Giters

snap-contrib / snapista

SNAP GPT thin layer for Python

Home Page:https://snap-contrib.github.io/snapista/

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Expected speed for SAR calibration

SimJeg opened this issue · comments

commented

Hello !

First thank you for developing and maintaining this snapista package. To install the package I had to first run conda install -c terradue -c conda-forge snap=8.0.0, it may be useful to precise it in the README.

I am applying the operations listed below on Sentinel-1 data (~1.5GB) and it took ~10mins on a machine with 8 CPUs. Is it what I sould expect ?

  • Manifest name: S1A_IW_GRDH_1SDV_20220113T015151_20220113T015216_041436_04ED43_BAD7.SAFE
  • Operations
    • Read
    • Apply-Orbit-File"
    • Remove-GRD-Border-Noise
    • Calibration
    • Write

I am currently adding the Terrain-Flattening and Terrain-Correction to see how long it takes

Thanks

commented

There may be a problem on my side as running the following code takes 6 minutes :

import os
from snapista import Graph, Operator

path_to_manifest = '/data/sentinel1/S1A_IW_GRDH_1SDV_20220113T015151_20220113T015216_041436_04ED43_BAD7.SAFE'

g = Graph()

g.add_node(
    operator=Operator(
        "Read",
        formatName="SENTINEL-1",
        file=path_to_manifest,
    ),
    node_id="read",
)
g.add_node(
    operator=Operator("Write", file='output.tif',
                      formatName="GeoTIFF-BigTIFF"),
    node_id="write",
    source="read",
)

g.run()