..
  Brief:
  This tutorial is geared towards new users of the Science Pipelines software.
  Our goal is to guide the reader through a small data processing project to show what it feels like to use the Science Pipelines.
  We want this tutorial to be kinetic; instead of getting bogged down in explanations and side-notes, we'll link to other documentation.
  Don't assume the user has any prior experience with the Pipelines; do assume a working knowledge of astronomy and the command line.

###############################################################################################
Getting started tutorial part 3: displaying exposures and source tables output by processCcd.py
###############################################################################################

In the :doc:`previous tutorial <processccd>` in the :ref:`series <getting-started-tutorial>` you used :command:`processCcd.py` to calibrate a set of raw Hyper Suprime-Cam images.
Now you'll learn how to use the LSST Science Pipelines to inspect :command:`processCcd.py`\ ’s outputs by displaying images and source catalogs in the `DS9 image viewer`_.
In doing so, you'll be introduced to some of the LSST Science Pipelines' Python APIs, including:

- Accessing datasets with the Butler.
- Displaying images in DS9 with ``lsst.afw.display``.
- Working with source catalogs using ``lsst.afw.table``.

.. figure:: ds9-screenshot.jpg
   :alt: Screenshot of DS9 showing an HSC image, mask planes, and source markers.

   In this tutorial, you'll create an image display like this one that includes mask planes and source markers.

Set up
======

Pick up your shell session where you left off in :doc:`part 2 <processccd>`.
That means your current working directory must *contain* the :file:`DATA` directory (the Butler repository).

The ``lsst_distrib`` package also needs to be set up in your shell environment.
See :doc:`/install/setup` for details on doing this.

You'll also need to `download and install the DS9 image viewer`_.

Launch DS9 and start a Python interpreter
=========================================

In this tutorial, you will use an interactive Python session to control DS9.

If you haven't already, launch the DS9 application.

Next, start up a Python interpreter.
You can use the default Python shell (:command:`python`), the `IPython shell`_, or even run from a `Jupyter Notebook`_.
Ensure that this Python session is running from the shell where you ran :command:`setup lsst_distrib`.

Creating a Butler client
========================

All data in the Pipelines flows through the Butler.
As you saw in the :doc:`previous tutorial <processccd>`, :command:`processCcd.py` read exposures from the Butler repository and persisted outputs back to the repository.
Although this Butler data repository is a directory on the filesystem (:file:`DATA`), we don't recommend directly accessing its files.
Instead, you use the Butler client from the ``lsst.daf.persistence`` module.
In the Python interpreter, run:

.. code-block:: python

   import lsst.daf.persistence as dafPersist
   butler = dafPersist.Butler(inputs='DATA/rerun/processCcdOutputs')

The Butler client reads from the data repository specified with the ``inputs`` argument.
In the previous tutorial, you created the ``processCcdOutputs`` rerun to isolate the outputs of the :command:`processCcd.py` command-line task.
Reruns act like repositories, so to work with the :command:`processCcd.py` outputs you specifically set ``inputs`` as the path to that rerun.

.. tip::

   Reruns are sub-directories of the :file:`rerun` directory of a root Butler data repository.

Listing available data IDs in the Butler
========================================

To get data from the Butler you need to know two things: the **dataset type** and the **data ID**.

Every dataset stored by the Butler has a well-defined type.
Tasks read specific dataset types and output other specific dataset types.
The :command:`processCcd.py` command reads in ``raw`` datasets and outputs ``calexp``, or *calibrated exposure*, datasets (among others).
It's ``calexp`` datasets that you'll display in this tutorial.

Data IDs let you reference specific instances of a dataset.
On the command line you select data IDs with ``--id`` arguments, filtering by keys like ``visit``, ``ccd``, and ``filter``.

Now, use the Butler client to find what data IDs are available for the ``calexp`` dataset type:

.. code-block:: python

   butler.queryMetadata('calexp', ['visit', 'ccd'], dataId={'filter': 'HSC-R'})   

The printed output is a list of ``(visit, ccd)`` key tuples for all data IDs where the ``filter`` key is the ``HSC-R`` band:

.. code-block:: text

   [(903334, 16),
    (903334, 22),
    (903334, 23),
    (903334, 100),
    (903336, 17),
    (903336, 24),
    (903338, 18),
    (903338, 25),
    (903342, 4),
    (903342, 10),
    (903342, 100),
    (903344, 0),
    (903344, 5),
    (903344, 11),
    (903346, 1),
    (903346, 6),
    (903346, 12)]

.. note::

