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authorRalf Gommers <ralf.gommers@googlemail.com>2012-03-03 21:20:13 +0100
committerRalf Gommers <ralf.gommers@googlemail.com>2012-03-03 22:34:02 +0100
commitf4dd54aa614b263950b7a57329eb0ab9a2f2eadf (patch)
treeef7178a9b7801f42c31290624faf76a1cf5b969c
parent91f87e1f613630ff0ad9864017f059afcd6e57f1 (diff)
downloadnumpy-f4dd54aa614b263950b7a57329eb0ab9a2f2eadf.tar.gz
DOC: merge wiki doc edits.
Diffstat
-rw-r--r--doc/source/dev/gitwash/following_latest.rst7
-rw-r--r--doc/source/reference/arrays.interface.rst2
-rw-r--r--doc/source/reference/swig.interface-file.rst7
-rw-r--r--numpy/add_newdocs.py21
-rw-r--r--numpy/core/code_generators/ufunc_docstrings.py4
-rw-r--r--numpy/core/defchararray.py11
-rw-r--r--numpy/core/fromnumeric.py2
-rw-r--r--numpy/core/memmap.py15
-rw-r--r--numpy/core/numeric.py24
-rw-r--r--numpy/fft/info.py22
-rw-r--r--numpy/lib/function_base.py10
-rw-r--r--numpy/lib/npyio.py69
-rw-r--r--numpy/lib/shape_base.py4
-rw-r--r--numpy/lib/twodim_base.py23
-rw-r--r--numpy/linalg/linalg.py1
-rw-r--r--numpy/random/mtrand/mtrand.c2386
-rw-r--r--numpy/random/mtrand/mtrand.pyx62
17 files changed, 1351 insertions, 1319 deletions
diff --git a/doc/source/dev/gitwash/following_latest.rst b/doc/source/dev/gitwash/following_latest.rst
index 5388ce104..8e0b6e797 100644
--- a/doc/source/dev/gitwash/following_latest.rst
+++ b/doc/source/dev/gitwash/following_latest.rst
@@ -10,7 +10,7 @@ These are the instructions if you just want to follow the latest
The steps are:
* :ref:`install-git`
-* get local copy of the git repository from github_
+* get local copy of the git repository from Github_
* update local copy from time to time
Get the local copy of the code
@@ -21,6 +21,9 @@ From the command line::
git clone git://github.com/numpy/numpy.git
You now have a copy of the code tree in the new ``numpy`` directory.
+If this doesn't work you can try the alternative read-only url::
+
+ git clone https://github.com/numpy/numpy.git
Updating the code
=================
@@ -34,4 +37,4 @@ From time to time you may want to pull down the latest code. Do this with::
The tree in ``numpy`` will now have the latest changes from the initial
repository.
-.. include:: git_links.inc
+.. _Github: https://github.com/numpy
diff --git a/doc/source/reference/arrays.interface.rst b/doc/source/reference/arrays.interface.rst
index 87ba15a9f..16abe5ce1 100644
--- a/doc/source/reference/arrays.interface.rst
+++ b/doc/source/reference/arrays.interface.rst
@@ -18,7 +18,7 @@ The Array Interface
uses the :pep:`3118` API; see the `Cython numpy
tutorial`__. Cython provides a way to write code that supports the buffer
protocol with Python versions older than 2.6 because it has a
- backward-compatible implementation utilizing the legacy array interface
+ backward-compatible implementation utilizing the array interface
described here.
__ http://cython.org/
diff --git a/doc/source/reference/swig.interface-file.rst b/doc/source/reference/swig.interface-file.rst
index 53bf68756..0a358bd6e 100644
--- a/doc/source/reference/swig.interface-file.rst
+++ b/doc/source/reference/swig.interface-file.rst
@@ -98,13 +98,13 @@ above will produce wrapper code that looks something like::
6 PyArrayObject *array1 = NULL ;
7 int is_new_object1 = 0 ;
8 PyObject * obj0 = 0 ;
- 9
+ 9
10 if (!PyArg_ParseTuple(args,(char *)"O:rms",&obj0)) SWIG_fail;
11 {
12 array1 = obj_to_array_contiguous_allow_conversion(
13 obj0, NPY_DOUBLE, &is_new_object1);
14 npy_intp size[1] = {
- 15 -1
+ 15 -1
16 };
17 if (!array1 || !require_dimensions(array1, 1) ||
18 !require_size(array1, size, 1)) SWIG_fail;
@@ -484,7 +484,7 @@ If you get a Python error that looks like the following::
and the argument you are passing is an integer extracted from a
NumPy array, then you have stumbled upon this problem. The
solution is to modify the `SWIG`_ type conversion system to accept
-`Numpy`_ array scalars in addition to the standard integer types.
+Numpy array scalars in addition to the standard integer types.
Fortunately, this capabilitiy has been provided for you. Simply copy
the file::
@@ -927,4 +927,3 @@ wrapper developers, including:
* Nine C macros and 13 C functions that can be used to write
specialized typemaps, extensions, or inlined functions that handle
cases not covered by the provided typemaps.
-
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py
index ab4dc5681..dd48191b6 100644
--- a/numpy/add_newdocs.py
+++ b/numpy/add_newdocs.py
@@ -1406,7 +1406,7 @@ add_newdoc('numpy.core.multiarray', 'arange',
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
- but returns a ndarray rather than a list.
+ but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better to use ``linspace`` for these cases.
@@ -1432,7 +1432,7 @@ add_newdoc('numpy.core.multiarray', 'arange',
Returns
-------
- out : ndarray
+ arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
@@ -1443,8 +1443,8 @@ add_newdoc('numpy.core.multiarray', 'arange',
See Also
--------
linspace : Evenly spaced numbers with careful handling of endpoints.
- ogrid: Arrays of evenly spaced numbers in N-dimensions
- mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions
+ ogrid: Arrays of evenly spaced numbers in N-dimensions.
+ mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
@@ -1584,6 +1584,17 @@ add_newdoc('numpy.core.multiarray', 'where',
[ 3., 4., -1.],
[-1., -1., -1.]])
+ Find the indices of elements of `x` that are in `goodvalues`.
+
+ >>> goodvalues = [3, 4, 7]
+ >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)
+ >>> ix
+ array([[False, False, False],
+ [ True, True, False],
+ [False, True, False]], dtype=bool)
+ >>> np.where(ix)
+ (array([1, 1, 2]), array([0, 1, 1]))
+
""")
@@ -2236,7 +2247,7 @@ add_newdoc('numpy.core', 'einsum',
add_newdoc('numpy.core', 'alterdot',
"""
- Change `dot`, `vdot`, and `innerproduct` to use accelerated BLAS functions.
+ Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.
Typically, as a user of Numpy, you do not explicitly call this function. If
Numpy is built with an accelerated BLAS, this function is automatically
diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py
index 2e1cdd460..8d7b83239 100644
--- a/numpy/core/code_generators/ufunc_docstrings.py
+++ b/numpy/core/code_generators/ufunc_docstrings.py
@@ -71,7 +71,7 @@ add_newdoc('numpy.core.umath', 'add',
Returns
-------
- y : ndarray or scalar
+ add : ndarray or scalar
The sum of `x1` and `x2`, element-wise. Returns a scalar if
both `x1` and `x2` are scalars.
@@ -173,7 +173,7 @@ add_newdoc('numpy.core.umath', 'arccosh',
Returns
-------
- y : ndarray
+ arccosh : ndarray
Array of the same shape as `x`.
