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-rw-r--r--numpy/lib/__init__.py26
-rw-r--r--numpy/lib/arraypad.py84
-rw-r--r--numpy/lib/arraysetops.py5
-rw-r--r--numpy/lib/financial.py177
-rw-r--r--numpy/lib/format.py3
-rw-r--r--numpy/lib/function_base.py56
-rw-r--r--numpy/lib/histograms.py20
-rw-r--r--numpy/lib/info.py160
-rw-r--r--numpy/lib/mixins.py4
-rw-r--r--numpy/lib/nanfunctions.py32
-rw-r--r--numpy/lib/npyio.py30
-rw-r--r--numpy/lib/polynomial.py8
-rw-r--r--numpy/lib/recfunctions.py10
-rw-r--r--numpy/lib/shape_base.py7
-rw-r--r--numpy/lib/tests/test_arraypad.py33
-rw-r--r--numpy/lib/tests/test_arraysetops.py7
-rw-r--r--numpy/lib/tests/test_financial.py42
-rw-r--r--numpy/lib/tests/test_function_base.py24
-rw-r--r--numpy/lib/tests/test_histograms.py11
-rw-r--r--numpy/lib/tests/test_index_tricks.py18
-rw-r--r--numpy/lib/tests/test_io.py2
-rw-r--r--numpy/lib/tests/test_recfunctions.py12
-rw-r--r--numpy/lib/utils.py7
23 files changed, 399 insertions, 379 deletions
diff --git a/numpy/lib/__init__.py b/numpy/lib/__init__.py
index c1757150e..2db12d9a4 100644
--- a/numpy/lib/__init__.py
+++ b/numpy/lib/__init__.py
@@ -1,14 +1,31 @@
+"""
+**Note:** almost all functions in the ``numpy.lib`` namespace
+are also present in the main ``numpy`` namespace. Please use the
+functions as ``np.<funcname>`` where possible.
+
+``numpy.lib`` is mostly a space for implementing functions that don't
+belong in core or in another NumPy submodule with a clear purpose
+(e.g. ``random``, ``fft``, ``linalg``, ``ma``).
+
+Most contains basic functions that are used by several submodules and are
+useful to have in the main name-space.
+
+"""
from __future__ import division, absolute_import, print_function
import math
-from .info import __doc__
from numpy.version import version as __version__
+# Public submodules
+# Note: recfunctions and (maybe) format are public too, but not imported
+from . import mixins
+from . import scimath as emath
+
+# Private submodules
from .type_check import *
from .index_tricks import *
from .function_base import *
-from .mixins import *
from .nanfunctions import *
from .shape_base import *
from .stride_tricks import *
@@ -16,9 +33,7 @@ from .twodim_base import *
from .ufunclike import *
from .histograms import *
-from . import scimath as emath
from .polynomial import *
-#import convertcode
from .utils import *
from .arraysetops import *
from .npyio import *
@@ -28,11 +43,10 @@ from .arraypad import *
from ._version import *
from numpy.core._multiarray_umath import tracemalloc_domain
-__all__ = ['emath', 'math', 'tracemalloc_domain']
+__all__ = ['emath', 'math', 'tracemalloc_domain', 'Arrayterator']
__all__ += type_check.__all__
__all__ += index_tricks.__all__
__all__ += function_base.__all__
-__all__ += mixins.__all__
__all__ += shape_base.__all__
__all__ += stride_tricks.__all__
__all__ += twodim_base.__all__
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py
index 62330e692..33e64708d 100644
--- a/numpy/lib/arraypad.py
+++ b/numpy/lib/arraypad.py
@@ -17,66 +17,6 @@ __all__ = ['pad']
# Private utility functions.
-def _linear_ramp(ndim, axis, start, stop, size, reverse=False):
- """
- Create a linear ramp of `size` in `axis` with `ndim`.
-
- This algorithm behaves like a vectorized version of `numpy.linspace`.
- The resulting linear ramp is broadcastable to any array that matches the
- ramp in `shape[axis]` and `ndim`.
-
- Parameters
- ----------
- ndim : int
- Number of dimensions of the resulting array. All dimensions except
- the one specified by `axis` will have the size 1.
- axis : int
- The dimension that contains the linear ramp of `size`.
- start : int or ndarray
- The starting value(s) of the linear ramp. If given as an array, its
- size must match `size`.
- stop : int or ndarray
- The stop value(s) (not included!) of the linear ramp. If given as an
- array, its size must match `size`.
- size : int
- The number of elements in the linear ramp. If this argument is 0 the
- dimensions of `ramp` will all be of length 1 except for the one given
- by `axis` which will be 0.
- reverse : bool
- If False, increment in a positive fashion, otherwise decrement.
-
- Returns
- -------
- ramp : ndarray
- Output array of dtype np.float64 that in- or decrements along the given
- `axis`.
-
- Examples
- --------
- >>> _linear_ramp(ndim=2, axis=0, start=np.arange(3), stop=10, size=2)
- array([[0. , 1. , 2. ],
- [5. , 5.5, 6. ]])
- >>> _linear_ramp(ndim=3, axis=0, start=2, stop=0, size=0)
- array([], shape=(0, 1, 1), dtype=float64)
- """
- # Create initial ramp
- ramp = np.arange(size, dtype=np.float64)
- if reverse:
- ramp = ramp[::-1]
-
- # Make sure, that ramp is broadcastable
- init_shape = (1,) * axis + (size,) + (1,) * (ndim - axis - 1)
- ramp = ramp.reshape(init_shape)
-
- if size != 0:
- # And scale to given start and stop values
- gain = (stop - start) / float(size)
- ramp = ramp * gain
- ramp += start
-
- return ramp
-
-
def _round_if_needed(arr, dtype):
"""
Rounds arr inplace if destination dtype is integer.
@@ -269,17 +209,25 @@ def _get_linear_ramps(padded, axis, width_pair, end_value_pair):
"""
edge_pair = _get_edges(padded, axis, width_pair)
- left_ramp = _linear_ramp(
- padded.ndim, axis, start=end_value_pair[0], stop=edge_pair[0],
- size=width_pair[0], reverse=False
+ left_ramp = np.linspace(
+ start=end_value_pair[0],
+ stop=edge_pair[0].squeeze(axis), # Dimensions is replaced by linspace
+ num=width_pair[0],
+ endpoint=False,
+ dtype=padded.dtype,
+ axis=axis,
)
- _round_if_needed(left_ramp, padded.dtype)
- right_ramp = _linear_ramp(
- padded.ndim, axis, start=end_value_pair[1], stop=edge_pair[1],
- size=width_pair[1], reverse=True
+ right_ramp = np.linspace(
+ start=end_value_pair[1],
+ stop=edge_pair[1].squeeze(axis), # Dimension is replaced by linspace
+ num=width_pair[1],
+ endpoint=False,
+ dtype=padded.dtype,
+ axis=axis,
)
- _round_if_needed(right_ramp, padded.dtype)
+ # Reverse linear space in appropriate dimension
+ right_ramp = right_ramp[_slice_at_axis(slice(None, None, -1), axis)]
return left_ramp, right_ramp
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index f3f4bc17e..2309f7e42 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -213,6 +213,7 @@ def unique(ar, return_index=False, return_inverse=False,
-----
When an axis is specified the subarrays indexed by the axis are sorted.
