AcadGIS 0.1.4 — layer gallery (manual check)¶

Every layer we built, each on the region that shows it best, eye-catching colours, fully commented. 10 layer functions in 0.1.4:

#	function	what	region here
1	plot + highlight_style	styled admin map, highlight a region	🇧🇷 Brazil
2	add_basemap / add_satellite	XYZ tiles (6 styles)	🇦🇪 Dubai
3	add_topography	DEM hillshade + hypsometry	🇨🇭 Swiss Alps
4	add_landcover	ESA WorldCover 10 m (curated)	🇨🇭 Switzerland
5	add_ndvi	Sentinel-2 NDVI (curated)	🇪🇬 Nile / Luxor
6	add_raster (rgb)	any GeoTIFF — true colour	🇮🇹 Venice
7	add_layer	any vector (poly/line/point) + labels	🇯🇵 Japan
8	add_cities	Natural Earth places, ranked	🇪🇺 Europe
9	add_roads	OpenStreetMap roads	🇫🇷 Paris

Continuous & categorical add_raster are also exercised by §3–5 (which call it internally). Network is needed for §2–6, §8–9. Only import acadgis as agis is needed (§6 adds rioxarray/requests for the demo fetch).

Bonus — 🇧🇩 Bangladesh field-research set (§10–13): your-own-data raster (numpy → GeoTIFF), categorical with custom classes, study sites over satellite, and a composed divisions + rivers + cities map.

In [1]:

import acadgis as agis
print("AcadGIS", agis.__version__)

import acadgis as agis print("AcadGIS", agis.__version__)

AcadGIS 0.1.4

1 · plot() + highlight_style — 🇧🇷 Brazil¶

In [2]:

# plot() + highlight_style — Brazil (big, colourful states).
br = agis.load_boundaries("Brazil", "state")          # 27 states (GADM adm1)
agis.plot(
    br,
    palette="vibrant",                                # eye-catching qualitative palette
    title="Brazil — states (Amazonas highlighted)",
    highlight="Amazonas",                             # name (or a list of names)
    highlight_style="overlay",                        # fill · overlay · rect · circle
    highlight_color="#ffd000", highlight_edge="#b30000",
    highlight_alpha=0.45, highlight_width=2.2,
    graticule={"grid": True, "sides": "lb", "grid_alpha": 0.25, "fontsize": 7},
    north_arrow={"style": "rose"}, scale_bar={"style": "double"},
    figsize=(9, 9),
)
agis.show()

# plot() + highlight_style — Brazil (big, colourful states). br = agis.load_boundaries("Brazil", "state") # 27 states (GADM adm1) agis.plot( br, palette="vibrant", # eye-catching qualitative palette title="Brazil — states (Amazonas highlighted)", highlight="Amazonas", # name (or a list of names) highlight_style="overlay", # fill · overlay · rect · circle highlight_color="#ffd000", highlight_edge="#b30000", highlight_alpha=0.45, highlight_width=2.2, graticule={"grid": True, "sides": "lb", "grid_alpha": 0.25, "fontsize": 7}, north_arrow={"style": "rose"}, scale_bar={"style": "double"}, figsize=(9, 9), ) agis.show()

2 · add_basemap / add_satellite — 🇦🇪 Dubai¶

In [3]:

# add_satellite — Palm Jumeirah, Dubai (iconic from space).
DUBAI = (55.10, 25.05, 55.185, 25.135)                # (minx, miny, maxx, maxy)
fig, ax = agis.plt.subplots(figsize=(9, 8))
ax.set_xlim(DUBAI[0], DUBAI[2]); ax.set_ylim(DUBAI[1], DUBAI[3])
agis.add_satellite(ax)                                # = add_basemap(style="satellite")
ax.set_title("add_satellite — Palm Jumeirah, Dubai", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_satellite — Palm Jumeirah, Dubai (iconic from space). DUBAI = (55.10, 25.05, 55.185, 25.135) # (minx, miny, maxx, maxy) fig, ax = agis.plt.subplots(figsize=(9, 8)) ax.set_xlim(DUBAI[0], DUBAI[2]); ax.set_ylim(DUBAI[1], DUBAI[3]) agis.add_satellite(ax) # = add_basemap(style="satellite") ax.set_title("add_satellite — Palm Jumeirah, Dubai", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

