Utilities (photometry.utilities)

Collection of utility functions that can be used throughout the photometry package.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

class photometry.utilities.ListHandler(*args, message_queue, **kwargs)[source]

Bases: Handler

A logging.Handler that writes messages into a list object.

The standard logging.QueueHandler/logging.QueueListener can not be used for this because the QueueListener runs in a private thread, not the main thread.

Warning

This handler is not thread-safe. Do not use it in threaded environments.

__init__(*args, message_queue, **kwargs)[source]

Initialize by copying the queue and sending everything else to superclass.

emit(record)[source]

Add the formatted log message (sans newlines) to the queue.

class photometry.utilities.LoggerWriter(logger, level=20)[source]

Bases: object

File-like object which passes input into a logger.

Can be used together with contextlib.redirect_stdout() or contextlib.redirect_stderr() to redirect streams to the given logger. Can be useful for wrapping codes which uses normal print() functions for logging.

1logger = logging.getLogger(__name__)
2with contextlib.redirect_stdout(LoggerWriter(logger, logging.INFO)):
3        print("This goes into the logger instead of STDOUT")

Warning

This object is not thread-safe. Do not use it in threaded environments.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

__init__(logger, level=20)[source]
flush()[source]
write(message)[source]
class photometry.utilities.TqdmLoggingHandler(*args, **kwargs)[source]

Bases: Handler

__init__(*args, **kwargs)[source]

Initializes the instance - basically setting the formatter to None and the filter list to empty.

emit(record)[source]

Do whatever it takes to actually log the specified logging record.

This version is intended to be implemented by subclasses and so raises a NotImplementedError.

photometry.utilities.add_proper_motion(ra, dec, pm_ra, pm_dec, bjd, epoch=2000.0)[source]

Project coordinates (ra,dec) with proper motions to new epoch.

Parameters:

  • ra (float) – Right ascension.

  • dec (float) – Declination.

  • pm_ra (float) – Proper motion in RA (mas/year).

  • pm_dec (float) – Proper motion in Declination (mas/year).

  • bjd (float) – Julian date to calculate coordinates for.

  • epoch (float, optional) – Epoch of ra and dec. Default=2000.

Returns:

RA and Declination at the specified date.

Return type:

(float, float)

photometry.utilities.cartesian_to_radec(xyz)[source]

Convert cartesian coordinates (x,y,z) to spherical coordinates in ra-dec form.

Parameters:

radec (ndarray) – Array with ra-dec pairs.

Returns:

ra-dec coordinates in degrees corresponding to input coordinates.

Return type:

ndarray

photometry.utilities.download_file(url, destination, desc=None, timeout=60, position_holders=None, position_lock=None, showprogress=None)[source]

Download file from URL and place into specified destination.

Parameters:

  • url (str) – URL to file to be downloaded.

  • destination (str) – Path where to save file.

  • desc (str, optional) – Description to write next to progress-bar.

  • timeout (float) – Time to wait for server response in seconds. Default=60.

  • showprogress (bool) – Force showing the progress bar. If None, the progressbar is shown based on the logging level and output type.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

photometry.utilities.download_parallel(urls, workers=4, timeout=60, showprogress=None)[source]

Download several files in parallel using multiple threads.

Parameters:

  • urls (iterable) – List of files to download. Each element should consist of a list or tuple, containing two elements: The URL to download, and the path to the destination where the file should be saved.

  • workers (int, optional) – Number of threads to use for downloading. Default=4.

  • timeout (float) – Time to wait for server response in seconds. Default=60.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

photometry.utilities.find_nearest(array, value)[source]

Search array for value and return the index where the value is closest.

Parameters:

  • array (ndarray) – Array to search.

  • value – Value to search array for.

Returns:

Index of array closest to value.

Return type:

int

Raises:

ValueError – If value is NaN.

Code author: Rasmus Handberg <rasmush@phys.au.dk>

photometry.utilities.integratedGaussian(x, y, flux, x_0, y_0, sigma=1)[source]

Evaluate a 2D symmetrical Gaussian integrated in pixels.

Parameters:

  • x (numpy.ndarray) – x coordinates at which to evaluate the PSF.

  • y (numpy.ndarray) – y coordinates at which to evaluate the PSF.

  • flux (float) – Integrated value.

  • x_0 (float) – Centroid position.

  • y_0 (float) – Centroid position.

  • sigma (float, optional) – Standard deviation of Gaussian. Default=1.

Returns:

2D Gaussian integrated pixel values at (x,y).

Return type:

numpy array

Note

Inspired by https://github.com/astropy/photutils/blob/master/photutils/psf/models.py

Example:

>>> import numpy as np
>>> X, Y = np.meshgrid(np.arange(-1,2), np.arange(-1,2))
>>> integratedGaussian(X, Y, 10, 0, 0)
array([[ 0.58433556,  0.92564571,  0.58433556],
        [ 0.92564571,  1.46631496,  0.92564571],
        [ 0.58433556,  0.92564571,  0.58433556]])
photometry.utilities.mag2flux(mag, zp=20.451)[source]

Convert from magnitude to flux using scaling relation from aperture photometry. This is an estimate.

The default scaling is based on TASOC Data Release 5 from sectors 1-5.

Parameters:

  • mag (ndarray) – Magnitude in TESS band.

  • zp (float) – Zero-point to use in scaling. Default is estimated from TASOC Data Release 5 from TESS sectors 1-5.

Returns:

Corresponding flux value

Return type:

ndarray

photometry.utilities.move_median_central(x, width_points, axis=0)[source]
photometry.utilities.radec_to_cartesian(radec)[source]

Convert spherical coordinates as (ra, dec) pairs to cartesian coordinates (x,y,z).

Parameters:

radec (ndarray) – Array with ra-dec pairs in degrees.

Returns:

(x,y,z) coordinates corresponding to input coordinates.

Return type:

ndarray

photometry.utilities.rms_timescale(time, flux, timescale=0.041666666666666664)[source]

Compute robust RMS on specified timescale. Using MAD scaled to RMS.

Parameters:

  • time (ndarray) – Timestamps in days.

  • flux (ndarray) – Flux to calculate RMS for.

  • timescale (float, optional) – Timescale to bin timeseries before calculating RMS. Default=1 hour.

Returns:

Robust RMS on specified timescale.

Return type:

float

Code author: Rasmus Handberg <rasmush@phys.au.dk>

photometry.utilities.sphere_distance(ra1, dec1, ra2, dec2)[source]

Calculate the great circle distance between two points using the Vincenty formulae.

Parameters:

  • ra1 (float or ndarray) – Longitude of first point in degrees.

  • dec1 (float or ndarray) – Lattitude of first point in degrees.

  • ra2 (float or ndarray) – Longitude of second point in degrees.

  • dec2 (float or ndarray) – Lattitude of second point in degrees.

Returns:

Distance between points in degrees.

Return type:

ndarray

Note

https://en.wikipedia.org/wiki/Great-circle_distance

photometry.utilities.to_tuple(inp, default=None)[source]

Convert iterable or single values to tuple.

This function is used for converting inputs, perticularly for preparing input to functions cached with functools.lru_cache(), to ensure inputs are hashable.

Parameters:

  • inp – Input to convert to tuple.

  • default – If input is None return this instead.

Returns:

inp converted to tuple.

Return type:

tuple

photometry.utilities.mad_to_sigma = 1.482602218505602

Constant for converting from MAD to SIGMA. Constant is 1/norm.ppf(3/4)