# User Manual

## Quick start

PyEPR provides Python bindings for the ENVISAT Product Reader C API (EPR API) for reading satellite data from ENVISAT ESA (European Space Agency) mission.

PyEPR, as well as the EPR API for C, supports ENVISAT MERIS, AATSR Level 1B and Level 2 and also ASAR data products. It provides access to the data either on a geophysical (decoded, ready-to-use pixel samples) or on a raw data layer. The raw data access makes it possible to read any data field contained in a product file.

Full access to the Python EPR API is provided by the `epr` module that have to be imported by the client program e.g. as follows:

    import epr

The following snippet open an ASAR product and dumps the “Main Processing Parameters” record to the standard output:

    import epr

    product = epr.Product(
        'ASA_IMP_1PNESA20060202_062238_000000152044_00435_20529_0000.N1'
    )
    dataset = product.get_dataset('MAIN_PROCESSING_PARAMS_ADS')
    record = dataset.read_record(0)
    print(record)
    product.close()

Since version 0.9 PyEPR also include update features that are not available in the EPR C API. The user can open a product in update mode (rb+) and call the `epr.Field.set_elem()` and `epr.Field.set_elems()` methods of `epr.Field` class to update its elements and write changes to disk.

See also

Update support and Update Field elements tutorial for details.

## Requirements

In order to use PyEPR it is needed that the following software are correctly installed and configured:

- Python3 >= 3.5 (including PyPy3)

- numpy >= 1.7.0

- EPR API >= 2.2 (optional, since PyEPR 0.7 the source tar-ball comes with a copy of the EPR C API sources)

- a reasonably updated C compiler [1] (build only)

- Cython >= 0.29.22 [2] (build only)

- pytest (optional and only needed for testing)

[1]

PyEPR has been developed and tested with gcc 4.

[2]

The source tarball of official releases also includes the C extension code generated by cython so users don’t strictly need cython to install PyEPR.

It is only needed to re-generate the C extension code (e.g. if one wants to build a development version of PyEPR).

## Download

Official source tar-balls can be downloaded form PyPi:

> https://pypi.org/project/pyepr

The source code of the development versions is available on the GitHub project page

> https://github.com/avalentino/pyepr

To clone the git repository the following command can be used:

    $ git clone https://github.com/avalentino/pyepr.git

To get also the EPR C API source code, the following commands are necessary:

    $ cd pyepr
    $ git submodule init
    Submodule 'extern/epr-api' (https://github.com/avalentino/epr-api.git) registered for path 'extern/epr-api'
    $ git submodule update
    Cloning into '/Users/antonio valentino/projects/av/pyepr/extern/epr-api'...
    Submodule path 'extern/epr-api': checked out '93c1f1efce26c64d508fe882d5c72a898a068f29'

## Installation

The easier way to install PyEPR is using tools like pip:

    $ python3 -m pip install pyepr

For a user specific installation please use:

    $ python3 -m pip install --user pyepr

To install PyEPR in a non-standard path:

    $ python3 -m pip install --install-option="--prefix=<TARGET_PATH>" pyepr

just make sure that `<TARGET_PATH>/lib/pythonX.Y/site-packages` is in the `PYTHONPATH`.

PyEPR can be installed from sources using the following command:

    $ python3 -m pip install .

The `setup.py` script by default checks for the availability of the EPR C API source code in the `<package-root>/epr-api-src` directory and tries to build PyEPR in standalone mode, i.e. without linking an external dynamic library of EPR-API.

If no EPR C API sources are found then the `setup.py` script automatically tries to link the EPR-API dynamic library. This can happen, for example, if the user is using a copy of the PyEPR sources cloned from a git repository. In this case it is assumed that the EPR API C library is properly installed in the system (see the Requirements section).

