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-rw-r--r--numpy/lib/_datasource.py24
-rw-r--r--numpy/lib/_iotools.py24
-rw-r--r--numpy/lib/_version.py5
-rw-r--r--numpy/lib/arraypad.py4
-rw-r--r--numpy/lib/arraysetops.py52
-rw-r--r--numpy/lib/arrayterator.py7
-rw-r--r--numpy/lib/financial.py26
-rw-r--r--numpy/lib/function_base.py317
-rw-r--r--numpy/lib/histograms.py9
-rw-r--r--numpy/lib/index_tricks.py10
-rw-r--r--numpy/lib/nanfunctions.py101
-rw-r--r--numpy/lib/npyio.py60
-rw-r--r--numpy/lib/polynomial.py47
-rw-r--r--numpy/lib/recfunctions.py102
-rw-r--r--numpy/lib/scimath.py45
-rw-r--r--numpy/lib/shape_base.py170
-rw-r--r--numpy/lib/tests/test_arraysetops.py8
-rw-r--r--numpy/lib/tests/test_function_base.py144
-rw-r--r--numpy/lib/tests/test_recfunctions.py6
-rw-r--r--numpy/lib/twodim_base.py48
-rw-r--r--numpy/lib/type_check.py37
-rw-r--r--numpy/lib/ufunclike.py12
-rw-r--r--numpy/lib/utils.py23
23 files changed, 651 insertions, 630 deletions
diff --git a/numpy/lib/_datasource.py b/numpy/lib/_datasource.py
index 30237b76f..3a0e67f60 100644
--- a/numpy/lib/_datasource.py
+++ b/numpy/lib/_datasource.py
@@ -20,17 +20,18 @@ gzip, bz2 and xz are supported.
Example::
>>> # Create a DataSource, use os.curdir (default) for local storage.
- >>> ds = datasource.DataSource()
+ >>> from numpy import DataSource
+ >>> ds = DataSource()
>>>
>>> # Open a remote file.
>>> # DataSource downloads the file, stores it locally in:
>>> # './www.google.com/index.html'
>>> # opens the file and returns a file object.
- >>> fp = ds.open('http://www.google.com/index.html')
+ >>> fp = ds.open('http://www.google.com/') # doctest: +SKIP
>>>
>>> # Use the file as you normally would
- >>> fp.read()
- >>> fp.close()
+ >>> fp.read() # doctest: +SKIP
+ >>> fp.close() # doctest: +SKIP
"""
from __future__ import division, absolute_import, print_function
@@ -156,6 +157,7 @@ class _FileOpeners(object):
Examples
--------
+ >>> import gzip
>>> np.lib._datasource._file_openers.keys()
[None, '.bz2', '.gz', '.xz', '.lzma']
>>> np.lib._datasource._file_openers['.gz'] is gzip.open
@@ -290,7 +292,7 @@ class DataSource(object):
URLs require a scheme string (``http://``) to be used, without it they
will fail::
- >>> repos = DataSource()
+ >>> repos = np.DataSource()
>>> repos.exists('www.google.com/index.html')
False
>>> repos.exists('http://www.google.com/index.html')
@@ -302,17 +304,17 @@ class DataSource(object):
--------
::
- >>> ds = DataSource('/home/guido')
- >>> urlname = 'http://www.google.com/index.html'
- >>> gfile = ds.open('http://www.google.com/index.html') # remote file
+ >>> ds = np.DataSource('/home/guido')
+ >>> urlname = 'http://www.google.com/'
+ >>> gfile = ds.open('http://www.google.com/')
>>> ds.abspath(urlname)
- '/home/guido/www.google.com/site/index.html'
+ '/home/guido/www.google.com/index.html'
- >>> ds = DataSource(None) # use with temporary file
+ >>> ds = np.DataSource(None) # use with temporary file
>>> ds.open('/home/guido/foobar.txt')
<open file '/home/guido.foobar.txt', mode 'r' at 0x91d4430>
>>> ds.abspath('/home/guido/foobar.txt')
- '/tmp/tmpy4pgsP/home/guido/foobar.txt'
+ '/tmp/.../home/guido/foobar.txt'
"""
diff --git a/numpy/lib/_iotools.py b/numpy/lib/_iotools.py
index 8a042f190..0ebd39b8c 100644
--- a/numpy/lib/_iotools.py
+++ b/numpy/lib/_iotools.py
@@ -146,11 +146,17 @@ def flatten_dtype(ndtype, flatten_base=False):
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
... ('block', int, (2, 3))])
>>> np.lib._iotools.flatten_dtype(dt)
- [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
+ [dtype('S4'), dtype('float64'), dtype('float64'), dtype('int64')]
>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
- [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
- dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
- dtype('int32')]
+ [dtype('S4'),
+ dtype('float64'),
+ dtype('float64'),
+ dtype('int64'),
+ dtype('int64'),
+ dtype('int64'),
+ dtype('int64'),
+ dtype('int64'),
+ dtype('int64')]
"""
names = ndtype.names
@@ -309,13 +315,13 @@ class NameValidator(object):
--------
>>> validator = np.lib._iotools.NameValidator()
>>> validator(['file', 'field2', 'with space', 'CaSe'])
- ['file_', 'field2', 'with_space', 'CaSe']
+ ('file_', 'field2', 'with_space', 'CaSe')
>>> validator = np.lib._iotools.NameValidator(excludelist=['excl'],
- deletechars='q',
- case_sensitive='False')
+ ... deletechars='q',
+ ... case_sensitive=False)
>>> validator(['excl', 'field2', 'no_q', 'with space', 'CaSe'])
- ['excl_', 'field2', 'no_', 'with_space', 'case']
+ ('EXCL', 'FIELD2', 'NO_Q', 'WITH_SPACE', 'CASE')
"""
#
@@ -599,7 +605,7 @@ class StringConverter(object):
--------
>>> import dateutil.parser
>>> import datetime
- >>> dateparser = datetustil.parser.parse
+ >>> dateparser = dateutil.parser.parse
>>> defaultdate = datetime.date(2000, 1, 1)
>>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
"""
diff --git a/numpy/lib/_version.py b/numpy/lib/_version.py
index c3563a7fa..8aa999fc9 100644
--- a/numpy/lib/_version.py
+++ b/numpy/lib/_version.py
@@ -47,9 +47,12 @@ class NumpyVersion():
>>> from numpy.lib import NumpyVersion
>>> if NumpyVersion(np.__version__) < '1.7.0':
... print('skip')
- skip
+ >>> # skip
>>> NumpyVersion('1.7') # raises ValueError, add ".0"
+ Traceback (most recent call last):
+ ...
+ ValueError: Not a valid numpy version string
"""
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py
index 4f6371058..b236cc449 100644
--- a/numpy/lib/arraypad.py
+++ b/numpy/lib/arraypad.py
@@ -1100,10 +1100,10 @@ def pad(array, pad_width, mode, **kwargs):
--------
>>> a = [1, 2, 3, 4, 5]
>>> np.pad(a, (2,3), 'constant', constant_values=(4, 6))
- array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
+ array([4, 4, 1, ..., 6, 6, 6])
>>> np.pad(a, (2, 3), 'edge')
- array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
+ array([1, 1, 1, ..., 5, 5, 5])
>>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index fd64ecbd6..b53d8c03f 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -82,7 +82,7 @@ def ediff1d(ary, to_end=None, to_begin=None):
array([ 1, 2, 3, -7])
>>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
- array([-99, 1, 2, 3, -7, 88, 99])
+ array([-99, 1, 2, ..., -7, 88, 99])
The returned array is always 1D.
@@ -94,8 +94,7 @@ def ediff1d(ary, to_end=None, to_begin=None):
# force a 1d array
ary = np.asanyarray(ary).ravel()
- # we have unit tests enforcing
- # propagation of the dtype of input
+ # enforce propagation of the dtype of input
# ary to returned result
dtype_req = ary.dtype
@@ -106,23 +105,22 @@ def ediff1d(ary, to_end=None, to_begin=None):
if to_begin is None:
l_begin = 0
else:
- to_begin = np.asanyarray(to_begin)
- if not np.can_cast(to_begin, dtype_req):
- raise TypeError("dtype of to_begin must be compatible "
- "with input ary")
-
- to_begin = to_begin.ravel()
+ _to_begin = np.asanyarray(to_begin, dtype=dtype_req)
+ if not np.all(_to_begin == to_begin):
+ raise ValueError("cannot convert 'to_begin' to array with dtype "
+ "'%r' as required for input ary" % dtype_req)
+ to_begin = _to_begin.ravel()
l_begin = len(to_begin)
if to_end is None:
l_end = 0
else:
- to_end = np.asanyarray(to_end)
- if not np.can_cast(to_end, dtype_req):
- raise TypeError("dtype of to_end must be compatible "
- "with input ary")
-
- to_end = to_end.ravel()
+ _to_end = np.asanyarray(to_end, dtype=dtype_req)
+ # check that casting has not overflowed
+ if not np.all(_to_end == to_end):
+ raise ValueError("cannot convert 'to_end' to array with dtype "
+ "'%r' as required for input ary" % dtype_req)
+ to_end = _to_end.ravel()
l_end = len(to_end)
# do the calculation in place and copy to_begin and to_end
@@ -241,13 +239,11 @@ def unique(ar, return_index=False, return_inverse=False,
>>> a = np.array(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = np.unique(a, return_index=True)
>>> u
- array(['a', 'b', 'c'],
- dtype='|S1')
+ array(['a', 'b', 'c'], dtype='<U1')
>>> indices
array([0, 1, 3])
>>> a[indices]
- array(['a', 'b', 'c'],
- dtype='|S1')
+ array(['a', 'b', 'c'], dtype='<U1')
Reconstruct the input array from the unique values:
@@ -256,9 +252,9 @@ def unique(ar, return_index=False, return_inverse=False,
>>> u
array([1, 2, 3, 4, 6])
>>> indices
- array([0, 1, 4, 3, 1, 2, 1])
+ array([0, 1, 4, ..., 1, 2, 1])
>>> u[indices]
- array([1, 2, 6, 4, 2, 3, 2])
+ array([1, 2, 6, ..., 2, 3, 2])
"""
ar = np.asanyarray(ar)
@@ -661,8 +657,8 @@ def isin(element, test_elements, assume_unique=False, invert=False):
>>> test_elements = [1, 2, 4, 8]
>>> mask = np.isin(element, test_elements)
>>> mask
- array([[ False, True],
- [ True, False]])
+ array([[False, True],
+ [ True, False]])
>>> element[mask]
array([2, 4])
@@ -676,7 +672,7 @@ def isin(element, test_elements, assume_unique=False, invert=False):
>>> mask = np.isin(element, test_elements, invert=True)
>>> mask
array([[ True, False],
- [ False, True]])
+ [False, True]])
>>> element[mask]
array([0, 6])
@@ -685,14 +681,14 @@ def isin(element, test_elements, assume_unique=False, invert=False):
>>> test_set = {1, 2, 4, 8}
>>> np.isin(element, test_set)
- array([[ False, False],
- [ False, False]])
+ array([[False, False],
+ [False, False]])
Casting the set to a list gives the expected result:
>>> np.isin(element, list(test_set))
- array([[ False, True],
- [ True, False]])
+ array([[False, True],
+ [ True, False]])
"""
element = np.asarray(element)
return in1d(element, test_elements, assume_unique=assume_unique,
diff --git a/numpy/lib/arrayterator.py b/numpy/lib/arrayterator.py
index f2d4fe9fd..c16668582 100644
--- a/numpy/lib/arrayterator.py
+++ b/numpy/lib/arrayterator.py
@@ -80,9 +80,8 @@ class Arrayterator(object):
>>> for subarr in a_itor:
... if not subarr.all():
- ... print(subarr, subarr.shape)
- ...
- [[[[0 1]]]] (1, 1, 1, 2)
+ ... print(subarr, subarr.shape) # doctest: +SKIP
+ >>> # [[[[0 1]]]] (1, 1, 1, 2)
"""
@@ -160,7 +159,7 @@ class Arrayterator(object):
... if not subarr:
... print(subarr, type(subarr))
...
