AstrodbWeb

Followers of this blog and our team's scientific endeavors may know we have a curated database of brown dwarfs we work with. An initial version of this database has been published in Filippazzo et al. 2015 and contains information for 198 objects. The database is also maintained on Github, where we welcome contributions from other researchers. We've developed a set of tools for astronomers to work with SQL databases, namely the Python package astrodbkit. This package can be applied to other SQL databases allowing astronomers from all fields of research to manage their data.
Here we introduce a new tool: AstrodbWeb, a web-based interface to explore the BDNYC database.
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Creating a Database with astrodbkit

NOTE: The information on this post may be outdated. We recommend looking into the up-to-date documentation at trac (internal) and ReadTheDocs.

The astrodbkit package can be used to modify an existing SQL database (such as The BDNYC Database) but it can also be used to create and populate a SQL database from scratch.

To do this, import the BDdb module and create a new database with
from astrodbkit import astrodb
dbpath = '/path/to/new_database.db'
astrodb.create_databse(dbpath)

Then load your new database with

db = astrodb.Database(dbpath)

and start adding tables! The db.table() method accepts as its arguments the table name, list of field names, and list of data types like so:

db.table('my_new_table', ['field1','field2'], ['INTEGER','TEXT'], new_table=True)

Note new_table=True is necessary to create a new table. Otherwise, it looks for an existing table to modify (which you could do as well!).

To populate your new database with data, read the documentation here or a summary at Adding Data to the BDNYC Database.

As always, I recommend the SQLite Browser for a nice GUI to make changes outside of the command line.

Happy databasing!

Updating the astrodbkit documentation

As you make changes to the astrodbkit repository on Github, you may find that the documentation needs updating. Luckily, we use the invaluable Sphinx and the awesome ReadTheDocs to generate the documentation so this is fairly simple.

First, make sure you update the appropriate doc strings (those informative green bits just below the function definition) as this is what Sphinx will use to generate the documentation!

Start in the top level astrodbkit directory and generate the documentation from the module docstrings with

sphinx-build docs astrodbkit -a

Then cd into the docs directory and rebuild the html files with

cd docs
make html

Now move back to the top level directory and add, commit, and push your changes to Github with something like

cd ..
git add /docs
git commit -m "Updated the documentation for methods x, y, and z."
git push origin <your_branch>

All set! Refresh the page (after a few minutes so ReadTheDocs can build the pages) and make sure everything is to your liking. Well done.

And just in case, here's a great tutorial for getting started with Sphinx and here's the official documentation.

Deploying Package Releases to PyPI

Registering the package

To register the package and deploy the initial release to PyPI, do:

python setup.py register -r pypi
python setup.py sdist upload -r pypi

Deploying a new release

So you've made some cool new improvements to your Python package and you want to deploy a new release. It's just a few easy steps.

  1. After your package modules have been updated, you have to update the version number in your setup.py file. The format is major.minor.micro depending on what you changed. For example, a small bug fix to v0.2.3 would increment to v0.2.4 while a new feature might increment to v0.3.0.
  2. Then make sure all the changes are committed and pushed to Github.
  3. Build with python setup.py sdist
  4. Twine with twine upload dist/compressed_package_filename

That's it!

Adding Data to a SQL Database using astrodbkit

NOTE: The information on this post may be outdated. We recommend looking into the up-to-date documentation at trac (internal) and ReadTheDocs.

To add data to any table, there are two easy steps. As our working example, we'll add some new objects to the SOURCES table.

Step 1: Create an ascii file of the data

First, you must choose a delimiter, which is just the character that will break up the data into columns. I recommend a pipe '|' character since they don't normally appear in text. This is better than a comma since some data fields may have comma-separated values.