See Also
diff --git a/numpy/core/defchararray.py b/numpy/core/defchararray.py
index 850e2dae2..a7b3fd1b7 100644
--- a/numpy/core/defchararray.py
+++ b/numpy/core/defchararray.py
@@ -265,12 +265,14 @@ def add(x1, x2):
Parameters
----------
x1 : array_like of str or unicode
+
x2 : array_like of str or unicode
Returns
-------
- out : ndarray
+ add : ndarray
Output array of `string_` or `unicode_`, depending on input types
+
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
@@ -608,14 +610,16 @@ def expandtabs(a, tabsize=8):
Return a copy of each string element where all tab characters are
replaced by one or more spaces, depending on the current column
and the given `tabsize`. The column number is reset to zero after
- each newline occurring in the string. If `tabsize` is not given, a
- tab size of 8 characters is assumed. This doesn't understand other
+ each newline occurring in the string. This doesn't understand other
non-printing characters or escape sequences.
Parameters
----------
a : array_like of str or unicode
+ Input array
tabsize : int, optional
+ Replace tabs with `tabsize` number of spaces. If not given defaults
+ to 8 spaces.
Returns
-------
@@ -625,6 +629,7 @@ def expandtabs(a, tabsize=8):
See also
--------
str.expandtabs
+
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'expandtabs', (tabsize,)))
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 8e7a556ac..8757b85bf 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -1986,7 +1986,7 @@ def alen(a):
Returns
-------
- l : int
+ alen : int
Length of the first dimension of `a`.
See Also
diff --git a/numpy/core/memmap.py b/numpy/core/memmap.py
index 1ab9f87fc..53f9a3f07 100644
--- a/numpy/core/memmap.py
+++ b/numpy/core/memmap.py
@@ -60,11 +60,16 @@ class memmap(ndarray):
Default is 'r+'.
offset : int, optional
In the file, array data starts at this offset. Since `offset` is
- measured in bytes, it should be a multiple of the byte-size of
- `dtype`. Requires ``shape=None``. The default is 0.
+ measured in bytes, it should normally be a multiple of the byte-size
+ of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
+ file are valid; The file will be extended to accommodate the
+ additional data. The default offset is 0.
shape : tuple, optional
- The desired shape of the array. By default, the returned array will be
- 1-D with the number of elements determined by file size and data-type.
+ The desired shape of the array. If ``mode == 'r'`` and the number
+ of remaining bytes after `offset` is not a multiple of the byte-size
+ of `dtype`, you must specify `shape`. By default, the returned array
+ will be 1-D with the number of elements determined by file size
+ and data-type.
order : {'C', 'F'}, optional
Specify the order of the ndarray memory layout: C (row-major) or
Fortran (column-major). This only has an effect if the shape is
@@ -79,7 +84,6 @@ class memmap(ndarray):
mode : str
File mode.
-
Methods
-------
close
@@ -89,6 +93,7 @@ class memmap(ndarray):
When you delete a memmap object, flush is called first to write
changes to disk before removing the object.
+
Notes
-----
The memmap object can be used anywhere an ndarray is accepted.
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index ba8fc1f52..bb13d573e 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -1663,12 +1663,11 @@ def fromfunction(function, shape, **kwargs):
Parameters
----------
function : callable
- The function is called with N parameters, each of which
- represents the coordinates of the array varying along a
- specific axis. For example, if `shape` were ``(2, 2)``, then
- the parameters would be two arrays, ``[[0, 0], [1, 1]]`` and
- ``[[0, 1], [0, 1]]``. `function` must be capable of operating on
- arrays, and should return a scalar value.
+ The function is called with N parameters, where N is the rank of
+ `shape`. Each parameter represents the coordinates of the array
+ varying along a specific axis. For example, if `shape`
+ were ``(2, 2)``, then the parameters in turn be (0, 0), (0, 1),
+ (1, 0), (1, 1).
shape : (N,) tuple of ints
Shape of the output array, which also determines the shape of
the coordinate arrays passed to `function`.
@@ -1678,10 +1677,11 @@ def fromfunction(function, shape, **kwargs):
Returns
-------
- out : any
+ fromfunction : any
The result of the call to `function` is passed back directly.
- Therefore the type and shape of `out` is completely determined by
- `function`.
+ Therefore the shape of `fromfunction` is completely determined by
+ `function`. If `function` returns a scalar value, the shape of
+ `fromfunction` would match the `shape` parameter.
See Also
--------
@@ -1689,7 +1689,7 @@ def fromfunction(function, shape, **kwargs):
Notes
-----
- Keywords other than `shape` and `dtype` are passed to `function`.
+ Keywords other than `dtype` are passed to `function`.
Examples
--------
@@ -1979,7 +1979,7 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8):
Returns
-------
- y : bool
+ allclose : bool
Returns True if the two arrays are equal within the given
tolerance; False otherwise. If either array contains NaN, then
False is returned.
@@ -2159,7 +2159,7 @@ def seterr(all=None, divide=None, over=None, under=None, invalid=None):
See also
--------
seterrcall : Set a callback function for the 'call' mode.
- geterr, geterrcall
+ geterr, geterrcall, errstate
Notes
-----
diff --git a/numpy/fft/info.py b/numpy/fft/info.py
index 890b2add2..f36a07ebf 100644
--- a/numpy/fft/info.py
+++ b/numpy/fft/info.py
@@ -4,7 +4,6 @@ Discrete Fourier Transform (:mod:`numpy.fft`)
.. currentmodule:: numpy.fft
-
Standard FFTs
-------------
@@ -31,7 +30,6 @@ Real FFTs
rfftn Real discrete Fourier transform in N dimensions.
irfftn Inverse real discrete Fourier transform in N dimensions.
-
Hermitian FFTs
--------------
@@ -41,7 +39,6 @@ Hermitian FFTs
hfft Hermitian discrete Fourier transform.
ihfft Inverse Hermitian discrete Fourier transform.
-
Helper routines
---------------
@@ -52,11 +49,12 @@ Helper routines
fftshift Shift zero-frequency component to center of spectrum.
ifftshift Inverse of fftshift.
+
Background information
----------------------
Fourier analysis is fundamentally a method for expressing a function as a
-sum of periodic components, and for recovering the signal from those
+sum of periodic components, and for recovering the function from those
components. When both the function and its Fourier transform are
replaced with discretized counterparts, it is called the discrete Fourier
transform (DFT). The DFT has become a mainstay of numerical computing in
@@ -74,6 +72,9 @@ referred to as a *signal*, which exists in the *time domain*. The output
is called a *spectrum* or *transform* and exists in the *frequency
domain*.
+Implementation details
+----------------------
+
There are many ways to define the DFT, varying in the sign of the
exponent, normalization, etc. In this implementation, the DFT is defined
as
@@ -97,7 +98,7 @@ For an even number of input points, ``A[n/2]`` represents both positive and
negative Nyquist frequency, and is also purely real for real input. For
an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
-The routine ``np.fft.fftfreq(A)`` returns an array giving the frequencies
+The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
of corresponding elements in the output. The routine
``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
@@ -117,7 +118,7 @@ It differs from the forward transform by the sign of the exponential
argument and the normalization by :math:`1/n`.
Real and Hermitian transforms
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-----------------------------
When the input is purely real, its transform is Hermitian, i.e., the
component at frequency :math:`f_k` is the complex conjugate of the
@@ -142,18 +143,21 @@ also be a faster way to compute large convolutions, using the property
that a convolution in the time domain is equivalent to a point-by-point
multiplication in the frequency domain.
+Higher dimensions
+-----------------
+
In two dimensions, the DFT is defined as
.. math::
A_{kl} = \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
- \\qquad k = 0, \\ldots, N-1;\\quad l = 0, \\ldots, M-1,
+ \\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
which extends in the obvious way to higher dimensions, and the inverses
in higher dimensions also extend in the same way.
References
-^^^^^^^^^^
+----------
.. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
machine calculation of complex Fourier series," *Math. Comput.*
@@ -164,7 +168,7 @@ References
12-13. Cambridge Univ. Press, Cambridge, UK.
