import os import re from typing import List, Tuple, Literal import cartopy.crs as ccrs import cartopy.feature as cfeature import geopandas as gpd import matplotlib matplotlib.use("Agg") # Use non-GUI backend for image generation import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from gadm_utils import GADMHandler def get_boundingbox(lats: np.ndarray, lons: np.ndarray) -> tuple[float, float, float, float]: """ Compute the bounding box of a set of latitudes and longitudes, returning in Nominatim convention: (lat_min, lat_max, lon_min, lon_max). Handles wraparound at the 0/360 meridian for longitudes. """ lat_min, lat_max = lats.min(), lats.max() lons_360 = np.mod(lons, 360) span_regular = lons.max() - lons.min() span_wrapped = lons_360.max() - lons_360.min() lons_used = lons_360 if span_wrapped < span_regular else lons lon_min, lon_max = lons_used.min(), lons_used.max() return lat_min, lat_max, lon_min, lon_max def scatter_plot(gadm_handler: GADMHandler, data: np.ndarray = None, is_categorical: bool = None, short_name: str = "plot", long_name: str = "", save_dir: str = "plots", session_name: str = None, cmap_float: str = "turbo", cmap_str: str = "tab20", scatter_size: float | Literal["auto"] = "auto", map_boundary_gadm_gids: List[str] = None, map_limits: Tuple[float, float, float, float] | Literal["data", "gadm_all", "gadm_first"] = "data", map_margin_ratio: float = 0.2, map_borders: bool = False, map_coastline: bool = False) -> str: """ Generate a scatter plot with optional GADM boundaries and save to file. """ # Validate and prepare data if data is not None: if data.ndim != 2 or data.shape[1] != 3: raise ValueError("`data` must be an Nx3 array: [lat, lon, value].") lats, lons, values = data[:, 0], data[:, 1], data[:, 2] else: lats = lons = values = np.array([]) has_data = data is not None and len(data) > 0 # Fail early if nothing to plot if not has_data and not map_boundary_gadm_gids: raise ValueError("Nothing to plot: both `data` and `map_boundary_gadm_gids` are empty.") # Determine map limits if map_limits == "data": if not has_data: raise ValueError("`map_limits='data'` requires `data` to be provided.") lat0, lat1, lon0, lon1 = get_boundingbox(lats, lons) elif "gadm" in map_limits: if not map_boundary_gadm_gids: raise ValueError("`map_boundary_gadm_gids` is required for GADM-based limits.") all_lats, all_lons = [], [] index_for_limits = 1 if map_limits == "gadm_first" else len(map_boundary_gadm_gids) for gid in map_boundary_gadm_gids[:index_for_limits]: polygon = gadm_handler.get_polygon(gid) if polygon is not None: min_x, min_y, max_x, max_y = polygon.bounds all_lons.extend([min_x, max_x]) all_lats.extend([min_y, max_y]) lat0, lat1, lon0, lon1 = get_boundingbox(np.array(all_lats), np.array(all_lons)) else: lat0, lat1, lon0, lon1 = map_limits # If data exists, expand map extent so scatter points are always visible if has_data: data_lat0 = float(np.min(lats)) data_lat1 = float(np.max(lats)) data_lon0 = float(np.min(lons)) data_lon1 = float(np.max(lons)) lat0 = min(lat0, data_lat0) lat1 = max(lat1, data_lat1) lon0 = min(lon0, data_lon0) lon1 = max(lon1, data_lon1) # Add margins if isinstance(map_limits, str): dlat = (lat1 - lat0) * map_margin_ratio or 1.0 dlon = (lon1 - lon0) * map_margin_ratio or 1.0 lat0, lat1 = lat0 - dlat, lat1 + dlat lon0, lon1 = lon0 - dlon, lon1 + dlon # Setup figure base = 6 width = lon1 - lon0 height = lat1 - lat0 plt.figure(figsize=(base * (width / height), base), dpi=200) ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([lon0, lon1, lat0, lat1], crs=ccrs.PlateCarree()) ax.set_aspect("equal", adjustable="box") tick_font = 8 if