# Source code for ctapipe.image.statistics

```
import numpy as np
from numba import njit
from ..containers import StatisticsContainer
__all__ = ["descriptive_statistics", "skewness", "kurtosis"]
@njit(cache=True)
def skewness(data, mean=None, std=None):
"""Calculate skewnewss (normalized third central moment)
with allowing precomputed mean and std.
With precomputed mean and std, this is ~10x faster than scipy.stats.skew
for our use case (1D arrays with ~100-1000 elements)
njit provides ~10% improvement over the non-jitted function.
Parameters
----------
data: ndarray
Data for which skewness is calculated
mean: float or None
pre-computed mean, if not given, mean is computed
std: float or None
pre-computed std, if not given, std is computed
Returns
-------
skewness: float
computed skewness
"""
if mean is None:
mean = np.mean(data)
if std is None:
std = np.std(data)
return np.mean(((data - mean) / std) ** 3)
@njit(cache=True)
def kurtosis(data, mean=None, std=None, fisher=True):
"""Calculate kurtosis (normalized fourth central moment)
with allowing precomputed mean and std.
With precomputed mean and std, this is ~10x faster than scipy.stats.skew
for our use case (1D arrays with ~100-1000 elements)
njit provides ~10% improvement over the non-jitted function.
Parameters
----------
data: ndarray
Data for which skewness is calculated
mean: float or None
pre-computed mean, if not given, mean is computed
std: float or None
pre-computed std, if not given, std is computed
fisher: bool
If True, Fisherâ€™s definition is used (normal ==> 0.0).
If False, Pearsonâ€™s definition is used (normal ==> 3.0).
Returns
-------
kurtosis: float
kurtosis
"""
if mean is None:
mean = np.mean(data)
if std is None:
std = np.std(data)
kurt = np.mean(((data - mean) / std) ** 4)
if fisher is True:
kurt -= 3.0
return kurt
[docs]def descriptive_statistics(
values, container_class=StatisticsContainer
) -> StatisticsContainer:
""" compute intensity statistics of an image """
mean = values.mean()
std = values.std()
return container_class(
max=values.max(),
min=values.min(),
mean=mean,
std=std,
skewness=skewness(values, mean=mean, std=std),
kurtosis=kurtosis(values, mean=mean, std=std),
)
```