Examples
-^^^^^^^^
+--------
For examples, see the various functions.
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 4ab1679e5..2de5c6193 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -2408,7 +2408,7 @@ def hamming(M):
-----
The Hamming window is defined as
- .. math:: w(n) = 0.54 + 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
+ .. math:: w(n) = 0.54 - 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and
@@ -3000,8 +3000,8 @@ def percentile(a, q, axis=None, out=None, overwrite_input=False):
Given a vector V of length N, the qth percentile of V is the qth ranked
value in a sorted copy of V. A weighted average of the two nearest
neighbors is used if the normalized ranking does not match q exactly.
- The same as the median if ``q=0.5``, the same as the minimum if ``q=0``
- and the same as the maximum if ``q=1``.
+ The same as the median if ``q=50``, the same as the minimum if ``q=0``
+ and the same as the maximum if ``q=100``.
Examples
--------
@@ -3108,7 +3108,7 @@ def trapz(y, x=None, dx=1.0, axis=-1):
Returns
-------
- out : float
+ trapz : float
Definite integral as approximated by trapezoidal rule.
See Also
@@ -3547,7 +3547,7 @@ def append(arr, values, axis=None):
Returns
-------
- out : ndarray
+ append : ndarray
A copy of `arr` with `values` appended to `axis`. Note that `append`
does not occur in-place: a new array is allocated and filled. If
`axis` is None, `out` is a flattened array.
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index c502e2cc5..82bac7d81 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -274,7 +274,7 @@ class NpzFile(object):
def load(file, mmap_mode=None):
"""
- Load a pickled, ``.npy``, or ``.npz`` binary file.
+ Load an array(s) or pickled objects from .npy, .npz, or pickled files.
Parameters
----------
@@ -283,13 +283,11 @@ def load(file, mmap_mode=None):
If the filename extension is ``.gz``, the file is first decompressed.
mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, then memory-map the file, using the given mode
- (see `numpy.memmap`). The mode has no effect for pickled or
- zipped files.
- A memory-mapped array is stored on disk, and not directly loaded
- into memory. However, it can be accessed and sliced like any
- ndarray. Memory mapping is especially useful for accessing
- small fragments of large files without reading the entire file
- into memory.
+ (see `numpy.memmap` for a detailed description of the modes).
+ A memory-mapped array is kept on disk. However, it can be accessed
+ and sliced like any ndarray. Memory mapping is especially useful for
+ accessing small fragments of large files without reading the entire
+ file into memory.
Returns
-------
@@ -309,9 +307,9 @@ def load(file, mmap_mode=None):
Notes
-----
- - If the file contains pickle data, then whatever is stored in the
- pickle is returned.
- - If the file is a ``.npy`` file, then an array is returned.
+ - If the file contains pickle data, then whatever object is stored
+ in the pickle is returned.
+ - If the file is a ``.npy`` file, then a single array is returned.
- If the file is a ``.npz`` file, then a dictionary-like object is
returned, containing ``{filename: array}`` key-value pairs, one for
each file in the archive.
@@ -321,7 +319,7 @@ def load(file, mmap_mode=None):
with load('foo.npz') as data:
a = data['a']
- The underlyling file descriptor is always closed when exiting the with block.
+ The underlyling file descriptor is closed when exiting the 'with' block.
Examples
--------
@@ -334,8 +332,10 @@ def load(file, mmap_mode=None):
Store compressed data to disk, and load it again:
- >>> np.savez('/tmp/123.npz', a=np.array([[1, 2, 3], [4, 5, 6]]), b=np.array([1, 2]))
- >>> data = np.load('/tmp/123.npy')
+ >>> a=np.array([[1, 2, 3], [4, 5, 6]])
+ >>> b=np.array([1, 2])
+ >>> np.savez('/tmp/123.npz', a=a, b=b)
+ >>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
[4, 5, 6]])
@@ -454,12 +454,12 @@ def savez(file, *args, **kwds):
Either the file name (string) or an open file (file-like object)
where the data will be saved. If file is a string, the ``.npz``
extension will be appended to the file name if it is not already there.
- *args : Arguments, optional
+ args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
know the names of the arrays outside `savez`, the arrays will be saved
with names "arr_0", "arr_1", and so on. These arguments can be any
expression.
- **kwds : Keyword arguments, optional
+ kwds : Keyword arguments, optional
Arrays to save to the file. Arrays will be saved in the file with the
keyword names.
@@ -471,6 +471,8 @@ def savez(file, *args, **kwds):
--------
save : Save a single array to a binary file in NumPy format.
savetxt : Save an array to a file as plain text.
+ numpy.savez_compressed : Save several arrays into a compressed .npz file
+ format
Notes
-----
@@ -491,7 +493,7 @@ def savez(file, *args, **kwds):
>>> x = np.arange(10)
>>> y = np.sin(x)
- Using `savez` with *args, the arrays are saved with default names.
+ Using `savez` with \\*args, the arrays are saved with default names.
>>> np.savez(outfile, x, y)
>>> outfile.seek(0) # Only needed here to simulate closing & reopening file
@@ -501,7 +503,7 @@ def savez(file, *args, **kwds):
>>> npzfile['arr_0']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
- Using `savez` with **kwds, the arrays are saved with the keyword names.
+ Using `savez` with \\**kwds, the arrays are saved with the keyword names.
>>> outfile = TemporaryFile()
>>> np.savez(outfile, x=x, y=y)
@@ -512,10 +514,6 @@ def savez(file, *args, **kwds):
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
- See Also
- --------
- numpy.savez_compressed : Save several arrays into a compressed .npz file format
-
"""
_savez(file, args, kwds, False)
@@ -1210,8 +1208,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
autostrip : bool, optional
Whether to automatically strip white spaces from the variables.
replace_space : char, optional
- Character(s) used in replacement of white spaces in the variables names.
- By default, use a '_'.
+ Character(s) used in replacement of white spaces in the variables
+ names. By default, use a '_'.
case_sensitive : {True, False, 'upper', 'lower'}, optional
If True, field names are case sensitive.
If False or 'upper', field names are converted to upper case.
@@ -1247,6 +1245,11 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
* Individual values are not stripped of spaces by default.
When using a custom converter, make sure the function does remove spaces.
+ References
+ ----------
+ .. [1] Numpy User Guide, section `I/O with Numpy
+ <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
+
Examples
---------
>>> from StringIO import StringIO
@@ -1770,8 +1773,9 @@ def ndfromtxt(fname, **kwargs):
"""
Load ASCII data stored in a file and return it as a single array.
- Complete description of all the optional input parameters is available in
- the docstring of the `genfromtxt` function.
+ Parameters
+ ----------
+ fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
@@ -1786,7 +1790,9 @@ def mafromtxt(fname, **kwargs):
"""
Load ASCII data stored in a text file and return a masked array.
- For a complete description of all the input parameters, see `genfromtxt`.
+ Parameters
+ ----------
+ fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
@@ -1804,8 +1810,9 @@ def recfromtxt(fname, **kwargs):
If ``usemask=False`` a standard `recarray` is returned,
if ``usemask=True`` a MaskedRecords array is returned.
- Complete description of all the optional input parameters is available in
- the docstring of the `genfromtxt` function.
+ Parameters
+ ----------
+ fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
@@ -1836,7 +1843,9 @@ def recfromcsv(fname, **kwargs):
`recarray`) or a masked record array (if ``usemask=True``,
see `ma.mrecords.MaskedRecords`).
- For a complete description of all the input parameters, see `genfromtxt`.