In [4]:

# All six basemap styles over the same spot.
styles = ["satellite", "osm", "light", "dark", "terrain", "toner"]
fig, axes = agis.plt.subplots(2, 3, figsize=(13, 7))
for sty, ax in zip(styles, axes.ravel()):
    ax.set_xlim(DUBAI[0], DUBAI[2]); ax.set_ylim(DUBAI[1], DUBAI[3])
    agis.add_basemap(ax, style=sty)
    ax.set_title(sty, fontsize=9, fontweight="bold"); ax.set_xticks([]); ax.set_yticks([])
fig.suptitle("add_basemap — 6 tile styles", fontsize=13, fontweight="bold")
agis.show()

# All six basemap styles over the same spot. styles = ["satellite", "osm", "light", "dark", "terrain", "toner"] fig, axes = agis.plt.subplots(2, 3, figsize=(13, 7)) for sty, ax in zip(styles, axes.ravel()): ax.set_xlim(DUBAI[0], DUBAI[2]); ax.set_ylim(DUBAI[1], DUBAI[3]) agis.add_basemap(ax, style=sty) ax.set_title(sty, fontsize=9, fontweight="bold"); ax.set_xticks([]); ax.set_yticks([]) fig.suptitle("add_basemap — 6 tile styles", fontsize=13, fontweight="bold") agis.show()

3 · add_topography — 🇨🇭 Swiss Alps¶

In [5]:

# add_topography — Swiss Alps (dramatic relief).
ALPS = (7.55, 45.85, 8.25, 46.45)                     # Jungfrau / Interlaken
fig, ax = agis.plt.subplots(figsize=(9, 8))
ax.set_xlim(ALPS[0], ALPS[2]); ax.set_ylim(ALPS[1], ALPS[3])
agis.add_topography(
    ax, area=ALPS,
    cmap="terrain", hillshade=True,                   # hypsometric tint + shaded relief
    colorbar=True, colorbar_label="Elevation (m)",
    max_size=900,                                     # DEM resolution cap (speed vs detail)
)
ax.set_title("add_topography — Swiss Alps (Jungfrau)", fontsize=12, fontweight="bold")
agis.show()

# add_topography — Swiss Alps (dramatic relief). ALPS = (7.55, 45.85, 8.25, 46.45) # Jungfrau / Interlaken fig, ax = agis.plt.subplots(figsize=(9, 8)) ax.set_xlim(ALPS[0], ALPS[2]); ax.set_ylim(ALPS[1], ALPS[3]) agis.add_topography( ax, area=ALPS, cmap="terrain", hillshade=True, # hypsometric tint + shaded relief colorbar=True, colorbar_label="Elevation (m)", max_size=900, # DEM resolution cap (speed vs detail) ) ax.set_title("add_topography — Swiss Alps (Jungfrau)", fontsize=12, fontweight="bold") agis.show()

4 · add_landcover — 🇨🇭 Switzerland¶

In [6]:

# add_landcover — Switzerland (forest, grassland, snow, water, built-up).
CH = (7.2, 46.0, 9.2, 47.3)                           # central Switzerland (multi-tile merge)
fig, ax = agis.plt.subplots(figsize=(10, 8))
ax.set_xlim(CH[0], CH[2]); ax.set_ylim(CH[1], CH[3])
agis.add_landcover(ax, CH, year=2021)                 # real ESA WorldCover 10 m + class legend
ax.set_title("add_landcover — Switzerland (ESA WorldCover 10 m)", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_landcover — Switzerland (forest, grassland, snow, water, built-up). CH = (7.2, 46.0, 9.2, 47.3) # central Switzerland (multi-tile merge) fig, ax = agis.plt.subplots(figsize=(10, 8)) ax.set_xlim(CH[0], CH[2]); ax.set_ylim(CH[1], CH[3]) agis.add_landcover(ax, CH, year=2021) # real ESA WorldCover 10 m + class legend ax.set_title("add_landcover — Switzerland (ESA WorldCover 10 m)", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

