# `distributions` module

Source code

    import numpy as np
    import scipy.stats as st

    # Bulge distributions
    class radialPlummer(st.rv_continuous):
        def _pdf(self, x):#(3M/4πa3)(1+(r/a)2)−5/2
            return 3/(4*np.pi**3) * (1 + x**2)**(-5/2) * 4*np.pi*x**2
    PLUMMER = radialPlummer(a=0, b=5, name='rPlummer')

    # [TODO: Check Hernquist. Not currently in use]
    class radialHernquist(st.rv_continuous):
        def _pdf(self, x):
            return 1/(2*np.pi) * 1/(x**4) * 4*np.pi*x**2
    HERNQUIST = radialHernquist(a=0, b=5, name='rHernquist')

    # Disk distributions
    class radialUniform(st.rv_continuous):
        def _pdf(self, x):
            return 2*x if x<1 else 0
    UNIFORM = radialUniform(a=0, b=10, name='rUniform')

    class radialExp(st.rv_continuous):
        def _pdf(self, x):
            return x*np.exp(-x)
    EXP = radialExp(a=0, b=10, name='rExp')

    # Halo distributions
    class radialNFW(st.rv_continuous):
        def _pdf(self, x):
            y = 1 / (x * (1 + x)**2) * x**2
            # Normalize pdf in (0, 5) range
            y /= (-5/6 + np.log(6))
    NFW = radialNFW(a=0, b=5, name='rNFW')

## Classes

`class radialExp (ancestors: scipy.stats._distn_infrastructure.rv_continuous, scipy.stats._distn_infrastructure.rv_generic)`

  A generic continuous random variable class meant for subclassing.

  `rv_continuous` is a base class to construct specific distribution classes and instances for continuous random variables. It cannot be used directly as a distribution.

  ## Parameters

  `momtype` :`int`, optional
    The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
  `a` :`float`, optional
    Lower bound of the support of the distribution, default is minus infinity.
  `b` :`float`, optional
    Upper bound of the support of the distribution, default is plus infinity.
  `xtol` :`float`, optional
    The tolerance for fixed point calculation for generic ppf.
  `badvalue` :`float`, optional
    The value in a result arrays that indicates a value that for which some argument restriction is violated, default is np.nan.
  `name` :`str`, optional
    The name of the instance. This string is used to construct the default example for distributions.
  `longname` :`str`, optional
    This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: `longname` exists for backwards compatibility, do not use for new subclasses.
  `shapes` :`str`, optional
    The shape of the distribution. For example `"m, n"` for a distribution that takes two integers as the two shape arguments for all its methods. If not provided, shape parameters will be inferred from the signature of the private methods, `_pdf` and `_cdf` of the instance.
  `extradoc` : `str`, optional, `deprecated`
    This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: `extradoc` exists for backwards compatibility, do not use for new subclasses.
  `seed` :`None` or `int` or `numpy.random.RandomState` `instance`, optional
    This parameter defines the RandomState object to use for drawing random variates. If None (or np.random), the global np.random state is used. If integer, it is used to seed the local RandomState instance. Default is None.

  ## Methods

  rvs pdf logpdf cdf logcdf sf logsf ppf isf moment stats entropy expect median mean std var interval call fit fit_loc_scale nnlf

  ## Notes

  Public methods of an instance of a distribution class (e.g., `pdf`, `cdf`) check their arguments and pass valid arguments to private, computational methods (`_pdf`, `_cdf`). For `pdf(x)`, `x` is valid if it is within the support of a distribution, `self.a <= x <= self.b`. Whether a shape parameter is valid is decided by an `_argcheck` method (which defaults to checking that its arguments are strictly positive.)

  Subclassing

  New random variables can be defined by subclassing the `rv_continuous` class and re-defining at least the `_pdf` or the `_cdf` method (normalized to location 0 and scale 1).

  If positive argument checking is not correct for your RV then you will also need to re-define the `_argcheck` method.

  Correct, but potentially slow defaults exist for the remaining methods but for speed and/or accuracy you can over-ride::

  _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf

  Rarely would you override `_isf`, `_sf` or `_logsf`, but you could.

  Methods that can be overwritten by subclasses ::

  _rvs _pdf _cdf _sf _ppf _isf _stats _munp _entropy _argcheck

  There are additional (internal and private) generic methods that can be useful for cross-checking and for debugging, but might work in all cases when directly called.

