feat(letsplot): implement slope-basic

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

@@ -1,7 +1,7 @@
 """ anyplot.ai
 slope-basic: Basic Slope Chart (Slopegraph)
-Library: letsplot 4.9.0 | Python 3.13.13
-Quality: 85/100 | Updated: 2026-04-30
+Library: letsplot 4.11.0 | Python 3.13.14
+Quality: 94/100 | Updated: 2026-07-26
 """
 
 import os
@@ -23,6 +23,7 @@
     labs,
     layer_tooltips,
     scale_color_manual,
+    scale_linetype_manual,
     scale_x_continuous,
     scale_y_continuous,
     theme,
@@ -40,26 +41,28 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 GRID_COLOR = "#C8C7BF" if THEME == "light" else "#333330"
 
-# Okabe-Ito: green = increase, orange = decrease
+# Imprint palette: brand green = increase, matte red = decrease
 COLOR_INCREASE = "#009E73"
-COLOR_DECREASE = "#AE3030"  # imprint red — decrease
+COLOR_DECREASE = "#AE3030"
 
-# Consumer electronics quarterly sales ($K): Q1 vs Q4
+# Consumer electronics quarterly sales ($K): Q1 vs Q4.
+# Values are spread with a minimum gap of ~20 at both endpoints so the
+# entity + value labels never sit close enough to overlap.
 data = {
     "entity": [
+        "Streaming Devices",
+        "Gaming Consoles",
         "Laptops",
-        "Smartphones",
+        "Tablets",
         "Smart TVs",
-        "Monitors",
         "Headphones",
-        "Cameras",
-        "Streaming Devices",
+        "Monitors",
         "Speakers",
-        "Tablets",
-        "Gaming Consoles",
+        "Cameras",
+        "Smartphones",
     ],
-    "Q1": [80, 210, 120, 155, 140, 195, 55, 175, 105, 90],
-    "Q4": [130, 175, 160, 120, 185, 215, 95, 140, 150, 60],
+    "Q1": [50, 72, 95, 118, 140, 162, 184, 206, 228, 250],
+    "Q4": [55, 90, 140, 160, 222, 245, 115, 200, 270, 180],
 }
 df = pd.DataFrame(data)
 df["change"] = df["Q4"] - df["Q1"]
@@ -89,7 +92,7 @@
     {"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
+# Endpoint labels: entity name + value for legibility
 df_left = pd.DataFrame(
     {
         "entity": df["entity"].values,
@@ -128,12 +131,12 @@
     # Background lines: dimmed to let top movers stand out
     + geom_segment(
         data=seg_normal,
-        mapping=aes(x="x_start", y="y_start", xend="x_end", yend="y_end", color="direction"),
+        mapping=aes(x="x_start", y="y_start", xend="x_end", yend="y_end", color="direction", linetype="direction"),
         size=1.8,
-        alpha=0.40,
+        alpha=0.55,
         tooltips=_tooltip_normal,
     )
-    # Top-mover lines: bold, fully opaque — emphasises biggest Q1→Q4 changes
+    # Top-mover lines: bold, fully opaque — emphasises the 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"),
@@ -142,33 +145,40 @@
         tooltips=_tooltip_top,
     )
     + 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)
+    + geom_text(
+        data=df_left, mapping=aes(x="x", y="value", label="label"), hjust=1, nudge_x=-0.07, size=4.2, color=INK_SOFT
+    )
+    + geom_text(
+        data=df_right, mapping=aes(x="x", y="value", label="label"), hjust=0, nudge_x=0.07, size=4.2, color=INK_SOFT
+    )
     + scale_color_manual(values={"Increase": COLOR_INCREASE, "Decrease": COLOR_DECREASE})
+    + scale_linetype_manual(values={"Increase": "solid", "Decrease": "dashed"}, guide="none")
     + 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 ($K)", color="Change")
+    + scale_y_continuous(limits=[20, 300])
+    + labs(title="slope-basic · python · letsplot · anyplot.ai", x="", y="Sales ($K)", color="Change")
     + theme_minimal()
     + theme(
         plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
-        panel_background=element_rect(fill=PAGE_BG),
+        # color matches fill so the rect draws no border (theme_minimal has no
+        # spines by default — the "remove all spines" clean-look alternative)
+        panel_background=element_rect(fill=PAGE_BG, color=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),
+        plot_title=element_text(size=16, color=INK),
+        axis_title_y=element_text(size=12, 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),
+        axis_text_x=element_text(size=12, color=INK_SOFT),
+        axis_text_y=element_text(size=10, 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),
+        legend_title=element_text(size=12, color=INK),
+        legend_text=element_text(size=10, color=INK_SOFT),
     )
-    + ggsize(1600, 900)
+    + ggsize(800, 450)
 )
 
-# Save PNG (scale 3x → 4800 × 2700 px)
-ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
+# Save PNG: scale=4 on ggsize(800, 450) -> 3200 x 1800 px (canonical landscape canvas)
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
 
 # Save HTML for letsplot interactive tooltips
 ggsave(plot, f"plot-{THEME}.html", path=".")