5 · add_ndvi — 🇪🇬 Nile at Luxor¶

In [7]:

# add_ndvi — the Nile at Luxor (green crops as a ribbon through desert).
LUXOR = (32.55, 25.60, 32.95, 25.95)
fig, ax = agis.plt.subplots(figsize=(8.5, 8))
ax.set_xlim(LUXOR[0], LUXOR[2]); ax.set_ylim(LUXOR[1], LUXOR[3])
agis.add_ndvi(ax, LUXOR, start="2023-01-01", end="2024-12-31",   # least-cloudy scene in window
              max_cloud=5, cmap="RdYlGn")
ax.set_title(f"add_ndvi — Nile at Luxor (Sentinel-2 {ax._acadgis_ndvi_date})",
             fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_ndvi — the Nile at Luxor (green crops as a ribbon through desert). LUXOR = (32.55, 25.60, 32.95, 25.95) fig, ax = agis.plt.subplots(figsize=(8.5, 8)) ax.set_xlim(LUXOR[0], LUXOR[2]); ax.set_ylim(LUXOR[1], LUXOR[3]) agis.add_ndvi(ax, LUXOR, start="2023-01-01", end="2024-12-31", # least-cloudy scene in window max_cloud=5, cmap="RdYlGn") ax.set_title(f"add_ndvi — Nile at Luxor (Sentinel-2 {ax._acadgis_ndvi_date})", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

6 · add_raster (rgb) — 🇮🇹 Venice true colour¶

In [8]:

# add_raster (rgb=True) — Venice true-colour from a Sentinel-2 "visual" COG.
import requests, rioxarray
from rasterio.warp import transform_bounds
VENICE = (12.30, 45.40, 12.42, 45.47)
body = {"collections": ["sentinel-2-l2a"], "bbox": list(VENICE),
        "datetime": "2023-04-01T00:00:00Z/2024-09-30T00:00:00Z",
        "query": {"eo:cloud_cover": {"lt": 5}}, "limit": 40}
feats = requests.post("https://earth-search.aws.element84.com/v1/search",
                      json=body, timeout=60).json()["features"]
feats.sort(key=lambda f: f["properties"]["eo:cloud_cover"])
href = feats[0]["assets"]["visual"]["href"]            # 3-band true-colour COG (UTM)
date = feats[0]["properties"]["datetime"][:10]
da = rioxarray.open_rasterio(href)
bx = transform_bounds("EPSG:4326", da.rio.crs, *VENICE)
da = da.rio.clip_box(*bx)                              # windowed read of just Venice
fig, ax = agis.plt.subplots(figsize=(9, 8))
agis.add_raster(ax, da, rgb=True)                      # generic add_raster, RGB mode
ax.set_title(f"add_raster (rgb) — Venice true colour, S-2 {date}", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_raster (rgb=True) — Venice true-colour from a Sentinel-2 "visual" COG. import requests, rioxarray from rasterio.warp import transform_bounds VENICE = (12.30, 45.40, 12.42, 45.47) body = {"collections": ["sentinel-2-l2a"], "bbox": list(VENICE), "datetime": "2023-04-01T00:00:00Z/2024-09-30T00:00:00Z", "query": {"eo:cloud_cover": {"lt": 5}}, "limit": 40} feats = requests.post("https://earth-search.aws.element84.com/v1/search", json=body, timeout=60).json()["features"] feats.sort(key=lambda f: f["properties"]["eo:cloud_cover"]) href = feats[0]["assets"]["visual"]["href"] # 3-band true-colour COG (UTM) date = feats[0]["properties"]["datetime"][:10] da = rioxarray.open_rasterio(href) bx = transform_bounds("EPSG:4326", da.rio.crs, *VENICE) da = da.rio.clip_box(*bx) # windowed read of just Venice fig, ax = agis.plt.subplots(figsize=(9, 8)) agis.add_raster(ax, da, rgb=True) # generic add_raster, RGB mode ax.set_title(f"add_raster (rgb) — Venice true colour, S-2 {date}", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

