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Interactive use of PyEPR_
-------------------------
.. highlight:: ipython
In this tutorial it is showed an example of how to use PyEPR_ interactively
to open, browse and display data of an ENVISAT_ ASAR_ product.
For the interactive session it is used the IPython_ interactive shell an
started with the :option:`ipython -pylab` option to enable interactive
plotting provided by the matplotlib_ package.
The ASAR_ product used in this example is a `free sample`_ available at the
ESA_ web site.
.. _PyEPR: https://github.com/avalentino/pyepr
.. _ENVISAT: http://envisat.esa.int
.. _ASAR: http://envisat.esa.int/handbooks/asar
.. _IPython: http://ipython.scipy.org/moin
.. _matplotlib: http://matplotlib.sourceforge.net
.. _`free sample`: http://earth.esa.int/services/sample_products/asar/IMP/ASA_IMP_1PNUPA20060202_062233_000000152044_00435_20529_3110.N1.gz
.. _ESA: http://earth.esa.int
:mod:`epr` module and classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
After starting the ipython shell with the following command:
.. code-block:: sh
$ ipython -pylab
one can import the :mod:`epr` module and start start taking confidence with
available classes and functions::
Python 2.6.6 (r266:84292, Sep 15 2010, 16:22:56)
Type "copyright", "credits" or "license" for more information.
IPython 0.10 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object'. ?object also works, ?? prints more.
Welcome to pylab, a matplotlib-based Python environment.
For more information, type 'help(pylab)'.
In [1]: import epr
In [2]: epr?
Base Class: <type 'module'>
String Form: <module 'epr' from 'epr.so'>
Namespace: Interactive
File: /home/antonio/projects/pyepr/epr.so
Docstring:
Python bindings for ENVISAT Product Reader C API
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_ is fully object oriented and, 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.
.. _PyEPR: http://avalentino.github.com/pyepr
.. _Python: http://www.python.org
.. _`EPR API`: https://github.com/bcdev/epr-api
.. _ENVISAT: http://envisat.esa.int
.. _ESA: http://earth.esa.int
In [3]: epr.__version__, epr.EPR_C_API_VERSION
Out[3]: ('0.7', '2.2')
Docstrings are available for almost all classes, methods and functions in
the :mod:`epr` and they can be displayed using the :func:`help` python_
command or the ``?`` IPython_ shortcut as showed above.
.. _python: http://www.python.org
Also IPython_ provides a handy tab completion mechanism to automatically
complete commands or to display available functions and classes::
In [4]: product = epr. [TAB]
epr.Band epr.E_TID_TIME
epr.__builtins__ epr.E_TID_UCHAR
epr.__class__ epr.E_TID_UINT
epr._CLib epr.E_TID_UNKNOWN
epr._close_api epr.E_TID_USHORT
epr.collections epr.Field
epr.create_bitmask_raster epr.__file__
epr.create_raster epr.__format__
epr.Dataset epr.__getattribute__
epr.data_type_id_to_str epr.get_data_type_size
epr.__delattr__ epr.get_sample_model_name
epr.__dict__ epr.get_scaling_method_name
epr.__doc__ epr.__hash__
epr.DSD epr.__init__
epr.EPR_C_API_VERSION epr.__name__
epr.EPRError epr.__new__
epr.EprObject epr.np
epr.EPRTime epr.open
epr.EPRValueError epr.__package__
epr.E_SMID_LIN epr.Product
epr.E_SMID_LOG epr.Raster
epr.E_SMID_NON epr.Record
epr.E_SMOD_1OF1 epr.__reduce__
epr.E_SMOD_1OF2 epr.__reduce_ex__
epr.E_SMOD_2OF2 epr.__repr__
epr.E_SMOD_2TOF epr.__revision__
epr.E_SMOD_3TOI epr.__setattr__
epr.E_TID_CHAR epr.__sizeof__
epr.E_TID_DOUBLE epr.so
epr.E_TID_FLOAT epr.__str__
epr.E_TID_INT epr.__subclasshook__
epr.E_TID_SHORT epr.sys
epr.E_TID_SPARE epr.__test__
epr.E_TID_STRING epr.__version__
:class:`epr.Product` navigation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The first thing to do is to use the :func:`epr.open` function to get an
instance of the desired ENVISAT_ :class:`epr.Product`::
In [4]: product = epr.open(\
'ASA_IMP_1PNUPA20060202_062233_000000152044_00435_20529_3110.N1')
In [4]: product.
