A project on survey design for precise self-calibration.
- Rory Holmes (MPIA)
- David W. Hogg (NYU)
Copyright 2012 the authors. All rights reserved.
If you want to license this code for use or re-use, get in touch.
- Hans-Walter Rix (MPIA)
Running the code:
A self-calibration simulation can be run by calling the
simulation.run_sim(parameter_dictionary) function, where the
parameter_dictionary contains all the parameters for this simulation run. This functions controls a full self-calibration simulation: it generates the synthetic sky, surveys the sky according to the specified survey strategy and then self-calibrates the resultant dataset. This function then returns the performance of the self-calibration procedure. See
default_parameters.py for all of the required simulation parameters. Running the
plot.py script on the output directory will produce plots from this self-calibration run. The package has been built in this way to allow for simple multiprocessing, as the
simulation.run_sim(parameter_dictionary) function can be called multiple times in parallel with different parameter dictionaries. For example, to test how the self-calibration procedure is sensitive to the number of sources, one can simply run this function with multiple parameter dictionaries in which the number of sources parameter is varied. A number of sample scripts have been included to show how this package can be used.
This package contains seven modules for the self-calibration simulations:
analysis.pycontains the functions used to analyze the fitted instrument response relative to the true instrument response.
save_out.pycontains the functions that save out all the data from the simulation. NB that
data_dirparameter in the parameter dictionary must be specified, otherwise no data is saved out from a simulation run.
self_calibration.pycontains the self-calibration procedures.
simulation.pycontains the master function that controls a full the full self-calibration simulation.
survey.pycontains the functions used to perform a survey of the synthetic sky, such as the imager measurement model.
transformations.pycontains useful transformation functions that are used throughout the code.
true_functions.pycontains all the functions representing the true parts of the simulations, such as the actual instrument response that is fitted for in the self-calibration simulations.
Plots for a simulation run can be generated by:
A complete parameter dictionary must be past to
simulation.run_sim() for each simulation run. The code does not check for missing parameters before beginning the simulations. See
default_parameters.py for a full list of required parameters.
Migration from svn:
Hogg migrated this from
svn with something like
cd git svn clone svn+ssh://astrometry.net/svn/trunk/projects/euclid/ --no-minimize-url --authors-file ~/authors cd euclid git remote add origin email@example.com:davidwhogg/SelfCalibration.git git push origin master