Merge branch 'main' into implementation/slope-basic/highcharts

Author: MarkusNeusinger

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

@@ -1,14 +1,34 @@
-""" pyplots.ai
+""" anyplot.ai
 slope-basic: Basic Slope Chart (Slopegraph)
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 94/100 | Created: 2025-12-17
+Library: altair 6.1.0 | Python 3.13.13
+Quality: 86/100 | Created: 2026-04-30
 """
 
-import altair as alt
+import os
+import sys
+
 import pandas as pd
 
 
-# Data - Product sales comparing Q1 vs Q4 (10 products)
+_script_dir = os.path.dirname(os.path.abspath(__file__))
+if _script_dir in sys.path:
+    sys.path.remove(_script_dir)
+
+import altair as alt  # noqa: E402
+
+
+# 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"
+
+# Okabe-Ito: position 1 = Increase, position 2 = Decrease
+COLOR_INCREASE = "#009E73"
+COLOR_DECREASE = "#D55E00"
+
+# Data
 data = pd.DataFrame(
     {
         "Product": [
@@ -28,84 +48,67 @@
     }
 )
 
-# Reshape data to long format for slope chart
 df_long = pd.melt(data, id_vars=["Product"], value_vars=["Q1 Sales", "Q4 Sales"], var_name="Period", value_name="Sales")
-
-# Determine direction of change for color coding
 data["Direction"] = data.apply(lambda row: "Increase" if row["Q4 Sales"] > row["Q1 Sales"] else "Decrease", axis=1)
 df_long = df_long.merge(data[["Product", "Direction"]], on="Product")
 
-# Create slope chart
+color_scale = alt.Scale(domain=["Increase", "Decrease"], range=[COLOR_INCREASE, COLOR_DECREASE])
+
+# Plot
 lines = (
     alt.Chart(df_long)
     .mark_line(strokeWidth=3, opacity=0.8)
     .encode(
-        x=alt.X("Period:N", axis=alt.Axis(labelFontSize=20, titleFontSize=24, title=None, labelAngle=0)),
+        x=alt.X("Period:N", axis=alt.Axis(labelFontSize=20, title=None, labelAngle=0)),
         y=alt.Y(
             "Sales:Q",
             axis=alt.Axis(labelFontSize=18, titleFontSize=22, title="Sales (units)"),
             scale=alt.Scale(zero=False),
         ),
         color=alt.Color(
-            "Direction:N",
-            scale=alt.Scale(domain=["Increase", "Decrease"], range=["#306998", "#FFD43B"]),
-            legend=alt.Legend(titleFontSize=20, labelFontSize=18, orient="top-right"),
+            "Direction:N", scale=color_scale, legend=alt.Legend(titleFontSize=20, labelFontSize=18, orient="top-right")
         ),
         detail="Product:N",
     )
 )
 
-# Add points at endpoints
 points = (
     alt.Chart(df_long)
     .mark_circle(size=200, opacity=0.9)
-    .encode(
-        x="Period:N",
-        y="Sales:Q",
-        color=alt.Color(
-            "Direction:N", scale=alt.Scale(domain=["Increase", "Decrease"], range=["#306998", "#FFD43B"]), legend=None
-        ),
-    )
+    .encode(x="Period:N", y="Sales:Q", color=alt.Color("Direction:N", scale=color_scale, legend=None))
 )
 
-# Add labels at left endpoint (Q1)
 labels_left = (
     alt.Chart(df_long[df_long["Period"] == "Q1 Sales"])
     .mark_text(align="right", dx=-15, fontSize=16)
-    .encode(
-        x="Period:N",
-        y="Sales:Q",
-        text="Product:N",
-        color=alt.Color(
-            "Direction:N", scale=alt.Scale(domain=["Increase", "Decrease"], range=["#306998", "#FFD43B"]), legend=None
-        ),
-    )
+    .encode(x="Period:N", y="Sales:Q", text="Product:N", color=alt.Color("Direction:N", scale=color_scale, legend=None))
 )
 
-# Add labels at right endpoint (Q4)
 labels_right = (
     alt.Chart(df_long[df_long["Period"] == "Q4 Sales"])
     .mark_text(align="left", dx=15, fontSize=16)
-    .encode(
-        x="Period:N",
-        y="Sales:Q",
-        text="Product:N",
-        color=alt.Color(
-            "Direction:N", scale=alt.Scale(domain=["Increase", "Decrease"], range=["#306998", "#FFD43B"]), legend=None
-        ),
-    )
+    .encode(x="Period:N", y="Sales:Q", text="Product:N", color=alt.Color("Direction:N", scale=color_scale, legend=None))
 )
 
