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-rw-r--r--numpy/lib/_iotools.py13
-rw-r--r--numpy/lib/arraypad.py9
-rw-r--r--numpy/lib/function_base.py7
-rw-r--r--numpy/lib/nanfunctions.py2
-rw-r--r--numpy/lib/npyio.py28
-rw-r--r--numpy/lib/recfunctions.py68
-rw-r--r--numpy/lib/tests/test_arraypad.py23
-rw-r--r--numpy/lib/tests/test_io.py11
-rw-r--r--numpy/lib/tests/test_recfunctions.py58
-rw-r--r--numpy/lib/type_check.py10
-rw-r--r--numpy/lib/utils.py87
11 files changed, 190 insertions, 126 deletions
diff --git a/numpy/lib/_iotools.py b/numpy/lib/_iotools.py
index 0ebd39b8c..c392929fd 100644
--- a/numpy/lib/_iotools.py
+++ b/numpy/lib/_iotools.py
@@ -121,7 +121,7 @@ def has_nested_fields(ndtype):
"""
for name in ndtype.names or ():
- if ndtype[name].names:
+ if ndtype[name].names is not None:
return True
return False
@@ -931,28 +931,27 @@ def easy_dtype(ndtype, names=None, defaultfmt="f%i", **validationargs):
names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)
ndtype = np.dtype(dict(formats=ndtype, names=names))
else:
- nbtypes = len(ndtype)
# Explicit names
if names is not None:
validate = NameValidator(**validationargs)
if isinstance(names, basestring):
names = names.split(",")
# Simple dtype: repeat to match the nb of names
- if nbtypes == 0:
+ if ndtype.names is None:
formats = tuple([ndtype.type] * len(names))
names = validate(names, defaultfmt=defaultfmt)
ndtype = np.dtype(list(zip(names, formats)))
# Structured dtype: just validate the names as needed
else:
- ndtype.names = validate(names, nbfields=nbtypes,
+ ndtype.names = validate(names, nbfields=len(ndtype.names),
defaultfmt=defaultfmt)
# No implicit names
- elif (nbtypes > 0):
+ elif ndtype.names is not None:
validate = NameValidator(**validationargs)
# Default initial names : should we change the format ?
- if ((ndtype.names == tuple("f%i" % i for i in range(nbtypes))) and
+ if ((ndtype.names == tuple("f%i" % i for i in range(len(ndtype.names)))) and
(defaultfmt != "f%i")):
- ndtype.names = validate([''] * nbtypes, defaultfmt=defaultfmt)
+ ndtype.names = validate([''] * len(ndtype.names), defaultfmt=defaultfmt)
# Explicit initial names : just validate
else:
ndtype.names = validate(ndtype.names, defaultfmt=defaultfmt)
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py
index f08d425d6..62330e692 100644
--- a/numpy/lib/arraypad.py
+++ b/numpy/lib/arraypad.py
@@ -323,6 +323,12 @@ def _get_stats(padded, axis, width_pair, length_pair, stat_func):
if right_length is None or max_length < right_length:
right_length = max_length
+ if (left_length == 0 or right_length == 0) \
+ and stat_func in {np.amax, np.amin}:
+ # amax and amin can't operate on an emtpy array,
+ # raise a more descriptive warning here instead of the default one
+ raise ValueError("stat_length of 0 yields no value for padding")
+
# Calculate statistic for the left side
left_slice = _slice_at_axis(
slice(left_index, left_index + left_length), axis)
@@ -340,6 +346,7 @@ def _get_stats(padded, axis, width_pair, length_pair, stat_func):
right_chunk = padded[right_slice]
right_stat = stat_func(right_chunk, axis=axis, keepdims=True)
_round_if_needed(right_stat, padded.dtype)
+
return left_stat, right_stat
@@ -835,7 +842,7 @@ def pad(array, pad_width, mode='constant', **kwargs):
raise ValueError("unsupported keyword arguments for mode '{}': {}"
.format(mode, unsupported_kwargs))
- stat_functions = {"maximum": np.max, "minimum": np.min,
+ stat_functions = {"maximum": np.amax, "minimum": np.amin,
"mean": np.mean, "median": np.median}
# Create array with final shape and original values
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 9d380e67d..21532838b 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -316,14 +316,17 @@ def average(a, axis=None, weights=None, returned=False):
The weights array can either be 1-D (in which case its length must be
the size of `a` along the given axis) or of the same shape as `a`.
If `weights=None`, then all data in `a` are assumed to have a
- weight equal to one.