This is done by making the specified axis the first dimension of the array
+ (move the axis to the first dimension to keep the order of the other axes)
and then flattening the subarrays in C order. The flattened subarrays are
then viewed as a structured type with each element given a label, with the
effect that we end up with a 1-D array of structured types that can be
@@ -264,7 +265,7 @@ def unique(ar, return_index=False, return_inverse=False,
# axis was specified and not None
try:
- ar = np.swapaxes(ar, axis, 0)
+ ar = np.moveaxis(ar, axis, 0)
except np.AxisError:
# this removes the "axis1" or "axis2" prefix from the error message
raise np.AxisError(axis, ar.ndim)
@@ -285,7 +286,7 @@ def unique(ar, return_index=False, return_inverse=False,
def reshape_uniq(uniq):
uniq = uniq.view(orig_dtype)
uniq = uniq.reshape(-1, *orig_shape[1:])
- uniq = np.swapaxes(uniq, 0, axis)
+ uniq = np.moveaxis(uniq, 0, axis)
return uniq
output = _unique1d(consolidated, return_index,
diff --git a/numpy/lib/financial.py b/numpy/lib/financial.py
index 216687475..a011e52a9 100644
--- a/numpy/lib/financial.py
+++ b/numpy/lib/financial.py
@@ -12,6 +12,7 @@ otherwise stated.
"""
from __future__ import division, absolute_import, print_function
+import warnings
from decimal import Decimal
import functools
@@ -19,6 +20,10 @@ import numpy as np
from numpy.core import overrides
+_depmsg = ("numpy.{name} is deprecated and will be removed from NumPy 1.20. "
+ "Use numpy_financial.{name} instead "
+ "(https://pypi.org/project/numpy-financial/).")
+
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
@@ -45,6 +50,8 @@ def _convert_when(when):
def _fv_dispatcher(rate, nper, pmt, pv, when=None):
+ warnings.warn(_depmsg.format(name='fv'),
+ DeprecationWarning, stacklevel=3)
return (rate, nper, pmt, pv)
@@ -53,6 +60,12 @@ def fv(rate, nper, pmt, pv, when='end'):
"""
Compute the future value.
+ .. deprecated:: 1.18
+
+ `fv` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Given:
* a present value, `pv`
* an interest `rate` compounded once per period, of which
@@ -100,7 +113,9 @@ def fv(rate, nper, pmt, pv, when='end'):
References
----------
- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
Open Document Format for Office Applications (OpenDocument)v1.2,
Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
Pre-Draft 12. Organization for the Advancement of Structured Information
@@ -109,6 +124,7 @@ def fv(rate, nper, pmt, pv, when='end'):
http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
OpenDocument-formula-20090508.odt
+
Examples
--------
What is the future value after 10 years of saving $100 now, with
@@ -139,6 +155,8 @@ def fv(rate, nper, pmt, pv, when='end'):
def _pmt_dispatcher(rate, nper, pv, fv=None, when=None):
+ warnings.warn(_depmsg.format(name='pmt'),
+ DeprecationWarning, stacklevel=3)
return (rate, nper, pv, fv)
@@ -147,6 +165,12 @@ def pmt(rate, nper, pv, fv=0, when='end'):
"""
Compute the payment against loan principal plus interest.
+ .. deprecated:: 1.18
+
+ `pmt` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Given:
* a present value, `pv` (e.g., an amount borrowed)
* a future value, `fv` (e.g., 0)
@@ -204,7 +228,9 @@ def pmt(rate, nper, pv, fv=0, when='end'):
References
----------
- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
Open Document Format for Office Applications (OpenDocument)v1.2,
Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
Pre-Draft 12. Organization for the Advancement of Structured Information
@@ -237,6 +263,8 @@ def pmt(rate, nper, pv, fv=0, when='end'):
def _nper_dispatcher(rate, pmt, pv, fv=None, when=None):
+ warnings.warn(_depmsg.format(name='nper'),
+ DeprecationWarning, stacklevel=3)
return (rate, pmt, pv, fv)
@@ -245,6 +273,12 @@ def nper(rate, pmt, pv, fv=0, when='end'):
"""
Compute the number of periodic payments.
+ .. deprecated:: 1.18
+
+ `nper` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
:class:`decimal.Decimal` type is not supported.
Parameters
@@ -270,6 +304,11 @@ def nper(rate, pmt, pv, fv=0, when='end'):
fv + pv + pmt*nper = 0
+ References
+ ----------
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+
Examples
--------
If you only had $150/month to pay towards the loan, how long would it take
@@ -311,6 +350,8 @@ def nper(rate, pmt, pv, fv=0, when='end'):
def _ipmt_dispatcher(rate, per, nper, pv, fv=None, when=None):
+ warnings.warn(_depmsg.format(name='ipmt'),
+ DeprecationWarning, stacklevel=3)
return (rate, per, nper, pv, fv)
@@ -319,6 +360,12 @@ def ipmt(rate, per, nper, pv, fv=0, when='end'):
"""
Compute the interest portion of a payment.
+ .. deprecated:: 1.18
+
+ `ipmt` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Parameters
----------
rate : scalar or array_like of shape(M, )
@@ -354,6 +401,11 @@ def ipmt(rate, per, nper, pv, fv=0, when='end'):
``pmt = ppmt + ipmt``
+ References
+ ----------
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+
Examples
--------
What is the amortization schedule for a 1 year loan of $2500 at
@@ -422,6 +474,8 @@ def _rbl(rate, per, pmt, pv, when):
def _ppmt_dispatcher(rate, per, nper, pv, fv=None, when=None):
+ warnings.warn(_depmsg.format(name='ppmt'),
+ DeprecationWarning, stacklevel=3)
return (rate, per, nper, pv, fv)
@@ -430,6 +484,12 @@ def ppmt(rate, per, nper, pv, fv=0, when='end'):
"""
Compute the payment against loan principal.
+ .. deprecated:: 1.18
+
+ `ppmt` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Parameters
----------
rate : array_like
@@ -450,12 +510,19 @@ def ppmt(rate, per, nper, pv, fv=0, when='end'):
--------
pmt, pv, ipmt
+ References
+ ----------
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+
"""
total = pmt(rate, nper, pv, fv, when)
return total - ipmt(rate, per, nper, pv, fv, when)
def _pv_dispatcher(rate, nper, pmt, fv=None, when=None):
+ warnings.warn(_depmsg.format(name='pv'),
+ DeprecationWarning, stacklevel=3)
return (rate, nper, nper, pv, fv)
@@ -464,6 +531,12 @@ def pv(rate, nper, pmt, fv=0, when='end'):
"""
Compute the present value.