product.bands product.get_num_dsds
product.__class__ product.get_scene_height
product.datasets product.get_scene_width
product.__delattr__ product.get_sph
product.__doc__ product.__hash__
product.file_path product.id_string
product.__format__ product.__init__
product.__getattribute__ product.meris_iodd_version
product.get_band product.__new__
product.get_band_at product.read_bitmask_raster
product.get_band_names product.__reduce__
product.get_dataset product.__reduce_ex__
product.get_dataset_at product.__repr__
product.get_dataset_names product.__setattr__
product.get_dsd_at product.__sizeof__
product.get_mph product.__str__
product.get_num_bands product.__subclasshook__
product.get_num_datasets product.tot_size
In [5]: product.tot_size / 1024.**2
Out[5]: 132.01041889190674
In [6]: print product
epr.Product(ASA_IMP_1PNUPA20060202_ ...) 7 datasets, 5 bands
epr.Dataset(MDS1_SQ_ADS) 1 records
epr.Dataset(MAIN_PROCESSING_PARAMS_ADS) 1 records
epr.Dataset(DOP_CENTROID_COEFFS_ADS) 1 records
epr.Dataset(SR_GR_ADS) 1 records
epr.Dataset(CHIRP_PARAMS_ADS) 1 records
epr.Dataset(GEOLOCATION_GRID_ADS) 11 records
epr.Dataset(MDS1) 8192 records
epr.Band(slant_range_time) of epr.Product(ASA_IMP_1PNUPA20060202_ ...)
epr.Band(incident_angle) of epr.Product(ASA_IMP_1PNUPA20060202_ ...)
epr.Band(latitude) of epr.Product(ASA_IMP_1PNUPA20060202 ...)
epr.Band(longitude) of epr.Product(ASA_IMP_1PNUPA20060202 ...)
epr.Band(proc_data) of epr.Product(ASA_IMP_1PNUPA20060202 ...)
A short summary of product contents can be displayed simply printing the
:class:`epr.Product` object as showed above.
Being able to display contents of each object it is easy to keep browsing and
get all desired information from the product::
In [7]: dataset = product.get_dataset('MAIN_PROCESSING_PARAMS_ADS')
In [8]: dataset
Out[8]: epr.Dataset(MAIN_PROCESSING_PARAMS_ADS) 1 records
In [9]: record = dataset.[TAB]
dataset.__class__ dataset.get_name dataset.__reduce__
dataset.create_record dataset.get_num_records dataset.__reduce_ex__
dataset.__delattr__ dataset.__hash__ dataset.__repr__
dataset.description dataset.__init__ dataset.__setattr__
dataset.__doc__ dataset.__iter__ dataset.__sizeof__
dataset.__format__ dataset.__new__ dataset.__str__
dataset.__getattribute__ dataset.product dataset.__subclasshook__
dataset.get_dsd dataset.read_record
dataset.get_dsd_name dataset.records
In [9]: record = dataset.read_record(0)
In [10]: record
Out[10]: <epr.Record object at 0x33570f0> 220 fields
In [11]: record.get_field_names()[:20]
Out[11]:
['first_zero_doppler_time',
'attach_flag',
'last_zero_doppler_time',
'work_order_id',
'time_diff',
'swath_id',
'range_spacing',
'azimuth_spacing',
'line_time_interval',
'num_output_lines',
'num_samples_per_line',
'data_type',
'spare_1',
'data_analysis_flag',
'ant_elev_corr_flag',
'chirp_extract_flag',
'srgr_flag',
'dop_cen_flag',
'dop_amb_flag',
'range_spread_comp_flag']
