XrContinuousRV

class xarray_einstats.stats.XrContinuousRV(dist, *args, **kwargs)

Wrapper for subclasses of rv_continuous.

Usage examples available at Intro to the stats module

See also

xarray_einstats.stats.XrDiscreteRV

Examples

Evaluate the ppf of a Student-T distribution from DataArrays that need broadcasting:

from xarray_einstats import tutorial
from xarray_einstats.stats import XrContinuousRV
from scipy import stats
ds = tutorial.generate_mcmc_like_dataset(3)
dist = XrContinuousRV(stats.t, 3, ds["mu"], ds["sigma"])
dist.ppf([.1, .5, .6])

<xarray.DataArray (quantile: 3, chain: 4, draw: 10, team: 6)> Size: 6kB
-3.796 -3.516 -2.506 -1.706 -3.562 -3.648 ... 0.7499 0.9269 1.166 0.9678 0.7404
Coordinates:
  * quantile  (quantile) float64 24B 0.1 0.5 0.6
  * chain     (chain) int64 32B 0 1 2 3
  * draw      (draw) int64 80B 0 1 2 3 4 5 6 7 8 9
  * team      (team) <U1 24B 'a' 'b' 'c' 'd' 'e' 'f'

xarray.DataArray

• quantile: 3
• chain: 4
• draw: 10
• team: 6

• -3.796 -3.516 -2.506 -1.706 -3.562 ... 0.9269 1.166 0.9678 0.7404

  array([[[[-3.79590643e+00, -3.51626437e+00, -2.50638029e+00,
            -1.70577315e+00, -3.56242742e+00, -3.64808572e+00],
           [-4.28859668e-01, -7.60812912e-01,  1.37555385e-01,
            -7.97098142e-01, -4.12145799e-01,  2.48423230e+00],
           [-1.67209975e+00, -6.53284674e-01, -1.42261506e+00,
             9.56244117e-02, -1.03096142e+00, -4.53819999e-01],
           [ 8.64250637e-02, -2.51353240e-02, -2.01063019e-01,
             9.62162391e-01,  6.91560190e-01,  1.01333638e+00],
           [-3.65841733e+00, -3.73739753e+00, -3.72220327e+00,
            -3.72807587e+00, -4.27065768e+00, -2.15513550e+00],
           [-3.38640347e+00, -4.73519393e+00, -3.34333336e+00,
            -1.73933738e+00, -4.59510477e+00, -4.02607757e+00],
           [-2.92974644e+00, -1.82808975e+00, -9.30529321e-01,
            -2.15182675e+00, -1.70255523e+00, -1.90061515e+00],
           [-3.96989871e-01,  6.15070971e-01, -7.66383515e-01,
             8.84987225e-01,  3.28495111e+00, -9.58559567e-01],
           [ 1.45801807e+00, -2.07845509e-01, -4.04529499e-01,
            -7.17336629e-01,  2.20738703e+00, -7.96293241e-01],
           [-2.59632145e+00, -3.45024771e+00, -1.54533508e+00,
            -2.90505797e+00,  1.91863814e-01, -3.85666446e+00]],
  ...
          [[ 1.57510205e-01,  6.01187331e-01,  2.36466681e+00,
             9.18317160e-01,  9.39919676e-01,  2.09492284e+00],
           [ 4.27349091e+00,  2.91412618e+00,  1.80479807e+00,
             7.53853150e-01,  1.50627104e+00,  9.56942117e-01],
           [ 3.77827834e+00,  1.18259087e+00,  2.43992650e+00,
             1.71211847e+00,  3.84014445e-01,  2.10944944e+00],
           [ 2.53566456e+00,  3.01859820e+00,  2.02613863e+00,
             2.99500067e+00,  1.73820014e+00,  1.58044689e+00],
           [ 1.40635380e-01,  1.48171703e+00,  2.04888967e+00,
             1.83499905e+00,  3.05529789e-01,  8.67585598e-02],
           [ 3.43195518e+00,  1.42694189e+00,  1.44830912e+00,
             1.41817600e+00,  1.23547914e+00,  4.73685690e-01],
           [ 2.04378940e+00,  1.59453681e+00,  5.18303346e+00,
             1.89794044e-01,  1.49440336e+00,  4.12162939e-01],
           [ 5.97638013e-01,  7.76050296e-01,  5.18919755e-01,
             2.09269176e+00,  9.81997440e-01,  1.06762080e+00],
           [ 3.43370256e-01,  1.05672401e+00,  3.78634511e+00,
             2.40224496e+00,  3.50579773e-01,  6.49594070e-01],
           [ 2.43369959e+00,  7.49895054e-01,  9.26892088e-01,
             1.16580175e+00,  9.67810181e-01,  7.40353924e-01]]]])

• Coordinates: (4)
  • quantile
    (quantile)
    float64
    0.1 0.5 0.6

    array([0.1, 0.5, 0.6])

  • chain
    (chain)
    int64
    0 1 2 3

    array([0, 1, 2, 3])

  • draw
    (draw)
    int64
    0 1 2 3 4 5 6 7 8 9

    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

  • team
    (team)
    <U1
    'a' 'b' 'c' 'd' 'e' 'f'

    array(['a', 'b', 'c', 'd', 'e', 'f'], dtype='<U1')

• Indexes: (4)
  • quantile
    PandasIndex

    PandasIndex(Index([0.1, 0.5, 0.6], dtype='float64', name='quantile'))

  • chain
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

  • draw
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64', name='draw'))

  • team
    PandasIndex

    PandasIndex(Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object', name='team'))

• Attributes: (0)

Methods

XrContinuousRV.__init__(dist, *args, **kwargs)
XrContinuousRV.cdf(*args[, apply_kwargs])	Method wrapping .cdf of the input distribution with xarray.apply_ufunc
XrContinuousRV.isf(*args[, apply_kwargs])	Method wrapping .isf of the input distribution with xarray.apply_ufunc
XrContinuousRV.logcdf(*args[, apply_kwargs])	Method wrapping .logcdf of the input distribution with xarray.apply_ufunc
XrContinuousRV.logpdf(*args[, apply_kwargs])	Method wrapping .logpdf of the input distribution with xarray.apply_ufunc
XrContinuousRV.logsf(*args[, apply_kwargs])	Method wrapping .logsf of the input distribution with xarray.apply_ufunc
XrContinuousRV.pdf(*args[, apply_kwargs])	Method wrapping .pdf of the input distribution with xarray.apply_ufunc
XrContinuousRV.ppf(*args[, apply_kwargs])	Method wrapping .ppf of the input distribution with xarray.apply_ufunc
XrContinuousRV.rvs(*args[, size, ...])	Method wrapping .rvs of the input distribution with xarray.apply_ufunc
XrContinuousRV.sf(*args[, apply_kwargs])	Method wrapping .sf of the input distribution with xarray.apply_ufunc