API Reference
This section provides a detailed reference for the code in the Star Log-extended eMulator
package.
SLM
augment_data_multiple_columns(X)
Augment the data matrix X with nonlinear terms for multiple variables.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
ndarray
|
The data matrix where each row is a variable, and each column is a snapshot in time. |
required |
Returns:
Name | Type | Description |
---|---|---|
augmented_X |
ndarray
|
The augmented data matrix with nonlinear terms. |
Source code in src/slmemulator/SLM.py
SLM(X, dt, error_threshold=0.0001, max_r=None)
Dynamic Mode decomposition for the augmented Data. Automatically determines the number of modes (r) based on an error threshold.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
ndarray
|
The data matrix where each row is a variable, and each column is a snapshot in time. Expected to be log-transformed where appropriate. |
required |
dt
|
float
|
The time difference of linear DMDs. |
required |
error_threshold
|
float
|
(Optional) The maximum allowed absolute difference between the original data and the DMD reconstruction. Defaults to 1e-4. |
0.0001
|
max_r
|
int
|
(Optional) The maximum number of modes to consider. If None, it will go up to the maximum possible rank (min(X.shape)). |
None
|
Source code in src/slmemulator/SLM.py
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solve_tov(fileName, tidal=False, parametric=False, mseos=True)
Solves the TOV equation and returns radius, mass and central pressure
Parameters:
Name | Type | Description | Default |
---|---|---|---|
fileName
|
str
|
Filename containing the EOS in the format nb (fm^-3), E (MeV), P (MeV/fm^3) |
required |
Returns:
Name | Type | Description |
---|---|---|
dataArray |
array
|
Data array containing radii, central pressure and mass. |
Source code in src/slmemulator/SLM.py
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|
cleanData
clean_directory(directory: str = None)
Cleans up specified files and directories within a given directory.
directory (str): The path to the directory to clean. Defaults to the current working directory if None.
Source code in src/slmemulator/cleanData.py
config
get_paths(output_base_dir: Path = None, use_mseos: bool = True, is_parametric_run: bool = True) -> dict
Returns a dictionary of resolved paths for data input/output and other project directories. Prioritizes explicit arguments for output_base_dir, then sensible defaults. Conditionally sets 'current' EOS/TOV/results paths based on the use_mseos flag. Adjusts base for general data directories (EOS_Data, TOV_data, testData, trainData) to output_base_dir if provided (for local working directories), otherwise defaults them to be under src_dir (for package internal use).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
output_base_dir
|
Path
|
The base directory for all generated outputs. Defaults to the project root. |
None
|
use_mseos
|
bool
|
If True, 'current' paths will point to MSEOS-related directories. If False, 'current' paths will point to QEOS-related directories. Defaults to True. |
True
|
is_parametric_run
|
bool
|
If True, indicates a parametric run.
This parameter is available for future path logic,
but currently the primary factor for
data directory location is |
True
|
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
A dictionary containing all relevant path configurations. |
Source code in src/slmemulator/config.py
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pSLM
ParametricSLM(fileList, filePath, tidal=False, error_threshold=0.0001, max_r=None)
Source code in src/slmemulator/pSLM.py
augment_data(X)
staticmethod
Augments the input data X by adding quadratic terms (X_i * X_j).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
ndarray
|
Original data matrix (n, m). |
required |
Returns:
Type | Description |
---|---|
np.ndarray (np.ndarray): Augmented data matrix. |
Source code in src/slmemulator/pSLM.py
fit()
Fits the Parametric SLM model by processing each file in fileList, performing DMD, and storing the DMD components along with extracted parameters.
Source code in src/slmemulator/pSLM.py
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predict(param_values, k=1, output_interp=False, distance_threshold=None)
Predicts the dynamics (Xdmd) for a given set of parameters by finding nearest neighbors in the parameter space and averaging their DMD components or using the closest one.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
param_values
|
list or ndarray
|
A list or array of parameter values to predict for (e.g., [Ls, Lv, zeta, xi] for MSEOS). This argument is mandatory. |
required |
k
|
int
|
Number of nearest neighbors to use for prediction. Defaults to 1 (pure nearest neighbor). |
1
|
output_interp
|
bool
|
If True, interpolate the final Xdmd outputs by averaging curves. If False (default) and k>1, averages the DMD components (Phi, omega, D, b). |
False
|
distance_threshold
|
float
|
If the distance to the closest neighbor exceeds this threshold, a warning is printed. |
None
|
Returns:
Name | Type | Description |
---|---|---|
tuple |
(Phi_avg/Phi_nn, omega_avg/omega_nn, D_avg/D_nn, b_avg/b_nn, Xdmd_predicted, t) - Reconstructed data (Xdmd_predicted) and related averaged DMD components. |
Raises: ValueError: If the model has not been fitted or no training data is available.
