switched null_space implementation from scipy to using numpys svd

This commit is contained in:
Nicolas Kruse 2025-11-17 13:47:30 +01:00
parent 7df98dda7b
commit 9bdf350c17
2 changed files with 25 additions and 4 deletions

View File

@ -2,7 +2,7 @@ import numpy as np
from numpy.typing import NDArray
from typing import Sequence, Any, TypeVar, Iterator, overload, Callable
from math import log as ln, ceil
from scipy.linalg import null_space
from ._numerics import null_space
from gaspype._phys_data import atomic_weights, db_reader
import re
import pkgutil
@ -90,7 +90,7 @@ class fluid_system:
self._t_offset = int(t_min)
self.species = species
self.active_species = species
self.active_species = species # for backward compatibility
element_compositions: list[dict[str, int]] = list()
for i, s in enumerate(species):
@ -220,7 +220,7 @@ class fluid:
The array can be multidimensional, the size of the last dimension
must match the number of species defined for the fluid_system.
The indices of the last dimension correspond to the indices in
the active_species list of the fluid_system.
the species list of the fluid_system.
fs: Reference to a fluid_system. Is optional if composition is
defined by a dict. If not specified a new fluid_system with
the components from the dict is created.
@ -585,7 +585,7 @@ class elements:
The array can be multidimensional, the size of the last dimension
must match the number of elements used in the fluid_system.
The indices of the last dimension correspond to the indices in
the active_species list of the fluid_system.
the species list of the fluid_system.
fs: Reference to a fluid_system.
shape: Tuple or list for the dimensions the fluid array. Can
only be used if composition argument is a dict. Otherwise

21
src/gaspype/_numerics.py Normal file
View File

@ -0,0 +1,21 @@
import numpy as np
from .typing import FloatArray
def null_space(A: FloatArray) -> FloatArray:
"""
Compute an orthonormal basis for the null space of A using NumPy SVD.
Args:
A: Input matrix of shape (m, n)
Return:
Null space vectors as columns, shape (n, n - rank)
"""
u, s, vh = np.linalg.svd(A, full_matrices=True)
M, N = u.shape[0], vh.shape[1]
rcond = np.finfo(s.dtype).eps * max(M, N)
tol = np.amax(s, initial=0.) * rcond
num = np.sum(s > tol, dtype=int)
Q = vh[num:,:].T.conj()
return Q