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-rw-r--r--numpy/core/numeric.py2
-rw-r--r--numpy/distutils/misc_util.py12
-rw-r--r--numpy/doc/glossary.py30
-rw-r--r--numpy/doc/structured_arrays.py4
-rw-r--r--numpy/linalg/linalg.py2
-rw-r--r--numpy/random/mtrand/mtrand.pyx54
6 files changed, 65 insertions, 39 deletions
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 77aaf6dec..1108d4667 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -2529,7 +2529,7 @@ def seterr(all=None, divide=None, over=None, under=None, invalid=None):
Notes
-----
- The floating-point exceptions are defined in the IEEE 754 standard [1]:
+ The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
diff --git a/numpy/distutils/misc_util.py b/numpy/distutils/misc_util.py
index 1d08942f6..cb7414a04 100644
--- a/numpy/distutils/misc_util.py
+++ b/numpy/distutils/misc_util.py
@@ -1218,15 +1218,15 @@ class Configuration(object):
#. file.txt -> (., file.txt)-> parent/file.txt
#. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt
#. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt
- #. *.txt -> parent/a.txt, parent/b.txt
- #. foo/*.txt -> parent/foo/a.txt, parent/foo/b.txt
- #. */*.txt -> (*, */*.txt) -> parent/c/a.txt, parent/d/b.txt
+ #. ``*``.txt -> parent/a.txt, parent/b.txt
+ #. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt
+ #. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt
#. (sun, file.txt) -> parent/sun/file.txt
#. (sun, bar/file.txt) -> parent/sun/file.txt
#. (sun, /foo/bar/file.txt) -> parent/sun/file.txt
- #. (sun, *.txt) -> parent/sun/a.txt, parent/sun/b.txt
- #. (sun, bar/*.txt) -> parent/sun/a.txt, parent/sun/b.txt
- #. (sun/*, */*.txt) -> parent/sun/c/a.txt, parent/d/b.txt
+ #. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+ #. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+ #. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt
An additional feature is that the path to a data-file can actually be
a function that takes no arguments and returns the actual path(s) to
diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py
index 9b7d613ba..0e1df495b 100644
--- a/numpy/doc/glossary.py
+++ b/numpy/doc/glossary.py
@@ -48,7 +48,7 @@ Glossary
array([(1, 2.0), (3, 4.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
- Fast element-wise operations, called :term:`ufuncs`, operate on arrays.
+ Fast element-wise operations, called a :term:`ufunc`, operate on arrays.
array_like
Any sequence that can be interpreted as an ndarray. This includes
@@ -62,6 +62,12 @@ Glossary
>>> x.shape
(3,)
+ big-endian
+ When storing a multi-byte value in memory as a sequence of bytes, the
+ sequence addresses/sends/stores the most significant byte first (lowest
+ address) and the least significant byte last (highest address). Common in
+ micro-processors and used for transmission of data over network protocols.
+
BLAS
`Basic Linear Algebra Subprograms <http://en.wikipedia.org/wiki/BLAS>`_
@@ -151,6 +157,11 @@ Glossary
For more information on dictionaries, read the
`Python tutorial <http://docs.python.org/tut>`_.
+ field
+ In a :term:`structured data type`, each sub-type is called a `field`.
+ The `field` has a name (a string), a type (any valid :term:`dtype`, and
+ an optional `title`. See :ref:`arrays.dtypes`
+
Fortran order
See `column-major`
@@ -158,6 +169,12 @@ Glossary
Collapsed to a one-dimensional array. See `numpy.ndarray.flatten`
for details.
+ homogenous
+ Describes a block of memory comprised of blocks, each block comprised of
+ items and of the same size, and blocks are interpreted in exactly the
+ same way. In the simplest case each block contains a single item, for
+ instance int32 or float64.
+
immutable
An object that cannot be modified after execution is called
immutable. Two common examples are strings and tuples.
@@ -224,6 +241,12 @@ Glossary
tutorial <http://docs.python.org/tut>`_. For a mapping
type (key-value), see *dictionary*.
+ little-endian
+ When storing a multi-byte value in memory as a sequence of bytes, the
+ sequence addresses/sends/stores the least significant byte first (lowest
+ address) and the most significant byte last (highest address). Common in
+ x86 processors.
