feat(bokeh): implement choropleth-basic

plots/choropleth-basic/implementations/bokeh.py

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+""" pyplots.ai
+choropleth-basic: Choropleth Map with Regional Coloring
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import math
+
+from bokeh.io import export_png
+from bokeh.models import ColorBar, ColumnDataSource, LabelSet, LogColorMapper
+from bokeh.palettes import Blues9
+from bokeh.plotting import figure
+
+
+# Data: US states arranged in a tile grid map layout (all 50 states + DC)
+# Grid positions (col, row) - approximating geographic positions
+regions = {
+    "AK": (0, 5),
+    "HI": (0, 1),
+    "WA": (1, 5),
+    "OR": (1, 4),
+    "CA": (0, 3),
+    "NV": (1, 3),
+    "ID": (2, 5),
+    "MT": (3, 5),
+    "WY": (3, 4),
+    "UT": (2, 3),
+    "AZ": (2, 2),
+    "CO": (3, 3),
+    "NM": (3, 2),
+    "ND": (4, 5),
+    "SD": (4, 4),
+    "NE": (4, 3),
+    "KS": (4, 2),
+    "OK": (4, 1),
+    "TX": (3, 1),
+    "MN": (5, 5),
+    "IA": (5, 4),
+    "MO": (5, 3),
+    "AR": (5, 2),
+    "LA": (5, 1),
+    "WI": (6, 5),
+    "IL": (6, 4),
+    "IN": (7, 4),
+    "MI": (7, 5),
+    "OH": (8, 4),
+    "KY": (7, 3),
+    "TN": (6, 3),
+    "MS": (6, 2),
+    "AL": (7, 2),
+    "GA": (8, 2),
+    "FL": (8, 1),
+    "SC": (9, 2),
+    "NC": (9, 3),
+    "VA": (9, 4),
+    "WV": (8, 3),
+    "PA": (10, 4),
+    "NY": (10, 5),
+    "VT": (11, 5),
+    "NH": (11, 4),
+    "ME": (12, 5),
+    "MA": (11, 3),
+    "RI": (12, 3),
+    "CT": (11, 2),
+    "NJ": (10, 3),
+    "DE": (10, 2),
+    "MD": (9, 1),
+    "DC": (10, 1),
+}
+
+# Population density values (people per sq mile) - realistic ranges
+density_values = {
+    "AK": 1,
+    "HI": 226,
+    "CA": 253,
+    "TX": 112,
+    "FL": 411,
+    "NY": 408,
+    "PA": 286,
+    "IL": 227,
+    "OH": 289,
+    "GA": 185,
+    "NC": 218,
+    "MI": 177,
+    "NJ": 1263,
+    "VA": 218,
+    "WA": 117,
+    "AZ": 64,
+    "MA": 901,
+    "TN": 167,
+    "IN": 189,
+    "MO": 89,
+    "MD": 636,
+    "WI": 108,
+    "CO": 57,
+    "MN": 71,
+    "SC": 173,
+    "AL": 99,
+    "LA": 107,
+    "KY": 114,
+    "OR": 44,
+    "OK": 58,
+    "CT": 733,
+    "IA": 57,
+    "MS": 63,
+    "AR": 58,
+    "UT": 40,
+    "NV": 28,
+    "KS": 36,
+    "NM": 17,
+    "NE": 25,
+    "WV": 74,
+    "ID": 23,
+    "ME": 44,
+    "NH": 154,
+    "RI": 1061,
+    "MT": 8,
+    "DE": 508,
+    "SD": 12,
+    "ND": 11,
+    "VT": 68,
+    "WY": 6,
+    # DC intentionally missing to demonstrate missing data handling
+}
+
+# Prepare data for rectangles
+xs = []
+ys = []
+widths = []
+heights = []
+colors = []
+abbrevs = []
+abbrev_x = []
+abbrev_y = []
+
+# Color mapping - use log scale to better differentiate low-density states
+min_val = max(1, min(density_values.values()))  # Log scale needs min > 0
+max_val = max(density_values.values())
+palette = list(reversed(Blues9))
+color_mapper = LogColorMapper(palette=palette, low=min_val, high=max_val, nan_color="#d0d0d0")
+
+# Process each region - rect is centered, so adjust coords to center of tile
+for abbrev, (col, row) in regions.items():
+    center_x = col * 1.1 + 0.5
+    center_y = row * 1.1 + 0.5
+    xs.append(center_x)
+    ys.append(center_y)
+    widths.append(1.0)
+    heights.append(1.0)
+    abbrev_x.append(center_x)
+    abbrev_y.append(center_y)
+    abbrevs.append(abbrev)
+
+    if abbrev in density_values:
+        density = density_values[abbrev]
+        # Map to color index using log scale for better low-value differentiation
+        log_min = math.log10(min_val)
+        log_max = math.log10(max_val)
+        log_val = math.log10(max(density, 1))  # Ensure positive for log