- 0 <type 'numpy.int32'>
+ 0 <class 'numpy.int64'>
"""
for block in self:
diff --git a/numpy/lib/financial.py b/numpy/lib/financial.py
index e1e297492..216687475 100644
--- a/numpy/lib/financial.py
+++ b/numpy/lib/financial.py
@@ -127,7 +127,7 @@ def fv(rate, nper, pmt, pv, when='end'):
>>> a = np.array((0.05, 0.06, 0.07))/12
>>> np.fv(a, 10*12, -100, -100)
- array([ 15692.92889434, 16569.87435405, 17509.44688102])
+ array([ 15692.92889434, 16569.87435405, 17509.44688102]) # may vary
"""
when = _convert_when(when)
@@ -275,7 +275,7 @@ def nper(rate, pmt, pv, fv=0, when='end'):
If you only had $150/month to pay towards the loan, how long would it take
to pay-off a loan of $8,000 at 7% annual interest?
- >>> print(round(np.nper(0.07/12, -150, 8000), 5))
+ >>> print(np.round(np.nper(0.07/12, -150, 8000), 5))
64.07335
So, over 64 months would be required to pay off the loan.
@@ -286,10 +286,10 @@ def nper(rate, pmt, pv, fv=0, when='end'):
>>> np.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12,
... -150 : -99 : 50 ,
... 8000 : 9001 : 1000]))
- array([[[ 64.07334877, 74.06368256],
- [ 108.07548412, 127.99022654]],
- [[ 66.12443902, 76.87897353],
- [ 114.70165583, 137.90124779]]])
+ array([[[ 64.07334877, 74.06368256],
+ [108.07548412, 127.99022654]],
+ [[ 66.12443902, 76.87897353],
+ [114.70165583, 137.90124779]]])
"""
when = _convert_when(when)
@@ -539,7 +539,7 @@ def pv(rate, nper, pmt, fv=0, when='end'):
>>> a = np.array((0.05, 0.04, 0.03))/12
>>> np.pv(a, 10*12, -100, 15692.93)
- array([ -100.00067132, -649.26771385, -1273.78633713])
+ array([ -100.00067132, -649.26771385, -1273.78633713]) # may vary
So, to end up with the same $15692.93 under the same $100 per month
"savings plan," for annual interest rates of 4% and 3%, one would
@@ -704,15 +704,15 @@ def irr(values):
Examples
--------
- >>> round(irr([-100, 39, 59, 55, 20]), 5)
+ >>> round(np.irr([-100, 39, 59, 55, 20]), 5)
0.28095
- >>> round(irr([-100, 0, 0, 74]), 5)
+ >>> round(np.irr([-100, 0, 0, 74]), 5)
-0.0955
- >>> round(irr([-100, 100, 0, -7]), 5)
+ >>> round(np.irr([-100, 100, 0, -7]), 5)
-0.0833
- >>> round(irr([-100, 100, 0, 7]), 5)
+ >>> round(np.irr([-100, 100, 0, 7]), 5)
0.06206
- >>> round(irr([-5, 10.5, 1, -8, 1]), 5)
+ >>> round(np.irr([-5, 10.5, 1, -8, 1]), 5)
0.0886
(Compare with the Example given for numpy.lib.financial.npv)
@@ -777,7 +777,7 @@ def npv(rate, values):
Examples
--------
>>> np.npv(0.281,[-100, 39, 59, 55, 20])
- -0.0084785916384548798
+ -0.0084785916384548798 # may vary
(Compare with the Example given for numpy.lib.financial.irr)
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 5f87c8b2c..b61a64b8e 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -218,12 +218,12 @@ def flip(m, axis=None):
[2, 3]],
[[4, 5],
[6, 7]]])
- >>> flip(A, 0)
+ >>> np.flip(A, 0)
array([[[4, 5],
[6, 7]],
[[0, 1],
[2, 3]]])
- >>> flip(A, 1)
+ >>> np.flip(A, 1)
array([[[2, 3],
[0, 1]],
[[6, 7],
@@ -239,7 +239,7 @@ def flip(m, axis=None):
[[1, 0],
[3, 2]]])
>>> A = np.random.randn(3,4,5)
- >>> np.all(flip(A,2) == A[:,:,::-1,...])
+ >>> np.all(np.flip(A,2) == A[:,:,::-1,...])
True
"""
if not hasattr(m, 'ndim'):
@@ -359,7 +359,7 @@ def average(a, axis=None, weights=None, returned=False):
Examples
--------
- >>> data = range(1,5)
+ >>> data = list(range(1,5))
>>> data
[1, 2, 3, 4]
>>> np.average(data)
@@ -373,11 +373,10 @@ def average(a, axis=None, weights=None, returned=False):
[2, 3],
[4, 5]])
>>> np.average(data, axis=1, weights=[1./4, 3./4])
- array([ 0.75, 2.75, 4.75])
+ array([0.75, 2.75, 4.75])
>>> np.average(data, weights=[1./4, 3./4])
-
Traceback (most recent call last):
- ...
+ ...
TypeError: Axis must be specified when shapes of a and weights differ.
>>> a = np.ones(5, dtype=np.float128)
@@ -586,7 +585,7 @@ def piecewise(x, condlist, funclist, *args, **kw):
``x >= 0``.
>>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
- array([ 2.5, 1.5, 0.5, 0.5, 1.5, 2.5])
+ array([2.5, 1.5, 0.5, 0.5, 1.5, 2.5])
Apply the same function to a scalar value.
@@ -671,7 +670,7 @@ def select(condlist, choicelist, default=0):
>>> condlist = [x<3, x>5]
>>> choicelist = [x, x**2]
>>> np.select(condlist, choicelist)
- array([ 0, 1, 2, 0, 0, 0, 36, 49, 64, 81])
+ array([ 0, 1, 2, ..., 49, 64, 81])
"""
# Check the size of condlist and choicelist are the same, or abort.
@@ -854,9 +853,9 @@ def gradient(f, *varargs, **kwargs):
--------
>>> f = np.array([1, 2, 4, 7, 11, 16], dtype=float)
>>> np.gradient(f)
- array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
+ array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
>>> np.gradient(f, 2)
- array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
+ array([0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
Spacing can be also specified with an array that represents the coordinates
of the values F along the dimensions.
@@ -864,13 +863,13 @@ def gradient(f, *varargs, **kwargs):
>>> x = np.arange(f.size)
>>> np.gradient(f, x)
- array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
+ array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
Or a non uniform one:
>>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=float)
>>> np.gradient(f, x)
- array([ 1. , 3. , 3.5, 6.7, 6.9, 2.5])
+ array([1. , 3. , 3.5, 6.7, 6.9, 2.5])
For two dimensional arrays, the return will be two arrays ordered by
axis. In this example the first array stands for the gradient in
@@ -878,8 +877,8 @@ def gradient(f, *varargs, **kwargs):
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float))
[array([[ 2., 2., -1.],
- [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ],
- [ 1. , 1. , 1. ]])]
+ [ 2., 2., -1.]]), array([[1. , 2.5, 4. ],
+ [1. , 1. , 1. ]])]
In this example the spacing is also specified:
uniform for axis=0 and non uniform for axis=1
@@ -888,17 +887,17 @@ def gradient(f, *varargs, **kwargs):
>>> y = [1., 1.5, 3.5]
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), dx, y)
[array([[ 1. , 1. , -0.5],
- [ 1. , 1. , -0.5]]), array([[ 2. , 2. , 2. ],
- [ 2. , 1.7, 0.5]])]
+ [ 1. , 1. , -0.5]]), array([[2. , 2. , 2. ],
+ [2. , 1.7, 0.5]])]
It is possible to specify how boundaries are treated using `edge_order`
>>> x = np.array([0, 1, 2, 3, 4])
>>> f = x**2
>>> np.gradient(f, edge_order=1)
- array([ 1., 2., 4., 6., 7.])
+ array([1., 2., 4., 6., 7.])
>>> np.gradient(f, edge_order=2)
- array([-0., 2., 4., 6., 8.])
+ array([0., 2., 4., 6., 8.])
The `axis` keyword can be used to specify a subset of axes of which the
gradient is calculated
@@ -1151,7 +1150,7 @@ def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
"""
Calculate the n-th discrete difference along the given axis.
- The first difference is given by ``out[n] = a[n+1] - a[n]`` along
+ The first difference is given by ``out[i] = a[i+1] - a[i]`` along
the given axis, higher differences are calculated by using `diff`
recursively.
@@ -1200,7 +1199,7 @@ def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
- array(255, np.uint8)
+ 255
If this is not desirable, then the array should be cast to a larger
integer type first:
@@ -1340,7 +1339,7 @@ def interp(x, xp, fp, left=None, right=None, period=None):
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
- array([ 3. , 3. , 2.5 , 0.56, 0. ])
+ array([3. , 3. , 2.5 , 0.56, 0. ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0
@@ -1364,7 +1363,7 @@ def interp(x, xp, fp, left=None, right=None, period=None):
>>> xp = [190, -190, 350, -350]
>>> fp = [5, 10, 3, 4]
>>> np.interp(x, xp, fp, period=360)
- array([7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75])
+ array([7.5 , 5. , 8.75, 6.25, 3. , 3.25, 3.5 , 3.75])
Complex interpolation:
@@ -1372,7 +1371,7 @@ def interp(x, xp, fp, left=None, right=None, period=None):
>>> xp = [2,3,5]
>>> fp = [1.0j, 0, 2+3j]
>>> np.interp(x, xp, fp)
- array([ 0.+1.j , 1.+1.5j])
+ array([0.+1.j , 1.+1.5j])
"""
@@ -1445,7 +1444,7 @@ def angle(z, deg=False):
Examples
--------
>>> np.angle([1.0, 1.0j, 1+1j]) # in radians
- array([ 0. , 1.57079633, 0.78539816])
+ array([ 0. , 1.57079633, 0.78539816]) # may vary
>>> np.angle(1+1j, deg=True) # in degrees
45.0
@@ -1505,9 +1504,9 @@ def unwrap(p, discont=pi, axis=-1):
>>> phase = np.linspace(0, np.pi, num=5)
>>> phase[3:] += np.pi
>>> phase
- array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531])
+ array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531]) # may vary
>>> np.unwrap(phase)
- array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ])
+ array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ]) # may vary
"""
p = asarray(p)
@@ -1547,10 +1546,10 @@ def sort_complex(a):
Examples
--------
>>> np.sort_complex([5, 3, 6, 2, 1])
- array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
+ array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
- array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
+ array([1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
@@ -1596,7 +1595,7 @@ def trim_zeros(filt, trim='fb'):
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
- array([0, 0, 0, 1, 2, 3, 0, 2, 1])
+ array([0, 0, 0, ..., 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
@@ -1958,11 +1957,11 @@ class vectorize(object):
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
- <type 'numpy.int32'>
+ <class 'numpy.int64'>
>>> vfunc = np.vectorize(myfunc, otypes=[float])
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
- <type 'numpy.float64'>
+ <class 'numpy.float64'>
The `excluded` argument can be used to prevent vectorizing over certain
arguments. This can be useful for array-like arguments of a fixed length
@@ -1990,18 +1989,18 @@ class vectorize(object):
>>> import scipy.stats
>>> pearsonr = np.vectorize(scipy.stats.pearsonr,
- ... signature='(n),(n)->(),()')
- >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
+ ... signature='(n),(n)->(),()')
+ >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
(array([ 1., -1.]), array([ 0., 0.]))
Or for a vectorized convolution:
>>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
>>> convolve(np.eye(4), [1, 2, 1])
- array([[ 1., 2., 1., 0., 0., 0.],
- [ 0., 1., 2., 1., 0., 0.],
- [ 0., 0., 1., 2., 1., 0.],
- [ 0., 0., 0., 1., 2., 1.]])
+ array([[1., 2., 1., 0., 0., 0.],
+ [0., 1., 2., 1., 0., 0.],
+ [0., 0., 1., 2., 1., 0.],
+ [0., 0., 0., 1., 2., 1.]])
See Also
--------
@@ -2311,10 +2310,14 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The
steps to compute the weighted covariance are as follows::
+ >>> m = np.arange(10, dtype=np.float64)
+ >>> f = np.arange(10) * 2
+ >>> a = np.arange(10) ** 2.