  A note on `shapes`: subclasses need not specify them explicitly. In this case, `shapes` will be automatically deduced from the signatures of the overridden methods (`pdf`, `cdf` etc). If, for some reason, you prefer to avoid relying on introspection, you can specify `shapes` explicitly as an argument to the instance constructor.

  Frozen Distributions

  Normally, you must provide shape parameters (and, optionally, location and scale parameters to each call of a method of a distribution.

  Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a "frozen" continuous RV object:

  rv = generic(<shape(s)>, loc=0, scale=1) frozen RV object with the same methods but holding the given shape, location, and scale fixed

  Statistics

  Statistics are computed using numerical integration by default. For speed you can redefine this using `_stats`:

  - take shape parameters and return mu, mu2, g1, g2
  - If you can't compute one of these, return it as None
  - Can also be defined with a keyword argument `moments`, which is a string composed of "m", "v", "s", and/or "k". Only the components appearing in string should be computed and returned in the order "m", "v", "s", or "k" with missing values returned as None.

  Alternatively, you can override `_munp`, which takes `n` and shape parameters and returns the n-th non-central moment of the distribution.

  ## Examples

  To create a new Gaussian distribution, we would do the following:

      >>> from scipy.stats import rv_continuous
      >>> class gaussian_gen(rv_continuous):
      ...     "Gaussian distribution"
      ...     def _pdf(self, x):
      ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
      >>> gaussian = gaussian_gen(name='gaussian')

  `scipy.stats` distributions are instances, so here we subclass `rv_continuous` and create an instance. With this, we now have a fully functional distribution with all relevant methods automagically generated by the framework.

  Note that above we defined a standard normal distribution, with zero mean and unit variance. Shifting and scaling of the distribution can be done by using `loc` and `scale` parameters: `gaussian.pdf(x, loc, scale)` essentially computes `y = (x - loc) / scale` and `gaussian._pdf(y) / scale`.

  Source code

      class radialExp(st.rv_continuous):
          def _pdf(self, x):
              return x*np.exp(-x)

`class radialHernquist (ancestors: scipy.stats._distn_infrastructure.rv_continuous, scipy.stats._distn_infrastructure.rv_generic)`

  A generic continuous random variable class meant for subclassing.

  `rv_continuous` is a base class to construct specific distribution classes and instances for continuous random variables. It cannot be used directly as a distribution.

  ## Parameters

  `momtype` :`int`, optional
    The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
  `a` :`float`, optional
    Lower bound of the support of the distribution, default is minus infinity.
  `b` :`float`, optional
    Upper bound of the support of the distribution, default is plus infinity.
  `xtol` :`float`, optional
    The tolerance for fixed point calculation for generic ppf.
  `badvalue` :`float`, optional
    The value in a result arrays that indicates a value that for which some argument restriction is violated, default is np.nan.
  `name` :`str`, optional
    The name of the instance. This string is used to construct the default example for distributions.
  `longname` :`str`, optional
    This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: `longname` exists for backwards compatibility, do not use for new subclasses.
  `shapes` :`str`, optional
    The shape of the distribution. For example `"m, n"` for a distribution that takes two integers as the two shape arguments for all its methods. If not provided, shape parameters will be inferred from the signature of the private methods, `_pdf` and `_cdf` of the instance.
  `extradoc` : `str`, optional, `deprecated`
    This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: `extradoc` exists for backwards compatibility, do not use for new subclasses.
  `seed` :`None` or `int` or `numpy.random.RandomState` `instance`, optional
    This parameter defines the RandomState object to use for drawing random variates. If None (or np.random), the global np.random state is used. If integer, it is used to seed the local RandomState instance. Default is None.

  ## Methods

  rvs pdf logpdf cdf logcdf sf logsf ppf isf moment stats entropy expect median mean std var interval call fit fit_loc_scale nnlf

  ## Notes

  Public methods of an instance of a distribution class (e.g., `pdf`, `cdf`) check their arguments and pass valid arguments to private, computational methods (`_pdf`, `_cdf`). For `pdf(x)`, `x` is valid if it is within the support of a distribution, `self.a <= x <= self.b`. Whether a shape parameter is valid is decided by an `_argcheck` method (which defaults to checking that its arguments are strictly positive.)

  Subclassing

  New random variables can be defined by subclassing the `rv_continuous` class and re-defining at least the `_pdf` or the `_cdf` method (normalized to location 0 and scale 1).

  If positive argument checking is not correct for your RV then you will also need to re-define the `_argcheck` method.