+        norm = (log_val - log_min) / (log_max - log_min)
+        idx = min(int(norm * (len(palette) - 1)), len(palette) - 1)
+        colors.append(palette[idx])
+    else:
+        # Missing data - gray with pattern indication
+        colors.append("#d0d0d0")
+
+# Create data sources
+rect_source = ColumnDataSource(data={"x": xs, "y": ys, "width": widths, "height": heights, "fill_color": colors})
+
+# Determine text colors based on background (using log scale)
+text_colors = []
+log_min = math.log10(min_val)
+log_max = math.log10(max_val)
+for abbrev in abbrevs:
+    if abbrev not in density_values:
+        text_colors.append("#333333")
+    else:
+        density = density_values[abbrev]
+        log_val = math.log10(max(density, 1))
+        norm = (log_val - log_min) / (log_max - log_min)
+        text_colors.append("white" if norm > 0.5 else "#306998")
+
+label_source = ColumnDataSource(data={"x": abbrev_x, "y": abbrev_y, "text": abbrevs, "text_color": text_colors})
+
+# Create figure (4800x2700 for landscape)
+p = figure(
+    width=4800,
+    height=2700,
+    title="choropleth-basic · bokeh · pyplots.ai",
+    x_axis_location=None,
+    y_axis_location=None,
+    tools="",
+    toolbar_location=None,
+    x_range=(-1, 14),
+    y_range=(0, 7),
+)
+
+# Remove grid and axes
+p.grid.grid_line_color = None
+p.outline_line_color = None
+p.xaxis.visible = False
+p.yaxis.visible = False
+
+# Draw rectangles for each state
+p.rect(
+    x="x",
+    y="y",
+    width="width",
+    height="height",
+    source=rect_source,
+    fill_color="fill_color",
+    fill_alpha=0.9,
+    line_color="white",
+    line_width=2,
+)
+
+# Add state abbreviation labels
+labels = LabelSet(
+    x="x",
+    y="y",
+    text="text",
+    text_color="text_color",
+    source=label_source,
+    text_align="center",
+    text_baseline="middle",
+    text_font_size="18pt",
+    text_font_style="bold",
+)
+p.add_layout(labels)
+
+# Title styling
+p.title.text_font_size = "32pt"
+p.title.align = "center"
+
+# Color bar for legend - made larger for prominence
+color_bar = ColorBar(
+    color_mapper=color_mapper,
+    width=60,
+    height=1200,
+    location=(0, 0),
+    title="Population Density (per sq mile)",
+    title_text_font_size="24pt",
+    major_label_text_font_size="20pt",
+    title_standoff=20,
+    padding=30,
+    margin=40,
+)
+p.add_layout(color_bar, "right")
+
+# Save output
+export_png(p, filename="plot.png")

plots/choropleth-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: choropleth-basic
+created: '2025-12-31T21:33:44Z'
+updated: '2025-12-31T21:41:36Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20627498185
+issue: 3069
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/choropleth-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/choropleth-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/choropleth-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of Bokeh-specific features including LogColorMapper for better differentiation
+    of low-density states
+  - Smart contrast handling with dynamic text colors (white on dark, blue on light
+    backgrounds)
+  - Good missing data handling demonstrated with DC shown in gray
+  - Clean tile grid approach that fits all 50 states + DC in a readable layout
+  - Well-implemented color bar with logarithmic scale and clear labeling
+  weaknesses:
+  - Title format should be exactly "choropleth-basic · bokeh · pyplots.ai" without
+    the "US Population Density" prefix
+  - Tile grid approach, while creative and readable, differs from traditional geographic
+    projection that spec mentions (Robinson for world, Albers for US)