+ Parameters
+ ----------
+ fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index 946cf172a..b88596ca4 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -29,7 +29,7 @@ def apply_along_axis(func1d,axis,arr,*args):
Returns
-------
- outarr : ndarray
+ apply_along_axis : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension, where the length of `outarr`
is equal to the size of the return value of `func1d`. If `func1d`
@@ -142,7 +142,7 @@ def apply_over_axes(func, a, axes):
Returns
-------
- val : ndarray
+ apply_over_axis : ndarray
The output array. The number of dimensions is the same as `a`,
but the shape can be different. This depends on whether `func`
changes the shape of its output with respect to its input.
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index 6fb348275..58d8250a1 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -358,7 +358,7 @@ def tri(N, M=None, k=0, dtype=float):
Returns
-------
- T : ndarray of shape (N, M)
+ tri : ndarray of shape (N, M)
Array with its lower triangle filled with ones and zero elsewhere;
in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
@@ -396,7 +396,7 @@ def tril(m, k=0):
Returns
-------
- L : ndarray, shape (M, N)
+ tril : ndarray, shape (M, N)
Lower triangle of `m`, of same shape and data-type as `m`.
See Also
@@ -790,9 +790,10 @@ def triu_indices(n, k=0):
Returns
-------
- inds : tuple of arrays
+ inds : tuple, shape(2) of ndarrays, shape(`n`)
The indices for the triangle. The returned tuple contains two arrays,
- each with the indices along one dimension of the array.
+ each with the indices along one dimension of the array. Can be used
+ to slice a ndarray of shape(`n`, `n`).
See also
--------
@@ -852,17 +853,21 @@ def triu_indices(n, k=0):
def triu_indices_from(arr, k=0):
"""
- Return the indices for the upper-triangle of an (n, n) array.
+ Return the indices for the upper-triangle of a (N, N) array.
See `triu_indices` for full details.
Parameters
----------
- arr : array_like
- The indices will be valid for square arrays whose dimensions are
- the same as arr.
+ arr : ndarray, shape(N, N)
+ The indices will be valid for square arrays.
k : int, optional
- Diagonal offset (see `triu` for details).
+ Diagonal offset (see `triu` for details).
+
+ Returns
+ -------
+ triu_indices_from : tuple, shape(2) of ndarray, shape(N)
+ Indices for the upper-triangle of `arr`.
See Also
--------
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index 4aaf260f8..aba656b5e 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -1454,6 +1454,7 @@ def matrix_rank(M, tol=None):
Examples
--------
+ >>> from numpy.linalg import matrix_rank
>>> matrix_rank(np.eye(4)) # Full rank matrix
4
>>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index 8f451eee2..9f82736b4 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 0.15 on Sat Mar 3 18:15:37 2012 */
+/* Generated by Cython 0.15 on Sat Mar 3 21:58:53 2012 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
@@ -802,36 +802,36 @@ static char __pyx_k_244[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n
static char __pyx_k_245[] = "RandomState.logistic (line 2921)";
static char __pyx_k_246[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n ";
static char __pyx_k_247[] = "RandomState.lognormal (line 3009)";
-static char __pyx_k_248[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color='r', linewidth=2)\n >>> plt.show()\n\n ";
-static char __pyx_k_249[] = "RandomState.rayleigh (line 3140)";
+static char __pyx_k_248[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n ";
+static char __pyx_k_249[] = "RandomState.rayleigh (line 3130)";
static char __pyx_k_250[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n ..[2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n ";
-static char __pyx_k_251[] = "RandomState.wald (line 3212)";
+static char __pyx_k_251[] = "RandomState.wald (line 3202)";
static char __pyx_k_252[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n ..[3] Wikipedia, \"Wald distributio""n\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n ";
-static char __pyx_k_253[] = "RandomState.triangular (line 3298)";
+static char __pyx_k_253[] = "RandomState.triangular (line 3288)";
static char __pyx_k_254[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n ..[1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n ... normed=True)\n >>> plt.show()\n\n ";
-static char __pyx_k_255[] = "RandomState.binomial (line 3386)";
+static char __pyx_k_255[] = "RandomState.binomial (line 3376)";
static char __pyx_k_256[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer > 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n "" .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n ";
-static char __pyx_k_257[] = "RandomState.negative_binomial (line 3494)";
+static char __pyx_k_257[] = "RandomState.negative_binomial (line 3484)";
static char __pyx_k_258[] = "\n negative_binomial(n, p, size=None)\n\n Draw samples from a negative_binomial distribution.\n\n Samples are drawn from a negative_Binomial distribution with specified\n parameters, `n` trials and `p` probability of success where `n` is an\n integer > 0 and `p` is in the interval [0, 1].\n\n Parameters\n ----------\n n : int\n Parameter, > 0.\n p : float\n Parameter, >= 0 and <=1.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. What is the probability\n of having one success for each successive well, that is what is the\n probability of a single success after drilling 5 wells, after 6 wells,\n etc.?\n\n >>> s = np.random.negative_binomial(1, 0.1, 100000)\n >>> for i in range(1, 11):\n ... probability = sum(s<i) / 100000.\n ... print i, \"wells drilled, probability of one success =\", probability\n\n ";
-static char __pyx_k_259[] = "RandomState.poisson (line 3589)";
+static char __pyx_k_259[] = "RandomState.poisson (line 3579)";
static char __pyx_k_260[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n ";
-static char __pyx_k_261[] = "RandomState.zipf (line 3660)";
+static char __pyx_k_261[] = "RandomState.zipf (line 3650)";
static char __pyx_k_262[] = "\n zipf(a, size=None)\n\n Draw samples from a Zipf distribution.\n\n Samples are drawn from a Zipf distribution with specified parameter\n `a` > 1.\n\n The Zipf distribution (also known as the zeta distribution) is a\n continuous probability distribution that satisfies Zipf's law: the\n frequency of an item is inversely proportional to its rank in a\n frequency table.\n\n Parameters\n ----------\n a : float > 1\n Distribution parameter.\n size : int or tuple of int, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 2. # parameter\n >>> s = np.random.zipf""(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n Truncate s values at 50 so plot is interesting\n >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n >>> x = np.arange(1., 50.)\n >>> y = x**(-a)/sps.zetac(a)\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n ";
-static char __pyx_k_263[] = "RandomState.geometric (line 3748)";
+static char __pyx_k_263[] = "RandomState.geometric (line 3738)";
static char __pyx_k_264[] = "\n geometric(p, size=None)\n\n Draw samples from the geometric distribution.\n\n Bernoulli trials are experiments with one of two outcomes:\n success or failure (an example of such an experiment is flipping\n a coin). The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. The output\n is shaped according to `size`.\n\n Returns\n -------\n out : ndarray\n Samples from the geometric distribution, shaped according to\n `size`.\n\n Examples\n --------\n Draw ten thousand values from the geometric distribution,\n with the probability of an individual success equal to 0.35:\n\n >>> z = np.random.geometric(p=0.35, size=10000)\n\n How many trials succeeded after a single run?\n\n >>> (z == 1).sum() / 10000.\n 0.34889999999999999 #random\n\n ";
-static char __pyx_k_265[] = "RandomState.hypergeometric (line 3814)";
+static char __pyx_k_265[] = "RandomState.hypergeometric (line 3804)";
static char __pyx_k_266[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : float (but truncated to an integer)\n parameter, > 0.\n nbad : float\n parameter, >= 0.\n nsample : float\n parameter, > 0 and <= ngood+nbad\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distribution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn wit""h\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n ";
-static char __pyx_k_267[] = "RandomState.logseries (line 3933)";
+static char __pyx_k_267[] = "RandomState.logseries (line 3923)";
static char __pyx_k_268[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n ";
-static char __pyx_k_269[] = "RandomState.multivariate_normal (line 4028)";
+static char __pyx_k_269[] = "RandomState.multivariate_normal (line 4018)";
static char __pyx_k_270[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we draw ""N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n ";
-static char __pyx_k_271[] = "RandomState.multinomial (line 4160)";
+static char __pyx_k_271[] = "RandomState.multinomial (line 4150)";
static char __pyx_k_272[] = "\n multinomial(n, pvals, size=None)\n\n Draw samples from a multinomial distribution.\n\n The multinomial distribution is a multivariate generalisation of the\n binomial distribution. Take an experiment with one of ``p``\n possible outcomes. An example of such an experiment is throwing a dice,\n where the outcome can be 1 through 6. Each sample drawn from the\n distribution represents `n` such experiments. Its values,\n ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n was ``i``.\n\n Parameters\n ----------\n n : int\n Number of experiments.\n pvals : sequence of floats, length p\n Probabilities of each of the ``p`` different outcomes. These\n should sum to 1 (however, the last element is always assumed to\n account for the remaining probability, as long as\n ``sum(pvals[:-1]) <= 1)``.\n size : tuple of ints\n Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n and the output shape becomes ``(M, N, K, p)``, since each sample\n has shape ``(p,)``.\n\n Examples\n --------\n Throw a dice 20 times:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=1)\n array([[4, 1, 7, 5, 2, 1]])\n\n It landed 4 times on 1, once on 2, etc.\n\n Now, throw the dice 20 times, and 20 times again:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=2)\n array([[3, 4, 3, 3, 4, 3],\n [2, 4, 3, 4, 0, 7]])\n\n For the first run, we threw 3 times 1, 4 times 2, etc. For the second,\n we threw 2 times 1, 4 times 2, etc.\n\n A loaded dice is more likely to land on number 6:\n\n >>> np.random.multinomial(100, [1/7.]*5)\n array([13, 16, 13, 16, 42])\n\n ";
-static char __pyx_k_273[] = "RandomState.dirichlet (line 4253)";
+static char __pyx_k_273[] = "RandomState.dirichlet (line 4243)";
