# GZSCORE

The GZSCORE node is based on a numpy or scipy function. The description of that function is as follows: Compute the geometric standard score. Compute the geometric z score of each strictly positive value in the sample, relative to the geometric mean and standard deviation. Mathematically the geometric z score can be evaluated as:: gzscore = log(a/gmu) / log(gsigma) where gmu (resp. gsigma) is the geometric mean (resp. standard deviation). Params: a : array_like Sample data. axis : int or None Axis along which to operate. Default is 0. If None, compute over the whole array 'a'. ddof : int Degrees of freedom correction in the calculation of the standard deviation. Default is 0. nan_policy : {'propagate', 'raise', 'omit'} Defines how to handle when input contains nan. 'propagate' returns nan, 'raise' throws an error, 'omit' performs the calculations ignoring nan values. Default is 'propagate'. Note that when the value is 'omit', nans in the input also propagate to the output, but they do not affect the geometric z scores computed for the non-nan values. 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 GZSCORE(
default: OrderedPair | Matrix,
axis: int = 0,
ddof: int = 0,
nan_policy: str = "propagate",
) -> OrderedPair | Matrix | Scalar:
"""The GZSCORE node is based on a numpy or scipy function.

The description of that function is as follows:

Compute the geometric standard score.

Compute the geometric z score of each strictly positive value in the sample, relative to the geometric mean and standard deviation.

Mathematically the geometric z score can be evaluated as::

gzscore = log(a/gmu) / log(gsigma)

where gmu (resp. gsigma) is the geometric mean (resp. standard
deviation).

Parameters
----------
a : array_like
Sample data.
axis : int or None, optional
Axis along which to operate.
Default is 0.
If None, compute over the whole array 'a'.
ddof : int, optional
Degrees of freedom correction in the calculation of the standard deviation.
Default is 0.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan values.
Default is 'propagate'.
Note that when the value is 'omit', nans in the input also propagate to the output,
but they do not affect the geometric z scores computed for the non-nan values.

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

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

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


Find this Flojoy Block on GitHub