In [12]: field = record.get_field('range_spacing')
In [13]: field.get [TAB]
field.get_description field.get_name field.get_unit
field.get_elem field.get_num_elems
field.get_elems field.get_type
In [13]: field.get_description()
Out[13]: 'Range sample spacing'
In [14]: epr.data_type_id_to_str(field.get_type())
Out[14]: 'float'
In [15]: field.get_num_elems()
Out[15]: 1
In [16]: field.get_unit()
Out[16]: 'm'
In [17]: print field
range_spacing = 12.500000
Iterating over :mod:`epr` objects
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:class:`epr.Record` objects are also iterable_ so one can write code like
the following::
In [18]: for field in record:
if field.get_num_elems() == 4:
print '%s: %d elements' % (field.get_name(), len(field))
....:
nominal_chirp.1.nom_chirp_amp: 4 elements
nominal_chirp.1.nom_chirp_phs: 4 elements
nominal_chirp.2.nom_chirp_amp: 4 elements
nominal_chirp.2.nom_chirp_phs: 4 elements
nominal_chirp.3.nom_chirp_amp: 4 elements
nominal_chirp.3.nom_chirp_phs: 4 elements
nominal_chirp.4.nom_chirp_amp: 4 elements
nominal_chirp.4.nom_chirp_phs: 4 elements
nominal_chirp.5.nom_chirp_amp: 4 elements
nominal_chirp.5.nom_chirp_phs: 4 elements
beam_merge_sl_range: 4 elements
beam_merge_alg_param: 4 elements
Image data
~~~~~~~~~~
Dealing with image data is simple as well::
In [19]: product.get_band_names()
Out[19]: ['slant_range_time',
'incident_angle',
'latitude',
'longitude',
'proc_data']
In [19]: band = product.get_band('proc_data')
In [20]: data = band. [TAB]
band.bm_expr band.read_as_array
band.__class__ band.read_raster
band.create_compatible_raster band.__reduce__
band.data_type band.__reduce_ex__
band.__delattr__ band.__repr__
band.description band.sample_model
band.__doc__ band.scaling_factor
band.__format__ band.scaling_method
band.__getattribute__ band.scaling_offset
band.get_name band.__setattr__
band.__hash__ band.__sizeof__
band.__init__ band.spectr_band_index
band.lines_mirrored band.__str__
band.__new__ band.__subclasshook__
band.product band.unit
band.__pyx_vtable__
In [20]: data = band.read_as_array(1000, 1000, xoffset=100, yoffset=6500, \
xstep=2, ystep=2)
In [21]: data
Out[21]:
array([[ 146., 153., 134., ..., 51., 55., 72.],
[ 198., 163., 146., ..., 26., 54., 57.],
[ 127., 205., 105., ..., 64., 76., 61.],
...,
[ 64., 78., 52., ..., 96., 176., 159.],
[ 66., 41., 45., ..., 200., 153., 203.],
[ 64., 71., 88., ..., 289., 182., 123.]], dtype=float32)
In [22]: data.shape
Out[22]: (500, 500)
In [23]: imshow(data, cmap=cm.gray, vmin=0, vmax=1000)
Out[23]: <matplotlib.image.AxesImage object at 0x60dcf10>
In [24]: title(band.description)
Out[24]: <matplotlib.text.Text object at 0x67e9950>
In [25]: colorbar()
Out[25]: <matplotlib.colorbar.Colorbar instance at 0x6b18cb0>
.. figure:: images/ASA_IMP_crop.*
:width: 100%
Image data read from the "proc_data" band
.. _iterable: http://docs.python.org/glossary.html#term-iterable
.. raw:: latex
\clearpage
|