+ .. deprecated:: 1.18
+
+ `pv` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Given:
* a future value, `fv`
* an interest `rate` compounded once per period, of which
@@ -510,7 +583,9 @@ def pv(rate, nper, pmt, fv=0, when='end'):
References
----------
- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
Open Document Format for Office Applications (OpenDocument)v1.2,
Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
Pre-Draft 12. Organization for the Advancement of Structured Information
@@ -567,6 +642,8 @@ def _g_div_gp(r, n, p, x, y, w):
def _rate_dispatcher(nper, pmt, pv, fv, when=None, guess=None, tol=None,
maxiter=None):
+ warnings.warn(_depmsg.format(name='rate'),
+ DeprecationWarning, stacklevel=3)
return (nper, pmt, pv, fv)
@@ -582,6 +659,12 @@ def rate(nper, pmt, pv, fv, when='end', guess=None, tol=None, maxiter=100):
"""
Compute the rate of interest per period.
+ .. deprecated:: 1.18
+
+ `rate` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Parameters
----------
nper : array_like
@@ -612,13 +695,16 @@ def rate(nper, pmt, pv, fv, when='end', guess=None, tol=None, maxiter=100):
References
----------
- Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document
- Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated
- Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12.
- Organization for the Advancement of Structured Information Standards
- (OASIS). Billerica, MA, USA. [ODT Document]. Available:
- http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
- OpenDocument-formula-20090508.odt
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).
+ Open Document Format for Office Applications (OpenDocument)v1.2,
+ Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,
+ Pre-Draft 12. Organization for the Advancement of Structured Information
+ Standards (OASIS). Billerica, MA, USA. [ODT Document].
+ Available:
+ http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula
+ OpenDocument-formula-20090508.odt
"""
when = _convert_when(when)
@@ -651,6 +737,8 @@ def rate(nper, pmt, pv, fv, when='end', guess=None, tol=None, maxiter=100):
def _irr_dispatcher(values):
+ warnings.warn(_depmsg.format(name='irr'),
+ DeprecationWarning, stacklevel=3)
return (values,)
@@ -659,6 +747,12 @@ def irr(values):
"""
Return the Internal Rate of Return (IRR).
+ .. deprecated:: 1.18
+
+ `irr` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
This is the "average" periodically compounded rate of return
that gives a net present value of 0.0; for a more complete explanation,
see Notes below.
@@ -693,13 +787,15 @@ def irr(values):
+ \\frac{55}{(1+r)^3} + \\frac{20}{(1+r)^4} = 0
In general, for `values` :math:`= [v_0, v_1, ... v_M]`,
- irr is the solution of the equation: [G]_
+ irr is the solution of the equation: [2]_
.. math:: \\sum_{t=0}^M{\\frac{v_t}{(1+irr)^{t}}} = 0
References
----------
- .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 348.
Examples
@@ -715,8 +811,6 @@ def irr(values):
>>> round(np.irr([-5, 10.5, 1, -8, 1]), 5)
0.0886
- (Compare with the Example given for numpy.lib.financial.npv)
-
"""
# `np.roots` call is why this function does not support Decimal type.
#
@@ -736,6 +830,8 @@ def irr(values):
def _npv_dispatcher(rate, values):
+ warnings.warn(_depmsg.format(name='npv'),
+ DeprecationWarning, stacklevel=3)
return (values,)
@@ -744,6 +840,12 @@ def npv(rate, values):
"""
Returns the NPV (Net Present Value) of a cash flow series.
+ .. deprecated:: 1.18
+
+ `npv` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Parameters
----------
rate : scalar
@@ -763,23 +865,48 @@ def npv(rate, values):
The NPV of the input cash flow series `values` at the discount
`rate`.
+ Warnings
+ --------
+ ``npv`` considers a series of cashflows starting in the present (t = 0).
+ NPV can also be defined with a series of future cashflows, paid at the
+ end, rather than the start, of each period. If future cashflows are used,
+ the first cashflow `values[0]` must be zeroed and added to the net
+ present value of the future cashflows. This is demonstrated in the
+ examples.
+
Notes
-----
- Returns the result of: [G]_
+ Returns the result of: [2]_
.. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}
References
----------
- .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
+ .. [2] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed.,
Addison-Wesley, 2003, pg. 346.
Examples
--------
- >>> np.npv(0.281,[-100, 39, 59, 55, 20])
- -0.0084785916384548798 # may vary
+ Consider a potential project with an initial investment of $40 000 and
+ projected cashflows of $5 000, $8 000, $12 000 and $30 000 at the end of
+ each period discounted at a rate of 8% per period. To find the project's
+ net present value:
+
+ >>> rate, cashflows = 0.08, [-40_000, 5_000, 8_000, 12_000, 30_000]
+ >>> np.npv(rate, cashflows).round(5)
+ 3065.22267
- (Compare with the Example given for numpy.lib.financial.irr)
+ It may be preferable to split the projected cashflow into an initial
+ investment and expected future cashflows. In this case, the value of
+ the initial cashflow is zero and the initial investment is later added
+ to the future cashflows net present value:
+
+ >>> initial_cashflow = cashflows[0]
+ >>> cashflows[0] = 0
+ >>> np.round(np.npv(rate, cashflows) + initial_cashflow, 5)
+ 3065.22267
"""
values = np.asarray(values)
@@ -787,6 +914,8 @@ def npv(rate, values):
def _mirr_dispatcher(values, finance_rate, reinvest_rate):
+ warnings.warn(_depmsg.format(name='mirr'),
+ DeprecationWarning, stacklevel=3)
return (values,)
@@ -795,6 +924,12 @@ def mirr(values, finance_rate, reinvest_rate):
"""
Modified internal rate of return.
+ .. deprecated:: 1.18
+
+ `mirr` is deprecated; for details, see NEP 32 [1]_.
+ Use the corresponding function in the numpy-financial library,
+ https://pypi.org/project/numpy-financial.
+
Parameters
----------
values : array_like
@@ -811,6 +946,10 @@ def mirr(values, finance_rate, reinvest_rate):
out : float
Modified internal rate of return
+ References
+ ----------
+ .. [1] NumPy Enhancement Proposal (NEP) 32,
+ https://numpy.org/neps/nep-0032-remove-financial-functions.html
"""
values = np.asarray(values)
n = values.size
diff --git a/numpy/lib/format.py b/numpy/lib/format.py
index 3bf818812..1ecd72815 100644
--- a/numpy/lib/format.py
+++ b/numpy/lib/format.py
@@ -173,6 +173,9 @@ from numpy.compat import (
)
+__all__ = []
+
+
MAGIC_PREFIX = b'\x93NUMPY'
MAGIC_LEN = len(MAGIC_PREFIX) + 2
ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 9d380e67d..3ad630a7d 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
@@ -679,11 +682,7 @@ def select(condlist, choicelist, default=0):
# Now that the dtype is known, handle the deprecated select([], []) case
if len(condlist) == 0:
- # 2014-02-24, 1.9
- warnings.warn("select with an empty condition list is not possible"
- "and will be deprecated",
- DeprecationWarning, stacklevel=3)
- return np.asarray(default)[()]
+ raise ValueError("select with an empty condition list is not possible")
choicelist = [np.asarray(choice) for choice in choicelist]
choicelist.append(np.asarray(default))
@@ -699,25 +698,11 @@ def select(condlist, choicelist, default=0):
choicelist = np.broadcast_arrays(*choicelist)
# If cond array is not an ndarray in boolean format or scalar bool, abort.
- deprecated_ints = False
for i in range(len(condlist)):
cond = condlist[i]
if cond.dtype.type is not np.bool_:
- if np.issubdtype(cond.dtype, np.integer):
- # A previous implementation accepted int ndarrays accidentally.