has_data else 10 title_font = 10 if has_data else 12 # Gridlines gl = ax.gridlines(draw_labels=True, linewidth=0.3, color='gray', alpha=0.7, linestyle='--') gl.top_labels = False gl.right_labels = False gl.xlabel_style = {'size': tick_font} gl.ylabel_style = {'size': tick_font} # Base map if map_coastline: ax.add_feature(cfeature.COASTLINE, linewidth=1.0, color="k") if map_borders: ax.add_feature(cfeature.BORDERS, linewidth=0.8, color="k") ax.add_feature(cfeature.LAND, facecolor="ivory") ax.add_feature(cfeature.OCEAN, facecolor="lightblue") # GADM boundaries if map_boundary_gadm_gids: for gid in map_boundary_gadm_gids: polygon = gadm_handler.get_polygon(gid) if polygon is not None: gpd.GeoSeries([polygon]).plot(ax=ax, facecolor='none', edgecolor='gray', linewidth=0.3) if map_limits == "gadm_first": gid = map_boundary_gadm_gids[0] polygon = gadm_handler.get_polygon(gid) if polygon is not None: gpd.GeoSeries([polygon]).plot(ax=ax, facecolor='none', edgecolor='black', linewidth=0.6) info = gadm_handler.get_full_info(gid) if info: lat_c = info["geometry"]["latitude"] lon_c = info["geometry"]["longitude"] name = info["full_name"].split(";")[0] plt.text(lon_c, lat_c, name, fontsize=tick_font, ha="center", va="center") # Scatter plot if has_data: if scatter_size == "auto": count = len(lats) base_size = 40 scatter_size = base_size / (np.log10(count + 1) + 1) scatter_size = np.clip(scatter_size, 5, 30) if is_categorical is None: is_categorical = np.issubdtype(values.dtype, np.str_) rasterize = len(lats) > 10000 if is_categorical: categories, values_encoded = np.unique(values.astype(str), return_inverse=True) num_categories = len(categories) cmap_base = plt.get_cmap(cmap_str) if hasattr(cmap_base, 'colors'): cmap_colors = cmap_base.colors[:num_categories] cmap_used = ListedColormap(cmap_colors) else: cmap_used = cmap_base sc = ax.scatter(lons, lats, c=values_encoded, cmap=cmap_used, s=scatter_size, linewidth=0.0, transform=ccrs.PlateCarree(), zorder=2, rasterized=rasterize, edgecolors='none') single_color_width = (num_categories - 1) / num_categories cbar = plt.colorbar(sc, ax=ax, orientation='vertical', shrink=0.6, ticks=np.linspace(single_color_width / 2, num_categories - 1 - single_color_width / 2, num_categories)) cbar.ax.set_yticklabels(categories) cbar.set_label(long_name, fontsize=tick_font) cbar.ax.tick_params(labelsize=tick_font) else: try: vmin = float(np.percentile(values, 1)) vmax = float(np.percentile(values, 99)) except Exception: # noqa vmin = vmax = None norm = plt.Normalize(vmin=vmin, vmax=vmax) if vmin is not None and vmax is not None else None sc = ax.scatter(lons, lats, c=values, cmap=cmap_float, s=scatter_size, linewidth=0.0, transform=ccrs.PlateCarree(), zorder=2, norm=norm, rasterized=rasterize, edgecolors='none') cbar = plt.colorbar(sc, ax=ax, orientation='vertical', shrink=0.6) cbar.set_label(long_name, fontsize=tick_font) cbar.ax.tick_params(labelsize=tick_font) ax.set_title(long_name, fontsize=title_font) # Save figure with safe filename if session_name is None or session_name == "": session_name = "default_session" safe_session = re.sub(r'[^a-zA-Z0-9_-]', '_', session_name) save_dir = os.path.join(save_dir, safe_session) os.makedirs(save_dir, exist_ok=True) # build base filename base_name = re.sub(r'[^a-zA-Z0-9_-]', '_', short_name) ext = ".png" filepath = os.path.join(save_dir, f"{base_name}{ext}") # if exists, append -1, -2, ... if os.path.exists(filepath): i = 1 while True: filepath = os.path.join(save_dir, f"{base_name}-{i}{ext}") if not os.path.exists(filepath): break i += 1 plt.savefig(filepath, bbox_inches="tight") plt.close() return filepath