7 · add_layer — 🇯🇵 Japan (polygons + lines + points)¶

In [9]:

# add_layer — Japan: prefectures (polygons) + rivers (lines) + cities (points).
jp = agis.load_boundaries("Japan", "province")         # 47 prefectures
fig, ax = agis.plt.subplots(figsize=(9, 9))
agis.plot(jp, ax=ax, palette="pastel", graticule=False, north_arrow=False, scale_bar=False)
agis.add_layer(ax, agis.load_rivers(scale="10m"), area=jp,         # any vector -> lines
               color="#2b7bba", linewidth=0.8, label="Rivers")
agis.add_layer(ax, agis.load_places(), area=jp,                    # points + labels
               labels=5, label_field="name", rank_field="pop",     # label the 5 biggest
               color="#c0392b", markersize=40, label_size=8, label="Cities")
ax.set_xlim(128, 146); ax.set_ylim(30.5, 46)           # main islands (skip far Ryukyu/Ogasawara)
ax.set_title("add_layer — Japan: prefectures + rivers + cities", fontsize=12, fontweight="bold")
ax.legend(loc="upper left", fontsize=8); agis.show()

# add_layer — Japan: prefectures (polygons) + rivers (lines) + cities (points). jp = agis.load_boundaries("Japan", "province") # 47 prefectures fig, ax = agis.plt.subplots(figsize=(9, 9)) agis.plot(jp, ax=ax, palette="pastel", graticule=False, north_arrow=False, scale_bar=False) agis.add_layer(ax, agis.load_rivers(scale="10m"), area=jp, # any vector -> lines color="#2b7bba", linewidth=0.8, label="Rivers") agis.add_layer(ax, agis.load_places(), area=jp, # points + labels labels=5, label_field="name", rank_field="pop", # label the 5 biggest color="#c0392b", markersize=40, label_size=8, label="Cities") ax.set_xlim(128, 146); ax.set_ylim(30.5, 46) # main islands (skip far Ryukyu/Ogasawara) ax.set_title("add_layer — Japan: prefectures + rivers + cities", fontsize=12, fontweight="bold") ax.legend(loc="upper left", fontsize=8); agis.show()

8 · add_cities — 🇪🇺 Europe¶

In [10]:

# add_cities — Europe (ranked by population, label control).
EUR = (-10, 36, 28, 60)
fig, ax = agis.plt.subplots(figsize=(11, 9))
ax.set_xlim(EUR[0], EUR[2]); ax.set_ylim(EUR[1], EUR[3])
agis.add_basemap(ax, style="light", alpha=0.9)
agis.add_cities(ax, top=25, labels=12,                 # show 25 biggest, label the top 12
                color="#d1495b", markersize=30, label_color="#111", label_size=8)
ax.set_title("add_cities — Europe (top 25, labelled top 12)", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_cities — Europe (ranked by population, label control). EUR = (-10, 36, 28, 60) fig, ax = agis.plt.subplots(figsize=(11, 9)) ax.set_xlim(EUR[0], EUR[2]); ax.set_ylim(EUR[1], EUR[3]) agis.add_basemap(ax, style="light", alpha=0.9) agis.add_cities(ax, top=25, labels=12, # show 25 biggest, label the top 12 color="#d1495b", markersize=30, label_color="#111", label_size=8) ax.set_title("add_cities — Europe (top 25, labelled top 12)", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

9 · add_roads — 🇫🇷 Paris¶

In [11]:

