Demo: area map

# --- Reserved parameters (injected by the Notebook Factory; defaults keep the notebook runnable standalone) ---
run_id = ""
inputs_dir = ""
output_dir = "outputs"
area = {}                      # {id, code, name, level, country, country_name, bbox}
area_geojson = ""              # FeatureCollection with the target area
subdivisions_geojson = ""      # FeatureCollection with the aggregation subdivisions
event = {}                     # Montandon STAC item (dict)
event_json = ""
montandon_stac_url = "https://montandon-eoapi-stage.ifrc.org/stac"
hazard = ""

# --- User parameters ---
label = "Hello from the Notebook Factory"
buffer_km = 5
show_subdivisions = True

# Parameters
run_id = "33e1b723-a17f-4601-b234-6ce86fcaa5ad"
inputs_dir = "/app/data/work/33e1b723-a17f-4601-b234-6ce86fcaa5ad/inputs"
output_dir = "/app/data/work/33e1b723-a17f-4601-b234-6ce86fcaa5ad/outputs"
area = {"id": 2, "code": "NPL.1_1", "name": "Central", "level": 1, "country": "NPL", "country_name": "Nepal", "bbox": [83.9218, 26.5684, 86.5752, 28.3869]}
area_geojson = "/app/data/work/33e1b723-a17f-4601-b234-6ce86fcaa5ad/inputs/area.geojson"
subdivisions_geojson = "/app/data/work/33e1b723-a17f-4601-b234-6ce86fcaa5ad/inputs/subdivisions.geojson"
event = {}
event_json = ""
montandon_stac_url = "https://montandon-eoapi-stage.ifrc.org/stac"
hazard = ""
label = "OVH proxy test"
buffer_km = 5
show_subdivisions = True

import json, math, os, warnings
from pathlib import Path
import numpy as np
import pandas as pd
import geopandas as gpd
import shapely
from shapely.geometry import box

warnings.filterwarnings("ignore", message=".*geographic CRS.*")
Path(output_dir).mkdir(parents=True, exist_ok=True)

def centroids(geoms):
    """Centroids computed in an equal-area projection (avoids the geographic-CRS warning)."""
    return geoms.to_crs(6933).centroid.to_crs(4326)

def load_area_and_subdivisions():
    """Read the boundaries prepared by the app, or build a small demo area when run standalone."""
    if area_geojson and Path(area_geojson).exists():
        area_gdf = gpd.read_file(area_geojson)
        subs = gpd.read_file(subdivisions_geojson) if subdivisions_geojson and Path(subdivisions_geojson).exists() else area_gdf.copy()
        return area_gdf, subs
    demo = gpd.GeoDataFrame({"name": ["Demo area"], "code": ["DEMO"], "level": [1]}, geometry=[box(83.5, 26.5, 88.0, 30.5)], crs=4326)
    cells, names = [], []
    minx, miny, maxx, maxy = demo.total_bounds
    for i in range(3):
        for j in range(3):
            cells.append(box(minx + i * (maxx - minx) / 3, miny + j * (maxy - miny) / 3, minx + (i + 1) * (maxx - minx) / 3, miny + (j + 1) * (maxy - miny) / 3))
            names.append(f"Demo district {i * 3 + j + 1}")
    subs = gpd.GeoDataFrame({"name": names, "code": [f"DEMO.{k}" for k in range(9)], "level": [2] * 9}, geometry=cells, crs=4326)
    return demo, subs

area_gdf, subdivisions = load_area_and_subdivisions()
area_name = area.get("name") or area_gdf.iloc[0].get("name", "Demo area")
print(f"Area: {area_name} · {len(subdivisions)} subdivisions")
subdivisions[["code", "name", "level"]].head()

# Visualisation: lonboard for maps, manywidgets for charts, controls and stats.
# All of these are anywidgets, so they stay interactive in the published (kernel-free) page.
from matplotlib import colormaps
from lonboard import Map, PolygonLayer, ScatterplotLayer
from manywidgets import Chart, Column, Legend, Row, Slider, Stat
from manywidgets.lonboard import LayerToggle

def colors_for(values, cmap="OrRd", vmin=None, vmax=None):
    v = np.asarray(values, dtype=float)
    vmin = np.nanmin(v) if vmin is None else vmin
    vmax = np.nanmax(v) if vmax is None else vmax
    norm = (v - vmin) / (vmax - vmin) if vmax > vmin else np.zeros_like(v)
    rgba = colormaps[cmap](np.nan_to_num(norm))
    return (rgba * 255).astype(np.uint8)

def legend_for(cmap, vmin, vmax, title="", n=5, fmt="{:.0%}"):
    stops = np.linspace(vmin, vmax, n)
    rgb = colors_for(stops, cmap, vmin, vmax)[:, :3]
    return Legend(entries=[[[int(c) for c in rgb[i]], fmt.format(stops[i])] for i in range(n)], title=title)

def bar_chart(names, values, title, y_label, color="#E4572E"):
    chart = Chart(chart_type="bar", title=title, y_label=y_label, height=max(280, 22 * len(names) + 120), legend_enabled=False)
    chart.set_options(scales={"x": {"type": "category", "ticks": {"autoSkip": False, "maxRotation": 60, "minRotation": 30}}})
    chart.add_series(x=[str(n) for n in names], y=[float(v) for v in values], name=y_label, series_type="bar", color=color)
    return chart

def fmt_int(v):
    return f"{float(v):,.0f}"

subs = subdivisions.copy()
subs["area_km2"] = subs.to_crs(6933).area / 1e6
Row(Stat(label="Area", value=area_name), Stat(label="Subdivisions", value=len(subs)), Stat(label="Total area", value=fmt_int(subs["area_km2"].sum()), unit="km²"), gap="16px")

outline = area_gdf.to_crs(3857).buffer(buffer_km * 1000).to_crs(4326)
outline_layer = PolygonLayer.from_geopandas(gpd.GeoDataFrame(geometry=outline, crs=4326), get_fill_color=[228, 87, 46, 40], get_line_color=[194, 67, 31], line_width_min_pixels=2)
subs_layer = PolygonLayer.from_geopandas(subs[["name", "area_km2", "geometry"]], get_fill_color=colors_for(subs["area_km2"], "Blues"), get_line_color=[80, 80, 80], line_width_min_pixels=1, opacity=0.6, visible=show_subdivisions)
Map([outline_layer, subs_layer])

from ipywidgets import jsdlink
slider = Slider(label="Buffer (km) — linked to the stat on the right", min=0, max=100, step=1, value=float(buffer_km))
stat = Stat(label="Slider value", value=float(buffer_km), unit="km")
jsdlink((slider, "value"), (stat, "value"))
Column(Row(LayerToggle(layer=subs_layer, label="Subdivisions"), LayerToggle(layer=outline_layer, label="Buffered outline"), gap="16px"), Row(slider, stat, gap="16px"), gap="12px")

print(label)
table = subs[["code", "name", "area_km2"]].copy()
table["area_km2"] = table["area_km2"].round(1)
table.to_csv(Path(output_dir) / "subdivisions.csv", index=False)
table