{"spec_id":"heatmap-geographic","library":"bokeh","language":"python","code":"\"\"\" anyplot.ai\nheatmap-geographic: Geographic Heatmap for Spatial Density\nLibrary: bokeh 3.9.0 | Python 3.13.13\nQuality: 87/100 | Updated: 2026-05-18\n\"\"\"\n\nimport sys\nfrom pathlib import Path\n\n\nscript_dir = str(Path(__file__).parent.absolute())\nsys.path = [p for p in sys.path if p != script_dir and p != \"\"]\n\nimport os\nimport time\n\nimport numpy as np\nfrom bokeh.io import output_file, save\nfrom bokeh.models import (\n    BasicTicker,\n    ColorBar,\n    ColumnDataSource,\n    HoverTool,\n    LinearColorMapper,\n    WMTSTileSource,\n)\nfrom bokeh.palettes import Viridis256\nfrom bokeh.plotting import figure\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\n\n\n# Theme tokens\nTHEME = os.getenv(\"ANYPLOT_THEME\", \"light\")\nPAGE_BG = \"#FAF8F1\" if THEME == \"light\" else \"#1A1A17\"\nELEVATED_BG = \"#FFFDF6\" if THEME == \"light\" else \"#242420\"\nINK = \"#1A1A17\" if THEME == \"light\" else \"#F0EFE8\"\nINK_SOFT = \"#4A4A44\" if THEME == \"light\" else \"#B8B7B0\"\nBRAND = \"#009E73\"  # Okabe-Ito position 1\n\n# Data: Simulated environmental monitoring stations across California\nnp.random.seed(42)\n\nn_points = 500\n\n# Create clusters representing different monitoring regions\ncoast_lat = np.random.normal(36.5, 0.8, n_points // 3)\ncoast_lon = np.random.normal(-121.0, 0.5, n_points // 3)\n\nsocal_lat = np.random.normal(34.0, 0.6, n_points // 3)\nsocal_lon = np.random.normal(-118.0, 0.7, n_points // 3)\n\nnorcal_lat = np.random.normal(38.5, 0.5, n_points // 3 + n_points % 3)\nnorcal_lon = np.random.normal(-121.5, 0.4, n_points // 3 + n_points % 3)\n\nlatitudes = np.concatenate([coast_lat, socal_lat, norcal_lat])\nlongitudes = np.concatenate([coast_lon, socal_lon, norcal_lon])\nvalues = np.random.exponential(scale=50, size=len(latitudes)) + 20\n\n# Convert lat/lon to Web Mercator projection\nmerc_x = longitudes * 20037508.34 / 180\nmerc_y = np.log(np.tan((90 + latitudes) * np.pi / 360)) / (np.pi / 180) * 20037508.34 / 180\n\nsource = ColumnDataSource(data={\"x\": merc_x, \"y\": merc_y, \"lat\": latitudes, \"lon\": longitudes, \"value\": values})\n\n# Map boundaries focused on California\nlat_min, lat_max = 32.5, 42.0\nlon_min, lon_max = -123.0, -114.0\nx_min = lon_min * 20037508.34 / 180\nx_max = lon_max * 20037508.34 / 180\ny_min = np.log(np.tan((90 + lat_min) * np.pi / 360)) / (np.pi / 180) * 20037508.34 / 180\ny_max = np.log(np.tan((90 + lat_max) * np.pi / 360)) / (np.pi / 180) * 20037508.34 / 180\n\n# Create 2D histogram for density estimation\ngrid_resolution = 100\nx_bins = np.linspace(x_min, x_max, grid_resolution)\ny_bins = np.linspace(y_min, y_max, grid_resolution)\n\nheatmap, x_edges, y_edges = np.histogram2d(merc_x, merc_y, bins=[x_bins, y_bins], weights=values, density=False)\n\n# Apply Gaussian smoothing for continuous appearance\nsigma = 3\nkernel_size = int(6 * sigma + 1)\nif kernel_size % 2 == 0:\n    kernel_size += 1\nkernel_x_arr = np.arange(kernel_size) - kernel_size // 2\nkernel_1d = np.exp(-(kernel_x_arr**2) / (2 * sigma**2))\nkernel_1d = kernel_1d / kernel_1d.sum()\n\nheatmap_smooth = np.apply_along_axis(lambda row: np.convolve(row, kernel_1d, mode=\"same\"), axis=0, arr=heatmap)\nheatmap_smooth = np.apply_along_axis(lambda col: np.convolve(col, kernel_1d, mode=\"same\"), axis=1, arr=heatmap_smooth)\n\n# Plot\np = figure(\n    width=4800,\n    height=2700,\n    x_range=(x_min, x_max),\n    y_range=(y_min, y_max),\n    x_axis_type=\"mercator\",\n    y_axis_type=\"mercator\",\n    title=\"California AQI Monitoring · heatmap-geographic · python · bokeh · anyplot.ai\",\n)\n\n# Theme-appropriate basemap tile\nif THEME == \"light\":\n    tile_url = \"https://cartodb-basemaps-a.