# add_roads — Paris (radial boulevards), OSM widths by class.
PARIS = (2.24, 48.81, 2.43, 48.91)
fig, ax = agis.plt.subplots(figsize=(9, 8.5))
ax.set_xlim(PARIS[0], PARIS[2]); ax.set_ylim(PARIS[1], PARIS[3])
agis.add_basemap(ax, style="light")
agis.add_roads(ax, classes=("motorway", "trunk", "primary", "secondary"),
               color="#e63946", by_class=True)         # width scales with road class
agis.add_cities(ax, top=1, labels="one", color="#111", label_color="#111", markersize=45)
ax.set_title("add_roads — Paris (OSM, by class)", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_roads — Paris (radial boulevards), OSM widths by class. PARIS = (2.24, 48.81, 2.43, 48.91) fig, ax = agis.plt.subplots(figsize=(9, 8.5)) ax.set_xlim(PARIS[0], PARIS[2]); ax.set_ylim(PARIS[1], PARIS[3]) agis.add_basemap(ax, style="light") agis.add_roads(ax, classes=("motorway", "trunk", "primary", "secondary"), color="#e63946", by_class=True) # width scales with road class agis.add_cities(ax, top=1, labels="one", color="#111", label_color="#111", markersize=45) ax.set_title("add_roads — Paris (OSM, by class)", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

🇧🇩 Bonus — Bangladesh field-research set¶

Practical patterns: your own raster (numpy → GeoTIFF), study sites over satellite, and a composed thematic map.

10 · add_raster — your own data (continuous field)¶

In [12]:

# add_raster — bring your OWN data: build a raster from a numpy array.
import numpy as np, rasterio, tempfile, os
from rasterio.transform import from_bounds
bd0 = agis.load_boundaries("Bangladesh", "country")
bd1 = agis.load_boundaries("Bangladesh", "division")
w, s, e, n = bd0.total_bounds
W = H = 480
LON, LAT = np.meshgrid(np.linspace(w, e, W), np.linspace(n, s, H))
# a smooth synthetic field (swap in any (H,W) array — model output, interpolation, …)
field = np.clip(0.55 + 0.25*np.cos(np.radians((LAT-23)*6)) + 0.20*np.sin(np.radians((LON-90)*5))
                + 0.18*np.exp(-(((LON-89.5)**2+(LAT-24.8)**2)/1.2))
                - 0.22*np.exp(-(((LON-90.4)**2+(LAT-23.8)**2)/0.25)), 0, 1).astype("float32")
tif = os.path.join(tempfile.gettempdir(), "bd_field.tif")
with rasterio.open(tif, "w", driver="GTiff", height=H, width=W, count=1, dtype="float32",
                   crs="EPSG:4326", transform=from_bounds(w, s, e, n, W, H), nodata=-9999) as d:
    d.write(field, 1)
fig, ax = agis.plt.subplots(figsize=(8, 9))
agis.add_raster(ax, tif, area=bd0, cmap="YlGn", vmin=0, vmax=1,
                colorbar=True, colorbar_label="value (your raster)")
agis.add_layer(ax, bd1, facecolor="none", edgecolor="#555", linewidth=0.6)
ax.set_xlim(w, e); ax.set_ylim(s, n)
ax.set_title("add_raster — your own data (continuous field)", fontsize=12, fontweight="bold")
agis.show()

# add_raster — bring your OWN data: build a raster from a numpy array. import numpy as np, rasterio, tempfile, os from rasterio.transform import from_bounds bd0 = agis.load_boundaries("Bangladesh", "country") bd1 = agis.load_boundaries("Bangladesh", "division") w, s, e, n = bd0.total_bounds W = H = 480 LON, LAT = np.meshgrid(np.linspace(w, e, W), np.linspace(n, s, H)) # a smooth synthetic field (swap in any (H,W) array — model output, interpolation, …) field = np.clip(0.55 + 0.25*np.cos(np.radians((LAT-23)*6)) + 0.20*np.sin(np.radians((LON-90)*5)) + 0.18*np.exp(-(((LON-89.5)**2+(LAT-24.8)**2)/1.2)) - 0.22*np.exp(-(((LON-90.4)**2+(LAT-23.8)**2)/0.25)), 0, 1).astype("float32") tif = os.path.join(tempfile.gettempdir(), "bd_field.tif") with rasterio.open(tif, "w", driver="GTiff", height=H, width=W, count=1, dtype="float32", crs="EPSG:4326", transform=from_bounds(w, s, e, n, W, H), nodata=-9999) as d: d.write(field, 1) fig, ax = agis.plt.subplots(figsize=(8, 9)) agis.add_raster(ax, tif, area=bd0, cmap="YlGn", vmin=0, vmax=1, colorbar=True, colorbar_label="value (your raster)") agis.add_layer(ax, bd1, facecolor="none", edgecolor="#555", linewidth=0.6) ax.set_xlim(w, e); ax.set_ylim(s, n) ax.set_title("add_raster — your own data (continuous field)", fontsize=12, fontweight="bold") agis.show()