static char __pyx_k_274[] = "\n dirichlet(alpha, size=None)\n\n Draw samples from the Dirichlet distribution.\n\n Draw `size` samples of dimension k from a Dirichlet distribution. A\n Dirichlet-distributed random variable can be seen as a multivariate\n generalization of a Beta distribution. Dirichlet pdf is the conjugate\n prior of a multinomial in Bayesian inference.\n\n Parameters\n ----------\n alpha : array\n Parameter of the distribution (k dimension for sample of\n dimension k).\n size : array\n Number of samples to draw.\n\n Returns\n -------\n samples : ndarray,\n The drawn samples, of shape (alpha.ndim, size).\n\n Notes\n -----\n .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n Uses the following property for computation: for each dimension,\n draw a random sample y_i from a standard gamma generator of shape\n `alpha_i`, then\n :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n Dirichlet distributed.\n\n References\n ----------\n .. [1] David McKay, \"Information Theory, Inference and Learning\n Algorithms,\" chapter 23,\n http://www.inference.phy.cam.ac.uk/mackay/\n .. [2] Wikipedia, \"Dirichlet distribution\",\n http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n Examples\n --------\n Taking an example cited in Wikipedia, this distribution can be used if\n one wanted to cut strings (each of initial length 1.0) into K pieces\n with different lengths, where each piece had, on average, a designated\n average length, but allowing some variation in the relative sizes of the\n pieces.\n\n >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n >>> plt.barh(range(20), s[0])\n >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n >>> plt.title(\"Lengths of Strings\")\n\n ";
-static char __pyx_k_275[] = "RandomState.shuffle (line 4369)";
+static char __pyx_k_275[] = "RandomState.shuffle (line 4359)";
static char __pyx_k_276[] = "\n shuffle(x)\n\n Modify a sequence in-place by shuffling its contents.\n\n Parameters\n ----------\n x : array_like\n The array or list to be shuffled.\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> arr = np.arange(10)\n >>> np.random.shuffle(arr)\n >>> arr\n [1 7 5 2 9 4 3 6 0 8]\n\n This function only shuffles the array along the first index of a\n multi-dimensional array:\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.shuffle(arr)\n >>> arr\n array([[3, 4, 5],\n [6, 7, 8],\n [0, 1, 2]])\n\n ";
-static char __pyx_k_277[] = "RandomState.permutation (line 4431)";
+static char __pyx_k_277[] = "RandomState.permutation (line 4421)";
static char __pyx_k_278[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n ";
static char __pyx_k__df[] = "df";
static char __pyx_k__mu[] = "mu";
@@ -13493,7 +13493,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_sel
*/
static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_37lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color='r', linewidth=2)\n >>> plt.show()\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_37lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n ";
static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
PyObject *__pyx_v_mean = 0;
PyObject *__pyx_v_sigma = 0;
@@ -13571,7 +13571,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
return NULL;
__pyx_L4_argument_unpacking_done:;
- /* "mtrand.pyx":3124
+ /* "mtrand.pyx":3114
* cdef double fmean, fsigma
*
* fmean = PyFloat_AsDouble(mean) # <<<<<<<<<<<<<<
@@ -13580,7 +13580,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
*/
__pyx_v_fmean = PyFloat_AsDouble(__pyx_v_mean);
- /* "mtrand.pyx":3125
+ /* "mtrand.pyx":3115
*
* fmean = PyFloat_AsDouble(mean)
* fsigma = PyFloat_AsDouble(sigma) # <<<<<<<<<<<<<<
@@ -13589,7 +13589,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
*/
__pyx_v_fsigma = PyFloat_AsDouble(__pyx_v_sigma);
- /* "mtrand.pyx":3127
+ /* "mtrand.pyx":3117
* fsigma = PyFloat_AsDouble(sigma)
*
* if not PyErr_Occurred(): # <<<<<<<<<<<<<<
@@ -13599,7 +13599,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
__pyx_t_1 = (!PyErr_Occurred());
if (__pyx_t_1) {
- /* "mtrand.pyx":3128
+ /* "mtrand.pyx":3118
*
* if not PyErr_Occurred():
* if fsigma <= 0: # <<<<<<<<<<<<<<
@@ -13609,23 +13609,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
__pyx_t_1 = (__pyx_v_fsigma <= 0.0);
if (__pyx_t_1) {
- /* "mtrand.pyx":3129
+ /* "mtrand.pyx":3119
* if not PyErr_Occurred():
* if fsigma <= 0:
* raise ValueError("sigma <= 0") # <<<<<<<<<<<<<<
* return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
*
*/
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3129; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L7;
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__pyx_L7:;
- /* "mtrand.pyx":3130
+ /* "mtrand.pyx":3120
* if fsigma <= 0:
* raise ValueError("sigma <= 0")
* return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma) # <<<<<<<<<<<<<<
@@ -13633,7 +13633,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
* PyErr_Clear()
*/
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+ __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_lognormal, __pyx_v_size, __pyx_v_fmean, __pyx_v_fsigma); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3120; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
__pyx_L6:;
- /* "mtrand.pyx":3132
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* return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
*
* PyErr_Clear() # <<<<<<<<<<<<<<
@@ -13651,52 +13651,52 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
*/
PyErr_Clear();
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* PyErr_Clear()
*
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* osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
* if np.any(np.less_equal(osigma, 0.0)):
*/
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*
* omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
* osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED) # <<<<<<<<<<<<<<
* if np.any(np.less_equal(osigma, 0.0)):
* raise ValueError("sigma <= 0.0")
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__pyx_t_2 = 0;
- /* "mtrand.pyx":3136
+ /* "mtrand.pyx":3126
* omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
* osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
* if np.any(np.less_equal(osigma, 0.0)): # <<<<<<<<<<<<<<
* raise ValueError("sigma <= 0.0")
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*/
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@@ -13704,40 +13704,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se
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- /* "mtrand.pyx":3137
+ /* "mtrand.pyx":3127
* osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
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* raise ValueError("sigma <= 0.0") # <<<<<<<<<<<<<<
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_110), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_Raise(__pyx_t_2, 0, 0, 0);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3137; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L8;
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__pyx_L8:;
- /* "mtrand.pyx":3138
+ /* "mtrand.pyx":3128
* if np.any(np.less_equal(osigma, 0.0)):
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+ __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_lognormal, __pyx_v_size, __pyx_v_omean, __pyx_v_osigma); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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return __pyx_r;
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+ __Pyx_RaiseArgtupleInvalid("rayleigh", 0, 0, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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if (__pyx_t_1) {
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_112), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3192; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_Raise(__pyx_t_2, 0, 0, 0);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L7;
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+ /* "mtrand.pyx":3193
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+ __pyx_t_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_fscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3193; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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}
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+ /* "mtrand.pyx":3195
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PyErr_Clear();
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+ __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3197; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2)));
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+ __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_3 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_3);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ __pyx_t_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(((PyObject *)__pyx_t_5));
__Pyx_INCREF(((PyObject *)__pyx_v_oscale));
PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_oscale));
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__Pyx_GIVEREF(__pyx_t_2);
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+ __pyx_t_2 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2);
__Pyx_GIVEREF(__pyx_t_2);
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+ __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
__Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0;
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
if (__pyx_t_1) {
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_114), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3199; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3199; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L8;
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__pyx_L8:;
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* if np.any(np.less_equal(oscale, 0.0)):
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+ __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3200; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
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return __pyx_r;
}
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+ __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 2:
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_120), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3293; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L9;
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__pyx_L9:;
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* elif np.any(np.less_equal(oscale,0.0)):
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*
*/
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- __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_wald, __pyx_v_size, __pyx_v_omean, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3294; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_wald, __pyx_v_size, __pyx_v_omean, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3284; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -14383,7 +14383,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P
