Usage

The ACDC package has many uses- custom dark correction, data analysis, database creation and querying, and superdark creation. Some common use examples are provided below.

Downloading Superdarks

To run the custom dark correction, you must have the ACDC superdarks. These darks can be obtained by running the download script like so:

download_superdarks <destdir>

Where <destdir> is the directory downloaded superdarks will be placed in. You must define the ACDC_SUPERDARKS environment variable as well (see Environment Variables).

Custom Dark Correction

The custom dark correction should be run on input corrtag files. At a minimum, the input and output directories should be specified when running acdc:

acdc -i <indir> -o <outdir>

Where <indir> is the input directory with corrtags you wish to calibrate, and <outdir> is the directory where custom-calibrated products should be written.

Additional run-time arguments are:

--x1d_outdir     Directory to write 1D spectra to (if not specified, the runtime will be used)
--binned         If toggled, specifies that binned superdarks should be used
-c or --clobber  If toggled, will overwrite any existing products
--hv             If specified, only corrtags of this HV will be processed
--segment        If specified, only corrtags of this segment will be processed

Data Analysis

TODO

Superdark Creation

You must have the SQLite database downloaded to create superdarks. TODO- document where DB can be downloaded from.

To create the pre-defined quiescent and active superdarks for each segment+HV combination, simply run:

make_all_superdarks

If you wish to create a custom superdark, you will need to do so interactively in a ipython/python session or create a script with the following commands:

from acdc.superdark.cos_fuv_superdark import Superdark
S = Superdark(hv=163, segment="FUVA", mjdstarts=[58000], mjdends=[58220],
              bin_pha=1, phastart=1, phaend=31, outfile="my_custom_superdark")

You may fill in the arguments as you see fit.

Database Querying

Many common queries have been pre-written for convenience. Some examples for queries on the SQLite database are below.

Return all dark events

Returns expstart, solar_flux, latitude, longitude, segment, hv, region, saa_distance, and all dark_pha<x> columns

from acdc.database.query_cos_dark import all_darks
df = all_darks()

Return filenames based on MJD, HV, and segment

Returns distinct filenames

from acdc.database.query_cos_dark import files_by_mjd
df = files_by_mjd(mjdstart, mjdend, segment, hv)

Return number of dark events based on MJD

Returns expstart, solar_flux, latitude, longitude, segment, hv, region, saa_distance, and all dark_pha<x> columns

from acdc.database.query_cos_dark import counts_by_mjd
df = counts_by_mjd(mjdstart, mjdend)

Some examples for queries on the MySQL database are below.

Return all dark events for a single HV

Unless otherwise specified, returns all columns. I.e. returncols is [xcorr, ycorr, pha, mjd, hv, segment, filename, and proposid].

from acdc.database.query_dark_events import all_rows
df = all_rows(hv, returncols)

Execute a SQL query

Execute a SQL command string.

from acdc.database.query_dark_events import sql_query
sql_query(hv, sqlquery)
# E.g. sql_query(163, "SELECT * FROM darkeventshv163 WHERE id=1")

Select rows based on column values

This is the equivalent of a WHERE col=val SQL command. Unless otherwise specified, returns all columns. I.e. returncols is [xcorr, ycorr, pha, mjd, hv, segment, filename, and proposid].

from acdc.database.query_dark_events import equals_query
equals_query(hv, returncols, **kwargs)

Select rows based on a range

Possible range to query across are: MJD, X position, Y position, and PHA.

from acdc.database.query_dark_events import range_query
range_query(163, mjdstart=58484, mjdend=58490)
range_query(163, x0=1000, x1=5000, y0=200, y1=512)
range_query(163, pha0=20, pha1=25)