+ weight equal to one. The 1-D calculation is::
+
+ avg = sum(a * weights) / sum(weights)
+
+ The only constraint on `weights` is that `sum(weights)` must not be 0.
returned : bool, optional
Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)
is returned, otherwise only the average is returned.
If `weights=None`, `sum_of_weights` is equivalent to the number of
elements over which the average is taken.
-
Returns
-------
retval, [sum_of_weights] : array_type or double
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 9a03d0b39..6cffab6ac 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -1443,7 +1443,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
the variance of the flattened array.
dtype : data-type, optional
Type to use in computing the variance. For arrays of integer type
- the default is `float32`; for arrays of float types it is the same as
+ the default is `float64`; for arrays of float types it is the same as
the array type.
out : ndarray, optional
Alternate output array in which to place the result. It must have
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index b9dc444f8..e57a6dd47 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -506,7 +506,9 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
Notes
-----
For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
-
+
+ Any data saved to the file is appended to the end of the file.
+
Examples
--------
>>> from tempfile import TemporaryFile
@@ -519,6 +521,15 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+ >>> with open('test.npy', 'wb') as f:
+ ... np.save(f, np.array([1, 2]))
+ ... np.save(f, np.array([1, 3]))
+ >>> with open('test.npy', 'rb') as f:
+ ... a = np.load(f)
+ ... b = np.load(f)
+ >>> print(a, b)
+ # [1 2] [1 3]
"""
own_fid = False
if hasattr(file, 'write'):
@@ -1776,12 +1787,13 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
replace_space=replace_space)
# Skip the first `skip_header` rows
- for i in range(skip_header):
- next(fhd)
-
- # Keep on until we find the first valid values
- first_values = None
try:
+ for i in range(skip_header):
+ next(fhd)
+
+ # Keep on until we find the first valid values
+ first_values = None
+
while not first_values:
first_line = _decode_line(next(fhd), encoding)
if (names is True) and (comments is not None):
@@ -2168,7 +2180,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
outputmask = np.array(masks, dtype=mdtype)
else:
# Overwrite the initial dtype names if needed
- if names and dtype.names:
+ if names and dtype.names is not None:
dtype.names = names
# Case 1. We have a structured type
if len(dtype_flat) > 1:
@@ -2218,7 +2230,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
#
output = np.array(data, dtype)
if usemask:
- if dtype.names:
+ if dtype.names is not None:
mdtype = [(_, bool) for _ in dtype.names]
else:
mdtype = bool
diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py
index 6e257bb3f..40060b41a 100644
--- a/numpy/lib/recfunctions.py
+++ b/numpy/lib/recfunctions.py
@@ -72,7 +72,7 @@ def recursive_fill_fields(input, output):
current = input[field]
except ValueError:
continue
- if current.dtype.names:
+ if current.dtype.names is not None:
recursive_fill_fields(current, output[field])
else:
output[field][:len(current)] = current
@@ -139,11 +139,11 @@ def get_names(adtype):
names = adtype.names
for name in names:
current = adtype[name]
- if current.names:
+ if current.names is not None:
listnames.append((name, tuple(get_names(current))))
else:
listnames.append(name)
- return tuple(listnames) or None
+ return tuple(listnames)
def get_names_flat(adtype):
@@ -176,9 +176,9 @@ def get_names_flat(adtype):
for name in names:
listnames.append(name)
current = adtype[name]
- if current.names:
+ if current.names is not None:
listnames.extend(get_names_flat(current))
- return tuple(listnames) or None
+ return tuple(listnames)
def flatten_descr(ndtype):
@@ -215,8 +215,8 @@ def _zip_dtype(seqarrays, flatten=False):
else:
for a in seqarrays:
current = a.dtype
- if current.names and len(current.names) <= 1:
- # special case - dtypes of 0 or 1 field are flattened
+ if current.names is not None and len(current.names) == 1:
+ # special case - dtypes of 1 field are flattened
newdtype.extend(_get_fieldspec(current))
else:
newdtype.append(('', current))
@@ -268,7 +268,7 @@ def get_fieldstructure(adtype, lastname=None, parents=None,):
names = adtype.names
for name in names:
current = adtype[name]
- if current.names:
+ if current.names is not None:
if lastname:
parents[name] = [lastname, ]
else:
@@ -281,7 +281,7 @@ def get_fieldstructure(adtype, lastname=None, parents=None,):
elif lastname:
lastparent = [lastname, ]
parents[name] = lastparent or []
- return parents or None
+ return parents
def _izip_fields_flat(iterable):
@@ -435,7 +435,7 @@ def merge_arrays(seqarrays, fill_value=-1, flatten=False,
if isinstance(seqarrays, (ndarray, np.void)):
seqdtype = seqarrays.dtype
# Make sure we have named fields
- if not seqdtype.names:
+ if seqdtype.names is None:
seqdtype = np.dtype([('', seqdtype)])
if not flatten or _zip_dtype((seqarrays,), flatten=True) == seqdtype:
# Minimal processing needed: just make sure everythng's a-ok
@@ -653,7 +653,7 @@ def rename_fields(base, namemapper):
for name in ndtype.names:
newname = namemapper.get(name, name)
current = ndtype[name]
- if current.names:
+ if current.names is not None:
newdtype.append(
(newname, _recursive_rename_fields(current, namemapper))
)
@@ -874,16 +874,35 @@ def _get_fields_and_offsets(dt, offset=0):
scalar fields in the dtype "dt", including nested fields, in left
to right order.
