=============
Get CENA data
=============

To get the CENA count rate data at a specific time, there are several implementations.

Iterate the CENA mass data matrix
=================================

You can iterate the 4-s CENA data (matrix).

First, create the data center (:ref:`util:data_center`) of CENA packet.

>>> from irfpy.cena import cena_data
>>> dc = cena_data.DataCenterCenaMassCount()
>>> print(dc)
irfpy DataCenter (Name: 'Unnamed DataCenter'): 514 data files combined

Then, specify the time range to look at.
For example, let's see the data from 08:00 to 08:05 on April 3, 2009.

>>> import datetime
>>> t0 = datetime.datetime(2009, 4, 3, 8, 0)
>>> t1 = datetime.datetime(2009, 4, 3, 8, 5)

Iterate the CENA data over the time specified.
The following for-loop is usually used.
The variable ``t`` here is the time, and the variable ``d`` is the data as matrix.

>>> for t, d in dc.iter(t0, t1):
...     print(t, d.shape)   # Or other processings here
2009-04-03 08:00:01.053000 (16, 7, 128)
2009-04-03 08:00:05.053000 (16, 7, 128)
2009-04-03 08:00:09.051000 (16, 7, 128)
2009-04-03 08:00:13.051000 (16, 7, 128)
...
2009-04-03 08:04:53.070000 (16, 7, 128)
2009-04-03 08:04:57.068000 (16, 7, 128)

Not only iteration, you can get a time series of data as matrix, or at a specific time.

>>> tlist, dlist = dc.get_array(t0, t1)
>>> dc.nearest(t0)
(datetime.datetime(2009, 4, 3, 8, 0, 1, 53000), masked_array(
   data=[[[--, --, --, ..., --, --, --],
          [--, --, --, ..., --, --, --],
          [--, --, --, ..., --, --, --],
          ...,


        
Iterate the CENA packet
=======================

You can iterate the CENA data packet by packet (4s-data for each).

It is very similar to what was done as above, but use another data center, :class:`irfpy.cena.cena_data.DataCenterCenaPacket`.

>>> from irfpy.cena import cena_data
>>> dc = cena_data.DataCenterCenaPacket()
>>> print(dc)
irfpy DataCenter (Name: 'Unnamed DataCenter'): 514 data files combined

Again, specify the time range to look at.
For example, let's see the data from 08:00 to 08:05 on April 3, 2009.

>>> import datetime
>>> t0 = datetime.datetime(2009, 4, 3, 8, 0)
>>> t1 = datetime.datetime(2009, 4, 3, 8, 5)

Then, iterate data, or get data as array, or as a single data.

>>> for t, d in dc.iter(t0, t1):
...     print(t, d)   # Or other processings here
2009-04-03 08:00:01.053000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...
2009-04-03 08:00:05.053000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...
2009-04-03 08:00:09.051000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...
2009-04-03 08:00:13.051000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...
2009-04-03 08:04:53.070000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...
2009-04-03 08:04:57.068000 <Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at ...

>>> tlist, dlist = dc.get_array(t0, t1)

>>> dc.nearest(t0)
(datetime.datetime(2009, 4, 3, 8, 0, 1, 53000),
<Sadppac.CenaPacket; proxy of <Swig Object of type 'IrfPac::SaraPac::CenaPacket *' at 0x7fedd4080750> >)



Get the CENA data (traditional way)
===================================

.. note:

        This section describes traditional way of getting the CENA data.
        It is recommended to use Data Center approach as described above.

.. code-block:: py

    >>> from irfpy.cena import cena_mass2 as cem
    >>> import datetime
    >>> t = datetime.datetime(2009, 2, 6, 7, 0, 0)
    >>> data = cem.getdataE16(t)
    >>> print(data.getDatetime())
    2009-02-06 06:59:59.294015

Here, the returned data is in the :class:`irfpy.util.julday.JdObject` object.

.. note::

    :class:`irfpy.util.julday.JdObject` is an old-fashioned class... We should update this.


The actual count rate data (matrix) is retrieved as

.. code-block:: py

    >>> matrix = data.getData()
    >>> print(matrix.shape)
    (16, 7, 128)

Here the shape (16, 7, 128) is the order of energy, direction, and mass bin.

Note that CENA covers 16 energy steps, while an instantaneous data covers only for 8 steps.
Thus, the missing data is masked.

.. code-block:: py

    >>> print(matrix[:, 3, 30])
    [-- -- -- -- 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -- -- -- --]

Get the energy setting of CENA
==============================

.. code-block:: py

    >>> from irfpy.cena import energy as cene
    >>> etbl = cene.getEnergyE16()
    >>> print(etbl)
    [   7.   11.   17.   25.   38.   57.   86.  129.  193.  290.  435.  652.
      978. 1467. 2200. 3300.]


Get the time series of CENA count data (Traditional way)
========================================================

.. note:

        This section describes traditional way of getting the CENA data.
        It is recommended to use Data Center approach as described above.

.. code-block:: py

    >>> import datetime
    >>> t0 = datetime.datetime(2009, 2, 6, 7, 0, 0)
    >>> t1 = datetime.datetime(2009, 2, 6, 7, 10, 0)
    >>> from irfpy.cena import cena_mass_time as cemt
    >>> tlist, datlist = cemt.getfulldata(t0, t1)
    >>> print(tlist[0])    # jdlist is a list of datetime
    2009-02-06 07:00:03.294000
    >>> print(len(tlist))
    150
    >>> print(datlist.shape)    # datlist is a numpy array
    (16, 7, 128, 150)

The order of the datalist dimension is energy, direction, mass, and time.


Get the time series of CENA differential flux
=============================================

.. note::

        As of v4.4.10, DataCenter-support is not yet implemented.

.. code-block:: py

    >>> import datetime
    >>> t0 = datetime.datetime(2009, 2, 6, 7, 0, 0)
    >>> t1 = datetime.datetime(2009, 2, 6, 7, 10, 0)
    >>> from irfpy.cena import cena_mass_time as cemt
    >>> tlist, datlist = cemt.getfullHdeflux(t0, t1)
    >>> print(tlist[0])    # jdlist is a list of datetime
    2009-02-06 07:00:03.294000
    >>> print(len(tlist))
    150
    >>> print(datlist.shape)
    (16, 7, 150)
    >>> print(datlist.min())
    0.0
    >>> print(datlist.max())
    205174.554877

The order of the data is energy, direction, and time.


Get times of CENA spectrum
==========================

.. note::

        As of v4.4.10, DataCenter-support is not yet implemented.

.. code-block:: py

    >>> from irfpy.cena import cena_mass2 as cem
    >>> tlist = cem.getobstime(orbit=1082)
    >>> print(tlist[0], tlist[-1])
    2009-02-06 06:50:54.125000 2009-02-06 08:48:56.370000
    >>> print(len(tlist))
    1770