Probability Distributions¶

class paramnormal.dist.normal[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a normal distribution.

Parameters:	mu : float The expected value (mean) of the underlying normal distribution. Acts as the location parameter of the distribution. sigma : float The standard deviation of the underlying normal distribution. Also acts as the scale parameter of distribution.

See also

scipy.stats.norm, numpy.random.normal

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html https://en.wikipedia.org/wiki/normal_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.normal(mu=5, sigma=2).rvs(size=3)
array([ 8.52810469,  5.80031442,  6.95747597])

>>> # english names and greek symbols are interchangeable
>>> numpy.random.seed(0)
>>> pn.normal(μ=5, σ=2).rvs(size=3)
array([ 8.52810469,  5.80031442,  6.95747597])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = numpy.random.normal(5, 2, size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.normal.fit(data)
params(mu=5.6480512782619359, sigma=2.1722505742582769)

>>> # estimate sigma when mu is fixed a known value:
>>> pn.normal.fit(data, mu=4.75)
params(mu=4.75, sigma=2.3505677305181645)

Methods

dist = <scipy.stats._continuous_distns.norm_gen object>¶

param_template¶

alias of params

name = 'normal'¶

class paramnormal.dist.lognormal[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a lognormal distribution.

Parameters:

mu : float The expected value (mean) of the underlying normal distribution. Acts as the scale parameter of the distribution. sigma : float The standard deviation of the underlying normal distribution. Also acts as the shape parameter of distribution. offset : float, optional The location parameter of the distribution. It’s effectively the lower bound of the distribution. In other works, if you’re investigating some quantity that cannot go below zero (e.g., pollutant concentrations), leave this as the default (zero). Note When fitting a lognormal distribution to a dataset, this will be fixed at its default value unless you explicitly set it to another value. Set it to None if wish that it be estimated entirely from scratch.

See also

scipy.stats.lognorm, numpy.random.lognormal

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html https://en.wikipedia.org/wiki/lognormal_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.lognormal(mu=5, sigma=2).rvs(size=3)
array([ 5054.85624027,   330.40342795,  1050.97750604])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.lognormal(μ=5, σ=2).rvs(size=3)
array([ 5054.85624027,   330.40342795,  1050.97750604])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = numpy.random.lognormal(5, 2, size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.lognormal.fit(data)
params(mu=5.6480511731060181, sigma=2.172250571711877, offset=0)

>>> # estimate sigma when mu is fixed a known value:
>>> pn.lognormal.fit(data, mu=4.75)
params(mu=4.75, sigma=2.3505859375000036, offset=0)

>>> # include `offset` in the estimate
>>> pn.lognormal.fit(data, offset=None)
params(mu=5.6538159643068386, sigma=2.1596452081058795, offset=-0.12039282461824304)

Methods

dist = <scipy.stats._continuous_distns.lognorm_gen object>¶

param_template¶

alias of params

name = 'lognormal'¶

classmethod fit(data, **guesses)[source]¶

class paramnormal.dist.weibull[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a weibull distribution.

Parameters:

k : float The shape parameter of the distribution. Note Strictly speaking, the weibull distribution has a second shape parameter, lambda. However, it seems to be always set to 1. So much so that scipy doesn’t give you any other option. loc, scale : floats, optional Location and scale parameters of the distribution. These default to, and should probably be left at, 0 and 1, respectively. Note When fitting a weibull distribution to a dataset, these will be fixed at their default values unless you explicitly set them to other values. Set them to None if you wish that they be estimated entirely from scratch.

See also

scipy.stats.weibull_min, scipy.stats.frechet_min, numpy.random.weibull

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.weibull_min.html https://en.wikipedia.org/wiki/weibull_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.weibull(k=5).rvs(size=3)
array([ 0.9553641 ,  1.04662991,  0.98415009])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = numpy.random.weibull(5, size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.weibull.fit(data)
params(k=5.4158203125000091, loc=0, scale=1)

>>> # include `loc` and `scale` in the estimate
>>> pn.weibull.fit(data, loc=None, scale=None)
params(k=14.120107702486127, loc=-1.389856535577052, scale=2.4320324339845572)

Methods

dist = <scipy.stats._continuous_distns.frechet_r_gen object>¶

param_template¶

alias of params

name = 'weibull'¶

class paramnormal.dist.alpha[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a alpha distribution.

Parameters:

alpha : float The shape parameter of the distribution. loc, scale : floats, optional Location and scale parameters of the distribution. These default to, and should probably be left at, 0 and 1, respectively. Note When fitting a alpha distribution to a dataset, these will be fixed at their default values unless you explicitly set them to other values. Set them to None if you wish that they be estimated entirely from scratch.

