FitsManager

class prose.FitsManager

A class for managing FITS files.

Parameters:

• folders (str or list of str, optional) – The folder(s) to search for FITS files. If not provided, files must be provided.

• files (str or list of str, optional) – The file(s) to read. If not provided, folders must be provided.

• depth (int, optional) – The subfolder depth to search for files in the folder(s). Default is 0 (search only in the provided folder(s)).

• hdu (int, optional) – The HDU to read from the FITS file. Default is 0 (the primary HDU).

• extension (str, optional) – The extension of the FITS file(s) to search for. Default is “.f*t*” (search for all FITS file extensions).

• file (str, optional) – The name of the SQLite database file to use. Default is “:memory:” (create an in-memory database).

• batch_size (int or bool, optional) – The number of files to store in the databse at once. If False, read all files at once. Default is False. This is to allow a scanning of a large number of files that are still saved in the database if an error occurs.

• scan (callable, optional) – The function used to retrieve files from a folder. Signature is scan(folder) -> list of file paths. Default is None.

• verbose (bool, optional) – Whether to display progress information. Default is True.

• to_df (function, optional) – A function to use for converting FITS files to pandas DataFrames. Default is None.

• telescope (str, optional) – The name of the telescope used to take the FITS files. Default is None.

con

The SQLite database connection.

Type:

sqlite3.Connection

cur

The SQLite database cursor.

Type:

sqlite3.Cursor

fits_to_df

The function used for converting FITS files to pandas DataFrames.

Type:

function

Methods

__init__([folders, files, depth, hdu, ...])
bias(i[, show])	Return the paths of the bias images associated to a given observation.
calibrations(**kwargs)	return a pandas DataFrame of calibrations observations given some metadata constraints in the form of wildcards.
darks(i[, show])	Return the paths of the dark images associated to a given observation.
files([id, path, exposure, tolerance])	Return a pandas DataFrame of files given some metadata constraints in the form of wildcards.
flats(i[, show])	Return the paths of the flat images associated to a given observation.
get_files(folders, extension[, scan, depth])	Return paths of files with specific extension in the specified folder(s)
images(i[, show])	Return the paths of the observation science images for a given observation id.
label(i)	Return a string label for the observation with the given index.
observation_files(i[, past, future, ...])	Return a dictionary of files for a given observation ID, along with calibration files.
observations([hide_exposure])	return a pandas DataFrame of observations given some metadata constraints in the form of wildcards
paths(**kwargs)	Get the paths of all files matching the kwargs query (see prose.FitsImage.files)
scan_files(files[, batch_size, verbose, ...])	Scan files and add data to database
to_pandas(query)	Execute a SQL query and return the result as a pandas DataFrame.

Attributes

all_bias	fits paths of the observation bias images
all_darks	fits paths of the observation dark images
all_flats	fits paths of the observation flats images
all_images	fits paths of the observation science images
obs_name	Observation name ({telescope}_{date}_{target}_{filter}) if a single observation is present
reduced	fits paths of the observation calibrated images if present
stack	fits paths of the observation stack image if present
unique_obs	Return whether the object contains a unique observation (observation is defined as a unique combinaison of date, telescope, target and filter).