Loading etl/assets/commons/__init__.py +1 −1 Original line number Original line Diff line number Diff line Loading @@ -410,7 +410,7 @@ def get_value_if_specified(parameters_dict: dict, return None return None def parse_input_main() -> Tuple[str, str, float, float, list, int]: def parse_input_main() -> Tuple[str, str, np.array, np.array, list, int]: """ """ Parse the stg and mdl configuration files, as well as the main one, to apply overrides, compute the arrays of Parse the stg and mdl configuration files, as well as the main one, to apply overrides, compute the arrays of temperatures and number densities and the type of models defined in the grid (isothermal/uniform density vs. temperatures and number densities and the type of models defined in the grid (isothermal/uniform density vs. Loading etl/main.py +1 −0 Original line number Original line Diff line number Diff line Loading @@ -210,6 +210,7 @@ def main_presentation_step(run_id: str, os.rmdir(scratches) os.rmdir(scratches) prs_inspection_main(run_id=run_id, prs_inspection_main(run_id=run_id, is_isothermal=_tdust_model_type == 'isothermal', is_isothermal=_tdust_model_type == 'isothermal', is_homogeneous=_model_type == 'homogeneous', engine=engine) engine=engine) cleanup_tmp_table(run_id=run_id, cleanup_tmp_table(run_id=run_id, engine=engine) engine=engine) Loading etl/prs/prs_inspect_results.py +92 −45 Original line number Original line Diff line number Diff line Loading @@ -3,13 +3,14 @@ import numpy as np import os import os import seaborn as sns import seaborn as sns import pandas as pd import pandas as pd import sqlalchemy import xarray as xr import xarray as xr from typing import Union, Tuple, List from typing import Union, Tuple, List from contextlib import closing from contextlib import closing from itertools import product from itertools import product from astropy.io import fits from astropy.io import fits from astropy import units as u from astropy import units as u from sqlalchemy.orm import Session from sqlalchemy.orm import Session, aliased from sqlalchemy import and_, or_ from sqlalchemy import and_, or_ from sqlalchemy import engine as sqla_engine from sqlalchemy import engine as sqla_engine from stg.stg_build_db_structure import (GridPars, from stg.stg_build_db_structure import (GridPars, Loading @@ -31,7 +32,9 @@ def get_aggregated_ratio_from_db( gas_density: float, gas_density: float, lines: Union[list, tuple], lines: Union[list, tuple], session: Session, session: Session, run_id: str) -> float: run_id: str, is_isothermal: bool = False, is_homogeneous: bool = False) -> float: """ """ Get the aggregated ratio from the database, according to the aggregation function specified in the pre config file; Get the aggregated ratio from the database, according to the aggregation function specified in the pre config file; this function works for a homogeneous model. For more complex models the query must be revised. this function works for a homogeneous model. For more complex models the query must be revised. Loading @@ -40,23 +43,50 @@ def get_aggregated_ratio_from_db( :param lines: the lines used to compute the ratio :param lines: the lines used to compute the ratio :param session: the SQLAlchemy session to use :param session: the SQLAlchemy session to use :param run_id: the id of the run :param run_id: the id of the run :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: the aggregated ratio :return: the aggregated ratio """ """ results = session.query(GridPars, RatioMaps) \ .join(StarsPars, isouter=True) \ density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, .join(ModelPars) \ is_isothermal=is_isothermal) .join(MomentZeroMaps) \ mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps) .join(RatioMaps, model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars) or_( results = session.query(RatioMaps) \ and_(RatioMaps.mom_zero_map_1, .join(mom_zero_1, ModelPars.iline == lines[0]), and_(RatioMaps.mom_zero_name_1 == mom_zero_1.mom_zero_name, and_(RatioMaps.mom_zero_map_2, RatioMaps.run_id == mom_zero_1.run_id)) \ ModelPars.iline == lines[1]))) \ .join(model_pars_1, and_(mom_zero_1.fits_cube_name == model_pars_1.fits_cube_name, mom_zero_1.run_id == model_pars_1.run_id)) \ .join(mom_zero_2, and_(RatioMaps.mom_zero_name_2 == mom_zero_2.mom_zero_name, RatioMaps.run_id == mom_zero_2.run_id)) \ .join(model_pars_2, and_(mom_zero_2.fits_cube_name == model_pars_2.fits_cube_name, mom_zero_2.run_id == model_pars_2.run_id)) \ .join(GridPars) \ .filter( .filter( and_(GridPars.dust_temperature_at_reference == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id, return results[1].aggregated_ratio model_pars_1.iline == lines[0], model_pars_2.iline == lines[1])).order_by(GridPars.created_on.desc()).first() return results.aggregated_ratio def get_filter_columns(is_homogeneous: bool, is_isothermal: bool) -> Tuple[sqlalchemy.Column, sqlalchemy.Column]: # Select correct column for filtering if is_isothermal is True: dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference if is_homogeneous is True: density_column = GridPars.central_density else: density_column = GridPars.density_at_reference return density_column, dust_temperature_column def get_results( def get_results( Loading @@ -65,7 +95,8 @@ def get_results( lines: Union[list, tuple], lines: Union[list, tuple], run_id: str, run_id: str, session: Session, session: Session, is_isothermal: bool = False) -> Tuple[float, str, str, float, str, str, tuple, np.array]: is_isothermal: bool = False, is_homogeneous: bool = False) -> Tuple[float, str, str, float, str, str, tuple, np.array]: """ """ Get results from the database, given the parameters of the model Get results from the database, given the parameters of the model :param dust_temperature: the characteristic dust temperature :param dust_temperature: the characteristic dust temperature Loading @@ -74,33 +105,40 @@ def get_results( :param run_id: the run_id of the model :param run_id: the run_id of the model :param session: a session to query the database :param session: a session to query the database :param is_isothermal: whether the model is isothermal :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: a tuple containing the aggregated line ratios, the moment zero maps names, the pixel size, the ratio map :return: a tuple containing the aggregated line ratios, the moment zero maps names, the pixel size, the ratio map name, the fits grid name (for the number density), the scaling factor between the grid and image pixel (must be name, the fits grid name (for the number density), the scaling factor between the grid and image pixel (must be integer!), and the pixel-by-pixel measured line ratios integer!), and the pixel-by-pixel measured line ratios """ """ # Select correct column for filtering density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, if is_isothermal is True: is_isothermal=is_isothermal) dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps) model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars) # Query database # Query database results = session.query(GridPars, RatioMaps, GridFiles, ModelPars) \ results = session.query(GridPars, RatioMaps, GridFiles, model_pars_1) \ .join(mom_zero_1, and_(RatioMaps.mom_zero_name_1 == mom_zero_1.mom_zero_name, RatioMaps.run_id == mom_zero_1.run_id)) \ .join(model_pars_1, and_(mom_zero_1.fits_cube_name == model_pars_1.fits_cube_name, mom_zero_1.run_id == model_pars_1.run_id)) \ .join(mom_zero_2, and_(RatioMaps.mom_zero_name_2 == mom_zero_2.mom_zero_name, RatioMaps.run_id == mom_zero_2.run_id)) \ .join(model_pars_2, and_(mom_zero_2.fits_cube_name == model_pars_2.fits_cube_name, mom_zero_2.run_id == model_pars_2.run_id)) \ .join(GridPars) \ .join(GridFiles) \ .join(GridFiles) \ .join(StarsPars, isouter=True) \ .join(StarsPars, isouter=True) \ .join(ModelPars) \ .join(MomentZeroMaps) \ .join(RatioMaps, or_( and_(RatioMaps.mom_zero_map_1, ModelPars.iline == lines[0]), and_(RatioMaps.mom_zero_map_2, ModelPars.iline == lines[1]))) \ .filter( .filter( and_(dust_temperature_column == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridFiles.quantity == 'gas_number_density', GridFiles.quantity == 'gas_number_density', GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id), model_pars_1.iline == lines[0], model_pars_2.iline == lines[1]).order_by(GridPars.created_on.desc()).first() with fits.open(os.path.join('prs', 'fits', 'ratios', results[1].ratio_map_name)) as ratio_fitsfile: with fits.open(os.path.join('prs', 'fits', 'ratios', results[1].ratio_map_name)) as ratio_fitsfile: ratio_values = ratio_fitsfile[0].data ratio_values = ratio_fitsfile[0].data assert (results[3].npix % results[0].grid_shape_1 == 0) and (results[3].npix % results[0].grid_shape_2 == 0) assert (results[3].npix % results[0].grid_shape_1 == 0) and (results[3].npix % results[0].grid_shape_2 == 0) Loading @@ -120,7 +158,8 @@ def get_grid_values( gas_density: float, gas_density: float, run_id: str, run_id: str, session: Session, session: Session, is_isothermal: bool = False) -> np.array: is_isothermal: bool = False, is_homogeneous: bool = False) -> np.array: """ """ Retrieve the grid values, given the model parameters Retrieve the grid values, given the model parameters :param quantity_name: the quantity to extract :param quantity_name: the quantity to extract Loading @@ -129,20 +168,18 @@ def get_grid_values( :param run_id: the run_id to process :param run_id: the run_id to process :param session: a session to query the database :param session: a session to query the database :param is_isothermal: whether the model is isothermal :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: the array of grid values :return: the array of grid values """ """ # Select correct column for filtering density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, if is_isothermal is True: is_isothermal=is_isothermal) dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference # Query database # Query database results = session.query(GridPars, GridFiles) \ results = session.query(GridPars, GridFiles) \ .join(GridFiles) \ .join(GridFiles) \ .filter( .filter( and_(dust_temperature_column == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridFiles.quantity == quantity_name, GridFiles.quantity == quantity_name, GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() with fits.open(os.path.join('prs', 'fits', 'grids', results[1].fits_grid_name)) as fitsfile: with fits.open(os.path.join('prs', 'fits', 'grids', results[1].fits_grid_name)) as fitsfile: Loading Loading @@ -171,6 +208,7 @@ def get_column_density_vs_mom0(volume_density_grid: np.array, def main(run_id: str, def main(run_id: str, is_isothermal: bool, is_isothermal: bool, is_homogeneous: bool, engine: sqla_engine = None): engine: sqla_engine = None): if engine is None: if engine is None: engine = get_pg_engine(logger=logger) engine = get_pg_engine(logger=logger) Loading Loading @@ -199,7 +237,9 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, lines=lines, lines=lines, session=session, session=session, run_id=run_id) run_id=run_id, is_isothermal=is_isothermal, is_homogeneous=is_homogeneous) results.loc[tdust, nh2] = aggregated_ratio results.loc[tdust, nh2] = aggregated_ratio logger.debug(f'The aggregated ratio for lines {lines}, using {nh2}, {tdust} is: {aggregated_ratio}') logger.debug(f'The aggregated ratio for lines {lines}, using {nh2}, {tdust} is: {aggregated_ratio}') ratio, \ ratio, \ Loading @@ -216,7 +256,8 @@ def main(run_id: str, lines=lines, lines=lines, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) density_grid = get_grid_values( density_grid = get_grid_values( quantity_name='gas_number_density', quantity_name='gas_number_density', Loading @@ -224,7 +265,8 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) temperature_grid = get_grid_values( temperature_grid = get_grid_values( quantity_name='dust_temperature', quantity_name='dust_temperature', Loading @@ -232,7 +274,8 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) avg_density_map = np.nansum(np.where(density_grid == 0, np.nan, density_grid ** 2), axis=2) / \ avg_density_map = np.nansum(np.where(density_grid == 0, np.nan, density_grid ** 2), axis=2) / \ np.nansum(np.where(density_grid == 0, np.nan, density_grid), axis=2) np.nansum(np.where(density_grid == 0, np.nan, density_grid), axis=2) Loading Loading @@ -279,6 +322,10 @@ def main(run_id: str, sns.kdeplot(x=np.log10(df_all['avg_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) sns.kdeplot(x=np.log10(df_all['avg_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) plt.savefig(os.path.join('prs', 'output', f'ratio_vs_avg_density_los_kde_{"-".join(lines)}.png')) plt.savefig(os.path.join('prs', 'output', f'ratio_vs_avg_density_los_kde_{"-".join(lines)}.png')) plt.clf() plt.clf() # for lines in line_pairs: # sns.kdeplot(x=np.log10(df_all['std_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) # plt.savefig(os.path.join('prs', 'output', f'ratio_vs_std_density_los_kde_{"-".join(lines)}.png')) # plt.clf() df_all.to_csv('full_dataset.csv') df_all.to_csv('full_dataset.csv') Loading Loading @@ -311,5 +358,5 @@ def plot_ratio_vs_density(lines: Tuple[str, str], if __name__ == '__main__': if __name__ == '__main__': main(run_id='0d0761f8-fcf3-4293-9d9d-ba1a37f79a19', is_isothermal=False) main(run_id='0d0761f8-fcf3-4293-9d9d-ba1a37f79a19', is_isothermal=False, is_homogeneous=False) # main(run_id='ba7fd3ed-5947-4dc6-bcef-38f151f19b77', is_isothermal=False) # main(run_id='ba7fd3ed-5947-4dc6-bcef-38f151f19b77', is_isothermal=False) Loading
etl/assets/commons/__init__.py +1 −1 Original line number Original line Diff line number Diff line Loading @@ -410,7 +410,7 @@ def get_value_if_specified(parameters_dict: dict, return None return None def parse_input_main() -> Tuple[str, str, float, float, list, int]: def parse_input_main() -> Tuple[str, str, np.array, np.array, list, int]: """ """ Parse the stg and mdl configuration files, as well as the main one, to apply overrides, compute the arrays of Parse the stg and mdl configuration files, as well as the main one, to apply overrides, compute the arrays of temperatures and number densities and the type of models defined in the grid (isothermal/uniform density vs. temperatures and number densities and the type of models defined in the grid (isothermal/uniform density vs. Loading
etl/main.py +1 −0 Original line number Original line Diff line number Diff line Loading @@ -210,6 +210,7 @@ def main_presentation_step(run_id: str, os.rmdir(scratches) os.rmdir(scratches) prs_inspection_main(run_id=run_id, prs_inspection_main(run_id=run_id, is_isothermal=_tdust_model_type == 'isothermal', is_isothermal=_tdust_model_type == 'isothermal', is_homogeneous=_model_type == 'homogeneous', engine=engine) engine=engine) cleanup_tmp_table(run_id=run_id, cleanup_tmp_table(run_id=run_id, engine=engine) engine=engine) Loading
etl/prs/prs_inspect_results.py +92 −45 Original line number Original line Diff line number Diff line Loading @@ -3,13 +3,14 @@ import numpy as np import os import os import seaborn as sns import seaborn as sns import pandas as pd import pandas as pd import sqlalchemy import xarray as xr import xarray as xr from typing import Union, Tuple, List from typing import Union, Tuple, List from contextlib import closing from contextlib import closing from itertools import product from itertools import product from astropy.io import fits from astropy.io import fits from astropy import units as u from astropy import units as u from sqlalchemy.orm import Session from sqlalchemy.orm import Session, aliased from sqlalchemy import and_, or_ from sqlalchemy import and_, or_ from sqlalchemy import engine as sqla_engine from sqlalchemy import engine as sqla_engine from