"""
+
+ # counts up elements in subarrays, including nested subarrays, and returns
+ # base dtype and count
+ def count_elem(dt):
+ count = 1
+ while dt.shape != ():
+ for size in dt.shape:
+ count *= size
+ dt = dt.base
+ return dt, count
+
fields = []
for name in dt.names:
field = dt.fields[name]
- if field[0].names is None:
- count = 1
- for size in field[0].shape:
- count *= size
- fields.append((field[0], count, field[1] + offset))
+ f_dt, f_offset = field[0], field[1]
+ f_dt, n = count_elem(f_dt)
+
+ if f_dt.names is None:
+ fields.append((np.dtype((f_dt, (n,))), n, f_offset + offset))
else:
- fields.extend(_get_fields_and_offsets(field[0], field[1] + offset))
+ subfields = _get_fields_and_offsets(f_dt, f_offset + offset)
+ size = f_dt.itemsize
+
+ for i in range(n):
+ if i == 0:
+ # optimization: avoid list comprehension if no subarray
+ fields.extend(subfields)
+ else:
+ fields.extend([(d, c, o + i*size) for d, c, o in subfields])
return fields
@@ -948,6 +967,12 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
fields = _get_fields_and_offsets(arr.dtype)
n_fields = len(fields)
+ if n_fields == 0 and dtype is None:
+ raise ValueError("arr has no fields. Unable to guess dtype")
+ elif n_fields == 0:
+ # too many bugs elsewhere for this to work now
+ raise NotImplementedError("arr with no fields is not supported")
+
dts, counts, offsets = zip(*fields)
names = ['f{}'.format(n) for n in range(n_fields)]
@@ -1039,6 +1064,9 @@ def unstructured_to_structured(arr, dtype=None, names=None, align=False,
if arr.shape == ():
raise ValueError('arr must have at least one dimension')
n_elem = arr.shape[-1]
+ if n_elem == 0:
+ # too many bugs elsewhere for this to work now
+ raise NotImplementedError("last axis with size 0 is not supported")
if dtype is None:
if names is None:
@@ -1051,7 +1079,11 @@ def unstructured_to_structured(arr, dtype=None, names=None, align=False,
raise ValueError("don't supply both dtype and names")
# sanity check of the input dtype
fields = _get_fields_and_offsets(dtype)
- dts, counts, offsets = zip(*fields)
+ if len(fields) == 0:
+ dts, counts, offsets = [], [], []
+ else:
+ dts, counts, offsets = zip(*fields)
+
if n_elem != sum(counts):
raise ValueError('The length of the last dimension of arr must '
'be equal to the number of fields in dtype')
diff --git a/numpy/lib/tests/test_arraypad.py b/numpy/lib/tests/test_arraypad.py
index b7630cdcd..b6dd3b31c 100644
--- a/numpy/lib/tests/test_arraypad.py
+++ b/numpy/lib/tests/test_arraypad.py
@@ -469,6 +469,29 @@ class TestStatistic(object):
)
assert_array_equal(a, b)
+ @pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning")
+ @pytest.mark.filterwarnings(
+ "ignore:invalid value encountered in (true_divide|double_scalars):"
+ "RuntimeWarning"
+ )
+ @pytest.mark.parametrize("mode", ["mean", "median"])
+ def test_zero_stat_length_valid(self, mode):
+ arr = np.pad([1., 2.], (1, 2), mode, stat_length=0)
+ expected = np.array([np.nan, 1., 2., np.nan, np.nan])
+ assert_equal(arr, expected)
+
+ @pytest.mark.parametrize("mode", ["minimum", "maximum"])
+ def test_zero_stat_length_invalid(self, mode):
+ match = "stat_length of 0 yields no value for padding"
+ with pytest.raises(ValueError, match=match):
+ np.pad([1., 2.], 0, mode, stat_length=0)
+ with pytest.raises(ValueError, match=match):
+ np.pad([1., 2.], 0, mode, stat_length=(1, 0))
+ with pytest.raises(ValueError, match=match):
+ np.pad([1., 2.], 1, mode, stat_length=0)
+ with pytest.raises(ValueError, match=match):
+ np.pad([1., 2.], 1, mode, stat_length=(1, 0))
+
class TestConstant(object):
def test_check_constant(self):
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 78f9f85f3..6ee17c830 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -1565,6 +1565,13 @@ M 33 21.99
test = np.genfromtxt(TextIO(data), delimiter=";",
dtype=ndtype, converters=converters)
+ # nested but empty fields also aren't supported
+ ndtype = [('idx', int), ('code', object), ('nest', [])]
+ with assert_raises_regex(NotImplementedError,
+ 'Nested fields.* not supported.*'):
