diff --git a/etl/assets/commons/__init__.py b/etl/assets/commons/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe82e70163f4ce868e1e78d74e4d47e3a11bba52
--- /dev/null
+++ b/etl/assets/commons/__init__.py
@@ -0,0 +1,269 @@
+import glob
+import logging
+import sys
+import yaml
+import numpy as np
+import shutil
+import os
+import urllib.request
+import json
+import pandas as pd
+from hashlib import sha1
+from typing import (List,
+ Union,
+ Tuple)
+from astropy import units as u
+from assets.constants import (leiden_url_mapping)
+
+
+def setup_logger(name: str,
+ log_level: str = None) -> logging.Logger:
+ """
+ Configure default logger
+ :param name: Logger name
+ :param log_level: Logging levels, as defined by logging
+ :return: the logger object
+ """
+ log_level = 'DEBUG' if log_level is None else log_level
+ """general logger configurator"""
+ logger = logging.getLogger(name)
+ logger.setLevel(log_level)
+ handler = logging.StreamHandler(sys.stdout)
+ handler.setLevel(log_level)
+ formatter = logging.Formatter("%(asctime)s - %(name)-7s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+ handler.setFormatter(formatter)
+ logger.addHandler(handler)
+ return logger
+
+
+def validate_parameter(param_to_validate,
+ default):
+ """
+ Substitute default value if the parameter is set to None
+ :param param_to_validate: input to validate
+ :param default: default value
+ :return: default value if parameter is set to None, else the passed value
+ """
+ return param_to_validate if param_to_validate is not None else default
+
+
+def load_config_file(config_file_path: str,
+ override_config: Union[dict, None] = None) -> dict:
+ """
+ Load the information in the YAML configuration file into a python dictionary
+ :param config_file_path: path to the configuration file
+ :param override_config: parameters of the input file to override (e.g. for grid creation)
+ :return: a dictionary with the parsed information
+ """
+ _override_config = validate_parameter(override_config, default={})
+ with open(config_file_path) as config_file:
+ config = yaml.load(config_file, Loader=yaml.FullLoader)
+ for key in _override_config:
+ if key not in config.keys():
+ config[key] = {}
+ for subkey in _override_config[key]:
+ try:
+ config[key][subkey] = _override_config[key][subkey]
+ except KeyError:
+ config[key] = {subkey: _override_config[key][subkey]}
+
+ try:
+ if config['density_powerlaw_idx'] == 0:
+ config['central_density'] = config['density_at_reference']
+ config['density_at_reference'] = None
+ except KeyError:
+ pass
+
+ try:
+ if config['dust_temperature_powerlaw_idx'] == 0:
+ config['dust_temperature'] = config['dust_temperature_at_reference']
+ config['dust_temperature_at_reference'] = None
+ except KeyError:
+ pass
+ return config
+
+
+def get_moldata(species_names: list,
+ logger: logging.Logger,
+ path: Union[str, None] = None,
+ use_cache: bool = False):
+ """
+ Downloads the molecular data from the Leiden molecular database; check whether the molecule in mapped in
+ leiden_url_mapping
+ :param species_names: the names of the species for which to download data
+ :param logger: logger for output printing
+ :param path: path where the files must be saved
+ :param use_cache: use molecular input files in cache, if possible
+ """
+ _path = validate_parameter(path, default=os.path.join('mdl', 'radmc_files'))
+ for species in species_names:
+ molecular_file_name = f'molecule_{species}.inp'
+ if use_cache is True:
+ shutil.copy(os.path.join('mdl', 'data', molecular_file_name),
+ os.path.join(_path, molecular_file_name))
+ if not os.path.isfile(os.path.join(_path, molecular_file_name)):
+ logger.info(f'Downloading file molecule_{species}.inp...')
+ data = urllib.request.urlopen(leiden_url_mapping[species]).read().decode()
+ with open(os.path.join(_path, f'molecule_{species}.inp'), 'w') as outfile:
+ outfile.writelines(data)
+ else:
+ logger.info(f'File molecule_{species}.inp found, skipping download...')
+
+
+def compute_unique_hash_filename(config: dict) -> str:
+ """
+ Compute a unique and reproducible filename given a configuration dictionary
+ :param config: the configuration dictionary to hash
+ :return: a unique hash of the dictionary, to use as a key and filename
+ """
+ hashed_dict = sha1(json.dumps(config, sort_keys=True).encode())
+ return f'{hashed_dict.hexdigest()}'
+
+
+def make_archive(output_filename: str,
+ source_dir: str,
+ archive_path: Union[str, None] = None):
+ """
+ Compresses all files in a directory into a .zip file
+ :param output_filename: the output filename
+ :param source_dir: the directory to compress
+ :param archive_path: path to store the compressed-grid archives
+ """
+ _archive_path = validate_parameter(archive_path,
+ default=os.path.join('stg', 'archive'))
+ filename_root, filename_format = output_filename.rsplit('.', maxsplit=1)
+ try:
+ os.remove(os.path.join(_archive_path, output_filename))
+ except FileNotFoundError:
+ pass
+ shutil.make_archive(base_name=os.path.join(_archive_path, filename_root),
+ format=filename_format,
+ root_dir=source_dir)
+
+
+def cleanup_directory(directory: str,
+ logger: logging.Logger):
+ """
+ Removes all files from the specified directory.
+ :param directory: directory to clean up
+ :param logger: logger to use
+ """
+ if os.path.isdir(directory):
+ file_list = glob.glob(f'{directory}/*')
+ for filename in file_list:
+ if os.path.isfile(filename) is True:
+ logger.debug(f'Removing file {filename}')
+ os.remove(filename)
+ else:
+ logger.debug(f'Skipping {filename}')
+
+
+def convert_frequency_to_wavelength(frequency: u.Quantity,
+ output_units: u.Unit):
+ return frequency.to(output_units, equivalencies=u.spectral())
+
+
+def get_value_if_specified(parameters_dict: dict,
+ key: str):
+ try:
+ return parameters_dict[key]
+ except KeyError:
+ return None
+
+
+def get_postprocessed_data(limit_rows: Union[None, int] = None,
+ use_model_for_inference: Union[None, str] = None) -> Tuple[List[List[str]], pd.DataFrame]:
+ """
+ Retrieve data and line pairs from the main config file
+ :param use_model_for_inference: the prs/output/run_type folder from which to get the data from inference;
+ defaults to fiducial model (constant_abundance_p15_q05)
+ :param limit_rows: the number of rows to use from the original dataset; useful to run tests and limit
+ computation time
+ :return: the line pairs list and the dataset
+ """
+ _use_model_for_inference = validate_parameter(
+ use_model_for_inference,
+ default='constant_abundance_p15_q05'
+ )
+ _data_file = os.path.join('prs', 'output', 'run_type', _use_model_for_inference, 'data', 'full_dataset.csv')
+ config = load_config_file(os.path.join('config', 'config.yml'))
+ line_pairs = config['overrides']['lines_to_process']
+ results_df_dict = {
+ 'ratios_and_los_averages': add_px_index_column(_data_file)
+ }
+ if limit_rows is not None:
+ data = results_df_dict['ratios_and_los_averages'].head(limit_rows)
+ else:
+ data = results_df_dict['ratios_and_los_averages']
+ return line_pairs, data
+
+
+def prepare_matrix(filename: str,
+ columns: list,
+ use_model_for_inference: Union[None, str] = None) -> pd.DataFrame:
+ _use_model_for_inference = validate_parameter(
+ use_model_for_inference,
+ default='constant_abundance_p15_q05'
+ )
+ df = pd.read_csv(os.path.join('prs', 'output', 'run_type', _use_model_for_inference, 'data', filename))
+ df['nh2'] = pd.Series(df['nh2'].round(1), dtype='string')
+ df['tdust'] = pd.Series(df['tdust'].round(1), dtype='string')
+ return df[columns]
+
+
+def get_data(limit_rows: Union[int, None] = None,
+ use_model_for_inference: Union[None, str] = None,
+ log_columns: Union[None, List] = None):
+ _use_model_for_inference = validate_parameter(
+ use_model_for_inference,
+ default='constant_abundance_p15_q05'
+ )
+ _log_columns = validate_parameter(
+ log_columns,
+ default=['log_nh2', 'log_tdust', 'avg_nh2', 'avg_tdust', 'molecule_column_density', 'std_nh2']
+ )
+ _, data = get_postprocessed_data(limit_rows=limit_rows,
+ use_model_for_inference=_use_model_for_inference)
+ data['nh2'] = pd.Series(data['nh2'].round(1), dtype='string')
+ data['tdust'] = pd.Series(data['tdust'].round(1), dtype='string')
+ df87 = prepare_matrix(filename='full_dataset_NH2_lines_87-86.csv',
+ columns=['px_index', 'nh2', 'tdust', 'mom_zero_87', 'mom_zero_86', 'molecule_column_density'],
+ use_model_for_inference=_use_model_for_inference)
+ df88 = prepare_matrix(filename='full_dataset_NH2_lines_88-86.csv',
+ columns=['px_index', 'nh2', 'tdust', 'mom_zero_88'],
+ use_model_for_inference=_use_model_for_inference)
+ df257 = prepare_matrix(filename='full_dataset_NH2_lines_257-256.csv',
+ columns=['px_index', 'nh2', 'tdust', 'mom_zero_257', 'mom_zero_256'],
+ use_model_for_inference=_use_model_for_inference)
+ df381 = prepare_matrix(filename='full_dataset_NH2_lines_381-380.csv',
+ columns=['px_index', 'nh2', 'tdust', 'mom_zero_381', 'mom_zero_380'],
+ use_model_for_inference=_use_model_for_inference)
+ merged = data.merge(df87, on=['px_index', 'nh2', 'tdust'])
+ merged = merged.merge(df88, on=['px_index', 'nh2', 'tdust'])
+ merged = merged.merge(df257, on=['px_index', 'nh2', 'tdust'])
+ merged = merged.merge(df381, on=['px_index', 'nh2', 'tdust'])
+ # These are transformed later on, to allow splitting
+ merged['nh2'] = pd.to_numeric(merged['nh2'])
+ merged['tdust'] = pd.to_numeric(merged['tdust'])
+ merged['log_nh2'] = pd.to_numeric(merged['nh2'])
+ merged['log_tdust'] = pd.to_numeric(merged['tdust'])
+ for column in _log_columns:
+ merged[column] = np.log10(merged[column])
+ npixels = 101
+ refpix = 50
+ merged['px_distance'] = np.sqrt(
+ ((merged['px_index'] / npixels).astype(int) - refpix) ** 2 + ((merged['px_index'] % npixels) - refpix) ** 2)
+ merged['core_radius_px'] = (1e8 / merged['nh2']) ** -(2 / 3) * 0.5 * 101 / 2
+ return merged
+
+
+def add_px_index_column(filename: str) -> pd.DataFrame:
+ """
+ Reset index to add pixel index column to data
+ :param filename: The filename of the csv file that contains the data
+ :return: a pandas dataframe with the px_index column
+ """
+ df = pd.read_csv(filename, index_col=0)
+ if 'px_index' not in df.columns:
+ df = df.reset_index().rename(columns={'index': 'px_index'})
+ return df
diff --git a/etl/assets/commons/db_utils.py b/etl/assets/commons/db_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e6a7ac8b5e0eacbf657cd1c42ed5eba19e786b9
--- /dev/null
+++ b/etl/assets/commons/db_utils.py
@@ -0,0 +1,71 @@
+import base64
+import logging
+import os
+from contextlib import closing
+from typing import Union, List
+import sqlalchemy as sqla
+import yaml
+from sqlalchemy.dialects.postgresql import insert
+from sqlalchemy.orm import Session
+from assets.commons import validate_parameter
+
+
+def get_credentials(logger: logging.Logger,
+ credentials_filename: Union[None, str] = None,
+ set_envs: bool = False) -> Union[None, dict]:
+ """
+ Retrieves credentials, if available, for logging in to the requested service.
+ :param domain: the name of the service, as it appears in the credentials file (case sensitive!).
+ :param logger: the logger to use.
+ :param credentials_filename: the name of the credentials file to be used. If None, uses the default.
+ :param set_envs: whether to set the credentials as environment variables.
+ :return: a dictionary with username and password, as retrieved from the credentials file or None.
+ """
+ _credentials_filename = credentials_filename if credentials_filename is not None \
+ else os.path.join('credentials', 'credentials.yml')
+ try:
+ with open(_credentials_filename) as credentials_file:
+ credentials = yaml.load(credentials_file, Loader=yaml.FullLoader)
+ credentials['DB_USER'] = base64.b64decode(credentials['DB_USER'].encode()).decode()
+ credentials['DB_PASS'] = base64.b64decode(credentials['DB_PASS'].encode()).decode()
+ if set_envs is True:
+ for key in credentials:
+ os.environ[key] = str(credentials[key])
+
+ return credentials
+ except FileNotFoundError:
+ logger.info('Credentials not found!')
+ return None
+
+
+def upsert(table_object: sqla.orm.decl_api.DeclarativeMeta,
+ row_dict: dict,
+ conflict_keys: List[sqla.Column],
+ engine: sqla.engine):
+ """
+ Upsert the row into the specified table, according to the indicated conflict columns
+ :param table_object: the table into which the row must be inserted
+ :param row_dict: the dictionary representing the row to insert
+ :param conflict_keys: the conflict columns list, representing the primary key of the table
+ :param engine: the SQLAlchemy engine to use
+ """
+ statement = insert(table_object).values(row_dict)
+ statement = statement.on_conflict_do_update(
+ index_elements=conflict_keys, set_=row_dict)
+ with closing(Session(engine)) as session:
+ session.execute(statement)
+ session.commit()
+
+
+def get_pg_engine(logger: logging.Logger, engine_kwargs: Union[dict, None] = None) -> sqla.engine.Engine:
+ """
+ Return the SQLAlchemy engine, given the credentials in the external file
+ :param logger: the logger to use
+ :return: the SQLAlchemy engine
+ """
+ _kwargs = validate_parameter(engine_kwargs, default={})
+ credentials = get_credentials(logger=logger,
+ credentials_filename=os.path.join('credentials', 'db_credentials.yml'))
+ url = f'postgresql://{credentials["DB_USER"]}:{credentials["DB_PASS"]}@{credentials["DB_HOST"]}:{credentials["DB_PORT"]}/{credentials["DB_NAME"]}'
+ engine = sqla.create_engine(url, **_kwargs)
+ return engine
diff --git a/etl/assets/commons/grid_utils.py b/etl/assets/commons/grid_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc05fed774a55b8b053c829a08255c99cf83b0df
--- /dev/null
+++ b/etl/assets/commons/grid_utils.py
@@ -0,0 +1,280 @@
+import logging
+import os
+from typing import Union, List
+import numpy as np
+from astropy import units as u
+from assets.commons import validate_parameter, load_config_file, setup_logger
+from assets.commons.parsing import get_grid_properties
+from assets.constants import mean_molecular_mass, radmc_grid_map, radmc_coord_system_map
+
+
+def compute_power_law_radial_profile(
+ central_value: float,
+ power_law_index: float,
+ distance_matrix: np.array,
+ maximum_radius: Union[float, None] = None,
+ value_at_reference: Union[float, None] = None,
+ distance_reference: Union[float, u.Quantity] = 1.0,
+ fill_reference_pixel: bool = True) -> np.array:
+ """
+ Compute a power law distribution over the specified grid
+ :param central_value: value in the center of the grid
+ :param power_law_index: index of the power law used to scale the profile
+ :param distance_matrix: the matrix of distances from the reference point
+ :param maximum_radius: the maximum radius to populate with gas within the grid in the same unit as the distance
+ matrix and distance reference
+ :param value_at_reference: value of the profile at the reference distance; defaults to central value if not provided
+ :param distance_reference: value of the reference distance
+ :param fill_reference_pixel: whether to fill the reference point with central_value and set this to the maximum in
+ the grid
+ :return: the distance matrix
+ """
+ _value_at_reference = validate_parameter(value_at_reference, default=central_value)
+ _distance_matrix = np.where(distance_matrix == 0, np.nanmin(distance_matrix[distance_matrix > 0]),
+ distance_matrix) if fill_reference_pixel is True else distance_matrix
+ try:
+ _distance_reference = distance_reference.to(u.cm).value
+ except AttributeError:
+ _distance_reference = distance_reference
+ profile = _value_at_reference * (_distance_matrix / _distance_reference) ** power_law_index
+ # If the routine fills the 0-distance point (the centre), it fixes the profile making the central value the maximum
+ if (fill_reference_pixel is True) and (power_law_index != 0):
+ profile = np.where(profile > central_value, central_value, profile)
+ if maximum_radius is not None:
+ profile = np.where(distance_matrix > maximum_radius + maximum_radius / 1e5, 0, profile)
+ return profile
+
+
+def compute_cartesian_coordinate_grid(indices: np.array,
+ physical_px_size: np.array) -> np.array:
+ """
+ Compute the physical coordinate grid, for a regular grid
+ :param indices: the indices of the grid pixels
+ :param physical_px_size: the array of physical pixel sizes or the pixel size, as astropy.Quantities
+ :return: the numpy.array with the physical grid coordinates
+ """
+ try:
+ _physical_px_size_cm = [px_size.to(u.cm).value for px_size in physical_px_size]
+ except TypeError:
+ _physical_px_size_cm = physical_px_size.to(u.cm).value
+ return (indices.T * _physical_px_size_cm).T
+
+
+def get_centered_indices(grid_metadata: dict) -> np.array:
+ """
+ Recompute the indices using as reference the reference pixel in the configuration file
+ :param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape and the reference pixel
+ :return: a numpy array of indices, wrt. the reference
+ """
+ return (np.indices(grid_metadata['grid_shape']).T - grid_metadata['grid_refpix']).T
+
+
+def get_grid_edges(grid_metadata: dict) -> np.array:
+ """
+ Compute the physical coordinates at the grid boundaries
+ :param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, the reference pixel, and the
+ physical pixel size
+ :return: a numpy array with the coordinates of the edges, per axis
+ """
+ grid_edges = []
+ for axis_idx in range(len(grid_metadata['grid_shape'])):
+ indices = np.arange(grid_metadata['grid_shape'][axis_idx] + 1) - grid_metadata['grid_refpix'][axis_idx] - 0.5
+ # valid for regular grid
+ grid_edges.append(compute_cartesian_coordinate_grid(indices=indices,
+ physical_px_size=grid_metadata['physical_px_size'][
+ axis_idx]))
+ # Transposed for consistent flattening
+ return np.array(grid_edges).T
+
+
+def get_distance_matrix(grid_metadata: dict,
+ indices: np.array) -> np.array:
+ """
+ Compute physical distance from the reference pixel, in cm
+ :param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, and the physical pixel size
+ :param indices: numpy array of the grid pixel indices
+ :return: a numpy array with the euclidean distance from the reference pixel
+ """
+ distance_matrix = np.zeros(grid_metadata['grid_shape'])
+ for axis_idx in range(len(grid_metadata['grid_shape'])):
+ distance_matrix += (indices[axis_idx, ...] * grid_metadata['physical_px_size'][axis_idx].to(u.cm).value) ** 2
+ return np.sqrt(distance_matrix)
+
+
+def get_physical_px_size(grid_metadata: dict) -> List[u.Quantity]:
+ """
+ Compute the physical pixel size
+ :param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, the grid size, and the grid
+ size units
+ :return: a list of physical pixel sizes, per axis
+ """
+ physical_px_size = []
+ for axis_idx in range(len(grid_metadata['grid_shape'])):
+ physical_px_size.append(
+ (grid_metadata['grid_size'][axis_idx] * u.Unit(grid_metadata['grid_size_units'][axis_idx])).to(u.cm) /
+ grid_metadata['grid_shape'][axis_idx])
+ return physical_px_size
+
+
+def compute_analytic_profile(central_value: float,
+ power_law_index: float,
+ maximum_radius: float,
+ value_at_reference: float,
+ distance_reference: float) -> np.array:
+ """
+ Compute the analytic radial profile based on a power-law model.
+
+ :param central_value (float): Central value of the profile, where the power-law could be undefined.
+ :param power_law_index (float): Power-law index determining the shape of the profile.
+ :param maximum_radius (float): Maximum radius within which to compute the profile; values outside this radius are
+ set to zero.
+ :param value_at_reference (float): Value of the profile at the reference distance.
+ :param distance_reference (float): Reference distance at which the profile value is specified.
+
+ :return: np.array: Computed radial profile.
+
+ Example:
+ # Compute an analytic profile with given parameters
+ profile = compute_analytic_profile(central_value=10.0,
+ power_law_index=-1.5,
+ maximum_radius=5.0,
+ value_at_reference=5.0,
+ distance_reference=1.0)
+ """
+ config = load_config_file(config_file_path=os.path.join('stg', 'config', 'config.yml'))
+ grid_metadata = extract_grid_metadata(config=config)
+ distance_matrix = get_distance_matrix(grid_metadata=grid_metadata,
+ indices=get_centered_indices(grid_metadata=grid_metadata))
+ profile = compute_power_law_radial_profile(central_value=central_value,
+ power_law_index=power_law_index,
+ distance_matrix=distance_matrix,
+ maximum_radius=maximum_radius,
+ value_at_reference=value_at_reference,
+ distance_reference=distance_reference)
+ return profile
+
+
+def compute_los_average_weighted_profile(profile: np.array,
+ weights: Union[float, np.array]) -> np.array:
+ """
+ Compute the line-of-sight (LOS) average weighted profile.
+ This function computes the weighted average profile along the LOS, where each profile is weighted
+ according to the provided weights. The computation is performed by summing the products of each
+ profile value and its corresponding weight across all LOS, and then dividing by the sum of weights
+ to obtain the average.
+
+ Note:
+ - If a profile value is 0, it is treated as missing data and not included in the computation.
+ - If a weight is 0, the corresponding profile is effectively excluded from the computation.
+
+ :param profile: (np.array) Array containing the profiles along different lines of sight (LOS).
+
+ :param weights: (Union[float, np.array]) Array containing the weights corresponding to each profile in the input array,
+ or a single weight value if uniform weighting is desired.
+
+ :return: (np.array) LOS average weighted profile.
+ """
+ return np.nansum(np.where(profile == 0, np.nan, profile * weights), axis=2) / \
+ np.nansum(np.where(profile == 0, np.nan, weights), axis=2)
+
+
+def compute_density_and_temperature_avg_profiles(density_profile: np.array,
+ temperature_profile: np.array,
+ molecular_abundance: float,
+ threshold: float,
+ abundance_jump: Union[float, int],
+ logger: Union[logging.Logger, None] = None) -> tuple:
+ """
+ Compute the average density and temperature profiles along the line-of-sight along with related quantities.
+ This function computes the average density and temperature profiles along the LOS, along with related quantities
+ such as molecular column density and the standard deviation of the species' number density. It first computes the
+ molecular number density grid using the given density, temperature, abundance, temperature threshold, and abundance
+ jump for simulating hot core-like abundance profiles. Then, it computes the average density and temperature profiles
+ using the molecular number density grid as weights along the LOS. Finally, it calculates the
+ molecular column density and its standard deviation.
+
+ :param density_profile (np.array): Array containing the density profiles.
+ :param temperature_profile (np.array): Array containing the temperature profiles.
+ :param molecular_abundance (float): Molecular abundance for computing molecular number density.
+ :param threshold (float): Threshold for adjusting the abundance in hot cores, where the molecule is potentially
+ evaporated from dust grains.
+ :param abundance_jump (Union[float, int]): Abundance jump in hot cores.
+ :param logger (Union[logging.Logger, None], optional): Logger object for logging messages. If not specified, creates
+ a generic logger for reporting the total mass.
+
+ :return: Tuple[np.array, np.array, np.array, np.array]
+ - Average density profile.
+ - Average temperature profile.
+ - Molecular column density.
+ - Standard deviation of molecular number density.
+ """
+ _logger = validate_parameter(logger, setup_logger('GENERIC'))
+ config = load_config_file(config_file_path=os.path.join('stg', 'config', 'config.yml'))
+ grid_metadata = extract_grid_metadata(config=config)
+ molecule_grid = compute_molecular_number_density_hot_core(gas_number_density_profile=density_profile,
+ abundance=molecular_abundance,
+ temperature_profile=temperature_profile,
+ threshold=threshold,
+ abundance_jump=abundance_jump)
+ avg_density_profile = compute_los_average_weighted_profile(profile=density_profile,
+ weights=molecule_grid)
+ mass = (np.nansum(density_profile * u.cm ** -3 * grid_metadata['physical_px_size'][0]
+ * grid_metadata['physical_px_size'][1]
+ * grid_metadata['physical_px_size'][2]) * mean_molecular_mass * m_p).to(u.M_sun)
+ _logger.debug(f'Total mass: {mass}')
+ avg_temperature_profile = compute_los_average_weighted_profile(profile=temperature_profile,
+ weights=molecule_grid)
+ return (avg_density_profile,
+ avg_temperature_profile,
+ np.nansum(molecule_grid, axis=2) * grid_metadata['physical_px_size'][2].value,
+ np.nanstd(molecule_grid, axis=2))
+
+
+def compute_molecular_number_density_hot_core(gas_number_density_profile: np.array,
+ abundance: float,
+ temperature_profile: np.array,
+ threshold: float,
+ abundance_jump: Union[float, int]) -> np.array:
+ """
+ Compute the molecular number density, using a step function abundance, changing above a specific temperature to
+ simulate evaporation
+ :param gas_number_density_profile: the gas number density profile array
+ :param abundance: the gas abundance of the species
+ :param temperature_profile: the temperature profile of the source
+ :param threshold: the threshold at which the species evaporate
+ :param abundance_jump: the factor describing the abundance variation wrt the bas level
+ :return: the array of molecular gas density computed using a step function profile
+ """
+ return np.where(temperature_profile < threshold,
+ gas_number_density_profile * abundance,
+ gas_number_density_profile * abundance * abundance_jump)
+
+
+def extract_grid_metadata(config: dict) -> dict:
+ """
+ Enrich grid metadata from the information in the configuration file
+ :param config: configuration dictionary
+ :return: a dictionary with the metadata
+ """
+ grid_config = config['grid']
+
+ grid_metadata = grid_config.copy()
+ grid_metadata['grid_type'] = radmc_grid_map[grid_config['grid_type']]
+ grid_metadata['coordinate_system'] = radmc_coord_system_map[grid_config['coordinate_system']]
+ grid_properties_keywords = ['shape', 'refpix', 'size', 'size_units']
+ for key in grid_properties_keywords:
+ grid_metadata[f'grid_{key}'] = get_grid_properties(grid_config=grid_config,
+ keyword=key)
+
+ grid_metadata['physical_px_size'] = get_physical_px_size(grid_metadata)
+
+ grid_metadata['centered_indices'] = get_centered_indices(grid_metadata)
+ grid_metadata['coordinate_grid'] = compute_cartesian_coordinate_grid(indices=grid_metadata['centered_indices'],
+ physical_px_size=grid_metadata[
+ 'physical_px_size'])
+
+ 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
diff --git a/etl/assets/commons/parsing.py b/etl/assets/commons/parsing.py
new file mode 100644
index 0000000000000000000000000000000000000000..638360e3d38e1f2daf6feeff3e137629998bb396
--- /dev/null
+++ b/etl/assets/commons/parsing.py
@@ -0,0 +1,93 @@
+import os
+from typing import Tuple
+
+import numpy as np
+
+from assets.commons import (load_config_file,
+ validate_parameter)
+
+
+def parse_grid_overrides(par_name: str,
+ config: dict) -> np.array:
+ """
+ Parse the global configuration for constructing the grid, computing the parameter values for which the grid has to
+ be computed.
+ :param par_name: parameter to parse
+ :param config: configuration dictionary to extract the grid properties
+ :return: the array of values to be considered in the grid
+ """
+ config_overrides = config['overrides']
+ if config_overrides[f'{par_name}_grid_type'] == 'linear':
+ grid_values = np.arange(float(config_overrides[f'{par_name}_limits'][0]),
+ float(config_overrides[f'{par_name}_limits'][1]),
+ float(config_overrides[f'{par_name}_step']))
+ else:
+ _step = float(config_overrides[f'{par_name}_step']) if float(config_overrides[f'{par_name}_step']) > 1 else 10 ** float(config_overrides[f'{par_name}_step'])
+ grid_values = 10 ** np.arange(np.log10(float(config_overrides[f'{par_name}_limits'][0])),
+ np.log10(float(config_overrides[f'{par_name}_limits'][1])),
+ np.log10(_step))
+ return np.around(grid_values, 2)
+
+
+def get_grid_properties(grid_config: dict,
+ keyword: str) -> list:
+ """
+ Compute the grid axes properties using the grid configuration dictionary. If less than 3 axes are provided, fill
+ the missing ones with the first axis.
+ :param grid_config: the dictionary containing the grid configuration metadata
+ :param keyword: the keyword to read
+ :return: a list of the requested keyword values over the three spatial axes
+ """
+ grid_property_list = [grid_config['dim1'][keyword]]
+ for axis_idx in range(2):
+ try:
+ grid_property_list.append(grid_config[f'dim{axis_idx + 2}'][keyword])
+ except KeyError:
+ grid_property_list.append(grid_config['dim1'][keyword])
+ return grid_property_list
+
+
+def parse_input_main() -> Tuple[str, str, np.array, np.array, list, int, str]:
+ """
+ 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.
+ power-law profiles)
+ :return: a tuple containing the dust profile type (isothermal/heated), the density profile (homogeneous/spherical),
+ the distinct values of dust temperatures and densities in the grid, the line pairs used to compute ratios,
+ the number of processes to be used in case of multiprocessing, and the model run_type string
+ """
+ default_config_name = 'config.yml'
+ stg_config = load_config_file(os.path.join('stg', 'config', default_config_name))
+ pl_density_idx = float(stg_config['grid']['density_powerlaw_idx'])
+ pl_dust_temperature_idx = float(stg_config['grid']['dust_temperature_powerlaw_idx'])
+ _model_type = 'spherical' if pl_density_idx != 0 else 'homogeneous'
+ _tdust_model_type = 'heated' if pl_dust_temperature_idx != 0 else 'isothermal'
+ config = load_config_file(os.path.join('config', default_config_name))
+ # grid definition
+ dust_temperatures = parse_grid_overrides(par_name='dust_temperature',
+ config=config)
+ densities = parse_grid_overrides(par_name='gas_density',
+ config=config)
+ line_pairs = config['overrides']['lines_to_process']
+ n_processes = validate_parameter(config['computation']['threads'], default=10)
+ return _tdust_model_type, _model_type, dust_temperatures, densities, line_pairs, n_processes, config['run_type']
+
+
+def read_abundance_variation_schema(line_config: dict) -> dict:
+ """
+ Read and fill the abundance variation schema, defined as a step function
+ :param line_config: the dictionary of the line configurations, for the abundances
+ :return: the dictionary of the abundance variations per species, with the abundance jump factor and the temperature
+ threshold at which evaporation happens
+ """
+ species_list = list(line_config['molecular_abundances'].keys())
+ results_dict = {}
+ for species in species_list:
+ results_dict[species] = {
+ 'threshold': 20,
+ 'abundance_jump': 1
+ }
+ if 'hot_core_specs' in line_config.keys():
+ if species in line_config['hot_core_specs']:
+ results_dict[species] = line_config['hot_core_specs'][species]
+ return results_dict
diff --git a/etl/assets/commons/training_utils.py b/etl/assets/commons/training_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8c30b8eb08e1540f3fb906d001b1bd32cda40b3
--- /dev/null
+++ b/etl/assets/commons/training_utils.py
@@ -0,0 +1,474 @@
+import logging
+import os
+import pickle
+from typing import Union, List, Tuple
+
+import numpy as np
+import pandas as pd
+from astropy import units as u
+from matplotlib import pyplot as plt
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.experimental import enable_halving_search_cv
+from sklearn.model_selection import HalvingGridSearchCV
+from sklearn.neighbors import KernelDensity
+from sklearn.preprocessing import QuantileTransformer
+from xgboost import XGBRegressor
+
+from assets.commons import (validate_parameter,
+ get_data)
+from assets.commons.grid_utils import (compute_analytic_profile,
+ compute_density_and_temperature_avg_profiles)
+
+
+def compute_and_add_similarity_cols(average_features_per_target_bin: pd.DataFrame,
+ input_df: pd.DataFrame,
+ similarity_bins: int,
+ columns_to_drop: Union[list, None] = None) -> pd.DataFrame:
+ """
+ Compute the cosine similarity between the input matrix and the average values split in bins of the target,
+ adding the columns to the input.
+ :param average_features_per_target_bin: the dataframe with the average values of the input per target bin
+ :param input_df: the input dataframe
+ :param columns_to_drop: the columns to drop from the input matrix before computing the cosine similarities
+ :param similarity_bins: number of bins used to group the target variable
+ :return: the input matrix with the similarity columns
+ """
+ _columns_to_drop = validate_parameter(columns_to_drop, default=[])
+ clean_df = input_df.copy().drop(columns=_columns_to_drop)
+ ordered_columns = average_features_per_target_bin.columns
+ similarities = cosine_similarity(clean_df[ordered_columns], average_features_per_target_bin[ordered_columns])
+ similarities = pd.DataFrame(similarities,
+ columns=[f'sim_{str(bin_idx).zfill(2)}' for bin_idx in range(similarity_bins)])
+ similarities.index = clean_df.index
+ return pd.merge(input_df, similarities, left_index=True, right_index=True, how='inner', validate='1:1', on=None)
+
+
+def plot_results(inferred_data: pd.DataFrame,
+ use_model_for_inference: str = None,
+ ratios_to_process: Union[List[List[str]], None] = None):
+ _use_model_for_inference = validate_parameter(
+ use_model_for_inference,
+ default='constant_abundance_p15_q05'
+ )
+ _ratios_to_process = validate_parameter(
+ ratios_to_process,
+ default=[['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']]
+ )
+
+ postprocessed_data = get_data(limit_rows=None,
+ use_model_for_inference=_use_model_for_inference)
+ plt.clf()
+ _, subplots = plt.subplots(nrows=2, ncols=3)
+ subplots[-1][-1].axis('off')
+ for idx, line_pairs in enumerate(_ratios_to_process):
+ subplots[int(idx / 3)][idx % 3].semilogx()
+ subplots[int(idx / 3)][idx % 3].scatter(10 ** inferred_data['avg_nh2'],
+ inferred_data[f'ratio_{line_pairs[0]}-{line_pairs[1]}'],
+ alpha=0.1)
+ subplots[int(idx / 3)][idx % 3].scatter(10 ** postprocessed_data['avg_nh2'],
+ postprocessed_data[f'ratio_{line_pairs[0]}-{line_pairs[1]}'],
+ alpha=0.01)
+ plt.savefig(f'training_results_{_use_model_for_inference}.png')
+
+
+def get_avg_profiles(
+ value_at_reference_density: float,
+ value_at_reference_temperature: float,
+ central_value_density: float = 1e8,
+ power_law_density: float = -1.5,
+ central_value_temperature: float = 2000,
+ power_law_temperature: float = -0.5,
+ maximum_radius: u.Quantity = 0.9 * u.pc,
+ distance_reference: u.Quantity = 0.5 * u.pc,
+ molecular_abundance: float = 1e-9,
+ threshold: float = 90,
+ abundance_jump: float = 1) -> Tuple[np.array, np.array, np.array, np.array]:
+ """
+ Get the average density, temperature, and standard deviation profiles along the line-of-sight, as well as the
+ molecular column density.
+
+ This function computes the average density, temperature, and standard deviation profiles along the LOS, together
+ with the molecular column density.
+ It first computes the density and temperature profiles analytically based on the specified parameters.
+ Then, it calculates the average density and temperature profiles along with related quantities such as the
+ molecular column density and the standard deviation of the species' number density using the computed
+ profiles and specified parameters.
+
+ :param value_at_reference_density (float): Reference value of the density profile.
+ :param value_at_reference_temperature (float): Reference value of the temperature profile.
+ :param central_value_density (float, optional): Central value of the density profile, and ceiling for this
+ parameter. Defaults to 1e8 cm^-3.
+ :param power_law_density (float, optional): Power-law index for the density profile. Defaults to -1.5.
+ :param central_value_temperature (float, optional): Central value of the temperature profile, and ceiling for
+ this parameter. Defaults to 2000 K.
+ :param power_law_temperature (float, optional): Power-law index for the temperature profile. Defaults to -0.5.
+ :param maximum_radius (u.Quantity, optional): Maximum radius within which to compute the profiles, larger radii
+ are assumed to be devoid of material. Defaults to 0.9 pc.
+ :param distance_reference (u.Quantity, optional): Reference distance at which the profiles values are specified.
+ Defaults to 0.5 pc.
+ :param molecular_abundance (float, optional): Molecular abundance for computing molecular number density.
+ Defaults to 1e-9 for E-CH3OH.
+ :param threshold (float, optional): Threshold in temperature above which E-CH3OH is sublimated from grains in
+ hot cores. Defaults to 90 K.
+ :param abundance_jump (float, optional): Abundance jump in hot cores. Defaults to 1, i.e. no jump.
+
+ :return: Tuple[np.array, np.array, np.array, np.array]
+ - Average density profile.
+ - Average temperature profile.
+ - Molecular column density.
+ - Standard deviation of molecular number density profile.
