"""Created on Aug 03 20:07:50 2024"""
from .backend import BaseDistribution, errorHandling as erH
from .utilities_d import gaussian_cdf_, gaussian_pdf_, gaussian_log_pdf_, gaussian_log_cdf_
[docs]
class GaussianDistribution(BaseDistribution):
r"""
Class for Gaussian distribution.
:param amplitude: The amplitude of the PDF. Defaults to 1.0. Ignored if **normalize** is ``True``.
:type amplitude: float, optional
:param mu: The mean parameter, :math:`\mu`. Defaults to 0.0.
:type mu: float, optional
:param std: The standard deviation parameter, :math:`\sigma`. Defaults to 1.0.
:type std: 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 NegativeStandardDeviationError: If the provided value of standard deviation is negative.
Examples
--------
Importing libraries:
.. literalinclude:: ../../../examples/basic/gaussian.py
:language: python
:linenos:
:lineno-start: 3
:lines: 3-7
Generating a standard Gaussian(:math:`\mu=0, \sigma = 1`) distribution with ``pyMultiFit`` and ``scipy``:
.. literalinclude:: ../../../examples/basic/gaussian.py
:language: python
:linenos:
:lineno-start: 9
:lines: 9-12
Plotting **PDF** and **CDF**:
.. literalinclude:: ../../../examples/basic/gaussian.py
:language: python
:linenos:
:lineno-start: 14
:lines: 14-29
.. image:: ../../../images/gaussian_example1.png
:alt: Gaussian(0, 1)
:align: center
Generating a translated Gaussian(:math:`\mu=3, \sigma=2`) distribution:
.. literalinclude:: ../../../examples/basic/gaussian.py
:language: python
:lineno-start: 32
:lines: 32
Plotting **PDF** and **CDF**:
.. literalinclude:: ../../../examples/basic/gaussian.py
:language: python
:lineno-start: 34
:lines: 34-49
.. image:: ../../../images/gaussian_example2.png
:alt: Gaussian(3, 2)
:align: center
"""
def __init__(self, amplitude: float = 1.0, mu: float = 0., std: float = 1., normalize: bool = False):
if not normalize and amplitude <= 0:
raise erH.NegativeAmplitudeError()
elif std <= 0:
raise erH.NegativeStandardDeviationError()
self.amplitude = 1. if normalize else amplitude
self.mu = mu
self.std_ = std
self.norm = normalize
[docs]
@classmethod
def scipy_like(cls, loc: float = 0.0, scale: float = 1.0):
"""
Instantiate GaussianDistribution with scipy parametrization.
Parameters
----------
loc: float, optional
The mean parameter. Defaults to 0.0.
scale: float, optional
The scale parameter. Defaults to 1.0.
Returns
-------
GaussianDistribution
An instance of normalized GaussianDistribution.
"""
return cls(mu=loc, std=scale, normalize=True)
[docs]
def pdf(self, x):
return gaussian_pdf_(x, amplitude=self.amplitude, mean=self.mu, std=self.std_, normalize=self.norm)
[docs]
def logpdf(self, x):
return gaussian_log_pdf_(x, amplitude=self.amplitude, mean=self.mu, std=self.std_, normalize=self.norm)
[docs]
def cdf(self, x):
return gaussian_cdf_(x, amplitude=self.amplitude, mean=self.mu, std=self.std_, normalize=self.norm)
[docs]
def logcdf(self, x):
return gaussian_log_cdf_(x, amplitude=self.amplitude, mean=self.mu, std=self.std_, normalize=self.norm)
[docs]
def stats(self):
m, s = self.mu, self.std_
return {'mean': m,
'median': m,
'mode': m,
'variance': s**2,
'std': s}