Paramnormal Activity

paramnormal.activity.random(distro: str | type[BaseDist_Mixin] | rv_frozen, *, shape: int | tuple[int, ...] | None = None, **params: float | None) → float | NDArray[float64][source]

Generate random data from a probability distribution.

Parameters:
distrostr or paramnormal class

The distribution from which the random data will be generated.

paramskeyword arguments of floats

The parameters required to define the distribution. See each distribution’s docstring for more info.

shapeint or tuple of ints, optional

The shape of the array into which the generated data will be placed. If None, a scalar will be retured.

Returns:
randomnumpy.array or scalar

The random array (or scalar) generated.

Examples

>>> import numpy
>>> import paramnormal
>>> numpy.random.seed(0)
>>> # define dist with a string
>>> paramnormal.activity.random('normal', mu=5, sigma=1.75, shape=1)
array([ 8.08709161])
>>> # or you can specify the actual class
>>> paramnormal.activity.random(paramnormal.normal, mu=5, sigma=1.75, shape=None)
5.700275114642641
>>> # greek letters still work
>>> paramnormal.activity.random('beta', α=2.5, β=1.2, shape=(3,3))
array([[ 0.43771761,  0.84131634,  0.4390664 ],
       [ 0.7037142 ,  0.88282672,  0.09080825],
       [ 0.98747135,  0.63227551,  0.98108498]])
paramnormal.activity.fit(distro: str | type[BaseDist_Mixin] | rv_frozen, data: float | int | number | NDArray[generic] | Sequence[float | int | number] | Sequence[NDArrayLike], as_params: Literal[True] = True, **guesses: float | None) → ParamsLike[source]
paramnormal.activity.fit(distro: str | type[BaseDist_Mixin] | rv_frozen, data: float | int | number | NDArray[generic] | Sequence[float | int | number] | Sequence[NDArrayLike], as_params: Literal[False], **guesses: float | None) → rv_frozen

Estimate the distribution parameters of sample data.

Parameters:
distrostr or paramnormal class

The distribution from which the random data will be generated.

dataarray-like

The data from which the distribution will be fit.

guessesnamed arguments of floats

Inital guess values for certain parameters of the distribution. See the class docstring for more information on the parameters.

Returns:
paramsnamedtuple

A namedtuple containing all of the paramaters of the distribution.

Examples

>>> import numpy
>>> import paramnormal
>>> numpy.random.seed(0)
>>> x = numpy.random.normal(loc=5.75, scale=2.25, size=37)
>>> paramnormal.activity.fit('normal', x)
params(mu=6.4790576880446782, sigma=2.4437818960405617)

>>> paramnormal.activity.fit('normal', x, sigma=2)
params(mu=6.4790576880446782, sigma=2)
paramnormal.activity.plot(distro: str | type[BaseDist_Mixin] | rv_frozen, which: str = 'PDF', data: float | int | number | NDArray[generic] | Sequence[float | int | number] | Sequence[float | int | number | NDArray[generic] | Sequence[float | int | number] | Sequence[NDArrayLike]] | None = None, fit_dist: bool = True, ax: Axes | None = None, pad: float = 0.05, xscale: Literal['linear', 'log'] = 'linear', line_opts: dict[str, str | float] | None = None, distplot: bool = False, distplot_opts: dict[str, Any] | None = None, **guesses: float | None) → Axes[source]

Plot the PDF of a dataset and other representations of the distribution (histogram, kernel density estimate, and rug plot).

Parameters:
distrostr or distribution

The (name of) the distribution to be plotted.

dataarray-like, optional

An array-like object that can be passed to distplot() and fit().

fit_distbool, optional

Toggles fitting distro to data. If False, distro must be a fully specified distribution so that the PDF can be plotted.

axmatplotlib.Axes, optional

Axes on which the everything gets drawn. If not provided, a new one is created.

padfloat, optional

The fraction of beyond min and max values of data where the PDF will be drawn.

xscalestr, optional

Specfifies a ‘log’ or ‘linear’ scale on the plot.

line_optsdict, optional

Plotting options passed to plot() when drawing the PDF.

distplotbool, Optional

Toggles the use of distplot(). The default is False.

distplot_optsdict, optional

Dictionary of parameters to be passed to distplot().

guesseskeyword arguments, optional

Additional parameters for specifying the distribution.

Returns:
axmatplotlib.Axes

See also

seaborn.distplot

Examples

Plot a simple PDF of a fully-specified normal distribution.

>>> import numpy
>>> import seaborn
>>> import paramnormal
>>> clean_bkgd = {'axes.facecolor': 'none', 'figure.facecolor': 'none'}
>>> seaborn.set(style='ticks', rc=clean_bkgd)
>>> norm_dist = paramnormal.normal(μ=5.4, σ=2.5)
>>> ax = paramnormal.activity.plot(norm_dist)
../_images/activity-1.png

Pass a data sample to fit the distribution on-the-fly.

>>> paramnormal.utils.seed(0)
>>> data = paramnormal.activity.random('normal', μ=5.4, σ=2.5, shape=(37))
>>> ax = paramnormal.activity.plot('normal', data=data)
../_images/activity-2.png

Use seaborn to show other representations of the distribution of real data:

>>> ax = paramnormal.activity.plot('normal', data=data, distplot=True)
>>> ax.legend(loc='upper left')
../_images/activity-3.png

Use line_opts and distplot_opts to customize more complex plots.

>>> paramnormal.utils.seed(0)
>>> data = paramnormal.activity.random('lognormal', μ=0.75, σ=1.2, shape=125)
>>> logdata = numpy.log10(data)
>>> line_opts = dict(color='firebrick', lw=3.5, label='Fit PDF')
>>> distplot_opts = dict(rug=True, kde=False, norm_hist=True)
>>> ax = paramnormal.activity.plot('lognormal', data=data, distplot=True,
...                                xscale='log', pad=0.01,
...                                line_opts=line_opts,
...                                distplot_opts=distplot_opts)
../_images/activity-4.png

Notice that the bins in log-space don’t work so well. We can compute them outselves.

>>> paramnormal.utils.seed(0)
>>> data = paramnormal.activity.random('lognormal', μ=0.75, σ=1.2, shape=125)
>>> logdata = numpy.log10(data)
>>> bins = numpy.logspace(logdata.min(), logdata.max(), num=30)
>>> distplot_opts = dict(rug=True, kde=False, norm_hist=True, bins=bins)
>>> ax = paramnormal.activity.plot('lognormal', data=data, distplot=True,
...                                xscale='log', pad=0.01,
...                                line_opts=line_opts,
...                                distplot_opts=distplot_opts)
../_images/activity-5.png