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GPF.writeProduct(target_product, File(), write_format, incremental, ProgressMonitor.NULL) Incremental = false # most writer don't support the incremental writing mode (update exsiting file), except BEAM-DIMAP.

# Alternative solution: Computations are faster when using GPF to write the product instead of ProductIO: ProductIO.writeProduct(target_product, , write_format) Write_format = 'BEAM-DIMAP' # in this case write as BEAM-DIMAP Target_product = GPF.createProduct(operator_name, parameters, p) The parameters must be named exactly with the String parameter name provided in GPT.įrom esa_snappy import ProductIO # package to be imported is now esa_snappy instead of snappy This parameter is a Java Hashmap, an object that is equivalent to a Python dictionary. In snappy, we provide the parameters through the second parameter of GPF.createProduct(). If you have added GPT to your environment variables, you may call GPT from cmd in order to check out the available Operators, their description and parameters. Its first parameter is a String denoting the name of the Operator as denoted in the Engine and available via GPT. SNAP Operators are available in snappy via GPF.createProduct(). Option A: Process a product using a SNAP Engine Operator and write the target product Option B is suited when you aim at doing custom computations for which you need to read data into Python numpy arrays.īoth options can, of course, occur in one workflow. Option A is suited when you aim at using only SNAP Engine Operators.

SNAPPY SNAPS HARROW HOW TO

Snappy generally offers to ways how to process data: You need to use a standard Python (CPython) interpreter installed on your computer (SNAP does not include a CPython interpreter.) The supported versions are Python 2.7, 3.3 to 3.10 64-bit (Linux + Darwin) and both 32-bit and 64-bit (Windows) as well as Anaconda distributions. With the standard Python approach extension of SNAP is currently limited to raster data processor ( Operator) plugins.

SNAPPY SNAPS HARROW CODE

your code has (or will have) dependencies on a lot of non-standard libraries.

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you do not require full portability on all platforms.

you do not plan to develop SNAP Desktop user interface extensions.

you plan to implement a fast data processor plugin in Python.

you already have CPython code and you want to incorporate SNAP functions.

you require using the Python scientific extension libraries such as numpy, scipy, matplotlib, etc.

With the recommended standard Python (CPython) approach, it is possible to call SNAP code from your Python programs/scripts and to extend SNAP by plugins written in Python. Documentation and examples how to use Jython in older SNAP versions are kept here. As of SNAP 8 the Jython support is not part of the SNAP standard distribution anymore. Note: For the most recent versions of SNAP we discourage the usage of Jython.

Use a standard Python (CPython) installation.

It is possible to use the SNAP Java API from Python and there are in principle two different ways to achieve this: According to the SNAP architecture, the reference SNAP Java API documentation of the two SNAP sub-systems SNAP Engine and SNAP Desktop nearly fully applies to use from Python as well. SNAP implementation language is Java and therefore SNAP's 'native' API is a Java API.

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