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-rw-r--r--numpy/lib/_datasource.py8
-rw-r--r--numpy/lib/arraysetops.py24
-rw-r--r--numpy/lib/function_base.py29
-rw-r--r--numpy/lib/nanfunctions.py16
-rw-r--r--numpy/lib/polynomial.py6
-rw-r--r--numpy/lib/tests/test__datasource.py27
-rw-r--r--numpy/lib/tests/test_arraysetops.py25
7 files changed, 96 insertions, 39 deletions
diff --git a/numpy/lib/_datasource.py b/numpy/lib/_datasource.py
index 6f1295f09..ab00b1444 100644
--- a/numpy/lib/_datasource.py
+++ b/numpy/lib/_datasource.py
@@ -37,6 +37,7 @@ from __future__ import division, absolute_import, print_function
import os
import sys
+import warnings
import shutil
import io
@@ -85,9 +86,10 @@ def _python2_bz2open(fn, mode, encoding, newline):
if "t" in mode:
# BZ2File is missing necessary functions for TextIOWrapper
- raise ValueError("bz2 text files not supported in python2")
- else:
- return bz2.BZ2File(fn, mode)
+ warnings.warn("Assuming latin1 encoding for bz2 text file in Python2",
+ RuntimeWarning, stacklevel=5)
+ mode = mode.replace("t", "")
+ return bz2.BZ2File(fn, mode)
def _python2_gzipopen(fn, mode, encoding, newline):
""" Wrapper to open gzip in text mode.
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index 5880ea154..d84455a8f 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -312,12 +312,12 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
return_indices : bool
- If True, the indices which correspond to the intersection of the
- two arrays are returned. The first instance of a value is used
- if there are multiple. Default is False.
-
- .. versionadded:: 1.15.0
-
+ If True, the indices which correspond to the intersection of the two
+ arrays are returned. The first instance of a value is used if there are
+ multiple. Default is False.
+
+ .. versionadded:: 1.15.0
+
Returns
-------
intersect1d : ndarray
@@ -326,7 +326,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
The indices of the first occurrences of the common values in `ar1`.
Only provided if `return_indices` is True.
comm2 : ndarray
- The indices of the first occurrences of the common values in `ar2`.
+ The indices of the first occurrences of the common values in `ar2`.
Only provided if `return_indices` is True.
@@ -345,7 +345,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
>>> from functools import reduce
>>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
array([3])
-
+
To return the indices of the values common to the input arrays
along with the intersected values:
>>> x = np.array([1, 1, 2, 3, 4])
@@ -355,8 +355,11 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
(array([0, 2, 4]), array([1, 0, 2]))
>>> xy, x[x_ind], y[y_ind]
(array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))
-
+
"""
+ ar1 = np.asanyarray(ar1)
+ ar2 = np.asanyarray(ar2)
+
if not assume_unique:
if return_indices:
ar1, ind1 = unique(ar1, return_index=True)
@@ -367,7 +370,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
else:
ar1 = ar1.ravel()
ar2 = ar2.ravel()
-
+
aux = np.concatenate((ar1, ar2))
if return_indices:
aux_sort_indices = np.argsort(aux, kind='mergesort')
@@ -389,6 +392,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
else:
return int1d
+
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 75a39beaa..2992e92bb 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -305,12 +305,17 @@ def average(a, axis=None, weights=None, returned=False):
Returns
-------
- average, [sum_of_weights] : array_type or double
- Return the average along the specified axis. When returned is `True`,
+ retval, [sum_of_weights] : array_type or double
+ Return the average along the specified axis. When `returned` is `True`,
return a tuple with the average as the first element and the sum
- of the weights as the second element. The return type is `Float`
- if `a` is of integer type, otherwise it is of the same type as `a`.
- `sum_of_weights` is of the same type as `average`.
+ of the weights as the second element. `sum_of_weights` is of the
+ same type as `retval`. The result dtype follows a genereal pattern.
+ If `weights` is None, the result dtype will be that of `a` , or ``float64``
+ if `a` is integral. Otherwise, if `weights` is not None and `a` is non-
+ integral, the result type will be the type of lowest precision capable of
+ representing values of both `a` and `weights`. If `a` happens to be
+ integral, the previous rules still applies but the result dtype will
+ at least be ``float64``.
Raises
------
@@ -327,6 +332,8 @@ def average(a, axis=None, weights=None, returned=False):
ma.average : average for masked arrays -- useful if your data contains
"missing" values
+ numpy.result_type : Returns the type that results from applying the
+ numpy type promotion rules to the arguments.
Examples
--------
@@ -346,10 +353,16 @@ def average(a, axis=None, weights=None, returned=False):
>>> np.average(data, axis=1, weights=[1./4, 3./4])
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)
+ >>> w = np.ones(5, dtype=np.complex64)
+ >>> avg = np.average(a, weights=w)
+ >>> print(avg.dtype)
+ complex256
"""
a = np.asanyarray(a)
@@ -1769,8 +1782,8 @@ class vectorize(object):
Generalized function class.
Define a vectorized function which takes a nested sequence of objects or
- numpy arrays as inputs and returns an single or tuple of numpy array as
- output. The vectorized function evaluates `pyfunc` over successive tuples
+ numpy arrays as inputs and returns a single numpy array or a tuple of numpy
+ arrays. The vectorized function evaluates `pyfunc` over successive tuples
of the input arrays like the python map function, except it uses the
broadcasting rules of numpy.