It is possible to control which EPR API C sources to use by means of the –epr-api-src option of the `setup.py` script:

    $ python3 setup.py install --epr-api-src=../epr-api/src

Also it is possible to switch off the standalone mode and force the link with the system EPR API C library:

    $ python3 setup.py install --epr-api-src=""

Please note that if the `setup.py` script is invoked directly, then the user must make sure that setup requirements are properly installed:

    $ python3 -m pip install cython numpy

## Testing

PyEPR package comes with a complete test suite. The test suite can be run from the package root directory using pytest:

    $ python3 -m pytest tests

or running the `test_all.py` script directly:

    $ python3 test_all.py

In the second case please make sure that the `epr` extension module is in the Python search path (see also `PYTHONPATH`).

The test script automatically downloads and decompresses the ENVISAT sample product necessary for testing, MER_LRC_2PTGMV20000620_104318_00000104X000_00000_00000_0001.N1, if it is not already available in the `tests` directory.

Note

please note that, unless the user already have a copy of the specified sample product correctly installed, an internet connection is necessary the first time that the test suite is run.

After the first run the sample product remains in the `tests` directory so the internet access is no longer necessary.

## Python vs C API

The Python EPR API is fully object oriented. The main structures of the C API have been implemented as objects while C function have been logically grouped and mapped onto object methods.

The entire process of defining an object oriented API for Python has been quite easy and straightforward thanks to the good design of the C API,

Of course there are also some differences that are illustrated in the following sections.

## Memory management

Being Python a very high level language uses have never to worry about memory allocation/de-allocation. They simply have to instantiate objects:

    product = epr.Product('filename.N1')

and use them freely.

Objects are automatically destroyed when there are no more references to them and memory is de-allocated automatically.

Even better, each object holds a reference to other objects it depends on so the user never have to worry about identifiers validity or about the correct order structures have to be freed.

For example: the C EPR_DatasetId structure has a field (product_id) that points to the product descriptor EPR_productId to which it belongs to.

The reference to the parent product is used, for example, when one wants to read a record using the epr_read_record function:

    EPR_SRecord* epr_read_record(EPR_SDatasetId* dataset_id, ...);

The function takes a EPR_SDatasetId as a parameter and assumes all fields (including `dataset->product_id`) are valid. It is responsibility of the programmer to keep all structures valid and free them at the right moment and in the correct order.

This is the standard way to go in C but not in Python.

In Python all is by far simpler, and the user can get a dateset object instance:

    dataset = product.get_dataset('MAIN_PROCESSING_PARAMS_ADS')

and then forget about the product instance it depends on. Even if the product variable goes out of scope and it is no more directly accessible in the program the dataset object keeps staying valid since it holds an internal reference to the product instance it depends on.

When record is destroyed automatically also the parent `epr.Product` object is destroyed (assumed there is no other reference to it).

The entire machinery is completely automatic and transparent to the user.

Note

of course when a product object is explicitly closed using the `epr.Product.close()` any I/O operation on it and on other objects (bands, datasets, etc) associated to it is no more possible.

## Arrays

PyEPR uses numpy in order to manage efficiently the potentially large amount of data contained in ENVISAT products.

- `epr.Field.get_elems()` return an 1D array containing elements of the field

- the Raster.data property is a 2D array exposes data contained in the `epr.Raster` object in form of `numpy.ndarray`

  Note

  `epr.Raster.data` directly exposes `epr.Raster` i.e. shares the same memory buffer with `epr.Raster`:

      >>> raster.get_pixel(i, j)
      5
      >>> raster.data[i, j]
      5
      >>> raster.data[i, j] = 3
      >>> raster.get_pixel(i, j)
      3

- `epr.Band.read_as_array()` is an additional method provided by the Python EPR API (does not exist any correspondent function in the C API). It is mainly a facility method that allows users to get access to band data without creating an intermediate `epr.Raster` object. It read a slice of data from the `epr.Band` and returns it as a 2D `numpy.ndarray`.

## Enumerators

Python does not have enumerators at language level (at least this is true for Python < 3.4). Enumerations are simply mapped as module constants that have the same name of the C enumerate but are spelled all in capital letters.