  Correct, but potentially slow defaults exist for the remaining methods but for speed and/or accuracy you can over-ride::

  _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf

  Rarely would you override `_isf`, `_sf` or `_logsf`, but you could.

  Methods that can be overwritten by subclasses ::

  _rvs _pdf _cdf _sf _ppf _isf _stats _munp _entropy _argcheck

  There are additional (internal and private) generic methods that can be useful for cross-checking and for debugging, but might work in all cases when directly called.

  A note on `shapes`: subclasses need not specify them explicitly. In this case, `shapes` will be automatically deduced from the signatures of the overridden methods (`pdf`, `cdf` etc). If, for some reason, you prefer to avoid relying on introspection, you can specify `shapes` explicitly as an argument to the instance constructor.

  Frozen Distributions

  Normally, you must provide shape parameters (and, optionally, location and scale parameters to each call of a method of a distribution.

  Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a "frozen" continuous RV object:

  rv = generic(<shape(s)>, loc=0, scale=1) frozen RV object with the same methods but holding the given shape, location, and scale fixed

  Statistics

  Statistics are computed using numerical integration by default. For speed you can redefine this using `_stats`:

  - take shape parameters and return mu, mu2, g1, g2
  - If you can't compute one of these, return it as None
  - Can also be defined with a keyword argument `moments`, which is a string composed of "m", "v", "s", and/or "k". Only the components appearing in string should be computed and returned in the order "m", "v", "s", or "k" with missing values returned as None.

  Alternatively, you can override `_munp`, which takes `n` and shape parameters and returns the n-th non-central moment of the distribution.

  ## Examples

  To create a new Gaussian distribution, we would do the following:

      >>> from scipy.stats import rv_continuous
      >>> class gaussian_gen(rv_continuous):
      ...     "Gaussian distribution"
      ...     def _pdf(self, x):
      ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
      >>> gaussian = gaussian_gen(name='gaussian')

  `scipy.stats` distributions are instances, so here we subclass `rv_continuous` and create an instance. With this, we now have a fully functional distribution with all relevant methods automagically generated by the framework.

  Note that above we defined a standard normal distribution, with zero mean and unit variance. Shifting and scaling of the distribution can be done by using `loc` and `scale` parameters: `gaussian.pdf(x, loc, scale)` essentially computes `y = (x - loc) / scale` and `gaussian._pdf(y) / scale`.

  Source code

      class radialHernquist(st.rv_continuous):
          def _pdf(self, x):
              return 1/(2*np.pi) * 1/(x**4) * 4*np.pi*x**2

`class radialNFW (ancestors: scipy.stats._distn_infrastructure.rv_continuous, scipy.stats._distn_infrastructure.rv_generic)`

  A generic continuous random variable class meant for subclassing.

  `rv_continuous` is a base class to construct specific distribution classes and instances for continuous random variables. It cannot be used directly as a distribution.

  ## Parameters

  `momtype` :`int`, optional
    The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
  `a` :`float`, optional
    Lower bound of the support of the distribution, default is minus infinity.
  `b` :`float`, optional
    Upper bound of the support of the distribution, default is plus infinity.
  `xtol` :`float`, optional
    The tolerance for fixed point calculation for generic ppf.
  `badvalue` :`float`, optional
    The value in a result arrays that indicates a value that for which some argument restriction is violated, default is np.nan.
  `name` :`str`, optional
    The name of the instance. This string is used to construct the default example for distributions.
  `longname` :`str`, optional
    This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: `longname` exists for backwards compatibility, do not use for new subclasses.
  `shapes` :`str`, optional
    The shape of the distribution. For example `"m, n"` for a distribution that takes two integers as the two shape arguments for all its methods. If not provided, shape parameters will be inferred from the signature of the private methods, `_pdf` and `_cdf` of the instance.
  `extradoc` : `str`, optional, `deprecated`
    This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: `extradoc` exists for backwards compatibility, do not use for new subclasses.
  `seed` :`None` or `int` or `numpy.random.RandomState` `instance`, optional
    This parameter defines the RandomState object to use for drawing random variates. If None (or np.random), the global np.random state is used. If integer, it is used to seed the local RandomState instance. Default is None.

  ## Methods

  rvs pdf logpdf cdf logcdf sf logsf ppf isf moment stats entropy expect median mean std var interval call fit fit_loc_scale nnlf

  ## Notes

  Public methods of an instance of a distribution class (e.g., `pdf`, `cdf`) check their arguments and pass valid arguments to private, computational methods (`_pdf`, `_cdf`). For `pdf(x)`, `x` is valid if it is within the support of a distribution, `self.a <= x <= self.b`. Whether a shape parameter is valid is decided by an `_argcheck` method (which defaults to checking that its arguments are strictly positive.)

