A python library of tools to process and analyse TraP data, including example scripts and Jupyter notebooks
Tools to access and query the TraP databases
- dbtools.py - contains:
- access(engine,host,port,user,password,database) - opens a connection to the TraP database using SQLalchemy
- GetVarParams(session,dataset_id) - returns the varmetric and runningcatalogue databases for a specific dataset id
Example Jupyter Notebooks and standalone scripts using these tools
- 4FreqVariablesPlot.py uses the output of FilterVariables.py run separately on each observing frequency in the dataset for 4 different observing frequencies. Outputs a 2x2 plot of the variability parameters at each observing frequency.
- CatalogueMatching.py is a jupyter notebook that can associate two catalogues, one from TraP, with each other.
- correctSystematicFluxOffset.py Sometimes there can be a systematic flux offest between images of the same field. This script uses the average flux density of point sources, as calculated using TraP, to correct the flux density scale in all the images.
- FilterVariables.py and FilterVariables.ipynb Are equivalent, one is a standalone script and the other is a Jupyter notebook. They plot the variability parameters for a specific dataset. FilterVariables.py is a more up to date version of the code - as needed for 4FreqVariablesPlot.py.
- filter_new_sources.py A filtering strategy using TraP outputs and deep catalogues of the field. For more details see Rowlinson et al. 2022
- plotLightcurve.py An example script to create a multi-frequency lightcurve of a source - where each individual frequency is in a different TraP job.
- plotLightcurve_v2.py An example script to create a multi-frequency lightcurve of a source from a single TraP run.
- SensitivityPlot.ipynb Code to make an image noise map using the TraP outputs
- dblogin.py contains the main login parameters required for the database
Tools to prepare images for TraP processing and to conduct initial image quality control
- script1.py This is a temporary summary
Various plotting tools used by the example scripts
Various other useful tools
- tools.py Includes:
- SigmaFit(data) - fits a gaussian distribution to data and returns the fit parameters
- extract_data(filename) - extracts data from a csv file
- write_data(filename,tmp) - writes data into a csv file
- animation_zoom.ipynb - IPython notebook which creates a movie from a collection of images, allowing for the user to specify a zoom-in window on a target of interest. Based on the animation prototype created by Mark in his scratchpad repo.