   That example ``butler.queryMetadata`` call is equivalent to this shell command that you used in the :doc:`previous tutorial <processccd>`:

   .. code-block:: bash

      processCcd.py DATA --rerun processCcdOutputs --id filter=HSC-R --show data

Get an exposure through the Butler
==================================

Knowing a specific data ID, let's get the dataset with the Butler client's ``get`` method:

.. code-block:: python

   calexp = butler.get('calexp', dataId={'filter': 'HSC-R', 'visit': 903334, 'ccd': 23})

This ``calexp`` is an ``ExposureF`` Python object.
Exposures are powerful representations of image data because they contain not only the image data, but also a variance image for uncertainty propagation, a bit mask image plane, and key-value metadata.
In the next steps you'll learn how to display an Exposure's image and mask.

Create a display
================

To display the ``calexp`` you will use the display framework, which is imported as:

.. code-block:: python

   import lsst.afw.display as afwDisplay

The display framework provides a uniform API for multiple display backends, including DS9_ and LSST's Firefly viewer.
For this tutorial you'll create a display with the ``ds9`` backend:

.. code-block:: python

   display = afwDisplay.getDisplay(backend='ds9')

Display the calexp (calibrated exposure)
========================================

Then use the display's ``mtv`` method to view the ``calexp`` in DS9:

.. code-block:: python

   display.mtv(calexp)

As soon as you execute the command a single Hyper Suprime-Cam calibrated exposure, the ``{'filter': 'HSC-R', 'visit': 903334, 'ccd': 23}`` data ID, should appear in the DS9 application.

Notice that the DS9 display is filled with colorful regions.
These are mask regions.
Each color reflects a different mask bit that correspond to detections and different types of detector artifacts.
You'll learn how to interpret these colors :ref:`later <getting-started-display-mask-colors>`, but first you'll likely want to adjust the image display.

Improving the image display
===========================

The display framework gives you control over the image display to help bring out image details.

To make masked regions semi-transparent, so that underlying image features are visible, try:

.. code-block:: python

   display.setMaskTransparency(60)

The ``setMaskTransparency`` method's argument can range from ``0`` (fully opaque) to ``100`` (fully transparent).

You can also control the colorbar scaling algorithm with the display's ``scale`` method.
Try an ``asinh`` stretch with the ``zscale`` algorithm for automatically selecting the white and black thresholds:

.. code-block:: python

   display.scale("asinh", "zscale")

Instead of an automatic algorithm like zscale (or ``minmax``) you can explicitly provide both a minimum (black) and maximum (white) value:

.. code-block:: python

   display.scale("asinh", -1, 30)

.. _getting-started-display-mask-colors:

Interpreting displayed mask colors
==================================

The display framework renders each plane of the mask in a different color (*plane* being a different bit in the mask).
To interpret these colors you can get a dictionary of mask planes from the ``calexp`` and query the display for the colors it rendered each mask plane with.
Run:

.. code-block:: python

   mask = calexp.getMask()
   for maskName, maskBit in mask.getMaskPlaneDict().items():
       print('{}: {}'.format(maskName, display.getMaskPlaneColor(maskName)))

As an example, this result is:

.. code-block:: text

   DETECTED_NEGATIVE: cyan
   CROSSTALK: None
   INTRP: green
   DETECTED: blue
   UNMASKEDNAN: None
   NO_DATA: orange
   BAD: red
   EDGE: yellow
   SUSPECT: yellow
   NOT_DEBLENDED: None
   CR: magenta
   SAT: green

Footprints of detected sources are rendered in blue and the saturated cores of bright stars are drawn in green.

.. tip::

   Try customizing the color of a mask plane with the ``Display.setMaskPlaneColor`` method.
   You can choose any `X11 color`_.
   For example:

   .. code-block:: python

      display.setMaskPlaneColor('DETECTED', 'dodgerblue')
      display.mtv(calexp)

Getting the source catalog generated by processCcd.py
=====================================================

Besides the calibrated exposure (``calexp``), :command:`processCcd.py` also creates a table of the sources it used for PSF estimation as well as astrometric and photometric calibration.
The dataset type of this table is ``src``, which you can get from the Butler:

.. code-block:: python

   src = butler.get('src', dataId={'filter': 'HSC-R', 'visit': 903334, 'ccd': 23})

This ``src`` dataset is a ``SourceTable``, which is a table object from the ``lsst.afw.table`` module.

