Longitudinal Investigation of Non-Transparency (LINT): using flats from CCDs to find the amount of dust, debris, or other contamination present on a sensor's surface. This program uses flats from CCDs to find the amount of dust, debris, or other contamination present on a sensor's surface.
The code will:
- Load flats (fits).
- Subtract overscan and mask overscan regions (if overscanSubtractBOOL is "True").
- Scale the flats to a common mean.
- Stack the flats using a median.
- Subtract the average sky value.
- Multiply the values by -1 to make to make attenuation spots appear positive to photometry code.
- Run Source Extractor on the image to detect the debris.
- Print a detailed map of the debris locations.
- Analyze and plot histograms showing the amount of contamination.
- Batch process data to show the accumulation of debris over time.
Programs, packages, and wrappers:
- Interpreter: python 3
- PyDev: 188.8.131.52603221110
- Numpy: 1.11.0
- Astropy: 1.3
- Python-dev: python-dev 2.7.5-5ubuntu3
- Eclipse: eclipse-platform 3.8.1-5.1
- SExtractor: 2.19.5-2
- Matplotlib: 1.5.1-1ubuntu1
- Test for issues with your astropy installation.
- Load fits files image data into a 3D array.
- Saves data to a fits file.
- Makes a directory for output products and move output products into it.
- Scans the images to insure that they are all the same size dimensionally.
- Load LINT.config.
- Subtract and remove overscan (if overscanSubtractBOOL bool is true).
- Scales the images using a common mean (find the mean of all of the data and scale the data so that every image has that same baseline mean).
- Stack the images, using a median, into a single flat field image (median the images: meaning that the resulting value in a pixel in the final image is the median of the values in that same pixel in the input images).
- Subtracts the average sky value from the median stacked image.
- Call SExtractor and send commands to process the stacked and inverted image.
- Record the output from SExtractor.
- Performs cuts to remove objects that are not debris.
- Flags: no gremlins in photometry.
- Flux: only want divots in original image.
- SNR: signal to noise ratio. Calculate the signal-to-noise ratio as FLUX_ISO / FLUXERR_ISO, that is, the isophotal flux (photometry derived from the counts above the threshold minus the background) divided by the RMS error for the isophotal flux; in other words, the signal divided by the noise.
- FWHM: Full Width at Half Maximum. Very small values may be anomalous and not represent debris.
- Make histograms of:
- Area of debris.
- Debris versus clean area.
- Create a list that groups the fits files by date.
- Run LINT on pre-grouped (by date) fits files.
- Plot the debris accumulation over time as: scatter plot.
- Add logger.
- Add help command in optional arguments.
- Check to see if dependencies are installed.
- Mask bad pixel columns.
- Test installation code.
This research makes use of:
- Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013).
- Bertin, E. & Arnouts, S. 1996: SExtractor: Software for source extraction, Astronomy & Astrophysics Supplement 317, 393
- PyDev for Eclipse 184.108.40.206603221110 org.python.pydev.feature.feature.group Fabio Zadrozny
- Hunter, J. D. Matplotlib: A 2D graphics environment. 2007