+
mask
A boolean array, used to select only certain elements for an operation::
@@ -285,7 +308,7 @@ Glossary
See *array*.
record array
- An :term:`ndarray` with :term:`structured data type`_ which has been
+ An :term:`ndarray` with :term:`structured data type` which has been
subclassed as ``np.recarray`` and whose dtype is of type ``np.record``,
making the fields of its data type to be accessible by attribute.
@@ -350,6 +373,9 @@ Glossary
>>> x[:, 1]
array([2, 4])
+ structure
+ See :term:`structured data type`
+
structured data type
A data type composed of other datatypes
diff --git a/numpy/doc/structured_arrays.py b/numpy/doc/structured_arrays.py
index af02e2173..ba667da59 100644
--- a/numpy/doc/structured_arrays.py
+++ b/numpy/doc/structured_arrays.py
@@ -284,7 +284,7 @@ the desired underlying dtype, and fields and flags will be copied from
``dtype``. This dtype is similar to a 'union' in C.
Indexing and Assignment to Structured arrays
-=============================================
+============================================
Assigning data to a Structured Array
------------------------------------
@@ -293,7 +293,7 @@ There are a number of ways to assign values to a structured array: Using python
tuples, using scalar values, or using other structured arrays.
Assignment from Python Native Types (Tuples)
-```````````````````````````````````````````
+````````````````````````````````````````````
The simplest way to assign values to a structured array is using python tuples.
Each assigned value should be a tuple of length equal to the number of fields
diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py
index f073abadf..4905690ad 100644
--- a/numpy/linalg/linalg.py
+++ b/numpy/linalg/linalg.py
@@ -1987,7 +1987,7 @@ def lstsq(a, b, rcond="warn"):
[ 2., 1.],
[ 3., 1.]])
- >>> m, c = np.linalg.lstsq(A, y)[0]
+ >>> m, c = np.linalg.lstsq(A, y, rcond=None)[0]
>>> print(m, c)
1.0 -0.95
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 1846a363f..4dabaa093 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -1284,7 +1284,7 @@ cdef class RandomState:
probability density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 15, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()
@@ -1495,7 +1495,7 @@ cdef class RandomState:
Display results as a histogram:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
+ >>> count, bins, ignored = plt.hist(dsums, 11, density=True)
>>> plt.show()
"""
@@ -1631,7 +1631,7 @@ cdef class RandomState:
the probability density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 30, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
... linewidth=2, color='r')
@@ -1874,7 +1874,7 @@ cdef class RandomState:
>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
- >>> count, bins, ignored = plt.hist(s, 50, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\
... (sps.gamma(shape) * scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
@@ -1964,7 +1964,7 @@ cdef class RandomState:
>>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
- >>> count, bins, ignored = plt.hist(s, 50, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1)*(np.exp(-bins/scale) /
... (sps.gamma(shape)*scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
@@ -2164,9 +2164,9 @@ cdef class RandomState:
>>> dfden = 20 # within groups degrees of freedom
>>> nonc = 3.0
>>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)
- >>> NF = np.histogram(nc_vals, bins=50, normed=True)
+ >>> NF = np.histogram(nc_vals, bins=50, density=True)
>>> c_vals = np.random.f(dfnum, dfden, 1000000)
- >>> F = np.histogram(c_vals, bins=50, normed=True)
+ >>> F = np.histogram(c_vals, bins=50, density=True)
>>> plt.plot(F[1][1:], F[0])
>>> plt.plot(NF[1][1:], NF[0])
>>> plt.show()
@@ -2342,7 +2342,7 @@ cdef class RandomState:
>>> import matplotlib.pyplot as plt
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
- ... bins=200, normed=True)
+ ... bins=200, density=True)
>>> plt.show()
Draw values from a noncentral chisquare with very small noncentrality,
@@ -2350,9 +2350,9 @@ cdef class RandomState:
>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),
- ... bins=np.arange(0., 25, .1), normed=True)
+ ... bins=np.arange(0., 25, .1), density=True)
>>> values2 = plt.hist(np.random.chisquare(3, 100000),
- ... bins=np.arange(0., 25, .1), normed=True)
+ ... bins=np.arange(0., 25, .1), density=True)
>>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')
>>> plt.show()
@@ -2361,7 +2361,7 @@ cdef class RandomState:
>>> plt.figure()
>>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),
- ... bins=200, normed=True)
+ ... bins=200, density=True)
>>> plt.show()
"""
@@ -2529,7 +2529,7 @@ cdef class RandomState:
>>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
>>> import matplotlib.pyplot as plt
- >>> h = plt.hist(s, bins=100, normed=True)
+ >>> h = plt.hist(s, bins=100, density=True)
For a one-sided t-test, how far out in the distribution does the t
statistic appear?