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index abd2da1a2..8d6b0f139 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -1178,13 +1178,15 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False,
This optional parameter specifies the interpolation method to
use when the desired quantile lies between two data points
``i < j``:
- * linear: ``i + (j - i) * fraction``, where ``fraction``
- is the fractional part of the index surrounded by ``i``
- and ``j``.
- * lower: ``i``.
- * higher: ``j``.
- * nearest: ``i`` or ``j``, whichever is nearest.
- * midpoint: ``(i + j) / 2``.
+
+ * linear: ``i + (j - i) * fraction``, where ``fraction``
+ is the fractional part of the index surrounded by ``i``
+ and ``j``.
+ * lower: ``i``.
+ * higher: ``j``.
+ * nearest: ``i`` or ``j``, whichever is nearest.
+ * midpoint: ``(i + j) / 2``.
+
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in
the result as dimensions with size one. With this option, the
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 0e691f56e..9f3b84732 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -396,7 +396,11 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
to points `(x, y)`. Returns a vector of coefficients `p` that minimises
- the squared error.
+ the squared error in the order `deg`, `deg-1`, ... `0`.
+
+ The `Polynomial.fit <numpy.polynomial.polynomial.Polynomial.fit>` class
+ method is recommended for new code as it is more stable numerically. See
+ the documentation of the method for more information.
Parameters
----------
diff --git a/numpy/lib/tests/test__datasource.py b/numpy/lib/tests/test__datasource.py
index 32812990c..85788941c 100644
--- a/numpy/lib/tests/test__datasource.py
+++ b/numpy/lib/tests/test__datasource.py
@@ -2,11 +2,14 @@ from __future__ import division, absolute_import, print_function
import os
import sys
+import pytest
from tempfile import mkdtemp, mkstemp, NamedTemporaryFile
from shutil import rmtree
-from numpy.testing import assert_, assert_equal, assert_raises, SkipTest
import numpy.lib._datasource as datasource
+from numpy.testing import (
+ assert_, assert_equal, assert_raises, assert_warns, SkipTest
+ )
if sys.version_info[0] >= 3:
import urllib.request as urllib_request
@@ -30,14 +33,14 @@ def urlopen_stub(url, data=None):
old_urlopen = None
-def setup():
+def setup_module():
global old_urlopen
old_urlopen = urllib_request.urlopen
urllib_request.urlopen = urlopen_stub
-def teardown():
+def teardown_module():
urllib_request.urlopen = old_urlopen
# A valid website for more robust testing
@@ -161,6 +164,24 @@ class TestDataSourceOpen(object):
fp.close()
assert_equal(magic_line, result)
+ @pytest.mark.skipif(sys.version_info[0] >= 3, reason="Python 2 only")
+ def test_Bz2File_text_mode_warning(self):
+ try:
+ import bz2
+ except ImportError:
+ # We don't have the bz2 capabilities to test.
+ raise SkipTest
+ # Test datasource's internal file_opener for BZip2 files.
+ filepath = os.path.join(self.tmpdir, 'foobar.txt.bz2')
+ fp = bz2.BZ2File(filepath, 'w')
+ fp.write(magic_line)
+ fp.close()
+ with assert_warns(RuntimeWarning):
+ fp = self.ds.open(filepath, 'rt')
+ result = fp.readline()
+ fp.close()
+ assert_equal(magic_line, result)
+
class TestDataSourceExists(object):
def setup(self):
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index dace5ade8..c76afb8e5 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -30,19 +30,30 @@ class TestSetOps(object):
ed = np.array([1, 2, 5])
c = intersect1d(a, b)
assert_array_equal(c, ed)
-
assert_array_equal([], intersect1d([], []))
-
+
+ def test_intersect1d_array_like(self):
+ # See gh-11772
+ class Test(object):
+ def __array__(self):
+ return np.arange(3)
+
+ a = Test()
+ res = intersect1d(a, a)
+ assert_array_equal(res, a)
+ res = intersect1d([1, 2, 3], [1, 2, 3])
+ assert_array_equal(res, [1, 2, 3])
+
def test_intersect1d_indices(self):
# unique inputs
- a = np.array([1, 2, 3, 4])
+ a = np.array([1, 2, 3, 4])
b = np.array([2, 1, 4, 6])
c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
ee = np.array([1, 2, 4])
assert_array_equal(c, ee)
assert_array_equal(a[i1], ee)
assert_array_equal(b[i2], ee)
-
+
# non-unique inputs
a = np.array([1, 2, 2, 3, 4, 3, 2])
b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
@@ -51,7 +62,7 @@ class TestSetOps(object):
assert_array_equal(c, ef)
assert_array_equal(a[i1], ef)
assert_array_equal(b[i2], ef)
-
+
# non1d, unique inputs
a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
@@ -61,7 +72,7 @@ class TestSetOps(object):
ea = np.array([2, 6, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
-
+
# non1d, not assumed to be uniqueinputs
a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
@@ -71,7 +82,7 @@ class TestSetOps(object):
ea = np.array([2, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
-
+
def test_setxor1d(self):
a = np.array([5, 7, 1, 2])
b = np.array([2, 4, 3, 1, 5])
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