"""Created on Aug 14 02:02:42 2024"""
from typing import Dict
import numpy as np
from .backend import errorHandling as erH, BaseDistribution
from .utilities_d import arc_sine_pdf_, arc_sine_cdf_, arc_sine_log_pdf_, arc_sine_log_cdf_
from .. import md_scipy_like
[docs]
class ArcSineDistribution(BaseDistribution):
r"""
Class for ArcSine distribution.
.. note::
The :class:`ArcSineDistribution` is a special case of the
:class:`~pymultifit.distributions.beta_d.BetaDistribution`,
* :math:`\alpha_\text{beta} = 0.5`,
* :math:`\lambda_\text{beta} = 0.5`.
:param amplitude: The amplitude of the PDF. Defaults to 1.0. Ignored if **normalize** is ``True``.
:type amplitude: float, optional
:param loc: The location parameter, :math:`-` shifting. Defaults to 0.0.
:type loc: float, optional
:param scale: The scale parameter, for shifting. Defaults to 1.0.
:type scale: float, optional
:param normalize: If ``True``, the distribution is normalized so that the total area under the PDF equals 1.
Defaults to ``False``.
:type normalize: bool, optional
:raise NegativeAmplitudeError: If the provided value of amplitude is negative.
:raise NegativeScaleError: If the provided value of scale is negative.
:example:
Importing libraries
.. literalinclude:: ../../../examples/basic/arcSine.py
:language: python
:linenos:
:lineno-start: 3
:lines: 3-7
Generating the ArcSine distribution with ``pyMultiFit`` and ``scipy``.
.. literalinclude:: ../../../examples/basic/arcSine.py
:language: python
:linenos:
:lineno-start: 9
:lines: 9-12
Plotting **PDF** and **CDF**
.. literalinclude:: ../../../examples/basic/arcSine.py
:language: python
:linenos:
:lineno-start: 14
:lines: 14-27
.. image:: ../../../images/arcsine_example.png
:alt: ArcSine distribution
:align: center
"""
def __init__(
self,
amplitude: float = 1.0,
loc: float = 0.0,
scale: float = 1.0,
normalize: bool = False,
):
if not normalize and amplitude <= 0:
raise erH.NegativeAmplitudeError()
self.amplitude = 1 if normalize else amplitude
self.loc = loc
self.scale = scale
self.norm = normalize
[docs]
@classmethod
@md_scipy_like('1.0.7')
def scipy_like(cls, loc: float = 0.0, scale: float = 1.0) -> 'ArcSineDistribution':
"""
Instantiate ArcSineDistribution with scipy parameterization.
Parameters
----------
loc: float, optional
The location parameter. Defaults to 0.0.
scale: float, optional
The scale parameter. Defaults to 1.0.
Returns
-------
ArcSineDistribution
An instance of normalized ArcSineDistribution.
"""
return cls(loc=loc, scale=scale, normalize=True)
[docs]
@classmethod
def from_scipy_params(cls, loc: float = 0.0, scale: float = 1.0) -> 'ArcSineDistribution':
"""
Instantiate ArcSineDistribution with scipy parameterization.
Parameters
----------
loc: float, optional
The location parameter. Defaults to 0.0.
scale: float, optional
The scale parameter. Defaults to 1.0.
Returns
-------
ArcSineDistribution
An instance of normalized ArcSineDistribution.
"""
return cls(loc=loc, scale=scale, normalize=True)
[docs]
def pdf(self, x: np.ndarray) -> np.ndarray:
return arc_sine_pdf_(
x,
amplitude=self.amplitude,
loc=self.loc,
scale=self.scale,
normalize=self.norm,
)
[docs]
def logpdf(self, x: np.ndarray) -> np.ndarray:
return arc_sine_log_pdf_(
x,
amplitude=self.amplitude,
loc=self.loc,
scale=self.scale,
normalize=self.norm,
)
[docs]
def cdf(self, x: np.ndarray) -> np.ndarray:
return arc_sine_cdf_(
x,
amplitude=self.amplitude,
loc=self.loc,
scale=self.scale,
normalize=self.norm,
)
[docs]
def logcdf(self, x: np.ndarray) -> np.ndarray:
return arc_sine_log_cdf_(
x,
amplitude=self.amplitude,
loc=self.loc,
scale=self.scale,
normalize=self.norm,
)
[docs]
def stats(self) -> Dict[str, float]:
s_, l_ = self.scale, self.loc
mean_ = (s_ * 0.5) + l_
median_ = (s_ * 0.5) + l_
variance_ = (1 / 8) * s_**2
return {
"mean": mean_,
"median": median_,
"variance": variance_,
"std": np.sqrt(variance_),
}