PyART.numerical.twopuncts_ID¶
Attributes¶
Classes¶
Class to perform linear transformation and its inverse |
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Class to setup initial data computation |
Module Contents¶
- class PyART.numerical.twopuncts_ID.LinearScaler(A, B, C, D)[source]¶
Class to perform linear transformation and its inverse between two intervals [A,B] and [C,D]
- transform(x)[source]¶
Transform x from interval [A,B] to [C,D].
- Parameters:
x (float or array-like) – Value(s) in the original interval [A,B] to be transformed.
- Returns:
Transformed value(s) in the target interval [C,D].
- Return type:
float or array-like
- inverse_transform(y)[source]¶
Inverse transform y from interval [C,D] to [A,B].
- Parameters:
y (float or array-like) – Value(s) in the target interval [C,D] to be inverse transformed.
- Returns:
Inverse transformed value(s) in the original interval [A,B].
- Return type:
float or array-like
- __lin_transf(A, B, C, D, x)[source]¶
Apply linear transformation from [A,B] to [C,D].
- Parameters:
A (float) – Lower bound of the original interval.
B (float) – Upper bound of the original interval.
C (float) – Lower bound of the target interval.
D (float) – Upper bound of the target interval.
x (float or array-like) – Value(s) to be transformed.
- Returns:
Transformed value(s).
- Return type:
float or array-like
- class PyART.numerical.twopuncts_ID.TwoPunctID(**kwargs)[source]¶
Bases:
objectClass to setup initial data computation using the TwoPunctures code. See: https://github.com/computationalrelativity/TwoPuncturesC
- create_TP_parfile(P, parfile=None, outdir=None)[source]¶
Create a parameter file for TwoPunctures with given momentum P. Assumes L along z axis and initial separation along x axis.
- Parameters:
P (float) – Magnitude of the linear momentum for each puncture.
parfile (str or None, optional) – Name of the parameter file to create (default is None, which uses “parfile_P{P:.10f}.par”).
outdir (str or None, optional) – Output directory to save the parameter file (default is None, which uses self.outdir).
- Returns:
Path to the created parameter file.
- Return type:
str
- run_TP_from_parfile(parfile)[source]¶
Run TwoPunctures with the given parameter file. :param parfile: Path to the parameter file to use. :type parfile: str
- Returns:
Path to the output file generated by TwoPunctures.
- Return type:
str
- read_TP_output(outfile)[source]¶
Read the output file from TwoPunctures and extract E_ADM, M1, and M2.
- Parameters:
outfile (str) – Path to the output file generated by TwoPunctures.
- Returns:
E_ADM (float) – Total ADM energy.
M1 (float) – ADM mass of puncture 1.
M2 (float) – ADM mass of puncture 2.
- run_TP_wrapper(P, parfile=None, verbose=None)[source]¶
Run the full TwoPunctures workflow: create parfile, run TP, read output. :param P: Magnitude of the linear momentum for each puncture. :type P: float :param parfile: Name of the parameter file to create (default is None, which uses “parfile_P{P:.10f}.par”). :type parfile: str or None, optional :param verbose: If True, print verbose output (default is None, which uses self.verbose). :type verbose: bool or None, optional
- Returns:
E_ADM (float) – Total ADM energy.
M1 (float) – ADM mass of puncture 1.
M2 (float) – ADM mass of puncture 2.
- run_TP_parallel(momenta, batch)[source]¶
Run TwoPunctures in parallel for a batch of momenta. :param momenta: Array of momenta to evaluate. :type momenta: array-like :param batch: List of indices in the momenta array to process in this batch. :type batch: list of int
- Returns:
energies – Array of computed ADM energies for the given momenta.
- Return type:
array-like
- fit_iter(P0, dP, npoints=4, poly_order=None, verbose=None, show_plot=False, save_plot=False, x0_position='centered', nproc=1)[source]¶
Fit a polynomial to find the momentum P that gives the desired energy self.E.
- Parameters:
P0 (float) – Initial guess for the momentum.
dP (float) – Step size for the momentum.
npoints (int, optional) – Number of points to evaluate in each iteration (default is 4).
poly_order (int or None, optional) – Order of the polynomial fit (default is None, which uses min(max(npoints-1,1),5)).
verbose (bool or None, optional) – If True, print verbose output (default is None, which uses self.verbose).
show_plot (bool, optional) – If True, show the fit plot (default is False).
save_plot (bool, optional) – If True, save the fit plot (default is False).
x0_position (str, optional) – Positioning of the points relative to P0: “centered”, “highest”, or “lowest” (default is “centered”).
nproc (int, optional) – Number of processors to use for parallel execution (default is 1).
- Returns:
Pf – Fitted momentum that gives the desired energy self.E.
- Return type:
float
- fit_iterations(P0=None, step_rel=0.15, tol=1e-10, resize_factor=100, itermax=5, npoints=4, poly_order=None, verbose=None, nproc=1, save_plot=False, show_plot=False)[source]¶
Iteratively fit to find the momentum P that gives the desired energy self.E. :param P0: Initial guess for the momentum (default is None, which uses an estimate based on self.E
and self.q).
- Parameters:
step_rel (float, optional) – Initial relative step size for the momentum (default is 0.15).
tol (float, optional) – Tolerance for the relative error in energy (default is 1e-10).
resize_factor (float, optional) – Factor to resize the step size in each iteration (default is 100).
itermax (int, optional) – Maximum number of iterations (default is 5).
npoints (int, optional) – Number of points to evaluate in each iteration (default is 4).
poly_order (int or None, optional) – Order of the polynomial fit (default is None, which uses min(max(npoints-1,1),5)).
verbose (bool or None, optional) – If True, print verbose output (default is None, which uses self.verbose).
nproc (int, optional) – Number of processors to use for parallel execution (default is 1).
save_plot (bool, optional) – If True, save the fit plots for each iteration (default is False).
show_plot (bool, optional) – If True, show the fit plots for each iteration (default is False).