The plugin esa-snappy enables Python developers to
- use the SNAP Java API from Python, and to
- extend SNAP by operator plug-ins for EO data processing written in the Python programming language.
It is worth mentioning that the esa-snappy plugin works with the standard CPython, so
that any native Python extension modules such as numpy and scipy can be used. Before you
read further you may have a look at the example code in examples/*.py for using the SNAP
Java API from Python.
The esa_snappy plugin depends on a bi-directional Java-Python bridge jpy that enables calls from Python into a Java virtual machine and, at the same time, the other way round. This bridge is implemented by the jpy Project and is independent of the snappy module.
Until SNAP version 9, the functionality was realized by an internal module 'snap-python' in snap-engine, finally providing a Python package named snappy, but this lead to conflicts as other common Python packages exist with this name. E.g., various state-of-the-art Python packages (such as xarray, dask) could not be used. Moreover, the snappy package could only be used with Python versions <= 3.6. The new esa_snappy plugin now provides a Python package with the same name esa_snappy which, along with the current jpy version, supports the more recent Python versions 3.6 to 3.10.
Before you can start using the SNAP API or developing SNAP operator plugins with Python you need to install esa_snappy in SNAP and configure SNAP for the desired Python version. This procedure is described in the following.
With the new esa_snappy plugin, a manual installation of jpy is no longer necessary, as jpy wheels with the required shared libraries for all supported Python versions and platforms are now included in the plugin. The jpy installation is done automatically as part of the esa_snappy installation and Python configuration, which in return can be done either
- from the command line using a configuration script which is provided within the SNAP installation (as for snappy in earlier SNAP versions)
- from a GUI as an optional step of the SNAP installation itself (as for snappy in earlier SNAP versions)
- from a GUI which can be invoked from SNAP Desktop if SNAP is already installed (in preparation, not yet functional)
On Darwin / Linux type:
cd <path to your SNAP installation>/bin
./snappy-conf <path/to/your/Python/installation>/bin/python [esa_snappy installation dir]
On Windows type:
cd <path to your SNAP installation>\bin
.\snappy-conf.bat <path\to\your\Python\installation>\python.exe [esa_snappy installation dir]
If no esa_snappy installation directory is specified, the installation will be done into <'user home directory'>/.snap/snap-python. This is likely the most common case.
If you encounter any problems during the esa_snappy installation, please do not hesitate to contact the SNAP user forum.
During the SNAP installation, a dedicated screen for the Python configuration will appear. Follow the instructions given there.
When esa_snappy is imported into your Python script or module, it will scan a SNAP installation for the available SNAP API components. To test if the import works, make sure that your Python executable is on your path, and then type
On Darwin / Linux:
python
>>> import sys
>>> sys.path.append('<esa_snappy installation dir>')
>>> import esa_snappy
On Windows:
python.exe
>>> import sys
>>> sys.path.append('<esa_snappy installation dir>')
>>> import esa_snappy
If the import is successful (no errors are raised) you can exit the Python interpreter and perform the test cases in the '<esa_snappy installation dir>/esa_snappy/tests' directory:
python test_snappy_mem.py
python test_snappy_perf.py
python test_snappy_product.py
These tests expect numpy to be installed in your Python.
They all require an EO data test product file as input named MER_FRS_L1B_SUBSET.dim,
which is an Envisat MERIS L1b product. This product is located in the
'<esa_snappy installation dir>/esa_snappy/testdata' directory.
Please note that the SNAP API is actually independent of specific data formats, the MERIS file in this case is only used as an example and for testing.
This error has been occasionally observed during the esa_snappy installation and Python configuration on Linux. Here, the environment variable LD_LIBRARY_PATH is likely not set correctly, and thus the shared library for the JVM cannot be found. This can be solved by performing the following steps:
locate libjvm.so- Output is, say,
/path/to/libjvm.so export LD_LIBRARY_PATH=/path/to/:${LD_LIBRARY_PATH}- Re-try the installation
After a successful esa_snappy installation and Python configuration, you might get an error
similar to
ImportError: libjvm.so: cannot open shared object file: No such file or directory
when doing import jpy within your Python script.
Again, the shared library for the JVM cannot be found. This might happen if the
LD_LIBRARY_PATH has previously been set correctly, but was changed or not set
permanently. In this case, set the LD_LIBRARY_PATH as described above and restart your Python script.
esa_snappy can be configured by an INI file snappy.ini. This file is read from the current working directory
or from the system-dependent location from which the installed Python esa_snappy module is loaded from.
Given here is an example of its content (Windows):
[DEFAULT]
snap_home: C:\Program Files\snap-10.0
java_class_path: ./target/classes
java_max_mem: 8G
debug: True
The examples for the API usage are simple tools that compute an output product from an input product. These examples expect MERIS L1 or L2 products as stated in each of the example scripts. You can download Envisat MERIS L1 or L2 files used as input from various archives, such as ESA Envisat MERIS Online Dissemination Service at ESA, or OceanColor Web at NASA.
Again, all examples expect numpy to be installed. Also note that all the scripts append the relative path
sys.path.append('../../')
as in this example setup the esa_snappy module is located two folder levels above the 'examples' subfolder. Adapt this path (or replace by the absolute path of your esa_snappy module) if the scripts shall be run from a different location.
Let's assume you have in the 'examples' subfolder a MERIS L1 and a MERIS L2 test product saved in Dimap format
named MER_RR__1P.dim and MER_RR__2P.dim in order to run the example code.