+ """
+ density_profile = compute_analytic_profile(central_value=central_value_density,
+ power_law_index=power_law_density,
+ maximum_radius=maximum_radius.to(u.cm).value,
+ value_at_reference=value_at_reference_density,
+ distance_reference=distance_reference.to(u.cm).value)
+ temperature_profile = compute_analytic_profile(central_value=central_value_temperature,
+ power_law_index=power_law_temperature,
+ maximum_radius=maximum_radius.to(u.cm).value,
+ value_at_reference=value_at_reference_temperature,
+ distance_reference=distance_reference.to(u.cm).value)
+ (avg_density_profile, avg_temperature_profile,
+ molecule_coldens, std_density) = compute_density_and_temperature_avg_profiles(
+ density_profile=density_profile,
+ temperature_profile=temperature_profile,
+ molecular_abundance=molecular_abundance,
+ threshold=threshold,
+ abundance_jump=abundance_jump
+ )
+ return (avg_density_profile.flatten('F'), avg_temperature_profile.flatten('F'),
+ molecule_coldens.flatten('F'), std_density.flatten('F'))
+
+
+def get_weights(data: pd.Series,
+ show_weights: bool = False,
+ bandwidth: float = 0.1,
+ subsample_fraction: float = 0.5) -> np.array:
+ """
+ Get weights for each data point using kernel density estimation (KDE).
+
+ :param data (pd.Series): Input data series for which weights are computed.
+ :param show_weights (bool, optional): Whether to interactively visualize the KDE and weights. Defaults to False.
+ :param bandwidth (float, optional): Bandwidth parameter for the KDE. Defaults to 0.1.
+ :param subsample_fraction (float, optional): Fraction of data to use for subsampling. Defaults to 0.5 to speed up
+ computation.
+
+ :return: np.array
+ Array of weights corresponding to each data point.
+
+ This function computes weights for each data point using kernel density estimation (KDE).
+ It fits a KDE model to the subsampled data and then evaluates the KDE at each data point
+ to obtain the weights. If specified, it also visualizes the KDE and weights.
+ """
+ kde = KernelDensity(bandwidth=bandwidth, kernel='epanechnikov')
+ kde.fit(np.array(data.sample(frac=subsample_fraction, random_state=3)).reshape(-1, 1))
+ tgt_min = data.min()
+ tgt_max = data.max()
+ ymin_kde = tgt_min - 1
+ ymax_kde = tgt_max + 1
+ grid = np.linspace(ymin_kde, ymax_kde, 100)
+ z = np.exp(kde.score_samples(list(grid.reshape(-1, 1))))
+ z_points = np.exp(kde.score_samples(list(data.values.reshape(-1, 1))))
+ weights = 1 / z_points
+ finite_weights_mask = np.isfinite(weights)
+ padding_value = np.nanmax(weights[finite_weights_mask])
+ weights = np.where(finite_weights_mask,
+ weights,
+ padding_value)
+ if show_weights is True:
+ plt.clf()
+ plt.plot(grid, z)
+ plt.scatter(data, z_points, marker='+')
+ plt.scatter(data, weights / weights.max(), marker='+')
+ plt.show()
+ return weights
+
+
+def split_data(merged: pd.DataFrame,
+ target_column: str,
+ predictor_columns: Union[list, None] = None,
+ test_models: Union[list, None] = None,
+ validation_models: Union[list, None] = None) -> Tuple[
+ np.array, np.array, np.array, np.array, np.array, np.array]:
+ """
+ This function splits the data into training, validation, and test sets. It also performs some data processing,
+ such as taking the logarithm for the specified columns.
+ :param merged (pd.DataFrame): The DataFrame containing both predictor and target columns.
+ :param target_column (str): The name of the target column in the DataFrame.
+ :param predictor_columns (Union[list, None], optional): A list of predictor column names to be used in training.
+ Defaults to None.
+ :param test_models (Union[list, None], optional): A list of values for the characteristic number density,
+ identifying the test data. If not specified, it uses 27000 cm^-3.
+ :param validation_models (Union[list, None], optional): A list of values for the characteristic number density,
+ identifying the validation data. If not specified, it uses 2187000 cm^-3
+
+ :return: Tuple[np.array, np.array, np.array, np.array, np.array, np.array]
+ - x_test: Predictor variables for the test dataset.
+ - x_train: Predictor variables for the training dataset.
+ - x_validation: Predictor variables for the validation dataset.
+ - y_test: Target variable for the test dataset.
+ - y_train: Target variable for the training dataset.
+ - y_validation: Target variable for the validation dataset.
+ """
+ _test_models = validate_parameter(test_models, default=[27000.0])
+ _validation_models = validate_parameter(validation_models, default=[2187000.0])
+ _predictor_columns = validate_parameter(
+ predictor_columns,
+ default=['nh2', 'tdust', 'avg_tdust', 'avg_nh2', 'molecule_column_density', 'std_nh2']
+ )
+ condition_test = merged['nh2'].isin(_test_models)
+ condition_validation = merged['nh2'].isin(_validation_models)
+ y = np.log10(merged[target_column].copy())
+ X = merged[_predictor_columns].copy()
+ nh2_list = [
+ 1.000e+03,
+ 3.000e+03,
+ 9.000e+03,
+ 2.700e+04,
+ 8.100e+04,
+ 2.430e+05,
+ 7.290e+05,
+ 2.187e+06,
+ 6.561e+06
+ ]
+ subsample = merged[
+ merged['nh2'].isin(nh2_list) & ~merged['nh2'].isin(_test_models) & ~merged['nh2'].isin(_validation_models)]
+ assert _test_models not in subsample.nh2.unique()
+ assert _validation_models not in subsample.nh2.unique()
+ y_sub = np.log10(subsample[target_column].copy())
+ x_sub = subsample[_predictor_columns].copy()
+ x_train = x_sub[(~condition_test) & (~condition_validation)].reset_index(drop=True)
+ y_train = y_sub[(~condition_test) & (~condition_validation)].reset_index(drop=True)
+ x_test = X[condition_test].reset_index(drop=True)
+ y_test = y[condition_test].reset_index(drop=True)
+ x_validation = X[condition_validation].reset_index(drop=True)
+ y_validation = y[condition_validation].reset_index(drop=True)
+ for x_df in (x_train, x_test, x_validation):
+ for column in ('nh2', 'tdust'):
+ x_df[column] = np.log10(x_df[column])
+ return x_test, x_train, x_validation, y_test, y_train, y_validation
+
+
+def train_ml_model(model_type: str,
+ model_kwargs: Union[dict, None],
+ x_train: pd.DataFrame,
+ y_train: pd.Series,
+ use_validation: bool = False,
+ x_validation: Union[None, pd.DataFrame] = None,
+ y_validation: Union[None, pd.Series] = None) -> Union[XGBRegressor, RandomForestRegressor]:
+ """
+ This function trains a machine learning model based on the specified model type and its corresponding parameters.
+ It supports training XGBoost, Random Forest, with hyperparamter optimization and potentially other models in the
+ future.
+ :param model_type (str): Type of the machine learning model to be trained. Allowed values are 'XGBoost',
+ 'XGBoost_gridsearch', 'auto_skl', 'RandomForest'.
+ :param model_kwargs (Union[dict, None]): Dictionary containing keyword arguments for initializing the model.
+ Defaults to None.
+ :param x_train (pd.DataFrame): Predictor variables for the training dataset.
+ :param y_train (pd.Series): Target variable for the training dataset.
+ :param use_validation (bool, optional): Whether to use a validation dataset for training during cross-validation and
+ optimization. Defaults to False.
+ :param x_validation (Union[None, pd.DataFrame], optional): Predictor variables for the validation dataset.
+ Defaults to None.
+ :param y_validation (Union[None, pd.Series], optional): Target variable for the validation dataset.
+ Defaults to None.
+
+ :return: Union[XGBRegressor, RandomForestRegressor]
+ Trained machine learning model.
+ """
+ allowed_model_types = ('XGBoost', 'XGBoost_gridsearch', 'auto_skl', 'RandomForest')
+ assert model_type in allowed_model_types
+ if model_type == 'XGBoost':
+ model = XGBRegressor(**model_kwargs)
+ elif model_type == 'XGBoost_gridsearch':
+ model = HalvingGridSearchCV(XGBRegressor(),
+ param_grid=model_kwargs['param_grid'],
+ **model_kwargs['param_gridsearch'],
+ refit=True,
+ verbose=0)
+ elif model_type == 'RandomForest':
+ model = RandomForestRegressor()
+ else:
+ raise RuntimeError('Model type requested not yet implemented')
+
+ if model_type in ('XGBoost', 'XGBoost_gridsearch', 'RandomForest'):
+ weights = get_weights(data=y_train, show_weights=False)
+ if use_validation is True:
+ model.fit(x_train, y_train, sample_weight=weights, eval_set=[(x_validation, y_validation)], verbose=0)
+ else:
+ model.fit(x_train, y_train, sample_weight=weights)
+ else:
+ model.fit(x_train, y_train)
+ return model
+
+
+def create_similarity_columns(model_root_folder: str,
+ target_id: str,
+ similarity_bins: int,
+ x_dataframes: dict,
+ y_train: pd.Series) -> dict:
+ """
+ This function evaluates and adds cosine-similarity columns based on the average features per target bin. It calculates
+ the average features per target bin using the training data, groups the data into bins based on target
+ values, and then computes similarity columns for each DataFrame in the input dictionary (train, test, validation).
+ Finally, it saves the average features per target bin to a pickle file and returns the updated DataFrame dictionary.
+
+ :param model_root_folder (str): The root folder where the trained model and other related files will be stored.
+ :param target_id (str): Identifier for the target variable.
+ :param similarity_bins (int): Number of bins for similarity calculation.
+ :param x_dataframes (dict): Dictionary containing DataFrame objects for training, validation, and test sets.
+ :param y_train (pd.Series): Target variable for the training dataset.
+
+ :return: dict
+ Dictionary containing updated DataFrame objects with added similarity columns.
+ """
+ pickle_outfile_name = os.path.join(model_root_folder, 'trained_model',
+ f'average_features_per_target_bin_{target_id}.pickle')
+ _x_dataframes = x_dataframes.copy()
+ bins = pd.cut(y_train, similarity_bins)
+ columns_to_drop = ([column for column in _x_dataframes['x_train'].columns if column.startswith('mom_zero_')]
+ + [column for column in _x_dataframes['x_train'].columns if column.startswith('ratio_')])
+ clean_train = _x_dataframes['x_train'].copy().drop(columns=columns_to_drop)
+ average_features_per_target_bin = clean_train.groupby(bins).mean()
+ for key in _x_dataframes.keys():
+ _x_dataframes[key] = compute_and_add_similarity_cols(
+ average_features_per_target_bin=average_features_per_target_bin,
+ columns_to_drop=columns_to_drop,
+ input_df=_x_dataframes[key],
+ similarity_bins=similarity_bins)
+ if os.path.isfile(pickle_outfile_name):
+ os.remove(pickle_outfile_name)
+ with open(pickle_outfile_name, 'wb') as outfile:
+ pickle.dump(average_features_per_target_bin, outfile)
+ return _x_dataframes
+
+
+def train_models_wrapper(target_name: str,
+ model_type: str,
+ model_kwargs: dict,
+ logger: logging.Logger,
+ similarity_bins: Union[None, int] = 50,
+ limit_rows: int = None,
+ model_root_folder: Union[str, None] = None,
+ use_validation: bool = False):
+ """
+ Train machine learning models for a specified target variable.
+
+ :param target_name (str): Name of the target variable in the training set.
+ :param model_type (str): Type of the machine learning model to be trained.
+ :param model_kwargs (dict): Dictionary containing keyword arguments for initializing the model.
+ :param similarity_bins (Union[None, int], optional): Number of bins for similarity calculation. Defaults to 50.
+ :param limit_rows (int, optional): Limit the number of rows for preprocessing. Defaults to None.
+ :param model_root_folder (Union[str, None], optional): Root folder where the trained model and other related files
+ will be stored. if not specified, the fiducial model is assumed, i.e. 'prs/output/run_type/constant_abundance_p15_q05'.
+ :param use_validation (bool, optional): Whether to use a validation dataset for training. Defaults to False.
+ :param logger (logging.Logger): Logger object for logging messages.
+ """
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ logger.info(f"Training for target {target_name}")
+ target_id = target_name.split('_')[-1]
+ x_dataframes, y_series = preprocess_data(model_root_folder=_model_root_folder,
+ similarity_bins=similarity_bins,
+ target_name=target_name,
+ limit_rows=limit_rows,
+ logger=logger)
+
+ model = train_ml_model(model_type=model_type,
+ model_kwargs=model_kwargs,
+ x_train=x_dataframes['x_train'],
+ y_train=y_series['y_train'],
+ x_validation=x_dataframes['x_test'],
+ y_validation=y_series['y_test'],
+ use_validation=use_validation)
+ with open(os.path.join(_model_root_folder, 'trained_model', f'ml_model_{target_id}.pickle'), 'wb') as outfile:
+ pickle.dump(model, outfile)
+ inspect_training(model=model,
+ x_dataframes=x_dataframes,
+ y_series=y_series,
+ logger=logger)
+
+
+def inspect_training(model: Union[XGBRegressor, RandomForestRegressor],
+ x_dataframes: dict,
+ y_series: dict,
+ logger: logging.Logger):
+ """
+ Inspect the training process results and evaluate the model.
+
+ :param model (Union[XGBRegressor, RandomForestRegressor]): Trained machine learning model.
+ :param x_dataframes (dict): Dictionary containing DataFrame objects for training, validation, and test sets.
+ :param y_series (dict): Dictionary containing target variable Series for training, validation, and test sets.
+ :param logger (logging.Logger): Logger object for logging messages.
+ """
+ pred = model.predict(x_dataframes['x_test'])
+ pred_val = model.predict(x_dataframes['x_validation'])
+ pred_train = model.predict(x_dataframes['x_train'])
+ mse = mean_squared_error(y_series['y_test'], pred)
+ mse_validation = mean_squared_error(y_series['y_validation'], pred_val)
+ mse_train = mean_squared_error(y_series['y_train'], pred_train)
+ logger.info(f'MSE {mse}, MSE validation {mse_validation}, MSE train {mse_train}')
+ plt.clf()
+ plt.scatter(10 ** y_series['y_test'], 10 ** pred)
+ plt.scatter(10 ** y_series['y_validation'], 10 ** pred_val)
+ plt.scatter(10 ** y_series['y_train'], 10 ** pred_train)
+ plt.loglog()
+ y_low = np.min(10 ** y_series['y_train'])
+ y_high = np.max(10 ** y_series['y_train'])
+ plt.plot([y_low, y_high], [y_low, y_high], color='r')
+ plt.show()
+
+
+def preprocess_data(model_root_folder: str,
+ similarity_bins: Union[None, int],
+ target_name: str,
+ logger: logging.Logger,
+ limit_rows: int = None) -> Tuple[dict, dict]:
+ """
+ Preprocess the data for training machine learning models.
+
+ :param model_root_folder (str): Root folder where the trained model and other related files will be stored.
+ :param similarity_bins (Union[None, int]): Number of bins for similarity calculation.
+ :param target_name (str): Name of the target variable.
+ :param limit_rows (int, optional): Limit the number of rows for preprocessing. Defaults to None.
+ :param logger (logging.Logger): Logger object for logging messages.
+ :return: Tuple[dict, dict]
+ - Dictionary containing DataFrame objects for training, validation, and test sets.
+ - Dictionary containing target variable Series for training, validation, and test sets.
+ """
+ target_id = target_name.split('_')[-1]
+ merged = get_data(limit_rows,
+ use_model_for_inference=model_root_folder.split(os.path.sep)[-1])
+ x_test, x_train, x_validation, y_test, y_train, y_validation = split_data(merged=merged,
+ target_column=target_name)
+ x_dataframes = {
+ 'x_train': x_train,
+ 'x_test': x_test,
+ 'x_validation': x_validation
+ }
+ if similarity_bins is not None:
+ x_dataframes = create_similarity_columns(model_root_folder=model_root_folder,
+ target_id=target_id,
+ similarity_bins=similarity_bins,
+ x_dataframes=x_dataframes,
+ y_train=y_train)
+ columns = x_dataframes['x_train'].columns
+ logger.info(f'Feature used for training {columns}')
+ scaler = QuantileTransformer(output_distribution='uniform')
+ scaler.fit(X=x_dataframes['x_train'])
+ for key in x_dataframes.keys():
+ x_dataframes[key] = pd.DataFrame(scaler.transform(X=x_dataframes[key]), columns=columns)
+ logger.info(f'Train {len(x_train)}, test {len(x_test)}, validation {len(x_validation)}')
+
+ with open(os.path.join(model_root_folder, 'trained_model', f'ml_scaler_{target_id}.pickle'), 'wb') as outfile:
+ pickle.dump(scaler, outfile)
+ y_series = {
+ 'y_train': y_train,
+ 'y_test': y_test,
+ 'y_validation': y_validation
+ }
+ return x_dataframes, y_series
diff --git a/etl/assets/constants/__init__.py b/etl/assets/constants/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7edfe121af620be158ac6307f746de2eb0ada70
--- /dev/null
+++ b/etl/assets/constants/__init__.py
@@ -0,0 +1,183 @@
+import numpy as np
+from .physical_constants import mean_molecular_mass
+
+polaris_grid_map = {
+ 'spherical': 30
+}
+
+polaris_data_map = {
+ "gas_number_density": 0,
+ "dust_temperature": 2,
+ "gas_temperature": 3,
+ "molecular_abundance_mass_ratio": 17
+}
+
+radmc_grid_map = {
+ 'regular': 0,
+ 'spherical': 100
+}
+
+radmc_coord_system_map = {
+ 'cartesian': 0,
+ 'polar': 100
+}
+
+radmc_input_headers = {
+ 'amr_grid.inp': ['iformat', 'grid_type', 'coordinate_system', 'grid_info', 'active_axes', 'ncells_per_axis'],
+ 'dust_density.inp': ['iformat', 'ncells', 'dust_species'],
+ 'dust_temperature.dat': ['iformat', 'ncells', 'dust_species'],
+ 'microturbulence.inp': ['iformat', 'ncells'],
+ 'gas_velocity.inp': ['iformat', 'ncells'],
+ 'gas_temperature.inp': ['iformat', 'ncells'],
+ 'wavelength_micron.inp': ['continuum_lambdas'],
+ 'numberdens_mol.inp': ['iformat', 'ncells'],
+ 'escprob_lengthscale.inp': ['iformat', 'ncells'],
+ 'stars.inp': ['iformat', 'nstars', 'continuum_lambdas', 'stars_properties', 'lambdas']
+}
+
+leiden_url_mapping = {
+ 'co': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/co.dat',
+ 'e-ch3oh': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/e-ch3oh.dat',
+ 'a-ch3oh': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/ch3oh_a.dat'
+}
+
+radmc_lines_mode_mapping = {
+ 'lte': 1,
+ 'user_defined_populations': 2,
+ 'lvg': 3,
+ 'optically_thin_non_lte': 4,
+}
+
+radmc_options_mapping = {
+ 'inclination': 'incl',
+ 'position_angle': 'phi',
+ 'imolspec': 'imolspec',
+ 'iline': 'iline',
+ 'width_kms': 'widthkms',
+ 'nchannels': 'linenlam',
+ 'npix': 'npix',
+ 'image_size_pc': 'sizepc',
+ 'threads': 'setthreads'
+}
+
+aggregation_function_mapping = {
+ 'mean': np.nanmean,
+ 'sum': np.nansum,
+ 'median': np.nanmedian
+}
+
+line_ratio_mapping = {
+ '87-86': r'$I(2_{0}-1_{0})/I(2_{-1}-1_{-1})$',
+ '88-86': r'$I(2_{1}-1_{1})/I(2_{-1}-1_{-1})$',
+ '88-87': r'$I(2_{1}-1_{1})/I(2_{0}-1_{0})$',
+ '257-256': r'$I(5_{-1}-4_{-1})/I(5_{0}-4_{0})$',
+ '381-380': r'$I(7_{-1}-6_{-1})/I(7_{0}-6_{0})$'
+}
+
+config_overrides = {
+ 'isothermal_p15': {
+ 'grid': {
+ 'power_law_temperature': 0
+ }
+ },
+ 'isothermal_p12': {
+ 'grid': {
+ 'power_law_density': -1.2,
+ 'power_law_temperature': 0
+ }
+ },
+ 'isothermal_p18': {
+ 'grid': {
+ 'power_law_density': -1.8,
+ 'power_law_temperature': 0
+ }
+ },
+ 'constant_abundance_p15_q05': {},
+ 'constant_abundance_p12_q05': {
+ 'grid': {
+ 'power_law_density': -1.2,
+ }
+ },
+ 'constant_abundance_p18_q05': {
+ 'grid': {
+ 'power_law_density': -1.8,
+ }
+ },
+ 'constant_abundance_p15_q05_x01': {
+ 'lines': {
+ 'molecular_abundances': {
+ "e-ch3oh": 1e-10
+ }
+ }
+ },
+ 'constant_abundance_p15_q05_x10': {
+ 'lines': {
+ 'molecular_abundances': {
+ "e-ch3oh": 1e-8
+ }
+ }
+ },
+ 'hot_core_p15_q05': {
+ 'lines': {
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 100
+ }
+ }
+ }
+ },
+ 'hot_core_p12_q05': {
+ 'grid': {
+ 'power_law_density': -1.2,
+ },
+ 'lines': {
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 100
+ }
+ }
+ }
+ },
+ 'hot_core_p18_q05': {
+ 'grid': {
+ 'power_law_density': -1.8,
+ },
+ 'lines': {
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 100
+ }
+ }
+ }
+ },
+ 'hot_core_x10': {
+ 'lines': {
+ 'molecular_abundances': {
+ "e-ch3oh": 1e-8
+ },
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 100
+ }
+ }
+ }
+ },
+ 'hot_core_x01': {
+ 'lines': {
+ 'molecular_abundances': {
+ "e-ch3oh": 1e-10
+ },
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 100
+ }
+ }
+ }
+ },
+ 'double_microturbulence': {},
+}
diff --git a/etl/assets/constants/physical_constants.py b/etl/assets/constants/physical_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f15a133768986cc1dec68941e3eb71357413d79
--- /dev/null
+++ b/etl/assets/constants/physical_constants.py
@@ -0,0 +1,2 @@
+mean_molecular_mass = 2.34
+speed_of_light = 2.99792458e10 #cm/s
diff --git a/etl/assets/data/ch3oh_data_top35.csv b/etl/assets/data/ch3oh_data_top35.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0a2dba5201d4a8b988981b978cbcaf473526222a
--- /dev/null
+++ b/etl/assets/data/ch3oh_data_top35.csv
@@ -0,0 +1,32 @@
+source_name,mass,distance,diameter,class,spectrum_index,tpeak_86,tpeak_unc_86,rms_noise_86,linewidth_mhz_86,linewidth_mhz_unc_86,linewidth_86,linewidth_unc_86,tpeak_87,tpeak_unc_87,rms_noise_87,linewidth_mhz_87,linewidth_mhz_unc_87,linewidth_87,linewidth_unc_87,tpeak_88,tpeak_unc_88,rms_noise_88,linewidth_mhz_88,linewidth_mhz_unc_88,linewidth_88,linewidth_unc_88,ratio_87-86,ratio_88-87,ratio_88-86,area_86,area_unc_86,area_87,area_unc_87,area_88,area_unc_88,tpeak_256,tpeak_unc_256,rms_noise_256,linewidth_mhz_256,linewidth_mhz_unc_256,linewidth_256,linewidth_unc_256,tpeak_257,tpeak_unc_257,rms_noise_257,linewidth_mhz_257,linewidth_mhz_unc_257,linewidth_257,linewidth_unc_257,ratio_257-256,area_256,area_unc_256,area_257,area_unc_257,tpeak_380,tpeak_unc_380,rms_noise_380,linewidth_mhz_380,linewidth_mhz_unc_380,linewidth_380,linewidth_unc_380,tpeak_381,tpeak_unc_381,rms_noise_381,linewidth_mhz_381,linewidth_mhz_unc_381,linewidth_381,linewidth_unc_381,ratio_381-380,area_381,area_unc_381,area_380,area_unc_380,AGAL_name,apex_name
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diff --git a/etl/assets/data/tab_top100_carsten_sort.csv b/etl/assets/data/tab_top100_carsten_sort.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a025d3ec583c819845704b9f7fbc06f2500f1637
--- /dev/null
+++ b/etl/assets/data/tab_top100_carsten_sort.csv
@@ -0,0 +1,113 @@
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diff --git a/etl/assets/data/top100_density.csv b/etl/assets/data/top100_density.csv
new file mode 100644
index 0000000000000000000000000000000000000000..049d372528f5648f98465cef2e32c7f80dde16c3
--- /dev/null
+++ b/etl/assets/data/top100_density.csv
@@ -0,0 +1,116 @@
+# select
+# agal_name
+# , round("N_H2_peak" / (2 * r_fwhm * 3.0856e18)) as volume_density
+# from top100_sed_data tsd
+"agal_name","volume_density"
+AGAL006.216-00.609,89795.0
+AGAL008.671-00.356,190969.0
+AGAL008.684-00.367,68138.0
+AGAL008.706-00.414,27785.0
+AGAL008.831-00.027,NaN
+AGAL010.444-00.017,22907.0
+AGAL010.472+00.027,366225.0
+AGAL010.624-00.384,299726.0
+AGAL012.804-00.199,375774.0
+AGAL013.178+00.059,68826.0
+AGAL013.658-00.599,52684.0
+AGAL014.114-00.574,75908.0
+AGAL014.194-00.194,78815.0
+AGAL014.492-00.139,74017.0
+AGAL014.632-00.577,127991.0
+AGAL015.029-00.669,209966.0
+AGAL018.606-00.074,43782.0
+AGAL018.734-00.226,19630.0
+AGAL018.888-00.474,56893.0
+AGAL019.609-00.234,92787.0
+AGAL019.882-00.534,136234.0
+AGAL022.376+00.447,82445.0
+AGAL023.206-00.377,163688.0
+AGAL024.416+00.101,15957.0
+AGAL024.629+00.172,15443.0
+AGAL024.651-00.169,7869.0
+AGAL024.789+00.082,108737.0
+AGAL028.564-00.236,37289.0
+AGAL028.861+00.066,19217.0
+AGAL030.818-00.056,386655.0
+AGAL030.848-00.081,23220.0
+AGAL030.893+00.139,49987.0
+AGAL031.412+00.307,389457.0
+AGAL034.258+00.154,1700013.0
+AGAL034.401+00.226,218025.0
+AGAL034.411+00.234,495365.0
+AGAL034.821+00.351,68475.0
+AGAL035.197-00.742,228801.0
+AGAL035.579+00.007,8844.0
+AGAL037.554+00.201,27842.0
+AGAL043.166+00.011,221873.0
+AGAL049.489-00.389,864473.0
+AGAL053.141+00.069,184095.0
+AGAL059.782+00.066,94354.0
+AGAL301.136-00.226,259527.0
+AGAL305.192-00.006,44264.0
+AGAL305.209+00.206,183284.0
+AGAL305.562+00.014,50295.0
+AGAL305.794-00.096,30607.0
+AGAL309.384-00.134,34424.0
+AGAL310.014+00.387,38275.0
+AGAL313.576+00.324,56581.0
+AGAL316.641-00.087,119766.0
+AGAL317.867-00.151,159134.0
+AGAL318.779-00.137,34706.0
+AGAL320.881-00.397,16371.0
+AGAL326.661+00.519,93292.0
+AGAL326.987-00.032,74912.0
+AGAL327.119+00.509,29492.0
+AGAL327.293-00.579,1286938.0
+AGAL327.393+00.199,36543.0
+AGAL328.809+00.632,350565.0
+AGAL329.029-00.206,34454.0
+AGAL329.066-00.307,14237.0
+AGAL330.879-00.367,218474.0
+AGAL330.954-00.182,322707.0
+AGAL331.709+00.582,23291.0
+AGAL332.094-00.421,83157.0
+AGAL332.826-00.549,407894.0
+AGAL333.134-00.431,200007.0
+AGAL333.284-00.387,117459.0
+AGAL333.314+00.106,53337.0
+AGAL333.604-00.212,210139.0
+AGAL333.656+00.059,21760.0
+AGAL335.789+00.174,135994.0
+AGAL336.958-00.224,28640.0
+AGAL337.176-00.032,11767.0
+AGAL337.258-00.101,40725.0
+AGAL337.286+00.007,21253.0
+AGAL337.406-00.402,266919.0
+AGAL337.704-00.054,76912.0
+AGAL337.916-00.477,386876.0
+AGAL338.066+00.044,15655.0
+AGAL338.786+00.476,38848.0
+AGAL338.926+00.554,154575.0
+AGAL339.623-00.122,40057.0
+AGAL340.374-00.391,66279.0
+AGAL340.746-01.001,43744.0
+AGAL340.784-00.097,30196.0
+AGAL341.217-00.212,77596.0
+AGAL342.484+00.182,20980.0
+AGAL343.128-00.062,280086.0
+AGAL343.756-00.164,319987.0
+AGAL344.227-00.569,609039.0
+AGAL345.003-00.224,303461.0
+AGAL345.488+00.314,225021.0
+AGAL345.504+00.347,168516.0
+AGAL345.718+00.817,90728.0
+AGAL351.131+00.771,43470.0
+AGAL351.161+00.697,743236.0
+AGAL351.244+00.669,238178.0
+AGAL351.416+00.646,2489024.0
+AGAL351.444+00.659,1201754.0
+AGAL351.496+00.691,78226.0
+AGAL351.571+00.762,61074.0
+AGAL351.581-00.352,234338.0
+AGAL351.774-00.537,2754463.0
+AGAL353.066+00.452,153199.0
+AGAL353.409-00.361,202379.0
+AGAL353.417-00.079,8376.0
+AGAL354.944-00.537,46271.0
diff --git a/etl/assets/utils.py b/etl/assets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a62eac3f0cf379a8e9629111a9d1cad780ccf339
--- /dev/null
+++ b/etl/assets/utils.py
@@ -0,0 +1,52 @@
+import os
+import pandas as pd
+import yaml
+from assets.constants import config_overrides
+from typing import (Union,
+ Tuple)
+from assets.commons import validate_parameter
+
+
+def get_poc_results(line_fit_filename: str) -> pd.DataFrame:
+ df_class = pd.read_csv(os.path.join('assets', 'data', line_fit_filename))
+ with open(os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05', 'volume_density_results.yml'),
+ 'r') as infile:
+ fit_results = yaml.load(infile, Loader=yaml.FullLoader)
+ df_results = pd.DataFrame.from_dict(fit_results, orient='index')
+ df_full = (df_results.merge(df_class, left_index=True, right_on='source_name', how='inner', validate='1:1')
+ .dropna(subset='best_fit'))
+ df_full['class_phys'] = 'HII'
+ df_full.loc[df_full['class'] == '70w', 'class_phys'] = 'Quiescent'
+ df_full.loc[df_full['class'] == 'IRw', 'class_phys'] = 'Protostellar'
+ df_full.loc[df_full['class'] == 'IRb', 'class_phys'] = 'MYSOs'
+ return df_full
+
+
+def get_profile_input(
+ model_root_folder: str,
+ species: Union[None, str] = None) -> Tuple[dict, dict]:
+ overrides = config_overrides[model_root_folder.split(os.path.sep)[-1]]
+ _species = validate_parameter(species, default='e-ch3oh')
+ try:
+ overrides_grid = overrides['grid']
+ except KeyError:
+ overrides_grid = {}
+ try:
+ overrides_lines = overrides['lines']
+ except KeyError:
+ overrides_lines = {}
+ overrides_abundance = {}
+
+ try:
+ overrides_abundance['molecular_abundance'] = overrides_lines['molecular_abundances'][_species]
+ except KeyError:
+ pass
+
+ for key in ['abundance_jump', 'threshold']:
+ try:
+ overrides_abundance[key] = overrides_lines['hot_core_specs'][_species][key]
+ except KeyError:
+ pass
+
+ return overrides_grid, overrides_abundance
+
diff --git a/etl/config/config.yml b/etl/config/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..dedec755ecf9770f1d315f2aa2b3ea646c370385
--- /dev/null
+++ b/etl/config/config.yml
@@ -0,0 +1,14 @@
+run_type: constant_abundance_p15_q05
+
+computation:
+ threads: 10
+
+overrides:
+ dust_temperature_grid_type: 'linear'
+ dust_temperature_limits: [10, 35]
+ dust_temperature_step: 2.5
+ gas_density_grid_type: 'log'
+ gas_density_limits: [1e3, 1e7]
+ gas_density_step: 3
+ gas_density_unit: cm^-3
+ lines_to_process: [['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']]
diff --git a/etl/config/density_inference_input.yml b/etl/config/density_inference_input.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ff8352ba5b0ff6f47d62da4b6f4fdc432113b317
--- /dev/null
+++ b/etl/config/density_inference_input.yml
@@ -0,0 +1,473 @@
+best_bandwidths:
+ 257-256: 0.4
+ 381-380: 0.2
+ 87-86: 0.1
+ 88-86: 0.1
+ 88-87: 0.1
+integrated_intensities_uncertainties:
+ '256':
+ G08.71-0.41: 0.3772371124870143
+ G10.45-0.02: 0.3438149090944644
+ G10.47+0.03: 4.67062615566759
+ G10.62-0.38: 5.717801509967671
+ G12.81-0.20: 2.088877254036277
+ G13.18+0.06: 1.43381518306444
+ G13.66-0.60: 1.1620189309770297
+ G14.11-0.57: 0.3933488163719442
+ G14.19-0.19: 1.9966333535808136
+ G14.49-0.14: 0.5232957299744114
+ G14.63-0.58: 0.6760059683722098
+ G15.03-0.67: 1.3201864103978675
+ G18.61-0.07: 0.449933406945426
+ G18.73-0.23: 1.1925529389006952
+ G18.89-0.47: 1.2420175138135696
+ G19.88-0.54: 1.1775666735052563
+ G22.37+0.45: 0.8397889239422417
+ G23.21-0.38: 2.366268748274346
+ G28.56-0.24: 0.7853900725084517
+ G28.86+0.07: 0.5
+ G30.85-0.08: 0.5698331975250233
+ G31.41+0.31: 2.794406439049449
+ G34.26+0.15: 5.571485634832507
+ G34.40+0.23: 2.4222527304195376
+ G34.41+0.23: 2.455950237377704
+ G34.82+0.35: 0.1947198188044887
+ G35.20-0.74: 1.9116488388286448
+ G37.55+0.20: 0.7800057066048043
+ G49.49-0.39: 13.79415939155848
+ G53.14+0.07: 0.9832922403050368
+ G59.78+0.07: 0.5
+ '257':
+ G08.71-0.41: 0.4136351969963581
+ G10.45-0.02: 0.3567836016634692
+ G10.47+0.03: 4.737191542189759
+ G10.62-0.38: 5.828217319934533
+ G12.81-0.20: 2.159742388795141
+ G13.18+0.06: 1.528153771626887
+ G13.66-0.60: 1.2364636458899043