- # Supported here deliberately, but deprecated.
- condlist[i] = condlist[i].astype(bool)
- deprecated_ints = True
- else:
- raise ValueError(
- 'invalid entry {} in condlist: should be boolean ndarray'.format(i))
-
- if deprecated_ints:
- # 2014-02-24, 1.9
- msg = "select condlists containing integer ndarrays is deprecated " \
- "and will be removed in the future. Use `.astype(bool)` to " \
- "convert to bools."
- warnings.warn(msg, DeprecationWarning, stacklevel=3)
+ raise TypeError(
+ 'invalid entry {} in condlist: should be boolean ndarray'.format(i))
if choicelist[0].ndim == 0:
# This may be common, so avoid the call.
@@ -1164,11 +1149,13 @@ def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
The axis along which the difference is taken, default is the
last axis.
prepend, append : array_like, optional
- Values to prepend or append to "a" along axis prior to
+ Values to prepend or append to `a` along axis prior to
performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of axis and the shape
of the input array in along all other axes. Otherwise the
- dimension and shape must match "a" except along axis.
+ dimension and shape must match `a` except along axis.
+
+ .. versionadded:: 1.16.0
Returns
-------
@@ -1327,9 +1314,13 @@ def interp(x, xp, fp, left=None, right=None, period=None):
Notes
-----
- Does not check that the x-coordinate sequence `xp` is increasing.
- If `xp` is not increasing, the results are nonsense.
- A simple check for increasing is::
+ The x-coordinate sequence is expected to be increasing, but this is not
+ explicitly enforced. However, if the sequence `xp` is non-increasing,
+ interpolation results are meaningless.
+
+ Note that, since NaN is unsortable, `xp` also cannot contain NaNs.
+
+ A simple check for `xp` being strictly increasing is::
np.all(np.diff(xp) > 0)
@@ -1902,7 +1893,7 @@ class vectorize(object):
typecode characters or a list of data type specifiers. There should
be one data type specifier for each output.
doc : str, optional
- The docstring for the function. If `None`, the docstring will be the
+ The docstring for the function. If None, the docstring will be the
``pyfunc.__doc__``.
excluded : set, optional
Set of strings or integers representing the positional or keyword
@@ -3310,13 +3301,6 @@ def sinc(x):
Text(0.5, 0, 'X')
>>> plt.show()
- It works in 2-D as well:
-
- >>> x = np.linspace(-4, 4, 401)
- >>> xx = np.outer(x, x)
- >>> plt.imshow(np.sinc(xx))
- <matplotlib.image.AxesImage object at 0x...>
-
"""
x = np.asanyarray(x)
y = pi * where(x == 0, 1.0e-20, x)
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 8474bd5d3..03c365ab6 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -22,6 +22,16 @@ array_function_dispatch = functools.partial(
_range = range
+def _ptp(x):
+ """Peak-to-peak value of x.
+
+ This implementation avoids the problem of signed integer arrays having a
+ peak-to-peak value that cannot be represented with the array's data type.
+ This function returns an unsigned value for signed integer arrays.
+ """
+ return _unsigned_subtract(x.max(), x.min())
+
+
def _hist_bin_sqrt(x, range):
"""
Square root histogram bin estimator.
@@ -40,7 +50,7 @@ def _hist_bin_sqrt(x, range):
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
- return x.ptp() / np.sqrt(x.size)
+ return _ptp(x) / np.sqrt(x.size)
def _hist_bin_sturges(x, range):
@@ -63,7 +73,7 @@ def _hist_bin_sturges(x, range):
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
- return x.ptp() / (np.log2(x.size) + 1.0)
+ return _ptp(x) / (np.log2(x.size) + 1.0)
def _hist_bin_rice(x, range):
@@ -87,7 +97,7 @@ def _hist_bin_rice(x, range):
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
- return x.ptp() / (2.0 * x.size ** (1.0 / 3))
+ return _ptp(x) / (2.0 * x.size ** (1.0 / 3))
def _hist_bin_scott(x, range):
@@ -137,7 +147,7 @@ def _hist_bin_stone(x, range):
"""
n = x.size
- ptp_x = np.ptp(x)
+ ptp_x = _ptp(x)
if n <= 1 or ptp_x == 0:
return 0
@@ -184,7 +194,7 @@ def _hist_bin_doane(x, range):
np.true_divide(temp, sigma, temp)
np.power(temp, 3, temp)
g1 = np.mean(temp)
- return x.ptp() / (1.0 + np.log2(x.size) +
+ return _ptp(x) / (1.0 + np.log2(x.size) +
np.log2(1.0 + np.absolute(g1) / sg1))
return 0.0
diff --git a/numpy/lib/info.py b/numpy/lib/info.py
deleted file mode 100644
index 8815a52f0..000000000
--- a/numpy/lib/info.py
+++ /dev/null
@@ -1,160 +0,0 @@
-"""
-Basic functions used by several sub-packages and
-useful to have in the main name-space.
-
-Type Handling
--------------
-================ ===================
-iscomplexobj Test for complex object, scalar result
-isrealobj Test for real object, scalar result
-iscomplex Test for complex elements, array result
-isreal Test for real elements, array result
-imag Imaginary part
-real Real part
-real_if_close Turns complex number with tiny imaginary part to real
-isneginf Tests for negative infinity, array result
-isposinf Tests for positive infinity, array result
-isnan Tests for nans, array result
-isinf Tests for infinity, array result
-isfinite Tests for finite numbers, array result
-isscalar True if argument is a scalar
-nan_to_num Replaces NaN's with 0 and infinities with large numbers
-cast Dictionary of functions to force cast to each type
-common_type Determine the minimum common type code for a group
- of arrays
-mintypecode Return minimal allowed common typecode.
-================ ===================
-
-Index Tricks
-------------
-================ ===================
-mgrid Method which allows easy construction of N-d
- 'mesh-grids'
-``r_`` Append and construct arrays: turns slice objects into
- ranges and concatenates them, for 2d arrays appends rows.
-index_exp Konrad Hinsen's index_expression class instance which
- can be useful for building complicated slicing syntax.
-================ ===================
-
-Useful Functions
-----------------
-================ ===================
-select Extension of where to multiple conditions and choices
-extract Extract 1d array from flattened array according to mask
-insert Insert 1d array of values into Nd array according to mask
-linspace Evenly spaced samples in linear space
-logspace Evenly spaced samples in logarithmic space
-fix Round x to nearest integer towards zero
-mod Modulo mod(x,y) = x % y except keeps sign of y
-amax Array maximum along axis
-amin Array minimum along axis
-ptp Array max-min along axis
-cumsum Cumulative sum along axis
-prod Product of elements along axis
-cumprod Cumluative product along axis
-diff Discrete differences along axis
-angle Returns angle of complex argument
-unwrap Unwrap phase along given axis (1-d algorithm)
-sort_complex Sort a complex-array (based on real, then imaginary)
-trim_zeros Trim the leading and trailing zeros from 1D array.
-vectorize A class that wraps a Python function taking scalar
- arguments into a generalized function which can handle
- arrays of arguments using the broadcast rules of
- numerix Python.