+ >>> ddof = 9 # N - 1
>>> w = f * a
>>> v1 = np.sum(w)
>>> v2 = np.sum(w * a)
- >>> m -= np.sum(m * w, axis=1, keepdims=True) / v1
+ >>> m -= np.sum(m * w, axis=None, keepdims=True) / v1
>>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)
Note that when ``a == 1``, the normalization factor
@@ -2346,14 +2349,14 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
>>> x = [-2.1, -1, 4.3]
>>> y = [3, 1.1, 0.12]
>>> X = np.stack((x, y), axis=0)
- >>> print(np.cov(X))
- [[ 11.71 -4.286 ]
- [ -4.286 2.14413333]]
- >>> print(np.cov(x, y))
- [[ 11.71 -4.286 ]
- [ -4.286 2.14413333]]
- >>> print(np.cov(x))
- 11.71
+ >>> np.cov(X)
+ array([[11.71 , -4.286 ], # may vary
+ [-4.286 , 2.144133]])
+ >>> np.cov(x, y)
+ array([[11.71 , -4.286 ], # may vary
+ [-4.286 , 2.144133]])
+ >>> np.cov(x)
+ array(11.71)
"""
# Check inputs
@@ -2590,12 +2593,12 @@ def blackman(M):
Examples
--------
+ >>> import matplotlib.pyplot as plt
>>> np.blackman(12)
- array([ -1.38777878e-17, 3.26064346e-02, 1.59903635e-01,
- 4.14397981e-01, 7.36045180e-01, 9.67046769e-01,
- 9.67046769e-01, 7.36045180e-01, 4.14397981e-01,
- 1.59903635e-01, 3.26064346e-02, -1.38777878e-17])
-
+ array([-1.38777878e-17, 3.26064346e-02, 1.59903635e-01, # may vary
+ 4.14397981e-01, 7.36045180e-01, 9.67046769e-01,
+ 9.67046769e-01, 7.36045180e-01, 4.14397981e-01,
+ 1.59903635e-01, 3.26064346e-02, -1.38777878e-17])
Plot the window and the frequency response:
@@ -2604,15 +2607,15 @@ def blackman(M):
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Blackman window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Blackman window')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Sample')
>>> plt.show()
>>> plt.figure()
- <matplotlib.figure.Figure object at 0x...>
+ <Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
@@ -2621,13 +2624,12 @@ def blackman(M):
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Blackman window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Frequency response of Blackman window')
>>> plt.ylabel("Magnitude [dB]")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
- <matplotlib.text.Text object at 0x...>
- >>> plt.axis('tight')
- (-0.5, 0.5, -100.0, ...)
+ Text(0.5, 0, 'Normalized frequency [cycles per sample]')
+ >>> _ = plt.axis('tight')
>>> plt.show()
"""
@@ -2699,8 +2701,9 @@ def bartlett(M):
Examples
--------
+ >>> import matplotlib.pyplot as plt
>>> np.bartlett(12)
- array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273,
+ array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273, # may vary
0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636,
0.18181818, 0. ])
@@ -2711,15 +2714,15 @@ def bartlett(M):
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Bartlett window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Bartlett window')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Sample')
>>> plt.show()
>>> plt.figure()
- <matplotlib.figure.Figure object at 0x...>
+ <Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
@@ -2728,13 +2731,12 @@ def bartlett(M):
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Bartlett window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Frequency response of Bartlett window')
>>> plt.ylabel("Magnitude [dB]")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
- <matplotlib.text.Text object at 0x...>
- >>> plt.axis('tight')
- (-0.5, 0.5, -100.0, ...)
+ Text(0.5, 0, 'Normalized frequency [cycles per sample]')
+ >>> _ = plt.axis('tight')
>>> plt.show()
"""
@@ -2801,26 +2803,27 @@ def hanning(M):
Examples
--------
>>> np.hanning(12)
- array([ 0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
- 0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
- 0.07937323, 0. ])
+ array([0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
+ 0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
+ 0.07937323, 0. ])
Plot the window and its frequency response:
+ >>> import matplotlib.pyplot as plt
>>> from numpy.fft import fft, fftshift
>>> window = np.hanning(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hann window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Hann window')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Sample')
>>> plt.show()
>>> plt.figure()
- <matplotlib.figure.Figure object at 0x...>
+ <Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
@@ -2829,13 +2832,13 @@ def hanning(M):
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of the Hann window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Frequency response of the Hann window')
>>> plt.ylabel("Magnitude [dB]")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Normalized frequency [cycles per sample]')
>>> plt.axis('tight')
- (-0.5, 0.5, -100.0, ...)
+ ...
>>> plt.show()
"""
@@ -2900,26 +2903,27 @@ def hamming(M):
Examples
--------
>>> np.hamming(12)
- array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594,
+ array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594, # may vary
0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909,
0.15302337, 0.08 ])
Plot the window and the frequency response:
+ >>> import matplotlib.pyplot as plt
>>> from numpy.fft import fft, fftshift
>>> window = np.hamming(51)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hamming window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Hamming window')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Sample')
>>> plt.show()
>>> plt.figure()
- <matplotlib.figure.Figure object at 0x...>
+ <Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
@@ -2928,13 +2932,13 @@ def hamming(M):
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Hamming window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Frequency response of Hamming window')
>>> plt.ylabel("Magnitude [dB]")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Normalized frequency [cycles per sample]')
>>> plt.axis('tight')
- (-0.5, 0.5, -100.0, ...)
+ ...
>>> plt.show()
"""
@@ -3083,9 +3087,9 @@ def i0(x):
Examples
--------
>>> np.i0([0.])
- array(1.0)
+ array(1.0) # may vary
>>> np.i0([0., 1. + 2j])
- array([ 1.00000000+0.j , 0.18785373+0.64616944j])
+ array([ 1.00000000+0.j , 0.18785373+0.64616944j]) # may vary
"""
x = atleast_1d(x).copy()
@@ -3180,11 +3184,12 @@ def kaiser(M, beta):
Examples
--------
+ >>> import matplotlib.pyplot as plt
>>> np.kaiser(12, 14)
- array([ 7.72686684e-06, 3.46009194e-03, 4.65200189e-02,
- 2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
- 9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
- 4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
+ array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary
+ 2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
+ 9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
+ 4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
Plot the window and the frequency response:
@@ -3194,15 +3199,15 @@ def kaiser(M, beta):
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Kaiser window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Kaiser window')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Sample')
>>> plt.show()
>>> plt.figure()
- <matplotlib.figure.Figure object at 0x...>
+ <Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
@@ -3211,13 +3216,13 @@ def kaiser(M, beta):
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Kaiser window")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Frequency response of Kaiser window')
>>> plt.ylabel("Magnitude [dB]")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'Normalized frequency [cycles per sample]')
>>> plt.axis('tight')
- (-0.5, 0.5, -100.0, ...)
+ (-0.5, 0.5, -100.0, ...) # may vary
>>> plt.show()
"""
@@ -3273,31 +3278,32 @@ def sinc(x):
Examples
--------
+ >>> import matplotlib.pyplot as plt
>>> x = np.linspace(-4, 4, 41)
>>> np.sinc(x)
- array([ -3.89804309e-17, -4.92362781e-02, -8.40918587e-02,
+ array([-3.89804309e-17, -4.92362781e-02, -8.40918587e-02, # may vary
-8.90384387e-02, -5.84680802e-02, 3.89804309e-17,
- 6.68206631e-02, 1.16434881e-01, 1.26137788e-01,
- 8.50444803e-02, -3.89804309e-17, -1.03943254e-01,
+ 6.68206631e-02, 1.16434881e-01, 1.26137788e-01,
+ 8.50444803e-02, -3.89804309e-17, -1.03943254e-01,
-1.89206682e-01, -2.16236208e-01, -1.55914881e-01,
- 3.89804309e-17, 2.33872321e-01, 5.04551152e-01,
- 7.56826729e-01, 9.35489284e-01, 1.00000000e+00,
- 9.35489284e-01, 7.56826729e-01, 5.04551152e-01,
- 2.33872321e-01, 3.89804309e-17, -1.55914881e-01,
- -2.16236208e-01, -1.89206682e-01, -1.03943254e-01,
- -3.89804309e-17, 8.50444803e-02, 1.26137788e-01,
- 1.16434881e-01, 6.68206631e-02, 3.89804309e-17,
+ 3.89804309e-17, 2.33872321e-01, 5.04551152e-01,
+ 7.56826729e-01, 9.35489284e-01, 1.00000000e+00,
+ 9.35489284e-01, 7.56826729e-01, 5.04551152e-01,
+ 2.33872321e-01, 3.89804309e-17, -1.55914881e-01,
+ -2.16236208e-01, -1.89206682e-01, -1.03943254e-01,
+ -3.89804309e-17, 8.50444803e-02, 1.26137788e-01,
+ 1.16434881e-01, 6.68206631e-02, 3.89804309e-17,
-5.84680802e-02, -8.90384387e-02, -8.40918587e-02,
-4.92362781e-02, -3.89804309e-17])
>>> plt.plot(x, np.sinc(x))
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Sinc Function")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 1.0, 'Sinc Function')
>>> plt.ylabel("Amplitude")
- <matplotlib.text.Text object at 0x...>
+ Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("X")
- <matplotlib.text.Text object at 0x...>
+ Text(0.5, 0, 'X')
>>> plt.show()
It works in 2-D as well:
@@ -3469,18 +3475,18 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
>>> np.median(a)
3.5
>>> np.median(a, axis=0)
- array([ 6.5, 4.5, 2.5])
+ array([6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
- array([ 7., 2.])
+ array([7., 2.])
>>> m = np.median(a, axis=0)
>>> out = np.zeros_like(m)
>>> np.median(a, axis=0, out=m)
- array([ 6.5, 4.5, 2.5])
+ array([6.5, 4.5, 2.5])
>>> m
- array([ 6.5, 4.5, 2.5])
+ array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
@@ -3647,23 +3653,23 @@ def percentile(a, q, axis=None, out=None,
>>> np.percentile(a, 50)
3.5
>>> np.percentile(a, 50, axis=0)
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> np.percentile(a, 50, axis=1)
- array([ 7., 2.])
+ array([7., 2.])
>>> np.percentile(a, 50, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ array([[7.],
+ [2.]])
>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=out)
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> m
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a == b)
The different types of interpolation can be visualized graphically:
@@ -3789,21 +3795,21 @@ def quantile(a, q, axis=None, out=None,
>>> np.quantile(a, 0.5)
3.5
>>> np.quantile(a, 0.5, axis=0)
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> np.quantile(a, 0.5, axis=1)
- array([ 7., 2.])
+ array([7., 2.])
>>> np.quantile(a, 0.5, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ array([[7.],
+ [2.]])
>>> m = np.quantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.quantile(a, 0.5, axis=0, out=out)
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> m
- array([[ 6.5, 4.5, 2.5]])
+ array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.quantile(b, 0.5, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a == b)
"""
q = np.asanyarray(q)
@@ -3950,8 +3956,6 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False,
r = add(x1, x2)
if np.any(n):
- warnings.warn("Invalid value encountered in percentile",
- RuntimeWarning, stacklevel=3)
if zerod:
if ap.ndim == 1:
if out is not None:
@@ -4032,9 +4036,9 @@ def trapz(y, x=None, dx=1.0, axis=-1):
array([[0, 1, 2],
[3, 4, 5]])
>>> np.trapz(a, axis=0)
- array([ 1.5, 2.5, 3.5])
+ array([1.5, 2.5, 3.5])
>>> np.trapz(a, axis=1)
- array([ 2., 8.])
+ array([2., 8.])
"""
y = asanyarray(y)
@@ -4152,17 +4156,17 @@ def meshgrid(*xi, **kwargs):
>>> y = np.linspace(0, 1, ny)
>>> xv, yv = np.meshgrid(x, y)
>>> xv
- array([[ 0. , 0.5, 1. ],
- [ 0. , 0.5, 1. ]])
+ array([[0. , 0.5, 1. ],
+ [0. , 0.5, 1. ]])
>>> yv
- array([[ 0., 0., 0.],
- [ 1., 1., 1.]])
+ array([[0., 0., 0.],
+ [1., 1., 1.]])
>>> xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays
>>> xv
- array([[ 0. , 0.5, 1. ]])
+ array([[0. , 0.5, 1. ]])
>>> yv
- array([[ 0.],
- [ 1.]])
+ array([[0.],
+ [1.]])
`meshgrid` is very useful to evaluate functions on a grid.
@@ -4224,7 +4228,7 @@ def delete(arr, obj, axis=None):
arr : array_like
Input array.
obj : slice, int or array of ints
- Indicate which sub-arrays to remove.
+ Indicate indices of sub-arrays to remove along the specified axis.
axis : int, optional
The axis along which to delete the subarray defined by `obj`.