See also

scipy.stats.alpha

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.alpha.html

Examples

>>> import numpy
>>> from scipy import stats
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.alpha(alpha=5).rvs(size=3)
array([ 0.20502995,  0.22566277,  0.21099298])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.alpha(α=5).rvs(size=3)
array([ 0.20502995,  0.22566277,  0.21099298])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = stats.alpha.rvs(5, size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.alpha.fit(data)
params(alpha=4.8356445312500096, loc=0, scale=1)

>>> # include `loc` and `scale` in the estimate
>>> pn.alpha.fit(data, loc=None, scale=None)
params(alpha=8.6781299501492342, loc=-0.15002784429644306, scale=3.1262971852456447)

Methods

dist = <scipy.stats._continuous_distns.alpha_gen object>¶

param_template¶

alias of params

name = 'alpha'¶

class paramnormal.dist.beta[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a beta distribution.

Parameters:

alpha, beta : float The (positive) shape parameters of the distribution. loc, scale : floats, optional Location and scale parameters of the distribution. These default to, and should probably be left at, 0 and 1, respectively. Note When fitting a beta distribution to a dataset, these will be fixed at their default values unless you explicitly set them to other values. Set them to None if you wish that they be estimated entirely from scratch.

See also

scipy.stats.beta, numpy.random.beta

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html https://en.wikipedia.org/wiki/beta_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.beta(alpha=2, beta=5).rvs(size=3)
array([ 0.47917138,  0.6550558 ,  0.21501632])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.beta(α=2, β=5).rvs(size=3)
array([ 0.47917138,  0.6550558 ,  0.21501632])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.beta(alpha=2, beta=5).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.beta.fit(data)
params(alpha=1.6784891179355115, beta=4.2459121691279398, loc=0, scale=1)

>>> # just estimate beta with a known alpha
>>> pn.beta.fit(data, alpha=2)
params(alpha=2, beta=4.9699264393421139, loc=0, scale=1)

>>> # include `loc` and `scale` in the estimate
>>> pn.beta.fit(data, loc=None, scale=None)
params(alpha=1.8111139255547926, beta=4.6972775768688697, loc=-0.0054013993799938431, scale=1.0388376932132561)

Methods

dist = <scipy.stats._continuous_distns.beta_gen object>¶

param_template¶

alias of params

name = 'beta'¶

class paramnormal.dist.gamma[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a gamma distribution.

Parameters:

k, theta : float The shape and scale parameters of the distribution, respectively. loc : float, optional Location parameter of the distribution. This defaults to, and should probably be left at, 0. Note When fitting a beta distribution to a dataset, this will be fixed at its default value unless you explicitly set it to other values. Set to None if you wish that it be estimated entirely from scratch.

See also

scipy.stats.gamma, numpy.random.gamma

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html https://en.wikipedia.org/wiki/gamma_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.gamma(k=2, theta=5).rvs(size=3)
array([ 25.69414788,  11.19240456,  27.13566137])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.gamma(k=2, θ=5).rvs(size=3)
array([ 25.69414788,  11.19240456,  27.13566137])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.gamma(k=2, θ=5).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.gamma.fit(data)
params(k=1.3379069223213471, loc=0, theta=7.5830062081633622)

>>> # just estimate theta with a known k
>>> pn.gamma.fit(data, theta=5)
params(k=1.8060453251225814, loc=0, theta=5)

>>> # include `loc` in the estimate
>>> pn.gamma.fit(data, loc=None)
params(k=1.0996117768860174, loc=0.29914735266576881, theta=8.9542450315590756)

Methods

dist = <scipy.stats._continuous_distns.gamma_gen object>¶

param_template¶

alias of params

name = 'gamma'¶

class paramnormal.dist.chi_squared[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a chi-squared distribution.

Parameters:

k : float The degrees of freedom of the distribution, respectively. loc, scale : floats, optional Location and scale parameters of the distribution. These default to, and should probably be left at, 0 and 1, respectively. Note When fitting a chi-squared distribution to a dataset, these will be fixed at their default value unless you explicitly set them to other values. Set to None if you wish that they be estimated entirely from scratch.