-================ ===================
-
-Shape Manipulation
-------------------
-================ ===================
-squeeze Return a with length-one dimensions removed.
-atleast_1d Force arrays to be >= 1D
-atleast_2d Force arrays to be >= 2D
-atleast_3d Force arrays to be >= 3D
-vstack Stack arrays vertically (row on row)
-hstack Stack arrays horizontally (column on column)
-column_stack Stack 1D arrays as columns into 2D array
-dstack Stack arrays depthwise (along third dimension)
-stack Stack arrays along a new axis
-split Divide array into a list of sub-arrays
-hsplit Split into columns
-vsplit Split into rows
-dsplit Split along third dimension
-================ ===================
-
-Matrix (2D Array) Manipulations
--------------------------------
-================ ===================
-fliplr 2D array with columns flipped
-flipud 2D array with rows flipped
-rot90 Rotate a 2D array a multiple of 90 degrees
-eye Return a 2D array with ones down a given diagonal
-diag Construct a 2D array from a vector, or return a given
- diagonal from a 2D array.
-mat Construct a Matrix
-bmat Build a Matrix from blocks
-================ ===================
-
-Polynomials
------------
-================ ===================
-poly1d A one-dimensional polynomial class
-poly Return polynomial coefficients from roots
-roots Find roots of polynomial given coefficients
-polyint Integrate polynomial
-polyder Differentiate polynomial
-polyadd Add polynomials
-polysub Subtract polynomials
-polymul Multiply polynomials
-polydiv Divide polynomials
-polyval Evaluate polynomial at given argument
-================ ===================
-
-Iterators
----------
-================ ===================
-Arrayterator A buffered iterator for big arrays.
-================ ===================
-
-Import Tricks
--------------
-================ ===================
-ppimport Postpone module import until trying to use it
-ppimport_attr Postpone module import until trying to use its attribute
-ppresolve Import postponed module and return it.
-================ ===================
-
-Machine Arithmetics
--------------------
-================ ===================
-machar_single Single precision floating point arithmetic parameters
-machar_double Double precision floating point arithmetic parameters
-================ ===================
-
-Threading Tricks
-----------------
-================ ===================
-ParallelExec Execute commands in parallel thread.
-================ ===================
-
-Array Set Operations
------------------------
-Set operations for numeric arrays based on sort() function.
-
-================ ===================
-unique Unique elements of an array.
-isin Test whether each element of an ND array is present
- anywhere within a second array.
-ediff1d Array difference (auxiliary function).
-intersect1d Intersection of 1D arrays with unique elements.
-setxor1d Set exclusive-or of 1D arrays with unique elements.
-in1d Test whether elements in a 1D array are also present in
- another array.
-union1d Union of 1D arrays with unique elements.
-setdiff1d Set difference of 1D arrays with unique elements.
-================ ===================
-
-"""
-from __future__ import division, absolute_import, print_function
-
-depends = ['core', 'testing']
-global_symbols = ['*']
diff --git a/numpy/lib/mixins.py b/numpy/lib/mixins.py
index 52ad45b68..f974a7724 100644
--- a/numpy/lib/mixins.py
+++ b/numpy/lib/mixins.py
@@ -5,8 +5,8 @@ import sys
from numpy.core import umath as um
-# Nothing should be exposed in the top-level NumPy module.
-__all__ = []
+
+__all__ = ['NDArrayOperatorsMixin']
def _disables_array_ufunc(obj):
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 9a03d0b39..18ccab3b8 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -244,8 +244,8 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
- expected output, but the type will be cast if necessary. See
- `doc.ufuncs` for details.
+ expected output, but the type will be cast if necessary. See
+ `ufuncs-output-type` for more details.
.. versionadded:: 1.8.0
keepdims : bool, optional
@@ -359,8 +359,8 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
- expected output, but the type will be cast if necessary. See
- `doc.ufuncs` for details.
+ expected output, but the type will be cast if necessary. See
+ `ufuncs-output-type` for more details.
.. versionadded:: 1.8.0
keepdims : bool, optional
@@ -585,8 +585,8 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
- `doc.ufuncs` for details. The casting of NaN to integer can yield
- unexpected results.
+ `ufuncs-output-type` for more details. The casting of NaN to integer
+ can yield unexpected results.
.. versionadded:: 1.8.0
keepdims : bool, optional
@@ -681,9 +681,9 @@ def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
- expected output, but the type will be cast if necessary. See
- `doc.ufuncs` for details. The casting of NaN to integer can yield
- unexpected results.
+ expected output, but the type will be cast if necessary. See
+ `ufuncs-output-type` for more details. The casting of NaN to integer
+ can yield unexpected results.
keepdims : bool, optional
If True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will
@@ -750,8 +750,8 @@ def nancumsum(a, axis=None, dtype=None, out=None):
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
- but the type will be cast if necessary. See `doc.ufuncs`
- (Section "Output arguments") for more details.
+ but the type will be cast if necessary. See `ufuncs-output-type` for
+ more details.
Returns
-------
@@ -888,8 +888,8 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
- expected output, but the type will be cast if necessary. See
- `doc.ufuncs` for details.
+ expected output, but the type will be cast if necessary. See
+ `ufuncs-output-type` for more details.
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,
@@ -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
@@ -1473,7 +1473,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
mean : Average
var : Variance while not ignoring NaNs
nanstd, nanmean
- numpy.doc.ufuncs : Section "Output arguments"
+ ufuncs-output-type
Notes
-----
@@ -1625,7 +1625,7 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
--------
var, mean, std
nanvar, nanmean
- numpy.doc.ufuncs : Section "Output arguments"
+ ufuncs-output-type
Notes
-----
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index e57a6dd47..7e1d4db4f 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -480,7 +480,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
file : file, str, or pathlib.Path
File or filename to which the data is saved. If file is a file-object,
then the filename is unchanged. If file is a string or Path, a ``.npy``
- extension will be appended to the file name if it does not already
+ extension will be appended to the filename if it does not already
have one.
arr : array_like
Array data to be saved.
@@ -506,9 +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.
-
+
+ Any data saved to the file is appended to the end of the file.
+
Examples
--------
>>> from tempfile import TemporaryFile
@@ -524,7 +524,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True):
>>> with open('test.npy', 'wb') as f:
... np.save(f, np.array([1, 2]))
- ... np.save(f, np.array([1, 3]))
+ ... np.save(f, np.array([1, 3]))
>>> with open('test.npy', 'rb') as f:
... a = np.load(f)
... b = np.load(f)
@@ -565,8 +565,7 @@ def _savez_dispatcher(file, *args, **kwds):
@array_function_dispatch(_savez_dispatcher)
def savez(file, *args, **kwds):
- """
- Save several arrays into a single file in uncompressed ``.npz`` format.
+ """Save several arrays into a single file in uncompressed ``.npz`` format.
If arguments are passed in with no keywords, the corresponding variable
names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
@@ -576,9 +575,9 @@ def savez(file, *args, **kwds):
Parameters
----------
file : str or file
- Either the file name (string) or an open file (file-like object)
+ Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
- ``.npz`` extension will be appended to the file name if it is not
+ ``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
@@ -611,6 +610,10 @@ def savez(file, *args, **kwds):
its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
+ When saving dictionaries, the dictionary keys become filenames
+ inside the ZIP archive. Therefore, keys should be valid filenames.
