Other — CIESIN GPWv4 population density¶
Gridded Population of the World v4 (CIESIN, ~1 km) over the Nile delta, reduced to a single mean over the 2020 entry.
Setup¶
First the imports. pyramids provides Dataset (GeoTIFF/NetCDF reading + plotting); earthlens provides the unified EarthLens entry point and the GEE Catalog.
import os
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset as PyramidsDataset
from earthlens import EarthLens
from earthlens.gee import Catalog
Output directory¶
Downloads land under a per-notebook out/other/ directory (it is .gitignored, so re-running overwrites it).
OUT_DIR = Path('out') / 'other'
OUT_DIR.mkdir(parents=True, exist_ok=True)
print(f'output directory: {OUT_DIR.resolve()}')
Credentials¶
The notebook reads the GEE service-account credentials from the GEE_SERVICE_ACCOUNT / GEE_SERVICE_KEY environment variables. Both must be set before running this cell.
SERVICE_ACCOUNT = os.environ['GEE_SERVICE_ACCOUNT']
SERVICE_KEY = os.environ['GEE_SERVICE_KEY']
Inspect the catalog entry¶
Before downloading anything, look at what the bundled catalog knows about the asset — bands, cadence, license, provider.
cat = Catalog()
ds = cat.get_dataset('CIESIN/GPWv4/population-density')
print(f'title: {ds.title}')
print(f'ee_type: {ds.ee_type}')
print(f'spatial_resolution: {ds.spatial_resolution} m')
print(f'extent.start_date: {ds.extent.start_date}')
print(f'extent.end_date: {ds.extent.end_date}')
print(f'default_reducer: {ds.default_reducer}')
print(f'license: {ds.license}')
print(f'provider: {ds.provider}')
print(f'#bands: {len(ds.bands)}')
print(f'band ids (first 5): {list(ds.bands)[:5]}')
Download¶
Tiny AOI ([29.0, 32.0] lat, [29.0, 33.0] lon) at 5000.0 m, raw cadence — keeps the synchronous download under EE's 32768-px per-axis cap.
Build the request, then authenticate it as a separate step (the construct -> authenticate split keeps each line easy to read and re-run). export_via defaults to "url", so this is a synchronous getDownloadURL round-trip.
gee = EarthLens(
data_source="gee",
start='2020-01-01',
end='2020-12-31',
dataset='CIESIN/GPWv4/population-density',
variables=['population-density'],
aoi=[29.0, 29.0, 33.0, 32.0],
cadence='raw',
path=str(OUT_DIR),
scale=5000.0,
reducer='mean',
)
gee.authenticate(service_account=SERVICE_ACCOUNT, service_key=SERVICE_KEY)
download() writes the composite to disk and returns the list of written paths.
paths = gee.download(progress_bar=False)
print(f'wrote {len(paths)} GeoTIFF(s):')
for p in paths:
print(f' {p} ({p.stat().st_size / 1024:.1f} KB)')
download_ok = True
Quick preview¶
Load the first written GeoTIFF through pyramids and render the single band. (pyramids.dataset.Dataset is the project's GeoTIFF/NetCDF wrapper.)
if not download_ok or not paths:
print('Skipping preview — no GeoTIFF was written.')
else:
pds = PyramidsDataset.read_file(str(paths[0]))
arr = pds.read_array()
if arr.ndim == 3:
arr = arr[0]
# Mask the dataset's nodata so the colormap doesn't get pinned to it.
nodata = pds.no_data_value
if nodata is not None:
try:
arr = np.ma.masked_equal(
arr, float(nodata[0] if isinstance(nodata, (list, tuple)) else nodata)
)
except (TypeError, ValueError):
pass
fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(arr, cmap='viridis')
ax.set_title(f'{'CIESIN/GPWv4/population-density'} / {'population-density'}')
ax.set_xlabel('x (px)')
ax.set_ylabel('y (px)')
fig.colorbar(im, ax=ax, shrink=0.8)
plt.tight_layout()
plt.show()
print(f'value range: [{float(np.nanmin(arr)):.4g}, {float(np.nanmax(arr)):.4g}]')
Tracking submitted jobs (asynchronous export)¶
The download above uses export_via="url" — a synchronous getDownloadURL round-trip. Nothing was queued, so there's no Earth Engine job to track.
To track an export instead, switch to an asynchronous sink (drive / gcs / asset) and pass wait_for_export=False so .download() returns a TaskInfo at submission time rather than blocking until completion. The cells below submit the same (asset_id, band, AOI, scale) request as an export_via="asset" task into the service account's own asset folder, then walk the four jobs-API calls (list_recent_tasks → wait_for_task_id → ee.data.getAsset → ee.data.deleteAsset) to make the job finish and tidy up. See track-batch-exports.ipynb for a deeper worked example.