  Subclassing

  New random variables can be defined by subclassing the `rv_continuous` class and re-defining at least the `_pdf` or the `_cdf` method (normalized to location 0 and scale 1).

  If positive argument checking is not correct for your RV then you will also need to re-define the `_argcheck` method.

  Correct, but potentially slow defaults exist for the remaining methods but for speed and/or accuracy you can over-ride::

  _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf

  Rarely would you override `_isf`, `_sf` or `_logsf`, but you could.

  Methods that can be overwritten by subclasses ::

  _rvs _pdf _cdf _sf _ppf _isf _stats _munp _entropy _argcheck

  There are additional (internal and private) generic methods that can be useful for cross-checking and for debugging, but might work in all cases when directly called.

  A note on `shapes`: subclasses need not specify them explicitly. In this case, `shapes` will be automatically deduced from the signatures of the overridden methods (`pdf`, `cdf` etc). If, for some reason, you prefer to avoid relying on introspection, you can specify `shapes` explicitly as an argument to the instance constructor.

  Frozen Distributions

  Normally, you must provide shape parameters (and, optionally, location and scale parameters to each call of a method of a distribution.

  Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a "frozen" continuous RV object:

  rv = generic(<shape(s)>, loc=0, scale=1) frozen RV object with the same methods but holding the given shape, location, and scale fixed

  Statistics

  Statistics are computed using numerical integration by default. For speed you can redefine this using `_stats`:

  - take shape parameters and return mu, mu2, g1, g2
  - If you can't compute one of these, return it as None
  - Can also be defined with a keyword argument `moments`, which is a string composed of "m", "v", "s", and/or "k". Only the components appearing in string should be computed and returned in the order "m", "v", "s", or "k" with missing values returned as None.

  Alternatively, you can override `_munp`, which takes `n` and shape parameters and returns the n-th non-central moment of the distribution.

  ## Examples

  To create a new Gaussian distribution, we would do the following:

      >>> from scipy.stats import rv_continuous
      >>> class gaussian_gen(rv_continuous):
      ...     "Gaussian distribution"
      ...     def _pdf(self, x):
      ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
      >>> gaussian = gaussian_gen(name='gaussian')

  `scipy.stats` distributions are instances, so here we subclass `rv_continuous` and create an instance. With this, we now have a fully functional distribution with all relevant methods automagically generated by the framework.

  Note that above we defined a standard normal distribution, with zero mean and unit variance. Shifting and scaling of the distribution can be done by using `loc` and `scale` parameters: `gaussian.pdf(x, loc, scale)` essentially computes `y = (x - loc) / scale` and `gaussian._pdf(y) / scale`.

  Source code

      class radialNFW(st.rv_continuous):
          def _pdf(self, x):
              y = 1 / (x * (1 + x)**2) * x**2
              # Normalize pdf in (0, 5) range
              y /= (-5/6 + np.log(6))

`class radialPlummer (ancestors: scipy.stats._distn_infrastructure.rv_continuous, scipy.stats._distn_infrastructure.rv_generic)`

  A generic continuous random variable class meant for subclassing.

  `rv_continuous` is a base class to construct specific distribution classes and instances for continuous random variables. It cannot be used directly as a distribution.

  ## Parameters

  `momtype` :`int`, optional
    The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
  `a` :`float`, optional
    Lower bound of the support of the distribution, default is minus infinity.
  `b` :`float`, optional
    Upper bound of the support of the distribution, default is plus infinity.
  `xtol` :`float`, optional
    The tolerance for fixed point calculation for generic ppf.
  `badvalue` :`float`, optional
    The value in a result arrays that indicates a value that for which some argument restriction is violated, default is np.nan.
  `name` :`str`, optional
    The name of the instance. This string is used to construct the default example for distributions.
  `longname` :`str`, optional
    This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: `longname` exists for backwards compatibility, do not use for new subclasses.
  `shapes` :`str`, optional
    The shape of the distribution. For example `"m, n"` for a distribution that takes two integers as the two shape arguments for all its methods. If not provided, shape parameters will be inferred from the signature of the private methods, `_pdf` and `_cdf` of the instance.
  `extradoc` : `str`, optional, `deprecated`
    This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: `extradoc` exists for backwards compatibility, do not use for new subclasses.
  `seed` :`None` or `int` or `numpy.random.RandomState` `instance`, optional
    This parameter defines the RandomState object to use for drawing random variates. If None (or np.random), the global np.random state is used. If integer, it is used to seed the local RandomState instance. Default is None.