+ E.g., avoid keys that begin with ``/`` or contain ``.``.
+
Examples
--------
>>> from tempfile import TemporaryFile
@@ -638,7 +641,6 @@ def savez(file, *args, **kwds):
['x', 'y']
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
-
"""
_savez(file, args, kwds, False)
@@ -656,15 +658,15 @@ def savez_compressed(file, *args, **kwds):
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
- If arguments are passed in with no keywords, then stored file names are
+ If arguments are passed in with no keywords, then stored filenames are
arr_0, arr_1, etc.
Parameters
----------
file : str or file
- Either the file name (string) or an open file (file-like object)
+ Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
- ``.npz`` extension will be appended to the file name if it is not
+ ``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
@@ -1469,7 +1471,7 @@ def fromregex(file, regexp, dtype, encoding=None):
Parameters
----------
file : str or file
- File name or file object to read.
+ Filename or file object to read.
regexp : str or regexp
Regular expression used to parse the file.
Groups in the regular expression correspond to fields in the dtype.
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 2c72f623c..3d07a0de4 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -479,10 +479,10 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
coefficients for `k`-th data set are in ``p[:,k]``.
residuals, rank, singular_values, rcond
- Present only if `full` = True. Residuals of the least-squares fit,
- the effective rank of the scaled Vandermonde coefficient matrix,
- its singular values, and the specified value of `rcond`. For more
- details, see `linalg.lstsq`.
+ Present only if `full` = True. Residuals is sum of squared residuals
+ of the least-squares fit, the effective rank of the scaled Vandermonde
+ coefficient matrix, its singular values, and the specified value of
+ `rcond`. For more details, see `linalg.lstsq`.
V : ndarray, shape (M,M) or (M,M,K)
Present only if `full` = False and `cov`=True. The covariance
diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py
index 40060b41a..927161ddb 100644
--- a/numpy/lib/recfunctions.py
+++ b/numpy/lib/recfunctions.py
@@ -200,7 +200,7 @@ def flatten_descr(ndtype):
descr = []
for field in names:
(typ, _) = ndtype.fields[field]
- if typ.names:
+ if typ.names is not None:
descr.extend(flatten_descr(typ))
else:
descr.append((field, typ))
@@ -527,6 +527,10 @@ def drop_fields(base, drop_names, usemask=True, asrecarray=False):
Nested fields are supported.
+ ..versionchanged: 1.18.0
+ `drop_fields` returns an array with 0 fields if all fields are dropped,
+ rather than returning ``None`` as it did previously.
+
Parameters
----------
base : array
@@ -566,7 +570,7 @@ def drop_fields(base, drop_names, usemask=True, asrecarray=False):
current = ndtype[name]
if name in drop_names:
continue
- if current.names:
+ if current.names is not None:
descr = _drop_descr(current, drop_names)
if descr:
newdtype.append((name, descr))
@@ -575,8 +579,6 @@ def drop_fields(base, drop_names, usemask=True, asrecarray=False):
return newdtype
newdtype = _drop_descr(base.dtype, drop_names)
- if not newdtype:
- return None
output = np.empty(base.shape, dtype=newdtype)
output = recursive_fill_fields(base, output)
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index a5d0040aa..92d52109e 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -782,7 +782,7 @@ def _split_dispatcher(ary, indices_or_sections, axis=None):
@array_function_dispatch(_split_dispatcher)
def split(ary, indices_or_sections, axis=0):
"""
- Split an array into multiple sub-arrays.
+ Split an array into multiple sub-arrays as views into `ary`.
Parameters
----------
@@ -809,7 +809,7 @@ def split(ary, indices_or_sections, axis=0):
Returns
-------
sub-arrays : list of ndarrays
- A list of sub-arrays.
+ A list of sub-arrays as views into `ary`.
Raises
------
@@ -854,8 +854,7 @@ def split(ary, indices_or_sections, axis=0):
if N % sections:
raise ValueError(
'array split does not result in an equal division')
- res = array_split(ary, indices_or_sections, axis)
- return res
+ return array_split(ary, indices_or_sections, axis)
def _hvdsplit_dispatcher(ary, indices_or_sections):
diff --git a/numpy/lib/tests/test_arraypad.py b/numpy/lib/tests/test_arraypad.py
index b6dd3b31c..65593dd29 100644
--- a/numpy/lib/tests/test_arraypad.py
+++ b/numpy/lib/tests/test_arraypad.py
@@ -2,7 +2,6 @@
"""
from __future__ import division, absolute_import, print_function
-from itertools import chain
import pytest
@@ -11,6 +10,12 @@ from numpy.testing import assert_array_equal, assert_allclose, assert_equal
from numpy.lib.arraypad import _as_pairs
+_numeric_dtypes = (
+ np.sctypes["uint"]
+ + np.sctypes["int"]
+ + np.sctypes["float"]
+ + np.sctypes["complex"]
+)
_all_modes = {
'constant': {'constant_values': 0},
'edge': {},
@@ -738,6 +743,24 @@ class TestLinearRamp(object):
assert_equal(a[0, :], 0.)
assert_equal(a[-1, :], 0.)
+ @pytest.mark.parametrize("dtype", _numeric_dtypes)
+ def test_negative_difference(self, dtype):
+ """
+ Check correct behavior of unsigned dtypes if there is a negative
+ difference between the edge to pad and `end_values`. Check both cases
+ to be independent of implementation. Test behavior for all other dtypes
+ in case dtype casting interferes with complex dtypes. See gh-14191.