return __pyx_r;
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*
* def triangular(self, left, mode, right, size=None): # <<<<<<<<<<<<<<
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values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mode);
if (likely(values[1])) kw_args--;
else {
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+ __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
case 2:
values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__right);
if (likely(values[2])) kw_args--;
else {
- __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3298; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 3:
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@@ -14452,7 +14452,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s
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+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "triangular") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
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+ __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3288; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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if (__pyx_t_1) {
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goto __pyx_L7;
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* if fmode > fright:
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_126), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_Raise(__pyx_t_2, 0, 0, 0);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3367; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L9;
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PyErr_Clear();
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__Pyx_Raise(__pyx_t_3, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3381; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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*/
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- /* "mtrand.pyx":3383
+ /* "mtrand.pyx":3373
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+ __pyx_t_3 = __pyx_f_6mtrand_cont3_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_triangular, __pyx_v_size, __pyx_v_oleft, __pyx_v_omode, __pyx_v_oright); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3372; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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return __pyx_r;
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+ __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3376; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3376; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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__Pyx_INCREF(__pyx_v_p);
PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_p);
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PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4);
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_p);
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return __pyx_r;
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else {
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+ __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3484; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 2:
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+ __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3484; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_154), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3728; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_2 = __pyx_f_6mtrand_discd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_zipf, __pyx_v_size, __pyx_v_fa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3729; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyErr_Clear();
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+ __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_a, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3733; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__less_equal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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goto __pyx_L8;
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__pyx_L8:;
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* if np.any(np.less_equal(oa, 1.0)):
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+ __pyx_t_2 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_zipf, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3736; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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return __pyx_r;
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+ __Pyx_RaiseArgtupleInvalid("geometric", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3738; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L8;
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@@ -16586,59 +16586,59 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se
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PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2);
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return __pyx_r;
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else {
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+ __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+ __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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case 3:
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+ /* "mtrand.pyx":3891
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@@ -16816,7 +16816,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx
*/
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+ /* "mtrand.pyx":3892
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*/
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+ /* "mtrand.pyx":3893
* lngood = PyInt_AsLong(ngood)
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+ /* "mtrand.pyx":3894
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+ /* "mtrand.pyx":3895
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+ __pyx_t_2 = PyObject_RichCompare(__pyx_v_ngood, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3895; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3895; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ /* "mtrand.pyx":3896
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__Pyx_Raise(__pyx_t_2, 0, 0, 0);
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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goto __pyx_L7;
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__Pyx_Raise(__pyx_t_2, 0, 0, 0);
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goto __pyx_L8;
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__pyx_L8:;
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__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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__pyx_L9:;
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__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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}
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}
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+ __pyx_t_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4209; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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* if kahan_sum(pix, d-1) > (1.0 + 1e-12):
* raise ValueError("sum(pvals[:-1]) > 1.0") # <<<<<<<<<<<<<<
*
* if size is None:
*/
- __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_188), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_188), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4214; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_Raise(__pyx_t_2, 0, 0, 0);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4214; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
goto __pyx_L6;
}
__pyx_L6:;
- /* "mtrand.pyx":4226
+ /* "mtrand.pyx":4216
* raise ValueError("sum(pvals[:-1]) > 1.0")
*
* if size is None: # <<<<<<<<<<<<<<
@@ -18510,16 +18510,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__pyx_t_3 = (__pyx_v_size == Py_None);
if (__pyx_t_3) {
- /* "mtrand.pyx":4227
+ /* "mtrand.pyx":4217
*
* if size is None:
* shape = (d,) # <<<<<<<<<<<<<<
* elif type(size) is int:
* shape = (size, d)
*/
- __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(((PyObject *)__pyx_t_4));
PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
__Pyx_GIVEREF(__pyx_t_2);
@@ -18529,7 +18529,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
goto __pyx_L7;
}
- /* "mtrand.pyx":4228
+ /* "mtrand.pyx":4218
* if size is None:
* shape = (d,)
* elif type(size) is int: # <<<<<<<<<<<<<<
@@ -18539,16 +18539,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__pyx_t_3 = (((PyObject *)Py_TYPE(__pyx_v_size)) == ((PyObject *)((PyObject*)(&PyInt_Type))));
if (__pyx_t_3) {
- /* "mtrand.pyx":4229
+ /* "mtrand.pyx":4219
* shape = (d,)
* elif type(size) is int:
* shape = (size, d) # <<<<<<<<<<<<<<
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- __pyx_t_4 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4229; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4229; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(((PyObject *)__pyx_t_2));
__Pyx_INCREF(__pyx_v_size);
PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size);
@@ -18562,21 +18562,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
}
/*else*/ {
- /* "mtrand.pyx":4231
+ /* "mtrand.pyx":4221
* shape = (size, d)
* else:
* shape = size + (d,) # <<<<<<<<<<<<<<
*
* multin = np.zeros(shape, int)
*/
- __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(((PyObject *)__pyx_t_4));
PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
__Pyx_GIVEREF(__pyx_t_2);
__pyx_t_2 = 0;
- __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0;
__pyx_v_shape = __pyx_t_2;
@@ -18584,19 +18584,19 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
}
__pyx_L7:;
- /* "mtrand.pyx":4233
+ /* "mtrand.pyx":4223
* shape = size + (d,)
*
* multin = np.zeros(shape, int) # <<<<<<<<<<<<<<
* mnarr = <ndarray>multin
* mnix = <long*>mnarr.data
*/
- __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
- __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(((PyObject *)__pyx_t_2));
__Pyx_INCREF(__pyx_v_shape);
PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape);
@@ -18604,14 +18604,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__Pyx_INCREF(((PyObject *)((PyObject*)(&PyInt_Type))));
PyTuple_SET_ITEM(__pyx_t_2, 1, ((PyObject *)((PyObject*)(&PyInt_Type))));
__Pyx_GIVEREF(((PyObject *)((PyObject*)(&PyInt_Type))));
- __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0;
__pyx_v_multin = __pyx_t_5;
__pyx_t_5 = 0;
- /* "mtrand.pyx":4234
+ /* "mtrand.pyx":4224
*
* multin = np.zeros(shape, int)
* mnarr = <ndarray>multin # <<<<<<<<<<<<<<