11 · add_raster — categorical (your own classes)¶

In [13]:

# add_raster — categorical with your OWN classes={value:(colour, label)}.
lc = np.full((H, W), 3, "uint8")                       # 3 cropland
lc[field > 0.72] = 4                                    # 4 forest
lc[(field > 0.45) & (field <= 0.55)] = 5               # 5 wetland
lc[field < 0.32] = 1                                    # 1 water
lc[np.exp(-(((LON-90.4)**2+(LAT-23.8)**2)/0.18)) > 0.5] = 2   # 2 built-up
lctif = os.path.join(tempfile.gettempdir(), "bd_lc.tif")
with rasterio.open(lctif, "w", driver="GTiff", height=H, width=W, count=1, dtype="uint8",
                   crs="EPSG:4326", transform=from_bounds(w, s, e, n, W, H), nodata=0) as d:
    d.write(lc, 1)
CLASSES = {1: ("#4a90d9", "Water"), 2: ("#d1495b", "Built-up"), 3: ("#e9d8a6", "Cropland"),
           4: ("#2a9d4a", "Forest"), 5: ("#76c893", "Wetland")}
fig, ax = agis.plt.subplots(figsize=(8, 9))
agis.add_raster(ax, lctif, area=bd0, categorical=True, classes=CLASSES, legend=True)
agis.add_layer(ax, bd1, facecolor="none", edgecolor="#333", linewidth=0.5)
ax.set_xlim(w, e); ax.set_ylim(s, n)
ax.set_title("add_raster — categorical (your own classes)", fontsize=12, fontweight="bold")
agis.show()

# add_raster — categorical with your OWN classes={value:(colour, label)}. lc = np.full((H, W), 3, "uint8") # 3 cropland lc[field > 0.72] = 4 # 4 forest lc[(field > 0.45) & (field <= 0.55)] = 5 # 5 wetland lc[field < 0.32] = 1 # 1 water lc[np.exp(-(((LON-90.4)**2+(LAT-23.8)**2)/0.18)) > 0.5] = 2 # 2 built-up lctif = os.path.join(tempfile.gettempdir(), "bd_lc.tif") with rasterio.open(lctif, "w", driver="GTiff", height=H, width=W, count=1, dtype="uint8", crs="EPSG:4326", transform=from_bounds(w, s, e, n, W, H), nodata=0) as d: d.write(lc, 1) CLASSES = {1: ("#4a90d9", "Water"), 2: ("#d1495b", "Built-up"), 3: ("#e9d8a6", "Cropland"), 4: ("#2a9d4a", "Forest"), 5: ("#76c893", "Wetland")} fig, ax = agis.plt.subplots(figsize=(8, 9)) agis.add_raster(ax, lctif, area=bd0, categorical=True, classes=CLASSES, legend=True) agis.add_layer(ax, bd1, facecolor="none", edgecolor="#333", linewidth=0.5) ax.set_xlim(w, e); ax.set_ylim(s, n) ax.set_title("add_raster — categorical (your own classes)", fontsize=12, fontweight="bold") agis.show()

12 · add_basemap + field study sites — Gazipur¶

In [14]:

