# GSTD

The GSTD node is based on a numpy or scipy function. The description of that function is as follows: Calculate the geometric standard deviation of an array. The geometric standard deviation describes the spread of a set of numbers where the geometric mean is preferred. It is a multiplicative factor, and so a dimensionless quantity. It is defined as the exponent of the standard deviation of log(a). Mathematically the population geometric standard deviation can be evaluated as:: gstd = exp(std(log(a))) .. versionadded:: 1.3.0 Params: a : array_like An array like object containing the sample data. axis : int, tuple or None Axis along which to operate. Default is 0. If None, compute over the whole array 'a'. ddof : int Degree of freedom correction in the calculation of the geometric standard deviation. Default is 1. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np

import scipy.stats

@flojoy
def GSTD(
default: OrderedPair | Matrix,
axis: int = 0,
ddof: int = 1,
) -> OrderedPair | Matrix | Scalar:
"""The GSTD node is based on a numpy or scipy function.

The description of that function is as follows:

Calculate the geometric standard deviation of an array.

The geometric standard deviation describes the spread of a set of numbers where the geometric mean is preferred.
It is a multiplicative factor, and so a dimensionless quantity.

It is defined as the exponent of the standard deviation of log(a).

Mathematically the population geometric standard deviation can be evaluated as::

gstd = exp(std(log(a)))

Parameters
----------
a : array_like
An array like object containing the sample data.
axis : int, tuple or None, optional
Axis along which to operate. Default is 0.
If None, compute over the whole array 'a'.
ddof : int, optional
Degree of freedom correction in the calculation of the geometric standard deviation.
Default is 1.

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

result = scipy.stats.gstd(
a=default.y,
axis=axis,
ddof=ddof,
)

if isinstance(result, np.ndarray):
result = OrderedPair(x=default.x, y=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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