+ G14.11-0.57: 0.4315807681683414
+ G14.19-0.19: 2.1114871424317903
+ G14.49-0.14: 0.5631062582040194
+ G14.63-0.58: 0.7362604233371383
+ G15.03-0.67: 1.3883181061379544
+ G18.61-0.07: 0.4918960784434188
+ G18.73-0.23: 1.2489705007902534
+ G18.89-0.47: 1.30997858860221
+ G19.88-0.54: 1.2556873263733366
+ G22.37+0.45: 0.9012219713131504
+ G23.21-0.38: 2.4463347346958884
+ G28.56-0.24: 0.8256372552866202
+ G28.86+0.07: 0.5
+ G30.85-0.08: 0.5939589107713851
+ G31.41+0.31: 2.830674270184918
+ G34.26+0.15: 5.689969110997285
+ G34.40+0.23: 2.5501966207773785
+ G34.41+0.23: 2.586820094338446
+ G34.82+0.35: 0.2153653834587912
+ G35.20-0.74: 2.009289929523078
+ G37.55+0.20: 0.8302500355158099
+ G49.49-0.39: 13.996345324993044
+ G53.14+0.07: 1.037736129080711
+ G59.78+0.07: 0.5
+ '380':
+ G08.71-0.41: 0.1719132347425028
+ G10.45-0.02: 0.3350748768496078
+ G10.47+0.03: 2.5238526974413102
+ G10.62-0.38: 1.9730421303059005
+ G12.81-0.20: 0.4938645969760634
+ G13.18+0.06: 0.3130453732259515
+ G13.66-0.60: 0.3896020198637059
+ G14.11-0.57: 0.1604869867792384
+ G14.19-0.19: 0.3718943617922549
+ G14.49-0.14: 0.3980987437341203
+ G14.63-0.58: 0.1942770443673398
+ G15.03-0.67: 0.4226996779770496
+ G18.61-0.07: 0.3104707669438686
+ G18.73-0.23: 0.3983825447868095
+ G18.89-0.47: 0.3283273384950035
+ G19.88-0.54: 0.297825582498799
+ G22.37+0.45: 0.2435883035421827
+ G23.21-0.38: 0.4720878606853434
+ G28.56-0.24: 0.3689771528703325
+ G28.86+0.07: 0.2133053661117121
+ G30.85-0.08: 0.4002321844656681
+ G31.41+0.31: 1.7677726204007878
+ G34.26+0.15: 2.2053525649662755
+ G34.40+0.23: 0.3157737464671486
+ G34.41+0.23: 0.552143182260122
+ G34.82+0.35: 0.3762696495121889
+ G35.20-0.74: 0.3277361642527309
+ G37.55+0.20: 0.3505786313165484
+ G49.49-0.39: 6.458962659096731
+ G53.14+0.07: 0.2659096119306583
+ G59.78+0.07: 0.1958691834258987
+ '381':
+ G08.71-0.41: 0.1912145881150111
+ G10.45-0.02: 0.348675803268881
+ G10.47+0.03: 2.527338233134208
+ G10.62-0.38: 2.000038955895338
+ G12.81-0.20: 0.5078276831867471
+ G13.18+0.06: 0.3327236010567068
+ G13.66-0.60: 0.4023699128083662
+ G14.11-0.57: 0.1778607797165793
+ G14.19-0.19: 0.3901217659238757
+ G14.49-0.14: 0.410805519891472
+ G14.63-0.58: 0.2096518477508132
+ G15.03-0.67: 0.4404189229260346
+ G18.61-0.07: 0.3250267274896626
+ G18.73-0.23: 0.4111428033705079
+ G18.89-0.47: 0.3562705793927619
+ G19.88-0.54: 0.3054447929243074
+ G22.37+0.45: 0.2565267403612208
+ G23.21-0.38: 0.47971744770208
+ G28.56-0.24: 0.3858032381571517
+ G28.86+0.07: 0.2244369344927569
+ G30.85-0.08: 0.4287274799425559
+ G31.41+0.31: 1.7849749705573348
+ G34.26+0.15: 2.2213861922198466
+ G34.40+0.23: 0.3283727163284978
+ G34.41+0.23: 0.5631980961359404
+ G34.82+0.35: 0.3873761358129582
+ G35.20-0.74: 0.3362751271550414
+ G37.55+0.20: 0.3531542428043672
+ G49.49-0.39: 6.498419983328799
+ G53.14+0.07: 0.278825242987693
+ G59.78+0.07: 0.2103971057794001
+ '86':
+ G08.71-0.41: 0.1141338092612959
+ G10.45-0.02: 0.1513227343419588
+ G10.47+0.03: 0.2436313155314365
+ G10.62-0.38: 0.2543853719744313
+ G12.81-0.20: 0.2120144583210657
+ G13.18+0.06: 0.1444849558699236
+ G13.66-0.60: 0.1880743486996585
+ G14.11-0.57: 0.0991920101690233
+ G14.19-0.19: 0.2591102799195194
+ G14.49-0.14: 0.1932137455379076
+ G14.63-0.58: 0.0920867956240891
+ G15.03-0.67: 0.1519740165468335
+ G18.61-0.07: 0.1336149687864388
+ G18.73-0.23: 0.1544601974519147
+ G18.89-0.47: 0.284814022368405
+ G19.88-0.54: 0.1238378758928978
+ G22.37+0.45: 0.1935045511182322
+ G23.21-0.38: 0.1601721389776916
+ G28.56-0.24: 0.1699145015331222
+ G28.86+0.07: 0.1037452815160559
+ G30.85-0.08: 0.1481775324641313
+ G31.41+0.31: 0.2194502880473983
+ G34.26+0.15: 0.1627530591328137
+ G34.40+0.23: 0.1351065672329636
+ G34.41+0.23: 0.2465062371409508
+ G34.82+0.35: 0.0865664840020827
+ G35.20-0.74: 0.1340658506805993
+ G37.55+0.20: 0.1253184895008262
+ G49.49-0.39: 0.2791842617996957
+ G53.14+0.07: 0.0768660912353171
+ G59.78+0.07: 0.0574168367111523
+ '87':
+ G08.71-0.41: 0.111937365388756
+ G10.45-0.02: 0.1491063536449446
+ G10.47+0.03: 0.2419807697773685
+ G10.62-0.38: 0.2507814699869695
+ G12.81-0.20: 0.2090805176901697
+ G13.18+0.06: 0.1418278810377906
+ G13.66-0.60: 0.1841371839860628
+ G14.11-0.57: 0.097470593166154
+ G14.19-0.19: 0.2519127822081297
+ G14.49-0.14: 0.188762628907307
+ G14.63-0.58: 0.0892839165361122
+ G15.03-0.67: 0.1499962128329128
+ G18.61-0.07: 0.1315931235851593
+ G18.73-0.23: 0.1517892338894
+ G18.89-0.47: 0.2784484436939946
+ G19.88-0.54: 0.121808079112921
+ G22.37+0.45: 0.1892876467707792
+ G23.21-0.38: 0.1576533484208159
+ G28.56-0.24: 0.1640239796382693
+ G28.86+0.07: 0.1023581134022235
+ G30.85-0.08: 0.145424188960802
+ G31.41+0.31: 0.2170064162555401
+ G34.26+0.15: 0.16145840413561
+ G34.40+0.23: 0.1324170108062351
+ G34.41+0.23: 0.2427485184738539
+ G34.82+0.35: 0.0851539809088609
+ G35.20-0.74: 0.1319126641393791
+ G37.55+0.20: 0.122848111292009
+ G49.49-0.39: 0.2773996831748604
+ G53.14+0.07: 0.075643530717601
+ G59.78+0.07: 0.0555749088316803
+ '88':
+ G08.71-0.41: 0.1118095857454675
+ G10.45-0.02: 0.1483259532833787
+ G10.47+0.03: 0.2409668716291754
+ G10.62-0.38: 0.2495329007627918
+ G12.81-0.20: 0.2084495919578603
+ G13.18+0.06: 0.1414209323698791
+ G13.66-0.60: 0.1831553534762108
+ G14.11-0.57: 0.0972920392752167
+ G14.19-0.19: 0.2507189304794556
+ G14.49-0.14: 0.1882827587313301
+ G14.63-0.58: 0.0890047760093749
+ G15.03-0.67: 0.1495172793656858
+ G18.61-0.07: 0.1313898333367211
+ G18.73-0.23: 0.1513505926924855
+ G18.89-0.47: 0.2779301291723239
+ G19.88-0.54: 0.121507432969849
+ G22.37+0.45: 0.1889958485649907
+ G23.21-0.38: 0.1568877828888521
+ G28.56-0.24: 0.1635890088005906
+ G28.86+0.07: 0.1020862031904043
+ G30.85-0.08: 0.1451689199961562
+ G31.41+0.31: 0.2158655413134478
+ G34.26+0.15: 0.1607951465382442
+ G34.40+0.23: 0.1320891607520888
+ G34.41+0.23: 0.2421195456012388
+ G34.82+0.35: 0.0850472508528964
+ G35.20-0.74: 0.1316317053747658
+ G37.55+0.20: 0.1222906026734552
+ G49.49-0.39: 0.2758738141000653
+ G53.14+0.07: 0.0755106793427082
+ G59.78+0.07: 0.0553352170005506
+measured_integrated_intensities:
+ '256':
+ G08.71-0.41: 0.2225977131194544
+ G10.45-0.02: 0.6483658492610417
+ G10.47+0.03: 12.781079959863508
+ G10.62-0.38: 14.026812217900536
+ G12.81-0.20: 4.594177385112016
+ G13.18+0.06: 2.2560621827614065
+ G13.66-0.60: 2.2460391103183417
+ G14.11-0.57: 0.4209070251778891
+ G14.19-0.19: 3.080405790891515
+ G14.49-0.14: 0.7458559310555399
+ G14.63-0.58: 1.2815176351639286
+ G15.03-0.67: 2.5627713663667446
+ G18.61-0.07: 0.6336927890231682
+ G18.73-0.23: 1.7197328016314029
+ G18.89-0.47: 2.044797825251944
+ G19.88-0.54: 2.689820011325686
+ G22.37+0.45: 1.0252721670025875
+ G23.21-0.38: 5.128448315345341
+ G28.56-0.24: 0.3548360822544717
+ G28.86+0.07: 0.1
+ G30.85-0.08: 0.5697324971999391
+ G31.41+0.31: 8.11815427336179
+ G34.26+0.15: 14.800657803071704
+ G34.40+0.23: 3.986394686990907
+ G34.41+0.23: 5.298745541451453
+ G34.82+0.35: 0.2244740966733627
+ G35.20-0.74: 3.557121906245741
+ G37.55+0.20: 1.45314493723405
+ G49.49-0.39: 36.47299209277803
+ G53.14+0.07: 1.769951086391564
+ G59.78+0.07: 0.1
+ '257':
+ G08.71-0.41: 1.6896248544507602
+ G10.45-0.02: 1.6172560812119725
+ G10.47+0.03: 17.90790753460651
+ G10.62-0.38: 23.51945555648909
+ G12.81-0.20: 9.576422834532323
+ G13.18+0.06: 6.614500597410293
+ G13.66-0.60: 5.099529057565239
+ G14.11-0.57: 2.328801003580471
+ G14.19-0.19: 8.648952235029466
+ G14.49-0.14: 2.4117084959115784
+ G14.63-0.58: 4.22783491577442
+ G15.03-0.67: 6.482321394374812
+ G18.61-0.07: 2.119319392382522
+ G18.73-0.23: 4.953380016604151
+ G18.89-0.47: 5.763854872422209
+ G19.88-0.54: 6.538781173715317
+ G22.37+0.45: 3.680142077328448
+ G23.21-0.38: 9.457619850247136
+ G28.56-0.24: 2.68014308698633
+ G28.86+0.07: 0.1
+ G30.85-0.08: 2.087101701007916
+ G31.41+0.31: 10.657245054323353
+ G34.26+0.15: 23.76415857208871
+ G34.40+0.23: 10.868969226850243
+ G34.41+0.23: 11.460979034617344
+ G34.82+0.35: 1.0392490902135514
+ G35.20-0.74: 9.236714367808798
+ G37.55+0.20: 3.44395156070276
+ G49.49-0.39: 53.88508148720575
+ G53.14+0.07: 4.882683930286012
+ G59.78+0.07: 0.1
+ '380':
+ G08.71-0.41: 0.1764976739921008
+ G10.45-0.02: 0.3956084197099886
+ G10.47+0.03: 17.205686694830824
+ G10.62-0.38: 16.829857896249838
+ G12.81-0.20: 3.77800331658647
+ G13.18+0.06: 1.7426684781184258
+ G13.66-0.60: 2.050893367850886
+ G14.11-0.57: 0.4213659117392063
+ G14.19-0.19: 3.0914972380702115
+ G14.49-0.14: 0.5203058400694469
+ G14.63-0.58: 0.7159602280790358
+ G15.03-0.67: 2.1151044891765216
+ G18.61-0.07: 0.6989085703534818
+ G18.73-0.23: 2.341561030185213
+ G18.89-0.47: 1.1824614382740015
+ G19.88-0.54: 5.43307241666044
+ G22.37+0.45: 0.8920116613769251
+ G23.21-0.38: 6.836653617571014
+ G28.56-0.24: 0.0440146014079702
+ G28.86+0.07: 1.4210884372776502
+ G30.85-0.08: 0.5707347171006769
+ G31.41+0.31: 12.47776135190902
+ G34.26+0.15: 22.863445215418928
+ G34.40+0.23: 3.531757873095086
+ G34.41+0.23: 7.573594043835107
+ G34.82+0.35: 0.5068421046687672
+ G35.20-0.74: 3.735886344534172
+ G37.55+0.20: 2.1481844033314563
+ G49.49-0.39: 59.72041757348511
+ G53.14+0.07: 2.1621252984074437
+ G59.78+0.07: 0.9953421793454204
+ '381':
+ G08.71-0.41: 0.6722454928574747
+ G10.45-0.02: 0.9251788351499816
+ G10.47+0.03: 18.155676944590947
+ G10.62-0.38: 26.31767222497715
+ G12.81-0.20: 6.359416798755954
+ G13.18+0.06: 5.105986039174396
+ G13.66-0.60: 3.5815529807897217
+ G14.11-0.57: 1.4017924550811132
+ G14.19-0.19: 7.297072065038203
+ G14.49-0.14: 1.440371736841573
+ G14.63-0.58: 3.008997339860133
+ G15.03-0.67: 5.842826943986542
+ G18.61-0.07: 1.8679581479443947
+ G18.73-0.23: 4.779917207406943
+ G18.89-0.47: 3.8325153184359935
+ G19.88-0.54: 10.285222566660105
+ G22.37+0.45: 2.6850343064569504
+ G23.21-0.38: 9.739053502995969
+ G28.56-0.24: 0.9680884829289998
+ G28.86+0.07: 2.7304343388726755
+ G30.85-0.08: 1.7951582288444188
+ G31.41+0.31: 15.666989012027273
+ G34.26+0.15: 27.670510995076157
+ G34.40+0.23: 8.838480648374599
+ G34.41+0.23: 12.543875121955834
+ G34.82+0.35: 0.912504455201019
+ G35.20-0.74: 8.213603364738846
+ G37.55+0.20: 2.427780024280013
+ G49.49-0.39: 71.80734717626261
+ G53.14+0.07: 4.9231567416211615
+ G59.78+0.07: 2.24196075698127
+ '86':
+ G08.71-0.41: 5.770415738358244
+ G10.45-0.02: 3.585944537913708
+ G10.47+0.03: 19.61767281185442
+ G10.62-0.38: 17.24610289004276
+ G12.81-0.20: 8.431310860817968
+ G13.18+0.06: 10.867003106610996
+ G13.66-0.60: 8.534961548014586
+ G14.11-0.57: 3.616973808521042
+ G14.19-0.19: 15.749893351960294
+ G14.49-0.14: 6.365582995343145
+ G14.63-0.58: 5.623662981046849
+ G15.03-0.67: 4.060482703151656
+ G18.61-0.07: 4.924693385096532
+ G18.73-0.23: 10.095062635647638
+ G18.89-0.47: 13.902280291349596
+ G19.88-0.54: 7.349704071521969
+ G22.37+0.45: 8.934333184369493
+ G23.21-0.38: 10.589381466381129
+ G28.56-0.24: 7.718943160772005
+ G28.86+0.07: 3.744479978305277
+ G30.85-0.08: 3.600081120187853
+ G31.41+0.31: 10.334408688143643
+ G34.26+0.15: 17.148782015804713
+ G34.40+0.23: 14.72159545617476
+ G34.41+0.23: 17.151716147853218
+ G34.82+0.35: 2.3557453894152305
+ G35.20-0.74: 11.441136922618975
+ G37.55+0.20: 5.218930784342653
+ G49.49-0.39: 40.047671620058914
+ G53.14+0.07: 3.998167013285548
+ G59.78+0.07: 2.88330892529155
+ '87':
+ G08.71-0.41: 1.3070165152749178
+ G10.45-0.02: 1.8754428022340208
+ G10.47+0.03: 14.654313712932716
+ G10.62-0.38: 10.36368884187998
+ G12.81-0.20: 4.037590978170881
+ G13.18+0.06: 3.998842732080239
+ G13.66-0.60: 3.965098726041119
+ G14.11-0.57: 1.0980674058983302
+ G14.19-0.19: 5.9799020315281455
+ G14.49-0.14: 1.94278246727516
+ G14.63-0.58: 1.7565446299584897
+ G15.03-0.67: 1.9599610541713943
+ G18.61-0.07: 1.4529504405718856
+ G18.73-0.23: 3.946558643004384
+ G18.89-0.47: 3.87746848292546
+ G19.88-0.54: 2.725369130194149
+ G22.37+0.45: 2.2334635415722928
+ G23.21-0.38: 5.3215576604812105
+ G28.56-0.24: 2.00135317412325
+ G28.86+0.07: 1.6724447166657392
+ G30.85-0.08: 1.0384292597032818
+ G31.41+0.31: 7.634235223264329
+ G34.26+0.15: 11.79339165343781
+ G34.40+0.23: 4.893358882791944
+ G34.41+0.23: 6.975706242571226
+ G34.82+0.35: 0.6233212838879257
+ G35.20-0.74: 4.120487302502623
+ G37.55+0.20: 2.3647012203938407
+ G49.49-0.39: 30.638278402401056
+ G53.14+0.07: 1.2755372740021984
+ G59.78+0.07: 1.051817414971558
+ '88':
+ G08.71-0.41: 0.2555605423861423
+ G10.45-0.02: 0.5654586375969508
+ G10.47+0.03: 10.621236220690491
+ G10.62-0.38: 6.590559744325095
+ G12.81-0.20: 2.2391932908285748
+ G13.18+0.06: 1.043058241991689
+ G13.66-0.60: 1.388687476672136
+ G14.11-0.57: 0.2400487988472261
+ G14.19-0.19: 1.3734457205383674
+ G14.49-0.14: 0.1719527527259724
+ G14.63-0.58: 0.6227318765911359
+ G15.03-0.67: 0.9484169569327036
+ G18.61-0.07: 0.2409772077030058
+ G18.73-0.23: 1.43132088364319
+ G18.89-0.47: 1.240400088439042
+ G19.88-0.54: 0.940430197609148
+ G22.37+0.45: 0.4850629557476013
+ G23.21-0.38: 1.900341099965392
+ G28.56-0.24: 0.3811019169488633
+ G28.86+0.07: 0.8361276696375168
+ G30.85-0.08: 0.2353506461831835
+ G31.41+0.31: 6.015384520155143
+ G34.26+0.15: 7.861370828025846
+ G34.40+0.23: 1.3090143608612992
+ G34.41+0.23: 3.094135827250596
+ G34.82+0.35: 0.1891164402500769
+ G35.20-0.74: 1.7277506260977236
+ G37.55+0.20: 0.934902424028627
+ G49.49-0.39: 19.50272354704647
+ G53.14+0.07: 0.4187857086494883
+ G59.78+0.07: 0.4564477452483653
+probability_threshold: 0.37
+ratio_limits: {
+ '87-86': [ 0, 1.7 ],
+ '88-86': [ 0, 1.7 ],
+ '88-87': [ 0, 1.7 ],
+ '257-256': [ 0, 15 ],
+ '381-380': [ 0, 4 ],
+}
+ratios_to_include:
+- 87-86
+- 88-86
+- 88-87
+- 257-256
+- 381-380
+recompute_kde: true
+simulated_ratio_realizations: 1000
+use_model_for_inference: PLACEHOLDER
diff --git a/etl/config/ml_modelling.yml b/etl/config/ml_modelling.yml
new file mode 100644
index 0000000000000000000000000000000000000000..20abcaa9f50232d04bed0b932e8119492c57ebc4
--- /dev/null
+++ b/etl/config/ml_modelling.yml
@@ -0,0 +1,71 @@
+retrain: True
+use_model_for_training: constant_abundance_p15_q05
+
+overrides: {
+ 'tdust_grid_type': 'linear',
+ 'tdust_limits': [ 10, 35 ],
+ 'tdust_step': 2.5,
+ 'nh2_grid_type': 'log',
+ 'nh2_limits': [ 1e3, 6.5e7 ],
+ 'nh2_step': 1.316074013
+}
+
+#model_type: 'XGBoost'
+#model_parameters: {
+# 'n_estimators': 300,
+# 'max_depth': 4,
+# 'learning_rate': 0.02,
+# 'subsample': 0.5
+#}
+
+use_validation: True
+
+model_type: 'XGBoost_gridsearch'
+model_parameters: {
+ 'param_grid':
+ {
+ 'n_estimators': [ 300, 320, 50 ],
+ 'max_depth': [ 5, 7, 2 ],
+ 'learning_rate': [ 0.05, 0.1, 0.03 ],
+ 'subsample': [ 1, 2, 1 ],
+ 'lambda': [ 10, 20, 10 ],
+ 'colsample_bynode': [ 0.75, 0.8, 0.1 ],
+# 'colsample_bylevel': [ 0.5, 1, 0.1 ],
+ 'colsample_bytree': [ 0.75, 0.8, 0.2 ],
+ 'min_child_weight': [ 100, 200, 100 ]
+ },
+ 'param_gridsearch':
+ {
+ 'cv': 5,
+ 'n_jobs': 16
+ },
+}
+#model_parameters: {
+# 'param_grid':
+# {
+# 'n_estimators': [ 400, 900, 100 ],
+# 'max_depth': [ 3, 7, 2 ],
+# 'learning_rate': [ 0.01, 0.06, 0.02 ],
+# 'subsample': [ 0.3, 0.9, 0.2 ]
+# },
+# 'param_gridsearch':
+# {
+# 'cv': 3,
+# 'n_jobs': 16
+# },
+#}
+
+#model_type: 'auto_skl'
+#model_parameters: {
+# 'time_left_for_this_task': 3600,
+# 'per_run_time_limit': 600,
+# 'n_jobs': 12
+#}
+
+#model_type: 'RandomForest'
+#model_parameters: {
+# 'n_estimators': 20,
+# 'criterion': 'friedman_mse',
+# 'max_samples': 0.5
+#}
+
diff --git a/etl/credentials/example_credentials.yml b/etl/credentials/example_credentials.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3c5893888fb8c30c6c31c73fbc44abd13d03a62b
--- /dev/null
+++ b/etl/credentials/example_credentials.yml
@@ -0,0 +1,6 @@
+# add a db_credentials.yml file with your credentials. The structure should be:
+#DB_USER: db_user B64-encoded
+#DB_PASS: db_pass B64-encoded
+#DB_HOST: db_host
+#DB_NAME: db_name
+#DB_PORT: db_port
\ No newline at end of file
diff --git a/etl/main.py b/etl/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd74caa900b5d065d77c48d03e118bb25f04debe
--- /dev/null
+++ b/etl/main.py
@@ -0,0 +1,284 @@
+import glob
+import os
+import uuid
+import sqlalchemy
+import argparse
+import sys
+from multiprocessing import Pool
+from stg.stg_build_db_structure import init_db, TmpExecutionQueue
+from itertools import product, chain
+from typing import Union, Tuple, Iterator
+from assets.commons import (cleanup_directory,
+ setup_logger,
+ validate_parameter)
+from assets.commons.parsing import parse_input_main
+from assets.commons.db_utils import upsert, get_pg_engine
+from stg.stg_radmc_input_generator import main as stg_main
+from mdl.mdl_execute_radmc_command import main as execute_radmc_script
+from prs.prs_compute_integrated_fluxes_and_ratios import main as prs_main
+from prs.prs_inspect_results import main as prs_inspection_main
+
+
+def compute_full_grid(tdust, nh2, line, density_keyword, dust_temperature_keyword) -> Tuple[float, float, int, str]:
+ scratch_dir = os.path.join('mdl', 'scratches', str(uuid.uuid4()))
+ stg_overrides = {
+ 'grid': {
+ dust_temperature_keyword: tdust,
+ density_keyword: nh2,
+ }
+ }
+ overrides = {
+ 'grid_lines': stg_overrides,
+ 'model': {
+ 'radmc_observation': {
+ 'iline': line
+ }
+ }
+ }
+ tarname = stg_main(override_config=overrides,
+ path_radmc_files=scratch_dir,
+ run_id=run_id)
+ cube_fits_name = execute_radmc_script(grid_zipfile=tarname,
+ override_config=overrides,
+ radmc_input_path=scratch_dir,
+ run_id=run_id)
+ return tdust, nh2, line, cube_fits_name
+
+
+def initialize_queue(engine: sqlalchemy.engine,
+ run_id: str,
+ run_arguments: Iterator):
+ is_initialized = engine.execute(f"select count(*) from tmp_execution_queue where run_id='{run_id}'").first()[0] != 0
+ if is_initialized is False:
+ for arguments in run_arguments:
+ raw_insert_entry = {'run_id': run_id,
+ 'dust_temperature': arguments[0],
+ 'density': arguments[1],
+ 'line': arguments[2],
+ 'density_keyword': arguments[3],
+ 'dust_temperature_keyword': arguments[4],
+ 'done': False}
+ upsert(
+ table_object=TmpExecutionQueue,
+ row_dict=raw_insert_entry,
+ conflict_keys=[
+ TmpExecutionQueue.run_id,
+ TmpExecutionQueue.dust_temperature,
+ TmpExecutionQueue.density,
+ TmpExecutionQueue.line,
+ TmpExecutionQueue.density_keyword,
+ TmpExecutionQueue.dust_temperature_keyword
+ ],
+ engine=engine
+ )
+
+
+def get_run_pars(engine: sqlalchemy.engine,
+ run_id: str):
+ sql_query = sqlalchemy.text(f"""UPDATE tmp_execution_queue
+ SET done = true
+ WHERE row_id = (SELECT row_id
+ FROM tmp_execution_queue
+ WHERE (run_id = '{run_id}')
+ AND (done is false)
+ AND pg_try_advisory_xact_lock(row_id)
+ LIMIT 1 FOR UPDATE)
+ RETURNING *""")
+ return engine.execution_options(autocommit=True).execute(sql_query).first()
+
+
+def verify_run(engine: sqlalchemy.engine,
+ run_id: str):
+ sql_query = sqlalchemy.text(f"""UPDATE tmp_execution_queue
+ SET done = false
+ WHERE row_id in (SELECT row_id
+ FROM tmp_execution_queue
+ WHERE run_id = '{run_id}'
+ AND (done is true)
+ AND (fits_cube_name is null))""")
+ engine.execution_options(autocommit=True).execute(sql_query)
+ _remaining_models = compute_remaining_models(run_id=run_id)
+ return True if _remaining_models == 0 else False
+
+
+def insert_fits_name(engine: sqlalchemy.engine,
+ row_id: int,
+ fits_cube_name: str):
+ sql_query = sqlalchemy.text(f"""UPDATE tmp_execution_queue
+ SET fits_cube_name = '{fits_cube_name}'
+ WHERE row_id = {row_id}""")
+ engine.execution_options(autocommit=True).execute(sql_query)
+
+
+def compute_grid_elements(run_id: str):
+ init_db()
+ parallel_args, _ = get_parallel_args_and_nprocesses()
+ engine = get_pg_engine(logger=logger)
+ initialize_queue(engine=engine,
+ run_id=run_id,
+ run_arguments=parallel_args)
+ engine.dispose()
+
+
+def get_parallel_args_and_nprocesses() -> Tuple[Iterator, int]:
+ _tdust_model_type, _model_type, dust_temperatures, densities, line_pairs, n_processes, _ = parse_input_main()
+ line_set = set(chain.from_iterable(line_pairs))
+ density_keyword = 'central_density' if _model_type == 'homogeneous' else 'density_at_reference'
+ dust_temperature_keyword = 'dust_temperature' if _tdust_model_type == 'isothermal' else 'dust_temperature_at_reference'
+ parallel_args = product(dust_temperatures, densities, line_set, [density_keyword], [dust_temperature_keyword])
+ return parallel_args, n_processes
+
+
+def compute_model(run_id: str):
+ engine = get_pg_engine(logger=logger)
+ parameters_set = get_run_pars(engine=engine,
+ run_id=run_id)
+ engine.dispose()
+ if parameters_set is not None:
+ _, _, _, fits_cube_name = compute_full_grid(tdust=parameters_set[2],
+ nh2=parameters_set[3],
+ line=parameters_set[4],
+ density_keyword=parameters_set[5],
+ dust_temperature_keyword=parameters_set[6])
+ engine = get_pg_engine(logger=logger)
+ insert_fits_name(engine=engine,
+ row_id=parameters_set[0],
+ fits_cube_name=fits_cube_name)
+ engine.dispose()
+ else:
+ logger.info('All models were completed.')
+
+
+def initialize_run():
+ if args.run_id is not None:
+ run_id = args.run_id
+ else:
+ logger.info('Generating new run_id')
+ run_id = str(uuid.uuid4())
+ compute_grid_elements(run_id=run_id)
+ sys.stdout.write(run_id)
+ with open('run_id.txt', 'w') as run_id_file:
+ run_id_file.write(f'{run_id}\n')
+ return run_id
+
+
+def compute_remaining_models(run_id: Union[None, str] = None) -> int:
+ _run_id = validate_parameter(run_id, default=os.getenv('run_id'))
+ logger.info(_run_id)
+ sql_query = sqlalchemy.text(f"""SELECT count(*)
+ FROM tmp_execution_queue
+ WHERE (run_id = '{run_id}')
+ AND ((done is false)
+ OR ((done is true) AND (fits_cube_name is null)))""")
+ engine = get_pg_engine(logger=logger)
+ n_models = engine.execution_options(autocommit=True).execute(sql_query).first()[0]
+ engine.dispose()
+ sys.stdout.write(str(n_models))
+ return n_models
+
+
+def get_results(engine: sqlalchemy.engine,
+ run_id: str):
+ sql_query = sqlalchemy.text(f"""SELECT dust_temperature
+ , density
+ , line
+ , fits_cube_name
+ FROM tmp_execution_queue
+ WHERE run_id = '{run_id}'""")
+ return engine.execution_options(autocommit=True).execute(sql_query).all()
+
+
+def cleanup_tmp_table(run_id: str,
+ engine: sqlalchemy.engine):
+ sql_query = sqlalchemy.text(f"""DELETE
+ FROM tmp_execution_queue
+ WHERE run_id = '{run_id}'""")
+ return engine.execution_options(autocommit=True).execute(sql_query)
+
+
+def main_presentation_step(run_id: str,
+ cleanup_scratches: bool = True,
+ results_dict: Union[dict, None] = None) -> bool:
+ _tdust_model_type, _model_type, dust_temperatures, densities, line_pairs, n_processes, run_type = parse_input_main()
+
+ for folder in ('data', 'figures', 'trained_model'):
+ os.makedirs(os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ run_type,
+ folder
+ ), exist_ok=True)
+
+ engine = get_pg_engine(logger=logger)
+ _results_dict = validate_parameter(results_dict,
+ default=get_results(engine=engine,
+ run_id=run_id))
+
+ results_map = {}
+ for (tdust, nh2, line, cube_fits_name) in _results_dict:
+ results_map[f'{str(nh2)}_{str(tdust)}_{line}'] = cube_fits_name
+
+ for line_pair in line_pairs:
+ for tdust, nh2 in product(dust_temperatures, densities):
+ prs_main(cube_fits_list=[results_map[f'{str(nh2)}_{str(tdust)}_{line_pair[0]}'],
+ results_map[f'{str(nh2)}_{str(tdust)}_{line_pair[1]}']],
+ run_id=run_id,
+ engine=engine)
+
+ if cleanup_scratches is True:
+ scratches_dirs = glob.glob(os.path.join('mdl', 'scratches', '*'))
+ for scratches in scratches_dirs:
+ cleanup_directory(directory=scratches, logger=logger)
+
+ prs_inspection_main(run_id=run_id,
+ is_isothermal=_tdust_model_type == 'isothermal',
+ is_homogeneous=_model_type == 'homogeneous',
+ engine=engine,
+ run_type=run_type)
+ _run_success = verify_run(run_id=run_id,
+ engine=engine)
+ if _run_success is True:
+ cleanup_tmp_table(run_id=run_id,
+ engine=engine)
+ engine.dispose()
+ return _run_success
+
+
+def process_models(distributed: bool = False) -> Tuple[Union[None, dict], int]:
+ if distributed is True:
+ compute_model(run_id=run_id)
+ results = None
+ remaining_models = compute_remaining_models(run_id)
+ else:
+ parallel_args, n_processes = get_parallel_args_and_nprocesses()
+ with Pool(n_processes) as pool:
+ results = pool.starmap(compute_full_grid, parallel_args)
+ remaining_models = 0
+ return results, remaining_models
+
+
+logger = setup_logger(name='MAIN')
+parser = argparse.ArgumentParser()
+parser.add_argument('--run_id')
+parser.add_argument('--cleanup_scratches')
+parser.add_argument('--distributed')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+ run_id = initialize_run()
+ assert run_id is not None
+ _distributed = validate_parameter(args.distributed, default='false').lower() == 'true'
+
+ results, remaining_models = process_models(distributed=_distributed)
+ if remaining_models == 0:
+ logger.info('All grid points processed. Summarizing results.')
+ _cleanup = validate_parameter(args.cleanup_scratches,
+ default='true').lower() == 'true'
+ _run_success = main_presentation_step(run_id=run_id,
+ cleanup_scratches=_cleanup,
+ results_dict=results)
+ if (_run_success is True) or (_distributed is False):
+ logger.info('The run completed successfully!')
+ else:
+ logger.error('The run was incomplete. I have reset the done flag in the database for incomplete models')
diff --git a/etl/mdl/config/config.yml b/etl/mdl/config/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ed2fd3fa5c73f031d4756494aa3a537ef8fb2f0a
--- /dev/null
+++ b/etl/mdl/config/config.yml
@@ -0,0 +1,16 @@
+radmc_postprocessing:
+ nphot: 1000000
+ scattering_mode_max: 0
+ iranfreqmode: 1
+ tgas_eq_tdust: 1
+ lines_slowlvg_as_alternative: 1
+
+radmc_observation:
+ inclination: 0
+ position_angle: 0
+ iline: 2
+ width_kms: 10
+ nchannels: 100
+ npix: 101
+ threads: 12
+ image_size_pc: 2
diff --git a/etl/mdl/data/dustkappa_silicate.inp b/etl/mdl/data/dustkappa_silicate.inp
new file mode 100644
index 0000000000000000000000000000000000000000..1cbfa5d5e2634e994c4d6608421324500a067134
--- /dev/null
+++ b/etl/mdl/data/dustkappa_silicate.inp
@@ -0,0 +1,1011 @@
+# Opacity file for Amorphous Olivine with 50% Mg and 50% Fe
+# Optical constants from Jaeger, Mutschke, Begemann, Dorschner, Henning (1994) A&A 292, 641-655; Dorschner, Begemann, Henning, Jaeger, Mutschke (1995) A&A 300, 503-520.
+# Please do not forget to cite in your publications the original paper of these optical constant measurements
+# Made with the makedustopac.py code by Cornelis Dullemond
+# using the bhmie.py Mie code of Bohren and Huffman (python version by Cornelis Dullemond, from original bhmie.f code by Bruce Draine)
+# Grain size = 1.000000e-05 cm
+# Material density = 3.710 g/cm^3
+3
+1000
+
+ 1.000000e-01 2.707028e+04 2.358565e+04 8.810943e-01
+ 1.011591e-01 2.714088e+04 2.357973e+04 8.796718e-01
+ 1.023317e-01 2.721083e+04 2.357984e+04 8.782258e-01
+ 1.035178e-01 2.728036e+04 2.358662e+04 8.767629e-01
+ 1.047177e-01 2.734975e+04 2.360056e+04 8.752888e-01
+ 1.059315e-01 2.741921e+04 2.362208e+04 8.738076e-01
+ 1.071593e-01 2.748878e+04 2.365149e+04 8.723212e-01
+ 1.084014e-01 2.755830e+04 2.368901e+04 8.708289e-01
+ 1.096579e-01 2.762738e+04 2.373487e+04 8.693281e-01
+ 1.109290e-01 2.769546e+04 2.378934e+04 8.678154e-01
+ 1.122148e-01 2.776195e+04 2.385275e+04 8.662881e-01
+ 1.135155e-01 2.782630e+04 2.392552e+04 8.647457e-01
+ 1.148312e-01 2.788817e+04 2.400818e+04 8.631908e-01
+ 1.161623e-01 2.794755e+04 2.410124e+04 8.616295e-01
+ 1.175087e-01 2.800473e+04 2.420519e+04 8.600712e-01
+ 1.188708e-01 2.806029e+04 2.432035e+04 8.585274e-01
+ 1.202486e-01 2.811500e+04 2.444685e+04 8.570097e-01
+ 1.216424e-01 2.816962e+04 2.458452e+04 8.555278e-01
+ 1.230524e-01 2.822476e+04 2.473293e+04 8.540878e-01
+ 1.244787e-01 2.828068e+04 2.489143e+04 8.526907e-01
+ 1.259216e-01 2.833722e+04 2.505921e+04 8.513323e-01
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+ 1.318621e-01 2.855216e+04 2.580911e+04 8.460674e-01
+ 1.333906e-01 2.859726e+04 2.601452e+04 8.447340e-01
+ 1.349367e-01 2.863712e+04 2.622731e+04 8.433802e-01
+ 1.365008e-01 2.867161e+04 2.644791e+04 8.420084e-01
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+
diff --git a/etl/mdl/data/dustopac.inp b/etl/mdl/data/dustopac.inp
new file mode 100644
index 0000000000000000000000000000000000000000..c9beb1bad8e7425ff4f94b684315cd8e21c0fd89
--- /dev/null
+++ b/etl/mdl/data/dustopac.inp
@@ -0,0 +1,7 @@
+2 Format number of this file
+1 Nr of dust species
+============================================================================
+1 Way in which this dust species is read
+0 0=Thermal grain
+silicate Extension of name of dustkappa_***.inp file
+----------------------------------------------------------------------------
diff --git a/etl/mdl/data/molecule_e-ch3oh.inp b/etl/mdl/data/molecule_e-ch3oh.inp
new file mode 100644
index 0000000000000000000000000000000000000000..0a51ceffe5175908a08bdd86ca0f4293b62269ee
--- /dev/null
+++ b/etl/mdl/data/molecule_e-ch3oh.inp
@@ -0,0 +1,35103 @@
+! MOLECULE
+E-CH3OH v=0
+! MASS
+ 32.
+! NUMBER OF ENERGY LEVELS
+ 256
+! LEVEL + ENERGY(CM-1) + WEIGHT + QUANTUM NOS. J_K
+ 1 5.4900000 3.0 1_-1
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+! NUMBER OF RADIATIVE TRANSITIONS
+ 2324
+! TRANS + UP + LOW + EINSTEINA(s^-1) + FREQ(GHz)+ E_up(K)
+ 1 80 79 3.882245E-11 2.925982 175.5
+ 2 15 14 1.593096E-15 3.387356 36.3
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+! NUMBER OF COLLISION PARTNERS
+1
+! COLLISION PARTNER
+1 E-CH3OH v=0 with p-H2 from Rabli & Flower (2010)
+! NUMBER OF COLLISIONAL TRANSITIONS
+ 32502
+! NUMBER OF COLLISION TEMPERATURES
+ 21
+! COLLISION TEMPERATURES
+ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. 110. 120. 130. 140. 150. 160. 170. 180. 190. 200. 2010.
