Loading etl/assets/commons/__init__.py +92 −21 Original line number Diff line number Diff line Loading @@ -3,7 +3,8 @@ import yaml import numpy as np import os import urllib.request from typing import List from typing import (List, Union) from astropy import units as u from assets.constants import (radmc_grid_map, radmc_coord_system_map, Loading @@ -11,6 +12,11 @@ from assets.constants import (radmc_grid_map, def load_config_file(config_file_path: str) -> dict: """ Load the information in the YAML configuration file into a python dictionary :param config_file_path: path to the configuration file :return: a dictionary with the parsed information """ with open(config_file_path) as config_file: config = yaml.load(config_file, Loader=yaml.FullLoader) return config Loading @@ -20,16 +26,39 @@ def compute_power_law_radial_profile( central_value: float, power_law_index: float, distance_matrix: np.array, value_at_reference: Union[float, None] = None, distance_reference: float = 1.0, fill_reference_pixel: bool = True ) -> np.array: 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 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 = value_at_reference if value_at_reference is not None else central_value _distance_matrix = np.where(distance_matrix == 0, 1, distance_matrix) if fill_reference_pixel is True else distance_matrix return central_value * (_distance_matrix / distance_reference) ** power_law_index 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: profile = np.where(profile > central_value, central_value, profile) return profile 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: Loading @@ -41,28 +70,38 @@ def get_grid_properties(grid_config: dict, 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] return indices * _physical_px_size_cm except TypeError: _physical_px_size_cm = physical_px_size.to(u.cm).value return (indices.T * _physical_px_size_cm).T 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_metadata['grid_shape'] = get_grid_properties(grid_config=grid_config, keyword='steps') grid_metadata['refpix'] = get_grid_properties(grid_config=grid_config, keyword='refpix') grid_metadata['grid_size'] = get_grid_properties(grid_config=grid_config, keyword='size') grid_metadata['grid_size_units'] = get_grid_properties(grid_config=grid_config, keyword='unit') 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) indices = np.indices(grid_metadata['grid_shape']).T - grid_metadata['refpix'] indices = get_centered_indices(grid_metadata) grid_metadata['coordinate_grid'] = compute_cartesian_coordinate_grid(indices=indices, physical_px_size=grid_metadata[ Loading @@ -74,18 +113,40 @@ def extract_grid_metadata(config: dict) -> dict: return grid_metadata def get_grid_edges(grid_metadata): 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( (np.arange(grid_metadata['grid_shape'][axis_idx] + 1) - grid_metadata['refpix'][axis_idx] - 0.5) * grid_metadata['physical_px_size'][axis_idx]) 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, indices): 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 Loading @@ -93,6 +154,12 @@ def get_distance_matrix(grid_metadata, indices): 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( Loading @@ -102,6 +169,11 @@ def get_physical_px_size(grid_metadata: dict) -> List[u.Quantity]: def get_moldata(species_names: list): """ 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 """ for species in species_names: data = urllib.request.urlopen(leiden_url_mapping[species]).read().decode() with open(os.path.join('mdl', 'radmc_input_files', f'molecule_{species}.inp'), 'w') as outfile: Loading @@ -111,4 +183,3 @@ def get_moldata(species_names: list): def convert_frequency_to_wavelength(frequency: astropy.units.Quantity, output_units: astropy.units.Unit): return frequency.to(output_units, equivalencies=u.spectral()) etl/stg/config/config.yml +1 −1 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ grid: central_density: 1e6 density_unit: "cm^-3" density_profile: "constant" dim1: {"size":0.1, "unit": "pc", "steps": 5, "refpix": 2} dim1: {"size":0.1, "size_units": "pc", "shape": 5, "refpix": 2} source_distance: 1000 source_distance_unit: "pc" Loading etl/tests/test_commons.py +64 −23 Original line number Diff line number Diff line Loading @@ -7,7 +7,7 @@ from assets.commons import (compute_power_law_radial_profile, get_grid_edges, extract_grid_metadata, get_physical_px_size, compute_cartesian_coordinate_grid, get_distance_matrix) compute_cartesian_coordinate_grid, get_distance_matrix, get_centered_indices) from unittest import TestCase Loading @@ -20,6 +20,9 @@ def create_test_config(config_dict: dict, class Test(TestCase): def setUp(self): self.config_filename = 'config.yml' 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) Loading @@ -42,43 +45,43 @@ class Test(TestCase): self.assertTrue(np.array_equal(radial_profile, 1.0 / distance_matrix)) def test_get_grid_properties_uniform(self): config_filename = os.path.join('test_files', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) config_dict = { 'dim1': {"size": 0.1, "unit": "pc", "steps": 5, "refpix": 2}, 'dim1': {"size": 0.1, "unit": "pc", "shape": 5, "refpix": 2}, } keywords = ['size', 