# astropy.uncertainty.distributions 源代码

```# -*- coding: utf-8 -*-

"""
Built-in distribution-creation functions.
"""
from warnings import warn

import numpy as np

from astropy import units as u
from .core import Distribution

__all__ = ['normal', 'poisson', 'uniform']

[文档]def normal(center, *, std=None, var=None, ivar=None, n_samples,
cls=Distribution, **kwargs):
"""
Create a Gaussian/normal distribution.

Parameters
----------
center : `~astropy.units.Quantity`
The center of this distribution
std : `~astropy.units.Quantity` or None
The standard deviation/σ of this distribution. Shape must match and unit
must be compatible with ``center``, or be `None` (if ``var`` or ``ivar``
are set).
var : `~astropy.units.Quantity` or None
The variance of this distribution. Shape must match and unit must be
compatible with ``center``, or be `None` (if ``std`` or ``ivar`` are set).
ivar : `~astropy.units.Quantity` or None
The inverse variance of this distribution. Shape must match and unit
must be compatible with ``center``, or be `None` (if ``std`` or ``var``
are set).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution.  Typically a
`Distribution` subclass.

Remaining keywords are passed into the constructor of the ``cls``

Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled Gaussian distribution.
The type will be the same as the parameter ``cls``.

"""
center = np.asanyarray(center)
if var is not None:
if std is None:
std = np.asanyarray(var)**0.5
else:
raise ValueError('normal cannot take both std and var')
if ivar is not None:
if std is None:
std = np.asanyarray(ivar)**-0.5
else:
raise ValueError('normal cannot take both ivar and '
'and std or var')
if std is None:
raise ValueError('normal requires one of std, var, or ivar')
else:
std = np.asanyarray(std)

randshape = np.broadcast(std, center).shape + (n_samples,)
samples = center[..., np.newaxis] + np.random.randn(*randshape) * std[..., np.newaxis]
return cls(samples, **kwargs)

COUNT_UNITS = (u.count, u.electron, u.dimensionless_unscaled, u.chan, u.bin, u.vox, u.bit, u.byte)

[文档]def poisson(center, n_samples, cls=Distribution, **kwargs):
"""
Create a Poisson distribution.

Parameters
----------
center : `~astropy.units.Quantity`
The center value of this distribution (i.e., λ).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution.  Typically a
`Distribution` subclass.

Remaining keywords are passed into the constructor of the ``cls``

Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled Poisson distribution.
The type will be the same as the parameter ``cls``.
"""
# we convert to arrays because np.random.poisson has trouble with quantities
has_unit = False
if hasattr(center, 'unit'):
has_unit = True
poissonarr = np.asanyarray(center.value)
else:
poissonarr = np.asanyarray(center)
randshape = poissonarr.shape + (n_samples,)

samples = np.random.poisson(poissonarr[..., np.newaxis], randshape)
if has_unit:
'units because they need the gain to be applied. It is '
'recommended you apply the gain to convert to e.g. electrons.')
elif center.unit not in COUNT_UNITS:
warn('Unit {} was provided to poisson, which is not one of {}, '
'and therefore suspect as a "counting" unit.  Ensure you mean '
'to use Poisson statistics.'.format(center.unit, COUNT_UNITS))

# re-attach the unit
samples = samples * center.unit

return cls(samples, **kwargs)

[文档]def uniform(*, lower=None, upper=None, center=None, width=None, n_samples,
cls=Distribution, **kwargs):
"""
Create a Uniform distriution from the lower and upper bounds.

Note that this function requires keywords to be explicit, and requires
either ``lower``/``upper`` or ``center``/``width``.

Parameters
----------
lower : array-like
The lower edge of this distribution. If a `~astropy.units.Quantity`, the
distribution will have the same units as ``lower``.
upper : `~astropy.units.Quantity`
The upper edge of this distribution. Must match shape and if a
`~astropy.units.Quantity` must have compatible units with ``lower``.
center : array-like
The center value of the distribution. Cannot be provided at the same
time as ``lower``/``upper``.
width : array-like
The width of the distribution.  Must have the same shape and compatible
units with ``center`` (if any).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution.  Typically a
`Distribution` subclass.

Remaining keywords are passed into the constructor of the ``cls``

Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled uniform distribution.
The type will be the same as the parameter ``cls``.
"""
if center is None and width is None:
lower = np.asanyarray(lower)
upper = np.asanyarray(upper)
if lower.shape != upper.shape:
raise ValueError('lower and upper must have consistent shapes')
elif upper is None and lower is None:
center = np.asanyarray(center)
width = np.asanyarray(width)
lower = center - width/2
upper = center + width/2
else:
raise ValueError('either upper/lower or center/width must be given '
'to uniform - other combinations are not valid')

newshape = lower.shape + (n_samples,)
if lower.shape == tuple() and upper.shape == tuple():
width = upper - lower  # scalar
else:
width = (upper - lower)[:, np.newaxis]
lower = lower[:, np.newaxis]
samples = lower + width * np.random.uniform(size=newshape)

return cls(samples, **kwargs)
```