If `axis` is None, `obj` is applied to the flattened array.
@@ -4245,6 +4249,7 @@ def delete(arr, obj, axis=None):
-----
Often it is preferable to use a boolean mask. For example:
+ >>> arr = np.arange(12) + 1
>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]
@@ -4476,7 +4481,7 @@ def insert(arr, obj, values, axis=None):
[2, 2],
[3, 3]])
>>> np.insert(a, 1, 5)
- array([1, 5, 1, 2, 2, 3, 3])
+ array([1, 5, 1, ..., 2, 3, 3])
>>> np.insert(a, 1, 5, axis=1)
array([[1, 5, 1],
[2, 5, 2],
@@ -4496,13 +4501,13 @@ def insert(arr, obj, values, axis=None):
>>> b
array([1, 1, 2, 2, 3, 3])
>>> np.insert(b, [2, 2], [5, 6])
- array([1, 1, 5, 6, 2, 2, 3, 3])
+ array([1, 1, 5, ..., 2, 3, 3])
>>> np.insert(b, slice(2, 4), [5, 6])
- array([1, 1, 5, 2, 6, 2, 3, 3])
+ array([1, 1, 5, ..., 2, 3, 3])
>>> np.insert(b, [2, 2], [7.13, False]) # type casting
- array([1, 1, 7, 0, 2, 2, 3, 3])
+ array([1, 1, 7, ..., 2, 3, 3])
>>> x = np.arange(8).reshape(2, 4)
>>> idx = (1, 3)
@@ -4666,7 +4671,7 @@ def append(arr, values, axis=None):
Examples
--------
>>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
- array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+ array([1, 2, 3, ..., 7, 8, 9])
When `axis` is specified, `values` must have the correct shape.
@@ -4676,8 +4681,8 @@ def append(arr, values, axis=None):
[7, 8, 9]])
>>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
Traceback (most recent call last):
- ...
- ValueError: arrays must have same number of dimensions
+ ...
+ ValueError: all the input arrays must have same number of dimensions
"""
arr = asanyarray(arr)
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 482eabe14..7b229cc89 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -645,7 +645,7 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None):
>>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
>>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
- >>> hist_0; hist1
+ >>> hist_0; hist_1
array([1, 1, 1])
array([2, 1, 1, 2])
>>> bins_0; bins_1
@@ -748,14 +748,14 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
- (array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
+ (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, density=True)
>>> hist
- array([ 0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
+ array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
>>> hist.sum()
2.4999999999999996
>>> np.sum(hist * np.diff(bin_edges))
@@ -770,8 +770,9 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
>>> rng = np.random.RandomState(10) # deterministic random data
>>> a = np.hstack((rng.normal(size=1000),
... rng.normal(loc=5, scale=2, size=1000)))
- >>> plt.hist(a, bins='auto') # arguments are passed to np.histogram
+ >>> _ = plt.hist(a, bins='auto') # arguments are passed to np.histogram
>>> plt.title("Histogram with 'auto' bins")
+ Text(0.5, 1.0, "Histogram with 'auto' bins")
>>> plt.show()
"""
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 56abe293a..64c491cfa 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -478,7 +478,7 @@ class RClass(AxisConcatenator):
Examples
--------
>>> np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])]
- array([1, 2, 3, 0, 0, 4, 5, 6])
+ array([1, 2, 3, ..., 4, 5, 6])
>>> np.r_[-1:1:6j, [0]*3, 5, 6]
array([-1. , -0.6, -0.2, 0.2, 0.6, 1. , 0. , 0. , 0. , 5. , 6. ])
@@ -538,7 +538,7 @@ class CClass(AxisConcatenator):
[2, 5],
[3, 6]])
>>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
- array([[1, 2, 3, 0, 0, 4, 5, 6]])
+ array([[1, 2, 3, ..., 4, 5, 6]])
"""
@@ -813,7 +813,7 @@ def fill_diagonal(a, val, wrap=False):
>>> # tall matrices no wrap
>>> a = np.zeros((5, 3),int)
- >>> fill_diagonal(a, 4)
+ >>> np.fill_diagonal(a, 4)
>>> a
array([[4, 0, 0],
[0, 4, 0],
@@ -823,7 +823,7 @@ def fill_diagonal(a, val, wrap=False):
>>> # tall matrices wrap
>>> a = np.zeros((5, 3),int)
- >>> fill_diagonal(a, 4, wrap=True)
+ >>> np.fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0],
[0, 4, 0],
@@ -833,7 +833,7 @@ def fill_diagonal(a, val, wrap=False):
>>> # wide matrices
>>> a = np.zeros((3, 5),int)
- >>> fill_diagonal(a, 4, wrap=True)
+ >>> np.fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0, 0, 0],
[0, 4, 0, 0, 0],
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index d73d84467..b3bf1880b 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -271,9 +271,9 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
- array([ 1., 2.])
+ array([1., 2.])
>>> np.nanmin(a, axis=1)
- array([ 1., 3.])
+ array([1., 3.])
When positive infinity and negative infinity are present:
@@ -384,9 +384,9 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
- array([ 3., 2.])
+ array([3., 2.])
>>> np.nanmax(a, axis=1)
- array([ 2., 3.])
+ array([2., 3.])
When positive infinity and negative infinity are present:
@@ -601,12 +601,15 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
- array([ 2., 1.])
+ array([2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
- >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
+ >>> from numpy.testing import suppress_warnings
+ >>> with suppress_warnings() as sup:
+ ... sup.filter(RuntimeWarning)
+ ... np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan
"""
@@ -677,7 +680,7 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nanprod(a)
6.0
>>> np.nanprod(a, axis=0)
- array([ 3., 2.])
+ array([3., 2.])
"""
a, mask = _replace_nan(a, 1)
@@ -738,16 +741,16 @@ def nancumsum(a, axis=None, dtype=None, out=None):
>>> np.nancumsum([1])
array([1])
>>> np.nancumsum([1, np.nan])
- array([ 1., 1.])
+ array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumsum(a)
- array([ 1., 3., 6., 6.])
+ array([1., 3., 6., 6.])
>>> np.nancumsum(a, axis=0)
- array([[ 1., 2.],
- [ 4., 2.]])
+ array([[1., 2.],
+ [4., 2.]])
>>> np.nancumsum(a, axis=1)
- array([[ 1., 3.],
- [ 3., 3.]])
+ array([[1., 3.],
+ [3., 3.]])
"""
a, mask = _replace_nan(a, 0)
@@ -805,16 +808,16 @@ def nancumprod(a, axis=None, dtype=None, out=None):
>>> np.nancumprod([1])
array([1])
>>> np.nancumprod([1, np.nan])
- array([ 1., 1.])
+ array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumprod(a)
- array([ 1., 2., 6., 6.])
+ array([1., 2., 6., 6.])
>>> np.nancumprod(a, axis=0)
- array([[ 1., 2.],
- [ 3., 2.]])
+ array([[1., 2.],
+ [3., 2.]])
>>> np.nancumprod(a, axis=1)
- array([[ 1., 2.],
- [ 3., 3.]])
+ array([[1., 2.],
+ [3., 3.]])
"""
a, mask = _replace_nan(a, 1)
@@ -895,9 +898,9 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
>>> np.nanmean(a)
2.6666666666666665
>>> np.nanmean(a, axis=0)
- array([ 2., 4.])
+ array([2., 4.])
>>> np.nanmean(a, axis=1)
- array([ 1., 3.5])
+ array([1., 3.5]) # may vary
"""
arr, mask = _replace_nan(a, 0)
@@ -1049,19 +1052,19 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValu
>>> a = np.array([[10.0, 7, 4], [3, 2, 1]])
>>> a[0, 1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ array([[10., nan, 4.],
+ [ 3., 2., 1.]])
>>> np.median(a)
nan
>>> np.nanmedian(a)
3.0
>>> np.nanmedian(a, axis=0)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> np.median(a, axis=1)
- array([ 7., 2.])
+ array([nan, 2.])
>>> b = a.copy()
>>> np.nanmedian(b, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.nanmedian(b, axis=None, overwrite_input=True)
@@ -1177,27 +1180,27 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False,
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ array([[10., nan, 4.],
+ [ 3., 2., 1.]])
>>> np.percentile(a, 50)
nan
>>> np.nanpercentile(a, 50)
- 3.5
+ 3.0
>>> np.nanpercentile(a, 50, axis=0)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> np.nanpercentile(a, 50, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ array([[7.],
+ [2.]])
>>> m = np.nanpercentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanpercentile(a, 50, axis=0, out=out)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> m
- array([ 6.5, 2. , 2.5])
+ array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
"""
@@ -1291,26 +1294,26 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False,
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
- array([[ 10., nan, 4.],
- [ 3., 2., 1.]])
+ array([[10., nan, 4.],
+ [ 3., 2., 1.]])
>>> np.quantile(a, 0.5)
nan
>>> np.nanquantile(a, 0.5)
- 3.5
+ 3.0
>>> np.nanquantile(a, 0.5, axis=0)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
- array([[ 7.],
- [ 2.]])
+ array([[7.],
+ [2.]])
>>> m = np.nanquantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanquantile(a, 0.5, axis=0, out=out)
- array([ 6.5, 2., 2.5])
+ array([6.5, 2. , 2.5])
>>> m
- array([ 6.5, 2. , 2.5])
+ array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
- array([ 7., 2.])
+ array([7., 2.])
>>> assert not np.all(a==b)
"""
a = np.asanyarray(a)
@@ -1465,12 +1468,12 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
- >>> np.var(a)
+ >>> np.nanvar(a)
1.5555555555555554
>>> np.nanvar(a, axis=0)
- array([ 1., 0.])
+ array([1., 0.])
>>> np.nanvar(a, axis=1)
- array([ 0., 0.25])
+ array([0., 0.25]) # may vary
"""
arr, mask = _replace_nan(a, 0)
@@ -1619,9 +1622,9 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
- array([ 1., 0.])
+ array([1., 0.])
>>> np.nanstd(a, axis=1)
- array([ 0., 0.5])
+ array([0., 0.5]) # may vary
"""
var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index db6a8e5eb..704fea108 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -168,13 +168,13 @@ class NpzFile(Mapping):
>>> x = np.arange(10)
>>> y = np.sin(x)
>>> np.savez(outfile, x=x, y=y)
- >>> outfile.seek(0)
+ >>> _ = outfile.seek(0)
>>> npz = np.load(outfile)
>>> isinstance(npz, np.lib.io.NpzFile)
True
- >>> npz.files
- ['y', 'x']
+ >>> sorted(npz.files)
+ ['x', 'y']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz.f.x # attribute lookup
@@ -502,7 +502,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
>>> x = np.arange(10)
>>> np.save(outfile, x)
- >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
+ >>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
@@ -597,10 +597,10 @@ def savez(file, *args, **kwds):
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
+ >>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> npzfile = np.load(outfile)
>>> npzfile.files
- ['arr_1', 'arr_0']
+ ['arr_0', 'arr_1']
>>> npzfile['arr_0']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
@@ -608,10 +608,10 @@ def savez(file, *args, **kwds):
>>> outfile = TemporaryFile()
>>> np.savez(outfile, x=x, y=y)
- >>> outfile.seek(0)
+ >>> _ = outfile.seek(0)
>>> npzfile = np.load(outfile)
- >>> npzfile.files
- ['y', 'x']
+ >>> sorted(npzfile.files)
+ ['x', 'y']
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
@@ -829,7 +829,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
`genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``.
Default: None.
skiprows : int, optional
- Skip the first `skiprows` lines; default: 0.
+ Skip the first `skiprows` lines, including comments; default: 0.
usecols : int or sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
@@ -891,21 +891,21 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
>>> from io import StringIO # StringIO behaves like a file object
>>> c = StringIO(u"0 1\\n2 3")
>>> np.loadtxt(c)
- array([[ 0., 1.],
- [ 2., 3.]])
+ array([[0., 1.],
+ [2., 3.]])
>>> d = StringIO(u"M 21 72\\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
... 'formats': ('S1', 'i4', 'f4')})
- array([('M', 21, 72.0), ('F', 35, 58.0)],
- dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
+ array([(b'M', 21, 72.), (b'F', 35, 58.)],
+ dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])
>>> c = StringIO(u"1,0,2\\n3,0,4")
>>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
- array([ 1., 3.])
+ array([1., 3.])
