FoldedNormal Distribution#

class FoldedNormalDistribution(amplitude: float = 1.0, mu: float = 0.0, sigma: float = 1.0, loc: float = 0.0, normalize: bool = False)[source]#

Bases: BaseDistribution

Class for FoldedNormal distribution.

Parameters:

amplitude (float, optional) – The amplitude of the PDF. Defaults to 1.0. Ignored if normalize is True.
mu (float, optional) – The mean parameter, \(\mu\). Defaults to 0.0.
sigma (float, optional) – The standard deviation parameter, \(\sigma\). Defaults to 1.0.
loc (float, optional) – The location parameter, for shifting. Defaults to 0.0.
normalize (bool, optional) – If True, the distribution is normalized so that the total area under the PDF equals 1. Defaults to False.

Raises:

NegativeAmplitudeError – If the provided value of amplitude is negative.
NegativeStandardDeviationError – If the provided value of standard deviation is negative.

Attributes:

mean: The mean of the distribution.
median: The median of the distribution.
mode: The mode of the distribution.
stddev: The standard deviation of the distribution.
variance: The variance of the distribution.

Methods

`cdf`(x)	Compute the cumulative density function (CDF) for the distribution.
`pdf`(x)	Compute the probability density function (PDF) for the distribution.
`scipy_like`(c[, loc, scale])	Instantiate FoldedNormalDistribution with scipy parametrization.
`stats`()	Computes and returns the statistical properties of the distribution, including,

Examples

Importing libraries:

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import foldnorm

from pymultifit.distributions import FoldedNormalDistribution

Generating a standard Folded Normal(\(\mu=0, \sigma = 1\)) distribution with pyMultiFit and scipy:

x_values = np.linspace(start=-10, stop=10, num=500)

y_multifit = FoldedNormalDistribution(normalize=True)
y_scipy = foldnorm

Plotting PDF and CDF:

f, ax = plt.subplots(1, 2, figsize=(12, 5))

ax[0].plot(x_values, y_scipy.pdf(x=x_values, c=0), label='Scipy Folded Normal')
ax[0].plot(x_values, y_multifit.pdf(x_values), 'k:', label='pyMultiFit Folded Normal')
ax[0].set_ylabel('f(x)')

ax[1].plot(x_values, y_scipy.cdf(x=x_values, c=0), label='Scipy Folded Normal')
ax[1].plot(x_values, y_multifit.cdf(x_values), 'k:', label='pyMultiFit Folded Normal')
ax[1].set_ylabel('F(x)')

f.suptitle('Folded Normal(0, 1)')

for i in ax:
    i.set_xlabel('X')
    i.legend()
plt.tight_layout()

Generating a translated Gaussian(\(\mu=2, \sigma=3\)) distribution with \(\text{loc}=3\):

y_multifit = FoldedNormalDistribution(mu=2, sigma=3, loc=3, normalize=True)

Plotting PDF and CDF:

f, ax = plt.subplots(1, 2, figsize=(12, 5))

ax[0].plot(x_values, y_scipy.pdf(x=x_values, c=2, scale=3, loc=3), label='Scipy translated Folded Normal')
ax[0].plot(x_values, y_multifit.pdf(x_values), 'k:', label='pyMultiFit translated Folded Normal')
ax[0].set_ylabel('f(x)')

ax[1].plot(x_values, y_scipy.cdf(x=x_values, c=2, scale=3, loc=3), label='Scipy translated Folded Normal')
ax[1].plot(x_values, y_multifit.cdf(x_values), 'k:', label='pyMultiFit translated Folded Normal')
ax[1].set_ylabel('F(x)')

f.suptitle(r'Folded Normal(2, 3, 3)')

for i in ax:
    i.set_xlabel('X')
    i.legend()
plt.tight_layout()

cdf(x: ndarray) → ndarray[source]#

Compute the cumulative density function (CDF) for the distribution.

Parameters:: x – Input array at which to evaluate the CDF.

pdf(x: ndarray) → ndarray[source]#

Compute the probability density function (PDF) for the distribution.

Parameters:: x – Input array at which to evaluate the PDF.

classmethod scipy_like(c, loc: float = 0.0, scale: float = 1.0)[source]#

Instantiate FoldedNormalDistribution with scipy parametrization.

Parameters:

c: float: The shape parameter.
loc: float, optional: The location parameter. Defaults to 0.0.
scale: float, optional: The scale parameter. Defaults to 1.0.

Returns:

FoldedNormalDistribution: An instance of normalized FoldedNormalDistribution.

stats() → Dict[str, Any][source]#

Computes and returns the statistical properties of the distribution, including,

mean,
median,
variance, and
standard deviation.

Returns:: A dictionary containing statistical properties such as mean, variance, etc.
Return type:: Dict[str, float]

Notes

If any of the parameter is not computable for a distribution, this method returns None.

property mean#: The mean of the distribution.

property median#: The median of the distribution.

property mode#: The mode of the distribution.

property stddev#: The standard deviation of the distribution.

property variance#: The variance of the distribution.

This class internally utilizes the following functions from utilities_d module:

Recommended Import#

from pymultifit.distributions import FoldedNormalDistribution

Full Import#

from pymultifit.distributions.foldedNormal_d import FoldedNormalDistribution