+! TRANS + UP + LOW + RATE COEFFS(cm^3 s^-1)
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diff --git a/etl/mdl/mdl_execute_radmc_command.py b/etl/mdl/mdl_execute_radmc_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..14d5613a5f163bb9cb94a800d4a1f00e78fd8edf
--- /dev/null
+++ b/etl/mdl/mdl_execute_radmc_command.py
@@ -0,0 +1,249 @@
+import os
+import sys
+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
+from stg.stg_build_db_structure import (LinePars,
+ SpeciesAndPartners,
+ ModelPars,
+ StarsPars)
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger,
+ compute_unique_hash_filename,
+ get_value_if_specified,
+ cleanup_directory)
+from assets.commons.parsing import read_abundance_variation_schema
+from assets.commons.db_utils import upsert, get_pg_engine
+from assets.constants import (radmc_options_mapping,
+ radmc_lines_mode_mapping)
+
+logger = setup_logger(name='MDL')
+
+
+def save_cube_as_fits(cube_out_name: Union[str, None] = None,
+ cube_out_path: Union[str, None] = None,
+ path_radmc_files: Union[str, None] = None):
+ """
+ Convert the RADMC output to fits
+ :param cube_out_name: outfile name
+ :param cube_out_path: outfile path
+ :param path_radmc_files: path where the radmc input/output files are stored
+ """
+ _cube_out_name = validate_parameter(cube_out_name, default='test_cube.fits')
+ _cube_out_path = validate_parameter(cube_out_path, default=os.path.join('prs', 'fits', 'cubes'))
+ _path_radmc_files = validate_parameter(path_radmc_files, default=os.path.join('mdl', 'radmc_files'))
+ imdata = image.readImage(fname=os.path.join(_path_radmc_files, 'image.out'))
+ output_name = os.path.join(_cube_out_path, _cube_out_name)
+ if os.path.isfile(output_name):
+ os.remove(output_name)
+ imdata.writeFits(fname=output_name)
+
+
+def check_config(config: dict) -> bool:
+ """
+ Check if all mandatory keys are present in the configuration dictionary.
+ :param config: the configuration dictionary
+ :return: True is all keys are present, False otherwise
+ """
+ mandatory_keys = {
+ 'inclination',
+ 'position_angle',
+ }
+ return set(mandatory_keys).difference(set(config.keys())) == set()
+
+
+def get_command_options(config: dict) -> set:
+ """
+ Parse the radmc3d command options from the configuration dictionary
+ :param config: the configuration dictionary
+ :return: a set of string to postpone to the radmc3d image command
+ """
+ options_list = []
+ for key in config["radmc_observation"]:
+ try:
+ options_list.append(f'{radmc_options_mapping[key]} {config["radmc_observation"][key]}')
+ except KeyError:
+ pass
+ return set(options_list)
+
+
+def populate_model_table(config_mdl: dict,
+ grid_zipfile: str,
+ cube_filename: str,
+ engine: sqlalchemy.engine,
+ executed_on: datetime.timestamp,
+ run_id: str):
+ """
+ Populate the model_parameters table in the DB
+ :param config_mdl: the model configuration
+ :param grid_zipfile: the name of the grid tarfile
+ :param cube_filename: the name of the fits cube
+ :param engine: the SQLAlchemy engine
+ :param executed_on: the timestamp of execution
+ :param run_id: the run unique identifier
+ """
+ model_pars_dict = {
+ 'zipped_grid_name': grid_zipfile,
+ 'fits_cube_name': cube_filename,
+ 'nphotons': config_mdl['radmc_postprocessing']['nphot'],
+ 'scattering_mode_max': int(config_mdl['radmc_postprocessing']['scattering_mode_max']),
+ 'iranfreqmode': config_mdl['radmc_postprocessing']['iranfreqmode'],
+ 'tgas_eq_tdust': config_mdl['radmc_postprocessing']['tgas_eq_tdust'],
+ 'inclination': config_mdl['radmc_observation']['inclination'],
+ 'position_angle': config_mdl['radmc_observation']['position_angle'],
+ 'imolspec': get_value_if_specified(config_mdl['radmc_observation'], 'imolspec'),
+ 'iline': get_value_if_specified(config_mdl['radmc_observation'], 'iline'),
+ 'width_kms': get_value_if_specified(config_mdl['radmc_observation'], 'width_kms'),
+ 'nchannels': get_value_if_specified(config_mdl['radmc_observation'], 'nchannels'),
+ 'npix': get_value_if_specified(config_mdl['radmc_observation'], 'npix'),
+ 'created_on': executed_on,
+ 'run_id': run_id
+ }
+ upsert(
+ table_object=ModelPars,
+ row_dict=model_pars_dict,
+ conflict_keys=[ModelPars.fits_cube_name, ModelPars.run_id],
+ engine=engine
+ )
+
+
+def populate_species_and_partner_table(config_lines: dict,
+ engine: sqlalchemy.engine,
+ executed_on: datetime.timestamp,
+ grid_zipfile: str,
+ run_id: str):
+ """
+ Populate the species_and_partners table in the DB
+ :param config_lines: the line configuration
+ :param engine: the SQLAlchemy engine
+ :param executed_on: the timestamp of execution
+ :param grid_zipfile: the name of the grid tarfile
+ :param run_id: the run unique identifier
+ """
+ hot_core_specs = read_abundance_variation_schema(line_config=config_lines)
+ for (species, collision_partner) in product(config_lines['species_to_include'], config_lines['collision_partners']):
+ species_partner_dict = {
+ 'zipped_grid_name': f'{grid_zipfile}',
+ 'species_to_include': species,
+ 'molecular_abundance': config_lines['molecular_abundances'][species],
+ 'threshold': hot_core_specs[species]['threshold'],
+ 'abundance_jump': hot_core_specs[species]['abundance_jump'],
+ 'collision_partner': collision_partner,
+ 'molecular_abundance_collision_partner': config_lines['molecular_abundances'][collision_partner],
+ 'created_on': executed_on,
+ 'run_id': run_id
+ }
+ upsert(
+ table_object=SpeciesAndPartners,
+ row_dict=species_partner_dict,
+ conflict_keys=[SpeciesAndPartners.zipped_grid_name,
+ SpeciesAndPartners.species_to_include,
+ SpeciesAndPartners.collision_partner,
+ SpeciesAndPartners.run_id],
+ engine=engine
+ )
+
+
+def populate_line_table(config_lines: dict,
+ engine: sqlalchemy.engine,
+ executed_on: datetime.timestamp,
+ grid_zipfile: str,
+ run_id: str):
+ """
+ Populate the lines table in the DB
+ :param config_lines: the dictionary containing the line configuration
+ :param engine: the SQLAlchemy engine to use
+ :param executed_on: the timestamp of execution, to add to the record
+ :param grid_zipfile: the grid tarfile name, to be used as key
+ :param run_id: the run unique identifier
+ """
+ line_pars_dict = {
+ 'zipped_grid_name': f'{grid_zipfile}',
+ 'lines_mode': config_lines['lines_mode'],
+ 'created_on': executed_on,
+ 'run_id': run_id
+ }
+ upsert(
+ table_object=LinePars,
+ row_dict=line_pars_dict,
+ conflict_keys=[LinePars.zipped_grid_name, LinePars.run_id],
+ engine=engine
+ )
+
+
+def main(grid_zipfile: str,
+ run_id: str,
+ override_config: Union[dict, None] = None,
+ radmc_input_path: Union[str, None] = None,
+ engine: sqlalchemy.engine = None) -> 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
+ _override_config = validate_parameter(override_config, default={'grid_lines': {}, 'model': {}})
+ executed_on = datetime.now()
+ _radmc_input_path = validate_parameter(radmc_input_path, default=os.path.join('mdl', 'radmc_files'))
+ config_stg = load_config_file(os.path.join('stg', 'config', 'config.yml'),
+ override_config=_override_config['grid_lines'])
+ config_lines = config_stg['lines']
+ config_mdl = load_config_file(os.path.join('mdl', 'config', 'config.yml'),
+ override_config=_override_config['model'])
+ assert check_config(config=config_mdl['radmc_observation'])
+ with open(os.path.join('mdl', 'radmc3d_postprocessing.sh'), 'w') as outfile:
+ outfile.write(f'cd {_radmc_input_path}\n')
+ options_set = get_command_options(config_mdl)
+ radmc_command = f'radmc3d image {" ".join(options_set)}'
+ outfile.write(radmc_command)
+
+ config_full = config_mdl.copy()
+ config_full.update(config_stg)
+ cube_filename = f'{compute_unique_hash_filename(config=config_full)}.fits'
+
+ # Execute radmc if not done already
+ if not os.path.isfile(os.path.join('prs', 'fits', 'cubes', cube_filename)):
+ logger.debug(f'Executing command: {radmc_command}')
+ execution_dir = os.getcwd()
+ os.chdir(_radmc_input_path)
+ os.system(radmc_command)
+ os.chdir(execution_dir)
+ logger.debug(f'Checking presence of file: {os.path.join(_radmc_input_path, "image.out")}')
+ assert os.path.isfile(os.path.join(_radmc_input_path, 'image.out'))
+
+ save_cube_as_fits(cube_out_name=cube_filename,
+ cube_out_path=os.path.join('prs', 'fits', 'cubes'),
+ path_radmc_files=radmc_input_path)
+ else:
+ logger.info('Computation performed already! Skipping...')
+
+ if engine is None:
+ engine = get_pg_engine(logger=logger, engine_kwargs={'pool_size': 2})
+
+ populate_line_table(config_lines=config_lines,
+ engine=engine,
+ executed_on=executed_on,
+ grid_zipfile=grid_zipfile,
+ run_id=run_id)
+
+ populate_species_and_partner_table(config_lines=config_lines,
+ engine=engine,
+ executed_on=executed_on,
+ grid_zipfile=grid_zipfile,
+ run_id=run_id)
+
+ populate_model_table(config_mdl=config_mdl,
+ grid_zipfile=grid_zipfile,
+ cube_filename=cube_filename,
+ engine=engine,
+ executed_on=executed_on,
+ run_id=run_id)
+ engine.dispose()
+ return cube_filename
+
+
+if __name__ == '__main__':
+ main(grid_zipfile='459295aa894dffa8c521e606d14dbb6927638a2c.zip',
+ run_id='test_run')
diff --git a/etl/mdl/radmc_files/readme b/etl/mdl/radmc_files/readme
new file mode 100644
index 0000000000000000000000000000000000000000..2a724d656eb449b1f067b149a82666deb79d9e6c
--- /dev/null
+++ b/etl/mdl/radmc_files/readme
@@ -0,0 +1 @@
+Here SAK will store the radmc files for each run.
\ No newline at end of file
diff --git a/etl/mdl/scratches/readme b/etl/mdl/scratches/readme
new file mode 100644
index 0000000000000000000000000000000000000000..46ab28e12838d1f87cd89a02cd4632ba79530868
--- /dev/null
+++ b/etl/mdl/scratches/readme
@@ -0,0 +1 @@
+Folder that contains the scratch files used by SAK in order to avoid concurrency when running in parallel.
\ No newline at end of file
diff --git a/etl/prs/config/config.yml b/etl/prs/config/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9f063dc1f33120efc5940e5f3b1bf8a04bc39cf6
--- /dev/null
+++ b/etl/prs/config/config.yml
@@ -0,0 +1,6 @@
+flux_computation:
+ central_frequency: 230.000
+ central_frequency_units: "GHz"
+ integration_limits: "all"
+ moment_zero_aggregation_function: 'sum'
+ aggregation_function: 'weighted_mean'
\ No newline at end of file
diff --git a/etl/prs/fits/cubes/readme b/etl/prs/fits/cubes/readme
new file mode 100644
index 0000000000000000000000000000000000000000..44f16ae3dc8e37f24fb1bd3d3f79d45adf6c209f
--- /dev/null
+++ b/etl/prs/fits/cubes/readme
@@ -0,0 +1 @@
+In this directory you will find the postprocessed fits cubes.
\ No newline at end of file
diff --git a/etl/prs/fits/grids/readme b/etl/prs/fits/grids/readme
new file mode 100644
index 0000000000000000000000000000000000000000..e58633c3d2bc837bb938a0ff809ad6b3789e37c7
--- /dev/null
+++ b/etl/prs/fits/grids/readme
@@ -0,0 +1 @@
+In this directory you will find the fits files representing the model grid.
\ No newline at end of file
diff --git a/etl/prs/fits/moments/readme b/etl/prs/fits/moments/readme
new file mode 100644
index 0000000000000000000000000000000000000000..a2762ec3c8148c62aee2fce34fda0b922f6a3581
--- /dev/null
+++ b/etl/prs/fits/moments/readme
@@ -0,0 +1 @@
+In this directory you will find the moment zero images of the lines.
\ No newline at end of file
diff --git a/etl/prs/fits/ratios/readme b/etl/prs/fits/ratios/readme
new file mode 100644
index 0000000000000000000000000000000000000000..e8d995c47858ebd85075c1675a8e557d364459a0
--- /dev/null
+++ b/etl/prs/fits/ratios/readme
@@ -0,0 +1 @@
+In this directory you will find the imagrs of the line ratios.
\ No newline at end of file
diff --git a/etl/prs/output/lte_approx/readme b/etl/prs/output/lte_approx/readme
new file mode 100644
index 0000000000000000000000000000000000000000..e03923e5d490d8c9ae47332556c3d2ce5e104a54
--- /dev/null
+++ b/etl/prs/output/lte_approx/readme
@@ -0,0 +1 @@
+Here you will find the figure referring to the LTE column density approximation comparison.
\ No newline at end of file
diff --git a/etl/prs/output/readme b/etl/prs/output/readme
new file mode 100644
index 0000000000000000000000000000000000000000..3c0bda48662446d5b1d01f235aef033287468bba
--- /dev/null
+++ b/etl/prs/output/readme
@@ -0,0 +1 @@
+In this directory you will find the final (summarized) output of the run(s).
\ No newline at end of file
diff --git a/etl/prs/output/run_type/readme b/etl/prs/output/run_type/readme
new file mode 100644
index 0000000000000000000000000000000000000000..bf208c691a3f98a72150c0afa4a7a790d4b8c72d
--- /dev/null
+++ b/etl/prs/output/run_type/readme
@@ -0,0 +1 @@
+Here you will find the results of the run, in the folder corresponding to the run type.
\ No newline at end of file
diff --git a/etl/prs/prepare_inference_input.py b/etl/prs/prepare_inference_input.py
new file mode 100644
index 0000000000000000000000000000000000000000..345a5e010f6d302750e41a11d62ee97742c62272
--- /dev/null
+++ b/etl/prs/prepare_inference_input.py
@@ -0,0 +1,49 @@
+import pandas as pd
+import yaml
+import os
+
+df = pd.read_csv(os.path.join('assets', 'data', 'ch3oh_data_top35.csv'))
+
+config_dict = {
+ 'simulated_ratio_realizations': 1000,
+ 'recompute_kde': True,
+ 'probability_threshold': 0.37,
+ 'best_bandwidths': {
+ '87-86': 0.1,
+ '88-86': 0.1,
+ '88-87': 0.1,
+ '257-256': 0.4,
+ '381-380': 0.2,
+ },
+ 'ratio_limits': {
+ '87-86': [0, 1.7],
+ '88-86': [0, 1.7],
+ '88-87': [0, 1.7],
+ '257-256': [0, 15],
+ '381-380': [0, 4],
+ },
+ 'use_model_for_inference': 'PLACEHOLDER',
+ 'ratios_to_include': [
+ "87-86",
+ "88-86",
+ "88-87",
+ "257-256",
+ "381-380"
+ ]
+}
+
+intensities_dict = {}
+uncertainties_dict = {}
+
+data_dict = df.to_dict(orient='records')
+for line_id in ['86', '87', '88', '257', '256', '381', '380']:
+ intensities_dict[line_id] = {}
+ uncertainties_dict[line_id] = {}
+ for row in data_dict:
+ intensities_dict[line_id][str(row['source_name'])] = row[f'area_{line_id}']
+ uncertainties_dict[line_id][str(row['source_name'])] = row[f'area_unc_{line_id}']
+config_dict['measured_integrated_intensities'] = intensities_dict
+config_dict['integrated_intensities_uncertainties'] = uncertainties_dict
+
+with open('config/density_inference_input.yml', 'w') as outfile:
+ yaml.dump(config_dict, outfile)
diff --git a/etl/prs/prs_analytical_representations.py b/etl/prs/prs_analytical_representations.py
new file mode 100644
index 0000000000000000000000000000000000000000..09e51956148c141385082634e7d13835aeff7089
--- /dev/null
+++ b/etl/prs/prs_analytical_representations.py
@@ -0,0 +1,77 @@
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+import pickle
+from scipy.interpolate import splrep, BSpline, RegularGridInterpolator
+from scipy.optimize import curve_fit
+from assets.commons import (get_data,
+ load_config_file,
+ setup_logger,
+ validate_parameter)
+from typing import Union, List
+
+
+def approx(x, x_shift, a, b, exponent, scale=1):
+ return (np.abs(np.arctanh(2 * (x - x_shift) / scale) + a)) ** exponent + b
+
+
+def main(ratios_to_fit: Union[List[str], None] = None):
+ config = load_config_file(config_file_path=os.path.join('config', 'config.yml'))
+ _ratio_to_fit = validate_parameter(
+ param_to_validate=ratios_to_fit,
+ default=['-'.join(lines_ratio) for lines_ratio in config['overrides']['lines_to_process']])
+ data = get_data(limit_rows=None,
+ use_model_for_inference=config['run_type'])
+ logger.info(f'Using {config["run_type"]} to fit data...')
+ logger.info(f'Data contains {data.shape[0]} rows')
+
+ bw = {
+ '87-86': 0.1,
+ '88-87': 0.1,
+ '88-86': 0.1,
+ '257-256': 0.4,
+ '381-380': 0.2}
+
+ x0 = {
+ '87-86': ((0.55, 2.3, 3.75, 0.9),),
+ '88-87': ((0.59, 2.3, 3.9, 1.05, 0.96),),
+ '88-86': ((0.55, 2.5, 4.3, 0.83, 1.08),),
+ '257-256': ((5, 2.5, 2.45, 0.75, 8), (5, 2.5, 3.33, 0.75, -8)),
+ '381-380': ((2.04, 2.5, 3.1, 0.6, 2.1), (2.18, 2.5, 3.7, 0.79, -2.4)),
+ }
+ min_ratio = {
+ '87-86': 0.08,
+ '88-87': 0.08,
+ '88-86': 0.03,
+ '257-256': 1,
+ '381-380': 1}
+ max_ratio = {
+ '87-86': 1.04,
+ '88-87': 1.02,
+ '88-86': 1.07,
+ '257-256': 7.5,
+ '381-380': 2.9}
+
+ for ratio_string in _ratio_to_fit:
+ _column_to_fit = f'ratio_{ratio_string}'
+ data.sort_values(by=_column_to_fit, inplace=True)
+ plt.clf()
+ plt.scatter(data[_column_to_fit], data['avg_nh2'], marker='+', alpha=0.1)
+ x_reg = np.linspace(min_ratio[ratio_string], max_ratio[ratio_string], 100)
+
+ data_clean = data[[_column_to_fit, 'avg_nh2']].loc[data[_column_to_fit] <= max_ratio[ratio_string]].copy()
+ kde = pickle.load(
+ open(f'prs/output/run_type/{config["run_type"]}/trained_model/ratio_density_kde_{ratio_string}.pickle',
+ 'rb'))
+ rgi = RegularGridInterpolator((kde['y'] * bw[ratio_string], kde['x'] * 0.2), kde['values'].reshape(200, 200).T)
+ density = rgi(data_clean[[_column_to_fit, 'avg_nh2']])
+ data_clean = data_clean.loc[(density > density.mean() - density.std())]
+ plt.scatter(data_clean[_column_to_fit], data_clean['avg_nh2'], marker='+', alpha=0.1, color='red')
+ for components in x0[ratio_string]:
+ plt.plot(x_reg, approx(x_reg, *components), color='cyan')
+ plt.show()
+
+
+if __name__ == '__main__':
+ logger = setup_logger(name='POLYFIT')
+ main()
diff --git a/etl/prs/prs_check_biases_poc_sample.py b/etl/prs/prs_check_biases_poc_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..b860235080cc85c32ea8d5ebf07b37529cc8b5c7
--- /dev/null
+++ b/etl/prs/prs_check_biases_poc_sample.py
@@ -0,0 +1,25 @@
+from assets.utils import get_poc_results
+from scipy.stats import anderson_ksamp
+from assets.commons import setup_logger
+import numpy as np
+
+
+logger = setup_logger(name='PRS - AD test')
+
+df_full = get_poc_results(line_fit_filename='ch3oh_data_top35.csv')
+
+df_full['best_fit'] /= 1e5
+df_full['best_fit'] = df_full['best_fit'].round(1)
+df_full['mass'] = (df_full['mass'] / 100).round(1)
+df_full['distance'] = (df_full['distance']).round(1)
+df_full['diameter'] = (df_full['diameter']).round(1)
+df_full = df_full[(df_full['mass'] < 100) & (df_full['mass'] > 3)]
+# df_full = df_full[df_full['class'] != 'HII']
+# df_full = df_full[df_full['class'].isin(['IRb', 'IRw'])]
+
+ad_results = {}
+for column in ['mass', 'distance', 'best_fit', 'diameter']:
+ array_list = [d[column].values for _, d in df_full.groupby(['class'])]
+ ad_results[column] = anderson_ksamp(array_list)[2]
+
+logger.info(f'The p values of the AD test are: {ad_results}')
diff --git a/etl/prs/prs_compute_integrated_fluxes_and_ratios.py b/etl/prs/prs_compute_integrated_fluxes_and_ratios.py
new file mode 100644
index 0000000000000000000000000000000000000000..949d91c09e5310d882ab06d5a40f21091f96ed48
--- /dev/null
+++ b/etl/prs/prs_compute_integrated_fluxes_and_ratios.py
@@ -0,0 +1,253 @@
+import os
+import sqlalchemy
+import numpy as np
+import astropy.units as u
+from datetime import datetime
+from astropy.io import fits
+from astropy import constants
+from typing import Union, List
+from assets.commons import (validate_parameter,
+ get_value_if_specified,
+ load_config_file,
+ setup_logger,
+ compute_unique_hash_filename)
+from assets.commons.db_utils import upsert, get_pg_engine
+from assets.constants import aggregation_function_mapping
+from stg.stg_build_db_structure import (MomentZeroMaps,
+ RatioMaps)
+
+logger = setup_logger(name='PRS')
+
+
+def populate_mom_zero_table(config_prs: dict,
+ fits_cube_name: str,
+ moment_zero_map_fits: str,
+ aggregated_moment_zero: float,
+ executed_on: datetime.timestamp,
+ engine: sqlalchemy.engine,
+ run_id: str):
+ """
+ Insert record in DB for the moment zero table
+ :param config_prs: configuration of the presentation layer
+ :param fits_cube_name: name of the fits cube
+ :param moment_zero_map_fits: filename of the moment zero map
+ :param aggregated_moment_zero: the aggregated value of the moment zero
+ :param executed_on: timestamp of execution
+ :param engine: SQLAlchemy engine to use
+ :param run_id: the run unique identifier
+ """
+ _integration_limit_low = get_value_if_specified(config_prs, 'integration_limits')[0] if get_value_if_specified(
+ config_prs, 'integration_limits') != 'all' else None
+ _integration_limit_high = get_value_if_specified(config_prs, 'integration_limits')[1] if get_value_if_specified(
+ config_prs, 'integration_limits') != 'all' else None
+ moment_zero_dict = {
+ 'mom_zero_name': f'{moment_zero_map_fits}',
+ 'fits_cube_name': f'{fits_cube_name}',
+ 'integration_limit_low': _integration_limit_low,
+ 'integration_limit_high': _integration_limit_high,
+ 'aggregated_moment_zero': aggregated_moment_zero,
+ 'aggregation_function': config_prs['moment_zero_aggregation_function'],
+ 'created_on': executed_on,
+ 'run_id': run_id,
+ }
+ upsert(
+ table_object=MomentZeroMaps,
+ row_dict=moment_zero_dict,
+ conflict_keys=[MomentZeroMaps.mom_zero_name,
+ MomentZeroMaps.run_id],
+ engine=engine
+ )
+
+
+def populate_line_ratios_table(config_prs: dict,
+ moment_zero_map_fits_list: List[str],
+ ratio_map_name: str,
+ aggregated_ratio: float,
+ engine: sqlalchemy.engine,
+ executed_on: datetime.timestamp,
+ run_id: str):
+ """
+ Insert record in DB for the line ratios table
+ :param config_prs: configuration of the presentation layer
+ :param moment_zero_map_fits_list: list of the moment 0 maps to use to compute the ratio
+ :param ratio_map_name: filename of the ratio map
+ :param aggregated_ratio: aggregated value of the ratio
+ :param engine: SQLAlchemy engine to use
+ :param executed_on: timestamp of execution
+ :param run_id: the run unique identifier
+ """
+ _integration_limit_low = get_value_if_specified(config_prs, 'integration_limits')[0] if get_value_if_specified(
+ config_prs, 'integration_limits') != 'all' else None
+ _integration_limit_high = get_value_if_specified(config_prs, 'integration_limits')[1] if get_value_if_specified(
+ config_prs, 'integration_limits') != 'all' else None
+ assert len(moment_zero_map_fits_list) == 2
+ line_ratio_dict = {
+ 'ratio_map_name': f'{ratio_map_name}',
+ 'mom_zero_name_1': f'{moment_zero_map_fits_list[0]}',
+ 'mom_zero_name_2': f'{moment_zero_map_fits_list[1]}',
+ 'aggregated_ratio': aggregated_ratio,
+ 'aggregation_function': config_prs['aggregation_function'],
+ 'created_on': executed_on,
+ 'run_id': run_id,
+ }
+ upsert(
+ table_object=RatioMaps,
+ row_dict=line_ratio_dict,
+ conflict_keys=[RatioMaps.ratio_map_name,
+ RatioMaps.run_id],
+ engine=engine
+ )
+
+
+def compute_moment_zero(cube: str,
+ config: dict,
+ cube_path: Union[str, None] = None,
+ moment_zero_path: Union[str, None] = None,
+ moment_zero_fits_name: Union[str, None] = None,
+ hdu_idx: int = 0) -> float:
+ """
+ Compute moment zero map, given a cube
+ :param cube: filename of the fits cube
+ :param config: configuration of the presentation layer
+ :param cube_path: path where the fits cube is to be found
+ :param moment_zero_path: path where the moment 0 map is to be saved
+ :param moment_zero_fits_name: filename for the moment zero map
+ :param hdu_idx: index of the HDU to extract the data and the header
+ :return: the aggregated value of the moment zero, according to the function specified in the configuration
+ """
+ _cube_path = validate_parameter(cube_path, default=os.path.join('prs', 'fits', 'cubes'))
+ _moment_zero_path = validate_parameter(moment_zero_path, default=os.path.join('prs', 'fits', 'moments'))
+ _moment_zero_fits_name = validate_parameter(moment_zero_fits_name, default='test_mom0.fits')
+
+ fitsfile = open_fits_file_duck_typing(fitsfile=cube, fits_path=_cube_path)
+ header = fitsfile[hdu_idx].header.copy()
+ data = fitsfile[hdu_idx].data
+ conversion = header['CUNIT3'].strip() == 'HZ'
+ spectral_unit = 'km/s' if conversion is True else 'Hz'
+ conversion_factor = 1 if conversion is False else (-constants.c * header['CDELT3'] / header['CRVAL3']).to('km/s').value
+ mom0 = (data.sum(axis=0) * abs(conversion_factor))
+ keywords_to_delete = ['NAXIS3', 'CRPIX3', 'CDELT3', 'CRVAL3', 'CUNIT3', 'CTYPE3']
+ header['BUNIT'] += f' {spectral_unit}'
+ header['BTYPE'] = 'MOMENT0'
+ header['NAXIS'] = 2
+ for key in keywords_to_delete:
+ del header[key]
+ fits.writeto(os.path.join(_moment_zero_path, _moment_zero_fits_name), data=mom0, header=header, overwrite=True)
+ try:
+ return aggregation_function_mapping[config['moment_zero_aggregation_function']](mom0)
+ except KeyError:
+ logger.warning(
+ 'Moment zero aggregation function not set or misconfigured. Trying to use the one for the ratio...')
+ return aggregation_function_mapping[config['aggregation_function']](mom0)
+
+
+def open_fits_file_duck_typing(fitsfile: Union[str, fits.PrimaryHDU],
+ fits_path: str = None) -> fits.PrimaryHDU:
+ _fits_path = validate_parameter(fits_path, default='.')
+ try:
+ hdu = fits.open(os.path.join(_fits_path, fitsfile))
+ except TypeError:
+ hdu = fitsfile
+ return hdu
+
+
+def compute_image_ratios(fits1: str,
+ fits2: str,
+ config: dict,
+ fits_path: Union[str, None] = None,
+ ratio_fits_path: Union[str, None] = None,
+ ratio_fits_name: Union[str, None] = None,
+ hdu1_idx: int = 0,
+ hdu2_idx: int = 0):
+ """
+ Compute the ratio of the images and save it to fits. The ratio is computed as fits1/fits2
+ :param fits1: filename of the first moment 0 map
+ :param fits2: filename of the second moment 0 map
+ :param config: configuration of the presentation layer
+ :param fits_path: path where the fits files are to be found
+ :param ratio_fits_path: path where the ratio map is to be saved
+ :param ratio_fits_name: filename for the ratio map
+ :param hdu1_idx: index of the HDU to extract the data and the header, for the first fits file
+ :param hdu2_idx: index of the HDU to extract the data and the header, for the second fits file
+ :return:
+ """
+ _fits_path = validate_parameter(fits_path, default=os.path.join('prs', 'fits', 'moments'))
+ _ratio_fits_path = validate_parameter(ratio_fits_path, default=os.path.join('prs', 'fits', 'ratios'))
+ _ratio_fits_name = validate_parameter(ratio_fits_name, default='ratio.fits')
+ hdu1 = open_fits_file_duck_typing(fitsfile=fits1,
+ fits_path=_fits_path)
+ hdu2 = open_fits_file_duck_typing(fitsfile=fits2,
+ fits_path=_fits_path)
+ ratio_image_data = hdu1[hdu1_idx].data / hdu2[hdu2_idx].data
+ if config['aggregation_function'] in ('mean', 'sum'):
+ aggregated_ratio = aggregation_function_mapping[config['aggregation_function']](ratio_image_data)
+ elif config['aggregation_function'] == 'weighted_mean':
+ aggregated_ratio = np.nansum(hdu1[hdu1_idx].data) / np.nansum(hdu2[hdu2_idx].data)
+ else:
+ raise NotImplementedError('Aggregation function not configured')
+ output_header = hdu1[hdu1_idx].header
+ output_header['BUNIT'] = ''
+ output_header['BTYPE'] = 'INT.RATIO'
+ fits.writeto(os.path.join(_ratio_fits_path, _ratio_fits_name),
+ data=ratio_image_data,
+ header=output_header,
+ overwrite=True)
+ return aggregated_ratio
+
+
+def main(cube_fits_list: List[str],
+ run_id: str,
+ mom0_out_cube1: Union[str, None] = None,
+ mom0_out_cube2: Union[str, None] = None,
+ engine: sqlalchemy.engine = None) -> str:
+ assert len(cube_fits_list) == 2
+ _mom0_out_cube1 = validate_parameter(mom0_out_cube1, default=cube_fits_list[0].replace('.fits', '_mom0.fits'))
+ _mom0_out_cube2 = validate_parameter(mom0_out_cube2, default=cube_fits_list[1].replace('.fits', '_mom0.fits'))
+ config_prs = load_config_file(os.path.join('prs', 'config', 'config.yml'))['flux_computation']
+ executed_on = datetime.now()
+ if engine is None:
+ engine = get_pg_engine(logger=logger)
+ config_prs.update({
+ 'cube_fits_list': cube_fits_list,
+ 'mom0_image_list': [_mom0_out_cube1, _mom0_out_cube2]
+ })
+ ratio_fits_name = f'{compute_unique_hash_filename(config=config_prs)}.fits'
+ aggregated_mom0_1 = compute_moment_zero(cube=cube_fits_list[0],
+ config=config_prs,
+ moment_zero_fits_name=_mom0_out_cube1)
+ aggregated_mom0_2 = compute_moment_zero(cube=cube_fits_list[1],
+ config=config_prs,
+ moment_zero_fits_name=_mom0_out_cube2)
+ aggregated_image_ratio = compute_image_ratios(fits1=_mom0_out_cube1,
+ fits2=_mom0_out_cube2,
+ config=config_prs,
+ ratio_fits_name=ratio_fits_name)
+ populate_mom_zero_table(config_prs=config_prs,
+ fits_cube_name=cube_fits_list[0],
+ moment_zero_map_fits=_mom0_out_cube1,
+ aggregated_moment_zero=aggregated_mom0_1,
+ executed_on=executed_on,
+ engine=engine,
+ run_id=run_id)
+ populate_mom_zero_table(config_prs=config_prs,
+ fits_cube_name=cube_fits_list[1],
+ aggregated_moment_zero=aggregated_mom0_2,
+ moment_zero_map_fits=_mom0_out_cube2,
+ executed_on=executed_on,
+ engine=engine,
+ run_id=run_id)
+ populate_line_ratios_table(config_prs=config_prs,
+ moment_zero_map_fits_list=[_mom0_out_cube1, _mom0_out_cube2],
+ ratio_map_name=ratio_fits_name,
+ aggregated_ratio=aggregated_image_ratio,
+ engine=engine,
+ executed_on=executed_on,
+ run_id=run_id)
+ return ratio_fits_name
+
+
+if __name__ == '__main__':
+ main(cube_fits_list=['test_cube.fits', 'test_cube.fits'],
+ mom0_out_cube1='test_cube_mom0.fits',
+ mom0_out_cube2='test_cube_mom0_1.fits',
+ run_id='test_run')
diff --git a/etl/prs/prs_density_inference.py b/etl/prs/prs_density_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c52911f414684fc18f5abd4f7e63b0c8f5d5da0a
--- /dev/null
+++ b/etl/prs/prs_density_inference.py
@@ -0,0 +1,626 @@
+import operator
+import pandas as pd
+import os
+import pickle
+import matplotlib.pyplot as plt
+import numpy as np
+from itertools import product
+import yaml
+from sklearn.neighbors import KernelDensity
+from sklearn.model_selection import GridSearchCV
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger,
+ get_postprocessed_data)
+from assets.constants import line_ratio_mapping
+from multiprocessing import Pool
+from typing import Tuple, Union, List
+from scipy.integrate import cumtrapz
+from scipy.stats import truncnorm
+from scipy.interpolate import RegularGridInterpolator
+from functools import reduce
+
+
+def get_truncated_normal(mean: float = 0,
+ sd: float = 1,
+ lower_bound: float = 0,
+ upper_bound: float = 10):
+ return truncnorm(
+ (lower_bound - mean) / sd, (upper_bound - mean) / sd, loc=mean, scale=sd)
+
+
+def train_kde_model(ratio: List[str],
+ training_data: pd.DataFrame,
+ points_per_axis: int,
+ best_bandwidth: Union[None, dict],
+ model_root_folder: Union[str, None] = None,
+ ratio_limits: Union[None, dict] = None,
+ rt_adjustment_factor: Union[int, float] = 2) -> Tuple[str, KernelDensity]:
+ """
+ Create the KDE from the modelled datapoints.