@@ -18621,7 +18621,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_multin)));
arrayObject_mnarr = ((PyArrayObject *)__pyx_v_multin);
- /* "mtrand.pyx":4235
+ /* "mtrand.pyx":4225
* multin = np.zeros(shape, int)
* mnarr = <ndarray>multin
* mnix = <long*>mnarr.data # <<<<<<<<<<<<<<
@@ -18630,7 +18630,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
*/
__pyx_v_mnix = ((long *)arrayObject_mnarr->data);
- /* "mtrand.pyx":4236
+ /* "mtrand.pyx":4226
* mnarr = <ndarray>multin
* mnix = <long*>mnarr.data
* i = 0 # <<<<<<<<<<<<<<
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*/
__pyx_v_i = 0;
- /* "mtrand.pyx":4237
+ /* "mtrand.pyx":4227
* mnix = <long*>mnarr.data
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__pyx_t_3 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr));
if (!__pyx_t_3) break;
- /* "mtrand.pyx":4238
+ /* "mtrand.pyx":4228
* i = 0
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*/
__pyx_v_Sum = 1.0;
- /* "mtrand.pyx":4239
+ /* "mtrand.pyx":4229
* while i < PyArray_SIZE(mnarr):
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*/
__pyx_v_dn = __pyx_v_n;
- /* "mtrand.pyx":4240
+ /* "mtrand.pyx":4230
* Sum = 1.0
* dn = n
* for j from 0 <= j < d-1: # <<<<<<<<<<<<<<
@@ -18678,7 +18678,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__pyx_t_6 = (__pyx_v_d - 1);
for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
- /* "mtrand.pyx":4241
+ /* "mtrand.pyx":4231
* dn = n
* for j from 0 <= j < d-1:
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@@ -18687,11 +18687,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
*/
if (unlikely(__pyx_v_Sum == 0)) {
PyErr_Format(PyExc_ZeroDivisionError, "float division");
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
(__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, __pyx_v_dn, ((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
- /* "mtrand.pyx":4242
+ /* "mtrand.pyx":4232
* for j from 0 <= j < d-1:
* mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
* dn = dn - mnix[i+j] # <<<<<<<<<<<<<<
@@ -18700,7 +18700,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
*/
__pyx_v_dn = (__pyx_v_dn - (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]));
- /* "mtrand.pyx":4243
+ /* "mtrand.pyx":4233
* mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
* dn = dn - mnix[i+j]
* if dn <= 0: # <<<<<<<<<<<<<<
@@ -18710,7 +18710,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__pyx_t_3 = (__pyx_v_dn <= 0);
if (__pyx_t_3) {
- /* "mtrand.pyx":4244
+ /* "mtrand.pyx":4234
* dn = dn - mnix[i+j]
* if dn <= 0:
* break # <<<<<<<<<<<<<<
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}
__pyx_L12:;
- /* "mtrand.pyx":4245
+ /* "mtrand.pyx":4235
* if dn <= 0:
* break
* Sum = Sum - pix[j] # <<<<<<<<<<<<<<
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}
__pyx_L11_break:;
- /* "mtrand.pyx":4246
+ /* "mtrand.pyx":4236
* break
* Sum = Sum - pix[j]
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@@ -18743,7 +18743,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
__pyx_t_3 = (__pyx_v_dn > 0);
if (__pyx_t_3) {
- /* "mtrand.pyx":4247
+ /* "mtrand.pyx":4237
* Sum = Sum - pix[j]
* if dn > 0:
* mnix[i+d-1] = dn # <<<<<<<<<<<<<<
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}
__pyx_L13:;
- /* "mtrand.pyx":4249
+ /* "mtrand.pyx":4239
* mnix[i+d-1] = dn
*
* i = i + d # <<<<<<<<<<<<<<
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__pyx_v_i = (__pyx_v_i + __pyx_v_d);
}
- /* "mtrand.pyx":4251
+ /* "mtrand.pyx":4241
* i = i + d
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* return multin # <<<<<<<<<<<<<<
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return __pyx_r;
}
-/* "mtrand.pyx":4253
+/* "mtrand.pyx":4243
* return multin
*
* def dirichlet(self, object alpha, size=None): # <<<<<<<<<<<<<<
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}
}
if (unlikely(kw_args > 0)) {
- if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4243; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
}
__pyx_v_alpha = values[0];
__pyx_v_size = values[1];
@@ -18870,37 +18870,37 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se
}
goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:;
- __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4253; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+ __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4243; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
__pyx_L3_error:;
__Pyx_AddTraceback("mtrand.RandomState.dirichlet", __pyx_clineno, __pyx_lineno, __pyx_filename);
__Pyx_RefNannyFinishContext();
return NULL;
__pyx_L4_argument_unpacking_done:;
- /* "mtrand.pyx":4339
+ /* "mtrand.pyx":4329
* cdef double acc, invacc
*
* k = len(alpha) # <<<<<<<<<<<<<<
* alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
* alpha_data = <double*>alpha_arr.data
*/
- __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4339; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4329; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_v_k = __pyx_t_1;
- /* "mtrand.pyx":4340
+ /* "mtrand.pyx":4330
*
* k = len(alpha)
* alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1) # <<<<<<<<<<<<<<
* alpha_data = <double*>alpha_arr.data
*
*/
- __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_2);
__Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2)));
__pyx_v_alpha_arr = ((PyArrayObject *)__pyx_t_2);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
- /* "mtrand.pyx":4341
+ /* "mtrand.pyx":4331
* k = len(alpha)
* alpha_arr = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
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@@ -18909,7 +18909,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se
*/
__pyx_v_alpha_data = ((double *)__pyx_v_alpha_arr->data);
- /* "mtrand.pyx":4343
+ /* "mtrand.pyx":4333
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@@ -18919,16 +18919,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se
__pyx_t_3 = (__pyx_v_size == Py_None);
if (__pyx_t_3) {
- /* "mtrand.pyx":4344
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+ __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
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goto __pyx_L6;
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- /* "mtrand.pyx":4345
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if (__pyx_t_3) {
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+ __pyx_t_4 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_INCREF(__pyx_v_size);
PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size);
@@ -18971,21 +18971,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_se
}
/*else*/ {
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+ __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
__Pyx_GIVEREF(__pyx_t_2);
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+ __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0;
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}
__pyx_L6:;
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+ __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_5 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__float64); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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+ __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_INCREF(__pyx_v_shape);
PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape);
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PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_5);
__Pyx_GIVEREF(__pyx_t_5);
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+ __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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__pyx_v_val_arr = ((PyArrayObject *)__pyx_v_diric);
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* diric = np.zeros(shape, np.float64)
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*/
__pyx_v_i = 0;
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*/
__pyx_v_totsize = PyArray_SIZE(__pyx_v_val_arr);
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__pyx_t_3 = (__pyx_v_i < __pyx_v_totsize);
if (!__pyx_t_3) break;
- /* "mtrand.pyx":4357
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*/
__pyx_v_acc = 0.0;
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__pyx_t_6 = __pyx_v_k;
for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
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+ /* "mtrand.pyx":4349
* acc = 0.0
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(__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = rk_standard_gamma(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, (__pyx_v_alpha_data[__pyx_v_j]));
- /* "mtrand.pyx":4360
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* for j from 0 <= j < k:
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__pyx_v_acc = (__pyx_v_acc + (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]));
}
- /* "mtrand.pyx":4361
+ /* "mtrand.pyx":4351
* val_data[i+j] = rk_standard_gamma(self.internal_state, alpha_data[j])
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- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4361; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
__pyx_v_invacc = (1.0 / __pyx_v_acc);
- /* "mtrand.pyx":4362
+ /* "mtrand.pyx":4352
* acc = acc + val_data[i+j]
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__pyx_t_6 = __pyx_v_k;
for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
- /* "mtrand.pyx":4363
+ /* "mtrand.pyx":4353
* invacc = 1/acc
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(__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = ((__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) * __pyx_v_invacc);
}
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+ /* "mtrand.pyx":4354
* for j from 0 <= j < k:
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__pyx_v_i = (__pyx_v_i + __pyx_v_k);
}
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return __pyx_r;
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+ __pyx_t_1 = PyObject_Length(__pyx_v_x); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_v_i = (__pyx_t_1 - 1);
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__Pyx_XGOTREF(__pyx_t_4);
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+ __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4397; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
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+ __pyx_t_1 = PyObject_Length(__pyx_t_5); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4397; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
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__pyx_L5_error:;
__Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
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+ if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_7) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4398; __pyx_clineno = __LINE__; goto __pyx_L7_except_error;}
