change")
+    + geom_point(data=df_long, mapping=aes(x="x", y="value", color="direction"), size=6)
+    + geom_text(data=df_left, mapping=aes(x="x", y="value", label="label"), hjust=1.08, size=10, color=INK_SOFT)
+    + geom_text(data=df_right, mapping=aes(x="x", y="value", label="label"), hjust=-0.08, size=10, color=INK_SOFT)
+    + scale_color_manual(values={"Increase": COLOR_INCREASE, "Decrease": COLOR_DECREASE})
+    + scale_x_continuous(breaks=[0, 1], labels=["Q1 Sales ($K)", "Q4 Sales ($K)"], limits=[-1.05, 2.05])
+    + scale_y_continuous(limits=[25, 245])
+    + labs(title="slope-basic · letsplot · anyplot.ai", x="", y="Sales ($K)", color="Change")
     + theme_minimal()
     + theme(
-        plot_title=element_text(size=28),
-        axis_title_y=element_text(size=22),
-        axis_title_x=element_blank(),
-        axis_text_x=element_text(size=20),
-        axis_text_y=element_text(size=18),
-        legend_title=element_text(size=20),
-        legend_text=element_text(size=18),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG),
+        panel_grid_major_y=element_line(color=GRID_COLOR, size=0.3),
         panel_grid_major_x=element_blank(),
         panel_grid_minor=element_blank(),
+        plot_title=element_text(size=28, color=INK),
+        axis_title_y=element_text(size=22, color=INK),
+        axis_title_x=element_blank(),
+        axis_text_x=element_text(size=20, color=INK_SOFT),
+        axis_text_y=element_text(size=18, color=INK_SOFT),
+        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+        legend_title=element_text(size=20, color=INK),
+        legend_text=element_text(size=18, color=INK_SOFT),
     )
     + ggsize(1600, 900)
 )
 
-# Save PNG (scale 3x to get 4800 x 2700 px)
-ggsave(plot, "plot.png", path=".", scale=3)
+# Save PNG (scale 3x → 4800 × 2700 px)
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
 
-# Save HTML for interactivity
-ggsave(plot, "plot.html", path=".")
+# Save HTML for letsplot interactive tooltips
+ggsave(plot, f"plot-{THEME}.html", path=".")