+ :param points_per_axis: number of point in the KDE grid evaluation
+ :param ratio: the line ratio to use in the model, in the form of a list of 2 line indices, according to the
+ collisional coefficients file
+ :param best_bandwidth: bandwidth to use for the KDE smoothing
+ :param training_data: the dataframe containing the data for the KDE modelling
+ :param model_root_folder: the folder referring to the model used for computation;
+ defaults to fiducial model (constant_abundance_p15_q05)
+ :param rt_adjustment_factor: a scaling factor to apply to the specified bandwidth for the computation of the KDE
+ for the sparser RT-only data
+ :param ratio_limits: fixed plotting limits
+ :return: the string representing the ratio modelled and the KDE itself
+ """
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ ratio_string = '-'.join(ratio)
+ scaled_grid, model, positions, x, y, z = get_kde(points_per_axis=points_per_axis,
+ ratio_string=ratio_string,
+ training_data=training_data,
+ best_bandwidth=best_bandwidth[ratio_string])
+ grid = scaled_grid.copy()
+ grid[0] = scaled_grid[0] * 0.2
+ grid[1] = scaled_grid[1] * best_bandwidth[ratio_string]
+ plot_kde_ratio_nh2(grid=grid,
+ values_on_grid=z.reshape(points_per_axis, points_per_axis),
+ ratio_string=ratio_string,
+ model_root_folder=_model_root_folder,
+ training_data=training_data,
+ suffix_outfile='_ml',
+ ratio_limits=ratio_limits[ratio_string])
+
+ scaled_grid, _, _, _, _, z_rt_only = get_kde(points_per_axis=points_per_axis,
+ ratio_string=ratio_string,
+ training_data=training_data[training_data['source'] == 'RT'],
+ best_bandwidth=best_bandwidth[ratio_string],
+ x=x / rt_adjustment_factor,
+ y=y / rt_adjustment_factor,
+ bw_adjustment_factor=rt_adjustment_factor)
+ grid *= rt_adjustment_factor
+ plot_kde_ratio_nh2(grid=grid,
+ values_on_grid=z_rt_only.reshape(points_per_axis, points_per_axis),
+ ratio_string=ratio_string,
+ model_root_folder=_model_root_folder,
+ training_data=training_data[training_data['source'] == 'RT'],
+ ratio_limits=ratio_limits[ratio_string])
+ with open(
+ os.path.join(_model_root_folder, 'trained_model', f'ratio_density_kde_{ratio_string}.pickle'), 'wb'
+ ) as outfile:
+ pickle.dump({'x': x, 'y': y, 'positions': positions, 'values': z, 'values_rt_only': z_rt_only}, outfile)
+ return ratio_string, model
+
+
+def get_kde(points_per_axis,
+ ratio_string,
+ training_data,
+ x=None,
+ y=None,
+ best_bandwidth: float = None,
+ bw_adjustment_factor: Union[float, int] = 1):
+ _best_bandwidth = bw_adjustment_factor * validate_parameter(best_bandwidth, 0.2)
+ _x_bandwidth = bw_adjustment_factor * 0.2
+ log_nh2 = np.log10(training_data['avg_nh2'])
+ xy_train = np.array([log_nh2, training_data[f'ratio_{ratio_string}']]).T
+ xy_train_scaled = xy_train / np.array([_x_bandwidth, _best_bandwidth])
+ # The grid search suggests a bandwidth of 0.1, but I need to make it larger due to the relatively large spacing
+ # between models
+ model = KernelDensity(bandwidth=1, kernel='epanechnikov')
+ model.fit(xy_train_scaled)
+ if x is None and y is None:
+ xmin = log_nh2.min() - 0.5
+ xmax = log_nh2.max() + 0.5
+ ymin = np.max([training_data[f'ratio_{ratio_string}'].min() - 0.5, 0])
+ ymax = training_data[f'ratio_{ratio_string}'].max() * 1.15
+ x = np.linspace(xmin, xmax, points_per_axis) / _x_bandwidth
+ y = np.linspace(ymin, ymax, points_per_axis) / _best_bandwidth
+ grid = np.meshgrid(x, y, indexing='ij')
+ positions = np.vstack([grid[0].ravel(), grid[1].ravel()])
+ z = np.exp(model.score_samples(positions.T)) + 1e-5
+ z /= z.max()
+ return grid, model, positions, x, y, z
+
+
+def plot_kde_ratio_nh2(grid: np.array,
+ values_on_grid: np.array,
+ ratio_string: str,
+ model_root_folder: str,
+ training_data: pd.DataFrame,
+ suffix_outfile: str = None,
+ ratio_limits: Union[None, list] = None):
+ plt.rcParams.update({'font.size': 20})
+ _suffix_outfile = validate_parameter(suffix_outfile, default='')
+ plt.clf()
+ plt.figure(figsize=(8, 6))
+ plt.scatter(training_data['avg_nh2'], training_data[f'ratio_{ratio_string}'], marker='+', alpha=0.1,
+ facecolor='grey')
+ plt.contour(10 ** grid[0], grid[1], values_on_grid, levels=np.arange(0.05, 0.95, 0.15))
+ plt.semilogx()
+ plt.xlabel(r'<$n$(H$_2$)> [cm$^{-3}$]')
+ plt.ylabel(f'Ratio {line_ratio_mapping[ratio_string]}')
+ plt.ylim(ratio_limits)
+ plt.tight_layout()
+ plt.savefig(os.path.join(
+ model_root_folder,
+ 'figures',
+ f'ratio_vs_avg_density_los_kde_{ratio_string}{_suffix_outfile}.png'))
+
+
+def get_results(x_array: np.array,
+ probability_density: np.array,
+ probability_threshold: float = 0.05,
+ interp_points: int = 100) -> Tuple[float, float, List[float]]:
+ """
+ Perform the data fitting, computing the best fit and the highest probability density interval
+ :param x_array: the density array
+ :param probability_density: the probability density values
+ :param probability_threshold: the probability mass contained in the wings
+ :param interp_points: the number of points used to interpolate the probability density
+ :return: the probability density threshold, the best-fit value for density, and the HPD interval
+ """
+ probability, centered_probability_density, ordered_idxs = \
+ get_probability_distribution(probability_density=probability_density,
+ x_array=x_array)
+ probability_density_threshold = get_probability_density_threshold(
+ ordered_probability=probability[ordered_idxs],
+ ordered_probability_density=centered_probability_density[ordered_idxs],
+ probability_threshold=probability_threshold
+ )
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=probability_density_threshold,
+ interp_points=interp_points)
+ return probability_density_threshold, x_array[ordered_idxs][-1], hpd_interval
+
+
+def get_probability_density_threshold(ordered_probability: np.array,
+ ordered_probability_density: np.array,
+ probability_threshold=0.05) -> float:
+ """
+ Compute the threshold in probability density that leaves out the requested probability mass
+ :param ordered_probability: the probability array, ordered according to increasing probability density
+ :param ordered_probability_density: the ordered probability density array, in ascending order
+ :param probability_threshold: the probability mass in the tails, outside the HPD
+ :return: the threshold in HPD that leaves the specified probability mass in the tails
+ """
+ ctrapz_ordered = np.cumsum(ordered_probability)
+ threshold_idx = np.argmin(np.abs(ctrapz_ordered - probability_threshold))
+
+ lower_index = np.max([threshold_idx - 1, 0])
+ upper_index = np.min([threshold_idx + 1, len(ctrapz_ordered) - 1])
+ x_interp = np.linspace(lower_index, upper_index, 200)
+ interpolated_probability = np.interp(x_interp,
+ [lower_index, threshold_idx, upper_index],
+ np.take(ctrapz_ordered, [lower_index, threshold_idx, upper_index]))
+ interp_idx = np.argmin(abs(interpolated_probability - probability_threshold))
+ weighted_probability_density_threshold = np.interp(x_interp,
+ [lower_index, threshold_idx, upper_index],
+ np.take(ordered_probability_density,
+ [lower_index, threshold_idx, upper_index]))
+ return weighted_probability_density_threshold[interp_idx]
+
+
+def get_hpd_interval(x_array: np.array,
+ probability_density: np.array,
+ hpd_threshold: float,
+ interp_points: int = 100) -> List[float]:
+ """
+ Compute the highest probability density interval, that leaves out the given probability density mass
+ :param x_array: the density array
+ :param probability_density: the probability density array
+ :param hpd_threshold: the probability density threshold
+ :param interp_points: the number of points used to interpolate the probability density
+ :return: a list of the boundaries fot the HPD (can be composed by multiple intervals
+ """
+ hpd_interval = []
+ elements_above_threshold = np.argwhere(probability_density - hpd_threshold >= 0).flatten()
+ intervals = np.split(elements_above_threshold, np.nonzero(np.diff(elements_above_threshold) != 1)[0] + 1)
+ idx_list = []
+ try:
+ for interval in intervals:
+ idx_list.append([np.min(interval) - 1, np.max(interval)])
+
+ flat_list = [item for sublist in idx_list for item in sublist]
+ for idx in flat_list:
+ if idx < 0:
+ hpd_interval.append(x_array[0])
+ elif idx == len(x_array) - 1:
+ hpd_interval.append(x_array[-1])
+ else:
+ x_interp = np.linspace(x_array[idx], x_array[idx + 1], interp_points)
+ interpolated_probability_density = np.interp(x_interp, x_array[idx:idx + 2],
+ probability_density[idx:idx + 2])
+ interp_idx = np.argmin(abs(interpolated_probability_density - hpd_threshold))
+ hpd_interval.append(x_interp[interp_idx])
+ except ValueError:
+ pass
+
+ return hpd_interval
+
+
+def get_probability_distribution(probability_density: np.array,
+ x_array: np.array) -> Tuple[np.array, np.array, np.array]:
+ """
+ Compute the probability mass distribution, and returns the centered probability density array, and the ordered
+ indices that sort the probability density in ascending order
+ :param probability_density: the probability density array
+ :param x_array: the density array
+ :return: the normalized probability mass distribution, the centered probability density array, and the indices that
+ sort the probability density array in ascending order
+ """
+ dx_array = np.diff(x_array)
+ avg_y_array = 0.5 * (probability_density[1:] + probability_density[0:-1])
+ probability = avg_y_array * dx_array
+ probability /= probability.sum()
+ ordered_idxs = np.argsort(avg_y_array)
+ return probability, avg_y_array, ordered_idxs
+
+
+def compute_best_bandwidth(data: pd.DataFrame,
+ bandwidths: List[float],
+ line_pairs: List[List[str]],
+ nthreads: int = 15,
+ crossvalidation_folds: int = 5,
+ training_thinning: int = 10,
+ kernel: str = None) -> List[float]:
+ """
+ Function that compute the best bandwidth for the KDE via cross-validation. Consider that due to the rough spacing
+ in the characteristic density of the clumps the best bandwidth is too small. We suggest scaling it up by a factor
+ of 2.
+ :param data: the data to be used for the cross validation
+ :param bandwidths: the list of bandwidths to be tested
+ :param line_pairs: the list of ratios for which the best bandwidth should be determined
+ :param nthreads: the number of threads to use
+ :param crossvalidation_folds: the number of folds for cross validation
+ :param training_thinning: thinning factor for training data set (reduces computational times)
+ :param kernel: the kernel type, as supported by sklearn; defaults to epanechnikov
+ :return: the list of best bandwidths, one for each ratio
+ """
+ _kernel = validate_parameter(kernel, 'epanechnikov')
+ best_bandwidths = []
+ for ratio in line_pairs:
+ ratio_string = '-'.join(ratio)
+ y_train = data[[f'ratio_{ratio_string}']]
+ model_y = KernelDensity(kernel=_kernel)
+ cv_grid_y = GridSearchCV(model_y, {'bandwidth': bandwidths}, cv=crossvalidation_folds, n_jobs=nthreads,
+ verbose=4)
+ cv_grid_y.fit(y_train.sample(frac=1 / training_thinning))
+ best_bandwidths.append(cv_grid_y.best_estimator_)
+ return best_bandwidths
+
+
+def recompute_and_save_kdes(data: pd.DataFrame,
+ line_pairs: List[List[str]],
+ points_per_axis: int,
+ nthreads: int = 10,
+ pickle_models_dict_filename: Union[None, str] = None,
+ model_root_folder: Union[None, str] = None,
+ best_bandwidths: Union[None, dict] = None,
+ ratio_limits: Union[None, dict] = None):
+ """
+ Retrieve the dictionary of the KDE models, either by computing it or unpickling it from previous runs
+ :param points_per_axis: number of points for the KDE grid evaluation
+ :param data: the data to be used for retraining
+ :param line_pairs: all the lines pairs that constitute the ratios
+ :param nthreads: the numbers of separate threads to use
+ :param pickle_models_dict_filename: the filename of the pickle file containing the model dictionary
+ :param model_root_folder: the folder referring to the model used for computation;
+ defaults to fiducial model (constant_abundance_p15_q05)
+ :param best_bandwidths: kernel bandwidths to use for each ratio
+ :param ratio_limits: fixed plotting limits
+ """
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ _pickle_models_dict_filename = validate_parameter(pickle_models_dict_filename, default='models_dict.pickle')
+
+ models_dict = {}
+ default_bandwidths = {
+ '87-86': 0.1,
+ '88-86': 0.1,
+ '88-87': 0.1,
+ '257-256': 0.4,
+ '381-380': 0.2,
+ }
+ _best_bandwidths = validate_parameter(best_bandwidths, default=default_bandwidths)
+ parallel_args = product(line_pairs, [data], [points_per_axis],
+ [_best_bandwidths], [_model_root_folder], [ratio_limits])
+ with Pool(nthreads) as pool:
+ results = pool.starmap(train_kde_model, parallel_args)
+
+ for ratio_string, model in results:
+ models_dict[ratio_string] = model
+
+ with open(os.path.join(_model_root_folder, 'trained_model', _pickle_models_dict_filename), 'wb') as outfile:
+ pickle.dump(models_dict, outfile)
+
+
+def plot_results(log_x_grid: np.array,
+ probability_density: np.array,
+ probability_density_threshold: float,
+ hpd_interval: List[float],
+ source_name: Union[str, None] = None,
+ model_root_folder: Union[None, str] = None):
+ """
+ Plot the results of the fitting, the posterior of the number density of H2
+ :param source_name: the source name string
+ :param hpd_interval: the list of HPD interval boundaries
+ :param log_x_grid: the logarithmic grid in number density
+ :param probability_density: the array with the probability density values
+ :param probability_density_threshold: the threshold where the probability density is to be cut to get the HPD
+ interval
+ :param model_root_folder: the folder referring to the model used for computation;
+ defaults to fiducial model (constant_abundance_p15_q05)
+ """
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ if (source_name is not None) and (source_name != 'default_source'):
+ _source_name_string = source_name
+ else:
+ _source_name_string = ''
+ plt.figure(figsize=(8, 6))
+ plt.rcParams.update({'font.size': 16})
+ plt.plot(10 ** log_x_grid, probability_density, marker='x')
+ plt.axhline(y=probability_density_threshold, color='r', linestyle='--')
+ plt.xlim(0.5 * np.min(np.array(hpd_interval)),
+ 2 * np.max(np.array(hpd_interval)))
+ plt.semilogx()
+ plt.xlabel(r'Number density [cm$^{-3}$]')
+ plt.ylabel(r'Probability density')
+ plt.title('$n$(H$_{2}$)' + f' posterior for {_source_name_string}')
+ plt.tight_layout()
+ plt.savefig(os.path.join(_model_root_folder, 'figures', f'density_pdf{_source_name_string}.png'))
+ plt.clf()
+
+
+def normalize_probability_density(log_x_grid: np.array,
+ probability_density: np.array) -> np.array:
+ """
+ Normalize the probability density, so that the integral of the probability is 1
+ :param log_x_grid: the logarithmic grid in number density
+ :param probability_density: the array with the probability density values
+ :return: the normalized probability density function
+ """
+ ecdf = cumtrapz(probability_density, 10 ** log_x_grid)
+ renorm_constant = ecdf[-1]
+ ecdf /= renorm_constant
+ probability_density /= renorm_constant
+ return probability_density, ecdf
+
+
+def compute_probability_density(x_grid: np.array,
+ measured_integrated_intensities: Tuple[float, float],
+ integrated_intensities_uncertainty: Tuple[float, float],
+ model: KernelDensity,
+ ratio_realizations: int = 1000,
+ best_bandwidth: Union[None, float] = None,
+ ratio_string: Union[None, str] = None) -> np.array:
+ """
+ Compute the PDF from the model and the realizations of the ratio, given its measurement and the uncertainties.
+ For now, the ratio meaaurement is assumed to have a gaussian uncertainty.
+ :param x_grid: the grid in number density
+ :param measured_integrated_intensities: the measured value of the integrated intensities
+ :param integrated_intensities_uncertainty: the rms uncertainty on the integrated intensity
+ :param model: the KDE model for the ratio
+ :param ratio_realizations: the number of realizations of the ratio, to compute the PDF
+ :param best_bandwidth: the bandwidth to use for KDE
+ :return: a dictionary containing the PDF of the density and the ratio string
+ """
+ _best_bandwidth = validate_parameter(best_bandwidth, 0.2)
+ _ratio_string = validate_parameter(ratio_string, default='not_specified')
+ simulated_integrated_intensities_0 = get_truncated_normal(mean=measured_integrated_intensities[0],
+ sd=integrated_intensities_uncertainty[0],
+ lower_bound=1e-5,
+ upper_bound=measured_integrated_intensities[0] + 10 *
+ integrated_intensities_uncertainty[0]) \
+ .rvs(size=ratio_realizations)
+ simulated_integrated_intensities_1 = get_truncated_normal(mean=measured_integrated_intensities[1],
+ sd=integrated_intensities_uncertainty[1],
+ lower_bound=1e-5,
+ upper_bound=measured_integrated_intensities[1] + 10 *
+ integrated_intensities_uncertainty[1]) \
+ .rvs(size=ratio_realizations)
+ simulated_ratios = simulated_integrated_intensities_0 / simulated_integrated_intensities_1
+ interp_function = RegularGridInterpolator((model['x'], model['y']),
+ model['values'].reshape(model['x'].shape[0], model['y'].shape[0]),
+ bounds_error=False,
+ fill_value=0)
+ positions = np.array(list(product(x_grid / 0.2, simulated_ratios / _best_bandwidth)))
+ z = interp_function(positions)
+ probability_density = np.nansum(z.reshape((len(x_grid), ratio_realizations)).T, axis=0)
+ probability_density /= np.trapz(probability_density, x_grid)
+ return {_ratio_string: probability_density}
+
+
+def main(measured_integrated_intensity_dict: dict,
+ integrated_intensity_uncertainty_dict: dict,
+ ratio_list: list,
+ probability_threshold: float,
+ source_names: list,
+ recompute_kde: bool = False,
+ points_per_axis: int = 200,
+ ratio_realizations: int = 1000,
+ nthreads: int = 10,
+ limit_rows: Union[None, int] = None,
+ use_model_for_inference: Union[None, str] = None,
+ best_bandwidths: Union[None, dict] = None,
+ ratio_limits: Union[None, dict] = None):
+ _use_model_for_inference = validate_parameter(
+ use_model_for_inference,
+ default='constant_abundance_p15_q05'
+ )
+ _model_root_folder = os.path.join('prs', 'output', 'run_type', _use_model_for_inference)
+ data, line_pairs = get_inference_data(use_model_for_inference=_use_model_for_inference,
+ limit_rows=limit_rows)
+ measured_integrated_intensity_coupled = {}
+ integrated_intensity_uncertainty_coupled = {}
+ if recompute_kde is True:
+ recompute_and_save_kdes(data=data,
+ line_pairs=line_pairs,
+ nthreads=nthreads,
+ points_per_axis=points_per_axis,
+ model_root_folder=_model_root_folder,
+ best_bandwidths=best_bandwidths,
+ ratio_limits=ratio_limits)
+
+ x_grid = np.linspace(np.log10(0.7 * np.nanmin(data['avg_nh2'])),
+ np.log10(1.3 * np.nanmax(data['avg_nh2'])), points_per_axis)
+
+ results_dict = {}
+ posteriors = {
+ 'PDF': {},
+ 'ECDF': {},
+ 'density_grid': {}
+ }
+ for source_name in source_names:
+ _ratio_list_per_source = []
+ for ratio_string in ratio_list:
+ line_ids = validate_line_ids(line_pairs, ratio_string)
+ if np.isnan(measured_integrated_intensity_dict[line_ids[0]][source_name]) or \
+ np.isnan(measured_integrated_intensity_dict[line_ids[1]][source_name]):
+ logger.warning(f'The ratio {ratio_string} is not available for source {source_name}. Proceeding with the remaining ratios...')
+ else:
+ measured_integrated_intensity_coupled[ratio_string] = [
+ measured_integrated_intensity_dict[line_ids[0]][source_name],
+ measured_integrated_intensity_dict[line_ids[1]][source_name]
+ ]
+ integrated_intensity_uncertainty_coupled[ratio_string] = [
+ integrated_intensity_uncertainty_dict[line_ids[0]][source_name],
+ integrated_intensity_uncertainty_dict[line_ids[1]][source_name]
+ ]
+ _ratio_list_per_source.append(ratio_string)
+
+ # Compute combined probability density as the product of the individual ratio probability densities
+ parallel_args = []
+ for ratio_string in _ratio_list_per_source:
+ with open(
+ os.path.join(_model_root_folder, 'trained_model', f'ratio_density_kde_{ratio_string}.pickle'), 'rb'
+ ) as infile:
+ model_dict = pickle.load(infile)
+ parallel_args.append([x_grid,
+ measured_integrated_intensity_coupled[ratio_string],
+ integrated_intensity_uncertainty_coupled[ratio_string],
+ model_dict,
+ ratio_realizations,
+ best_bandwidths[ratio_string],
+ ratio_string])
+ with Pool(nthreads) as pool:
+ results = pool.starmap(compute_probability_density, parallel_args)
+ results = reduce(operator.ior, results, {})
+ combined_probability_density = np.array(list(results.values())).prod(axis=0)
+ if np.isnan(combined_probability_density.sum()):
+ logger.warning(f'Source {source_name} does not have ratios compatible with modelling with this grid')
+ problematic_ratios = [key for key in results.keys() if np.isnan(results[key].sum())]
+ logger.warning(f'The ratios outside the simulated boundaries are {problematic_ratios}')
+ results_dict[source_name] = {
+ 'best_fit': np.nan,
+ 'hpd_interval': [np.nan, np.nan]
+ }
+ else:
+ combined_probability_density, combined_ecdf = normalize_probability_density(x_grid,
+ combined_probability_density)
+
+ probability_density_threshold, best_fit, hpd_interval = \
+ get_results(x_array=10 ** x_grid,
+ probability_density=combined_probability_density,
+ probability_threshold=probability_threshold)
+ plot_results(log_x_grid=x_grid,
+ probability_density=combined_probability_density,
+ probability_density_threshold=probability_density_threshold,
+ hpd_interval=hpd_interval,
+ source_name=source_name,
+ model_root_folder=_model_root_folder)
+ logger.info(f'Processed source {source_name}')
+ logger.info(f'The best fit value is {best_fit}, with an HPD interval of {hpd_interval}')
+ logger.info(f'The threshold is {probability_density_threshold}')
+ results_dict[source_name] = {
+ 'best_fit': float(best_fit),
+ 'hpd_interval': [float(element) for element in hpd_interval]
+ }
+ posteriors['PDF'][source_name] = combined_probability_density
+ posteriors['ECDF'][source_name] = combined_ecdf
+ posteriors['density_grid'][source_name] = 10 ** x_grid
+ with open(os.path.join(_model_root_folder, 'volume_density_results.yml'), 'w') as outfile:
+ yaml.dump(results_dict, outfile)
+ with open(os.path.join(_model_root_folder, 'posteriors.pickle'), 'wb') as outfile:
+ pickle.dump(posteriors, outfile)
+
+
+def validate_line_ids(line_pairs, ratio_string):
+ line_ids = ratio_string.split('-')
+ if line_ids not in line_pairs:
+ raise ValueError(f'Specified ratio {ratio_string} not present in the model ({line_pairs})')
+ return line_ids
+
+
+def get_inference_data(use_model_for_inference: str, limit_rows: int):
+ line_pairs, data = get_postprocessed_data(limit_rows=limit_rows,
+ use_model_for_inference=use_model_for_inference)
+ data['source'] = 'RT'
+ try:
+ _inferred_data_file = os.path.join(os.path.join('prs', 'output', 'run_type', use_model_for_inference),
+ 'data',
+ 'inferred_data.csv')
+ inferred_data = pd.read_csv(_inferred_data_file, index_col=0)
+ inferred_data['source'] = 'ML'
+ data = pd.concat([data, inferred_data], axis=0, ignore_index=True)
+ except IOError:
+ logger.warning('Inferred data not found. Proceeding with RT-generated data only...')
+ return data, line_pairs
+
+
+if __name__ == '__main__':
+ external_input = load_config_file(config_file_path='config/density_inference_input.yml')
+ logger = setup_logger(name='PRS - DENSITY INFERENCE')
+ try:
+ limit_rows = external_input['limit_rows']
+ except KeyError:
+ limit_rows = None
+ try:
+ use_model_for_inference = external_input['use_model_for_inference']
+ if use_model_for_inference == 'PLACEHOLDER':
+ logger.warning('No model specified for inference in density_inference_input.yml. Using fiducial.')
+ use_model_for_inference = 'constant_abundance_p15_q05'
+ except KeyError:
+ use_model_for_inference = None
+ try:
+ points_per_axis = external_input['points_per_axis']
+ except KeyError:
+ points_per_axis = 200
+ try:
+ nthreads = external_input['nthreads']
+ except KeyError:
+ nthreads = 1
+ try:
+ sources = set()
+ for line_id in external_input['measured_integrated_intensities']:
+ sources = sources.union(set(external_input['measured_integrated_intensities'][line_id].keys()))
+ _intensities_dict = external_input['measured_integrated_intensities']
+ _uncertainty_dict = external_input['integrated_intensities_uncertainties']
+ except AttributeError:
+ sources = ['default_source']
+ _intensities_dict = {line_id: {'default_source': external_input['measured_integrated_intensities'][line_id]}
+ for line_id in external_input['measured_integrated_intensities']}
+ _uncertainty_dict = {
+ line_id: {'default_source': external_input['integrated_intensities_uncertainties'][line_id]}
+ for line_id in external_input['integrated_intensities_uncertainties']}
+ sources = list(sources)
+ sources.sort()
+
+ logger.info(f'Using {use_model_for_inference} for inference')
+ main(measured_integrated_intensity_dict=_intensities_dict,
+ integrated_intensity_uncertainty_dict=_uncertainty_dict,
+ source_names=sources,
+ ratio_list=external_input['ratios_to_include'],
+ probability_threshold=external_input['probability_threshold'],
+ recompute_kde=external_input['recompute_kde'],
+ points_per_axis=points_per_axis,
+ ratio_realizations=external_input['simulated_ratio_realizations'],
+ nthreads=nthreads,
+ limit_rows=limit_rows,
+ use_model_for_inference=use_model_for_inference,
+ best_bandwidths=external_input['best_bandwidths'],
+ ratio_limits=external_input['ratio_limits'])
diff --git a/etl/prs/prs_expand_modelling_with_ml.py b/etl/prs/prs_expand_modelling_with_ml.py
new file mode 100644
index 0000000000000000000000000000000000000000..56a501255c052c098f590bb670358cfbc15f17fb
--- /dev/null
+++ b/etl/prs/prs_expand_modelling_with_ml.py
@@ -0,0 +1,125 @@
+import pandas as pd
+import os
+import pickle
+import numpy as np
+from itertools import product
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger)
+from assets.commons.parsing import parse_grid_overrides
+from assets.commons.training_utils import (compute_and_add_similarity_cols,
+ plot_results,
+ get_avg_profiles)
+from assets.utils import get_profile_input
+from typing import Union, List
+
+
+def main(species: Union[str, None] = None,
+ tdust: float = 21.0,
+ nh2: float = 1e5,
+ model_root_folder: Union[str, None] = None,
+ ratios_to_process: Union[List[List[str]], None] = None) -> pd.DataFrame:
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ _ratios_to_process = validate_parameter(
+ ratios_to_process,
+ default=[['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']]
+ )
+ line_set = set([line_id for ratio in _ratios_to_process for line_id in ratio])
+ (overrides_grid, overrides_abundances) = get_profile_input(species=species,
+ model_root_folder=_model_root_folder)
+ (avg_density_profile, avg_temperature_profile,
+ molecule_coldens, std_density) = \
+ get_avg_profiles(
+ value_at_reference_density=nh2,
+ value_at_reference_temperature=tdust,
+ **overrides_abundances,
+ **overrides_grid
+ )
+ x_interp = pd.DataFrame()
+
+ x_interp['avg_nh2'] = np.log10(avg_density_profile)
+ x_interp['avg_tdust'] = np.log10(avg_temperature_profile)
+ x_interp['molecule_column_density'] = np.log10(molecule_coldens)
+ x_interp['px_index'] = x_interp.index.copy()
+ x_interp['nh2'] = np.log10(nh2)
+ x_interp['tdust'] = np.log10(tdust)
+ x_interp['std_nh2'] = np.log10(std_density)
+
+ results_df = pd.DataFrame()
+ x_interp['px_index'] = x_interp.index.copy()
+ x_interp = x_interp.dropna(subset=['avg_nh2'])
+ indices = x_interp['px_index'].reset_index(drop=True)
+ results_df['px_index'] = indices
+ for line in line_set:
+ with open(os.path.join(_model_root_folder, 'trained_model', f'average_features_per_target_bin_{line}.pickle'),
+ 'rb') as infile:
+ average_features_per_taget_bin = pickle.load(infile)
+ x_interp = compute_and_add_similarity_cols(average_features_per_target_bin=average_features_per_taget_bin,
+ input_df=x_interp,
+ columns_to_drop=['px_index'],
+ similarity_bins=50)
+ with open(os.path.join(_model_root_folder, 'trained_model', f'ml_scaler_{line}.pickle'), 'rb') as infile:
+ scaler = pickle.load(infile)
+ with open(os.path.join(_model_root_folder, 'trained_model', f'ml_model_{line}.pickle'), 'rb') as infile:
+ model = pickle.load(infile)
+ columns = model.feature_names_in_
+ x_interp_transformed = pd.DataFrame(scaler.transform(x_interp[columns]), columns=columns)
+ results_df[f'mom_zero_{line}'] = model.predict(x_interp_transformed)
+ for column in x_interp.columns:
+ if column.startswith('sim_'):
+ x_interp.drop(columns=column, inplace=True)
+
+ results_df = results_df.merge(x_interp, on=['px_index'], how='inner', validate='1:1')
+ ratio_string_list = []
+ for line_pairs in _ratios_to_process:
+ ratio_string = f'ratio_{line_pairs[0]}-{line_pairs[1]}'
+ ratio_string_list.append(ratio_string)
+ results_df[ratio_string] = \
+ 10 ** results_df[f'mom_zero_{line_pairs[0]}'] / 10 ** results_df[f'mom_zero_{line_pairs[1]}']
+ return results_df[['nh2', 'tdust', 'avg_tdust', 'avg_nh2', 'px_index'] + ratio_string_list]
+
+
+if __name__ == '__main__':
+ config = load_config_file(config_file_path=os.path.join('config', 'config.yml'))
+ external_input = load_config_file(config_file_path=os.path.join('config', 'ml_modelling.yml'))
+ logger = setup_logger(name='PRS - ML modelling')
+ try:
+ limit_rows = external_input['limit_rows']
+ except KeyError:
+ limit_rows = None
+ try:
+ use_model_for_training = external_input['use_model_for_training']
+ except KeyError:
+ use_model_for_training = None
+
+ _use_model_for_training = validate_parameter(
+ use_model_for_training,
+ default='constant_abundance_p15_q05'
+ )
+ _model_root_folder = os.path.join('prs', 'output', 'run_type', _use_model_for_training)
+ logger.info(f'Using {_use_model_for_training} for training in folder {_model_root_folder}')
+
+ tdust_grid = parse_grid_overrides(par_name='tdust', config=external_input)
+ nh2_grid = parse_grid_overrides(par_name='nh2', config=external_input)
+
+ inferred_data_list = []
+ for (tdust, nh2) in product(tdust_grid, nh2_grid):
+ logger.info(f'Producing emulated data for {nh2}, {tdust}')
+ if nh2 not in [1e3, 3e3, 9e3, 27e3, 81e3, 243e3, 729e3, 2187e3]:
+ inferred_data_list.append(
+ main(nh2=nh2,
+ tdust=tdust,
+ model_root_folder=_model_root_folder,
+ ratios_to_process=config['overrides']['lines_to_process'])
+ )
+
+ inferred_data = pd.concat(inferred_data_list, axis=0, ignore_index=True)
+ plot_results(inferred_data=inferred_data,
+ use_model_for_inference=_use_model_for_training,
+ ratios_to_process=config['overrides']['lines_to_process'])
+ inferred_data['avg_nh2'] = 10 ** inferred_data['avg_nh2']
+ inferred_data.to_csv(os.path.join(_model_root_folder, 'data', 'inferred_data.csv'))
+ logger.info('Completed emulation')
diff --git a/etl/prs/prs_expand_ratio_modelling_with_ml.py b/etl/prs/prs_expand_ratio_modelling_with_ml.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e9a348807d64ad5b9ae8b427754a9b816138d7a
--- /dev/null
+++ b/etl/prs/prs_expand_ratio_modelling_with_ml.py
@@ -0,0 +1,120 @@
+import pandas as pd
+import os
+import pickle
+import numpy as np
+from itertools import product
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger)
+from assets.commons.parsing import parse_grid_overrides
+from assets.commons.training_utils import compute_and_add_similarity_cols
+from assets.commons.training_utils import plot_results, get_avg_profiles
+from typing import Tuple, Union, List
+from assets.utils import get_profile_input
+
+
+def main(species: Union[str, None] = None,
+ tdust: float = 21.0,
+ nh2: float = 1e5,
+ model_root_folder: Union[str, None] = None,
+ ratios_to_process: Union[List[List[str]], None] = None) -> pd.DataFrame:
+ _model_root_folder = validate_parameter(
+ model_root_folder,
+ default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
+ )
+ _ratios_to_process = validate_parameter(
+ ratios_to_process,
+ default=[['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']]
+ )
+ (overrides_grid, overrides_abundances) = get_profile_input(species=species,
+ model_root_folder=_model_root_folder)
+ (avg_density_profile, avg_temperature_profile,
+ molecule_coldens, std_density) = \
+ get_avg_profiles(
+ value_at_reference_density=nh2,
+ value_at_reference_temperature=tdust,
+ **overrides_abundances,
+ **overrides_grid
+ )
+ x_interp = pd.DataFrame()
+
+ x_interp['avg_nh2'] = np.log10(avg_density_profile)
+ x_interp['avg_tdust'] = np.log10(avg_temperature_profile)
+ x_interp['molecule_column_density'] = np.log10(molecule_coldens)
+ x_interp['px_index'] = x_interp.index.copy()
+ x_interp['nh2'] = np.log10(nh2)
+ x_interp['tdust'] = np.log10(tdust)
+ x_interp['std_nh2'] = np.log10(std_density)
+
+ results_df = pd.DataFrame()
+ x_interp['px_index'] = x_interp.index.copy()
+ x_interp = x_interp.dropna(subset=['avg_nh2'])
+ indices = x_interp['px_index'].reset_index(drop=True)
+ results_df['px_index'] = indices
+ ratio_string_list = []
+ for line_ratio in _ratios_to_process:
+ line_ratio_string = '-'.join(line_ratio)
+ ratio_string_list.append(f'ratio_{line_ratio_string}')
+ with open(os.path.join(_model_root_folder, 'trained_model', f'average_features_per_target_bin_{line_ratio_string}.pickle'),
+ 'rb') as infile:
+ average_features_per_taget_bin = pickle.load(infile)
+ x_interp = compute_and_add_similarity_cols(average_features_per_target_bin=average_features_per_taget_bin,
+ input_df=x_interp,
+ columns_to_drop=['px_index'],
+ similarity_bins=50)
+ with open(os.path.join(_model_root_folder, 'trained_model', f'ml_scaler_{line_ratio_string}.pickle'), 'rb') as infile:
+ scaler = pickle.load(infile)
+ with open(os.path.join(_model_root_folder, 'trained_model', f'ml_model_{line_ratio_string}.pickle'), 'rb') as infile:
+ model = pickle.load(infile)
+ columns = model.feature_names_in_
+ x_interp_transformed = pd.DataFrame(scaler.transform(x_interp[columns]), columns=columns)
+ results_df[f'ratio_{line_ratio_string}'] = 10 ** model.predict(x_interp_transformed)
+ for column in x_interp.columns:
+ if column.startswith('sim_'):
+ x_interp.drop(columns=column, inplace=True)
+
+ results_df = results_df.merge(x_interp, on=['px_index'], how='inner', validate='1:1')
+ return results_df[['nh2', 'tdust', 'avg_tdust', 'avg_nh2', 'px_index'] + ratio_string_list]
+
+
+if __name__ == '__main__':
+ config = load_config_file(config_file_path=os.path.join('config', 'config.yml'))
+ external_input = load_config_file(config_file_path=os.path.join('config', 'ml_modelling.yml'))
+ logger = setup_logger(name='PRS - ML modelling')
+ try:
+ limit_rows = external_input['limit_rows']
+ except KeyError:
+ limit_rows = None
+ try:
+ use_model_for_training = external_input['use_model_for_training']
+ except KeyError:
+ use_model_for_training = None
+
+ _use_model_for_training = validate_parameter(
+ use_model_for_training,
+ default='constant_abundance_p15_q05'
+ )
+ _model_root_folder = os.path.join('prs', 'output', 'run_type', _use_model_for_training)
+ logger.info(f'Using {_use_model_for_training} for training in folder {_model_root_folder}')
+
+ tdust_grid = parse_grid_overrides(par_name='tdust', config=external_input)
+ nh2_grid = parse_grid_overrides(par_name='nh2', config=external_input)
+
+ inferred_data_list = []
+ for (tdust, nh2) in product(tdust_grid, nh2_grid):
+ logger.info(f'Producing emulated data for {nh2}, {tdust}')
+ if nh2 not in [1e3, 3e3, 9e3, 27e3, 81e3, 243e3, 729e3, 2187e3]:
+ inferred_data_list.append(
+ main(nh2=nh2,
+ tdust=tdust,
+ model_root_folder=_model_root_folder,
+ ratios_to_process=config['overrides']['lines_to_process'])
+ )
+
+ inferred_data = pd.concat(inferred_data_list, axis=0, ignore_index=True)
+ plot_results(inferred_data=inferred_data,
+ use_model_for_inference=_use_model_for_training,
+ ratios_to_process=config['overrides']['lines_to_process'])
+ inferred_data['avg_nh2'] = 10 ** inferred_data['avg_nh2']
+ inferred_data.to_csv(os.path.join(_model_root_folder, 'data', 'inferred_data.csv'))
+ logger.info('Completed emulation')
diff --git a/etl/prs/prs_inspect_results.py b/etl/prs/prs_inspect_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffd735bbd65c09c0f12bebc97d4dfc94d1ac23f7
--- /dev/null
+++ b/etl/prs/prs_inspect_results.py
@@ -0,0 +1,524 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import seaborn as sns
+import pandas as pd
+import sqlalchemy
+import xarray as xr
+from typing import Union, Tuple, List
+from contextlib import closing
+from itertools import product
+from astropy.io import fits
+from astropy import units as u
+from sqlalchemy.orm import Session, aliased
+from sqlalchemy import and_, or_, func, cast, Numeric
+from sqlalchemy import engine as sqla_engine
+from stg.stg_build_db_structure import (GridPars,
+ GridFiles,
+ StarsPars,
+ ModelPars,
+ RatioMaps,
+ MomentZeroMaps)
+from assets.commons import (load_config_file,
+ setup_logger,
+ validate_parameter)
+from assets.commons.parsing import parse_grid_overrides
+from assets.commons.grid_utils import compute_los_average_weighted_profile
+from assets.commons.db_utils import get_pg_engine
+from assets.constants import line_ratio_mapping
+
+plt.rcParams.update({'font.size': 20})
+logger = setup_logger(name='PRS - INSPECT')
+
+
+def get_clump_avg_density(density_at_ref_value: np.array,
+ avg_densities_dict: dict) -> np.array:
+ """
+ Create the numpy array of the average densities over the entire clump for all the models in the grid.