  ## Methods

  rvs pdf logpdf cdf logcdf sf logsf ppf isf moment stats entropy expect median mean std var interval call fit fit_loc_scale nnlf

  ## Notes

  Public methods of an instance of a distribution class (e.g., `pdf`, `cdf`) check their arguments and pass valid arguments to private, computational methods (`_pdf`, `_cdf`). For `pdf(x)`, `x` is valid if it is within the support of a distribution, `self.a <= x <= self.b`. Whether a shape parameter is valid is decided by an `_argcheck` method (which defaults to checking that its arguments are strictly positive.)

  Subclassing

  New random variables can be defined by subclassing the `rv_continuous` class and re-defining at least the `_pdf` or the `_cdf` method (normalized to location 0 and scale 1).

  If positive argument checking is not correct for your RV then you will also need to re-define the `_argcheck` method.

  Correct, but potentially slow defaults exist for the remaining methods but for speed and/or accuracy you can over-ride::

  _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf

  Rarely would you override `_isf`, `_sf` or `_logsf`, but you could.

  Methods that can be overwritten by subclasses ::

  _rvs _pdf _cdf _sf _ppf _isf _stats _munp _entropy _argcheck

  There are additional (internal and private) generic methods that can be useful for cross-checking and for debugging, but might work in all cases when directly called.

  A note on `shapes`: subclasses need not specify them explicitly. In this case, `shapes` will be automatically deduced from the signatures of the overridden methods (`pdf`, `cdf` etc). If, for some reason, you prefer to avoid relying on introspection, you can specify `shapes` explicitly as an argument to the instance constructor.

  Frozen Distributions

  Normally, you must provide shape parameters (and, optionally, location and scale parameters to each call of a method of a distribution.

  Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a "frozen" continuous RV object:

  rv = generic(<shape(s)>, loc=0, scale=1) frozen RV object with the same methods but holding the given shape, location, and scale fixed

  Statistics

  Statistics are computed using numerical integration by default. For speed you can redefine this using `_stats`:

  - take shape parameters and return mu, mu2, g1, g2
  - If you can't compute one of these, return it as None
  - Can also be defined with a keyword argument `moments`, which is a string composed of "m", "v", "s", and/or "k". Only the components appearing in string should be computed and returned in the order "m", "v", "s", or "k" with missing values returned as None.

  Alternatively, you can override `_munp`, which takes `n` and shape parameters and returns the n-th non-central moment of the distribution.

  ## Examples

  To create a new Gaussian distribution, we would do the following:

      >>> from scipy.stats import rv_continuous
      >>> class gaussian_gen(rv_continuous):
      ...     "Gaussian distribution"
      ...     def _pdf(self, x):
      ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
      >>> gaussian = gaussian_gen(name='gaussian')

  `scipy.stats` distributions are instances, so here we subclass `rv_continuous` and create an instance. With this, we now have a fully functional distribution with all relevant methods automagically generated by the framework.

  Note that above we defined a standard normal distribution, with zero mean and unit variance. Shifting and scaling of the distribution can be done by using `loc` and `scale` parameters: `gaussian.pdf(x, loc, scale)` essentially computes `y = (x - loc) / scale` and `gaussian._pdf(y) / scale`.

  Source code

      class radialPlummer(st.rv_continuous):
          def _pdf(self, x):#(3M/4πa3)(1+(r/a)2)−5/2
              return 3/(4*np.pi**3) * (1 + x**2)**(-5/2) * 4*np.pi*x**2

`class radialUniform (ancestors: scipy.stats._distn_infrastructure.rv_continuous, scipy.stats._distn_infrastructure.rv_generic)`

  A generic continuous random variable class meant for subclassing.

  `rv_continuous` is a base class to construct specific distribution classes and instances for continuous random variables. It cannot be used directly as a distribution.