Put the data to be added in an ascii file with the following formatting:

  1. The first line must be the |-separated column names to insert/update, e.g. ra|dec|publication_id. Note that the column names in the ascii file need not be in the same order as the table. Also, only the column names that match will be added and non-matching or missing column names will be ignored, e.g. spectral_type|ra|publication_id|dec will ignore the spectral_type values as this is not a column in the SOURCES table and input the other columns in the correct places.
  2. If a record (i.e. a line in your ascii file) has no value for a particular column, type nothing. E.g. for the given column names ra|dec|publication_id|comments, a record with no publication_id should read 34.567|12.834||This object is my favorite!.

Step 2: Add the data to the specified table

To add the data to the table (in our example, the SOURCES table), import astrodbkit and initialize the .db file. Then run the add_data() method with the path to the ascii file as the first argument and the table to add the data to as the second argument. Be sure to specify your delimiter with delim='|'. Here's what that looks like:

from astrodbkit import astrodb
db = astrodb.Database('/path/to/the/database/file.db')
db.add_data('/path/to/the/upload/file.csv', 'sources', delim='|')

That's it!

Documentation using Sphinx

Sphinx is a free, online tool that helps generate documentation for primarily Python projects.  We use it for HTML format outputs, but it also supports LaTeX, ePub, Texinfo, manual pages, and plain text formats.  The markup language of Sphinx is reStructuredText (reST) (for documentation see http://sphinx-doc.org/rest.html).  Of Sphinx's many features, the one most useful thus far is its auto-documentation of Python files, the steps of which this post details below.

Sphinx auto-documentation steps:

  1. Install Sphinx at the command line using: easy_install -U Sphinx
  2. Read through the Sphinx documentation at http://sphinx-doc.org/.
  3. Run Sphinx's Getting Started function Sphinx-quickstart to quickly set up directories and configurations. NOTE: Since I have already completed this setup, it DOES NOT need to be run again for the BDNYC documentation.
    1. Access the documentation directories in Dropbox through the shared folder BDNYCdb/Documentation
    2. The Documentation folder contains the .rst and .py files that Sphinx uses to build documentation.  Place any files for which documentation is to be made within that directory since Sphinx automatically looks there.
      Screen Shot 2014-08-07 at 2.46.31 PMBDdb.html within the Documentation directories
  4. The index.rst file is the master file that contains the table of contents (known as a TOCtree) for the entire documentation.  Within  the TOCtree, Sphinx knows that all the files are .rst, so there is no need to specify the file extension or path.  The index also serves as a welcome page for users, so it may include an introduction or other information that would be useful for outside reference.  Within the TOCtree (and all of reST) indentation and notation is especially important.  Below is an example of a functioning TOCtree.  .. toctree::
    :maxdepth: 2
    # blank line
    BDdb
    utilities
    astrotools
    load_db
    Database_instructions

    The "maxdepth" function specifies the depth of nesting allowed.  In order to keep the index clean and aesthetically simple, a maximum of one nested page is reasonable. In other files, however, highly nested pages are not only common, but well-supported by reST.
    Screen Shot 2014-08-07 at 2.52.07 PM
    Screenshot of index.rst
  5. For each item in the TOCtree of the index file, there must be a corresponding .rst file in the main directory for Sphinx to nest within the index HTML file.  In the above example, a Database_instructions.rst file might contain in plain English how to interact with the database, while the BDdb.rst file contains the auto-documentation of BDdb.py.
  6. The .rst file within the TOCtree of the index must be formatted like below to correctly call for the Python files. [name of module]
    ================    # make page header (see http://sphinx-doc.org/rest.html)
    .. automodule:: [name of Python file w/o extension]
    :members:    # tells Sphinx to pull from the file
    # blank line
    :undoc-members:
    :show-inheritance:

