# SLOGDET

The SLOGDET node is based on a numpy or scipy function. The description of that function is as follows: Compute the sign and (natural) logarithm of the determinant of an array. If an array has a very small or very large determinant, then a call to 'det' may overflow or underflow. This routine is more robust against such issues, because it computes the logarithm of the determinant rather than the determinant itself. Params: select_return : 'sign', 'logdet' Select the desired object to return. See the respective function documents for descriptors. a : (..., M, M) array_like Input array, has to be a square 2-D array. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import flojoy, Matrix, Scalar
import numpy as np
from typing import Literal

import numpy.linalg

@flojoy
def SLOGDET(
default: Matrix,
select_return: Literal["sign", "logdet"] = "sign",
) -> Matrix | Scalar:
"""The SLOGDET node is based on a numpy or scipy function.

The description of that function is as follows:

Compute the sign and (natural) logarithm of the determinant of an array.

If an array has a very small or very large determinant, then a call to 'det' may overflow or underflow.

This routine is more robust against such issues, because it computes the logarithm of the determinant rather than the determinant itself.

Parameters
----------
select_return : 'sign', 'logdet'
Select the desired object to return.
See the respective function documents for descriptors.
a : (..., M, M) array_like
Input array, has to be a square 2-D array.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = numpy.linalg.slogdet(
a=default.m,
)

return_list = ["sign", "logdet"]
if isinstance(result, tuple):
res_dict = {}
num = min(len(result), len(return_list))
for i in range(num):
res_dict[return_list[i]] = result[i]
result = res_dict[select_return]
else:
result = result._asdict()
result = result[select_return]

if isinstance(result, np.ndarray):
result = Matrix(m=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))

return result


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