>>> y
- array([ 2., 4.])
+ array([2., 4.])
"""
# Type conversions for Py3 convenience
@@ -1481,17 +1481,17 @@ def fromregex(file, regexp, dtype, encoding=None):
Examples
--------
>>> f = open('test.dat', 'w')
- >>> f.write("1312 foo\\n1534 bar\\n444 qux")
+ >>> _ = f.write("1312 foo\\n1534 bar\\n444 qux")
>>> f.close()
>>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything]
>>> output = np.fromregex('test.dat', regexp,
... [('num', np.int64), ('key', 'S3')])
>>> output
- array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
- dtype=[('num', '<i8'), ('key', '|S3')])
+ array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')],
+ dtype=[('num', '<i8'), ('key', 'S3')])
>>> output['num']
- array([1312, 1534, 444], dtype=int64)
+ array([1312, 1534, 444])
"""
own_fh = False
@@ -1674,26 +1674,26 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
- array((1, 1.3, 'abcde'),
- dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
+ array((1, 1.3, b'abcde'),
+ dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
Using dtype = None
- >>> s.seek(0) # needed for StringIO example only
+ >>> _ = s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
- array((1, 1.3, 'abcde'),
- dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
+ array((1, 1.3, b'abcde'),
+ dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
Specifying dtype and names
- >>> s.seek(0)
+ >>> _ = s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
- array((1, 1.3, 'abcde'),
- dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
+ array((1, 1.3, b'abcde'),
+ dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
An example with fixed-width columns
@@ -1701,8 +1701,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
- array((1, 1.3, 'abcde'),
- dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
+ array((1, 1.3, b'abcde'),
+ dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', 'S5')])
"""
if max_rows is not None:
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index e3defdca2..b55764b5d 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -110,7 +110,7 @@ def poly(seq_of_zeros):
Given a sequence of a polynomial's zeros:
>>> np.poly((0, 0, 0)) # Multiple root example
- array([1, 0, 0, 0])
+ array([1., 0., 0., 0.])
The line above represents z**3 + 0*z**2 + 0*z + 0.
@@ -119,14 +119,14 @@ def poly(seq_of_zeros):
The line above represents z**3 - z/4
- >>> np.poly((np.random.random(1.)[0], 0, np.random.random(1.)[0]))
- array([ 1. , -0.77086955, 0.08618131, 0. ]) #random
+ >>> np.poly((np.random.random(1)[0], 0, np.random.random(1)[0]))
+ array([ 1. , -0.77086955, 0.08618131, 0. ]) # random
Given a square array object:
>>> P = np.array([[0, 1./3], [-1./2, 0]])
>>> np.poly(P)
- array([ 1. , 0. , 0.16666667])
+ array([1. , 0. , 0.16666667])
Note how in all cases the leading coefficient is always 1.
@@ -295,7 +295,7 @@ def polyint(p, m=1, k=None):
>>> p = np.poly1d([1,1,1])
>>> P = np.polyint(p)
>>> P
- poly1d([ 0.33333333, 0.5 , 1. , 0. ])
+ poly1d([ 0.33333333, 0.5 , 1. , 0. ]) # may vary
>>> np.polyder(P) == p
True
@@ -310,7 +310,7 @@ def polyint(p, m=1, k=None):
0.0
>>> P = np.polyint(p, 3, k=[6,5,3])
>>> P
- poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ])
+ poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ]) # may vary
Note that 3 = 6 / 2!, and that the constants are given in the order of
integrations. Constant of the highest-order polynomial term comes first:
@@ -404,7 +404,7 @@ def polyder(p, m=1):
>>> np.polyder(p, 3)
poly1d([6])
>>> np.polyder(p, 4)
- poly1d([ 0.])
+ poly1d([0.])
"""
m = int(m)
@@ -552,28 +552,29 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
>>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
>>> z = np.polyfit(x, y, 3)
>>> z
- array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254])
+ array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) # may vary
It is convenient to use `poly1d` objects for dealing with polynomials:
>>> p = np.poly1d(z)
>>> p(0.5)
- 0.6143849206349179
+ 0.6143849206349179 # may vary
>>> p(3.5)
- -0.34732142857143039
+ -0.34732142857143039 # may vary
>>> p(10)
- 22.579365079365115
+ 22.579365079365115 # may vary
High-order polynomials may oscillate wildly:
>>> p30 = np.poly1d(np.polyfit(x, y, 30))
- /... RankWarning: Polyfit may be poorly conditioned...
+ ...
+ >>> # RankWarning: Polyfit may be poorly conditioned...
>>> p30(4)
- -0.80000000000000204
+ -0.80000000000000204 # may vary
>>> p30(5)
- -0.99999999999999445
+ -0.99999999999999445 # may vary
>>> p30(4.5)
- -0.10547061179440398
+ -0.10547061179440398 # may vary
Illustration:
@@ -703,6 +704,8 @@ def polyval(p, x):
for polynomials of high degree the values may be inaccurate due to
rounding errors. Use carefully.
+ If `x` is a subtype of `ndarray` the return value will be of the same type.
+
References
----------
.. [1] I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng.
@@ -714,18 +717,18 @@ def polyval(p, x):
>>> np.polyval([3,0,1], 5) # 3 * 5**2 + 0 * 5**1 + 1
76
>>> np.polyval([3,0,1], np.poly1d(5))
- poly1d([ 76.])
+ poly1d([76.])
>>> np.polyval(np.poly1d([3,0,1]), 5)
76
>>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5))
- poly1d([ 76.])
+ poly1d([76.])
"""
p = NX.asarray(p)
if isinstance(x, poly1d):
y = 0
else:
- x = NX.asarray(x)
+ x = NX.asanyarray(x)
y = NX.zeros_like(x)
for i in range(len(p)):
y = y * x + p[i]
@@ -951,7 +954,7 @@ def polydiv(u, v):
>>> x = np.array([3.0, 5.0, 2.0])
>>> y = np.array([2.0, 1.0])
>>> np.polydiv(x, y)
- (array([ 1.5 , 1.75]), array([ 0.25]))
+ (array([1.5 , 1.75]), array([0.25]))
"""
truepoly = (isinstance(u, poly1d) or isinstance(u, poly1d))
@@ -1046,7 +1049,7 @@ class poly1d(object):
>>> p.r
array([-1.+1.41421356j, -1.-1.41421356j])
>>> p(p.r)
- array([ -4.44089210e-16+0.j, -4.44089210e-16+0.j])
+ array([ -4.44089210e-16+0.j, -4.44089210e-16+0.j]) # may vary
These numbers in the previous line represent (0, 0) to machine precision
@@ -1073,7 +1076,7 @@ class poly1d(object):
poly1d([ 1, 4, 10, 12, 9])
>>> (p**3 + 4) / p
- (poly1d([ 1., 4., 10., 12., 9.]), poly1d([ 4.]))
+ (poly1d([ 1., 4., 10., 12., 9.]), poly1d([4.]))
``asarray(p)`` gives the coefficient array, so polynomials can be
used in all functions that accept arrays:
@@ -1095,7 +1098,7 @@ class poly1d(object):
Construct a polynomial from its roots:
>>> np.poly1d([1, 2], True)
- poly1d([ 1, -3, 2])
+ poly1d([ 1., -3., 2.])
This is the same polynomial as obtained by:
diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py
index fcc0d9a7a..5ff35f0bb 100644
--- a/numpy/lib/recfunctions.py
+++ b/numpy/lib/recfunctions.py
@@ -57,11 +57,10 @@ def recursive_fill_fields(input, output):
Examples
--------
>>> from numpy.lib import recfunctions as rfn
- >>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)])
+ >>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> rfn.recursive_fill_fields(a, b)
- array([(1, 10.0), (2, 20.0), (0, 0.0)],
- dtype=[('A', '<i4'), ('B', '<f8')])
+ array([(1, 10.), (2, 20.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')])
"""
newdtype = output.dtype
@@ -89,11 +88,11 @@ def get_fieldspec(dtype):
Examples
--------
- >>> dt = np.dtype([(('a', 'A'), int), ('b', float, 3)])
+ >>> dt = np.dtype([(('a', 'A'), np.int64), ('b', np.double, 3)])
>>> dt.descr
- [(('a', 'A'), '<i4'), ('b', '<f8', (3,))]
+ [(('a', 'A'), '<i8'), ('b', '<f8', (3,))]
>>> get_fieldspec(dt)
- [(('a', 'A'), dtype('int32')), ('b', dtype(('<f8', (3,))))]
+ [(('a', 'A'), dtype('int64')), ('b', dtype(('<f8', (3,))))]
"""
if dtype.names is None:
@@ -120,10 +119,15 @@ def get_names(adtype):
Examples
--------
>>> from numpy.lib import recfunctions as rfn
- >>> rfn.get_names(np.empty((1,), dtype=int)) is None
- True
+ >>> rfn.get_names(np.empty((1,), dtype=int))
+ Traceback (most recent call last):
+ ...
+ AttributeError: 'numpy.ndarray' object has no attribute 'names'
+
>>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]))
- ('A', 'B')
+ Traceback (most recent call last):
+ ...
+ AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names(adtype)
('a', ('b', ('ba', 'bb')))
@@ -153,9 +157,13 @@ def get_names_flat(adtype):
--------
>>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=int)) is None
- True
+ Traceback (most recent call last):
+ ...
+ AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', float)]))
- ('A', 'B')
+ Traceback (most recent call last):
+ ...
+ AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names_flat(adtype)
('a', 'b', 'ba', 'bb')
@@ -403,20 +411,18 @@ def merge_arrays(seqarrays, fill_value=-1, flatten=False,
--------
>>> from numpy.lib import recfunctions as rfn
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
- masked_array(data = [(1, 10.0) (2, 20.0) (--, 30.0)],
- mask = [(False, False) (False, False) (True, False)],
- fill_value = (999999, 1e+20),
- dtype = [('f0', '<i4'), ('f1', '<f8')])
-
- >>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])),
- ... usemask=False)
- array([(1, 10.0), (2, 20.0), (-1, 30.0)],
- dtype=[('f0', '<i4'), ('f1', '<f8')])
- >>> rfn.merge_arrays((np.array([1, 2]).view([('a', int)]),
+ array([( 1, 10.), ( 2, 20.), (-1, 30.)],
+ dtype=[('f0', '<i8'), ('f1', '<f8')])
+
+ >>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
+ ... np.array([10., 20., 30.])), usemask=False)
+ array([(1, 10.0), (2, 20.0), (-1, 30.0)],
+ dtype=[('f0', '<i8'), ('f1', '<f8')])
+ >>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
... np.array([10., 20., 30.])),
... usemask=False, asrecarray=True)
- rec.array([(1, 10.0), (2, 20.0), (-1, 30.0)],
- dtype=[('a', '<i4'), ('f1', '<f8')])
+ rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
+ dtype=[('a', '<i8'), ('f1', '<f8')])
Notes
-----
@@ -547,16 +553,14 @@ def drop_fields(base, drop_names, usemask=True, asrecarray=False):
--------
>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
- ... dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+ ... dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])])
>>> rfn.drop_fields(a, 'a')
- array([((2.0, 3),), ((5.0, 6),)],
- dtype=[('b', [('ba', '<f8'), ('bb', '<i4')])])
+ array([((2., 3),), ((5., 6),)],
+ dtype=[('b', [('ba', '<f8'), ('bb', '<i8')])])
>>> rfn.drop_fields(a, 'ba')
- array([(1, (3,)), (4, (6,))],
- dtype=[('a', '<i4'), ('b', [('bb', '<i4')])])
+ array([(1, (3,)), (4, (6,))], dtype=[('a', '<i8'), ('b', [('bb', '<i8')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
- array([(1,), (4,)],
- dtype=[('a', '<i4')])
+ array([(1,), (4,)], dtype=[('a', '<i8')])
"""
if _is_string_like(drop_names):
drop_names = [drop_names]
@@ -648,8 +652,8 @@ def rename_fields(base, namemapper):
>>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
... dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
>>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
- array([(1, (2.0, [3.0, 30.0])), (4, (5.0, [6.0, 60.0]))],
- dtype=[('A', '<i4'), ('b', [('ba', '<f8'), ('BB', '<f8', 2)])])
+ array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))],
+ dtype=[('A', '<i8'), ('b', [('ba', '<f8'), ('BB', '<f8', (2,))])])
"""
def _recursive_rename_fields(ndtype, namemapper):
@@ -834,18 +838,18 @@ def repack_fields(a, align=False, recurse=False):
... print("offsets:", [d.fields[name][1] for name in d.names])
... print("itemsize:", d.itemsize)
...