Source code in src/slmemulator/pSLM.py
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is_on_boundary(param, param_min, param_max, tolerance=1e-05)
Checks if a parameter set is on the boundary of the parameter space.
Source code in src/slmemulator/pSLM.py
recombination
gaussian_rbf(r, epsilon)
recombination_thinning(A_matrix, y_vector, initial_solution_x=None, tolerance=1e-09)
Conceptual implementation of the recombination thinning process based on the paper. This function aims to find a sparse solution x' to the linear system Ax = y.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
A_matrix
|
array
|
The matrix A in the linear system Ax = y. In the context of GRIM, this matrix relates features and linear functionals. |
required |
y_vector
|
array
|
The vector y in the linear system Ax = y. In the context of GRIM, this relates to the target function evaluated by linear functionals. |
required |
initial_solution_x
|
array
|
An initial solution vector x. If None, a least squares solution is computed. |
None
|
tolerance
|
float
|
Tolerance for numerical stability, especially for SVD and checking non-zero components. |
1e-09
|
Returns:
Type | Description |
---|---|
np.array: A sparser solution vector x' for the system Ax = y. Returns None if the system is inconsistent or other issues. |
Source code in src/slmemulator/recombination.py
scaledTOV
Information about the code: This code solves TOV equations for mass radius relations. This can also plot the mass-radius curve.
USE: To use the code, here are the steps: 1) Include the file in your main code e.g. import tov_class as tc 2) Load the EoS using the ToV loader, tc.ToV(filename, arraysize) 3) call the solver as tc.ToV.mass_radius(min_pressure, max_pressure) 4) To plot, follow the code in main() on creating the dictionary of inputs
Updates: Version 0.0.1-1 Solves ToV, can only take inputs of pressure (MeV/fm^3), energy density in MeV, baryon density in fm^-3 in ascending order.
TOV(filename, imax)
Solves TOV equations and gives data-table, mass-radius plot and max. mass, central pressure and central density by loading an EoS datafile.
Source code in src/slmemulator/scaledTOV.py
pressure_from_nb(nb)
Evaluate pressure from number density using interpolation
energy_from_pressure(pressure)
Evaluate energy density from pressure using interpolation
Source code in src/slmemulator/scaledTOV.py
pressure_from_energy(energy)
Evaluate pressure from energy density using interpolation
baryon_from_energy(energy)
Evaluate number density from energy using interpolation
RK4(f, x0, t0, te, N)
A simple RK4 solver to avoid overhead of calculating with solve_ivp or any other adaptive step-size function.
Example
tov.RK4(f=func, x0=1., t0=1., te=10., N=100)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
f
|
func
|
A Python function for the ODE(s) to be solved. Able to solve N coupled ODEs. |
required |
x0
|
float
|
Guess for the function(s) to be solved. |
required |
t0
|
float
|
Initial point of the grid. |
required |
te
|
float
|
End point of the grid. |
required |
N
|
int
|
The number of steps to take in the range (te-t0). |
required |
Returns:
Name | Type | Description |
---|---|---|
times |
array
|
The grid of solution steps. |
solution |
array
|
The solutions of each function at each point in the grid. |
Source code in src/slmemulator/scaledTOV.py
TOV_class
TOV(eos_filepath=None, tidal=False, solver='RK4', solve_ivp_kwargs=None, sol_pts=4000)
inertia using RK4. Also includes uncertainty quantification techniques through the highest posterior density interval (HPD or HDI) calculation. Able to accept one EOS from a single curve or draws from an EOS, such as from a Gaussian Process.