    Screen Shot 2014-08-07 at 2.57.41 PM

    Screenshot of BDdb.rst

  7. The referenced Python files for auto-documentation can either be in located in the main directory, or their location can be specified.  Since we will be constantly pushing and pulling from Github, it is best for those doing documentation to change the file path to their local copy of BDdb.py, astrotools.py, etc, before making documentation.  This can be achieved as follows:
  8. Open conf.py, which is the configuration file for Sphinx.  It was produced during the "quickstart" function to allow easy modifications of aesthetic features (i.e., theme, font), and facilitate the HTML building process.
    1. Change the file path in line 21 to the applicable local copy.
    2. Comment out the file path of previous users in order to make it easier for others to update documentation.
  9. In order to make the HTML files, move Terminal to the directory of the documentation, i.e., '.../Dropbox/BDNYCdb/Documentation' and run make html.
    1. The make htmlfunction comes build-in through running "quickstart" and uses the makefile in the main directory.  It can take other functions as well, including make clean, which deletes the entire contents of the "build" directory.
    2. Sphinx should put the generated HTML files in the /_build/html directory.
  10. To make the HTML files live on BDNYC.org/database, use the application Cyberduck, which can be downloaded at http://cyberduck.en.softonic.com/mac.
    1. Click "Open Connection" and put in the server, username, and password.
    2. Drag and drop any .rst and HTML files that were modified into the appropriate directory and the documentation should go live immediately!

 

References:

http://sphinx-doc.org/ -- Sphinx documentation homepage

http://sphinx-doc.org/contents.html -- Outline of Sphinx documentation

http://sphinx-doc.org/rest.html -- Using reStructuredText in Sphinx

http://sphinx-doc.org/ext/autodoc.html -- Auto-documentation documentation

http://cyberduck.en.softonic.com/mac -- Download Cyberduck

Use of the DISTINCT function in an SQLite query

When using an sqlite query to pull from our database, you may run into the issue where you only want to see the desired field entry once, and the resulting list or dictionary is giving it to you multiple times.

An Example

Say you want to get the source_id from the spectra table where the spectral_type is an L dwarf and the spectra regime is NIR. Meaning, you want to know which L dwarfs we have NIR spectra for. Well, the output will give you a list of all of the entries we have spectra for, even if some entries overlap on source_id. Each source_id will show up in the list per the number of separate NIR spectra we have for that object. If  you only want to see the source_id once, we can use a function tag called DISTINCT. It follows like:

SELECT DISTINCT sp.source_id FROM spectra AS sp JOIN spectral_types AS spt ON spt.source_id=sp.source_id WHERE spt.spectral_type BETWEEN 10 AND 19 AND sp.regime='NIR'

(See the joining tables post if confused on how to do so.)

*NOTE: Here I used another function BETWEEN instead of writing out spt.spectral_type>=10 AND spt.spectral_type<20

Using astroquery

If you're like me, you've spent a lot of time downloading a table from VizieR, and then trying to insert it into whatever database or table you are currently using. It's annoying, particularly because VizieR either won't output information in a machine-readable format or won't put an empty line into a massive list of results when there's no match in the catalog.
Astroquery is better. It's a python package affiliated with astropy that can grab data from all your favorite catalog servers (Vizier, SIMBAD, IRSA, NED, etc) directly, and use it; combine that with Astropy's ability (or Python's capacity) to print out tables to files, and it's a lot easier to write a script to do it all for you.

Astroquery is in the Python Package Index, so you can install and update it with
pip install astroquery

You also need the 'requests' module to do the web-interfacing (and Astropy v3); it's available on the Python Package Index and comes preloaded in Anaconda. Using it is simple (this part is almost entirely taken from their pages on using VizieR and IRSA):
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BDNYC Database Setup

WARNING: The information on this is outdated. We recommend looking into the up-to-date documentation at trac (internal) and ReadTheDocs.

Read this post first if your development environment needs setting up. Then...

Setting Up the Database

To install the database, open a Terminal session and do:

pip install BDNYCdb

Then download the public database file BDNCY198.db here or get the unpublished BDNYC.db file from Dropbox. (Ask a group member for the invite!)

Accessing the Database

Now you can load the entire database into a Python variable simply by launching the Python interpreter and pointing the get_db() function to the database file by doing:

In [1]: from BDNYCdb import BDdb
In [2]: db = BDdb.get_db('/path/to/your/Dropbox/BDNYCdb/BDNYC.db')

Voila!