See also

scipy.stats.chi2, numpy.random.chisquare

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html https://en.wikipedia.org/wiki/Chi-squared_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.chi_squared(k=2).rvs(size=3)
array([ 1.59174902,  2.51186153,  1.84644629])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.chi_squared(k=2).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.chi_squared.fit(data)
params(k=2.2668945312500028, loc=0, scale=1)

>>> # include `loc` in the estimate
>>> pn.chi_squared.fit(data, loc=None)
params(k=1.9361813889429524, loc=0.037937143324767775, scale=1)

Methods

dist = <scipy.stats._continuous_distns.chi2_gen object>¶

param_template¶

alias of params

nane = 'chi_squared'¶

class paramnormal.dist.pareto[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a pareto distribution.

Parameters:

alpha : float The shape parameter of the distribution. loc, scale : floats, optional Location and scale parameters of the distribution. These default to, and should probably be left at, 0 and 1, respectively. Note When fitting a pareto distribution to a dataset, this will be fixed at its default value unless you explicitly set it to other values. Set to None if you wish that it be estimated entirely from scratch.

See also

scipy.stats.pareto, numpy.random.pareto

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pareto.html https://en.wikipedia.org/wiki/pareto_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.pareto(alpha=2).rvs(size=3)
array([ 1.48875061,  1.87379424,  1.58662889])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.pareto(α=2).rvs(size=3)
array([ 1.48875061,  1.87379424,  1.58662889])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.pareto(alpha=2).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.pareto.fit(data)
params(alpha=1.7850585937500019, loc=0, scale=1)

>>> # include `loc` in the estimate
>>> pn.pareto.fit(data, loc=None)
params(alpha=1.8040853559635659, loc=0.009529403810858695, scale=1)

Methods

dist = <scipy.stats._continuous_distns.pareto_gen object>¶

param_template¶

alias of params

name = 'pareto'¶

class paramnormal.dist.exponential[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to an exponential distribution.

Parameters:	lambda_ : float The shape parameter of the distribution. loc : float, optional Location parameter of the distribution. This defaults to, and should probably be left at, 0. Note When fitting an exponential distribution to a dataset, this will be fixed at its default value unless you explicitly set it to other values. Set to None if you wish that it be estimated entirely from scratch.

See also

scipy.stats.expon, numpy.random.exponential

References

http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html https://en.wikipedia.org/wiki/exponential_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.exponential(lambda_=2).rvs(size=3)
array([ 0.39793725,  0.62796538,  0.46161157])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.exponential(λ=2).rvs(size=3)
array([ 0.39793725,  0.62796538,  0.46161157])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.exponential(λ=2).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.exponential.fit(data)
params(lambda_=1.7849050026146085, loc=0)

>>> # include `loc` in the estimate
>>> pn.exponential.fit(data, loc=None)
params(lambda_=1.8154701618164411, loc=0.0094842718426853996)

Methods

dist = <scipy.stats._continuous_distns.expon_gen object>¶

param_template¶

alias of params

name = 'exponential'¶

classmethod fit(data, **guesses)[source]¶

class paramnormal.dist.rice[source]¶

Bases: paramnormal.dist.BaseDist_Mixin

Create and fit data to a Rice distribution.

Parameters:

R : float The shape parameter of the distribution. sigma : float The standard deviate of the distribution. loc : float, optional Location parameter of the distribution. This defaults to, and should probably be left at, 0. Note When fitting an Rice distribution to a dataset, this will be fixed at its default value unless you explicitly set it to other values. Set to None if you wish that it be estimated entirely from scratch.

See also

scipy.stats.rice, numpy.random.exponential

References

http://scipy.github.io/devdocs/generated/scipy.stats.rice https://en.wikipedia.org/wiki/Rice_distribution

Examples

>>> import numpy
>>> import paramnormal as pn
>>> numpy.random.seed(0)
>>> pn.rice(R=10, sigma=2).rvs(size=3)
array([ 15.67835764,  13.36907874,  10.37753817])

>>> # you can also use greek letters
>>> numpy.random.seed(0)
>>> pn.rice(R=10, σ=2).rvs(size=3)
array([ 15.67835764,  13.36907874,  10.37753817])

>>> # silly fake data
>>> numpy.random.seed(0)
>>> data = pn.rice(R=10, sigma=2).rvs(size=37)
>>> # pretend `data` is unknown and we want to fit a dist. to it
>>> pn.rice.fit(data)
params(R=10.100674084593422, sigma=1.759817171541185, loc=0)

>>> # include `loc` in the estimate (bad idea)
>>> pn.rice.fit(data, loc=None)
params(R=4.249154300734, sigma=1.862167512728, loc=5.570921659394)

Methods

dist = <scipy.stats._continuous_distns.rice_gen object>¶

param_template¶

alias of params

name = 'rice'¶

classmethod fit(data, **guesses)[source]¶