A Python standard star fitter inspired by the SDSS mtpipe excal code.
- Ensure that you have the necessary dependencies installed on your computer:
- Python 2.7 or higher
- The numpy python module (pyExcal definitely works with numpy 1.11.1; other versions should work, too.)
- The scipy python module (pyExcal definitely works with numpy 0.18.0; other versions should work, too.)
- Run the appropriate setup script in the pyExcal bin directory, depending on which shell you are using (bash or tcsh):
source pyExcal/bin/setup.bash
orsource pyExcal/bin/setup.csh
**Note: you will need to modify the value for PYEXCAL_DIR in the setup script before the first time you ever run it.** - Run the following command:
pyExcal.py --inputFile $PYEXCAL_DIR/test/std-rguiz-test.g.csv --band g
bash$ pyExcal.py --inputFile $PYEXCAL_DIR/test/std-rguiz-test.g.csv --band g Initial parameter values: [1.6509999999999998, 0.0, 0.0] Converged Converged with chi squared 0.481103286398 degrees of freedom, dof 40 RMS of residuals (i.e. sqrt(chisq/dof)) 0.109670334001 Reduced chisq (i.e. variance of residuals) 0.01202758216 Fitted parameters at minimum, with 68% C.I.: a 1.34904 +/- 0.0629574 (4.666840 percent) b -0.00829816 +/- 0.0502757 (605.865115 percent) k 0.214706 +/- 0.0391504 (18.234481 percent) Correlation matrix: a b k a 1.000000 b -0.101816 1.000000 k -0.904639 -0.244643 1.000000 Your fit equation is: g_inst - g_std = 1.349 + -0.008*((g-r) - 0.530) + 0.215*X Outputting QA plot qa-std-rguiz-test.g.csv_airmass.g-band.png Outputting QA plot qa-std-rguiz-test.g.csv_color.g-band.png That's all, folks!
The a, b, and k parameters are the familiar a, b, and k from the mtpipe photometric equations. Currently, I have ignored the "c" term coefficient (a.k.a., the second-order extinction), but that could be added if needed. (The concern is that it is so small that we won't get a significant fit for it, and will just end up adding noise to the fit.)
Please use the issues to post requests for additions or other updates to the code or to this FAQ .