# add_basemap + study sites — the field-research workflow (sites over satellite).
gz = agis.load_boundaries("Bangladesh", "district", within="Dhaka")
gz = gz[gz["NAME_2"] == "Gazipur"]
gw, gs2, ge, gn = gz.total_bounds
rng = np.random.default_rng(3)
xs = gw + (ge - gw) * rng.uniform(.25, .75, 7)
ys = gs2 + (gn - gs2) * rng.uniform(.30, .75, 7)
sites = agis.gpd.GeoDataFrame({"id": [f"S{i+1}" for i in range(7)],
        "geometry": agis.gpd.points_from_xy(xs, ys)}, crs="EPSG:4326")
fig, ax = agis.plt.subplots(figsize=(8.5, 8))
ax.set_xlim(gw - .03, ge + .03); ax.set_ylim(gs2 - .03, gn + .03)
agis.add_satellite(ax)
agis.add_layer(ax, gz, facecolor="none", edgecolor="#ffd000", linewidth=2.2)
agis.add_layer(ax, sites, color="#ff2e63", markersize=85, edgecolor="white",
               labels="all", label_field="id", label_color="white", label_size=8,
               label="Field sites", legend=True)
ax.set_title("add_basemap (satellite) + field sites — Gazipur", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_basemap + study sites — the field-research workflow (sites over satellite). gz = agis.load_boundaries("Bangladesh", "district", within="Dhaka") gz = gz[gz["NAME_2"] == "Gazipur"] gw, gs2, ge, gn = gz.total_bounds rng = np.random.default_rng(3) xs = gw + (ge - gw) * rng.uniform(.25, .75, 7) ys = gs2 + (gn - gs2) * rng.uniform(.30, .75, 7) sites = agis.gpd.GeoDataFrame({"id": [f"S{i+1}" for i in range(7)], "geometry": agis.gpd.points_from_xy(xs, ys)}, crs="EPSG:4326") fig, ax = agis.plt.subplots(figsize=(8.5, 8)) ax.set_xlim(gw - .03, ge + .03); ax.set_ylim(gs2 - .03, gn + .03) agis.add_satellite(ax) agis.add_layer(ax, gz, facecolor="none", edgecolor="#ffd000", linewidth=2.2) agis.add_layer(ax, sites, color="#ff2e63", markersize=85, edgecolor="white", labels="all", label_field="id", label_color="white", label_size=8, label="Field sites", legend=True) ax.set_title("add_basemap (satellite) + field sites — Gazipur", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

13 · add_layer — composed thematic map (Bangladesh)¶

In [15]:

# add_layer — composed thematic map: divisions + rivers + ranked cities.
fig, ax = agis.plt.subplots(figsize=(8.5, 9))
agis.plot(bd1, ax=ax, palette="pastel", graticule=False, north_arrow=False, scale_bar=False)
agis.add_layer(ax, agis.load_rivers(scale="10m"), area=bd0, color="#2b7bba",
               linewidth=0.9, label="Rivers")
agis.add_cities(ax, area=bd0, top=6, labels="all", color="#111", marker="s",
                markersize=45, label_color="#111")
ax.set_xlim(w, e); ax.set_ylim(s, n)
ax.legend(loc="upper left", fontsize=8)
ax.set_title("add_layer — Bangladesh: divisions + rivers + cities", fontsize=12, fontweight="bold")
agis.show()

# add_layer — composed thematic map: divisions + rivers + ranked cities. fig, ax = agis.plt.subplots(figsize=(8.5, 9)) agis.plot(bd1, ax=ax, palette="pastel", graticule=False, north_arrow=False, scale_bar=False) agis.add_layer(ax, agis.load_rivers(scale="10m"), area=bd0, color="#2b7bba", linewidth=0.9, label="Rivers") agis.add_cities(ax, area=bd0, top=6, labels="all", color="#111", marker="s", markersize=45, label_color="#111") ax.set_xlim(w, e); ax.set_ylim(s, n) ax.legend(loc="upper left", fontsize=8) ax.set_title("add_layer — Bangladesh: divisions + rivers + cities", fontsize=12, fontweight="bold") agis.show()

🇧🇩 Real-data Bangladesh trio¶

The same curated layers as §3–5, here on Bangladesh: real land cover & NDVI over Dhaka, and topography over the Chittagong Hill Tracts.