+ :param density_at_ref_value: the array with the characteristic values of the density
+ :param avg_densities_dict: the dictionary of the clump densities, indexed by the characteristic density at reference value
+ :return: an array with the sorted average number densities
+ """
+ _avg_densities = np.zeros(shape=len(density_at_ref_value))
+ for (idx, dens) in enumerate(density_at_ref_value):
+ _avg_densities[idx] = avg_densities_dict[dens]
+ return _avg_densities
+
+
+def get_aggregated_ratio_from_db(
+ dust_temperature: float,
+ gas_density: float,
+ lines: Union[list, tuple],
+ session: Session,
+ 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;
+ this function works for a homogeneous model. For more complex models the query must be revised.
+ :param dust_temperature: the dust temperature of the model
+ :param gas_density: the gas density of the model
+ :param lines: the lines used to compute the ratio
+ :param session: the SQLAlchemy session to use
+ :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
+ """
+ _dust_temperature = np.round(dust_temperature, 2)
+ _gas_density = np.round(gas_density, 2)
+ density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous,
+ is_isothermal=is_isothermal)
+ mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps)
+ model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars)
+ results = session.query(RatioMaps) \
+ .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) \
+ .filter(
+ and_(func.round(cast(dust_temperature_column, Numeric), 2) == _dust_temperature,
+ func.round(cast(density_column, Numeric), 2) == _gas_density,
+ GridPars.run_id == run_id,
+ 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]:
+ """
+ Get the appropriate columns for filtering based on the input parameters.
+ :param is_homogeneous: Flag indicating if the grid is homogeneous or not.
+ :param is_isothermal: Flag indicating if the grid is isothermal or not.
+ :return: A tuple containing the appropriate density and dust temperature
+ columns for the given filter conditions.
+ """
+ # 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(
+ dust_temperature: float,
+ gas_density: float,
+ lines: Union[list, tuple],
+ run_id: str,
+ session: Session,
+ 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
+ :param dust_temperature: the characteristic dust temperature
+ :param gas_density: the characteristic number density of molecular hydrogen
+ :param lines: the lines used to compute the ratio
+ :param run_id: the run_id of the model
+ :param session: a session to query the database
+ :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
+ 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
+ """
+ density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous,
+ is_isothermal=is_isothermal)
+
+ mom_zero_1, mom_zero_2 = aliased(MomentZeroMaps), aliased(MomentZeroMaps)
+ model_pars_1, model_pars_2 = aliased(ModelPars), aliased(ModelPars)
+ # Query database
+ 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(StarsPars, isouter=True) \
+ .filter(
+ and_(dust_temperature_column == dust_temperature,
+ density_column == gas_density,
+ GridFiles.quantity == 'gas_number_density',
+ 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:
+ 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)
+ return results[1].aggregated_ratio, \
+ results[1].mom_zero_name_1, \
+ results[1].mom_zero_name_2, \
+ (results[0].grid_size_3 * u.pc).to(u.cm).value / results[0].grid_shape_3, \
+ results[1].ratio_map_name, \
+ results[2].fits_grid_name, \
+ (int(results[3].npix / results[0].grid_shape_1), int(results[3].npix / results[0].grid_shape_2)), \
+ ratio_values
+
+
+def get_grid_values(
+ quantity_name: str,
+ dust_temperature: float,
+ gas_density: float,
+ run_id: str,
+ session: Session,
+ is_isothermal: bool = False,
+ is_homogeneous: bool = False) -> np.array:
+ """
+ Retrieve the grid values, given the model parameters
+ :param quantity_name: the quantity to extract
+ :param dust_temperature: the characteristic dust temperature
+ :param gas_density: the characteristic number density of molecular hydrogen
+ :param run_id: the run_id to process
+ :param session: a session to query the database
+ :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
+ """
+ density_column, dust_temperature_column = get_filter_columns(is_homogeneous=is_homogeneous,
+ is_isothermal=is_isothermal)
+
+ # Query database
+ results = session.query(GridPars, GridFiles) \
+ .join(GridFiles) \
+ .filter(
+ and_(dust_temperature_column == dust_temperature,
+ density_column == gas_density,
+ GridFiles.quantity == quantity_name,
+ 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:
+ data = fitsfile[0].data
+ return data
+
+
+def get_column_density_vs_mom0(molecule_column_density_grid: np.array,
+ h2_volume_density_grid: np.array,
+ mom_zero_name_1: str,
+ mom_zero_name_2: str,
+ grid_pixel_size: float,
+ lines: Tuple[str, str],
+ gas_density: float,
+ dust_temperature: float,
+ zoom_ratios: Tuple[int, int]) -> pd.DataFrame:
+ """
+ Get a DataFrame containing the column density of H2 and the moment zero.
+
+ :param molecule_column_density_grid: Array of column densities for the molecule of interest.
+ :param h2_volume_density_grid: Array of column densities for H2.
+ :param mom_zero_name_1: Name of the first moment file.
+ :param mom_zero_name_2: Name of the second moment file.
+ :param grid_pixel_size: Size of the grid pixels.
+ :param lines: Tuple of the names of the lines id for which to compute the moment zero.
+ :param gas_density: Gas density at the scaling radius.
+ :param dust_temperature: Dust temperature at the scaling radius.
+ :param zoom_ratios: Tuple of the horizontal and vertical zoom ratios.
+ :return: A DataFrame containing the column density of H2 and the molecule of interest as a function of mom0.
+ """
+ h2_column_density_map = np.nansum(h2_volume_density_grid * grid_pixel_size, axis=2).flatten()
+ molecule_column_density_map = np.nansum(molecule_column_density_grid * grid_pixel_size, axis=2).flatten()
+ df_results = pd.DataFrame(data=h2_column_density_map, columns=['H2_column_density'])
+ df_results['molecule_column_density'] = molecule_column_density_map
+ df_results['nh2'] = gas_density
+ df_results['tdust'] = dust_temperature
+ with fits.open(os.path.join('prs', 'fits', 'moments', mom_zero_name_1)) as fitsfile:
+ df_results[f'mom_zero_{lines[0]}'] = fitsfile[0].data[::zoom_ratios[0],
+ ::zoom_ratios[1]].byteswap().newbyteorder().flatten()
+ with fits.open(os.path.join('prs', 'fits', 'moments', mom_zero_name_2)) as fitsfile:
+ df_results[f'mom_zero_{lines[1]}'] = fitsfile[0].data[::zoom_ratios[0],
+ ::zoom_ratios[1]].byteswap().newbyteorder().flatten()
+ return df_results[df_results['H2_column_density'] > 0]
+
+
+def plot_coldens_vs_integrated_intensity(df_coldens_mom0_list: List[pd.DataFrame],
+ lines: Tuple[str, str],
+ run_type: str) -> pd.DataFrame:
+ """
+ Plots the column densities vs integrated intensity for each line.
+
+ :param df_col_dens_list: List of DataFrames containing the column densities of interest.
+ :param lines: Tuple of the id of the lines.
+ :return: A DataFrame containing the column densities of interest vs. the moment zero.
+ """
+ df_coldens_mom0 = pd.concat(df_coldens_mom0_list)
+ plt.xlabel('H_2 column density')
+ plt.ylabel('Moment 0')
+ plt.loglog()
+ plt.scatter(df_coldens_mom0['H2_column_density'], df_coldens_mom0[f'mom_zero_{lines[0]}'],
+ label=f'mom_zero_{lines[0]}')
+ plt.scatter(df_coldens_mom0['H2_column_density'], df_coldens_mom0[f'mom_zero_{lines[1]}'],
+ label=f'mom_zero_{lines[1]}')
+ plt.legend()
+ plt.savefig(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ run_type,
+ 'figures',
+ f'coldens_moments_lines_{"-".join(lines)}.png'
+ )
+ )
+ plt.clf()
+ return df_coldens_mom0
+
+
+def plot_ratio_vs_density(lines: Tuple[str, str],
+ results: xr.DataArray,
+ avg_density: np.array,
+ run_type: str):
+ """
+ Plots the line ratio vs. the average LOS volume density, and the integrated ratio as a function of the
+ model-specific density and temperature at the scaling radius.
+
+ :param lines: Tuple of the id of the lines.
+ :param results: DataArray containing the ratios and volume densities.
+ :param avg_density: numpy array with the overall average densities of the clumps.
+ :param run_type: string identification of the model type.
+ """
+ plt.figure(figsize=(8, 6))
+ plt.semilogx()
+ plt.xlabel(r'<n(H$_2$) [cm$^{-3}$]>')
+ plt.ylabel('simulated ratio')
+ plt.title(f'Ratio {line_ratio_mapping["-".join(lines)]}')
+ plt.tight_layout()
+ plt.savefig(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ run_type,
+ 'figures',
+ f'ratio_vs_avg_density_los_{"-".join(lines)}.png'
+ )
+ )
+ plt.clf()
+ plt.figure(figsize=(8, 6))
+ results.plot(x='dust_temperature', y='gas_density', yscale='log', cbar_kwargs={'pad': 0.2})
+ plt.xlabel('$T_{dust}(r_0)$ [K]')
+ plt.ylabel('$n$(H$_2$, r$_0$) [cm$^{-3}$]')
+ # plt.title(f'Integrated line ratio ({"-".join(lines)}) grid', y=1.02)
+ ref_density_limits = plt.gca().get_ylim()
+ ref_density_values_limits = np.array([min(results['gas_density'].data), max(results['gas_density'].data)])
+ ax2 = plt.twinx()
+ # Aligning the secondary axis values with the pixel centres
+ ax2.set_ylim(np.array([ref_density_limits[0] / ref_density_values_limits[0] * min(avg_density),
+ ref_density_limits[1] / ref_density_values_limits[1] * max(avg_density)]))
+ ax2.semilogy()
+ ax2.set_ylabel(r'<n(H$_2$) [cm$^{-3}$]>')
+ plt.tight_layout()
+ plt.savefig(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ run_type,
+ 'figures',
+ f'ratio_grid_lines_{"-".join(lines)}.png'
+ )
+ )
+ plt.clf()
+
+
+def main(run_id: str,
+ is_isothermal: bool,
+ is_homogeneous: bool,
+ molecule_used_to_weight_los_quantity: Union[str, None] = None,
+ engine: sqla_engine = None,
+ run_type: Union[str, None] = None):
+ if engine is None:
+ engine = get_pg_engine(logger=logger)
+ config = load_config_file(os.path.join('config', 'config.yml'))
+ config_stg = load_config_file(os.path.join('stg', 'config', 'config.yml'))
+
+ _run_type = validate_parameter(
+ run_type,
+ default='constant_abundance_p15_q05'
+ )
+
+ # grid definition
+ dust_temperatures = parse_grid_overrides(par_name='dust_temperature',
+ config=config)
+ central_densities = parse_grid_overrides(par_name='gas_density',
+ config=config)
+ line_pairs = config['overrides']['lines_to_process']
+
+ # If not specified, assumes that the first molecule should be used for weighting
+ _molecule_used_to_weight_los_quantity = validate_parameter(molecule_used_to_weight_los_quantity,
+ default=config_stg['lines']['species_to_include'][0])
+
+ results = xr.DataArray(np.empty(shape=[len(dust_temperatures), len(central_densities)]),
+ dims=('dust_temperature', 'gas_density'),
+ coords={
+ 'dust_temperature': dust_temperatures,
+ 'gas_density': central_densities
+ })
+ results_dict = {}
+ avg_densities_dict = {}
+ with closing(Session(engine)) as session:
+ for lines in line_pairs:
+ df_coldens_mom0_list = []
+ results_dict[f'{"-".join(lines)}'] = {}
+ for (tdust, nh2) in product(dust_temperatures, central_densities):
+ aggregated_ratio = get_aggregated_ratio_from_db(dust_temperature=tdust,
+ gas_density=nh2,
+ lines=lines,
+ session=session,
+ run_id=run_id,
+ is_isothermal=is_isothermal,
+ is_homogeneous=is_homogeneous)
+ results.loc[tdust, nh2] = aggregated_ratio
+ logger.debug(f'The aggregated ratio for lines {lines}, using {nh2}, {tdust} is: {aggregated_ratio}')
+ _, \
+ mom0_name_1, \
+ mom0_name_2, \
+ grid_pixel_size, \
+ _, _, \
+ zoom_ratios, \
+ ratio_values = \
+ get_results(
+ dust_temperature=tdust,
+ gas_density=nh2,
+ lines=lines,
+ session=session,
+ run_id=run_id,
+ is_isothermal=is_isothermal,
+ is_homogeneous=is_homogeneous
+ )
+ density_grid = get_grid_values(
+ quantity_name='gas_number_density',
+ dust_temperature=tdust,
+ gas_density=nh2,
+ session=session,
+ run_id=run_id,
+ is_isothermal=is_isothermal,
+ is_homogeneous=is_homogeneous
+ )
+ temperature_grid = get_grid_values(
+ quantity_name='dust_temperature',
+ dust_temperature=tdust,
+ gas_density=nh2,
+ session=session,
+ run_id=run_id,
+ is_isothermal=is_isothermal,
+ is_homogeneous=is_homogeneous
+ )
+ molecule_grid = get_grid_values(
+ quantity_name=_molecule_used_to_weight_los_quantity,
+ dust_temperature=tdust,
+ gas_density=nh2,
+ session=session,
+ run_id=run_id,
+ is_isothermal=is_isothermal,
+ is_homogeneous=is_homogeneous
+ )
+ avg_density_map = compute_los_average_weighted_profile(profile=density_grid, weights=molecule_grid)
+ avg_density = np.nansum(np.where(density_grid == 0, np.nan, density_grid * molecule_grid)) / \
+ np.nansum(np.where(density_grid == 0, np.nan, molecule_grid))
+ avg_densities_dict[nh2] = avg_density
+ std_density_map = np.nanstd(np.where(density_grid == 0, np.nan, molecule_grid), axis=2)
+ avg_temperature_map = compute_los_average_weighted_profile(profile=temperature_grid,
+ weights=molecule_grid)
+ correlation_data = np.array([avg_density_map.flatten(),
+ avg_temperature_map.flatten(),
+ std_density_map.flatten(),
+ ratio_values[::zoom_ratios[0], ::zoom_ratios[1]].flatten()])
+ results_dict[f'{"-".join(lines)}'][f'{"_".join([str(tdust), str(nh2)])}'] = correlation_data
+ plt.scatter(correlation_data[0], correlation_data[3])
+ df_coldens_mom0_list.append(get_column_density_vs_mom0(molecule_column_density_grid=molecule_grid,
+ h2_volume_density_grid=density_grid,
+ mom_zero_name_1=mom0_name_1,
+ mom_zero_name_2=mom0_name_2,
+ lines=lines,
+ gas_density=nh2,
+ dust_temperature=tdust,
+ zoom_ratios=zoom_ratios,
+ grid_pixel_size=grid_pixel_size))
+ avg_densities = get_clump_avg_density(density_at_ref_value=results['gas_density'].data,
+ avg_densities_dict=avg_densities_dict)
+ plot_ratio_vs_density(lines=lines,
+ results=results,
+ avg_density=avg_densities,
+ run_type=_run_type)
+
+ df_coldens_mom0 = plot_coldens_vs_integrated_intensity(df_coldens_mom0_list=df_coldens_mom0_list,
+ lines=lines,
+ run_type=_run_type).reset_index(). \
+ rename(columns={'index': 'px_index'})
+ df_coldens_mom0.to_csv(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ _run_type,
+ 'data',
+ f'full_dataset_NH2_lines_{"-".join(lines)}.csv'
+ )
+ )
+
+ df_list_concat = []
+ for (tdust, nh2) in product(dust_temperatures, central_densities):
+ df_list = []
+ for lines in line_pairs:
+ df = pd.DataFrame(
+ data=results_dict[f'{"-".join(lines)}'][f'{"_".join([str(tdust), str(nh2)])}'].transpose(),
+ columns=['avg_nh2', 'avg_tdust', 'std_nh2', f'ratio_{"-".join(lines)}'])
+ df_list.append(df.dropna())
+ df_tmp = pd.concat(df_list, axis=1)
+ df_tmp = df_tmp.loc[:, ~df_tmp.columns.duplicated()].copy()
+ df_tmp['nh2'] = nh2
+ df_tmp['tdust'] = tdust
+ df_list_concat.append(df_tmp)
+ df_all = pd.concat(df_list_concat).reset_index(). \
+ rename(columns={'index': 'px_index'})
+ for lines in line_pairs:
+ g = sns.jointplot(x=np.log10(df_all['avg_nh2']), y=df_all[f'ratio_{"-".join(lines)}'],
+ kind='kde', joint_kws={'bw_adjust': 2})
+ g.plot_joint(sns.scatterplot, s=100, alpha=.5, marker='+', color='orange')
+
+ plt.savefig(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ _run_type,
+ 'figures',
+ f'ratio_vs_avg_density_los_kde_{"-".join(lines)}.png'
+ )
+ )
+ plt.clf()
+ df_all.to_csv(
+ os.path.join(
+ 'prs',
+ 'output',
+ 'run_type',
+ _run_type,
+ 'data',
+ 'full_dataset.csv'
+ )
+ )
+
+
+if __name__ == '__main__':
+ main(run_id='7dd5b365-875e-4857-ae11-2707820a33c1', is_isothermal=True, is_homogeneous=True, run_type='uniform')
diff --git a/etl/prs/prs_ml_training.py b/etl/prs/prs_ml_training.py
new file mode 100644
index 0000000000000000000000000000000000000000..d99569e2d766bd4b5b7c7429ffcf41304ba9d3a8
--- /dev/null
+++ b/etl/prs/prs_ml_training.py
@@ -0,0 +1,63 @@
+import os
+import numpy as np
+import argparse
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger)
+from assets.commons.training_utils import train_models_wrapper
+
+if __name__ == '__main__':
+ external_input = load_config_file(config_file_path='config/ml_modelling.yml')
+ logger = setup_logger(name='PRS - ML training')
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--line', default=None)
+ parser.add_argument('--distributed', default='true')
+ args = parser.parse_args()
+ try:
+ limit_rows = external_input['limit_rows']
+ except KeyError:
+ limit_rows = None
+ try:
+ use_model_for_training = external_input['use_model_for_training']
+ except KeyError:
+ use_model_for_training = None
+ _distributed = validate_parameter(args.distributed, default='false').lower() == 'true'
+
+ _use_model_for_training = validate_parameter(
+ use_model_for_training,
+ default='constant_abundance_p15_q05'
+ )
+ _model_root_folder = os.path.join('prs', 'output', 'run_type', _use_model_for_training)
+ logger.info(f'Using {_use_model_for_training} for training in folder {_model_root_folder}')
+
+ if external_input['retrain'] is True:
+ if external_input['model_type'] == 'XGBoost_gridsearch':
+ _model_kwargs = external_input['model_parameters']['param_grid'].copy()
+ for par in _model_kwargs:
+ _model_kwargs[par] = np.arange(_model_kwargs[par][0], _model_kwargs[par][1], _model_kwargs[par][2])
+ external_input['model_parameters']['param_grid'] = _model_kwargs
+ if _distributed is True:
+ _line = validate_parameter(args.line, default='86')
+ if args.line is None:
+ logger.warning(f'No line ID specified, proceeding with {_line}')
+ train_models_wrapper(target_name=f'mom_zero_{_line}',
+ model_type=external_input['model_type'],
+ model_kwargs=external_input['model_parameters'],
+ limit_rows=limit_rows,
+ model_root_folder=_model_root_folder,
+ use_validation=external_input['use_validation'],
+ logger=logger)
+ else:
+ lines_to_process = (args.line,) if (args.line is not None) \
+ else ('86', '87', '88', '256', '257', '381', '380')
+ for line in lines_to_process:
+ train_models_wrapper(target_name=f'mom_zero_{line}',
+ model_type=external_input['model_type'],
+ model_kwargs=external_input['model_parameters'],
+ limit_rows=limit_rows,
+ model_root_folder=_model_root_folder,
+ use_validation=external_input['use_validation'],
+ logger=logger)
+
+ logger.info(f'Completed training for line {args.line}')
diff --git a/etl/prs/prs_ml_training_ratio.py b/etl/prs/prs_ml_training_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..c99243277a43b459f55cc3c4fc10cbe9efacdabe
--- /dev/null
+++ b/etl/prs/prs_ml_training_ratio.py
@@ -0,0 +1,63 @@
+import os
+import numpy as np
+import argparse
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger)
+from assets.commons.training_utils import train_models_wrapper
+
+if __name__ == '__main__':
+ external_input = load_config_file(config_file_path=os.path.join('config', 'ml_modelling.yml'))
+ logger = setup_logger(name='PRS - ML training')
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--line_ratio', default=None)
+ parser.add_argument('--distributed', default='true')
+ args = parser.parse_args()
+ try:
+ limit_rows = external_input['limit_rows']
+ except KeyError:
+ limit_rows = None
+ try:
+ use_model_for_training = external_input['use_model_for_training']
+ except KeyError:
+ use_model_for_training = None
+ _distributed = validate_parameter(args.distributed, default='false').lower() == 'true'
+
+ _use_model_for_training = validate_parameter(
+ use_model_for_training,
+ default='constant_abundance_p15_q05'
+ )
+ _model_root_folder = os.path.join('prs', 'output', 'run_type', _use_model_for_training)
+ logger.info(f'Using {_use_model_for_training} for training in folder {_model_root_folder}')
+
+ if external_input['retrain'] is True:
+ if external_input['model_type'] == 'XGBoost_gridsearch':
+ _model_kwargs = external_input['model_parameters']['param_grid'].copy()
+ for par in _model_kwargs:
+ _model_kwargs[par] = np.arange(_model_kwargs[par][0], _model_kwargs[par][1], _model_kwargs[par][2])
+ external_input['model_parameters']['param_grid'] = _model_kwargs
+ if _distributed is True:
+ _line_ratio = validate_parameter(args.line_ratio, default='87-86')
+ if args.line_ratio is None:
+ logger.warning(f'No line ratio specified, proceeding with {_line_ratio}')
+ train_models_wrapper(target_name=f'ratio_{_line_ratio}',
+ model_type=external_input['model_type'],
+ model_kwargs=external_input['model_parameters'],
+ limit_rows=limit_rows,
+ model_root_folder=_model_root_folder,
+ use_validation=external_input['use_validation'],
+ logger=logger)
+ else:
+ ratios_to_process = (args.line_ratio,) if (args.line_ratio is not None) \
+ else (('86', '87'), ('88', '87'), ('88', '86'), ('256', '257'), ('381', '380'))
+ for ratio in ratios_to_process:
+ train_models_wrapper(target_name=f'ratio_{ratio}',
+ model_type=external_input['model_type'],
+ model_kwargs=external_input['model_parameters'],
+ limit_rows=limit_rows,
+ model_root_folder=_model_root_folder,
+ use_validation=external_input['use_validation'],
+ logger=logger)
+
+ logger.info(f'Completed training for line ratio {args.line_ratio}')
diff --git a/etl/prs/prs_poc_figures.py b/etl/prs/prs_poc_figures.py
new file mode 100644
index 0000000000000000000000000000000000000000..40993c8c0c9828777dcc66261e693e60b28c8100
--- /dev/null
+++ b/etl/prs/prs_poc_figures.py
@@ -0,0 +1,108 @@
+import os
+import pandas as pd
+import seaborn as sns
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.constants import G, m_p
+import astropy.units as u
+from assets.utils import get_poc_results
+plt.rcParams.update({'font.size': 16})
+
+
+def make_ecdf_figure(df_full: pd.DataFrame):
+ sns.kdeplot(data=df_full, x='best_fit', hue='Class')
+ plt.clf()
+ plt.figure(figsize=(8, 6))
+ sns.ecdfplot(data=df_full, x='best_fit', hue='Class', hue_order=['Quiescent', 'Protostellar', 'MYSOs', 'HII'])
+ plt.semilogx()
+ plt.xlim([1e5, 5e6])
+ plt.xlabel('<$n$(H$_{2}$)> [cm$^{-3}$]')
+ plt.ylabel('Cumulative probability')
+ plt.title('Volume density ECDF per class')
+ plt.tight_layout()
+ plt.savefig(os.path.join('prs', 'output', 'poc_figures', 'best_fit_nh2.png'))
+
+
+def make_violin_figure(df_full: pd.DataFrame, nsamples: int = 10000):
+ with open(os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05', 'posteriors.pickle'),
+ 'rb') as infile:
+ posteriors = pickle.load(infile)
+ df_list = []
+ for source in posteriors['PDF']:
+ tmp_df = pd.DataFrame(columns=['source_name', 'Class', 'density'])
+ try:
+ class_phys = df_full[df_full['source_name'] == source]['Class'].iloc[0]
+ samples = sampler(ecdf=posteriors['ECDF'][source], density_grid=posteriors['density_grid'][source],
+ nsamples=nsamples)
+ tmp_df['density'] = samples
+ tmp_df['source_name'] = source
+ tmp_df['Class'] = class_phys
+ df_list.append(tmp_df)
+ except IndexError:
+ pass
+
+ plt.clf()
+ plt.figure(figsize=(8, 6))
+ df_generated_densities = pd.concat(df_list)
+ sns.violinplot(df_generated_densities,
+ x='Class',
+ y='density',
+ order=['Quiescent', 'Protostellar', 'MYSOs', 'HII'],
+ bw=0.2)
+ plt.semilogy()
+ plt.ylim([5e4, 5e6])
+ plt.ylabel('<$n$(H$_{2}$)> [cm$^{-3}$]')
+ plt.title('Bootstrapped volume density distributions')
+ plt.tight_layout()
+ plt.savefig(os.path.join('prs', 'output', 'poc_figures', 'nh2_violin.png'))
+
+
+def sampler(ecdf, density_grid, nsamples):
+ generated_densities = np.zeros(shape=nsamples)
+ for idx in range(nsamples):
+ a = np.random.default_rng().uniform(0, 1)
+ generated_index = np.argmax(ecdf >= a) - 1
+ # In principle this can be interpolated for a smoother density distribution
+ generated_densities[idx] = density_grid[generated_index]
+ return generated_densities
+
+
+def make_volume_densities_comparison_figure(df_full: pd.DataFrame):
+ df_volume_density_sed = pd.read_csv(os.path.join('assets', 'data', 'top100_density.csv'),
+ skiprows=4)
+ df_merge = df_full.merge(df_volume_density_sed, left_on='AGAL_name', right_on='agal_name')
+ hpd = df_full['hpd_interval'].values
+ filtered_intervals = []
+ for intervals in hpd:
+ filtered_intervals.append(intervals[-2:])
+ hpd_array = np.array(filtered_intervals)
+ uncertainty_array = np.abs(hpd_array - np.array(df_full['best_fit'])[:, np.newaxis]).T
+ plt.clf()
+ plt.figure(figsize=(8, 6))
+ plt.scatter(df_merge['volume_density'],
+ df_merge['best_fit'],)
+ plt.errorbar(df_merge['volume_density'],
+ df_merge['best_fit'],
+ yerr=uncertainty_array,
+ fmt='none')
+ plt.plot([2e4, 8e5], [2e4, 8e6], color='red')
+ plt.loglog()
+ plt.xlabel('<$n$(H$_{2, dust}$)> [cm$^{-3}$]')
+ plt.ylabel('<$n$(H$_{2, SAK}$)> [cm$^{-3}$]')
+ plt.title('Comparison of the inferred volume density')
+ plt.tight_layout()
+ plt.savefig(os.path.join('prs', 'output', 'poc_figures', 'volume_density_comparison.png'))
+
+
+def main():
+ df_full = get_poc_results(line_fit_filename='ch3oh_data_top35.csv')
+ df_full.rename(columns={'class_phys': 'Class'}, inplace=True)
+ df_full = df_full[(df_full['mass'] < 10000) & (df_full['mass'] > 300)]
+ make_ecdf_figure(df_full=df_full)
+ make_violin_figure(df_full=df_full)
+ make_volume_densities_comparison_figure(df_full=df_full)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/etl/prs/prs_poc_latex_table.py b/etl/prs/prs_poc_latex_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f65fb8de409c8a62fe1d474bbdbda0fb6561917
--- /dev/null
+++ b/etl/prs/prs_poc_latex_table.py
@@ -0,0 +1,123 @@
+import os
+import pandas as pd
+import numpy as np
+from assets.utils import get_poc_results
+from collections import OrderedDict
+
+
+def divide_chunks(l, n):
+ for i in range(0, len(l), n):
+ yield list(l[i:i + n])
+
+
+def mask_non_detections(data: pd.DataFrame,
+ transition_id: str) -> pd.DataFrame:
+ _data = data.copy()
+ _data.loc[_data[f'tpeak_{transition_id}'] < 2 * _data[f'rms_noise_{transition_id}'],
+ f'tpeak_{transition_id}'] = -1
+ return _data
+
+
+df_full = get_poc_results(line_fit_filename='ch3oh_data_top35.csv')
+
+# Make POC LaTex table
+pi230_beam_eff = 0.7
+flash_beam_eff = 0.73
+df_table = df_full.copy()
+df_table[['tpeak_256', 'tpeak_257', 'rms_noise_256', 'rms_noise_257']] /= pi230_beam_eff
+df_table[['area_256', 'area_257', 'area_unc_256', 'area_unc_257']] /= pi230_beam_eff
+df_table[['tpeak_380', 'tpeak_381', 'rms_noise_380', 'rms_noise_381']] /= flash_beam_eff
+df_table[['area_380', 'area_381', 'area_unc_380', 'area_unc_381']] /= flash_beam_eff
+for transition_id in ['86', '87', '88', '256', '257', '380', '381']:
+ df_table = mask_non_detections(data=df_table, transition_id=transition_id)
+
+df_table['best_fit'] /= 1e5
+df_table['best_fit'] = df_table['best_fit'].round(1)
+df_table['hpd_interval'] = df_table['hpd_interval'].apply(lambda row: (np.array(row) / 1e5).round(1))
+df_table['hpd_interval'] = df_table['hpd_interval'].apply(lambda row: list(divide_chunks(row, 2)))
+df_table['mass'] = (df_table['mass'] / 100).round(1).astype(str)
+df_table['distance'] = (df_table['distance']).round(1).astype(str)
+density_format = '$[10^{5}\,\mathrm{cm^{-3}}]$'
+fwhm_units = '[km s$^{-1}$]'
+
+df_fit_results = df_table[['AGAL_name', 'class_phys', 'best_fit', 'hpd_interval','mass', 'distance']].copy()
+df_source_properties = df_table[['AGAL_name', 'tpeak_86', 'tpeak_87', 'tpeak_88', 'tpeak_256', 'tpeak_257', 'tpeak_380',
+ 'tpeak_381', 'rms_noise_86', 'rms_noise_87', 'rms_noise_88', 'rms_noise_256',
+ 'rms_noise_257', 'rms_noise_380', 'rms_noise_381', 'linewidth_86', 'linewidth_87', 'linewidth_88', 'linewidth_256', 'linewidth_257', 'linewidth_380',
+ 'linewidth_381', 'linewidth_unc_86', 'linewidth_unc_87', 'linewidth_unc_88', 'linewidth_unc_256',
+ 'linewidth_unc_257', 'linewidth_unc_380', 'linewidth_unc_381']].copy()
+for transition_id in ['86', '87', '88', '256', '257', '380', '381']:
+ df_source_properties[f'tpeak_{transition_id}'] = '$' + df_source_properties[f'tpeak_{transition_id}'].round(2).astype(str) + ' \pm ' + df_table[f'rms_noise_{transition_id}'].round(2).astype(str) + '$'
+ df_source_properties[f'linewidth_{transition_id}'] = '$' + df_source_properties[f'linewidth_{transition_id}'].round(2).astype(str) + ' \pm ' + df_table[f'linewidth_unc_{transition_id}'].round(2).astype(str) + '$'
+ df_source_properties.loc[df_source_properties[f'tpeak_{transition_id}'].str.startswith('$-1.0'), [f'tpeak_{transition_id}', f'linewidth_{transition_id}']] = '-1.00'
+ df_source_properties.loc[df_source_properties[f'tpeak_{transition_id}'].str.startswith('$nan'), [f'tpeak_{transition_id}', f'linewidth_{transition_id}']] = 'N/A'
+ df_source_properties.drop(columns=[f'rms_noise_{transition_id}', f'linewidth_unc_{transition_id}'], inplace=True)
+
+remap_columns = OrderedDict([
+ ('AGAL_name', ('Source', '')),
+ ('class_phys', ('Classification', '')),
+ ('best_fit', ('best fit($n\mathrm{H_2}$)', density_format)),
+ ('hpd_interval', ('67\% HPD', density_format)),
+ ('mass', ('Mass', '$[10^2 \mathrm{M_\odot}]$')),
+ ('distance', ('Distance', '[kpc]')),
+ ('tpeak_86', ('$T_{MB, 86}$', '[K]')),
+ ('linewidth_86', ('$FWHM_{(2_K-1_K)}$', fwhm_units)),
+ ('tpeak_87', ('$T_{MB, 87}$', '[K]')),
+ ('linewidth_87', ('$FWHM_{87}$', '[km s$^{-1}]$')),
+ ('tpeak_88', ('$T_{MB, 88}$', '[K]')),
+ ('linewidth_88', ('$FWHM_{88}$', '[km s$^{-1}]$')),
+ # ('rms_noise_86', ('$\sigma_{96.7GHz}$', '[K]')),
+ ('tpeak_256', ('$T_{MB, 256}$', '[K]')),
+ ('linewidth_256', ('$FWHM_{(5_K-4_K)}$', fwhm_units)),
+ ('tpeak_257', ('$T_{MB, 257}$', '[K]')),
+ ('linewidth_257', ('$FWHM_{257}$', fwhm_units)),
+ # ('rms_noise_256', ('$\sigma_{241.7GHz}$', '[K]')),
+ ('tpeak_380', ('$T_{MB, 380}$', '[K]')),
+ ('linewidth_380', ('$FWHM_{(7_K-6_K)}$', fwhm_units)),
+ ('tpeak_381', ('$T_{MB, 381}$', '[K]')),
+ ('linewidth_381', ('$FWHM_{381}$', fwhm_units)),
+ # ('rms_noise_380', ('$\sigma_{338.3GHz}$', '[K]')),
+])
+df_table = df_table[remap_columns.keys()]
+df_table.rename(columns=remap_columns, inplace=True)
+df_table.columns = pd.MultiIndex.from_tuples(df_table.columns)
+df_source_properties.rename(columns=remap_columns, inplace=True)
+df_source_properties.columns = pd.MultiIndex.from_tuples(df_source_properties.columns)
+df_fit_results.rename(columns=remap_columns, inplace=True)
+df_fit_results.columns = pd.MultiIndex.from_tuples(df_fit_results.columns)
+
+
+caption = 'Sources included in the proof-of-concept, classification, distance, mass, and number density results. We indicate the highest-probability density interval containing the 67\% of the probability mass as HPD 67\%.'