You'll explore ``SourceTable``\ s more in a later tutorial, but you can check its length with Python's `len` function:

.. code-block:: python

   print(len(src))

The columns of a table are defined in its schema.
You can print out the schema to see each column's name, data type, and description:

.. code-block:: python

   print(src.getSchema())

To get just the names of columns, run:

.. code-block:: python

   print(src.getSchema().getNames())

To get metadata about a specific column, like ``calib_pdfUsed``:

.. code-block:: python

   print(src.schema.find("calib_psfUsed"))

Given a name, you can get a column's values as a familiar Numpy array like this:

.. code-block:: python

   print(src['base_PsfFlux_flux'])

.. tip::

   If you are working in a Jupyter notebook you can see an HTML table rendering of any ``lsst.afw.table`` table object by getting an `astropy.table.Table`_ version of it:

   .. code-block:: python

      src.asAstropy()

   The returned Astropy Table is a view, not a copy, so it doesn't consume much additional memory.

Plotting sources on the display
===============================

Now you'll overplot sources from the ``src`` table onto the image display using the ``Display``\ ’s ``dot`` method for plotting markers.
``Display.dot`` plots markers individually, so you'll need to iterate over rows in the ``SourceTable``.
It's more efficient to send a batch of updates to the display, though, so enclose the loop in a ``display.Buffering`` context, like this:

.. code-block:: python

   with display.Buffering():
       for s in src:
           display.dot("o", s.getX(), s.getY(), size=10, ctype='orange')

Now orange circles should appear in the DS9 window over every detected source.

.. note::

   Notice the ``getX`` and ``getY`` methods for getting the (x,y) centroid of each source.
   These methods are shortcuts, using the table's *slot* system.
   Because the the ``src`` catalog contains measurements from several measurement plugins, slots are a way of easily using the pre-configured best measurements of a source.

Clearing markers
================

``Display.dot`` always adds new markers to the display.
To clear the display of all markers, use the ``erase`` method:

.. code-block:: python

   display.erase()

Selecting PSF-fitting sources to plot on the display
====================================================

Next, use the display to understand what sources were used for PSF measurement.

The ``src`` table's ``calib_psfUsed`` column describes whether the source was used for PSF measurement.
Since columns are Numpy arrays we can iterate over rows where ``src['calib_psfUsed']`` is ``True`` with Numpy's boolean array indexing:

.. code-block:: python

   with display.Buffering():
       for s in src[src['calib_psfUsed']]:
           display.dot("x", s.getX(), s.getY(), size=10, ctype='red')

Red **x** symbols on the display mark all stars used by PSF measurement.

Some sources might be considered as PSF candidates, but later rejected.
In this statement, you can use a logical ``&`` (and) operator to combine boolean index arrays where both ``src['calib_psfCandidate']`` is ``True`` and ``src['calib_psfUsed'] == False`` as well:

.. code-block:: python

   rejectedPsfSources = src[src['calib_psfCandidate'] &
                            (src['calib_psfUsed'] == False)]
   with display.Buffering():
       for s in rejectedPsfSources:
           display.dot("+", s.getX(), s.getY(), size=10, ctype='green')

Now all green plus (**+**) symbols on the display mark rejected PSF measurement sources.

The display framework, as you've seen, is a useful facility for inspecting images and tables.
This tutorial only covered the framework's basic functionality.
Explore the display framework documentation to learn how to display multiple images at once, and to work with different display backends.

.. TODO: link to lsst.display docs when available

Wrap up
=======

In this tutorial you've worked with the LSST Science Pipelines Python API to display images and tables.
Here are some key takeaways:

- Use the ``lsst.daf.persistence.Butler`` class to read and write data from repositories.
- The ``lsst.afw.display`` module provides a flexible framework for sending data from LSST Science Pipelines code to image displays.
  You used the DS9 backend in this tutorial, but other backends are available.
- Exposure objects have image data, mask data, and metadata.
  When you display an exposure, the display framework automatically overlays mask planes.
- Tables have well-defined schemas. Use methods like ``getSchema`` to understand the contents of a table.
  You can also use the ``asAstropy`` table method to view the table as an `astropy.table.Table`.

Continue this tutorial series in :doc:`part 4, where you'll coadd these processed images <coaddition>` into deeper mosaics.

.. _`DS9 image viewer`:
.. _`DS9`: http://ds9.si.edu/site/Home.html
.. _`download and install the DS9 image viewer`: http://ds9.si.edu/site/Download.html
.. _`IPython shell`: http://ipython.readthedocs.io/en/stable/
.. _`Jupyter Notebook`: http://jupyter-notebook.readthedocs.io/en/latest/
.. _`X11 color`: https://en.wikipedia.org/wiki/X11_color_names
.. _`astropy.table.Table`: http://docs.astropy.org/en/stable/table/index.html