+ """
+ x = np.array([3], dtype=dtype)
+ result = np.pad(x, 3, mode="linear_ramp", end_values=0)
+ expected = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype)
+ assert_equal(result, expected)
+
+ x = np.array([0], dtype=dtype)
+ result = np.pad(x, 3, mode="linear_ramp", end_values=3)
+ expected = np.array([3, 2, 1, 0, 1, 2, 3], dtype=dtype)
+ assert_equal(result, expected)
+
class TestReflect(object):
def test_check_simple(self):
@@ -1330,13 +1353,7 @@ def test_memory_layout_persistence(mode):
assert np.pad(x, 5, mode).flags["F_CONTIGUOUS"]
-@pytest.mark.parametrize("dtype", chain(
- # Skip "other" dtypes as they are not supported by all modes
- np.sctypes["int"],
- np.sctypes["uint"],
- np.sctypes["float"],
- np.sctypes["complex"]
-))
+@pytest.mark.parametrize("dtype", _numeric_dtypes)
@pytest.mark.parametrize("mode", _all_modes.keys())
def test_dtype_persistence(dtype, mode):
arr = np.zeros((3, 2, 1), dtype=dtype)
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index dd8a38248..fd21a7f76 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -600,8 +600,11 @@ class TestUnique(object):
assert_array_equal(unique(data, axis=1), result.astype(dtype), msg)
msg = 'Unique with 3d array and axis=2 failed'
- data3d = np.dstack([data] * 3)
- result = data3d[..., :1]
+ data3d = np.array([[[1, 1],
+ [1, 0]],
+ [[0, 1],
+ [0, 0]]]).astype(dtype)
+ result = np.take(data3d, [1, 0], axis=2)
assert_array_equal(unique(data3d, axis=2), result, msg)
uniq, idx, inv, cnt = unique(data, axis=0, return_index=True,
diff --git a/numpy/lib/tests/test_financial.py b/numpy/lib/tests/test_financial.py
index 524915041..cb67f7c0f 100644
--- a/numpy/lib/tests/test_financial.py
+++ b/numpy/lib/tests/test_financial.py
@@ -1,5 +1,6 @@
from __future__ import division, absolute_import, print_function
+import warnings
from decimal import Decimal
import numpy as np
@@ -8,16 +9,35 @@ from numpy.testing import (
)
+def filter_deprecation(func):
+ def newfunc(*args, **kwargs):
+ with warnings.catch_warnings(record=True) as ws:
+ warnings.filterwarnings('always', category=DeprecationWarning)
+ func(*args, **kwargs)
+ assert_(all(w.category is DeprecationWarning for w in ws))
+ return newfunc
+
+
class TestFinancial(object):
+ @filter_deprecation
+ def test_npv_irr_congruence(self):
+ # IRR is defined as the rate required for the present value of a
+ # a series of cashflows to be zero i.e. NPV(IRR(x), x) = 0
+ cashflows = np.array([-40000, 5000, 8000, 12000, 30000])
+ assert_allclose(np.npv(np.irr(cashflows), cashflows), 0, atol=1e-10, rtol=0)
+
+ @filter_deprecation
def test_rate(self):
assert_almost_equal(
np.rate(10, 0, -3500, 10000),
0.1107, 4)
+ @filter_deprecation
def test_rate_decimal(self):
rate = np.rate(Decimal('10'), Decimal('0'), Decimal('-3500'), Decimal('10000'))
assert_equal(Decimal('0.1106908537142689284704528100'), rate)
+ @filter_deprecation
def test_irr(self):
v = [-150000, 15000, 25000, 35000, 45000, 60000]
assert_almost_equal(np.irr(v), 0.0524, 2)
@@ -37,20 +57,25 @@ class TestFinancial(object):
v = [-1, -2, -3]
assert_equal(np.irr(v), np.nan)
+ @filter_deprecation
def test_pv(self):
assert_almost_equal(np.pv(0.07, 20, 12000, 0), -127128.17, 2)
+ @filter_deprecation
def test_pv_decimal(self):
assert_equal(np.pv(Decimal('0.07'), Decimal('20'), Decimal('12000'), Decimal('0')),
Decimal('-127128.1709461939327295222005'))
+ @filter_deprecation
def test_fv(self):
assert_equal(np.fv(0.075, 20, -2000, 0, 0), 86609.362673042924)
+ @filter_deprecation
def test_fv_decimal(self):
assert_equal(np.fv(Decimal('0.075'), Decimal('20'), Decimal('-2000'), 0, 0),
Decimal('86609.36267304300040536731624'))
+ @filter_deprecation
def test_pmt(self):
res = np.pmt(0.08 / 12, 5 * 12, 15000)
tgt = -304.145914
@@ -65,6 +90,7 @@ class TestFinancial(object):
tgt = np.array([[-166.66667, -19311.258], [-626.90814, -19311.258]])
assert_allclose(res, tgt)
+ @filter_deprecation
def test_pmt_decimal(self):
res = np.pmt(Decimal('0.08') / Decimal('12'), 5 * 12, 15000)
tgt = Decimal('-304.1459143262052370338701494')
@@ -88,18 +114,22 @@ class TestFinancial(object):
assert_equal(res[1][0], tgt[1][0])
assert_equal(res[1][1], tgt[1][1])
+ @filter_deprecation
def test_ppmt(self):
assert_equal(np.round(np.ppmt(0.1 / 12, 1, 60, 55000), 2), -710.25)
+ @filter_deprecation
def test_ppmt_decimal(self):
assert_equal(np.ppmt(Decimal('0.1') / Decimal('12'), Decimal('1'), Decimal('60'), Decimal('55000')),
Decimal('-710.2541257864217612489830917'))
# Two tests showing how Decimal is actually getting at a more exact result
# .23 / 12 does not come out nicely as a float but does as a decimal
+ @filter_deprecation
def test_ppmt_special_rate(self):
assert_equal(np.round(np.ppmt(0.23 / 12, 1, 60, 10000000000), 8), -90238044.232277036)
+ @filter_deprecation
def test_ppmt_special_rate_decimal(self):
# When rounded out to 8 decimal places like the float based test, this should not equal the same value
# as the float, substituted for the decimal
@@ -112,31 +142,38 @@ class TestFinancial(object):
assert_equal(np.ppmt(Decimal('0.23') / Decimal('12'), 1, 60, Decimal('10000000000')),
Decimal('-90238044.2322778884413969909'))
+ @filter_deprecation
def test_ipmt(self):
assert_almost_equal(np.round(np.ipmt(0.1 / 12, 1, 24, 2000), 2), -16.67)
+ @filter_deprecation
def test_ipmt_decimal(self):
result = np.ipmt(Decimal('0.1') / Decimal('12'), 1, 24, 2000)
assert_equal(result.flat[0], Decimal('-16.66666666666666666666666667'))
+ @filter_deprecation
def test_nper(self):
assert_almost_equal(np.nper(0.075, -2000, 0, 100000.),
21.54, 2)
+ @filter_deprecation
def test_nper2(self):
assert_almost_equal(np.nper(0.0, -2000, 0, 100000.),
50.0, 1)
+ @filter_deprecation
def test_npv(self):
assert_almost_equal(
np.npv(0.05, [-15000, 1500, 2500, 3500, 4500, 6000]),
122.89, 2)
+ @filter_deprecation
def test_npv_decimal(self):
assert_equal(
np.npv(Decimal('0.05'), [-15000, 1500, 2500, 3500, 4500, 6000]),
Decimal('122.894854950942692161628715'))
+ @filter_deprecation
def test_mirr(self):
val = [-4500, -800, 800, 800, 600, 600, 800, 800, 700, 3000]
assert_almost_equal(np.mirr(val, 0.08, 0.055), 0.0666, 4)
@@ -150,6 +187,7 @@ class TestFinancial(object):
val = [39000, 30000, 21000, 37000, 46000]
assert_(np.isnan(np.mirr(val, 0.10, 0.12)))
+ @filter_deprecation
def test_mirr_decimal(self):
val = [Decimal('-4500'), Decimal('-800'), Decimal('800'), Decimal('800'),
Decimal('600'), Decimal('600'), Decimal('800'), Decimal('800'),
@@ -168,6 +206,7 @@ class TestFinancial(object):
val = [Decimal('39000'), Decimal('30000'), Decimal('21000'), Decimal('37000'), Decimal('46000')]
assert_(np.isnan(np.mirr(val, Decimal('0.10'), Decimal('0.12'))))
+ @filter_deprecation
def test_when(self):
# begin
assert_equal(np.rate(10, 20, -3500, 10000, 1),
@@ -232,6 +271,7 @@ class TestFinancial(object):
assert_equal(np.nper(0.075, -2000, 0, 100000., 0),
np.nper(0.075, -2000, 0, 100000., 'end'))
+ @filter_deprecation
def test_decimal_with_when(self):
"""Test that decimals are still supported if the when argument is passed"""
# begin
@@ -306,6 +346,7 @@ class TestFinancial(object):
np.ipmt(Decimal('0.1') / Decimal('12'), Decimal('1'), Decimal('24'), Decimal('2000'),
Decimal('0'), 'end').flat[0])
+ @filter_deprecation
def test_broadcast(self):
assert_almost_equal(np.nper(0.075, -2000, 0, 100000., [0, 1]),
[21.5449442, 20.76156441], 4)
@@ -323,6 +364,7 @@ class TestFinancial(object):
[-74.998201, -75.62318601, -75.62318601,
-76.88882405, -76.88882405], 4)
+ @filter_deprecation
def test_broadcast_decimal(self):
