Quickstart

This short example shows the typical PyRawPh workflow:

load a local ΦSat-2 L1 product,
inspect the event,
access one band,
build an RGB image,
compute NDVI,
create tiles,
export a result to GeoTIFF.

Load a product

from pyrawph.l1.l1_event import L1_event

ev = L1_event.from_path(
    product_folder="path/to/product_folder",
    scene_id=0,
    product_kind="BC",
)

Inspect the event

ev.show_event_info()

Access one band

Bands can be selected by: - integer index, for example 3, - wavelength in nanometers, for example 842.0, - string selectors such as "B3" or "842nm", - aliases such as "RED" or "NIR".

nir = ev.get_band("NIR")
red = ev.get_band("RED")

Build an RGB image

rgb = ev.rgb()

Compute NDVI

ndvi = ev.index("NDVI")

Create tiles

tiles = ev.to_tiles(tile_size=512, overlap=0)
print(ev.get_tiles_names())

Inspect one tile

t0 = ev.get_tile(0)
t0.show_bands()

Export a result

ev.export_to_tif(
    out_path="outputs/ndvi.tif",
    arr=ndvi,
    meta=ev.get_meta(),
)

Notes

The local reader expects a ΦSat-2 product folder containing a bands/ directory and may also use sidecar files such as:

geolocation/GL_scene_<id>.json
processing_config.json
session_*_metadata.json

When available, these files enrich the event metadata with geospatial information and band wavelengths.