- >>> dt = np.dtype('u1,i4,f4', align=True)
+ >>> dt = np.dtype('u1,<i4,<f4', align=True)
>>> dt
- dtype({'names':['f0','f1','f2'], 'formats':['u1','<i4','<f8'], 'offsets':[0,4,8], 'itemsize':16}, align=True)
+ dtype({'names':['f0','f1','f2'], 'formats':['u1','<i8','<f8'], 'offsets':[0,8,16], 'itemsize':24}, align=True)
>>> print_offsets(dt)
- offsets: [0, 4, 8]
- itemsize: 16
+ offsets: [0, 8, 16]
+ itemsize: 24
>>> packed_dt = repack_fields(dt)
>>> packed_dt
- dtype([('f0', 'u1'), ('f1', '<i4'), ('f2', '<f8')])
+ dtype([('f0', 'u1'), ('f1', '<i8'), ('f2', '<f8')])
>>> print_offsets(packed_dt)
- offsets: [0, 1, 5]
- itemsize: 13
+ offsets: [0, 1, 9]
+ itemsize: 17
"""
if not isinstance(a, np.dtype):
@@ -1244,15 +1248,16 @@ def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
- ... dtype=[('A', '|S3'), ('B', float), ('C', float)])
+ ... dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)])
>>> test = rfn.stack_arrays((z,zz))
>>> test
- masked_array(data = [('A', 1.0, --) ('B', 2.0, --) ('a', 10.0, 100.0) ('b', 20.0, 200.0)
- ('c', 30.0, 300.0)],
- mask = [(False, False, True) (False, False, True) (False, False, False)
- (False, False, False) (False, False, False)],
- fill_value = ('N/A', 1e+20, 1e+20),
- dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])
+ masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0),
+ (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)],
+ mask=[(False, False, True), (False, False, True),
+ (False, False, False), (False, False, False),
+ (False, False, False)],
+ fill_value=(b'N/A', 1.e+20, 1.e+20),
+ dtype=[('A', 'S3'), ('B', '<f8'), ('C', '<f8')])
"""
if isinstance(arrays, ndarray):
@@ -1331,7 +1336,10 @@ def find_duplicates(a, key=None, ignoremask=True, return_index=False):
>>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
... mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
>>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
- ... # XXX: judging by the output, the ignoremask flag has no effect
+ (masked_array(data=[(1,), (1,), (2,), (2,)],
+ mask=[(False,), (False,), (False,), (False,)],
+ fill_value=(999999,),
+ dtype=[('a', '<i8')]), array([0, 1, 3, 4]))
"""
a = np.asanyarray(a).ravel()
# Get a dictionary of fields
diff --git a/numpy/lib/scimath.py b/numpy/lib/scimath.py
index 9ca006841..5ac790ce9 100644
--- a/numpy/lib/scimath.py
+++ b/numpy/lib/scimath.py
@@ -59,7 +59,7 @@ def _tocomplex(arr):
>>> a = np.array([1,2,3],np.short)
>>> ac = np.lib.scimath._tocomplex(a); ac
- array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
+ array([1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> ac.dtype
dtype('complex64')
@@ -70,7 +70,7 @@ def _tocomplex(arr):
>>> b = np.array([1,2,3],np.double)
>>> bc = np.lib.scimath._tocomplex(b); bc
- array([ 1.+0.j, 2.+0.j, 3.+0.j])
+ array([1.+0.j, 2.+0.j, 3.+0.j])
>>> bc.dtype
dtype('complex128')
@@ -81,13 +81,13 @@ def _tocomplex(arr):
>>> c = np.array([1,2,3],np.csingle)
>>> cc = np.lib.scimath._tocomplex(c); cc
- array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
+ array([1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> c *= 2; c
- array([ 2.+0.j, 4.+0.j, 6.+0.j], dtype=complex64)
+ array([2.+0.j, 4.+0.j, 6.+0.j], dtype=complex64)
>>> cc
- array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
+ array([1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
"""
if issubclass(arr.dtype.type, (nt.single, nt.byte, nt.short, nt.ubyte,
nt.ushort, nt.csingle)):
@@ -170,7 +170,7 @@ def _fix_real_abs_gt_1(x):
array([0, 1])
>>> np.lib.scimath._fix_real_abs_gt_1([0,2])
- array([ 0.+0.j, 2.+0.j])
+ array([0.+0.j, 2.+0.j])
"""
x = asarray(x)
if any(isreal(x) & (abs(x) > 1)):
@@ -212,14 +212,14 @@ def sqrt(x):
>>> np.lib.scimath.sqrt(1)
1.0
>>> np.lib.scimath.sqrt([1, 4])
- array([ 1., 2.])
+ array([1., 2.])
But it automatically handles negative inputs:
>>> np.lib.scimath.sqrt(-1)
- (0.0+1.0j)
+ 1j
>>> np.lib.scimath.sqrt([-1,4])
- array([ 0.+1.j, 2.+0.j])
+ array([0.+1.j, 2.+0.j])
"""
x = _fix_real_lt_zero(x)
@@ -317,7 +317,7 @@ def log10(x):
1.0
>>> np.emath.log10([-10**1, -10**2, 10**2])
- array([ 1.+1.3644j, 2.+1.3644j, 2.+0.j ])
+ array([1.+1.3644j, 2.+1.3644j, 2.+0.j ])
"""
x = _fix_real_lt_zero(x)
@@ -354,9 +354,9 @@ def logn(n, x):
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.logn(2, [4, 8])
- array([ 2., 3.])
+ array([2., 3.])
>>> np.lib.scimath.logn(2, [-4, -8, 8])
- array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
+ array([2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
@@ -405,7 +405,7 @@ def log2(x):
>>> np.emath.log2(8)
3.0
>>> np.emath.log2([-4, -8, 8])
- array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
+ array([2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
@@ -451,9 +451,9 @@ def power(x, p):
>>> np.lib.scimath.power([2, 4], 2)
array([ 4, 16])
>>> np.lib.scimath.power([2, 4], -2)
- array([ 0.25 , 0.0625])
+ array([0.25 , 0.0625])
>>> np.lib.scimath.power([-2, 4], 2)
- array([ 4.+0.j, 16.+0.j])
+ array([ 4.-0.j, 16.+0.j])
"""
x = _fix_real_lt_zero(x)
@@ -499,7 +499,7 @@ def arccos(x):
0.0
>>> np.emath.arccos([1,2])
- array([ 0.-0.j , 0.+1.317j])
+ array([0.-0.j , 0.-1.317j])
"""
x = _fix_real_abs_gt_1(x)
@@ -545,7 +545,7 @@ def arcsin(x):
0.0
>>> np.emath.arcsin([0,1])
- array([ 0. , 1.5708])
+ array([0. , 1.5708])
"""
x = _fix_real_abs_gt_1(x)
@@ -589,11 +589,14 @@ def arctanh(x):
--------
>>> np.set_printoptions(precision=4)
- >>> np.emath.arctanh(np.eye(2))
- array([[ Inf, 0.],
- [ 0., Inf]])
+ >>> from numpy.testing import suppress_warnings
+ >>> with suppress_warnings() as sup:
+ ... sup.filter(RuntimeWarning)
+ ... np.emath.arctanh(np.eye(2))
+ array([[inf, 0.],
+ [ 0., inf]])
>>> np.emath.arctanh([1j])
- array([ 0.+0.7854j])
+ array([0.+0.7854j])
"""
x = _fix_real_abs_gt_1(x)
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index f56c4f4db..e088a6c4a 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -129,7 +129,7 @@ def take_along_axis(arr, indices, axis):
[40, 50, 60]])
>>> ai = np.argsort(a, axis=1); ai
array([[0, 2, 1],
- [1, 2, 0]], dtype=int64)
+ [1, 2, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 20, 30],
[40, 50, 60]])
@@ -142,7 +142,7 @@ def take_along_axis(arr, indices, axis):
>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai
array([[1],
- [0], dtype=int64)
+ [0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[30],
[60]])
@@ -152,10 +152,10 @@ def take_along_axis(arr, indices, axis):
>>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)
>>> ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)
- >>> ai = np.concatenate([ai_min, ai_max], axis=axis)
- >> ai
+ >>> ai = np.concatenate([ai_min, ai_max], axis=1)
+ >>> ai
array([[0, 1],
- [1, 0]], dtype=int64)
+ [1, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 30],
[40, 60]])
@@ -243,7 +243,7 @@ def put_along_axis(arr, indices, values, axis):
>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
>>> ai
array([[1],
- [0]], dtype=int64)
+ [0]])
>>> np.put_along_axis(a, ai, 99, axis=1)
>>> a
array([[10, 99, 20],
@@ -330,9 +330,9 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs):
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
- array([ 4., 5., 6.])
+ array([4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
- array([ 2., 5., 8.])
+ array([2., 5., 8.])
For a function that returns a 1D array, the number of dimensions in
`outarr` is the same as `arr`.
@@ -732,11 +732,11 @@ def array_split(ary, indices_or_sections, axis=0):
--------
>>> x = np.arange(8.0)
>>> np.array_split(x, 3)
- [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]
+ [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7.])]
>>> x = np.arange(7.0)
>>> np.array_split(x, 3)
- [array([ 0., 1., 2.]), array([ 3., 4.]), array([ 5., 6.])]
+ [array([0., 1., 2.]), array([3., 4.]), array([5., 6.])]
"""
try:
@@ -828,14 +828,14 @@ def split(ary, indices_or_sections, axis=0):
--------
>>> x = np.arange(9.0)
>>> np.split(x, 3)
- [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7., 8.])]
+ [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
>>> x = np.arange(8.0)
>>> np.split(x, [3, 5, 6, 10])
- [array([ 0., 1., 2.]),
- array([ 3., 4.]),
- array([ 5.]),
- array([ 6., 7.]),
+ [array([0., 1., 2.]),
+ array([3., 4.]),
+ array([5.]),
+ array([6., 7.]),
array([], dtype=float64)]
"""
@@ -872,43 +872,43 @@ def hsplit(ary, indices_or_sections):
--------
>>> x = np.arange(16.0).reshape(4, 4)
>>> x
- array([[ 0., 1., 2., 3.],
- [ 4., 5., 6., 7.],
- [ 8., 9., 10., 11.],
- [ 12., 13., 14., 15.]])
+ array([[ 0., 1., 2., 3.],
+ [ 4., 5., 6., 7.],
+ [ 8., 9., 10., 11.],
+ [12., 13., 14., 15.]])
>>> np.hsplit(x, 2)
[array([[ 0., 1.],
[ 4., 5.],
[ 8., 9.],
- [ 12., 13.]]),
+ [12., 13.]]),
array([[ 2., 3.],
[ 6., 7.],
- [ 10., 11.],
- [ 14., 15.]])]
+ [10., 11.],
+ [14., 15.]])]
>>> np.hsplit(x, np.array([3, 6]))
- [array([[ 0., 1., 2.],
- [ 4., 5., 6.],
- [ 8., 9., 10.],
- [ 12., 13., 14.]]),
- array([[ 3.],
- [ 7.],
- [ 11.],
- [ 15.]]),
- array([], dtype=float64)]
+ [array([[ 0., 1., 2.],
+ [ 4., 5., 6.],
+ [ 8., 9., 10.],
+ [12., 13., 14.]]),
+ array([[ 3.],
+ [ 7.],
+ [11.],
+ [15.]]),
+ array([], shape=(4, 0), dtype=float64)]
With a higher dimensional array the split is still along the second axis.
>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
- array([[[ 0., 1.],
- [ 2., 3.]],
- [[ 4., 5.],
- [ 6., 7.]]])
+ array([[[0., 1.],
+ [2., 3.]],
+ [[4., 5.],
+ [6., 7.]]])
>>> np.hsplit(x, 2)
- [array([[[ 0., 1.]],
- [[ 4., 5.]]]),
- array([[[ 2., 3.]],
- [[ 6., 7.]]])]
+ [array([[[0., 1.]],
+ [[4., 5.]]]),
+ array([[[2., 3.]],
+ [[6., 7.]]])]