Example
tov = TOVSolver(eos_filepath='path/to/eos', tidal=True, moment=True)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
eos_filepath
|
str
|
The path to the EOS data table to be used. |
None
|
tidal
|
bool
|
Whether to calculate tidal deformability or not. Default is False. |
False
|
moment
|
bool
|
Whether to calculate moment of inertia or not. Default is False. |
required |
Returns:
Type | Description |
---|---|
None. |
Source code in src/slmemulator/TOV_class.py
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|
RK4(f, x0, t0, te, N)
A simple RK4 solver to avoid overhead of calculating with solve_ivp or any other adaptive step-size function.
Example
tov.RK4(f=func, x0=1., t0=1., te=10., N=100)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
f
|
func
|
A Python function for the ODE(s) to be solved. Able to solve N coupled ODEs. |
required |
x0
|
float
|
Guess for the function(s) to be solved. |
required |
t0
|
float
|
Initial point of the grid. |
required |
te
|
float
|
End point of the grid. |
required |
N
|
int
|
The number of steps to take in the range (te-t0). |
required |
Returns:
Name | Type | Description |
---|---|---|
times |
array
|
The grid of solution steps. |
solution |
array
|
The solutions of each function at each point in the grid. |
Source code in src/slmemulator/TOV_class.py
RK2(f, x0, t0, te, N)
A simple RK2 solver using the Heun's method. This is a low-fidelity solver.
Example
tov.RK2(f=func, x0=1., t0=1., te=10., N=100)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
f
|
func
|
A Python function for the ODE(s) to be solved. Able to solve N coupled ODEs. |
required |
x0
|
float
|
Guess for the function(s) to be solved. |
required |
t0
|
float
|
Initial point of the grid. |
required |
te
|
float
|
End point of the grid. |
required |
N
|
int
|
The number of steps to take in the range (te-t0). |
required |
Returns:
Name | Type | Description |
---|---|---|
times |
array
|
The grid of solution steps. |
solution |
array
|
The solutions of each function at each point in the grid. |
Source code in src/slmemulator/TOV_class.py
euler(f, x0, t0, te, N)
A simple forward euler solver to avoid overhead of calculating with solve_ivp or any other adaptive step-size function. This is a low fidelity solver!
Example
tov.euler(f=func, x0=1., t0=1., te=10., N=100)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
f
|
func
|
A Python function for the ODE(s) to be solved. Able to solve N coupled ODEs. |
required |
x0
|
float
|
Guess for the function(s) to be solved. |
required |
t0
|
float
|
Initial point of the grid. |
required |
te
|
float
|
End point of the grid. |
required |
N
|
int
|
The number of steps to take in the range (te-t0). |
required |
Returns:
Name | Type | Description |
---|---|---|
times |
array
|
The grid of solution steps. |
solution |
array
|
The solutions of each function at each point in the grid. |
Source code in src/slmemulator/TOV_class.py
tov_equations_scaled(x, y0)
The Tolman-Oppenheimer-Volkoff equations in scaled format, to be solved with the RK4 routine. If selected, the tidal deformability and moment of inertia will be included and solved simultaneously.
Example
tov.tov_equations_scaled(x=0.2, y0=[m_init, p_init])
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
float
|
A point in the scaled radius grid. |
required |
y0
|
list
|
The list of initial guesses for each function solved. |
required |
Returns:
Type | Description |
---|---|
The solutions, in array format, of each function to be solved. |
Source code in src/slmemulator/TOV_class.py
f_x(x, mass, pres, eps)
A function in the tidal deformability calculation.
Example
tov.f_x(x=0.2, mass=1.06, pres=2.34, eps=6.0)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
float
|
The current gridpoint in scaled radius. |
required |
mass
|
float
|
The current mass. |
required |
pres
|
float
|
The current pressure from the EOS. |
required |
eps
|
float
|
The current energy density from the EOS. |
required |
Returns:
Type | Description |
---|---|
The value of F(x) at the current radius. |
Source code in src/slmemulator/TOV_class.py
q_x(x, mass, pres, eps, cs2)
A function in the calculation of the tidal deformability.
