Precipitation — CHIRPS daily rainfall¶
CHIRPS daily rainfall (5.5 km, 1981-present) over the Lake Victoria region, reduced to one monthly mean over June 2024.
Setup¶
The imports for the whole notebook. pyramids provides Dataset (GeoTIFF/NetCDF
reading + plotting); earthlens provides the unified EarthLens entry point and the
Earth Engine Catalog.
import os
from pathlib import Path
import ee
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, cancel_task, list_recent_tasks, wait_for_task_id
Output directory¶
Every written GeoTIFF lands under a per-notebook out/ directory (which is
.gitignored — re-running overwrites it).
OUT_DIR = Path('out') / 'precipitation'
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('UCSB-CHG/CHIRPS/DAILY')
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 ([-2.0, 2.0] lat, [29.0, 33.0] lon) at 5566.0 m, monthly cadence — keeps
the synchronous download under EE's 32768-px per-axis cap. We build the request
first, then authenticate on its own line.
gee = EarthLens(
data_source="gee",
start='2024-06-01',
end='2024-06-30',
dataset='UCSB-CHG/CHIRPS/DAILY',
variables=['precipitation'],
aoi=[29.0, -2.0, 33.0, 2.0],
cadence='monthly',
path=str(OUT_DIR),
scale=5566.0,
reducer='mean',
)
gee.authenticate(service_account=SERVICE_ACCOUNT, service_key=SERVICE_KEY)
Run the synchronous download and report the GeoTIFF(s) written to disk.
paths = gee.download(progress_bar=False)
download_ok = True
print(f'wrote {len(paths)} GeoTIFF(s):')
for p in paths:
print(f' {p} ({p.stat().st_size / 1024:.1f} KB)')
Quick preview¶
Load the first written GeoTIFF through pyramids and read its single band into an
array. (pyramids.dataset.Dataset is the project's GeoTIFF/NetCDF wrapper.) We also
mask the dataset's nodata so the colormap isn't pinned to it.
if not download_ok or not paths:
print('Skipping preview — no GeoTIFF was written.')
arr = None
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
Render the band¶
Plot the precipitation array with matplotlib and print its value range.
if arr is not None:
fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(arr, cmap='viridis')
ax.set_title(f'{'UCSB-CHG/CHIRPS/DAILY'} / {'precipitation'}')
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.
The demo asset folder¶
Name a Folder asset 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).
# 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-precipitation'
print(f'demo folder: {DEMO_FOLDER}')
Best-effort cleanup of any leftover children from a previous run, then (re)create the empty parent folder — EE 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. Build the request and
authenticate first.
async_gee = EarthLens(
data_source="gee",
start='2024-06-01',
end='2024-06-30',
dataset='UCSB-CHG/CHIRPS/DAILY',
variables=['precipitation'],
aoi=[29.0, -2.0, 33.0, 2.0],
cadence='monthly',
path=str(OUT_DIR),
scale=5566.0,
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 — no blocking.
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 submitted tasks¶
list_recent_tasks(description_prefix=...) returns every matching task across the
current project, so we can confirm ours is queued before waiting on it.
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}')
else:
print('Skipping list — async submission did not succeed.')
Wait for completion¶
wait_for_task_id blocks until the task reaches a terminal state. A real workflow
would poll later from a separate process — the wait here exists so the notebook shows
the full success path end-to-end. On FAILED / CANCELLED it raises, and we
cancel-if-still-running so no in-flight task leaks on notebook restart.
if submitted_ok and task_info is not None:
try:
final = wait_for_task_id(
task_info.id,
poll_seconds=10,
progress_bar=False,
)
print(f'final 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 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)')