+label = 'tab:poc_results'
+latex_table = (df_fit_results.style.hide(axis='index').format({
+ ('best fit($n\mathrm{H_2}$)', '$[10^{5}\,\mathrm{cm^{-3}}]$'): '{:,.1f}'})
+ .to_latex(caption=caption, label=label, hrules=True, environment='table*', position_float='centering'))
+with open(os.path.join('prs', 'output', 'poc_tables', 'fit_results.tex'), 'w') as outfile:
+ outfile.write(latex_table)
+
+table_cols = [
+ [('Source', ''), ('$T_{MB, 86}$', '[K]'), ('$T_{MB, 87}$', '[K]'), ('$T_{MB, 88}$', '[K]'), ('$FWHM_{(2_K-1_K)}$',
+ fwhm_units)],
+ [('Source', ''), ('$T_{MB, 256}$', '[K]'), ('$T_{MB, 257}$', '[K]'), ('$FWHM_{(5_K-4_K)}$', fwhm_units), ('$T_{MB, 380}$', '[K]'), ('$T_{MB, 381}$', '[K]'), ('$FWHM_{(7_K-6_K)}$',
+ fwhm_units), ]
+]
+captions = [
+ 'Line properties of the lines in the $(2_K-1_K)$ methanol band. Only one FWHM is listed because the fit is performed forcing all lines to have the same width.',
+ 'Line properties of the lines in the $(5_K-4_K)$ and $(7_K-6_K)$ methanol bands. Only one FWHM is listed per band because the fit is performed forcing all lines to have the same width.',
+]
+labels = [
+ 'tab:poc_lines_3mm',
+ 'tab:poc_lines_hf',
+]
+outfiles = [
+ 'line_table_3mm.tex',
+ 'line_table_hf.tex',
+]
+for cols, caption, label, filename in zip(table_cols, captions, labels, outfiles):
+ latex_table = (df_source_properties[cols].style.hide(axis='index')
+ .to_latex(caption=caption, label=label, hrules=True, environment='table*', position_float='centering'))
+ latex_table = latex_table.replace('[[', '[').replace(']]', ']')
+ latex_table = latex_table.replace('-1.00', '\dots').replace('-1.00', '\dots')
+ with open(os.path.join('prs', 'output', 'poc_tables', filename), 'w') as outfile:
+ outfile.write(latex_table)
+
diff --git a/etl/requirements.txt b/etl/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ecc11c5883093444b8e39aa6d6928d3bbc36908b
--- /dev/null
+++ b/etl/requirements.txt
@@ -0,0 +1,14 @@
+numpy==1.23.4
+PyYAML==6.0
+astropy==5.1
+matplotlib==3.6.2
+scipy==1.9.3
+tqdm==4.64.1
+xarray==2022.10.0
+sqlalchemy==1.4.42
+psycopg2-binary==2.9.5
+seaborn==0.12.2
+scikit-learn==1.3.0
+xgboost==2.0.0
+astropy==5.1
+keras==2.12.0
\ No newline at end of file
diff --git a/etl/reset_output.sh b/etl/reset_output.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7b7713ca4306881382c706d5a66c74439c2b6667
--- /dev/null
+++ b/etl/reset_output.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+read -p "This will delete all of the results, are you sure? " -n 1 -r
+echo
+
+if [[ $REPLY =~ ^[Yy]$ ]]
+then
+ folder_list=("prs/fits/cubes" "prs/fits/moments" "prs/fits/ratios" "prs/fits/grids")
+ for folder_name in ${folder_list[@]}
+ do
+ rm $folder_name/*.fits
+ done
+ rm prs/output/*.png
+ rm stg/archive/*.zip
+ rm full_dataset*.csv
+fi
+
diff --git a/etl/stg/config/config.yml b/etl/stg/config/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..793dab2f3b8adbe1a1e1c17654a3dcdadcc2dc7b
--- /dev/null
+++ b/etl/stg/config/config.yml
@@ -0,0 +1,46 @@
+grid:
+ grid_type: regular
+ coordinate_system: cartesian
+ central_density: 1e8
+ density_unit: "cm^-3"
+ density_powerlaw_idx: -1.5
+ density_at_reference: 1e6
+ maximum_radius: 0.9
+ maximum_radius_unit: 'pc'
+ distance_reference: 0.5
+ distance_reference_unit: 'pc'
+ dust_temperature: 2000
+ dust_temperature_at_reference: 30
+ dust_temperature_unit: 'K'
+ dust_temperature_powerlaw_idx: -0.5
+ microturbulence: 1.5
+ microturbulence_unit: 'km/s'
+ dim1: {"size": 2, "size_units": "pc", "shape": 101, "refpix": 50}
+ velocity_field: 'solid'
+ 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: {
+ "e-ch3oh": 1e-9,
+ "p-h2": 1,
+ }
+ hot_core_specs: {
+ "e-ch3oh": {
+ "threshold": 90,
+ "abundance_jump": 1
+ }
+ }
+ lines_mode: 'lvg'
+ collision_partners: ['p-h2']
diff --git a/etl/stg/stg_build_db_structure.py b/etl/stg/stg_build_db_structure.py
new file mode 100644
index 0000000000000000000000000000000000000000..a44b6c4a48ac923e790f53bd07952e2d959ed83a
--- /dev/null
+++ b/etl/stg/stg_build_db_structure.py
@@ -0,0 +1,222 @@
+import logging
+import os
+import time
+
+from sqlalchemy import (Column,
+ ForeignKey,
+ Integer,
+ Sequence,
+ String,
+ Float,
+ DateTime,
+ Boolean,
+ ARRAY,
+ ForeignKeyConstraint)
+from assets.commons import (setup_logger)
+from assets.commons.db_utils import get_pg_engine
+from sqlalchemy.orm import declarative_base
+from sqlalchemy.orm import relationship
+from sqlalchemy.exc import OperationalError
+
+logger = setup_logger(name='DB_SETUP')
+Base = declarative_base()
+
+
+class GridFiles(Base):
+ __tablename__ = "grid_files"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('zipped_grid_name', 'run_id'),
+ ['grid_parameters.zipped_grid_name', 'grid_parameters.run_id']
+ ),
+ )
+ zipped_grid_name = Column(String(150), primary_key=True)
+ quantity = Column(String(30), primary_key=True)
+ fits_grid_name = Column(String)
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class GridPars(Base):
+ __tablename__ = "grid_parameters"
+ zipped_grid_name = Column(String(150), primary_key=True)
+ species_and_partners = relationship("SpeciesAndPartners", cascade="all, delete-orphan")
+ fits_cube_name = relationship("ModelPars", cascade="all, delete-orphan")
+ line_pars = relationship("LinePars", cascade="all, delete-orphan")
+ stars_pars = relationship("StarsPars", cascade="all, delete-orphan")
+ grid_type = Column(String)
+ coordinate_system = Column(String)
+ central_density = Column(Float)
+ density_powerlaw_index = Column(Float)
+ density_at_reference = Column(Float)
+ dust_temperature = Column(Float)
+ dust_temperature_powerlaw_index = Column(Float)
+ dust_temperature_at_reference = Column(Float)
+ microturbulence = Column(Float)
+ velocity_field = Column(String)
+ velocity_gradient = Column(String)
+ velocity_powerlaw_index = Column(Float)
+ velocity_at_reference = Column(Float)
+ distance_reference = Column(Float)
+ maximum_radius = Column(Float)
+ grid_size_1 = Column(Float)
+ grid_shape_1 = Column(Float)
+ grid_refpix_1 = Column(Float)
+ grid_size_2 = Column(Float)
+ grid_shape_2 = Column(Float)
+ grid_refpix_2 = Column(Float)
+ grid_size_3 = Column(Float)
+ grid_shape_3 = Column(Float)
+ grid_refpix_3 = Column(Float)
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class StarsPars(Base):
+ __tablename__ = "stars_parameters"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('zipped_grid_name', 'run_id'),
+ ['grid_parameters.zipped_grid_name', 'grid_parameters.run_id']
+ ),
+ )
+ zipped_grid_name = Column(String(150), primary_key=True)
+ nstars = Column(Integer)
+ rstars = Column(ARRAY(Float))
+ mstars = Column(ARRAY(Float))
+ star_positions = Column(ARRAY(Float))
+ star_fluxes = Column(ARRAY(Float))
+ nlambdas = Column(Integer)
+ spacing = Column(String)
+ lambdas_micron_limits = Column(ARRAY(Float))
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class LinePars(Base):
+ __tablename__ = "lines_parameters"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('zipped_grid_name', 'run_id'),
+ ['grid_parameters.zipped_grid_name', 'grid_parameters.run_id']
+ ),
+ )
+ zipped_grid_name = Column(String(150), primary_key=True)
+ lines_mode = Column(String(20))
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class SpeciesAndPartners(Base):
+ __tablename__ = "species_and_partners"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('zipped_grid_name', 'run_id'),
+ ['grid_parameters.zipped_grid_name', 'grid_parameters.run_id']
+ ),
+ )
+ zipped_grid_name = Column(String(150), primary_key=True)
+ species_to_include = Column(String(100), primary_key=True)
+ molecular_abundance = Column(Float)
+ threshold = Column(Float)
+ abundance_jump = Column(Float)
+ collision_partner = Column(String(100), primary_key=True)
+ molecular_abundance_collision_partner = Column(Float)
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class ModelPars(Base):
+ __tablename__ = "model_parameters"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('zipped_grid_name', 'run_id'),
+ ['grid_parameters.zipped_grid_name', 'grid_parameters.run_id']
+ ),
+ )
+ zipped_grid_name = Column(String(150), nullable=False)
+ fits_cube_name = Column(String(150), primary_key=True)
+ mom_zero_name = relationship("MomentZeroMaps", cascade="all, delete-orphan")
+ nphotons = Column(Float)
+ scattering_mode_max = Column(Integer)
+ iranfreqmode = Column(Integer)
+ tgas_eq_tdust = Column(Integer)
+ inclination = Column(Float)
+ position_angle = Column(Float)
+ imolspec = Column(Integer)
+ iline = Column(Integer)
+ width_kms = Column(Float)
+ nchannels = Column(Integer)
+ npix = Column(Integer)
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class MomentZeroMaps(Base):
+ __tablename__ = "moment_zero_maps"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('fits_cube_name', 'run_id'),
+ ['model_parameters.fits_cube_name', 'model_parameters.run_id']
+ ),
+ )
+ mom_zero_name = Column(String(150), primary_key=True)
+ fits_cube_name = Column(String(150), nullable=False)
+ integration_limit_low = Column(Float)
+ integration_limit_high = Column(Float)
+ aggregated_moment_zero = Column(Float)
+ aggregation_function = Column(String(20))
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+
+
+class RatioMaps(Base):
+ __tablename__ = "ratio_maps"
+ __table_args__ = (
+ ForeignKeyConstraint(
+ ('mom_zero_name_1', 'run_id'),
+ ['moment_zero_maps.mom_zero_name', 'moment_zero_maps.run_id'],
+ ),
+ ForeignKeyConstraint(
+ ('mom_zero_name_2', 'run_id'),
+ ['moment_zero_maps.mom_zero_name', 'moment_zero_maps.run_id'],
+ ),
+ )
+ ratio_map_name = Column(String(150), primary_key=True)
+ mom_zero_name_1 = Column(String(150), nullable=False)
+ mom_zero_name_2 = Column(String(150), nullable=False)
+ aggregated_ratio = Column(Float)
+ aggregation_function = Column(String(20))
+ created_on = Column(DateTime)
+ run_id = Column(String, primary_key=True)
+ mom_zero_map_1 = relationship("MomentZeroMaps", foreign_keys=[mom_zero_name_1, run_id])
+ mom_zero_map_2 = relationship("MomentZeroMaps", foreign_keys=[mom_zero_name_2, run_id])
+
+
+class TmpExecutionQueue(Base):
+ __tablename__ = "tmp_execution_queue"
+ row_id = Column(Integer, Sequence('row_id_seq'))
+ run_id = Column(String, primary_key=True)
+ dust_temperature = Column(Float, primary_key=True)
+ density = Column(Float, primary_key=True)
+ line = Column(Integer, primary_key=True)
+ density_keyword = Column(String, primary_key=True)
+ dust_temperature_keyword = Column(String, primary_key=True)
+ fits_cube_name = Column(String)
+ done = Column(Boolean)
+
+
+def init_db():
+ engine = get_pg_engine(logger=logger)
+ try:
+ Base.metadata.create_all(bind=engine)
+ except OperationalError:
+ logger.error('Connection failed. Sleeping and retrying')
+ time.sleep(3)
+ Base.metadata.create_all(bind=engine)
+ logger.info('Connection successful! DB initialized as needed.')
+ engine.dispose()
+
+
+if __name__ == '__main__':
+ init_db()
diff --git a/etl/stg/stg_radmc_input_generator.py b/etl/stg/stg_radmc_input_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e73878057d1a4482bafef27f86e7cd79caf243ff
--- /dev/null
+++ b/etl/stg/stg_radmc_input_generator.py
@@ -0,0 +1,697 @@
+import logging
+import uuid
+
+import numpy as np
+import os
+import shutil
+from datetime import datetime
+from typing import Union, List, Tuple
+from sqlalchemy import engine as sqla_engine
+from assets.commons import (load_config_file,
+ get_moldata,
+ compute_unique_hash_filename,
+ make_archive,
+ validate_parameter,
+ setup_logger,
+ cleanup_directory,
+ get_value_if_specified)
+from assets.commons.grid_utils import (compute_molecular_number_density_hot_core,
+ compute_power_law_radial_profile,
+ extract_grid_metadata)
+from assets.commons.parsing import read_abundance_variation_schema
+from assets.commons.db_utils import upsert, get_pg_engine
+from assets.constants import (mean_molecular_mass,
+ radmc_input_headers,
+ radmc_lines_mode_mapping)
+from stg.stg_build_db_structure import GridPars, GridFiles, StarsPars
+from astropy import units as u
+from astropy import constants as cst
+from astropy.io import fits
+
+logger = setup_logger(name='STG')
+
+
+def write_radmc_input(filename: str,
+ quantity: np.array,
+ grid_metadata: dict,
+ path: Union[None, str] = None,
+ override_defaults: Union[None, dict] = None,
+ flatten_style: Union[None, str] = None):
+ rt_metadata_default = {
+ 'iformat': 1,
+ 'grid_type': grid_metadata['grid_type'],
+ 'coordinate_system': grid_metadata['coordinate_system'],
+ 'grid_info': 0, ##
+ 'grid_shape': grid_metadata['grid_shape'],
+ 'ncells': np.prod(grid_metadata['grid_shape']),
+ 'ncells_per_axis': ' '.join([str(axis_size) for axis_size in grid_metadata['grid_shape']]),
+ 'dust_species': 1, ##
+ 'active_axes': ' '.join(['1'] * len(grid_metadata['grid_shape'])),
+ 'continuum_lambdas': grid_metadata['continuum_lambdas'], ##
+ }
+ _path = validate_parameter(path, default='.')
+ _filename = 'numberdens_mol.inp' if filename.startswith('numberdens_') else filename
+ _flatten_style = validate_parameter(flatten_style, default='F')
+ with open(os.path.join(_path, filename), "w") as outfile:
+ for key in radmc_input_headers[_filename]:
+ try:
+ header_line = override_defaults[key]
+ except (KeyError, TypeError):
+ header_line = rt_metadata_default[key]
+ outfile.write(f'{header_line}\n')
+ outfile.write('\n'.join(quantity.flatten(_flatten_style).astype(str)))
+
+
+def write_radmc_lines_input(line_config: dict,
+ logger: logging.Logger,
+ path: Union[None, str] = None):
+ _path = validate_parameter(path, default='.')
+
+ if line_config['lines_mode'] != 'lte':
+ assert len(line_config['collision_partners']) > 0
+ else:
+ logger.warning('Some collision partners were specified, but LTE populations were requested. Ignoring collision '
+ 'partner data...')
+ line_config['collision_partners'] = []
+
+ with open(os.path.join(_path, 'lines.inp'), "w") as outfile:
+ # iformat
+ outfile.write('2\n')
+ outfile.write(f'{str(len(line_config["species_to_include"]))}\n')
+ for species in line_config["species_to_include"]:
+ outfile.write(
+ f'{species} leiden 0 0 {str(len(line_config["collision_partners"]))}\n')
+ for coll_partner in line_config['collision_partners']:
+ outfile.write(f'{coll_partner}\n')
+
+
+def copy_additional_files(files_to_transfer: Union[None, List[str]] = None,
+ dst_path: Union[str, None] = None):
+ """
+ Copy constant files from cache
+ :param files_to_transfer: list of files to transfer
+ :param dst_path: destination path of the files
+ """
+ _dst_path = validate_parameter(dst_path, default=os.path.join('mdl', 'radmc_files'))
+ _files_to_transfer = validate_parameter(files_to_transfer, default=['dustkappa_silicate.inp',
+ 'dustopac.inp'])
+ _path = os.path.join('mdl', 'data')
+ if not os.path.isdir(_dst_path):
+ os.mkdir(_dst_path)
+ for filename in _files_to_transfer:
+ shutil.copy2(os.path.join(_path, filename), _dst_path)
+
+
+def write_radmc_main_input_file(config_mdl: dict,
+ config_lines: dict,
+ path: Union[None, str] = None):
+ """
+ Creates the main input file for RADMC, which can be used to reprocess the input
+ :param config_mdl: the model configuration
+ :param config_lines: te line configuration, from the STG layer
+ :param path: the path where to put the input file
+ """
+ _path = validate_parameter(path, default='.')
+ with open(os.path.join(_path, 'radmc3d.inp'), "w") as outfile:
+ for parameter in config_mdl["radmc_postprocessing"]:
+ outfile.write(f'{parameter} = {config_mdl["radmc_postprocessing"][parameter]}\n')
+ outfile.write(f'lines_mode = {radmc_lines_mode_mapping[config_lines["lines_mode"]]}\n')
+
+
+def get_solid_body_rotation_y(grid_metadata: dict) -> Tuple[np.array, np.array]:
+ """
+ Generate the velocity field for solid body rotation around the y-axis according to the configuration file
+ :param grid_metadata: the grid metadata
+ :return: a tuple with the velocity along the x- and y-axes
+ """
+ distance_xz = grid_metadata['distance_matrix'][:, grid_metadata['grid_refpix'][1], :]
+ gradient = float(grid_metadata['velocity_gradient']) * u.Unit(grid_metadata['velocity_gradient_unit'])
+ radial_velocity = (distance_xz * u.cm * gradient).to(u.cm / u.second)
+ angle_in_radians = np.arctan(
+ abs(grid_metadata['centered_indices'][2, ...] / grid_metadata['centered_indices'][0, ...]))
+ velocity_x = radial_velocity[:, np.newaxis, :] * -np.sin(angle_in_radians) * np.sign(
+ grid_metadata['centered_indices'][2, ...])
+ velocity_z = radial_velocity[:, np.newaxis, :] * np.cos(angle_in_radians) * np.sign(
+ grid_metadata['centered_indices'][0, ...])
+ velocity_x = np.where(np.isnan(velocity_x), 0, velocity_x)
+ velocity_z = np.where(np.isnan(velocity_z), 0, velocity_z)
+ return velocity_x, velocity_z
+
+
+def get_profiles(grid_metadata: dict) -> dict:
+ """
+ Function that computes the profiles of the physical quantities needed for the model.
+ :param grid_metadata: the grid metadata obtained parsing the config file
+ :return: a dictionary of arrays containing the profiles of physical quantities
+ """
+ density_ref = get_reference_value(grid_metadata=grid_metadata,
+ quantity_name='density',
+ desired_unit='cm^-3')
+ tdust_ref = get_reference_value(grid_metadata=grid_metadata,
+ quantity_name='dust_temperature',
+ desired_unit='K')
+ profiles_mapping = {
+ 'gas_number_density': {
+ 'central_value': (float(grid_metadata['central_density']) * u.Unit(grid_metadata['density_unit']))
+ .to(u.cm ** -3).value,
+ 'power_law_index': float(grid_metadata['density_powerlaw_idx']),
+ 'value_at_reference': density_ref['value_at_reference'],
+ 'distance_reference': density_ref['distance_reference']
+ },
+ 'dust_temperature': {
+ 'central_value': float(grid_metadata['dust_temperature']),
+ 'power_law_index': float(grid_metadata['dust_temperature_powerlaw_idx']),
+ 'value_at_reference': tdust_ref['value_at_reference'],
+ 'distance_reference': tdust_ref['distance_reference']
+ },
+ 'microturbulence': {
+ 'central_value': (float(grid_metadata['microturbulence']) * u.Unit(grid_metadata['microturbulence_unit']))
+ .to(u.cm / u.second).value,
+ 'power_law_index': 0,
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ },
+ 'escprob_lengthscale': {
+ 'central_value': min((np.max(grid_metadata['grid_size']) *
+ u.Unit(grid_metadata['grid_size_units'][np.argmax(grid_metadata['grid_size'])]))
+ .to(u.cm).value,
+ (2 * grid_metadata['maximum_radius'] * u.Unit(grid_metadata['maximum_radius_unit']))
+ .to(u.cm).value),
+ 'power_law_index': 0,
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ },
+ 'velocity_x': {
+ 'central_value': 0,
+ 'power_law_index': 0,
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ },
+ 'velocity_y': {
+ 'central_value': 0,
+ 'power_law_index': 0,
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ },
+ 'velocity_z': {
+ 'central_value': 0,
+ 'power_law_index': 0,
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ },
+ }
+
+ profiles = {}
+
+ for profile in profiles_mapping:
+ profiles[profile] = compute_power_law_radial_profile(
+ central_value=profiles_mapping[profile]['central_value'],
+ power_law_index=profiles_mapping[profile]['power_law_index'],
+ distance_matrix=grid_metadata['distance_matrix'],
+ value_at_reference=profiles_mapping[profile]['value_at_reference'],
+ distance_reference=profiles_mapping[profile]['distance_reference'],
+ maximum_radius=grid_metadata['maximum_radius'] * u.Unit(grid_metadata['maximum_radius_unit']).to(u.cm)
+ )
+
+ if grid_metadata['velocity_field'] == 'solid':
+ profiles['velocity_x'], profiles['velocity_z'] = get_solid_body_rotation_y(grid_metadata=grid_metadata)
+ profiles['velocity_field'] = np.array([profiles['velocity_x'], profiles['velocity_y'], profiles['velocity_z']])
+
+ if 'floor_temperature' in grid_metadata.keys():
+ profiles['dust_temperature'] = np.where(
+ profiles['dust_temperature'] < float(grid_metadata['floor_temperature']),
+ float(grid_metadata['floor_temperature']),
+ profiles['dust_temperature']
+ )
+ return profiles
+
+
+def get_reference_value(grid_metadata: dict,
+ quantity_name: str,
+ desired_unit: str) -> dict:
+ """
+ Compile a dictionary of reference values for the grid, applying a conversion, if necessary
+ :param grid_metadata: the grid metadata
+ :param quantity_name: the quantity to process
+ :param desired_unit: the output unit to use
+ :return: the dictionary with the value at reference in the desired units and the distance reference used to scale
+ the power law
+ """
+ try:
+ reference_value_dict = {
+ 'value_at_reference': (
+ (float(grid_metadata[f'{quantity_name}_at_reference']) * u.Unit(grid_metadata[f'{quantity_name}_unit']))
+ .to(u.Unit(desired_unit))).value,
+ 'distance_reference': (float(grid_metadata['distance_reference']) * u.Unit(
+ grid_metadata['distance_reference_unit'])).to(u.cm).value
+ }
+ except (KeyError, AttributeError):
+ reference_value_dict = {
+ 'value_at_reference': None,
+ 'distance_reference': 1.0
+ }
+ return reference_value_dict
+
+
+def write_grid_input_files(profiles: dict,
+ grid_metadata: dict,
+ wavelengths_micron: np.array,
+ path: Union[str, None] = None):
+ """
+ Write the grid input files needed by RADMC3d
+ :param profiles: the dictionary of profiles for physical quantities that define the model
+ :param grid_metadata: the grid metadata
+ :param wavelengths_micron: the array of wavelengths to model
+ :param path: the output path
+ """
+ _path = validate_parameter(path, default=os.path.join('mdl', 'radmc_files'))
+
+ quantity_mapping = {
+ 'amr_grid.inp': {
+ 'quantity': grid_metadata['grid_edges'],
+ },
+ 'dust_density.inp': {
+ 'quantity': (profiles['gas_number_density'] * u.cm ** -3 / 100. * cst.m_p.to(
+ u.gram) * mean_molecular_mass).value,
+ },
+ 'dust_temperature.dat': {
+ 'quantity': profiles['dust_temperature'],
+ },
+ 'microturbulence.inp': {
+ 'quantity': profiles['microturbulence'],
+ },
+ 'gas_velocity.inp': {
+ 'quantity': profiles['velocity_field'],
+ },
+ 'wavelength_micron.inp': {
+ 'quantity': wavelengths_micron,
+ },
+ 'escprob_lengthscale.inp': {
+ 'quantity': profiles['escprob_lengthscale'],
+ },
+ }
+ for filename in quantity_mapping:
+ write_radmc_input(filename=filename,
+ quantity=quantity_mapping[filename]['quantity'],
+ path=_path,
+ grid_metadata=grid_metadata)
+
+
+def write_molecular_number_density_profiles(profiles: dict,
+ line_config: dict,
+ grid_metadata: dict,
+ path: Union[str, None] = None) -> dict:
+ """
+ Write the molecular number density profiles. A different function is used wrt the other profiles because the
+ molecular ones depend on the others, and on the abundance (profiles)
+ :param profiles: the dictionary of profiles for physical quantities that define the model
+ :param line_config: the dictionary of the line configurations, for the abundances
+ :param grid_metadata: the grid metadata
+ :param path: the output path
+ """
+ _path = validate_parameter(path, default=os.path.join('mdl', 'radmc_files'))
+ hot_core_specs = read_abundance_variation_schema(line_config=line_config)
+ species_profiles = {}
+ for species in line_config['species_to_include'] + line_config['collision_partners']:
+ species_profiles[species] = compute_molecular_number_density_hot_core(
+ gas_number_density_profile=profiles['gas_number_density'],
+ abundance=float(line_config['molecular_abundances'][species]),
+ temperature_profile=profiles['dust_temperature'],
+ threshold=float(hot_core_specs[species]['threshold']),
+ abundance_jump=float(hot_core_specs[species]['abundance_jump']))
+ write_radmc_input(filename=f'numberdens_{species}.inp',
+ quantity=species_profiles[species],
+ path=_path,
+ grid_metadata=grid_metadata)
+ return species_profiles
+
+
+def save_fits_grid_profile(quantity: np.array,
+ filename: str,
+ path: str = None):
+ """
+ Save the model grid to a fits file
+ :param quantity: the array to be stored in the fits file
+ :param filename: the name of the file to be created
+ :param path: the path where the file will be stored
+ """
+ _path = validate_parameter(path, default=os.path.join('prs', 'fits', 'grids'))
+ if not os.path.isfile(os.path.join(_path, filename)):
+ try:
+ fits.writeto(os.path.join(_path, filename), quantity)
+ except OSError:
+ pass
+ else:
+ logger.info('Skipping saving of fits grid. File already present!')
+
+
+def convert_dimensional_unit(value: Union[float, None, str],
+ current_unit: str,
+ desired_unit: str) -> Union[float, None]:
+ """
+ Convert the quantities specified in the configuration file to standard ones
+ :param value: the value to convert
+ :param current_unit: unit specified in the config file
+ :param desired_unit: the output unit
+ :return: the converted value, if defined
+ """
+ if value is not None:
+ return (float(value) * u.Unit(current_unit)).to(u.Unit(desired_unit)).value
+ else:
+ return None
+
+
+def remap_metadata_to_grid_row(zipped_grid_name: str,
+ grid_metadata: dict,
+ config: dict) -> dict:
+ """
+ Create a dictionary that can be persisted in the database, according to the GridPars table structure
+ :param zipped_grid_name: the name of the compressed archive that contains grid files
+ :param grid_metadata: the grid metadata
+ :param config: the configuration dictionary, to store human-readable grid- and coordinate types
+ :return: the dictionary to be inserted in the database
+ """
+ _config = config['grid']
+ dimensional_quantities = {
+ 'central_density': {'unit_key': 'density_unit', 'desired_unit': 'cm^-3'},
+ 'density_at_reference': {'unit_key': 'density_unit', 'desired_unit': 'cm^-3'},
+ 'microturbulence': {'unit_key': 'microturbulence_unit', 'desired_unit': 'km/s'},
+ 'velocity_gradient': {'unit_key': 'velocity_gradient_unit', 'desired_unit': 'km/(s pc)'},
+ 'velocity_at_reference': {'unit_key': 'velocity_unit', 'desired_unit': 'km/s'},
+ 'distance_reference': {'unit_key': 'distance_reference_unit', 'desired_unit': 'pc'},
+ 'maximum_radius': {'unit_key': 'maximum_radius_unit', 'desired_unit': 'pc'},
+ }
+ grid_row = {
+ 'zipped_grid_name': zipped_grid_name,
+ 'grid_type': _config['grid_type'],
+ 'coordinate_system': _config['coordinate_system'],
+ 'central_density': get_value_if_specified(parameters_dict=grid_metadata, key='central_density'),
+ 'density_powerlaw_index': get_value_if_specified(parameters_dict=grid_metadata, key='density_powerlaw_idx'),
+ 'density_at_reference': get_value_if_specified(parameters_dict=grid_metadata, key='density_at_reference'),
+ 'dust_temperature': get_value_if_specified(parameters_dict=grid_metadata, key='dust_temperature'),
+ 'dust_temperature_powerlaw_index': get_value_if_specified(parameters_dict=grid_metadata,
+ key='dust_temperature_powerlaw_idx'),
+ 'dust_temperature_at_reference': get_value_if_specified(parameters_dict=grid_metadata,
+ key='dust_temperature_at_reference'),
+ 'microturbulence': get_value_if_specified(parameters_dict=grid_metadata, key='microturbulence'),
+ 'velocity_field': get_value_if_specified(parameters_dict=grid_metadata, key='velocity_field'),
+ 'velocity_gradient': get_value_if_specified(parameters_dict=grid_metadata, key='velocity_gradient'),
+ 'velocity_powerlaw_index': get_value_if_specified(parameters_dict=grid_metadata, key='velocity_powerlaw_index'),
+ 'velocity_at_reference': get_value_if_specified(parameters_dict=grid_metadata, key='velocity_at_reference'),
+ 'distance_reference': get_value_if_specified(parameters_dict=grid_metadata, key='distance_reference'),
+ 'maximum_radius': get_value_if_specified(parameters_dict=grid_metadata, key='maximum_radius'),
+ 'grid_size_1': grid_metadata['grid_size'][0],
+ 'grid_shape_1': grid_metadata['grid_shape'][0],
+ 'grid_refpix_1': grid_metadata['grid_refpix'][0],
+ 'grid_size_2': grid_metadata['grid_size'][1],
+ 'grid_shape_2': grid_metadata['grid_shape'][1],
+ 'grid_refpix_2': grid_metadata['grid_refpix'][1],
+ 'grid_size_3': grid_metadata['grid_size'][2],
+ 'grid_shape_3': grid_metadata['grid_shape'][2],
+ 'grid_refpix_3': grid_metadata['grid_refpix'][2],
+ }
+ for qty in dimensional_quantities:
+ try:
+ grid_row[qty] = convert_dimensional_unit(value=grid_row[qty],
+ current_unit=u.Unit(
+ grid_metadata[dimensional_quantities[qty]['unit_key']]),
+ desired_unit=u.Unit(dimensional_quantities[qty]['desired_unit']))
+ except KeyError:
+ grid_row[qty] = None
+ for dim_idx in range(2):
+ grid_row[f'grid_size_{dim_idx + 1}'] = (
+ grid_row[f'grid_size_{dim_idx + 1}'] * u.Unit(grid_metadata['grid_size_units'][dim_idx])).to(
+ u.pc).value
+ return grid_row
+
+
+def populate_grid_table(config: dict,
+ engine: sqla_engine,
+ grid_metadata: dict,
+ run_id: str) -> str:
+ """
+ Upsert the grid data to the postgres DB
+ :param config: the configuration dictionary
+ :param engine: the SQLAlchemy engine to use
+ :param grid_metadata: the grid metadata
+ :return: the compressed archive filename
+ """
+ output_filename = f'{compute_unique_hash_filename(config=config)}.zip'
+ remapped_row = remap_metadata_to_grid_row(
+ zipped_grid_name=output_filename,
+ grid_metadata=grid_metadata,
+ config=config
+ )
+ remapped_row['run_id'] = run_id
+ remapped_row['created_on'] = datetime.now()
+ upsert(
+ table_object=GridPars,
+ row_dict=remapped_row,
+ conflict_keys=[GridPars.zipped_grid_name, GridPars.run_id],
+ engine=engine
+ )
+ return output_filename
+
+
+def populate_grid_files(quantity_name: str,
+ engine: sqla_engine,
+ zip_filename: str,
+ filename: str,
+ run_id: str):
+ """
+ Stores into the DB the grid filename for later reference
+ :param quantity_name: the name of the quantity to store
+ :param engine: the SQLAlchemy engine
+ :param zip_filename: the name of the grid archive containing the radmc3d input files
+ :param filename: the fits filename where the grid is stored
+ :param run_id: the ID of the run
+ """
+ raw_insert_entry = {'zipped_grid_name': zip_filename,
+ 'quantity': quantity_name,
+ 'fits_grid_name': filename,
+ 'created_on': datetime.now(),
+ 'run_id': run_id}
+ upsert(
+ table_object=GridFiles,
+ row_dict=raw_insert_entry,
+ conflict_keys=[GridFiles.zipped_grid_name, GridFiles.quantity, GridFiles.run_id],
+ engine=engine
+ )
+
+
+def populate_stars_table(config_stars: dict,
+ engine: sqla_engine,
+ grid_zipfile: str,
+ run_id: str):
+ """
+ Populate the stars table in the DB
+ :param config_stars: the dictionary containing the stars configurations
+ :param engine: the SQLAlchemy engine to use
+ :param executed_on: the timestamp of execution, to add to the record
+ :param grid_zipfile: the grid tarfile name, to be used as key
+ :param run_id: the run unique identifier
+ """
+ _config_stars = config_stars.copy()
+ _config_stars['zipped_grid_name'] = grid_zipfile
+ _config_stars['created_on'] = datetime.now()
+ _config_stars['run_id'] = run_id
+
+ upsert(
+ table_object=StarsPars,
+ row_dict=_config_stars,
+ conflict_keys=[StarsPars.zipped_grid_name, StarsPars.run_id],
+ engine=engine
+ )
+
+
+def write_stellar_input_file(stars_metadata: dict,
+ grid_metadata: dict,
+ path: str,
+ wavelengths_micron: np.array):
+ """
+ Create the input file to insert stars in the computation
+ :param stars_metadata: the metadata about the stars, as specified in the configuration file
+ :param grid_metadata: the grid metadata
+ :param path: the path where the files will be created
+ :param wavelengths_micron: the wavelength to consider
+ """
+ star_properties = [' '.join([str(rstar), str(mstar), str(pos[0]), str(pos[1]), str(pos[2])]) for rstar, mstar, pos
+ in zip(stars_metadata['rstars'], stars_metadata['mstars'], stars_metadata['star_positions'])]
+ override_defaults = {
+ 'iformat': 2,
+ 'nstars': stars_metadata['nstars'],
+ 'continuum_lambdas': stars_metadata['nlambdas'],
+ 'stars_properties': '\n'.join(star_properties),
+ 'lambdas': ' \n'.join(wavelengths_micron.astype(str)),
+ }
+ write_radmc_input(filename='stars.inp',
+ path=path,
+ quantity=np.array(stars_metadata['star_fluxes']),
+ grid_metadata=grid_metadata,
+ override_defaults=override_defaults,
+ flatten_style='C')
+
+
+def get_grid_name(method: Union[str, None] = None,
+ zip_filename: Union[str, None] = None,
+ quantity_name: Union[str, None] = None):
+ """
+ Compute the name of the grid file
+ :param method: method used to compute the file name; uuid generates a random unique identifier, composite_grid
+ appends the quantity name to the zipped archive name
+ :param zip_filename: the zipped archive name
+ :param quantity_name: the name of the quantity that should be processed
+ :return: the name of the file
+ """
+ _method = validate_parameter(method, default='uuid')
+ allowed_methods = ('uuid', 'composite_grid')
+ if method == 'uuid':
+ return f'{str(uuid.uuid4())}.fits'
+ elif method == 'composite_grid':
+ assert ((zip_filename is not None) and (quantity_name is not None))
+ return f'{".".join(zip_filename.split(".")[0:-1])}_{quantity_name}.fits'
+ else:
+ raise NotImplementedError(
+ f'The chosen method is not available. Allowed options are: {" ".join(allowed_methods)}')
+
+
+def main(run_id: str,
+ override_config: Union[dict, None] = None,
+ path_radmc_files: Union[str, None] = None,
+ compute_dust_temperature: bool = True,
+ engine: sqla_engine = None) -> str:
+ _override_config = validate_parameter(override_config, default={'grid_lines': {}, 'model': {}})
+ config = load_config_file(os.path.join('stg', 'config', 'config.yml'),
+ override_config=_override_config['grid_lines'])
+ config_lines = config['lines']
+ config_mdl = load_config_file(os.path.join('mdl', 'config', 'config.yml'),
+ override_config=_override_config['model'])
+ input_files_dir = validate_parameter(path_radmc_files, default=os.path.join('mdl', 'radmc_files'))
+ cleanup_directory(directory=input_files_dir,
+ logger=logger)
+ copy_additional_files(dst_path=input_files_dir)
+
+ grid_metadata = extract_grid_metadata(config=config)
+ if 'stars' in config:
+ wavelengths_micron = manage_wavelength_grid_with_stars(config, grid_metadata, input_files_dir)
+ else:
+ wavelengths_micron = np.logspace(np.log10(5),
+ np.log10(1300),
+ 100)
+ grid_metadata['continuum_lambdas'] = len(wavelengths_micron)
+
+ profiles = get_profiles(grid_metadata=grid_metadata)
+ write_grid_input_files(grid_metadata=grid_metadata,
+ profiles=profiles,
+ path=input_files_dir,
+ wavelengths_micron=wavelengths_micron)
+ get_moldata(species_names=config['lines']['species_to_include'],
+ logger=logger,
+ path=input_files_dir,
+ use_cache=True)
+ write_radmc_lines_input(line_config=config['lines'],
+ path=input_files_dir,
+ logger=logger)
+
+ molecular_number_density_profiles = write_molecular_number_density_profiles(profiles=profiles,
+ grid_metadata=grid_metadata,
+ line_config=config['lines'],
+ path=input_files_dir)
+ if engine is None:
+ engine = get_pg_engine(logger=logger)
+ zip_filename = populate_grid_table(config=config,
+ engine=engine,
+ grid_metadata=grid_metadata,
+ run_id=run_id)
+ engine.dispose()
+
+ write_radmc_main_input_file(config_mdl=config_mdl,
+ config_lines=config_lines,
+ path=input_files_dir)
+ execution_dir = os.getcwd()
+ os.chdir(input_files_dir)
+ # Recompute dust temperature distribution if needed, based on star positions and properties
+ if 'stars' in config:
+ if compute_dust_temperature is True:
+ logger.info('Computing dust temperature distribution using the stars in the configuration file')
+ try:
+ _threads = config_mdl['radmc_observation']['threads']
+ except KeyError:
+ _threads = 4
+ os.system(f'radmc3d mctherm setthreads {str(_threads)}')
+ populate_stars_table(config_stars=config['stars'],
+ engine=engine,
+ grid_zipfile=zip_filename,
+ run_id=run_id)
+ 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'))
+ # TODO: Should I apply the floor temperature here as well?
