Configuration

astromodels includes a configuration that allows users to set certain variables from the beginning

from astromodels import astromodels_config, show_configuration

22:58:47 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48
                  available                                                                                        

         WARNING   The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  functions.py:69
                  will not be available.                                                                           

/Users/runner/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numba/core/decorators.py:262: NumbaDeprecationWarning: numba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.
  warnings.warn(msg, NumbaDeprecationWarning)

/Users/runner/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/numba/core/decorators.py:262: NumbaDeprecationWarning: numba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.
  warnings.warn(msg, NumbaDeprecationWarning)

show_configuration()

config
┣━━ logging
┃   ┣━━ path: ~/.astromodels/log
┃   ┣━━ developer: False
┃   ┣━━ usr: True
┃   ┣━━ console: True
┃   ┣━━ level: 20
┃   ┣━━ startup_warnings: True
┃   ┣━━ info_style: medium_spring_green
┃   ┣━━ warn_style: medium_orchid
┃   ┣━━ error_style: blink bold bright_red
┃   ┣━━ debug_style: blue_violet
┃   ┗━━ message_style: bold grey78
┣━━ absorption_models
┃   ┣━━ tbabs_table: AbsTables.WILM
┃   ┣━━ phabs_table: AbsTables.AG89
┃   ┗━━ ebl_table: EBLTable.dominguez
┗━━ modeling
    ┣━━ use_memoization: True
    ┣━━ use_parameter_transforms: True
    ┗━━ ignore_parameter_bounds: False

The configuration can be accessed and altered during runtime:

astromodels_config.modeling.use_memoization = False

show_configuration()

config
┣━━ logging
┃   ┣━━ path: ~/.astromodels/log
┃   ┣━━ developer: False
┃   ┣━━ usr: True
┃   ┣━━ console: True
┃   ┣━━ level: 20
┃   ┣━━ startup_warnings: True
┃   ┣━━ info_style: medium_spring_green
┃   ┣━━ warn_style: medium_orchid
┃   ┣━━ error_style: blink bold bright_red
┃   ┣━━ debug_style: blue_violet
┃   ┗━━ message_style: bold grey78
┣━━ absorption_models
┃   ┣━━ tbabs_table: AbsTables.WILM
┃   ┣━━ phabs_table: AbsTables.AG89
┃   ┗━━ ebl_table: EBLTable.dominguez
┗━━ modeling
    ┣━━ use_memoization: False
    ┣━━ use_parameter_transforms: True
    ┗━━ ignore_parameter_bounds: False

The user can create a configuration YAML file with any name and the extension .yml and place it in the ~/.config/astromodels/ folder. An example file:

logging:
    developer: False # do not store debug log file
    usr: True # store a log file
    console: True # print logs to screen
    level: DEBUG # turn on debug message


modeling:
    use_parameter_transforms: no # turn off parameter transforms
    ignore_parameter_bounds: yes # ignore parameter bounds

Not all options are required to be set and the defaults will be applied to anything not set.

Configuration options

There are a few special configuration options

use_memoization

By default, astromodels functions memoize or cache their output. This is useful for various processes like optimization as speeds of the evaluation of repeated function calls with the same values. However, there is a slight overhead when caching values and when performing Bayesian fits, this can slow down the evaluation as chance of hitting the exact same values more than once should be low. Thus, it is possible to turn of memoization directly in the configuration.

use_parameter_transforms

Parameters can have transforms assigned to them. These transforms are used during optimization to transform the parameter into a different space, such as log10. However, this may not be desirable and is not needed (or used) during Bayesian fits. There is also a small overhead in computing these transforms. Thus, this can be turned off via the configuration.

ignore_parameter_bounds

The bounds of parameters can be used in during optimization but are not used during Bayesian fits (the prior on a parameter controls its bounds if any). Thus, it is possible to turn off errors occuring from trying to set parameters outside of thier bounds in the configuration.