stg.stg_build_db_structure import (GridPars, from stg.stg_build_db_structure import (GridPars, Loading @@ -31,7 +32,9 @@ def get_aggregated_ratio_from_db( gas_density: float, gas_density: float, lines: Union[list, tuple], lines: Union[list, tuple], session: Session, session: Session, run_id: str) -> float: run_id: str, is_isothermal: bool = False, is_homogeneous: bool = False) -> float: """ """ Get the aggregated ratio from the database, according to the aggregation function specified in the pre config file; Get the aggregated ratio from the database, according to the aggregation function specified in the pre config file; this function works for a homogeneous model. For more complex models the query must be revised. this function works for a homogeneous model. For more complex models the query must be revised. Loading @@ -40,23 +43,50 @@ def get_aggregated_ratio_from_db( :param lines: the lines used to compute the ratio :param lines: the lines used to compute the ratio :param session: the SQLAlchemy session to use :param session: the SQLAlchemy session to use :param run_id: the id of the run :param run_id: the id of the run :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: the aggregated ratio :return: the aggregated ratio """ """ results = session.query(GridPars, RatioMaps) \ .join(StarsPars, isouter=True) \ density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, .join(ModelPars) \ is_isothermal=is_isothermal) .join(MomentZeroMaps) \ mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps) .join(RatioMaps, model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars) or_( results = session.query(RatioMaps) \ and_(RatioMaps.mom_zero_map_1, .join(mom_zero_1, ModelPars.iline == lines[0]), and_(RatioMaps.mom_zero_name_1 == mom_zero_1.mom_zero_name, and_(RatioMaps.mom_zero_map_2, RatioMaps.run_id == mom_zero_1.run_id)) \ ModelPars.iline == lines[1]))) \ .join(model_pars_1, and_(mom_zero_1.fits_cube_name == model_pars_1.fits_cube_name, mom_zero_1.run_id == model_pars_1.run_id)) \ .join(mom_zero_2, and_(RatioMaps.mom_zero_name_2 == mom_zero_2.mom_zero_name, RatioMaps.run_id == mom_zero_2.run_id)) \ .join(model_pars_2, and_(mom_zero_2.fits_cube_name == model_pars_2.fits_cube_name, mom_zero_2.run_id == model_pars_2.run_id)) \ .join(GridPars) \ .filter( .filter( and_(GridPars.dust_temperature_at_reference == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id, return results[1].aggregated_ratio model_pars_1.iline == lines[0], model_pars_2.iline == lines[1])).order_by(GridPars.created_on.desc()).first() return results.aggregated_ratio def get_filter_columns(is_homogeneous: bool, is_isothermal: bool) -> Tuple[sqlalchemy.Column, sqlalchemy.Column]: # Select correct column for filtering if is_isothermal is True: dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference if is_homogeneous is True: density_column = GridPars.central_density else: density_column = GridPars.density_at_reference return density_column, dust_temperature_column def get_results( def get_results( Loading @@ -65,7 +95,8 @@ def get_results( lines: Union[list, tuple], lines: Union[list, tuple], run_id: str, run_id: str, session: Session, session: Session, is_isothermal: bool = False) -> Tuple[float, str, str, float, str, str, tuple, np.array]: is_isothermal: bool = False, is_homogeneous: bool = False) -> Tuple[float, str, str, float, str, str, tuple, np.array]: """ """ Get results from the database, given the parameters of the model Get results from the database, given the parameters of the model :param dust_temperature: the characteristic dust temperature :param dust_temperature: the characteristic dust temperature Loading @@ -74,33 +105,40 @@ def get_results( :param run_id: the run_id of the model :param run_id: the run_id of the model :param session: a session to query the database :param session: a session to query the database :param is_isothermal: whether the model is isothermal :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: a tuple containing the aggregated line ratios, the moment zero maps names, the pixel size, the ratio map :return: a tuple containing the aggregated line ratios, the moment zero maps names, the pixel size, the ratio map name, the fits grid name (for the number density), the scaling factor between the grid and image pixel (must be name, the fits grid name (for the number density), the scaling factor between the grid and image pixel (must be integer!), and the pixel-by-pixel measured line ratios integer!), and the pixel-by-pixel measured line ratios """ """ # Select correct column for filtering density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, if is_isothermal is True: is_isothermal=is_isothermal) dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps) model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars) # Query database # Query database results = session.query(GridPars, RatioMaps, GridFiles, ModelPars) \ results = session.query(GridPars, RatioMaps, GridFiles, model_pars_1) \ .join(mom_zero_1, and_(RatioMaps.mom_zero_name_1 == mom_zero_1.mom_zero_name, RatioMaps.run_id == mom_zero_1.run_id)) \ .join(model_pars_1, and_(mom_zero_1.fits_cube_name == model_pars_1.fits_cube_name, mom_zero_1.run_id == model_pars_1.run_id)) \ .join(mom_zero_2, and_(RatioMaps.mom_zero_name_2 == mom_zero_2.mom_zero_name, RatioMaps.run_id == mom_zero_2.run_id)) \ .join(model_pars_2, and_(mom_zero_2.fits_cube_name == model_pars_2.fits_cube_name, mom_zero_2.run_id == model_pars_2.run_id)) \ .join(GridPars) \ .join(GridFiles) \ .join(GridFiles) \ .join(StarsPars, isouter=True) \ .join(StarsPars, isouter=True) \ .join(ModelPars) \ .join(MomentZeroMaps) \ .join(RatioMaps, or_( and_(RatioMaps.mom_zero_map_1, ModelPars.iline == lines[0]), and_(RatioMaps.mom_zero_map_2, ModelPars.iline == lines[1]))) \ .filter( .filter( and_(dust_temperature_column == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridFiles.quantity == 'gas_number_density', GridFiles.quantity == 'gas_number_density', GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id), model_pars_1.iline == lines[0], model_pars_2.iline == lines[1]).order_by(GridPars.created_on.desc()).first() with fits.open(os.path.join('prs', 'fits', 'ratios', results[1].ratio_map_name)) as ratio_fitsfile: with fits.open(os.path.join('prs', 'fits', 'ratios', results[1].ratio_map_name)) as ratio_fitsfile: ratio_values = ratio_fitsfile[0].data ratio_values = ratio_fitsfile[0].data assert (results[3].npix % results[0].grid_shape_1 == 0) and (results[3].npix % results[0].grid_shape_2 == 0) assert (results[3].npix % results[0].grid_shape_1 == 0) and (results[3].npix % results[0].grid_shape_2 == 0) Loading @@ -120,7 +158,8 @@ def get_grid_values( gas_density: float, gas_density: float, run_id: str, run_id: str, session: Session, session: Session, is_isothermal: bool = False) -> np.array: is_isothermal: bool = False, is_homogeneous: bool = False) -> np.array: """ """ Retrieve the grid values, given the model parameters Retrieve the grid values, given the model parameters :param quantity_name: the quantity to extract :param quantity_name: the quantity to extract Loading @@ -129,20 +168,18 @@ def get_grid_values( :param run_id: the run_id to process :param run_id: the run_id to process :param session: a session to query the database :param session: a session to query the database :param is_isothermal: whether the model is isothermal :param is_isothermal: whether the model is isothermal :param is_homogeneous: whether the model has a homogeneous density distribution :return: the array of grid values :return: the array of grid values """ """ # Select correct column for filtering density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous, if is_isothermal is True: is_isothermal=is_isothermal) dust_temperature_column = GridPars.dust_temperature else: dust_temperature_column = GridPars.dust_temperature_at_reference # Query database # Query database results = session.query(GridPars, GridFiles) \ results = session.query(GridPars, GridFiles) \ .join(GridFiles) \ .join(GridFiles) \ .filter( .filter( and_(dust_temperature_column == dust_temperature, and_(dust_temperature_column == dust_temperature, GridPars.density_at_reference == gas_density, density_column == gas_density, GridFiles.quantity == quantity_name, GridFiles.quantity == quantity_name, GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() GridPars.run_id == run_id)).order_by(GridPars.created_on.desc()).first() with fits.open(os.path.join('prs', 'fits', 'grids', results[1].fits_grid_name)) as fitsfile: with fits.open(os.path.join('prs', 'fits', 'grids', results[1].fits_grid_name)) as fitsfile: Loading Loading @@ -171,6 +208,7 @@ def get_column_density_vs_mom0(volume_density_grid: np.array, def main(run_id: str, def main(run_id: str, is_isothermal: bool, is_isothermal: bool, is_homogeneous: bool, engine: sqla_engine = None): engine: sqla_engine = None): if engine is None: if engine is None: engine = get_pg_engine(logger=logger) engine = get_pg_engine(logger=logger) Loading Loading @@ -199,7 +237,9 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, lines=lines, lines=lines, session=session, session=session, run_id=run_id) run_id=run_id, is_isothermal=is_isothermal, is_homogeneous=is_homogeneous) results.loc[tdust, nh2] = aggregated_ratio results.loc[tdust, nh2] = aggregated_ratio logger.debug(f'The aggregated ratio for lines {lines}, using {nh2}, {tdust} is: {aggregated_ratio}') logger.debug(f'The aggregated ratio for lines {lines}, using {nh2}, {tdust} is: {aggregated_ratio}') ratio, \ ratio, \ Loading @@ -216,7 +256,8 @@ def main(run_id: str, lines=lines, lines=lines, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) density_grid = get_grid_values( density_grid = get_grid_values( quantity_name='gas_number_density', quantity_name='gas_number_density', Loading @@ -224,7 +265,8 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) temperature_grid = get_grid_values( temperature_grid = get_grid_values( quantity_name='dust_temperature', quantity_name='dust_temperature', Loading @@ -232,7 +274,8 @@ def main(run_id: str, gas_density=nh2, gas_density=nh2, session=session, session=session, run_id=run_id, run_id=run_id, is_isothermal=is_isothermal is_isothermal=is_isothermal, is_homogeneous=is_homogeneous ) ) avg_density_map = np.nansum(np.where(density_grid == 0, np.nan, density_grid ** 2), axis=2) / \ avg_density_map = np.nansum(np.where(density_grid == 0, np.nan, density_grid ** 2), axis=2) / \ np.nansum(np.where(density_grid == 0, np.nan, density_grid), axis=2) np.nansum(np.where(density_grid == 0, np.nan, density_grid), axis=2) Loading Loading @@ -279,6 +322,10 @@ def main(run_id: str, sns.kdeplot(x=np.log10(df_all['avg_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) sns.kdeplot(x=np.log10(df_all['avg_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) plt.savefig(os.path.join('prs', 'output', f'ratio_vs_avg_density_los_kde_{"-".join(lines)}.png')) plt.savefig(os.path.join('prs', 'output', f'ratio_vs_avg_density_los_kde_{"-".join(lines)}.png')) plt.clf() plt.clf() # for lines in line_pairs: # sns.kdeplot(x=np.log10(df_all['std_nh2']), y=df_all[f'ratio_{"-".join(lines)}'], bw_adjust=2) # plt.savefig(os.path.join('prs', 'output', f'ratio_vs_std_density_los_kde_{"-".join(lines)}.png')) # plt.clf() df_all.to_csv('full_dataset.csv') df_all.to_csv('full_dataset.csv') Loading Loading @@ -311,5 +358,5 @@ def plot_ratio_vs_density(lines: Tuple[str, str], if __name__ == '__main__': if __name__ == '__main__': main(run_id='0d0761f8-fcf3-4293-9d9d-ba1a37f79a19', is_isothermal=False) main(run_id='0d0761f8-fcf3-4293-9d9d-ba1a37f79a19', is_isothermal=False, is_homogeneous=False) # main(run_id='ba7fd3ed-5947-4dc6-bcef-38f151f19b77', is_isothermal=False) # main(run_id='ba7fd3ed-5947-4dc6-bcef-38f151f19b77', is_isothermal=False)