  ## Parameters

  `momtype` :`int`, optional
    The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
  `a` :`float`, optional
    Lower bound of the support of the distribution, default is minus infinity.
  `b` :`float`, optional
    Upper bound of the support of the distribution, default is plus infinity.
  `xtol` :`float`, optional
    The tolerance for fixed point calculation for generic ppf.
  `badvalue` :`float`, optional
    The value in a result arrays that indicates a value that for which some argument restriction is violated, default is np.nan.
  `name` :`str`, optional
    The name of the instance. This string is used to construct the default example for distributions.
  `longname` :`str`, optional
    This string is used as part of the first line of the docstring returned when a subclass has no docstring of its own. Note: `longname` exists for backwards compatibility, do not use for new subclasses.
  `shapes` :`str`, optional
    The shape of the distribution. For example `"m, n"` for a distribution that takes two integers as the two shape arguments for all its methods. If not provided, shape parameters will be inferred from the signature of the private methods, `_pdf` and `_cdf` of the instance.
  `extradoc` : `str`, optional, `deprecated`
    This string is used as the last part of the docstring returned when a subclass has no docstring of its own. Note: `extradoc` exists for backwards compatibility, do not use for new subclasses.
  `seed` :`None` or `int` or `numpy.random.RandomState` `instance`, optional
    This parameter defines the RandomState object to use for drawing random variates. If None (or np.random), the global np.random state is used. If integer, it is used to seed the local RandomState instance. Default is None.

  ## Methods

  rvs pdf logpdf cdf logcdf sf logsf ppf isf moment stats entropy expect median mean std var interval call fit fit_loc_scale nnlf

  ## Notes

  Public methods of an instance of a distribution class (e.g., `pdf`, `cdf`) check their arguments and pass valid arguments to private, computational methods (`_pdf`, `_cdf`). For `pdf(x)`, `x` is valid if it is within the support of a distribution, `self.a <= x <= self.b`. Whether a shape parameter is valid is decided by an `_argcheck` method (which defaults to checking that its arguments are strictly positive.)

  Subclassing

  New random variables can be defined by subclassing the `rv_continuous` class and re-defining at least the `_pdf` or the `_cdf` method (normalized to location 0 and scale 1).

  If positive argument checking is not correct for your RV then you will also need to re-define the `_argcheck` method.

  Correct, but potentially slow defaults exist for the remaining methods but for speed and/or accuracy you can over-ride::

  _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf

  Rarely would you override `_isf`, `_sf` or `_logsf`, but you could.

  Methods that can be overwritten by subclasses ::

  _rvs _pdf _cdf _sf _ppf _isf _stats _munp _entropy _argcheck

  There are additional (internal and private) generic methods that can be useful for cross-checking and for debugging, but might work in all cases when directly called.

  A note on `shapes`: subclasses need not specify them explicitly. In this case, `shapes` will be automatically deduced from the signatures of the overridden methods (`pdf`, `cdf` etc). If, for some reason, you prefer to avoid relying on introspection, you can specify `shapes` explicitly as an argument to the instance constructor.

  Frozen Distributions

  Normally, you must provide shape parameters (and, optionally, location and scale parameters to each call of a method of a distribution.

  Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a "frozen" continuous RV object:

  rv = generic(<shape(s)>, loc=0, scale=1) frozen RV object with the same methods but holding the given shape, location, and scale fixed

  Statistics

  Statistics are computed using numerical integration by default. For speed you can redefine this using `_stats`:

  - take shape parameters and return mu, mu2, g1, g2
  - If you can't compute one of these, return it as None
  - Can also be defined with a keyword argument `moments`, which is a string composed of "m", "v", "s", and/or "k". Only the components appearing in string should be computed and returned in the order "m", "v", "s", or "k" with missing values returned as None.

  Alternatively, you can override `_munp`, which takes `n` and shape parameters and returns the n-th non-central moment of the distribution.

  ## Examples

  To create a new Gaussian distribution, we would do the following:

      >>> from scipy.stats import rv_continuous
      >>> class gaussian_gen(rv_continuous):
      ...     "Gaussian distribution"
      ...     def _pdf(self, x):
      ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
      >>> gaussian = gaussian_gen(name='gaussian')

  `scipy.stats` distributions are instances, so here we subclass `rv_continuous` and create an instance. With this, we now have a fully functional distribution with all relevant methods automagically generated by the framework.

  Note that above we defined a standard normal distribution, with zero mean and unit variance. Shifting and scaling of the distribution can be done by using `loc` and `scale` parameters: `gaussian.pdf(x, loc, scale)` essentially computes `y = (x - loc) / scale` and `gaussian._pdf(y) / scale`.

  Source code

      class radialUniform(st.rv_continuous):
          def _pdf(self, x):
              return 2*x if x<1 else 0