Usage

To use roman_photoz, follow these steps:

1. Interactive mode:

   import matplotlib.pyplot as plt
   import numpy as np
   from roman_photoz.roman_catalog_process import RomanCatalogProcess
   
   # create a RomanCatalogProcess object with default configuration
   # and custom model filename
   rcp = RomanCatalogProcess(
       config_filename="",  # use default config
       model_filename="custom_model.pkl"  # specify custom model filename
   )
   
   # process the catalog
   rcp.process(
       input_path="/path/to/input/file/",
       input_filename="roman_simulated_catalog.asdf",
       output_path="/path/to/output/file/",
       output_filename="output_filename.asdf",
       save_results=True,
   )
   
   # examples of visualization
   # plot the estimated PDF for the first object
   zgrid = np.linspace(0, 7, 200)
   plt.plot(zgrid, np.squeeze(rcp.estimated.data.pdf(zgrid)[0]))
   
   # plot the estimated redshift ("Z_BEST") vs. the actual redshift ("ZSPEC")
   plt.plot(rcp.estimated.data.ancil["ZSPEC"], rcp.estimated.data.ancil["Z_BEST"], "o")
   
   # plot the redshift vs. the simulated magnitude in all filters
   plt.plot(rcp.estimated.data.ancil["ZSPEC"], rcp.estimated.data.ancil["MAG_OBS()"], "o")
   

2. Non-interactive mode:

   from roman_photoz import roman_catalog_process
   
   argv = [
       "--model_filename",
       "custom_model.pkl",
       "--input_path",
       "/path/to/input/file/",
       "--input_filename",
       "roman_simulated_catalog.asdf",
       "--output_path",
       "/path/to/output/file/",
       "--output_filename",
       "output_filename.asdf",
       "--save_results",
       "True",
   ]
   
   roman_catalog_process.main(argv)
   

3. Command line mode:

   python -m roman_photoz --model_filename=custom_model.pkl --input_path=/path/to/input/file/ --input_filename=roman_simulated_catalog.asdf --output_path=/path/to/output/file/ --output_filename=output_filename.asdf --save_results=True