Imports and the demo folder¶
Bring in the jobs-API helpers and compute the asset folder we own. GEE._export_via_batch writes the image at <asset_id>/<prefix>, so asset_id is the parent folder, not the final image path.
import ee
from earthlens.gee import cancel_task, list_recent_tasks, wait_for_task_id
# The asset goes into a `Folder` asset that we own. `GEE._export_via_batch`
# writes the actual image at `<asset_id>/<prefix>`, so `asset_id` here is
# the parent FOLDER (not the final image path). Both must be cleaned up.
_proj = ee.data._get_projects_path().removeprefix('projects/')
DEMO_FOLDER = f'projects/{_proj}/assets/earthlens-demo-other'
print(f'demo folder: {DEMO_FOLDER}')
Reset and create the folder¶
Best-effort clear of any leftover children from a previous run, then create the empty parent folder (Earth Engine requires it to exist before a child write).
# Best-effort cleanup of leftover children from a previous run (so the
# folder is empty before we try to delete it below).
try:
for child in ee.data.listAssets({'parent': DEMO_FOLDER}).get('assets', []):
ee.data.deleteAsset(child['name'])
print(f'cleared leftover child: {child["name"]}')
ee.data.deleteAsset(DEMO_FOLDER)
print(f'cleared leftover folder: {DEMO_FOLDER}')
except Exception:
pass
# Create the parent folder — EE requires it to exist before a child write.
ee.data.createAsset({'type': 'Folder'}, DEMO_FOLDER)
print(f'created folder: {DEMO_FOLDER}')
Submit¶
Same (asset_id, band, AOI, scale) request as the sync download above, just routed through export_via="asset" + wait_for_export=False. download() returns a TaskInfo per submitted bucket at the moment the task is queued — no blocking.
Build the asynchronous request and authenticate it on its own line, mirroring the sync download above but routed through export_via="asset" + wait_for_export=False.
submitted_ok = False
task_info = None
async_gee = EarthLens(
data_source="gee",
start='2020-01-01',
end='2020-12-31',
dataset='CIESIN/GPWv4/population-density',
variables=['population-density'],
aoi=[29.0, 29.0, 33.0, 32.0],
cadence='raw',
path=str(OUT_DIR),
scale=5000.0,
reducer='mean',
export_via='asset',
asset_id=DEMO_FOLDER,
wait_for_export=False,
)
async_gee.authenticate(service_account=SERVICE_ACCOUNT, service_key=SERVICE_KEY)
download() returns a TaskInfo per submitted bucket at the moment the task is queued — it does not block on completion.
submitted = async_gee.download(progress_bar=False)
task_info = submitted[0]
submitted_ok = True
print(f'submitted: id={task_info.id} state={task_info.state}')
print(f' description={task_info.description}')
List + wait¶
list_recent_tasks(description_prefix=...) returns every matching task across the current project; wait_for_task_id blocks until the one we care about reaches a terminal state. A real workflow would just poll later from a separate process — the wait here exists so the notebook shows the full success path end-to-end.
if submitted_ok and task_info is not None:
recent = list_recent_tasks(
description_prefix=task_info.description,
max_age_min=10,
)
print(f'list_recent_tasks matched {len(recent)} task(s):')
for t in recent:
print(f' {t.id} {t.state:<12} {t.description}')
try:
final = wait_for_task_id(
task_info.id,
poll_seconds=10,
progress_bar=False,
)
print(f'\nfinal state: {final.state}')
except RuntimeError as exc:
# Raised on FAILED / CANCELLED — cancel-if-still-running
# so we don't leak an in-flight task on notebook restart.
print(f'wait_for_task_id raised: {exc}')
try:
cancel_task(task_info.id)
except Exception:
pass
else:
print('Skipping list/wait — async submission did not succeed.')
Verify + clean up¶
Confirm the produced asset exists on Earth Engine, then delete it (and the surrounding demo folder) so we don't leak storage between notebook runs. The backend wrote the image at <DEMO_FOLDER>/<task description>.
if submitted_ok and task_info is not None:
produced = f'{DEMO_FOLDER}/{task_info.description}'
try:
meta = ee.data.getAsset(produced)
print(f'asset exists: type={meta.get("type")} ' f'name={meta.get("name")}')
ee.data.deleteAsset(produced)
print('asset deleted')
except Exception as exc:
print(f'verify/delete skipped: {exc}')
# Always try to tear down the parent folder.
try:
ee.data.deleteAsset(DEMO_FOLDER)
print(f'folder deleted: {DEMO_FOLDER}')
except Exception as exc:
print(f'folder delete skipped: {exc}')
What's on disk¶
The GeoTIFF (or empty list, if the EE call was tolerated) is left under the per-notebook out/ directory for you to inspect. That directory is .gitignored — re-running the notebook overwrites it.
for p in sorted(OUT_DIR.iterdir()) if OUT_DIR.exists() else []:
print(f'{p} ({p.stat().st_size / 1024:.1f} KB)')