+ os.chdir(execution_dir)
+
+ if engine is None:
+ engine = get_pg_engine(logger=logger)
+ for quantity_name in ('gas_number_density', 'dust_temperature'):
+ save_and_persist_grid(engine=engine,
+ profiles=profiles,
+ run_id=run_id,
+ zip_filename=zip_filename,
+ quantity_name=quantity_name)
+ for species in molecular_number_density_profiles:
+ save_and_persist_grid(engine=engine,
+ profiles=molecular_number_density_profiles,
+ run_id=run_id,
+ zip_filename=zip_filename,
+ quantity_name=species)
+
+ engine.dispose()
+
+ make_archive(output_filename=zip_filename,
+ source_dir=input_files_dir,
+ archive_path=os.path.join('stg', 'archive'))
+ return zip_filename
+
+
+def manage_wavelength_grid_with_stars(config: dict,
+ grid_metadata: dict,
+ input_files_dir: str) -> np.array:
+ stars_metadata = config['stars']
+ if stars_metadata['spacing'] == 'log':
+ wavelengths_micron = np.logspace(np.log10(stars_metadata['lambdas_micron_limits'][0]),
+ np.log10(stars_metadata['lambdas_micron_limits'][1]),
+ stars_metadata['nlambdas'])
+ elif stars_metadata['spacing'] == 'linear':
+ wavelengths_micron = np.linspace(stars_metadata['lambdas_micron_limits'][0],
+ stars_metadata['lambdas_micron_limits'][1],
+ stars_metadata['nlambdas'])
+ else:
+ raise (NotImplemented('Spacing not defined. Choose between {linear, log}'))
+ write_stellar_input_file(stars_metadata=stars_metadata,
+ grid_metadata=grid_metadata,
+ path=input_files_dir,
+ wavelengths_micron=wavelengths_micron)
+ return wavelengths_micron
+
+
+def save_and_persist_grid(engine: sqla_engine,
+ profiles: dict,
+ run_id: str,
+ zip_filename: str,
+ quantity_name: str,
+ method: Union[str, None] = None):
+ _method = validate_parameter(method, default='composite_grid')
+ grid_file_name = get_grid_name(method=_method,
+ zip_filename=zip_filename,
+ quantity_name=quantity_name)
+ save_fits_grid_profile(quantity=profiles[quantity_name],
+ filename=grid_file_name)
+ populate_grid_files(quantity_name=quantity_name,
+ engine=engine,
+ zip_filename=zip_filename,
+ filename=grid_file_name,
+ run_id=run_id)
+
+
+if __name__ == '__main__':
+ main(run_id='test_run')
diff --git a/etl/tests/__init__.py b/etl/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/etl/tests/test_commons.py b/etl/tests/test_commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..7901f98727e606d66190c209396a564b1c904ae0
--- /dev/null
+++ b/etl/tests/test_commons.py
@@ -0,0 +1,431 @@
+import os
+from unittest import TestCase
+import numpy as np
+import pandas as pd
+from astropy import units as u
+from assets.commons import (load_config_file,
+ validate_parameter,
+ setup_logger,
+ get_moldata)
+from assets.commons.db_utils import get_credentials
+from assets.commons.grid_utils import (get_grid_edges,
+ get_physical_px_size,
+ compute_cartesian_coordinate_grid,
+ compute_power_law_radial_profile,
+ get_distance_matrix,
+ get_centered_indices,
+ extract_grid_metadata,
+ compute_los_average_weighted_profile)
+from assets.commons.parsing import (get_grid_properties,
+ parse_grid_overrides)
+from assets.commons.training_utils import compute_and_add_similarity_cols
+
+
+def create_test_config(config_dict: dict,
+ config_filename: str):
+ with open(config_filename, 'w') as config_file:
+ config_file.write('grid:\n')
+ for key in config_dict:
+ config_file.write(f' {key}: {config_dict[key]}\n')
+
+
+class TestCommons(TestCase):
+ def setUp(self):
+ self.config_filename = 'config.yml'
+ self.logger = setup_logger(name='TEST_COMMONS')
+
+ def test_compute_power_law_radial_profile_flat(self):
+ indices = np.indices([3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2)
+ radial_profile = compute_power_law_radial_profile(
+ central_value=1,
+ power_law_index=0,
+ distance_matrix=distance_matrix
+ )
+ self.assertTrue(np.array_equal(radial_profile, np.ones([3, 3])))
+
+ def test_compute_power_law_radial_profile_pl(self):
+ indices = np.indices([3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2)
+ distance_matrix[1, 1] = 1.0
+ radial_profile = compute_power_law_radial_profile(
+ central_value=1,
+ power_law_index=-1,
+ distance_matrix=distance_matrix
+ )
+ self.assertTrue(np.array_equal(radial_profile, 1.0 / distance_matrix))
+
+ def test_compute_power_law_radial_profile_pl_with_reference(self):
+ indices = np.indices([3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2)
+ radial_profile = compute_power_law_radial_profile(
+ central_value=2,
+ value_at_reference=1,
+ distance_reference=1,
+ power_law_index=-1,
+ distance_matrix=distance_matrix
+ )
+ censored_rp = radial_profile.copy()
+ censored_rp[1, 1] = np.nan
+ self.assertEqual(radial_profile[1, 1], np.nanmax(censored_rp))
+
+ def test_get_grid_properties_uniform(self):
+ _config_filename = os.path.join('test_files', self.config_filename)
+ config_dict = {
+ 'dim1': {"size": 0.1, "unit": "pc", "shape": 5, "refpix": 2},
+ }
+ keywords = ['size', 'unit', 'shape', 'refpix']
+ expected_result = {
+ 'size': [0.1] * 3,
+ 'unit': ['pc'] * 3,
+ 'shape': [5] * 3,
+ 'refpix': [2] * 3,
+ }
+ create_test_config(config_dict=config_dict,
+ config_filename=_config_filename)
+ config = load_config_file(_config_filename)
+ for key in keywords:
+ grid_properties = get_grid_properties(grid_config=config['grid'],
+ keyword=key)
+ self.assertListEqual(grid_properties, expected_result[key])
+
+ def test_get_grid_properties(self):
+ _config_filename = os.path.join('test_files', self.config_filename)
+ config_dict = {
+ 'dim1': {"size": 0.1, "unit": "pc", "shape": 5, "refpix": 2},
+ 'dim2': {"size": 0.2, "unit": "pc", "shape": 10, "refpix": 4.5},
+ 'dim3': {"size": 0.3, "unit": "pc", "shape": 15, "refpix": 7},
+ }
+ keywords = ['size', 'unit', 'shape', 'refpix']
+ expected_result = {
+ 'size': [0.1, 0.2, 0.3],
+ 'unit': ['pc'] * 3,
+ 'shape': [5, 10, 15],
+ 'refpix': [2, 4.5, 7],
+ }
+ create_test_config(config_dict=config_dict,
+ config_filename=_config_filename)
+
+ config = load_config_file(_config_filename)
+ for key in keywords:
+ grid_properties = get_grid_properties(grid_config=config['grid'],
+ keyword=key)
+ self.assertListEqual(grid_properties, expected_result[key])
+
+ def test_get_grid_edges(self):
+ grid_metadata = {
+ 'grid_shape': [2, 2],
+ 'physical_px_size': [1 * u.cm, 1 * u.cm],
+ 'grid_refpix': [0.5, 0.5]
+ }
+ expected_results_per_axis = np.array([-1, 0, 1])
+ grid_edges = get_grid_edges(grid_metadata=grid_metadata)
+ for axis_idx in range(len(grid_metadata['grid_shape'])):
+ self.assertTrue(np.array_equal(expected_results_per_axis, grid_edges[:, axis_idx]))
+
+ def test_get_physical_px_size(self):
+ _config_filename = os.path.join('test_files', self.config_filename)
+ grid_size = 0.2
+ npix = 5
+ config_dict = {
+ 'grid_type': 'regular',
+ 'coordinate_system': 'cartesian',
+ 'central_density': '1e6',
+ 'density_unit': 'cm^-3',
+ 'dim1': {"size": grid_size, "size_units": "pc", "shape": npix, "refpix": 2},
+ }
+ expected_result = [(grid_size * u.pc).to(u.cm) / npix] * 3
+ create_test_config(config_dict=config_dict,
+ config_filename=_config_filename)
+ config = load_config_file(_config_filename)
+ grid_metadata = extract_grid_metadata(config=config)
+ physical_px_size = get_physical_px_size(grid_metadata)
+ self.assertListEqual(expected_result, physical_px_size)
+
+ def test_compute_cartesian_coordinate_grid(self):
+ indices = np.array([0, 1, 2])
+ physical_px_size = [0.1 * u.pc]
+ expected_results = indices * (physical_px_size[0]).to(u.cm).value
+ computed_grid = compute_cartesian_coordinate_grid(indices=indices,
+ physical_px_size=physical_px_size)
+ self.assertTrue(np.array_equal(expected_results, computed_grid))
+
+ def test_compute_cartesian_coordinate_grid_2d(self):
+ indices = np.indices([3, 4])
+ physical_px_size = [1 * u.cm, 1 * u.cm]
+ expected_results = indices
+ computed_grid = compute_cartesian_coordinate_grid(indices=indices,
+ physical_px_size=physical_px_size)
+ self.assertTrue(np.array_equal(expected_results, computed_grid))
+
+ def test_get_distance_matrix_2d(self):
+ grid_metadata = {
+ 'grid_shape': [2, 2],
+ 'physical_px_size': [1 * u.cm, 1 * u.cm]
+ }
+ indices = np.indices(grid_metadata['grid_shape'])
+ expected_results = np.array([[0, 1], [1, np.sqrt(2)]])
+ distance_matrix = get_distance_matrix(grid_metadata=grid_metadata,
+ indices=indices)
+ self.assertTrue(np.array_equal(expected_results, distance_matrix))
+
+ def test_get_distance_matrix(self):
+ grid_metadata = {
+ 'grid_shape': [2, 2, 2],
+ 'physical_px_size': [1 * u.cm, 1 * u.cm, 1 * u.cm]
+ }
+ indices = np.indices(grid_metadata['grid_shape'])
+ expected_results = np.array([[[0, 1], [1, np.sqrt(2)]],
+ [[1, np.sqrt(2)], [np.sqrt(2), np.sqrt(3)]]])
+ distance_matrix = get_distance_matrix(grid_metadata=grid_metadata,
+ indices=indices)
+ self.assertTrue(np.array_equal(expected_results, distance_matrix))
+
+ def test_get_distance_matrix_symmetric(self):
+ grid_metadata = {
+ 'grid_shape': [5, 5, 5],
+ 'physical_px_size': [1 * u.cm, 1 * u.cm, 1 * u.cm]
+ }
+ indices = np.indices(grid_metadata['grid_shape']) - 3
+ distance_matrix = get_distance_matrix(grid_metadata=grid_metadata,
+ indices=indices)
+ self.assertTrue(np.array_equal(distance_matrix, distance_matrix.T))
+
+ def test_get_centered_indices(self):
+ grid_metadata = {
+ 'grid_shape': [5, 3],
+ 'grid_refpix': [2, 1]
+ }
+ expected_indices = [[[-2, -2, -2],
+ [-1, -1, -1],
+ [0, 0, 0],
+ [1, 1, 1],
+ [2, 2, 2]],
+ [[-1, 0, 1],
+ [-1, 0, 1],
+ [-1, 0, 1],
+ [-1, 0, 1],
+ [-1, 0, 1]]]
+ indices = get_centered_indices(grid_metadata=grid_metadata)
+ self.assertTrue(np.array_equal(np.array(expected_indices), indices))
+
+ def test_validate_parameter_none(self):
+ parameter = None
+ default = 10
+ result = validate_parameter(param_to_validate=parameter,
+ default=default)
+ self.assertEqual(default, result)
+
+ def test_validate_parameter(self):
+ parameter = 100
+ default = 10
+ result = validate_parameter(param_to_validate=parameter,
+ default=default)
+ self.assertEqual(parameter, result)
+
+ def test_parse_grid_overrides_linear(self):
+ config = {
+ 'overrides': {
+ 'dust_temperature_grid_type': 'linear',
+ 'dust_temperature_limits': [10, 30],
+ 'dust_temperature_step': 1,
+ }
+ }
+ expected_results = np.arange(config['overrides']['dust_temperature_limits'][0],
+ config['overrides']['dust_temperature_limits'][1],
+ config['overrides']['dust_temperature_step'])
+ grid_values = parse_grid_overrides(par_name='dust_temperature',
+ config=config)
+ self.assertTrue(np.array_equal(expected_results, grid_values))
+
+ def test_parse_grid_overrides_log(self):
+ config = {
+ 'overrides': {
+ 'dust_temperature_grid_type': 'log',
+ 'dust_temperature_limits': [10, 200],
+ 'dust_temperature_step': 2,
+ }
+ }
+ expected_results = np.array([10, 20, 40, 80, 160])
+ grid_values = parse_grid_overrides(par_name='dust_temperature',
+ config=config)
+ self.assertTrue(np.array_equal(expected_results, grid_values.astype(int)))
+
+ def test_parse_grid_overrides_log_exponent(self):
+ exponent = 0.25
+ config = {
+ 'overrides': {
+ 'dust_temperature_grid_type': 'log',
+ 'dust_temperature_limits': [10, 100],
+ 'dust_temperature_step': exponent,
+ }
+ }
+ expected_results = np.array(
+ [10, 10 * 10 ** exponent, 10 * 10 ** (2 * exponent), 10 * 10 ** (3 * exponent), 10 * 10 ** (4 * exponent)])
+ grid_values = parse_grid_overrides(par_name='dust_temperature',
+ config=config)
+ self.assertTrue(np.allclose(expected_results, grid_values, 5))
+
+ def test_get_credentials(self):
+ expected_results = {
+ 'DB_USER': 'pippo',
+ 'DB_PASS': 'pluto',
+ 'DB_HOST': 'localhost',
+ 'DB_NAME': 'postgres',
+ 'DB_PORT': 5432,
+ }
+ credentials = get_credentials(logger=self.logger,
+ credentials_filename=os.path.join('test_files', 'credentials.yaml'),
+ set_envs=True)
+ self.assertDictEqual(credentials, expected_results)
+ for key in expected_results:
+ self.assertEqual(str(expected_results[key]), os.getenv(key))
+
+ def test_get_moldata_from_cache(self):
+ species = 'e-ch3oh'
+ os.chdir('..')
+ expected_path = os.path.join('tests', 'test_files', f'molecule_{species}.inp')
+ try:
+ os.remove(expected_path)
+ except IOError:
+ pass
+ get_moldata(species_names=[species],
+ logger=self.logger,
+ path=os.path.join('tests', 'test_files'),
+ use_cache=True)
+ self.assertTrue(os.path.isfile(expected_path))
+ os.remove(expected_path)
+ os.chdir('tests')
+
+ def test_get_moldata_negate_cache(self):
+ species = 'e-ch3oh'
+ os.chdir('..')
+ expected_path = os.path.join('tests', 'test_files', f'molecule_{species}.inp')
+ try:
+ os.remove(expected_path)
+ except IOError:
+ pass
+ get_moldata(species_names=[species],
+ logger=self.logger,
+ path=os.path.join('tests', 'test_files'),
+ use_cache=False)
+ self.assertTrue(os.path.isfile(expected_path))
+ os.remove(expected_path)
+ os.chdir('tests')
+
+ def test_compute_los_average_weighted_profile(self):
+ indices = np.indices([3, 3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2 + indices[2, :, :] ** 2)
+ radial_profile = compute_power_law_radial_profile(
+ central_value=1,
+ power_law_index=0,
+ distance_matrix=distance_matrix
+ )
+ rng = np.random.default_rng(seed=3)
+ weights = rng.random(radial_profile.shape)
+ los_average = compute_los_average_weighted_profile(profile=radial_profile,
+ weights=weights)
+ expected_result = np.ones_like(los_average)
+ self.assertTrue(np.array_equal(expected_result, los_average))
+
+ def test_compute_los_average_weighted_profile_pl(self):
+ indices = np.indices([3, 3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2 + indices[2, :, :] ** 2)
+ radial_profile = compute_power_law_radial_profile(
+ central_value=1,
+ power_law_index=-1,
+ distance_matrix=distance_matrix
+ )
+ weights = np.ones_like(radial_profile)
+ los_average = compute_los_average_weighted_profile(profile=radial_profile,
+ weights=weights)
+ expected_result = np.where(~np.isfinite(np.mean(1.0 / distance_matrix, axis=2)),
+ 1,
+ np.mean(1.0 / distance_matrix, axis=2))
+ self.assertTrue(np.array_equal(expected_result, los_average))
+
+ def test_compute_los_average_weighted_profile_pl_weights(self):
+ indices = np.indices([3, 3, 3]) - 1
+ distance_matrix = np.sqrt(indices[0, :, :] ** 2 + indices[1, :, :] ** 2 + indices[2, :, :] ** 2)
+ radial_profile = compute_power_law_radial_profile(
+ central_value=1,
+ power_law_index=-1,
+ distance_matrix=distance_matrix
+ )
+ rng = np.random.default_rng(seed=3)
+ weights = rng.random(radial_profile.shape)
+ los_average = compute_los_average_weighted_profile(profile=radial_profile,
+ weights=weights)
+ weights /= np.sum(weights, axis=2, keepdims=True)
+ expected_radial_profile = np.where(~np.isfinite(1.0 / distance_matrix),
+ 1, 1.0 / distance_matrix)
+ self.assertTrue(np.array_equal(radial_profile, expected_radial_profile))
+ expected_result = np.where(~np.isfinite(1.0 / distance_matrix),
+ 1, np.sum(radial_profile * weights, axis=2))
+ self.assertTrue(np.allclose(expected_result, los_average, 1e-5))
+
+
+class TestTraining(TestCase):
+ def setUp(self):
+ feature_names = ['feature_01', 'feature_02']
+
+ self.df_data = pd.DataFrame(
+ data=[
+ [1, -1],
+ [1, 1],
+ [-1, -1],
+ [-1, 0],
+ [2, 1],
+ [0, 1],
+ [0, 0]
+ ],
+ columns=feature_names
+ )
+ self.average_features_per_target_bin = pd.DataFrame(
+ data=[
+ [1, 1],
+ [-1, -1]
+ ],
+ columns=feature_names
+ )
+
+ def test_compute_and_add_similarity_cols_output_features(self):
+ test_df = compute_and_add_similarity_cols(average_features_per_target_bin=self.average_features_per_target_bin,
+ input_df=self.df_data,
+ similarity_bins=2)
+ self.assertListEqual(
+ list1=list(test_df.columns),
+ list2=list(self.df_data.columns) + [f'sim_{str(idx).zfill(2)}' for idx in range(2)]
+ )
+
+ def test_compute_and_add_similarity_cols_drop_features_output(self):
+ df_data_with_additional_column = self.df_data.copy()
+ df_data_with_additional_column['additional_column'] = 1
+ assert 'additional_column' in df_data_with_additional_column.columns
+ test_df = compute_and_add_similarity_cols(average_features_per_target_bin=self.average_features_per_target_bin,
+ input_df=df_data_with_additional_column,
+ similarity_bins=2)
+ self.assertListEqual(
+ list1=list(test_df.columns),
+ list2=list(df_data_with_additional_column.columns) + [f'sim_{str(idx).zfill(2)}' for idx in range(2)]
+ )
+
+ def test_compute_and_add_similarity_cols(self):
+ test_df = compute_and_add_similarity_cols(average_features_per_target_bin=self.average_features_per_target_bin,
+ input_df=self.df_data,
+ similarity_bins=2)
+ df_data_l2_norm = np.sqrt(np.sum(self.df_data.values ** 2, axis=1)).reshape(len(self.df_data), 1)
+ average_features_per_target_bin_l2_norm = (np.sqrt(np.sum(self.average_features_per_target_bin.values ** 2,
+ axis=1))
+ .reshape(1, len(self.average_features_per_target_bin)))
+ expected_result = np.dot(
+ self.df_data,
+ self.average_features_per_target_bin.T
+ ) / np.dot(df_data_l2_norm,
+ average_features_per_target_bin_l2_norm)
+ self.assertTrue(
+ np.array_equal(
+ test_df[['sim_00', 'sim_01']].values.round(5),
+ np.nan_to_num(expected_result.round(5), nan=0)
+ )
+ )
diff --git a/etl/tests/test_files/credentials.yaml b/etl/tests/test_files/credentials.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..366cb8fe95b5327c1068f5e23e8dfb2c3701332b
--- /dev/null
+++ b/etl/tests/test_files/credentials.yaml
@@ -0,0 +1,5 @@
+DB_USER: cGlwcG8=
+DB_PASS: cGx1dG8=
+DB_HOST: localhost
+DB_NAME: postgres
+DB_PORT: 5432
\ No newline at end of file
diff --git a/etl/tests/test_lte_approx.py b/etl/tests/test_lte_approx.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc548b49db4d2173f0fb1761ce8bcaf2eb5f56c0
--- /dev/null
+++ b/etl/tests/test_lte_approx.py
@@ -0,0 +1,103 @@
+from itertools import groupby
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+from assets.commons import (get_data)
+from astropy import units as u
+from astropy import constants
+from sklearn.linear_model import LinearRegression
+from typing import Tuple
+plt.rcParams.update({'font.size': 16})
+
+
+def get_moldata() -> pd.DataFrame:
+ source_file = 'mdl/data/molecule_e-ch3oh.inp'
+ rows_list = []
+
+ with open(source_file, 'r') as infile:
+ file_content = infile.readlines()
+ file_content_sections = [list(g) for k, g in groupby(file_content, lambda x: x.startswith('!')) if not k]
+ levels = file_content_sections[3]
+
+ for line in levels:
+ split_line = line.split()
+ rows_list.append({
+ 'original_index': int(split_line[0]),
+ 'energy': float(split_line[1]),
+ 'g': float(split_line[2]),
+ 'weight': 2 * float(split_line[2]) + 1,
+ 'J_K': f'{split_line[3]}'
+ })
+
+ energy_levels = pd.DataFrame(rows_list)
+ return energy_levels.sort_values(by='energy')
+
+
+def compute_partition_function(temperature: float,
+ energies_and_gs: pd.DataFrame) -> float:
+ partition_function = 0
+ for level in energies_and_gs.iterrows():
+ partition_function += float(level[1]['g']) * np.exp(-float(level[1]['energy']) / temperature)
+ return partition_function
+
+
+def get_coldens_z_temperature(energies: np.array, y_rotational_diagram: np.array) -> Tuple[np.array, np.array]:
+ coldens_z = []
+ temperature = []
+ for pix_y_rotdia in y_rot_dia.value:
+ reg = LinearRegression().fit(np.log10(np.e) * energies.reshape(-1, 1), np.log10(pix_y_rotdia))
+ coldens_z.append(reg.intercept_)
+ temperature.append(-1 / reg.coef_[0])
+ temperature = np.array(temperature)
+ temperature = np.where((temperature > 200) | (temperature <= 0), 200, temperature)
+ return np.array(coldens_z), temperature
+
+
+def get_y_rotational_diagram(mom_zero_data: pd.DataFrame,
+ frequencies: np.array,
+ einstein_a: np.array,
+ gs: np.array):
+ integrated_intensities = np.array(
+ mom_zero_data[['mom_zero_86', 'mom_zero_87', 'mom_zero_88']]) * u.Jansky.decompose() * u.km / u.s * \
+ (constants.c.cgs ** 2 / (2 * constants.k_B.decompose() * (frequencies * u.GHz).to(
+ u.Hz) ** 2).decompose()) / (np.arcsin(2 / 101)) ** 2
+ integrated_intensities = integrated_intensities.decompose().cgs
+ y_rot_dia = 8 * np.pi * constants.k_B.cgs * (frequencies * u.GHz).to(u.Hz) ** 2 / \
+ (constants.h.cgs * constants.c.cgs ** 3 * einstein_a * (u.s ** -1)) * \
+ integrated_intensities / gs
+ y_rot_dia = y_rot_dia.decompose().cgs
+ return y_rot_dia
+
+
+# Transitions data
+# 86, 87, 88
+gs = np.array([5, 5, 5])
+energies = np.array([12.5, 20.1, 28.0])
+einstein_a = np.array([2.557794E-06, 3.407341E-06, 2.624407E-06])
+frequencies = np.array([96.739358, 96.744545, 96.75550])
+
+data = get_data(limit_rows=None)
+molecular_data = get_moldata()
+
+y_rot_dia = get_y_rotational_diagram(mom_zero_data=data,
+ frequencies=frequencies,
+ einstein_a=einstein_a,
+ gs=gs)
+
+coldens_z, temperature = get_coldens_z_temperature(energies=energies,
+ y_rotational_diagram=y_rot_dia)
+Z = compute_partition_function(energies_and_gs=molecular_data, temperature=temperature)
+data['lte_coldens'] = coldens_z + np.log10(Z) #np.mean(log_n, axis=1)
+
+
+
+plt.clf()
+plt.scatter(10**data.lte_coldens, 10**data.molecule_column_density, alpha=0.3)
+# sns.kdeplot(x=10**data.lte_coldens, y=10**data.molecule_column_density)
+plt.loglog()
+plt.plot([1e11, 1e17], [1e11, 1e17], color='red')
+plt.xlabel(r'N(CH$_3$OH) LTE [cm$^{-2}$]')
+plt.ylabel(r'N(CH$_3$OH) Model [cm$^{-2}$]')
+plt.tight_layout()
+plt.savefig(os.path.join('prs', 'output', 'lte_approx', 'lte_approximation_coldens.png'))
diff --git a/etl/tests/test_mdl_execute_radmc_command.py b/etl/tests/test_mdl_execute_radmc_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..b66ac2eb8ea6c9079353cf42bb444008f186e5f3
--- /dev/null
+++ b/etl/tests/test_mdl_execute_radmc_command.py
@@ -0,0 +1,37 @@
+from mdl.mdl_execute_radmc_command import (get_command_options,
+ check_config)
+from unittest import TestCase
+
+
+class Test(TestCase):
+ def test_get_command_options(self):
+ config = {
+ 'radmc_observation': {
+ 'inclination': 30,
+ 'position_angle': 45,
+ 'imolspec': 1,
+ 'iline': 3
+ }
+ }
+ options_set = get_command_options(config=config)
+ expected_result = {
+ 'incl 30',
+ 'phi 45',
+ 'imolspec 1',
+ 'iline 3'
+ }
+ self.assertSetEqual(options_set, expected_result)
+
+ def test_check_config_pass(self):
+ config = {
+ 'inclination': 0,
+ 'position_angle': 0
+ }
+ self.assertTrue(check_config(config))
+
+ def test_check_config_fail(self):
+ config = {
+ 'inclination': 0,
+ 'imolspec': 1
+ }
+ self.assertFalse(check_config(config))
diff --git a/etl/tests/test_prs_density_inference.py b/etl/tests/test_prs_density_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc4c0e46ec4da269151f9bc3971f42b1176a30d5
--- /dev/null
+++ b/etl/tests/test_prs_density_inference.py
@@ -0,0 +1,121 @@
+import numpy as np
+from unittest import TestCase
+from prs.prs_density_inference import (get_probability_density_threshold,
+ get_probability_distribution,
+ get_results,
+ get_hpd_interval)
+
+
+class Test(TestCase):
+ def test_get_probability_density_threshold(self):
+ x_array = np.arange(8)
+ probability_density = np.array([0, 5, 15, 30, 30, 15, 5, 0]) / 100.
+ expected_result = 0.025
+ probability, centered_probability_density, ordered_idxs = get_probability_distribution(
+ probability_density=probability_density,
+ x_array=x_array)
+ computed_threshold = get_probability_density_threshold(
+ ordered_probability=probability[ordered_idxs],
+ ordered_probability_density=centered_probability_density[ordered_idxs],
+ probability_threshold=0.05
+ )
+
+ self.assertAlmostEqual(expected_result, computed_threshold, 5)
+
+ def test_get_hpd_interval_last(self):
+ x_array = np.arange(8)
+ probability_density = np.array([0, 5, 15, 30, 10, 15, 15, 15]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.05,
+ interp_points=1000)
+ expected_hpd_interval = [1, 7]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_first(self):
+ x_array = np.arange(8)
+ probability_density = np.array([15, 15, 15, 10, 30, 15, 5, 0]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.05,
+ interp_points=1000)
+ expected_hpd_interval = [0, 6]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_double(self):
+ x_array = np.arange(8)
+ probability_density = np.array([10, 20, 15, 10, 30, 15, 5, 0]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.15,
+ interp_points=1000)
+ expected_hpd_interval = [0.5, 2, 3.25, 5]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_edge_case_below(self):
+ x_array = np.arange(8)
+ probability_density = np.array([30, 20, 15, 10, 10, 15, 5, 0]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.15,
+ interp_points=1000)
+ expected_hpd_interval = [0, 2, 5, 5]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_edge_case_constant(self):
+ x_array = np.arange(8)
+ probability_density = np.array([30, 20, 15, 15, 10, 10, 5, 0]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.1,
+ interp_points=1000)
+ expected_hpd_interval = [0, 5]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_edge_case_constant2(self):
+ x_array = np.arange(8)
+ probability_density = np.array([10, 10, 35, 25, 10, 10, 5, 0]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.1,
+ interp_points=1000)
+ expected_hpd_interval = [0, 5]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_hpd_interval_edge_case_above(self):
+ x_array = np.arange(8)
+ probability_density = np.array([30, 20, 15, 10, 11, 15, 5, 1]) / 100.
+ hpd_interval = get_hpd_interval(x_array=x_array,
+ probability_density=probability_density,
+ hpd_threshold=0.1,
+ interp_points=1000)
+ expected_hpd_interval = [0, 5.5]
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_results(self):
+ x_array = np.arange(8)
+ probability_density = np.array([0, 5, 15, 30, 30, 15, 5, 0]) / 100.
+ computed_threshold, best_fit, hpd_interval = get_results(x_array=x_array,
+ probability_density=probability_density,
+ probability_threshold=0.05,
+ interp_points=1000)
+ expected_threshold = 0.025
+ expected_best_fit = 3
+ expected_hpd_interval = [0.5, 6.5]
+ self.assertAlmostEqual(expected_threshold, computed_threshold, 5)
+ self.assertEqual(expected_best_fit, best_fit)
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-2))
+
+ def test_get_results_asymmetric(self):
+ x_array = np.array([1, 2, 4, 8, 16, 32, 64, 128])
+ probability_density = np.array([0, 5, 15, 34, 40, 5, 1, 0]) / 100.
+ computed_threshold, best_fit, hpd_interval = get_results(x_array=x_array,
+ probability_density=probability_density,
+ probability_threshold=0.05,
+ interp_points=1000)
+ expected_threshold = 0.02557788944723618
+ expected_best_fit = 8
+ expected_hpd_interval = [1.5115, 51.5395]
+ self.assertAlmostEqual(expected_threshold, computed_threshold, 5)
+ self.assertEqual(expected_best_fit, best_fit)
+ self.assertTrue(np.allclose(np.array(expected_hpd_interval), hpd_interval, rtol=1e-4))
diff --git a/etl/tests/test_stg_radmc_input_generator.py b/etl/tests/test_stg_radmc_input_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..d75404f362e06604f14ba7936a4c8fd311611d81
--- /dev/null
+++ b/etl/tests/test_stg_radmc_input_generator.py
@@ -0,0 +1,157 @@
+import os
+import numpy as np
+from stg.stg_radmc_input_generator import (get_solid_body_rotation_y,
+ get_grid_name)
+from assets.commons.parsing import read_abundance_variation_schema
+from astropy import units as u
+from unittest import TestCase
+from assets.commons import load_config_file
+from assets.commons.grid_utils import (compute_molecular_number_density_hot_core,
+ extract_grid_metadata,
+ compute_power_law_radial_profile)
+
+
+def create_test_config(config_dict: dict,
+ config_filename: str):
+ with open(config_filename, 'w') as config_file:
+ config_file.write('grid:\n')
+ for key in config_dict:
+ config_file.write(f' {key}: {config_dict[key]}\n')
+
+
+class Test(TestCase):
+ def setUp(self):
+ self.config_filename = 'config.yml'
+
+ def test_compute_power_law_radial_profile(self):
+ profile = compute_power_law_radial_profile(central_value=2000.0,
+ power_law_index=0,
+ distance_matrix=np.array([
+ [np.sqrt(2), 1, np.sqrt(2)],
+ [1, 0, 1],
+ [np.sqrt(2), 1, np.sqrt(2)],
+ ]),
+ maximum_radius=np.sqrt(2),
+ value_at_reference=15.0,
+ distance_reference=3e18)
+ expected_result = np.array([
+ [15, 15, 15],
+ [15, 15, 15],
+ [15, 15, 15],
+ ])
+ self.assertTrue(np.allclose(profile, expected_result, 1.0e-5))
+
+ def test_read_abundance_variation_schema(self):
+ line_config = {
+ 'species_to_include': ['e-ch3oh'],
+ 'molecular_abundances': {
+ "e-ch3oh": 1e-9,
+ "p-h2": 1,
+ },
+ 'hot_core_specs': {
+ "e-ch3oh": {
+ 'threshold': 90,
+ 'abundance_jump': 100
+ }
+ },
+ 'lines_mode': 'lvg',
+ 'collision_partners': ['p-h2']
+ }
+ result_dict = read_abundance_variation_schema(line_config=line_config)
+ expected_dict = {
+ 'e-ch3oh': {
+ 'threshold': 90,
+ 'abundance_jump': 100
+ },
+ 'p-h2': {
+ 'threshold': 20,
+ 'abundance_jump': 1
+ },
+ }
+ self.assertDictEqual(result_dict, expected_dict)
+
+ def test_compute_molecular_number_density_hot_core(self):
+ gas_number_density_profile = np.array([1, 1, 2, 3])
+ temperature_profile = np.array([10, 100, 200, 20])
+ abundance_array = compute_molecular_number_density_hot_core(
+ gas_number_density_profile=gas_number_density_profile,
+ abundance=1e-9,
+ temperature_profile=temperature_profile,
+ abundance_jump=100,
+ threshold=90)
+ expected_results = np.array([1e-9, 1e-7, 2e-7, 3e-9])
+ self.assertTrue(np.allclose(abundance_array, expected_results, 1e-7))
+
+ def test_get_grid_filename_composite(self):
+ grid_name = get_grid_name(method='composite_grid',
+ zip_filename='abc.def.zip',
+ quantity_name='h2_density')
+ self.assertEqual(grid_name, 'abc.def_h2_density.fits')
+
+ def test_get_grid_filename_missing_info(self):
+ with self.assertRaises(AssertionError):
+ _ = get_grid_name(method='composite_grid',
+ quantity_name='h2_density')
+ with self.assertRaises(AssertionError):
+ _ = get_grid_name(method='composite_grid',
+ zip_filename='abc.def.zip')
+
+ def test_get_grid_filename_undefined_method(self):
+ with self.assertRaises(NotImplementedError):
+ get_grid_name(method='puzzidilontano')
+
+ def test_get_solid_body_rotation_y(self):
+ _config_filename = os.path.join('test_files', self.config_filename)
+ grid_size = 3
+ npix = 3
+ config_dict = {
+ 'grid_type': 'regular',
+ 'coordinate_system': 'cartesian',
+ 'central_density': '1e6',
+ 'density_unit': 'cm^-3',
+ 'dim1': {"size": grid_size, "size_units": "cm", "shape": npix, "refpix": 1},
+ 'velocity_field': 'solid',
+ 'velocity_gradient': 1,
+ 'velocity_gradient_unit': "cm/s cm",
+ }
+ expected_result_x = [
+ [
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ ],
+ [
+ [1, 0, -1],
+ [1, 0, -1],
+ [1, 0, -1],
+ ],
+ [
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ [np.sqrt(2) * np.sin(np.pi / 4), 0, -np.sqrt(2) * np.sin(np.pi / 4)],
+ ],
+ ]
+ expected_result_z = [
+ [
+ [-np.sqrt(2) * np.cos(np.pi / 4), -1.0, -np.sqrt(2) * np.cos(np.pi / 4)],
+ [-np.sqrt(2) * np.cos(np.pi / 4), -1.0, -np.sqrt(2) * np.cos(np.pi / 4)],
+ [-np.sqrt(2) * np.cos(np.pi / 4), -1.0, -np.sqrt(2) * np.cos(np.pi / 4)],
+ ],
+ [
+ [0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0],
+ ],
+ [
+ [np.sqrt(2) * np.cos(np.pi / 4), 1.0, np.sqrt(2) * np.cos(np.pi / 4)],
+ [np.sqrt(2) * np.cos(np.pi / 4), 1.0, np.sqrt(2) * np.cos(np.pi / 4)],
+ [np.sqrt(2) * np.cos(np.pi / 4), 1.0, np.sqrt(2) * np.cos(np.pi / 4)],
+ ],
+ ]
+ create_test_config(config_dict=config_dict,
+ config_filename=_config_filename)
+ config = load_config_file(_config_filename)
+ grid_metadata = extract_grid_metadata(config=config)
+ velocity_x, velocity_z = get_solid_body_rotation_y(grid_metadata=grid_metadata)
+ self.assertTrue(np.array_equal(np.array(expected_result_x), velocity_x.value))
+ self.assertTrue(np.array_equal(np.array(expected_result_z), velocity_z.value))