CHC (CHIRPS) — daily rainfall quickstart (live)¶

Download a few days of CHIRPS-2.0 daily precipitation for a small area over Colombia, then read and plot the cropped GeoTIFF. CHC uses anonymous FTP, so no credentials are needed.

This notebook performs a real network download from data.chc.ucsb.edu.

Setup¶

Imports plus a bit of log-suppression. earthlens provides the unified EarthLens entry point; pyramids provides Dataset for reading and plotting the GeoTIFF later. We disable the earthlens logger for a clean notebook.

In [ ]:

import tempfile

from loguru import logger

from earthlens import EarthLens

logger.disable('earthlens')  # quieten the download logs for a clean notebook

import tempfile from loguru import logger from earthlens import EarthLens logger.disable('earthlens') # quieten the download logs for a clean notebook

Build the request¶

Configure the CHC backend: the daily CHIRPS-2.0 product, a three-day window in January 2009, the precipitation variable, and a small bounding box over Colombia. Output GeoTIFFs go to a fresh temporary directory.

In [ ]:

lens = EarthLens(
    data_source='chc',
    temporal_resolution='daily',
    start='2009-01-01',
    end='2009-01-03',
    variables=['precipitation'],
    lat_lim=[4.19, 4.64],
    lon_lim=[-75.65, -74.73],
    path=tempfile.mkdtemp(prefix='chirps_'),
)

lens = EarthLens( data_source='chc', temporal_resolution='daily', start='2009-01-01', end='2009-01-03', variables=['precipitation'], lat_lim=[4.19, 4.64], lon_lim=[-75.65, -74.73], path=tempfile.mkdtemp(prefix='chirps_'), )

Download the daily tiles¶

download() fetches each global daily tile, crops it to the requested bbox, and returns the list of written GeoTIFF paths.

In [ ]:

paths = lens.download(progress_bar=False)  # returns the written GeoTIFF paths
print(f'{len(paths)} files written')

paths = lens.download(progress_bar=False) # returns the written GeoTIFF paths print(f'{len(paths)} files written')

The backend writes one global-daily_precipitation_<date>.tif per day.

In [ ]:

for p in sorted(paths):
    print(p.name)

for p in sorted(paths): print(p.name)

Read and plot the cropped raster¶

Read the first day's GeoTIFF with pyramids and pull its array. CHIRPS encodes no-data as -9999, so we mask negatives to NaN before plotting.

In [ ]:

import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset

ds = Dataset.read_file(str(sorted(paths)[0]))
arr = np.asarray(ds.read_array(), dtype='float32')
arr = arr[0] if arr.ndim == 3 else arr
arr = np.where(arr < 0, np.nan, arr)  # CHIRPS no-data is -9999
print('EPSG', ds.epsg, '| shape', arr.shape)

import matplotlib.pyplot as plt import numpy as np from pyramids.dataset import Dataset ds = Dataset.read_file(str(sorted(paths)[0])) arr = np.asarray(ds.read_array(), dtype='float32') arr = arr[0] if arr.ndim == 3 else arr arr = np.where(arr < 0, np.nan, arr) # CHIRPS no-data is -9999 print('EPSG', ds.epsg, '| shape', arr.shape)

Plot the masked precipitation field for the first day.

In [ ]:

plt.figure(figsize=(6, 5))
plt.imshow(arr, cmap='Blues')
plt.colorbar(label='precipitation (mm/day)')
plt.title('CHIRPS daily rainfall — 2009-01-01')
plt.axis('off')
plt.show()

plt.figure(figsize=(6, 5)) plt.imshow(arr, cmap='Blues') plt.colorbar(label='precipitation (mm/day)') plt.title('CHIRPS daily rainfall — 2009-01-01') plt.axis('off') plt.show()