In the database, each source has a unique identifier (or 'primary key' in SQL speak). To see an inventory of all data for one of these sources, just do:

In [3]: db.inventory(id)

where id is the source_id.

Now that you have the database at your fingertips, you’ll want to get some info out of it. To do this, you can use SQL queries.

Here is a detailed post about how to write an SQL query.

Further documentation for sqlite3 can be found here. Most commands involve wrapping SQL language inside python functions. The main method we will use to fetch data from the database is list():

In [5]: data = db.list( "SQL_query_goes_here" ).fetchall()

Example Queries

Some SQL query examples to put in the command above (wrapped in quotes of course):

  1. SELECT shortname, ra, dec FROM sources WHERE (222<ra AND ra<232) AND (5<dec AND dec<15)
  2. SELECT band, magnitude, magnitude_unc FROM photometry WHERE source_id=58
  3. SELECT source_id, band, magnitude FROM photometry WHERE band='z' AND magnitude<15
  4. SELECT wavelength, flux, unc FROM spectra WHERE observation_id=75”

As you hopefully gathered:

  1. Returns the shortname, ra and dec of all objects in a 10 square degree patch of sky centered at RA = 227, DEC = 10
  2. Returns all the photometry and uncertainties available for object 58
  3. Returns all objects and z magnitudes with z less than 15
  4. Returns the wavelength, flux and uncertainty arrays for all spectra of object 75

The above examples are for querying individual tables only. We can query from multiple tables at the same time with the JOIN command like so:

  1. SELECT t.name, p.band, p.magnitude, p.magnitude_unc FROM telescopes as t JOIN photometry AS p ON p.telescope_id=t.id WHERE p.source_id=58
  2. SELECT p1.magnitude-p2.magnitude FROM photometry AS p1 JOIN photometry AS p2 ON p1.source_id=p2.source_id WHERE p1.band='J' AND p2.band='H'
  3. SELECT src.designation, src.unum, spt.spectral_type FROM sources AS src JOIN spectral_types AS spt ON spt.source_id=src.id WHERE spt.spectral_type>=10 AND spt.spectral_type<20 AND spt.regime='optical'
  4. SELECT s.unum, p.parallax, p.parallax_unc, p.publication_id FROM sources as s JOIN parallaxes AS p ON p.source_id=s.id

As you may have gathered:

  1. Returns the survey, band and magnitude for all photometry of source 58
  2. Returns the J-H color for every object
  3. Returns the designation, U-number and optical spectral type for all L dwarfs
  4. Returns the parallax measurements and publications for all sources

Alternative Output

As shown above, the result of a SQL query is typically a list of tuples where we can use the indices to print the values. For example, this source's g-band magnitude:

In [9]: data = db.list("SELECT band,magnitude FROM photometry WHERE source_id=58").fetchall()
In [10]: data
Out[10]: [('u', 25.70623),('g', 25.54734),('r', 23.514),('i', 21.20863),('z', 18.0104)]
In [11]: data[1][1]
Out[11]: 25.54734

However we can also query the database a little bit differently so that the fields and records are returned as a dictionary. Instead of db.query.execute() we can do db.dict() like this:

In [12]: data = db.dict("SELECT * FROM photometry WHERE source_id=58").fetchall()
In [13]: data
Out[13]: [<sqlite3.Row at 0x107798450>, <sqlite3.Row at 0x107798410>, <sqlite3.Row at 0x107798430>, <sqlite3.Row at 0x1077983d0>, <sqlite3.Row at 0x1077982f0>]
In [14]: data[1]['magnitude']
Out[14]: 25.54734

Database Schema and Browsing

In order to write the SQL queries above you of course need to know what the names of the tables and fields in the database are. One way to do this is:

In [15]: db.list("SELECT sql FROM sqlite_master").fetchall()

This will print a list of each table, the possible fields, and the data type (e.g. TEXT, INTEGER, ARRAY) for that field.