'unit', 'steps', 'refpix'] keywords = ['size', 'unit', 'shape', 'refpix'] expected_result = { 'size': [0.1] * 3, 'unit': ['pc'] * 3, 'steps': [5] * 3, 'shape': [5] * 3, 'refpix': [2] * 3, } create_test_config(config_dict=config_dict, config_filename=config_filename) config = load_config_file(config_filename) 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', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) config_dict = { 'dim1': {"size": 0.1, "unit": "pc", "steps": 5, "refpix": 2}, 'dim2': {"size": 0.2, "unit": "pc", "steps": 10, "refpix": 4.5}, 'dim3': {"size": 0.3, "unit": "pc", "steps": 15, "refpix": 7}, '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', 'steps', 'refpix'] keywords = ['size', 'unit', 'shape', 'refpix'] expected_result = { 'size': [0.1, 0.2, 0.3], 'unit': ['pc'] * 3, 'steps': [5, 10, 15], 'shape': [5, 10, 15], 'refpix': [2, 4.5, 7], } create_test_config(config_dict=config_dict, config_filename=config_filename) config_filename=_config_filename) config = load_config_file(config_filename) config = load_config_file(_config_filename) for key in keywords: grid_properties = get_grid_properties(grid_config=config['grid'], keyword=key) Loading @@ -88,7 +91,7 @@ class Test(TestCase): grid_metadata = { 'grid_shape': [2, 2], 'physical_px_size': [1 * u.cm, 1 * u.cm], 'refpix': [0.5, 0.5] 'grid_refpix': [0.5, 0.5] } expected_results_per_axis = np.array([-1, 0, 1]) grid_edges = get_grid_edges(grid_metadata=grid_metadata) Loading @@ -96,7 +99,7 @@ class Test(TestCase): 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', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) grid_size = 0.2 npix = 5 config_dict = { Loading @@ -104,14 +107,14 @@ class Test(TestCase): 'coordinate_system': 'cartesian', 'central_density': '1e6', 'density_unit': 'cm^-3', 'dim1': {"size": grid_size, "unit": "pc", "steps": npix, "refpix": 2}, 'dim1': {"size": grid_size, "size_units": "pc", "shape": npix, "refpix": 2}, 'source_distance': '1000', 'source_distance_unit': 'pc' } 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) 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) Loading @@ -124,7 +127,15 @@ class Test(TestCase): physical_px_size=physical_px_size) self.assertTrue(np.array_equal(expected_results, computed_grid)) def test_get_distance_matrix(self): def test_compute_cartesian_coordinate_grid(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] Loading @@ -134,3 +145,33 @@ class Test(TestCase): 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_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)) Loading
etl/assets/commons/__init__.py +92 −21 Original line number Diff line number Diff line Loading @@ -3,7 +3,8 @@ import yaml import numpy as np import os import urllib.request from typing import List from typing import (List, Union) from astropy import units as u from assets.constants import (radmc_grid_map, radmc_coord_system_map, Loading @@ -11,6 +12,11 @@ from assets.constants import (radmc_grid_map, def load_config_file(config_file_path: str) -> dict: """ Load the information in the YAML configuration file into a python dictionary :param config_file_path: path to the configuration file :return: a dictionary with the parsed information """ with open(config_file_path) as config_file: config = yaml.load(config_file, Loader=yaml.FullLoader) return config Loading @@ -20,16 +26,39 @@ def compute_power_law_radial_profile( central_value: float, power_law_index: float, distance_matrix: np.array, value_at_reference: Union[float, None] = None, distance_reference: float = 1.0, fill_reference_pixel: bool = True ) -> np.array: 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 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 = value_at_reference if value_at_reference is not None else central_value _distance_matrix = np.where(distance_matrix == 0, 1, distance_matrix) if fill_reference_pixel is True else distance_matrix return central_value * (_distance_matrix / distance_reference) ** power_law_index 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: profile = np.where(profile > central_value, central_value, profile) return profile 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: Loading @@ -41,28 +70,38 @@ def get_grid_properties(grid_config: dict, 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] return indices * _physical_px_size_cm except TypeError: _physical_px_size_cm = physical_px_size.to(u.cm).value return (indices.T * _physical_px_size_cm).T 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_metadata['grid_shape'] = get_grid_properties(grid_config=grid_config, keyword='steps') grid_metadata['refpix'] = get_grid_properties(grid_config=grid_config, keyword='refpix') grid_metadata['grid_size'] = get_grid_properties(grid_config=grid_config, keyword='size') grid_metadata['grid_size_units'] = get_grid_properties(grid_config=grid_config, keyword='unit') 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) indices = np.indices(grid_metadata['grid_shape']).T - grid_metadata['refpix'] indices = get_centered_indices(grid_metadata) grid_metadata['coordinate_grid'] = compute_cartesian_coordinate_grid(indices=indices, physical_px_size=grid_metadata[ Loading @@ -74,18 +113,40 @@ def extract_grid_metadata(config: dict) -> dict: return grid_metadata def get_grid_edges(grid_metadata): 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( (np.arange(grid_metadata['grid_shape'][axis_idx] + 1) - grid_metadata['refpix'][axis_idx] - 0.5) * grid_metadata['physical_px_size'][axis_idx]) 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, indices): 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 Loading @@ -93,6 +154,12 @@ def get_distance_matrix(grid_metadata, indices): 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( Loading @@ -102,6 +169,11 @@ def get_physical_px_size(grid_metadata: dict) -> List[u.Quantity]: def get_moldata(species_names: list): """ 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 """ for species in species_names: data = urllib.request.urlopen(leiden_url_mapping[species]).read().decode() with open(os.path.join('mdl', 'radmc_input_files', f'molecule_{species}.inp'), 'w') as outfile: Loading @@ -111,4 +183,3 @@ def get_moldata(species_names: list): def convert_frequency_to_wavelength(frequency: astropy.units.Quantity, output_units: astropy.units.Unit): return frequency.to(output_units, equivalencies=u.spectral())
etl/stg/config/config.yml +1 −1 Original line number Diff line number Diff line Loading @@ -4,7 +4,7 @@ grid: central_density: 1e6 density_unit: "cm^-3" density_profile: "constant" dim1: {"size":0.1, "unit": "pc", "steps": 5, "refpix": 2} dim1: {"size":0.1, "size_units": "pc", "shape": 5, "refpix": 2} source_distance: 1000 source_distance_unit: "pc" Loading
etl/tests/test_commons.py +64 −23 Original line number Diff line number Diff line Loading @@ -7,7 +7,7 @@ from assets.commons import (compute_power_law_radial_profile, get_grid_edges, extract_grid_metadata, get_physical_px_size, compute_cartesian_coordinate_grid, get_distance_matrix) compute_cartesian_coordinate_grid, get_distance_matrix, get_centered_indices) from unittest import TestCase Loading @@ -20,6 +20,9 @@ def create_test_config(config_dict: dict, class Test(TestCase): def setUp(self): self.config_filename = 'config.yml' 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) Loading @@ -42,43 +45,43 @@ class Test(TestCase): self.assertTrue(np.array_equal(radial_profile, 1.0 / distance_matrix)) def test_get_grid_properties_uniform(self): config_filename = os.path.join('test_files', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) config_dict = { 'dim1': {"size": 0.1, "unit": "pc", "steps": 5, "refpix": 2}, 'dim1': {"size": 0.1, "unit": "pc", "shape": 5, "refpix": 2}, } keywords = ['size', 'unit', 'steps', 'refpix'] keywords = ['size', 'unit', 'shape', 'refpix'] expected_result = { 'size': [0.1] * 3, 'unit': ['pc'] * 3, 'steps': [5] * 3, 'shape': [5] * 3, 'refpix': [2] * 3, } create_test_config(config_dict=config_dict, config_filename=config_filename) config = load_config_file(config_filename) 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', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) config_dict = { 'dim1': {"size": 0.1, "unit": "pc", "steps": 5, "refpix": 2}, 'dim2': {"size": 0.2, "unit": "pc", "steps": 10, "refpix": 4.5}, 'dim3': {"size": 0.3, "unit": "pc", "steps": 15, "refpix": 7}, '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', 'steps', 'refpix'] keywords = ['size', 'unit', 'shape', 'refpix'] expected_result = { 'size': [0.1, 0.2, 0.3], 'unit': ['pc'] * 3, 'steps': [5, 10, 15], 'shape': [5, 10, 15], 'refpix': [2, 4.5, 7], } create_test_config(config_dict=config_dict, config_filename=config_filename) config_filename=_config_filename) config = load_config_file(config_filename) config = load_config_file(_config_filename) for key in keywords: grid_properties = get_grid_properties(grid_config=config['grid'], keyword=key) Loading @@ -88,7 +91,7 @@ class Test(TestCase): grid_metadata = { 'grid_shape': [2, 2], 'physical_px_size': [1 * u.cm, 1 * u.cm], 'refpix': [0.5, 0.5] 'grid_refpix': [0.5, 0.5] } expected_results_per_axis = np.array([-1, 0, 1]) grid_edges = get_grid_edges(grid_metadata=grid_metadata) Loading @@ -96,7 +99,7 @@ class Test(TestCase): 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', 'config.yml') _config_filename = os.path.join('test_files', self.config_filename) grid_size = 0.2 npix = 5 config_dict = { Loading @@ -104,14 +107,14 @@ class Test(TestCase): 'coordinate_system': 'cartesian', 'central_density': '1e6', 'density_unit': 'cm^-3', 'dim1': {"size": grid_size, "unit": "pc", "steps": npix, "refpix": 2}, 'dim1': {"size": grid_size, "size_units": "pc", "shape": npix, "refpix": 2}, 'source_distance': '1000', 'source_distance_unit': 'pc' } 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) 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) Loading @@ -124,7 +127,15 @@ class Test(TestCase): physical_px_size=physical_px_size) self.assertTrue(np.array_equal(expected_results, computed_grid)) def test_get_distance_matrix(self): def test_compute_cartesian_coordinate_grid(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] Loading @@ -134,3 +145,33 @@ class Test(TestCase): 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_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))