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__pyx_L12_try_end:;
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if (__pyx_t_8) {
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if (!__pyx_t_8) break;
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__pyx_v_j = rk_interval(__pyx_v_i, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
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+ __pyx_k_tuple_161 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_161)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_158));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_161));
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+ /* "mtrand.pyx":3896
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+ __pyx_k_tuple_163 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_163)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3896; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_162));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_163));
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+ /* "mtrand.pyx":3898
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+ __pyx_k_tuple_165 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_165)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_164));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_165));
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+ /* "mtrand.pyx":3900
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+ __pyx_k_tuple_167 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_167)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_166));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_167));
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+ /* "mtrand.pyx":3902
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+ __pyx_k_tuple_169 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_169)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_168));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_169));
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+ __pyx_k_tuple_170 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_170)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_162));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_170));
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+ /* "mtrand.pyx":3915
* raise ValueError("ngood < 1")
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* raise ValueError("nbad < 1") # <<<<<<<<<<<<<<
* if np.any(np.less(onsample, 1)):
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+ __pyx_k_tuple_171 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_171)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_164));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_171));
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+ /* "mtrand.pyx":3917
* raise ValueError("nbad < 1")
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* if np.any(np.less(np.add(ongood, onbad),onsample)):
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+ __pyx_k_tuple_172 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_172)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3917; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_166));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_172));
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+ /* "mtrand.pyx":3919
* raise ValueError("nsample < 1")
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_168));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_173));
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_174));
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_176));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_177));
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_174));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_178));
- /* "mtrand.pyx":4024
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_176));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_179));
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_180));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_181));
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__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_183));
- /* "mtrand.pyx":4131
+ /* "mtrand.pyx":4121
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__Pyx_GIVEREF(((PyObject *)__pyx_kp_s_184));
__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_185));
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+ /* "mtrand.pyx":4214
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@@ -22148,744 +22148,744 @@ PyMODINIT_FUNC PyInit_mtrand(void)
__Pyx_GIVEREF(__pyx_t_4);
__pyx_t_4 = 0;
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__pyx_k_111 = __pyx_t_4;
__Pyx_GIVEREF(__pyx_t_4);
__pyx_t_4 = 0;
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__Pyx_GIVEREF(__pyx_t_4);
__pyx_t_4 = 0;
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__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
- /* "mtrand.pyx":4521
+ /* "mtrand.pyx":4511
* multivariate_normal = _rand.multivariate_normal
* multinomial = _rand.multinomial
* dirichlet = _rand.dirichlet # <<<<<<<<<<<<<<
*
* shuffle = _rand.shuffle
*/
- __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4511; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__dirichlet); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__dirichlet); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4511; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
- if (PyObject_SetAttr(__pyx_m, __pyx_n_s__dirichlet, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s__dirichlet, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4511; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- /* "mtrand.pyx":4523
+ /* "mtrand.pyx":4513
* dirichlet = _rand.dirichlet
*
* shuffle = _rand.shuffle # <<<<<<<<<<<<<<
* permutation = _rand.permutation
*/
- __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
- __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__shuffle); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__shuffle); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- if (PyObject_SetAttr(__pyx_m, __pyx_n_s__shuffle, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s__shuffle, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
- /* "mtrand.pyx":4524
+ /* "mtrand.pyx":4514
*
* shuffle = _rand.shuffle
* permutation = _rand.permutation # <<<<<<<<<<<<<<
*/
- __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__permutation); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_1 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__permutation); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_1);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
- if (PyObject_SetAttr(__pyx_m, __pyx_n_s__permutation, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyObject_SetAttr(__pyx_m, __pyx_n_s__permutation, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
/* "mtrand.pyx":1
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 8f208edcc..90ec0cafb 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -3012,22 +3012,27 @@ cdef class RandomState:
Return samples drawn from a log-normal distribution.
- Draw samples from a log-normal distribution with specified mean, standard
- deviation, and shape. Note that the mean and standard deviation are not the
- values for the distribution itself, but of the underlying normal
- distribution it is derived from.
-
+ Draw samples from a log-normal distribution with specified mean,
+ standard deviation, and array shape. Note that the mean and standard
+ deviation are not the values for the distribution itself, but of the
+ underlying normal distribution it is derived from.
Parameters
----------
mean : float
Mean value of the underlying normal distribution
- sigma : float, >0.
+ sigma : float, > 0.
Standard deviation of the underlying normal distribution
size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
+ Returns
+ -------
+ samples : ndarray or float
+ The desired samples. An array of the same shape as `size` if given,
+ if `size` is None a float is returned.
+
See Also
--------
scipy.stats.lognorm : probability density function, distribution,
@@ -3036,41 +3041,28 @@ cdef class RandomState:
Notes
-----
A variable `x` has a log-normal distribution if `log(x)` is normally
- distributed.
-
- The probability density function for the log-normal distribution is
+ distributed. The probability density function for the log-normal
+ distribution is:
.. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}
e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}
- where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation
- of the normally distributed logarithm of the variable.
-
- A log-normal distribution results if a random variable is the *product* of
- a large number of independent, identically-distributed variables in the
- same way that a normal distribution results if the variable is the *sum*
- of a large number of independent, identically-distributed variables
- (see the last example). It is one of the so-called "fat-tailed"
- distributions.
-
- The log-normal distribution is commonly used to model the lifespan of units
- with fatigue-stress failure modes. Since this includes
- most mechanical systems, the log-normal distribution has widespread
- application.
-
- It is also commonly used to model oil field sizes, species abundance, and
- latent periods of infectious diseases.
+ where :math:`\\mu` is the mean and :math:`\\sigma` is the standard
+ deviation of the normally distributed logarithm of the variable.
+ A log-normal distribution results if a random variable is the *product*
+ of a large number of independent, identically-distributed variables in
+ the same way that a normal distribution results if the variable is the
+ *sum* of a large number of independent, identically-distributed
+ variables.
References
----------
- .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, "Log-normal
- Distributions across the Sciences: Keys and Clues", May 2001
- Vol. 51 No. 5 BioScience
- http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
- .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
- Values, Birkhauser Verlag, Basel, pp 31-32.
- .. [3] Wikipedia, "Lognormal distribution",
- http://en.wikipedia.org/wiki/Lognormal_distribution
+ Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions
+ across the Sciences: Keys and Clues," *BioScience*, Vol. 51, No. 5,
+ May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
+
+ Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,
+ Basel: Birkhauser Verlag, 2001, pp. 31-32.
Examples
--------
@@ -3104,9 +3096,7 @@ cdef class RandomState:
... b.append(np.product(a))
>>> b = np.array(b) / np.min(b) # scale values to be positive
-
>>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')
-
>>> sigma = np.std(np.log(b))
>>> mu = np.mean(np.log(b))
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