-# Combine layers
+# Style
 chart = (
     (lines + points + labels_left + labels_right)
-    .properties(
-        width=1400, height=850, title=alt.Title(text="slope-basic · altair · pyplots.ai", fontSize=28, anchor="middle")
+    .properties(width=1400, height=850, background=PAGE_BG, title="slope-basic · altair · anyplot.ai")
+    .configure_title(color=INK, fontSize=28, anchor="middle")
+    .configure_axis(
+        domainColor=INK_SOFT,
+        tickColor=INK_SOFT,
+        grid=True,
+        gridColor=INK,
+        gridOpacity=0.10,
+        gridDash=[4, 4],
+        labelColor=INK_SOFT,
+        titleColor=INK,
     )
-    .configure_axis(grid=True, gridOpacity=0.3, gridDash=[4, 4])
-    .configure_view(strokeWidth=0)
+    .configure_view(fill=PAGE_BG, stroke=INK_SOFT)
+    .configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
 )
 
-# Save as PNG (4800 × 2700 px) and HTML
-chart.save("plot.png", scale_factor=3.0)
-chart.save("plot.html")
+# Save
+chart.save(f"plot-{THEME}.png", scale_factor=3.0)
+chart.save(f"plot-{THEME}.html")

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

@@ -1,16 +1,26 @@
-""" pyplots.ai
+""" anyplot.ai
 slope-basic: Basic Slope Chart (Slopegraph)
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 84/100 | Updated: 2026-04-30
 """
 
+import os
+
 from bokeh.io import export_png, save
-from bokeh.models import Label
+from bokeh.models import ColumnDataSource, HoverTool, Label
 from bokeh.plotting import figure
 from bokeh.resources import CDN
 
 
-# Data - Product sales comparison Q1 vs Q4 (10 products)
+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"
+
+INCREASE_COLOR = "#009E73"  # Okabe-Ito position 1 (brand green)
+DECREASE_COLOR = "#D55E00"  # Okabe-Ito position 2 (vermillion)
+
 products = [
     "Product A",
     "Product B",
@@ -26,66 +36,115 @@
 q1_sales = [85, 72, 91, 45, 68, 53, 78, 62, 40, 88]
 q4_sales = [92, 65, 88, 71, 74, 48, 95, 58, 67, 82]
 
-# Determine direction for color coding
-colors = []
-for start, end in zip(q1_sales, q4_sales, strict=True):
-    if end > start:
-        colors.append("#306998")  # Python Blue - increase
-    elif end < start:
-        colors.append("#FFD43B")  # Python Yellow - decrease
-    else:
-        colors.append("#888888")  # Gray - no change
-
-# Create figure
+colors = [INCREASE_COLOR if end > start else DECREASE_COLOR for start, end in zip(q1_sales, q4_sales, strict=True)]
+
+
+def spread_labels(ys, min_gap=4.5):
+    """Shift label y-positions apart so dense clusters don't overlap."""
+    n = len(ys)
+    order = sorted(range(n), key=lambda i: ys[i])
+    adjusted = [float(ys[i]) for i in order]
+    for _ in range(30):
+        changed = False
+        for i in range(1, n):
+            if adjusted[i] - adjusted[i - 1] < min_gap:
+                mid = (adjusted[i] + adjusted[i - 1]) / 2
+                adjusted[i - 1] = mid - min_gap / 2
+                adjusted[i] = mid + min_gap / 2
+                changed = True
+        if not changed:
+            break
+    result = [0.0] * n
+    for new_i, orig_i in enumerate(order):
+        result[orig_i] = adjusted[new_i]
+    return result
+
+
+left_y = spread_labels(q1_sales)
+right_y = spread_labels(q4_sales)
+
 p = figure(
     width=4800,
     height=2700,
-    title="slope-basic · bokeh · pyplots.ai",
+    title="slope-basic · bokeh · anyplot.ai",
     x_range=(-0.5, 1.5),
-    y_range=(25, 105),
+    y_range=(25, 112),
     toolbar_location=None,
 )
 
-# Style title and axes
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = None
+
 p.title.text_font_size = "32pt"
 p.title.align = "center"
+p.title.text_color = INK
 