# Use almost equal because precision is tested in the explicit tests, this test is to ensure
# broadcast with Decimal is not broken.
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index eae52c002..1eae8ccfb 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -423,27 +423,17 @@ class TestSelect(object):
assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0])
def test_deprecated_empty(self):
- with warnings.catch_warnings(record=True):
- warnings.simplefilter("always")
- assert_equal(select([], [], 3j), 3j)
-
- with warnings.catch_warnings():
- warnings.simplefilter("always")
- assert_warns(DeprecationWarning, select, [], [])
- warnings.simplefilter("error")
- assert_raises(DeprecationWarning, select, [], [])
+ assert_raises(ValueError, select, [], [], 3j)
+ assert_raises(ValueError, select, [], [])
def test_non_bool_deprecation(self):
choices = self.choices
conditions = self.conditions[:]
- with warnings.catch_warnings():
- warnings.filterwarnings("always")
- conditions[0] = conditions[0].astype(np.int_)
- assert_warns(DeprecationWarning, select, conditions, choices)
- conditions[0] = conditions[0].astype(np.uint8)
- assert_warns(DeprecationWarning, select, conditions, choices)
- warnings.filterwarnings("error")
- assert_raises(DeprecationWarning, select, conditions, choices)
+ conditions[0] = conditions[0].astype(np.int_)
+ assert_raises(TypeError, select, conditions, choices)
+ conditions[0] = conditions[0].astype(np.uint8)
+ assert_raises(TypeError, select, conditions, choices)
+ assert_raises(TypeError, select, conditions, choices)
def test_many_arguments(self):
# This used to be limited by NPY_MAXARGS == 32
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 4895a722c..dbf189f3e 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -8,6 +8,7 @@ from numpy.testing import (
assert_array_almost_equal, assert_raises, assert_allclose,
assert_array_max_ulp, assert_raises_regex, suppress_warnings,
)
+import pytest
class TestHistogram(object):
@@ -591,6 +592,16 @@ class TestHistogramOptimBinNums(object):
msg += " with datasize of {0}".format(testlen)
assert_equal(len(a), numbins, err_msg=msg)
+ @pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott',
+ 'stone', 'rice', 'sturges'])
+ def test_signed_integer_data(self, bins):
+ # Regression test for gh-14379.
+ a = np.array([-2, 0, 127], dtype=np.int8)
+ hist, edges = np.histogram(a, bins=bins)
+ hist32, edges32 = np.histogram(a.astype(np.int32), bins=bins)
+ assert_array_equal(hist, hist32)
+ assert_array_equal(edges, edges32)
+
def test_simple_weighted(self):
"""
Check that weighted data raises a TypeError
diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py
index a5cdda074..dbe445c2c 100644
--- a/numpy/lib/tests/test_index_tricks.py
+++ b/numpy/lib/tests/test_index_tricks.py
@@ -175,6 +175,24 @@ class TestRavelUnravelIndex(object):
assert_raises_regex(
ValueError, "out of bounds", np.unravel_index, [1], ())
+ @pytest.mark.parametrize("mode", ["clip", "wrap", "raise"])
+ def test_empty_array_ravel(self, mode):
+ res = np.ravel_multi_index(
+ np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode)
+ assert(res.shape == (0,))
+
+ with assert_raises(ValueError):
+ np.ravel_multi_index(
+ np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode)
+
+ def test_empty_array_unravel(self):
+ res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0))
+ # res is a tuple of three empty arrays
+ assert(len(res) == 3)
+ assert(all(a.shape == (0,) for a in res))
+
+ with assert_raises(ValueError):
+ np.unravel_index([1], (2, 1, 0))
class TestGrid(object):
def test_basic(self):
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index 6ee17c830..1181fe986 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -1871,7 +1871,7 @@ M 33 21.99
data = ["1, 1, 1, 1, -1.1"] * 50
mdata = TextIO("\n".join(data))
- converters = {4: lambda x: "(%s)" % x}
+ converters = {4: lambda x: "(%s)" % x.decode()}
kwargs = dict(delimiter=",", converters=converters,
dtype=[(_, int) for _ in 'abcde'],)
assert_raises(ValueError, np.genfromtxt, mdata, **kwargs)
diff --git a/numpy/lib/tests/test_recfunctions.py b/numpy/lib/tests/test_recfunctions.py
index 0c839d486..fa5f4dec2 100644
--- a/numpy/lib/tests/test_recfunctions.py
+++ b/numpy/lib/tests/test_recfunctions.py
@@ -91,8 +91,10 @@ class TestRecFunctions(object):
control = np.array([(1,), (4,)], dtype=[('a', int)])
assert_equal(test, control)
+ # dropping all fields results in an array with no fields
test = drop_fields(a, ['a', 'b'])
- assert_(test is None)
+ control = np.array([(), ()], dtype=[])
+ assert_equal(test, control)
def test_rename_fields(self):
# Test rename fields
@@ -378,8 +380,8 @@ class TestMergeArrays(object):
z = np.array(
[('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)])
w = np.array(
- [(1, (2, 3.0)), (4, (5, 6.0))],
- dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+ [(1, (2, 3.0, ())), (4, (5, 6.0, ()))],
+ dtype=[('a', int), ('b', [('ba', float), ('bb', int), ('bc', [])])])
self.data = (w, x, y, z)
def test_solo(self):
@@ -450,8 +452,8 @@ class TestMergeArrays(object):
test = merge_arrays((x, w), flatten=False)
controldtype = [('f0', int),
('f1', [('a', int),
- ('b', [('ba', float), ('bb', int)])])]
- control = np.array([(1., (1, (2, 3.0))), (2, (4, (5, 6.0)))],
+ ('b', [('ba', float), ('bb', int), ('bc', [])])])]
+ control = np.array([(1., (1, (2, 3.0, ()))), (2, (4, (5, 6.0, ())))],
dtype=controldtype)
assert_equal(test, control)
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index 8bcbd8e86..3c71d2a7c 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -788,13 +788,8 @@ def lookfor(what, module=None, import_modules=True, regenerate=False,
if kind in ('module', 'object'):
# don't show modules or objects
continue
- ok = True
doc = docstring.lower()
- for w in whats:
- if w not in doc:
- ok = False
- break
- if ok:
+ if all(w in doc for w in whats):
found.append(name)
# Relevance sort
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