"""
if _nx.ndim(ary) == 0:
@@ -936,35 +936,31 @@ def vsplit(ary, indices_or_sections):
--------
>>> x = np.arange(16.0).reshape(4, 4)
>>> x
- array([[ 0., 1., 2., 3.],
- [ 4., 5., 6., 7.],
- [ 8., 9., 10., 11.],
- [ 12., 13., 14., 15.]])
+ array([[ 0., 1., 2., 3.],
+ [ 4., 5., 6., 7.],
+ [ 8., 9., 10., 11.],
+ [12., 13., 14., 15.]])
>>> np.vsplit(x, 2)
- [array([[ 0., 1., 2., 3.],
- [ 4., 5., 6., 7.]]),
- array([[ 8., 9., 10., 11.],
- [ 12., 13., 14., 15.]])]
+ [array([[0., 1., 2., 3.],
+ [4., 5., 6., 7.]]), array([[ 8., 9., 10., 11.],
+ [12., 13., 14., 15.]])]
>>> np.vsplit(x, np.array([3, 6]))
- [array([[ 0., 1., 2., 3.],
- [ 4., 5., 6., 7.],
- [ 8., 9., 10., 11.]]),
- array([[ 12., 13., 14., 15.]]),
- array([], dtype=float64)]
+ [array([[ 0., 1., 2., 3.],
+ [ 4., 5., 6., 7.],
+ [ 8., 9., 10., 11.]]), array([[12., 13., 14., 15.]]), array([], shape=(0, 4), dtype=float64)]
With a higher dimensional array the split is still along the first axis.
>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
- array([[[ 0., 1.],
- [ 2., 3.]],
- [[ 4., 5.],
- [ 6., 7.]]])
+ array([[[0., 1.],
+ [2., 3.]],
+ [[4., 5.],
+ [6., 7.]]])
>>> np.vsplit(x, 2)
- [array([[[ 0., 1.],
- [ 2., 3.]]]),
- array([[[ 4., 5.],
- [ 6., 7.]]])]
+ [array([[[0., 1.],
+ [2., 3.]]]), array([[[4., 5.],
+ [6., 7.]]])]
"""
if _nx.ndim(ary) < 2:
@@ -989,30 +985,28 @@ def dsplit(ary, indices_or_sections):
--------
>>> x = np.arange(16.0).reshape(2, 2, 4)
>>> x
- array([[[ 0., 1., 2., 3.],
- [ 4., 5., 6., 7.]],
- [[ 8., 9., 10., 11.],
- [ 12., 13., 14., 15.]]])
+ array([[[ 0., 1., 2., 3.],
+ [ 4., 5., 6., 7.]],
+ [[ 8., 9., 10., 11.],
+ [12., 13., 14., 15.]]])
>>> np.dsplit(x, 2)
- [array([[[ 0., 1.],
- [ 4., 5.]],
- [[ 8., 9.],
- [ 12., 13.]]]),
- array([[[ 2., 3.],
- [ 6., 7.]],
- [[ 10., 11.],
- [ 14., 15.]]])]
+ [array([[[ 0., 1.],
+ [ 4., 5.]],
+ [[ 8., 9.],
+ [12., 13.]]]), array([[[ 2., 3.],
+ [ 6., 7.]],
+ [[10., 11.],
+ [14., 15.]]])]
>>> np.dsplit(x, np.array([3, 6]))
- [array([[[ 0., 1., 2.],
- [ 4., 5., 6.]],
- [[ 8., 9., 10.],
- [ 12., 13., 14.]]]),
- array([[[ 3.],
- [ 7.]],
- [[ 11.],
- [ 15.]]]),
- array([], dtype=float64)]
-
+ [array([[[ 0., 1., 2.],
+ [ 4., 5., 6.]],
+ [[ 8., 9., 10.],
+ [12., 13., 14.]]]),
+ array([[[ 3.],
+ [ 7.]],
+ [[11.],
+ [15.]]]),
+ array([], shape=(2, 2, 0), dtype=float64)]
"""
if _nx.ndim(ary) < 3:
raise ValueError('dsplit only works on arrays of 3 or more dimensions')
@@ -1092,15 +1086,15 @@ def kron(a, b):
Examples
--------
>>> np.kron([1,10,100], [5,6,7])
- array([ 5, 6, 7, 50, 60, 70, 500, 600, 700])
+ array([ 5, 6, 7, ..., 500, 600, 700])
>>> np.kron([5,6,7], [1,10,100])
- array([ 5, 50, 500, 6, 60, 600, 7, 70, 700])
+ array([ 5, 50, 500, ..., 7, 70, 700])
>>> np.kron(np.eye(2), np.ones((2,2)))
- array([[ 1., 1., 0., 0.],
- [ 1., 1., 0., 0.],
- [ 0., 0., 1., 1.],
- [ 0., 0., 1., 1.]])
+ array([[1., 1., 0., 0.],
+ [1., 1., 0., 0.],
+ [0., 0., 1., 1.],
+ [0., 0., 1., 1.]])
>>> a = np.arange(100).reshape((2,5,2,5))
>>> b = np.arange(24).reshape((2,3,4))
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index a17fc66e5..93d4b279f 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -136,8 +136,8 @@ class TestSetOps(object):
np.nan),
# should fail because attempting
# to downcast to smaller int type:
- (np.array([1, 2, 3], dtype=np.int32),
- np.array([5, 7, 2], dtype=np.int64),
+ (np.array([1, 2, 3], dtype=np.int16),
+ np.array([5, 1<<20, 2], dtype=np.int32),
None),
# should fail because attempting to cast
# two special floating point values
@@ -152,8 +152,8 @@ class TestSetOps(object):
# specifically, raise an appropriate
# Exception when attempting to append or
# prepend with an incompatible type
- msg = 'must be compatible'
- with assert_raises_regex(TypeError, msg):
+ msg = 'cannot convert'
+ with assert_raises_regex(ValueError, msg):
ediff1d(ary=ary,
to_end=append,
to_begin=prepend)
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 3d4b0e3b2..d9a97db1b 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -4,6 +4,7 @@ import operator
import warnings
import sys
import decimal
+import types
import pytest
import numpy as np
@@ -24,6 +25,7 @@ from numpy.lib import (
from numpy.compat import long
+PY2 = sys.version_info[0] == 2
def get_mat(n):
data = np.arange(n)
@@ -353,9 +355,9 @@ class TestAverage(object):
assert_equal(type(np.average(a, weights=w)), subclass)
def test_upcasting(self):
- types = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
+ typs = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')]
- for at, wt, rt in types:
+ for at, wt, rt in typs:
a = np.array([[1,2],[3,4]], dtype=at)
w = np.array([[1,2],[3,4]], dtype=wt)
assert_equal(np.average(a, weights=w).dtype, np.dtype(rt))
@@ -1498,6 +1500,49 @@ class TestVectorize(object):
f(x)
+class TestLeaks(object):
+ class A(object):
+ iters = 20
+
+ def bound(self, *args):
+ return 0
+
+ @staticmethod
+ def unbound(*args):
+ return 0
+
+ @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+ @pytest.mark.parametrize('name, incr', [
+ ('bound', A.iters),
+ ('unbound', 0),
+ ])
+ def test_frompyfunc_leaks(self, name, incr):
+ # exposed in gh-11867 as np.vectorized, but the problem stems from
+ # frompyfunc.
+ # class.attribute = np.frompyfunc(<method>) creates a
+ # reference cycle if <method> is a bound class method. It requires a
+ # gc collection cycle to break the cycle (on CPython 3)
+ import gc
+ A_func = getattr(self.A, name)
+ gc.disable()
+ try:
+ refcount = sys.getrefcount(A_func)
+ for i in range(self.A.iters):
+ a = self.A()
+ a.f = np.frompyfunc(getattr(a, name), 1, 1)
+ out = a.f(np.arange(10))
+ a = None
+ if PY2:
+ assert_equal(sys.getrefcount(A_func), refcount)
+ else:
+ # A.func is part of a reference cycle if incr is non-zero
+ assert_equal(sys.getrefcount(A_func), refcount + incr)
+ for i in range(5):
+ gc.collect()
+ assert_equal(sys.getrefcount(A_func), refcount)
+ finally:
+ gc.enable()
+
class TestDigitize(object):
def test_forward(self):
@@ -2391,11 +2436,8 @@ class TestPercentile(object):
assert_equal(np.percentile(x, 100), 3.5)
assert_equal(np.percentile(x, 50), 1.75)
x[1] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(x, 0), np.nan)
- assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan)
- assert_(w[0].category is RuntimeWarning)
+ assert_equal(np.percentile(x, 0), np.nan)
+ assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan)
def test_api(self):
d = np.ones(5)
@@ -2733,85 +2775,63 @@ class TestPercentile(object):
def test_nan_behavior(self):
a = np.arange(24, dtype=float)
a[2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, 0.3), np.nan)
- assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
- assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
- np.array([np.nan] * 2))
- assert_(w[0].category is RuntimeWarning)
- assert_(w[1].category is RuntimeWarning)
- assert_(w[2].category is RuntimeWarning)
+ assert_equal(np.percentile(a, 0.3), np.nan)
+ assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
+ assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
+ np.array([np.nan] * 2))
a = np.arange(24, dtype=float).reshape(2, 3, 4)
a[1, 2, 3] = np.nan
a[1, 1, 2] = np.nan
# no axis
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, 0.3), np.nan)
- assert_equal(np.percentile(a, 0.3).ndim, 0)
- assert_(w[0].category is RuntimeWarning)
+ assert_equal(np.percentile(a, 0.3), np.nan)
+ assert_equal(np.percentile(a, 0.3).ndim, 0)
# axis0 zerod
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0)
b[2, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, 0.3, 0), b)
+ assert_equal(np.percentile(a, 0.3, 0), b)
# axis0 not zerod
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
[0.3, 0.6], 0)
b[:, 2, 3] = np.nan
b[:, 1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, [0.3, 0.6], 0), b)
+ assert_equal(np.percentile(a, [0.3, 0.6], 0), b)
# axis1 zerod
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1)
b[1, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, 0.3, 1), b)
+ assert_equal(np.percentile(a, 0.3, 1), b)
# axis1 not zerod
b = np.percentile(
np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1)
b[:, 1, 3] = np.nan
b[:, 1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, [0.3, 0.6], 1), b)
+ assert_equal(np.percentile(a, [0.3, 0.6], 1), b)
# axis02 zerod
b = np.percentile(
np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2))
b[1] = np.nan
b[2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, 0.3, (0, 2)), b)
+ assert_equal(np.percentile(a, 0.3, (0, 2)), b)
# axis02 not zerod
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
[0.3, 0.6], (0, 2))
b[:, 1] = np.nan
b[:, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
+ assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
# axis02 not zerod with nearest interpolation
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
[0.3, 0.6], (0, 2), interpolation='nearest')
b[:, 1] = np.nan
b[:, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.percentile(
- a, [0.3, 0.6], (0, 2), interpolation='nearest'), b)
+ assert_equal(np.percentile(
+ a, [0.3, 0.6], (0, 2), interpolation='nearest'), b)
class TestQuantile(object):
@@ -2858,10 +2878,7 @@ class TestMedian(object):
# check array scalar result
assert_equal(np.median(a).ndim, 0)
a[1] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a).ndim, 0)
- assert_(w[0].category is RuntimeWarning)
+ assert_equal(np.median(a).ndim, 0)
def test_axis_keyword(self):
a3 = np.array([[2, 3],
@@ -2960,58 +2977,43 @@ class TestMedian(object):
def test_nan_behavior(self):
a = np.arange(24, dtype=float)
a[2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a), np.nan)
- assert_equal(np.median(a, axis=0), np.nan)
- assert_(w[0].category is RuntimeWarning)
- assert_(w[1].category is RuntimeWarning)
+ assert_equal(np.median(a), np.nan)
+ assert_equal(np.median(a, axis=0), np.nan)
a = np.arange(24, dtype=float).reshape(2, 3, 4)
a[1, 2, 3] = np.nan
a[1, 1, 2] = np.nan
# no axis
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a), np.nan)
- assert_equal(np.median(a).ndim, 0)
- assert_(w[0].category is RuntimeWarning)
+ assert_equal(np.median(a), np.nan)
+ assert_equal(np.median(a).ndim, 0)
# axis0
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0)
b[2, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a, 0), b)
- assert_equal(len(w), 1)
+ assert_equal(np.median(a, 0), b)
# axis1
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1)
b[1, 3] = np.nan
b[1, 2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a, 1), b)
- assert_equal(len(w), 1)
+ assert_equal(np.median(a, 1), b)
# axis02
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2))
b[1] = np.nan
b[2] = np.nan
- with warnings.catch_warnings(record=True) as w:
- warnings.filterwarnings('always', '', RuntimeWarning)
- assert_equal(np.median(a, (0, 2)), b)
- assert_equal(len(w), 1)
+ assert_equal(np.median(a, (0, 2)), b)
def test_empty(self):
- # empty arrays
+ # mean(empty array) emits two warnings: empty slice and divide by 0
a = np.array([], dtype=float)
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', RuntimeWarning)
assert_equal(np.median(a), np.nan)
assert_(w[0].category is RuntimeWarning)
+ assert_equal(len(w), 2)
# multiple dimensions
a = np.array([], dtype=float, ndmin=3)
diff --git a/numpy/lib/tests/test_recfunctions.py b/numpy/lib/tests/test_recfunctions.py
index 11f8a5afa..069693613 100644
--- a/numpy/lib/tests/test_recfunctions.py
+++ b/numpy/lib/tests/test_recfunctions.py
@@ -221,9 +221,9 @@ class TestRecFunctions(object):
( 5, ( 6., 7), [ 8., 9.]),
(10, (11., 12), [13., 14.]),
(15, (16., 17), [18., 19.])],
- dtype=[('a', '<i4'),
- ('b', [('f0', '<f4'), ('f1', '<u2')]),
- ('c', '<f4', (2,))])
+ dtype=[('a', 'i4'),
+ ('b', [('f0', 'f4'), ('f1', 'u2')]),
+ ('c', 'f4', (2,))])
assert_equal(out, want)
d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py
index 27d848608..e165c9b02 100644
--- a/numpy/lib/twodim_base.py
+++ b/numpy/lib/twodim_base.py
@@ -77,13 +77,13 @@ def fliplr(m):
--------
>>> A = np.diag([1.,2.,3.])