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"\nelse:\n    tile_url = \"https://cartodb-basemaps-a.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"\ntile_source = WMTSTileSource(url=tile_url)\np.add_tile(tile_source)\n\n# Heatmap color mapper using viridis for perceptually uniform density representation\nmax_density = np.percentile(heatmap_smooth[heatmap_smooth > 0], 95) if np.any(heatmap_smooth > 0) else 1\ncolor_mapper = LinearColorMapper(palette=Viridis256, low=0, high=max_density, nan_color=\"rgba(0, 0, 0, 0)\")\n\nheatmap_display = heatmap_smooth.copy()\nheatmap_display[heatmap_display < max_density * 0.01] = np.nan\n\np.image(\n    image=[heatmap_display.T],\n    x=x_min,\n    y=y_min,\n    dw=x_max - x_min,\n    dh=y_max - y_min,\n    color_mapper=color_mapper,\n    alpha=0.7,\n)\n\n# Scatter monitoring stations with Okabe-Ito first series color\nstation_renderer = p.scatter(\n    x=\"x\", y=\"y\", source=source, size=12, color=BRAND, alpha=0.6, legend_label=\"Monitoring Stations\"\n)\n\n# HoverTool for interactive station details\nhover = HoverTool(\n    renderers=[station_renderer], tooltips=[(\"AQI Value\", \"@value{0.0}\"), (\"Lat / Lon\", \"@lat{0.00}° / @lon{0.00}°\")]\n)\np.add_tools(hover)\n\n# Colorbar with larger title for visibility at canvas scale\ncolor_bar = ColorBar(\n    color_mapper=color_mapper,\n    ticker=BasicTicker(),\n    label_standoff=16,\n    border_line_color=None,\n    location=(0, 0),\n    title=\"AQI Density\",\n    title_text_font_size=\"22pt\",\n    title_text_color=INK,\n    major_label_text_font_size=\"18pt\",\n    major_label_text_color=INK_SOFT,\n    width=40,\n    background_fill_color=PAGE_BG,\n)\np.add_layout(color_bar, \"right\")\n\n# Theme-adaptive chrome\np.background_fill_color = PAGE_BG\np.border_fill_color = PAGE_BG\np.outline_line_color = INK_SOFT\n\np.title.text_font_size = \"28pt\"\np.title.text_color = INK\n\np.xaxis.axis_label = \"Longitude (°)\"\np.yaxis.axis_label = \"Latitude (°)\"\np.xaxis.axis_label_text_font_size = \"22pt\"\np.yaxis.axis_label_text_font_size = \"22pt\"\np.xaxis.axis_label_text_color = INK\np.yaxis.axis_label_text_color = INK\np.xaxis.major_label_text_font_size = \"18pt\"\np.yaxis.major_label_text_font_size = \"18pt\"\np.xaxis.major_label_text_color = INK_SOFT\np.yaxis.major_label_text_color = INK_SOFT\np.xaxis.axis_line_color = INK_SOFT\np.yaxis.axis_line_color = INK_SOFT\np.xaxis.major_tick_line_color = INK_SOFT\np.yaxis.major_tick_line_color = INK_SOFT\n\np.xgrid.grid_line_color = INK\np.ygrid.grid_line_color = INK\np.xgrid.grid_line_alpha = 0.10\np.ygrid.grid_line_alpha = 0.10\n\np.legend.location = \"top_right\"\np.legend.label_text_font_size = \"16pt\"\np.legend.background_fill_color = ELEVATED_BG\np.legend.border_line_color = INK_SOFT\np.legend.label_text_color = INK_SOFT\n\n# Save HTML (required catalog artifact)\noutput_file(f\"plot-{THEME}.html\", title=\"Geographic Heatmap for Spatial Density\")\nsave(p)\n\n# Screenshot with headless Chrome via Selenium\nW, H = 4800, 2700\nopts = Options()\nfor arg in (\n    \"--headless=new\",\n    \"--no-sandbox\",\n    \"--disable-dev-shm-usage\",\n    \"--disable-gpu\",\n    f\"--window-size={W},{H}\",\n    \"--hide-scrollbars\",\n):\n    opts.add_argument(arg)\ndriver = webdriver.Chrome(options=opts)\ndriver.set_window_size(W, H)\ndriver.get(f\"file://{Path(f'plot-{THEME}.html').resolve()}\")\ntime.sleep(3)\ndriver.save_screenshot(f\"plot-{THEME}.png\")\ndriver.quit()\n"}