Computing a Fluorescence Line Height (FLH) product from water-leaving reflectances:
python snappy_flh.py MER_RR__2P.dim
Computing a Normalized Difference Vegetation Index (NDVI) product from top-of-atmosphere radiances:
python snappy_ndvi.py MER_RR__1P.dim
python snappy_ndvi_with_masks.py MER_RR__1P.dim
Performing arbitrary band maths:
python snappy_bmaths.py MER_RR__1P.dim
Tailoring any input product to a spatial subset:
python snappy_subset.py MER_RR__2P.dim "POLYGON((15.786082 45.30223, 11.798364 46.118263, 10.878688 43.61961, 14.722727 42.85818, 15.786082 45.30223))"
There are many more possibilities to use the SNAP API. In principle, all Java classes of the SNAP API can be used. As the SNAP API can be used from Python in a similar way as from Java, all of the Java API documentation applies as well. Please check:
The esa_snappy module imports the most frequently used Java API classes by default:
String = jpy.get_type('java.lang.String')File = jpy.get_type('java.io.File')Point = jpy.get_type('java.awt.Point')Rectangle = jpy.get_type('java.awt.Rectangle')Arrays = jpy.get_type('java.util.Arrays')Collections = jpy.get_type('java.util.Collections')List = jpy.get_type('java.util.List')Map = jpy.get_type('java.util.Map')Set = jpy.get_type('java.util.Set')ArrayList = jpy.get_type('java.util.ArrayList')HashMap = jpy.get_type('java.util.HashMap')HashSet = jpy.get_type('java.util.HashSet')
Product tree & associates:
Product = jpy.get_type('org.esa.snap.core.datamodel.Product')VectorDataNode = jpy.get_type('org.esa.snap.core.datamodel.VectorDataNode')RasterDataNode = jpy.get_type('org.esa.snap.core.datamodel.RasterDataNode')TiePointGrid = jpy.get_type('org.esa.snap.core.datamodel.TiePointGrid')AbstractBand = jpy.get_type('org.esa.snap.core.datamodel.AbstractBand')Band = jpy.get_type('org.esa.snap.core.datamodel.Band')VirtualBand = jpy.get_type('org.esa.snap.core.datamodel.VirtualBand')Mask = jpy.get_type('org.esa.snap.core.datamodel.Mask')GeneralFilterBand = jpy.get_type('org.esa.snap.core.datamodel.GeneralFilterBand')ConvolutionFilterBand = jpy.get_type('org.esa.snap.core.datamodel.ConvolutionFilterBand')
Product tree associates:
ProductData = jpy.get_type('org.esa.snap.core.datamodel.ProductData')GeoCoding = jpy.get_type('org.esa.snap.core.datamodel.GeoCoding')TiePointGeoCoding = jpy.get_type('org.esa.snap.core.datamodel.TiePointGeoCoding')PixelGeoCoding = jpy.get_type('org.esa.snap.core.datamodel.PixelGeoCoding')PixelGeoCoding2 = jpy.get_type('org.esa.snap.core.datamodel.PixelGeoCoding2')CrsGeoCoding = jpy.get_type('org.esa.snap.core.datamodel.CrsGeoCoding')GeoPos = jpy.get_type('org.esa.snap.core.datamodel.GeoPos')PixelPos = jpy.get_type('org.esa.snap.core.datamodel.PixelPos')FlagCoding = jpy.get_type('org.esa.snap.core.datamodel.FlagCoding')ProductNodeGroup = jpy.get_type('org.esa.snap.core.datamodel.ProductNodeGroup')
Graph Processing Framework:
GPF = jpy.get_type('org.esa.snap.core.gpf.GPF')Operator = jpy.get_type('org.esa.snap.core.gpf.Operator')Tile = jpy.get_type('org.esa.snap.core.gpf.Tile')
Utilities:
EngineConfig = jpy.get_type('org.esa.snap.runtime.EngineConfig')Engine = jpy.get_type('org.esa.snap.runtime.Engine')SystemUtils = jpy.get_type('org.esa.snap.core.util.SystemUtils')ProductIO = jpy.get_type('org.esa.snap.core.dataio.ProductIO')ProductUtils = jpy.get_type('org.esa.snap.core.util.ProductUtils')GeoUtils = jpy.get_type('org.esa.snap.core.util.GeoUtils')ProgressMonitor = jpy.get_type('com.bc.ceres.core.ProgressMonitor')PlainFeatureFactory = jpy.get_type('org.esa.snap.core.datamodel.PlainFeatureFactory')FeatureUtils = jpy.get_type('org.esa.snap.core.util.FeatureUtils')
GeoTools:
DefaultGeographicCRS = jpy.get_type('org.geotools.referencing.crs.DefaultGeographicCRS')ListFeatureCollection = jpy.get_type('org.geotools.data.collection.ListFeatureCollection')SimpleFeatureBuilder = jpy.get_type('org.geotools.feature.simple.SimpleFeatureBuilder')
JTS:
Geometry = jpy.get_type('org.locationtech.jts.geom.Geometry')WKTReader = jpy.get_type('org.locationtech.jts.io.WKTReader')
To import other Java API classes, get the fully qualified type name from the API reference and import it using jpy. For example:
jpy = esa_snappy.jpy
Color = jpy.get_type('java.awt.Color')
ColorPoint = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef$Point')
ColorPaletteDef = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef')
ImageInfo = jpy.get_type('org.esa.snap.core.datamodel.ImageInfo')
ImageManager = jpy.get_type('org.esa.snap.core.image.ImageManager')
JAI = jpy.get_type('javax.media.jai.JAI')
The esa-snappy module also enables Python developers to write operators for SNAP. See the comprehensive documentation:
Finally, please don't hesitate to contact the SNAP User Forum.
Have fun!