For example:

C | Python
--- | ---
e_tid_double | E_TID_DOUBLE
e_smod_1OF1 | E_SMOD_1OF1
e_smid_log | E_SMID_LOG

## Error handling and logging

Currently error handling and logging functions of the EPR C API are not exposed to python.

Internal library logging is completely silenced and errors are converted to Python exceptions. Where appropriate standard Python exception types are use in other cases custom exception types (e.g. `epr.EPRError`, `epr.EPRValueError`) are used.

## Library initialization

Differently from the C API library initialization is not needed: it is performed internally the first time the `epr` module is imported in Python.

## High level API

PyEPR provides some utility method that has no correspondent in the C API:

- `epr.Record.fields()`

- `epr.Record.get_field_names()`

- `epr.Dataset.records()`

- `epr.Product.get_dataset_names()`

- `epr.Product.get_band_names()`

- `epr.Product.datasets()`

- `epr.Product.bands()`

Example:

    for dataset in product.datasets():
        for record in dataset.records():
            print(record)
            print()

Another example:

    if 'proc_data' in product.band_names():
        band = product.get_band('proc_data')
        print(band)

## Special methods

The Python EPR API also implements some special method in order to make EPR programming even handy and, in short, pythonic.

The `__repr__` methods have been overridden to provide a little more information with respect to the standard implementation.

In some cases `__str__` method have been overridden to output a verbose string representation of the objects and their contents.

If the EPR object has a `print` method (like e.g. `epr.Record.print()` and `epr.Field.print()`) then the string representation of the object will have the same format used by the `print` method. So writing:

    fd.write(str(record))

giver the same result of:

    record.print(fd)

Of course the `epr.Record.print()` method is more efficient for writing to file.

Also `epr.Dataset` and `epr.Record` classes implement the `__iter__` special method for iterating over records and fields respectively. So it is possible to write code like the following:

    for record in dataset:
        for index, field in enumerate(record):
            print(index, field)

`epr.DSD` and `epr.Field` classes implement the `__eq__` and `__ne__` methods for objects comparison:

    if filed1 == field2:
        print('field 1 and field2 are equal')
        print(field1)
    else:
        print('field1:', field1)
        print('field2:', field2)

`epr.Field` object also implement the `__len__` special method that returns the number of elements in the field:

    if field.get_type() != epr.E_TID_STRING:
        assert field.get_num_elems() == len(field)
    else:
        assert len(field) == len(field.get_elem())

Note

differently from the `epr.Field.get_num_elems()` method `len(field)` return the number of elements if the field type is not `epr.E_TID_STRING`. If the field contains a string then the string length is returned.

Finally the `epr.Product` class acts as a context manager (i.e. it implements the `__enter__` and `__exit__` methods).

This allows the user to write code like the following:

    with epr.open('ASA_IMS_ ... _4650.N1') as product:
        print(product)

that ensure that the product is closed as soon as the program exits the `with` block.

## Update support

It is not possible to create new ENVISAT products for scratch with the EPR API. Indeed EPR means “ENVISAT Product Reader”. Anyway, since version 0.9, PyEPR also include basic update features. This means that, while it is still not possible to create new `Products`, the user can update existing ones changing the contents of any `Field` in any record with the only exception of MPH and SPH `Field`s.

The user can open a product in update mode (rb+):

    product = epr.open('ASA_IMS_ ... _4650.N1', 'rb+')

and update the `epr.Field` element at a specific index:

    field.set_elem(new_value, index)

or also update all elements of the `epr.Field` in one shot:

    field.set_elems(new_values)

Note

unfortunately there are some limitations to the update support. Many of the internal structures of the EPR C API are loaded when the `Product` is opened and are not automatically updated when the `epr.Field.set_elem()` and `epr.Field.set_elems()` methods are called. In particular `epr.Band`s contents may depend on several `epr.Field` values, e.g. the contents of Scaling_Factor_GADS `epr.Dataset`. For this reason the user may need to close and re-open the `epr.Product` in order to have all changes effectively applied.

See also

Update Field elements