+ test = np.genfromtxt(TextIO(data), delimiter=";",
+ dtype=ndtype, converters=converters)
+
def test_userconverters_with_explicit_dtype(self):
# Test user_converters w/ explicit (standard) dtype
data = TextIO('skip,skip,2001-01-01,1.0,skip')
@@ -1681,6 +1688,10 @@ M 33 21.99
test = np.genfromtxt(data)
assert_equal(test, np.array([]))
+ # when skip_header > 0
+ test = np.genfromtxt(data, skip_header=1)
+ assert_equal(test, np.array([]))
+
def test_fancy_dtype_alt(self):
# Check that a nested dtype isn't MIA
data = TextIO('1,2,3.0\n4,5,6.0\n')
diff --git a/numpy/lib/tests/test_recfunctions.py b/numpy/lib/tests/test_recfunctions.py
index 0126ccaf8..0c839d486 100644
--- a/numpy/lib/tests/test_recfunctions.py
+++ b/numpy/lib/tests/test_recfunctions.py
@@ -115,6 +115,14 @@ class TestRecFunctions(object):
test = get_names(ndtype)
assert_equal(test, ('a', ('b', ('ba', 'bb'))))
+ ndtype = np.dtype([('a', int), ('b', [])])
+ test = get_names(ndtype)
+ assert_equal(test, ('a', ('b', ())))
+
+ ndtype = np.dtype([])
+ test = get_names(ndtype)
+ assert_equal(test, ())
+
def test_get_names_flat(self):
# Test get_names_flat
ndtype = np.dtype([('A', '|S3'), ('B', float)])
@@ -125,6 +133,14 @@ class TestRecFunctions(object):
test = get_names_flat(ndtype)
assert_equal(test, ('a', 'b', 'ba', 'bb'))
+ ndtype = np.dtype([('a', int), ('b', [])])
+ test = get_names_flat(ndtype)
+ assert_equal(test, ('a', 'b'))
+
+ ndtype = np.dtype([])
+ test = get_names_flat(ndtype)
+ assert_equal(test, ())
+
def test_get_fieldstructure(self):
# Test get_fieldstructure
@@ -147,6 +163,11 @@ class TestRecFunctions(object):
'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}
assert_equal(test, control)
+ # 0 fields
+ ndtype = np.dtype([])
+ test = get_fieldstructure(ndtype)
+ assert_equal(test, {})
+
def test_find_duplicates(self):
# Test find_duplicates
a = ma.array([(2, (2., 'B')), (1, (2., 'B')), (2, (2., 'B')),
@@ -248,7 +269,8 @@ class TestRecFunctions(object):
# including uniform fields with subarrays unpacked
d = np.array([(1, [2, 3], [[ 4, 5], [ 6, 7]]),
(8, [9, 10], [[11, 12], [13, 14]])],
- dtype=[('x0', 'i4'), ('x1', ('i4', 2)), ('x2', ('i4', (2, 2)))])
+ dtype=[('x0', 'i4'), ('x1', ('i4', 2)),
+ ('x2', ('i4', (2, 2)))])
dd = structured_to_unstructured(d)
ddd = unstructured_to_structured(dd, d.dtype)
assert_(dd.base is d)
@@ -262,6 +284,40 @@ class TestRecFunctions(object):
assert_equal(res, np.zeros((10, 6), dtype=int))
+ # test nested combinations of subarrays and structured arrays, gh-13333
+ def subarray(dt, shape):
+ return np.dtype((dt, shape))
+
+ def structured(*dts):
+ return np.dtype([('x{}'.format(i), dt) for i, dt in enumerate(dts)])
+
+ def inspect(dt, dtype=None):
+ arr = np.zeros((), dt)
+ ret = structured_to_unstructured(arr, dtype=dtype)
+ backarr = unstructured_to_structured(ret, dt)
+ return ret.shape, ret.dtype, backarr.dtype
+
+ dt = structured(subarray(structured(np.int32, np.int32), 3))
+ assert_equal(inspect(dt), ((6,), np.int32, dt))
+
+ dt = structured(subarray(subarray(np.int32, 2), 2))
+ assert_equal(inspect(dt), ((4,), np.int32, dt))
+
+ dt = structured(np.int32)
+ assert_equal(inspect(dt), ((1,), np.int32, dt))
+
+ dt = structured(np.int32, subarray(subarray(np.int32, 2), 2))
+ assert_equal(inspect(dt), ((5,), np.int32, dt))
+
+ dt = structured()
+ assert_raises(ValueError, structured_to_unstructured, np.zeros(3, dt))
+
+ # these currently don't work, but we may make it work in the future
+ assert_raises(NotImplementedError, structured_to_unstructured,
+ np.zeros(3, dt), dtype=np.int32)
+ assert_raises(NotImplementedError, unstructured_to_structured,
+ np.zeros((3,0), dtype=np.int32))
+
def test_field_assignment_by_name(self):
a = np.ones(2, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
newdt = [('b', 'f4'), ('c', 'u1')]
diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py
index ac4b03a6c..586824743 100644
--- a/numpy/lib/type_check.py
+++ b/numpy/lib/type_check.py
@@ -395,19 +395,27 @@ def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Default is True.