@@ -2630,7 +2630,7 @@ cdef class RandomState:
>>> import matplotlib.pyplot as plt
>>> from scipy.special import i0
- >>> plt.hist(s, 50, normed=True)
+ >>> plt.hist(s, 50, density=True)
>>> x = np.linspace(-np.pi, np.pi, num=51)
>>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))
>>> plt.plot(x, y, linewidth=2, color='r')
@@ -2744,7 +2744,7 @@ cdef class RandomState:
density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, _ = plt.hist(s, 100, normed=True)
+ >>> count, bins, _ = plt.hist(s, 100, density=True)
>>> fit = a*m**a / bins**(a+1)
>>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
>>> plt.show()
@@ -2957,17 +2957,17 @@ cdef class RandomState:
>>> powpdf = stats.powerlaw.pdf(xx,5)
>>> plt.figure()
- >>> plt.hist(rvs, bins=50, normed=True)
+ >>> plt.hist(rvs, bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('np.random.power(5)')
>>> plt.figure()
- >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
+ >>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('inverse of 1 + np.random.pareto(5)')
>>> plt.figure()
- >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)
+ >>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')
>>> plt.title('inverse of stats.pareto(5)')
@@ -3055,7 +3055,7 @@ cdef class RandomState:
the probability density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 30, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> x = np.arange(-8., 8., .01)
>>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)
>>> plt.plot(x, pdf)
@@ -3171,7 +3171,7 @@ cdef class RandomState:
the probability density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 30, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
... * np.exp( -np.exp( -(bins - mu) /beta) ),
... linewidth=2, color='r')
@@ -3186,7 +3186,7 @@ cdef class RandomState:
... a = np.random.normal(mu, beta, 1000)
... means.append(a.mean())
... maxima.append(a.max())
- >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)
+ >>> count, bins, ignored = plt.hist(maxima, 30, density=True)
>>> beta = np.std(maxima) * np.sqrt(6) / np.pi
>>> mu = np.mean(maxima) - 0.57721*beta
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
@@ -3381,7 +3381,7 @@ cdef class RandomState:
the probability density function:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')
+ >>> count, bins, ignored = plt.hist(s, 100, density=True, align='mid')
>>> x = np.linspace(min(bins), max(bins), 10000)
>>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
@@ -3403,7 +3403,7 @@ 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='mid')
+ >>> count, bins, ignored = plt.hist(b, 100, density=True, align='mid')
>>> sigma = np.std(np.log(b))
>>> mu = np.mean(np.log(b))
@@ -3480,7 +3480,7 @@ cdef class RandomState:
--------
Draw values from the distribution and plot the histogram
- >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)
+ >>> values = hist(np.random.rayleigh(3, 100000), bins=200, density=True)
Wave heights tend to follow a Rayleigh distribution. If the mean wave
height is 1 meter, what fraction of waves are likely to be larger than 3
@@ -3572,7 +3572,7 @@ cdef class RandomState:
Draw values from the distribution and plot the histogram:
>>> import matplotlib.pyplot as plt
- >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)
+ >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, density=True)
>>> plt.show()
"""
@@ -3659,7 +3659,7 @@ cdef class RandomState:
>>> import matplotlib.pyplot as plt
>>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,
- ... normed=True)
+ ... density=True)
>>> plt.show()
"""
@@ -3969,7 +3969,7 @@ cdef class RandomState:
Display histogram of the sample:
>>> import matplotlib.pyplot as plt
- >>> count, bins, ignored = plt.hist(s, 14, normed=True)
+ >>> count, bins, ignored = plt.hist(s, 14, density=True)
>>> plt.show()
Draw each 100 values for lambda 100 and 500:
@@ -4066,7 +4066,7 @@ cdef class RandomState:
Truncate s values at 50 so plot is interesting:
- >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)
+ >>> count, bins, ignored = plt.hist(s[s<50], 50, density=True)
>>> x = np.arange(1., 50.)
>>> y = x**(-a) / special.zetac(a)
>>> plt.plot(x, y/max(y), linewidth=2, color='r')
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