-# Remove x axis tick labels and set custom labels for time points
 p.xaxis.visible = False
 p.yaxis.axis_label = "Sales (thousands)"
 p.yaxis.axis_label_text_font_size = "22pt"
+p.yaxis.axis_label_text_color = INK
 p.yaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_color = INK_SOFT
+p.yaxis.axis_line_color = INK_SOFT
+p.yaxis.major_tick_line_color = INK_SOFT
+
+p.xgrid.grid_line_color = None
+p.ygrid.grid_line_color = INK_SOFT
+p.ygrid.grid_line_alpha = 0.10
 
-# Add time point labels at bottom
-p.add_layout(Label(x=0, y=32, text="Q1", text_font_size="28pt", text_align="center", text_baseline="top"))
-p.add_layout(Label(x=1, y=32, text="Q4", text_font_size="28pt", text_align="center", text_baseline="top"))
+# Time point labels
+for x_pos, label in [(0, "Q1"), (1, "Q4")]:
+    p.add_layout(
+        Label(
+            x=x_pos, y=27, text=label, text_font_size="28pt", text_align="center", text_baseline="top", text_color=INK
+        )
+    )
+
+# Direction legend in upper-center (above data range)
+for y_pos, legend_text, color in [(109, "— Increase", INCREASE_COLOR), (103, "— Decrease", DECREASE_COLOR)]:
+    p.add_layout(
+        Label(
+            x=0.5,
+            y=y_pos,
+            text=legend_text,
+            text_font_size="20pt",
+            text_align="center",
+            text_baseline="middle",
+            text_color=color,
+        )
+    )
 
-# Draw slope lines connecting Q1 to Q4 for each product
+# ColumnDataSource for scatter enables HoverTool
+scatter_data: dict[str, list] = {"x": [], "y": [], "color": [], "product": [], "period": [], "value": []}
 for product, start, end, color in zip(products, q1_sales, q4_sales, colors, strict=True):
-    # Draw the connecting line
-    p.line(x=[0, 1], y=[start, end], line_width=4, line_color=color, line_alpha=0.8)
+    scatter_data["x"].extend([0, 1])
+    scatter_data["y"].extend([start, end])
+    scatter_data["color"].extend([color, color])
+    scatter_data["product"].extend([product, product])
+    scatter_data["period"].extend(["Q1", "Q4"])
+    scatter_data["value"].extend([start, end])
 
-    # Add markers at both endpoints
-    p.scatter(x=[0, 1], y=[start, end], size=18, color=color, alpha=0.9)
+source = ColumnDataSource(data=scatter_data)
 
-    # Add labels at start (Q1) - left aligned
+# Draw slope lines and endpoint labels
+for i, (product, start, end, color) in enumerate(zip(products, q1_sales, q4_sales, colors, strict=True)):
+    p.line(x=[0, 1], y=[start, end], line_width=4, line_color=color, line_alpha=0.8)
     p.add_layout(
         Label(
             x=-0.05,
-            y=start,
+            y=left_y[i],
             text=f"{product}: {start}",
             text_font_size="18pt",
             text_align="right",
             text_baseline="middle",
             text_color=color,
         )
     )
-
-    # Add labels at end (Q4) - right aligned
     p.add_layout(
         Label(
             x=1.05,
-            y=end,
+            y=right_y[i],
             text=f"{end}: {product}",
             text_font_size="18pt",
             text_align="left",
@@ -94,16 +153,12 @@
         )
     )
 
-# Style grid
-p.xgrid.grid_line_color = None
-p.ygrid.grid_line_alpha = 0.3
-p.ygrid.grid_line_dash = "dashed"
-
-# Style outline
-p.outline_line_color = None
-
-# Save as PNG
-export_png(p, filename="plot.png")
+dots = p.scatter(x="x", y="y", size=18, color="color", source=source, alpha=0.9)
+p.add_tools(
+    HoverTool(
+        renderers=[dots], tooltips=[("Product", "@product"), ("Period", "@period"), ("Sales", "@value{0} thousand")]
+    )
+)
 
-# Also save as HTML for interactive viewing
-save(p, filename="plot.html", resources=CDN, title="slope-basic · bokeh · pyplots.ai")
+export_png(p, filename=f"plot-{THEME}.png")
+save(p, filename=f"plot-{THEME}.html", resources=CDN, title="slope-basic · bokeh · anyplot.ai")