>>> A
- array([[ 1., 0., 0.],
- [ 0., 2., 0.],
- [ 0., 0., 3.]])
+ array([[1., 0., 0.],
+ [0., 2., 0.],
+ [0., 0., 3.]])
>>> np.fliplr(A)
- array([[ 0., 0., 1.],
- [ 0., 2., 0.],
- [ 3., 0., 0.]])
+ array([[0., 0., 1.],
+ [0., 2., 0.],
+ [3., 0., 0.]])
>>> A = np.random.randn(2,3,5)
>>> np.all(np.fliplr(A) == A[:,::-1,...])
@@ -129,13 +129,13 @@ def flipud(m):
--------
>>> A = np.diag([1.0, 2, 3])
>>> A
- array([[ 1., 0., 0.],
- [ 0., 2., 0.],
- [ 0., 0., 3.]])
+ array([[1., 0., 0.],
+ [0., 2., 0.],
+ [0., 0., 3.]])
>>> np.flipud(A)
- array([[ 0., 0., 3.],
- [ 0., 2., 0.],
- [ 1., 0., 0.]])
+ array([[0., 0., 3.],
+ [0., 2., 0.],
+ [1., 0., 0.]])
>>> A = np.random.randn(2,3,5)
>>> np.all(np.flipud(A) == A[::-1,...])
@@ -191,9 +191,9 @@ def eye(N, M=None, k=0, dtype=float, order='C'):
array([[1, 0],
[0, 1]])
>>> np.eye(3, k=1)
- array([[ 0., 1., 0.],
- [ 0., 0., 1.],
- [ 0., 0., 0.]])
+ array([[0., 1., 0.],
+ [0., 0., 1.],
+ [0., 0., 0.]])
"""
if M is None:
@@ -378,9 +378,9 @@ def tri(N, M=None, k=0, dtype=float):
[1, 1, 1, 1, 1]])
>>> np.tri(3, 5, -1)
- array([[ 0., 0., 0., 0., 0.],
- [ 1., 0., 0., 0., 0.],
- [ 1., 1., 0., 0., 0.]])
+ array([[0., 0., 0., 0., 0.],
+ [1., 0., 0., 0., 0.],
+ [1., 1., 0., 0., 0.]])
"""
if M is None:
@@ -540,7 +540,7 @@ def vander(x, N=None, increasing=False):
of the differences between the values of the input vector:
>>> np.linalg.det(np.vander(x))
- 48.000000000000043
+ 48.000000000000043 # may vary
>>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
48
@@ -644,7 +644,7 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None,
Examples
--------
- >>> import matplotlib as mpl
+ >>> from matplotlib.image import NonUniformImage
>>> import matplotlib.pyplot as plt
Construct a 2-D histogram with variable bin width. First define the bin
@@ -666,6 +666,7 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None,
>>> ax = fig.add_subplot(131, title='imshow: square bins')
>>> plt.imshow(H, interpolation='nearest', origin='low',
... extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
+ <matplotlib.image.AxesImage object at 0x...>
:func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:
@@ -673,13 +674,14 @@ def histogram2d(x, y, bins=10, range=None, normed=None, weights=None,
... aspect='equal')
>>> X, Y = np.meshgrid(xedges, yedges)
>>> ax.pcolormesh(X, Y, H)
+ <matplotlib.collections.QuadMesh object at 0x...>
:class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to
display actual bin edges with interpolation:
>>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
... aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
- >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')
+ >>> im = NonUniformImage(ax, interpolation='bilinear')
>>> xcenters = (xedges[:-1] + xedges[1:]) / 2
>>> ycenters = (yedges[:-1] + yedges[1:]) / 2
>>> im.set_data(xcenters, ycenters, H)
@@ -829,7 +831,7 @@ def tril_indices(n, k=0, m=None):
Both for indexing:
>>> a[il1]
- array([ 0, 4, 5, 8, 9, 10, 12, 13, 14, 15])
+ array([ 0, 4, 5, ..., 13, 14, 15])
And for assigning values:
@@ -944,7 +946,7 @@ def triu_indices(n, k=0, m=None):
Both for indexing:
>>> a[iu1]
- array([ 0, 1, 2, 3, 5, 6, 7, 10, 11, 15])
+ array([ 0, 1, 2, ..., 10, 11, 15])
And for assigning values:
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index 90b1e9a6e..f55517732 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -105,11 +105,11 @@ def asfarray(a, dtype=_nx.float_):
Examples
--------
>>> np.asfarray([2, 3])
- array([ 2., 3.])
+ array([2., 3.])
>>> np.asfarray([2, 3], dtype='float')
- array([ 2., 3.])
+ array([2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
- array([ 2., 3.])
+ array([2., 3.])
"""
if not _nx.issubdtype(dtype, _nx.inexact):
@@ -146,13 +146,13 @@ def real(val):
--------
>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.real
- array([ 1., 3., 5.])
+ array([1., 3., 5.])
>>> a.real = 9
>>> a
- array([ 9.+2.j, 9.+4.j, 9.+6.j])
+ array([9.+2.j, 9.+4.j, 9.+6.j])
>>> a.real = np.array([9, 8, 7])
>>> a
- array([ 9.+2.j, 8.+4.j, 7.+6.j])
+ array([9.+2.j, 8.+4.j, 7.+6.j])
>>> np.real(1 + 1j)
1.0
@@ -192,10 +192,10 @@ def imag(val):
--------
>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.imag
- array([ 2., 4., 6.])
+ array([2., 4., 6.])
>>> a.imag = np.array([8, 10, 12])
>>> a
- array([ 1. +8.j, 3.+10.j, 5.+12.j])
+ array([1. +8.j, 3.+10.j, 5.+12.j])
>>> np.imag(1 + 1j)
1.0
@@ -422,11 +422,13 @@ def nan_to_num(x, copy=True):
0.0
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
- array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
- -1.28000000e+002, 1.28000000e+002])
+ array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
+ -1.28000000e+002, 1.28000000e+002])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
+ array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
+ -1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(y)
- array([ 1.79769313e+308 +0.00000000e+000j,
+ array([ 1.79769313e+308 +0.00000000e+000j, # may vary
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
"""
@@ -490,12 +492,12 @@ def real_if_close(a, tol=100):
Examples
--------
>>> np.finfo(float).eps
- 2.2204460492503131e-16
+ 2.2204460492503131e-16 # may vary
>>> np.real_if_close([2.1 + 4e-14j], tol=1000)
- array([ 2.1])
+ array([2.1])
>>> np.real_if_close([2.1 + 4e-13j], tol=1000)
- array([ 2.1 +4.00000000e-13j])
+ array([2.1+4.e-13j])
"""
a = asanyarray(a)
@@ -538,7 +540,6 @@ def asscalar(a):
--------
>>> np.asscalar(np.array([24]))
24
-
"""
# 2018-10-10, 1.16
@@ -672,11 +673,11 @@ def common_type(*arrays):
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
- <type 'numpy.float32'>
+ <class 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
- <type 'numpy.float64'>
+ <class 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
- <type 'numpy.complex128'>
+ <class 'numpy.complex128'>
"""
is_complex = False
diff --git a/numpy/lib/ufunclike.py b/numpy/lib/ufunclike.py
index 9a9e6f9dd..5c411e8c8 100644
--- a/numpy/lib/ufunclike.py
+++ b/numpy/lib/ufunclike.py
@@ -154,11 +154,11 @@ def isposinf(x, out=None):
Examples
--------
>>> np.isposinf(np.PINF)
- array(True, dtype=bool)
+ True
>>> np.isposinf(np.inf)
- array(True, dtype=bool)
+ True
>>> np.isposinf(np.NINF)
- array(False, dtype=bool)
+ False
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True])
@@ -224,11 +224,11 @@ def isneginf(x, out=None):
Examples
--------
>>> np.isneginf(np.NINF)
- array(True, dtype=bool)
+ True
>>> np.isneginf(np.inf)
- array(False, dtype=bool)
+ False
>>> np.isneginf(np.PINF)
- array(False, dtype=bool)
+ False
>>> np.isneginf([-np.inf, 0., np.inf])
array([ True, False, False])
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index 84edf4021..6b112f37a 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -150,10 +150,8 @@ def deprecate(*args, **kwargs):
Warning:
>>> olduint = np.deprecate(np.uint)
+ DeprecationWarning: `uint64` is deprecated! # may vary
>>> olduint(6)
- /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:
- DeprecationWarning: uint32 is deprecated
- warnings.warn(str1, DeprecationWarning, stacklevel=2)
6
"""
@@ -201,8 +199,8 @@ def byte_bounds(a):
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
- >>> I = np.eye(2, dtype='G'); I.dtype
- dtype('complex192')
+ >>> I = np.eye(2); I.dtype
+ dtype('float64')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
@@ -263,17 +261,17 @@ def who(vardict=None):
>>> np.who()
Name Shape Bytes Type
===========================================================
- a 10 40 int32
+ a 10 80 int64
b 20 160 float64
- Upper bound on total bytes = 200
+ Upper bound on total bytes = 240
>>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
... 'idx':5}
>>> np.who(d)
Name Shape Bytes Type
===========================================================
- y 3 24 float64
x 2 16 float64
+ y 3 24 float64
Upper bound on total bytes = 40
"""
@@ -733,7 +731,7 @@ def lookfor(what, module=None, import_modules=True, regenerate=False,
Examples
--------
- >>> np.lookfor('binary representation')
+ >>> np.lookfor('binary representation') # doctest: +SKIP
Search results for 'binary representation'
------------------------------------------
numpy.binary_repr
@@ -1104,7 +1102,7 @@ def safe_eval(source):
>>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
Traceback (most recent call last):
...
- SyntaxError: Unsupported source construct: compiler.ast.CallFunc
+ ValueError: malformed node or string: <_ast.Call object at 0x...>
"""
# Local import to speed up numpy's import time.
@@ -1142,17 +1140,12 @@ def _median_nancheck(data, result, axis, out):
n = n.filled(False)
if result.ndim == 0:
if n == True:
- warnings.warn("Invalid value encountered in median",
- RuntimeWarning, stacklevel=3)
if out is not None:
out[...] = data.dtype.type(np.nan)
result = out
else:
result = data.dtype.type(np.nan)
elif np.count_nonzero(n.ravel()) > 0:
- warnings.warn("Invalid value encountered in median for" +
- " %d results" % np.count_nonzero(n.ravel()),
- RuntimeWarning, stacklevel=3)
result[n] = np.nan
return result
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