+
+ .. versionadded:: 1.13
nan : int, float, optional
Value to be used to fill NaN values. If no value is passed
then NaN values will be replaced with 0.0.
+
+ .. versionadded:: 1.17
posinf : int, float, optional
Value to be used to fill positive infinity values. If no value is
passed then positive infinity values will be replaced with a very
large number.
+
+ .. versionadded:: 1.17
neginf : int, float, optional
Value to be used to fill negative infinity values. If no value is
passed then negative infinity values will be replaced with a very
small (or negative) number.
+
+ .. versionadded:: 1.17
- .. versionadded:: 1.13
+
Returns
-------
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index c7dbcc5f9..8bcbd8e86 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -1003,93 +1003,6 @@ def _getmembers(item):
if hasattr(item, x)]
return members
-#-----------------------------------------------------------------------------
-
-# The following SafeEval class and company are adapted from Michael Spencer's
-# ASPN Python Cookbook recipe: https://code.activestate.com/recipes/364469/
-#
-# Accordingly it is mostly Copyright 2006 by Michael Spencer.
-# The recipe, like most of the other ASPN Python Cookbook recipes was made
-# available under the Python license.
-# https://en.wikipedia.org/wiki/Python_License
-
-# It has been modified to:
-# * handle unary -/+
-# * support True/False/None
-# * raise SyntaxError instead of a custom exception.
-
-class SafeEval(object):
- """
- Object to evaluate constant string expressions.
-
- This includes strings with lists, dicts and tuples using the abstract
- syntax tree created by ``compiler.parse``.
-
- .. deprecated:: 1.10.0
-
- See Also
- --------
- safe_eval
-
- """
- def __init__(self):
- # 2014-10-15, 1.10
- warnings.warn("SafeEval is deprecated in 1.10 and will be removed.",
- DeprecationWarning, stacklevel=2)
-
- def visit(self, node):
- cls = node.__class__
- meth = getattr(self, 'visit' + cls.__name__, self.default)
- return meth(node)
-
- def default(self, node):
- raise SyntaxError("Unsupported source construct: %s"
- % node.__class__)
-
- def visitExpression(self, node):
- return self.visit(node.body)
-
- def visitNum(self, node):
- return node.n
-
- def visitStr(self, node):
- return node.s
-
- def visitBytes(self, node):
- return node.s
-
- def visitDict(self, node,**kw):
- return dict([(self.visit(k), self.visit(v))
- for k, v in zip(node.keys, node.values)])
-
- def visitTuple(self, node):
- return tuple([self.visit(i) for i in node.elts])
-
- def visitList(self, node):
- return [self.visit(i) for i in node.elts]
-
- def visitUnaryOp(self, node):
- import ast
- if isinstance(node.op, ast.UAdd):
- return +self.visit(node.operand)
- elif isinstance(node.op, ast.USub):
- return -self.visit(node.operand)
- else:
- raise SyntaxError("Unknown unary op: %r" % node.op)
-
- def visitName(self, node):
- if node.id == 'False':
- return False
- elif node.id == 'True':
- return True
- elif node.id == 'None':
- return None
- else:
- raise SyntaxError("Unknown name: %s" % node.id)
-
- def visitNameConstant(self, node):
- return node.value
-
def safe_eval(source):
"""
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