Example
tov.q_x(x=0.1, mass=2.0, pres=1.0, eps=3.0, cs2=0.33)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
float
|
The current gridpoint in scaled radius. |
required |
mass
|
float
|
The current mass. |
required |
pres
|
float
|
The current pressure from the EOS. |
required |
eps
|
float
|
The current energy density from the EOS. |
required |
cs2
|
float
|
The current speed of sound from the EOS. |
required |
Returns:
Type | Description |
---|---|
The value of Q(x) at the current radius. |
Source code in src/slmemulator/TOV_class.py
tidal_def(yR, mass, radius)
The calculation of the tidal deformability, Lambda, and the tidal Love number, k2. This function is calculated after the RK4 routine has been completed.
Example
tov.tidal_def(yR=np.array, mass=np.array, radius=np.array)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
yR
|
float
|
The array of y at the maximum radii points. |
required |
mass
|
float
|
The array of mass at the maximum radii. |
required |
radius
|
float
|
The maximum radii array. |
required |
Returns:
Name | Type | Description |
---|---|---|
tidal_deform |
array
|
The tidal deformability solved at each point in the maximum radius. |
k2 |
array
|
The value of the Love number calculated at the compactness M/R and the value of y at maximum radius. |
Source code in src/slmemulator/TOV_class.py
tov_routine(verbose=False, write_to_file=False)
The TOV routine to solve each set of coupled ODEs and to output the quantities needed to display the M-R curve, as well as the tidal deformability and moment of inertia if desired.
Example
tov.tov_routine(verbose=True, write_to_file=True)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
verbose
|
bool
|
Whether to plot quantities and display the full maximum mass array. Default is False. |
False
|
write_to_file
|
bool
|
Choice to write the TOV results to a file located in a folder of the user's choice. Default is False. |
False
|
Returns:
Type | Description |
---|---|
self.total_radius (array): The array of total maximum radius values. |
|
self.total_pres_central (array): The array of total central pressure values. |
|
self.total_max_mass (array): The array of total maximum mass values. |
Source code in src/slmemulator/TOV_class.py
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max_arrays()
Returns the max arrays needed for the interval calculation.
Returns:
Type | Description |
---|---|
self.max_radius_arr (array): Maximum radius array. |
|
self.max_pres_arr (array): Maximum central pressure array. |
|
self.max_mass_arr (array): Maximum mass array. |
Source code in src/slmemulator/TOV_class.py
central_dens(pres_arr=None)
Calculation to determine the central density of the star at the maximum mass and radius determined from the tov_routine().
Example
tov.central_dens()
Parameters:
Name | Type | Description | Default |
---|---|---|---|
pres_arr
|
array
|
An optional pressure array to use for calculating central densities at places other than the absolute TOV maximum mass of each curve. Default is None, and code will use absolute TOV maximum mass central pressure class array. |
None
|
Returns:
Name | Type | Description |
---|---|---|
c_dens |
array
|
The array of central densities for each EOS used. |
Source code in src/slmemulator/TOV_class.py
canonical_NS_radius()
Calculation of the radius of a 1.4 M_sol neutron star.
Example
tov.canonical_NS_radius()
Returns:
Name | Type | Description |
---|---|---|
rad_14 |
array
|
The array of values of the radius for each EOS used. |
Source code in src/slmemulator/TOV_class.py
tovScaledRev
Information about the code: This code solves TOV equations for mass radius relations. This can also plot the mass-radius curve.
The code solves dr/dp and dm/dp instead of the regular way.
USE: To use the code, here are the steps: 1) Include the file in your main code e.g. import tov_class as tc 2) Load the EoS using the ToV loader, tc.ToV(filename, arraysize) 3) call the solver as tc.ToV.mass_radius(min_pressure, max_pressure) 4) To plot, follow the code in main() on creating the dictionary of inputs
Updates: Version 0.0.1-1 Solves ToV, can only take inputs of pressure (MeV/fm^3), energy density in MeV, baryon density in fm^-3 in ascending order.
TOV(filename, imax, tidal=False)
Solves TOV equations and gives data-table, mass-radius plot and max. mass, central pressure and central density by loading an EoS datafile.
Source code in src/slmemulator/tovScaledRev.py
pressure_from_nb(nb)
Evaluate pressure from number density using interpolation
energy_from_pressure(pressure)
Evaluate energy density from pressure using interpolation
Source code in src/slmemulator/tovScaledRev.py
pressure_from_energy(energy)
Evaluate pressure from energy density using interpolation
baryon_from_energy(energy)
Evaluate number density from energy using interpolation