14 · add_landcover — Dhaka (real ESA WorldCover)¶

In [16]:

# add_landcover — real ESA WorldCover 10 m for Dhaka (urban core in red).
DHK = (90.28, 23.62, 90.55, 23.95)
dhk = agis.load_boundaries("Bangladesh", "district", within="Dhaka")
fig, ax = agis.plt.subplots(figsize=(8.5, 8))
ax.set_xlim(DHK[0], DHK[2]); ax.set_ylim(DHK[1], DHK[3])
agis.add_landcover(ax, DHK, year=2021)                 # real 10 m land cover + class legend
agis.add_layer(ax, dhk, facecolor="none", edgecolor="#111", linewidth=0.7)
ax.set_title("add_landcover — Dhaka (ESA WorldCover 10 m)", fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_landcover — real ESA WorldCover 10 m for Dhaka (urban core in red). DHK = (90.28, 23.62, 90.55, 23.95) dhk = agis.load_boundaries("Bangladesh", "district", within="Dhaka") fig, ax = agis.plt.subplots(figsize=(8.5, 8)) ax.set_xlim(DHK[0], DHK[2]); ax.set_ylim(DHK[1], DHK[3]) agis.add_landcover(ax, DHK, year=2021) # real 10 m land cover + class legend agis.add_layer(ax, dhk, facecolor="none", edgecolor="#111", linewidth=0.7) ax.set_title("add_landcover — Dhaka (ESA WorldCover 10 m)", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

15 · add_ndvi — Dhaka (real Sentinel-2)¶

In [17]:

# add_ndvi — real Sentinel-2 NDVI for Dhaka.
fig, ax = agis.plt.subplots(figsize=(8.5, 8))
ax.set_xlim(DHK[0], DHK[2]); ax.set_ylim(DHK[1], DHK[3])
agis.add_ndvi(ax, DHK, start="2024-01-01", end="2024-04-01", max_cloud=8, cmap="RdYlGn")
agis.add_layer(ax, dhk, facecolor="none", edgecolor="#111", linewidth=0.7)
ax.set_title(f"add_ndvi — Dhaka (Sentinel-2 {ax._acadgis_ndvi_date})",
             fontsize=12, fontweight="bold")
ax.set_xticks([]); ax.set_yticks([]); agis.show()

# add_ndvi — real Sentinel-2 NDVI for Dhaka. fig, ax = agis.plt.subplots(figsize=(8.5, 8)) ax.set_xlim(DHK[0], DHK[2]); ax.set_ylim(DHK[1], DHK[3]) agis.add_ndvi(ax, DHK, start="2024-01-01", end="2024-04-01", max_cloud=8, cmap="RdYlGn") agis.add_layer(ax, dhk, facecolor="none", edgecolor="#111", linewidth=0.7) ax.set_title(f"add_ndvi — Dhaka (Sentinel-2 {ax._acadgis_ndvi_date})", fontsize=12, fontweight="bold") ax.set_xticks([]); ax.set_yticks([]); agis.show()

16 · add_topography — Chittagong Hill Tracts¶

In [18]:

# add_topography — Chittagong Hill Tracts (Bangladesh's hill country).
CHT = (91.7, 21.9, 92.5, 23.2)
fig, ax = agis.plt.subplots(figsize=(8, 8.5))
ax.set_xlim(CHT[0], CHT[2]); ax.set_ylim(CHT[1], CHT[3])
agis.add_topography(ax, area=CHT, cmap="terrain", hillshade=True,
                    colorbar=True, colorbar_label="Elevation (m)", max_size=900)
ax.set_title("add_topography — Chittagong Hill Tracts", fontsize=12, fontweight="bold")
agis.show()

# add_topography — Chittagong Hill Tracts (Bangladesh's hill country). CHT = (91.7, 21.9, 92.5, 23.2) fig, ax = agis.plt.subplots(figsize=(8, 8.5)) ax.set_xlim(CHT[0], CHT[2]); ax.set_ylim(CHT[1], CHT[3]) agis.add_topography(ax, area=CHT, cmap="terrain", hillshade=True, colorbar=True, colorbar_label="Elevation (m)", max_size=900) ax.set_title("add_topography — Chittagong Hill Tracts", fontsize=12, fontweight="bold") agis.show()