Merge branch 'stellar_heating'

#### The staging configuration file (`etl/stg/config/config.yml`)

The staging config file has two main categories:

The staging config file has three main categories:

* **grid:**

    * grid_type: regular or spherical

    * velocity_gradient: the value of the velocity gradient for solid-body roation.

    * velocity_gradient_unit: the unit of the velocity gradient, e.g. "km/s/pc"

* **stars:**

  Adding the star configuration section makes the program compute the dust temperature distribution given the properties

  of the stars included. _Caveat:_ be careful, test runs show unexpectedtly low dust temperatures using a blackbody.

    * nstars: the number of stars to include

    * rstars: the radius of the star in cm; it is normally ignored, unless the parameter `istar_sphere` is set to 1

    * mstars: the mass in grams; not used in the current version of RADMC

    * star_positions: x, y, z coordinates of the star in cm, expressed as a list of lists

    * star_fluxes: stellar fluxes at each of the computed lambdas in erg cm$^-2$ s$-1$ Hz$-1$; if negative, interpreted

      as the peak temperature of a blackbody

    * nlambdas: number of wavelengths to compute

    * spacing: log or linear

    * lambdas_micron_limits: the limits in wavelength to consider in the run, expressed in micron

* **lines:**

    * species_to_include: the list of molecular species to include in the RADMC postprocessing, e.g. ['e-ch3oh']

    * molecular_abundances: a dict-like parameter, containing the species name and the corresponding fractional

    grid_metadata['distance_matrix'] = get_distance_matrix(grid_metadata, grid_metadata['centered_indices'])

    grid_metadata['grid_edges'] = get_grid_edges(grid_metadata)

    grid_metadata['continuum_lambdas'] = 100

    return grid_metadata

    'wavelength_micron.inp': ['continuum_lambdas'],

    'numberdens_mol.inp': ['iformat', 'ncells'],

    'escprob_lengthscale.inp': ['iformat', 'ncells'],

    'stars.inp': ['iformat', 'nstars', 'continuum_lambdas', 'stars_properties', 'lambdas']

}

from itertools import product

from typing import Union

import sqlalchemy

import shutil

from astropy import units as u

from datetime import datetime

from radmc3dPy import image

def main(grid_tarfile: str,

         run_id: str,

         override_config: Union[dict, None] = None,

         radmc_input_path: Union[str, None] = None) -> str:

         radmc_input_path: Union[str, None] = None,

         compute_dust_temperature: bool = True) -> str:

    # This is necessary, because the lines_mode is needed both in the lines.inp and radmc3d.inp files

    # The reason for splitting the main input file from the rest is that some parameters can be changed

    # independently of the grid for the modeling. The mdl hash should depend on all the mdl parameters, not a subset

    engine = get_pg_engine(logger=logger)

    # Execute radmc

    logger.debug(f'Executing command: {radmc_command}')

    execution_dir = os.getcwd()

    os.chdir(_radmc_input_path)

    if 'stars' in config_stg:

        if compute_dust_temperature is True:

            logger.info('Computing dust temperature distribution using the stars in the configuration file')

            os.system('radmc3d mctherm setthreads 16')

        else:

            logger.info('Using cached dust temperature distribution')

            shutil.copy(os.path.join(execution_dir, 'model', 'data', 'dust_temperature.dat'),

                        os.path.join('.', 'dust_temperature.dat'))

    os.system(radmc_command)

    os.chdir(execution_dir)

    logger.debug(f'Checking presence of file: {os.path.join(_radmc_input_path, "image.out")}')

    velocity_gradient: 2

    velocity_gradient_unit: "km/(s pc)"

#stars:

#    nstars: 1

#    rstars: [6.96e10, ]

#    mstars: [1.99e36, ]

#    star_positions: [[0, 0, 0]]

#    star_fluxes: [[-32000,], ]

#    nlambdas: 300

#    spacing: 'log'

#    lambdas_micron_limits: [0.001, 1300]

lines:

    species_to_include: ['e-ch3oh']

    molecular_abundances: {