SQL browserEven easier is to use the DB Browser for SQLite pictured at left which lets you expand and collapse each table, sort and order columns, and other fun stuff.

It even allows you to manually create/edit/destroy records with a very nice GUI.

IMPORTANT: If you are using the private database keep in mind that if you change a database record, you immediately change it for everyone since we share the same database file on Dropbox. Be careful!

Always check and double-check that you are entering the correct data for the correct source before you save any changes with the SQLite Database Browser.

SQL Queries

An SQL database is comprised of a bunch of tables (kind of like a spreadsheet) that have fields (column names) and records (rows of data). For example, our database might have a table called students that looks like this:

id first last grade GPA
1 Al Einstein 6 2.7
2 Annie Cannon 6 3.8
3 Artie Eddington 8 3.2
4 Carlie Herschel 8 3.2

So in our students table, the fields are [id, first, last, grade, GPA], and there are a total of four records, each with a required yet arbitrary id in the first column.

To pull these records out, we tell SQL to SELECT values for the following fields FROM a certain table. In SQL this looks like:

In [1]: db.execute("SELECT id, first, last, grade, GPA FROM students").fetchall()
Out[1]: [(1,'Al','Einstein',6,2.7),(2,'Annie','Cannon',6,3.8),(3,'Artie','Eddington',8,3.2),(4,'Carlie','Herschel',8,3.2)]

Or equivalently, we can just use a wildcard "*" if we want to return all fields with the SQL query "SELECT * FROM students".

We can modify our SQL query to change the order of fields or only return certain ones as well. For example:

In [2]: db.execute("SELECT last, first, GPA FROM students").fetchall()
Out[1]: [('Einstein','Al',2.7),('Cannon','Annie',3.8),('Eddington','Artie',3.2),('Herschel','Carlie',3.2)]

Now that we know how to get records from tables, we can restrict which records it returns with the WHERE statement:

In [3]: db.execute("SELECT last, first, GPA FROM students WHERE GPA>3.1").fetchall()
Out[3]: [('Cannon','Annie',3.8),('Eddington','Artie',3.2),('Herschel','Carlie',3.2)]

Notice the first student had a GPA less than 3.1 so he was omitted from the result.

Now let's say we have a second table called quizzes which is a table of every quiz grade for all students that looks like this:

id student_id quiz_number score
1 1 3 89
2 2 3 96
3 3 3 94
4 4 3 92
5 1 4 78
6 3 4 88
7 4 4 91

Now if we want to see only Al's grades, we have to JOIN the tables ON some condition. In this case, we want to tell SQL that the student_id (not the id) in the quizzes table should match the id in the students table (since only those grades are Al's). This looks like:

In [4]: db.execute("SELECT quizzes.quiz_number, quizzes.score FROM quizzes JOIN students ON students.id=quizzes.student_id WHERE students.last='Einstein'").fetchall()
Out[4]: [(3,89),(4,78)]

So students.id=quizzes.student_id associates each quiz with a student from the students table and students.last='Einstein' specifies that we only want the grades from the student with last name Einstein.

Similarly, we can see who scored 90 or greater on which quiz with:

In [5]: db.execute("SELECT students.last, quizzes.quiz_number, quizzes.score FROM quizzes JOIN students ON students.id=quizzes.student_id WHERE quizzes.score>=90").fetchall()
Out[5]: [('Cannon',3,96),('Eddington',3,94),('Herschel',3,92),('Herschel',4,91)]

That's it! We can JOIN as many tables as we want with as many restrictions we need to pull out data in the desired form.

This is powerful, but the queries can become lengthy. A slight shortcut is to use the AS statement to assign a table to a variable (e.g. students => s, quizzes => q) like such:

In [6]: db.execute("SELECT s.last, q.quiz_number, q.score FROM quizzes AS q JOIN students AS s ON s.id=q.student_id WHERE q.score>=90").fetchall()
Out[6]: [('Cannon',3,96),('Eddington',3,94),('Herschel',3,92),('Herschel',4,91)]