diff options
39 files changed, 434 insertions, 860 deletions
diff --git a/.circleci/config.yml b/.circleci/config.yml index a54f85110..cc01e8851 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -39,14 +39,14 @@ jobs: . venv/bin/activate cd doc git submodule update --init - make html + SPHINXOPTS=-q make -e html - run: name: build neps command: | . venv/bin/activate cd doc/neps - make html + SPHINXOPTS=-q make -e html - store_artifacts: path: doc/build/html/ diff --git a/azure-pipelines.yml b/azure-pipelines.yml index c085c38f3..d65d1e41f 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -59,21 +59,33 @@ jobs: # two C compilers, but with homebrew looks like we're # now stuck getting the full gcc toolchain instead of # just pulling in gfortran - - script: HOMEBREW_NO_AUTO_UPDATE=1 brew install gcc + - script: | + # same version of gfortran as the wheel builds + brew install gcc49 + # manually link critical gfortran libraries + ln -s /usr/local/Cellar/gcc@4.9/4.9.4_1/lib/gcc/4.9/libgfortran.3.dylib /usr/local/lib/libgfortran.3.dylib + ln -s /usr/local/Cellar/gcc@4.9/4.9.4_1/lib/gcc/4.9/libquadmath.0.dylib /usr/local/lib/libquadmath.0.dylib + # manually symlink gfortran-4.9 to plain gfortran + # for f2py + ln -s /usr/local/bin/gfortran-4.9 /usr/local/bin/gfortran displayName: 'make gfortran available on mac os vm' + # use the pre-built openblas binary that most closely + # matches our MacOS wheel builds -- currently based + # primarily on file size / name details + - script: | + wget "https://3f23b170c54c2533c070-1c8a9b3114517dc5fe17b7c3f8c63a43.ssl.cf2.rackcdn.com/openblas-v0.3.3-186-g701ea883-macosx_10_9_intel-gf_1becaaa.tar.gz" + tar -zxvf openblas-v0.3.3-186-g701ea883-macosx_10_9_intel-gf_1becaaa.tar.gz + # manually link to appropriate system paths + cp ./usr/local/lib/* /usr/local/lib/ + cp ./usr/local/include/* /usr/local/include/ + displayName: 'install pre-built openblas' - script: python -m pip install --upgrade pip setuptools wheel displayName: 'Install tools' - script: python -m pip install cython nose pytz pytest pickle5 vulture docutils sphinx>=1.8.3 numpydoc matplotlib displayName: 'Install dependencies; some are optional to avoid test skips' - script: /bin/bash -c "! vulture . --min-confidence 100 --exclude doc/,numpy/distutils/ | grep 'unreachable'" displayName: 'Check for unreachable code paths in Python modules' - # NOTE: init_dgelsd failed init issue with current ACCELERATE / - # LAPACK configuration on Azure macos image; at the time of writing - # this plagues homebrew / macports NumPy builds, but we will - # circumvent for now by aggressively disabling acceleration for - # macos NumPy builds / tests; ACCELERATE=None on its own is not - # sufficient - # also, might as well prefer usage of clang over gcc proper + # prefer usage of clang over gcc proper # to match likely scenario on many user mac machines - script: python setup.py build -j 4 install displayName: 'Build NumPy' diff --git a/doc/DISTUTILS.rst.txt b/doc/DISTUTILS.rst.txt index 515fc871b..3cb812176 100644 --- a/doc/DISTUTILS.rst.txt +++ b/doc/DISTUTILS.rst.txt @@ -300,8 +300,72 @@ in writing setup scripts: Template files -------------- -XXX: Describe how files with extensions ``.f.src``, ``.pyf.src``, -``.c.src``, etc. are pre-processed by the ``build_src`` command. +NumPy Distutils preprocesses C source files (extension: :file:`.c.src`) written +in a custom templating language to generate C code. The :c:data:`@` symbol is +used to wrap macro-style variables to empower a string substitution mechansim +that might describe (for instance) a set of data types. + +As a more detailed scenario, a loop in the NumPy C source code may +have a :c:data:`@TYPE@` variable, targeted for string substitution, +which is preprocessed to a number of otherwise identical loops with +several strings such as :c:data:`INT, LONG, UINT, ULONG`. The :c:data:`@TYPE@` +style syntax thus reduces code duplication and maintenance burden by +mimicking languages that have generic type support. By convention, +and as required by the preprocessor, generically typed blocks are preceded +by comment blocks that enumerate the intended string substitutions. + +The template language blocks are delimited by :c:data:`/**begin repeat` +and :c:data:`/**end repeat**/` lines, which may also be nested using +consecutively numbered delimiting lines such as :c:data:`/**begin repeat1` +and :c:data:`/**end repeat1**/`. String replacement specifications are started +and terminated using :c:data:`#`. This may be clearer in the following +template source example: + +.. code-block:: C + :linenos: + :emphasize-lines: 3, 13, 29, 31 + + /* TIMEDELTA to non-float types */ + + /**begin repeat + * + * #TOTYPE = BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, + * LONGLONG, ULONGLONG, DATETIME, + * TIMEDELTA# + * #totype = npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, npy_uint, + * npy_long, npy_ulong, npy_longlong, npy_ulonglong, + * npy_datetime, npy_timedelta# + */ + + /**begin repeat1 + * + * #FROMTYPE = TIMEDELTA# + * #fromtype = npy_timedelta# + */ + static void + @FROMTYPE@_to_@TOTYPE@(void *input, void *output, npy_intp n, + void *NPY_UNUSED(aip), void *NPY_UNUSED(aop)) + { + const @fromtype@ *ip = input; + @totype@ *op = output; + + while (n--) { + *op++ = (@totype@)*ip++; + } + } + /**end repeat1**/ + + /**end repeat**/ + +The preprocessing of generically typed C source files (whether in NumPy +proper or in any third party package using NumPy Distutils) is performed +by `conv_template.py`_. +The type specific C files generated (extension: :file:`.c`) by these modules +during the build process are ready to be compiled. This form +of generic typing is also supported for C header files (preprocessed to +produce :file:`.h` files). + +.. _conv_template.py: https://github.com/numpy/numpy/blob/master/numpy/distutils/conv_template.py Useful functions in ``numpy.distutils.misc_util`` ------------------------------------------------- diff --git a/doc/release/1.17.0-notes.rst b/doc/release/1.17.0-notes.rst index b26938e18..b9b27da79 100644 --- a/doc/release/1.17.0-notes.rst +++ b/doc/release/1.17.0-notes.rst @@ -18,13 +18,22 @@ New functions Deprecations ============ +``np.polynomial`` functions warn when passed ``float``s in place of ``int``s +---------------------------------------------------------------------------- +Previously functions in this module would accept ``float``s provided their +values were integral. For consistency with the rest of numpy, doing so is now +deprecated, and in future will raise a ``TypeError``. + +Similarly, passing a float like ``0.5`` in place of an integer will now raise a +``TypeError`` instead of the previous ``ValueError``. + Deprecate ``numpy.distutils.exec_command`` and ``numpy.distutils.temp_file_name`` --------------------------------------------------------------------------------- - The internal use of these functions has been refactored and there are better alternatives. Relace ``exec_command`` with `subprocess.Popen` and ``temp_file_name`` with `tempfile.mkstemp`. + Future Changes ============== @@ -44,7 +53,6 @@ now be rounded up instead of down, changing the last bit (ULP) of the result. Signed zero when using divmod ----------------------------- - Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero when using the ``divmod`` and ``floor_divide`` functions when the result was zero. For example:: @@ -60,11 +68,11 @@ zero:: Do not lookup ``__buffer__`` attribute in `numpy.frombuffer` ------------------------------------------------------------ - Looking up ``__buffer__`` attribute in `numpy.frombuffer` was undocumented and non-functional. This code was removed. If needed, use ``frombuffer(memoryview(obj), ...)`` instead. + C API changes ============= @@ -101,7 +109,6 @@ available bits add zero padding. Negative counts trim bits off the end instead of counting from the beginning. None counts implement the existing behavior of unpacking everything. - ``np.linalg.svd`` and ``np.linalg.pinv`` can be faster on hermitian inputs -------------------------------------------------------------------------- These functions now accept a ``hermitian`` argument, matching the one added @@ -126,7 +133,6 @@ tolerances. Replacement of the `fftpack`-based FFT module by the `pocketfft` library ------------------------------------------------------------------------ - Both implementations have the same ancestor (Fortran77 `FFTPACK` by Paul N. Swarztrauber), but `pocketfft` contains additional modifications which improve both accuracy and performance in some circumstances. For FFT lengths @@ -176,7 +182,6 @@ Changes ``median`` and ``percentile`` family of functions no longer warn about ``nan`` ------------------------------------------------------------------------------ - `numpy.median`, `numpy.percentile`, and `numpy.quantile` used to emit a ``RuntimeWarning`` when encountering an `numpy.nan`. Since they return the ``nan`` value, the warning is redundant and has been removed. diff --git a/doc/scipy-sphinx-theme b/doc/scipy-sphinx-theme -Subproject d990ab9134199f6496b9ac8567f10791f04a720 +Subproject f0d96ae2bf3b010ce53adadde1e38997497a513 diff --git a/numpy/compat/py3k.py b/numpy/compat/py3k.py index 141a21fed..c9ed9d52c 100644 --- a/numpy/compat/py3k.py +++ b/numpy/compat/py3k.py @@ -11,6 +11,7 @@ __all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar', 'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike'] import sys +import os try: from pathlib import Path, PurePath except ImportError: @@ -173,7 +174,6 @@ else: """ import imp - import os if info is None: path = os.path.dirname(fn) fo, fn, info = imp.find_module(name, [path]) @@ -195,7 +195,6 @@ else: # Backport os.fs_path, os.PathLike, and PurePath.__fspath__ if sys.version_info[:2] >= (3, 6): - import os os_fspath = os.fspath os_PathLike = os.PathLike else: diff --git a/numpy/core/_exceptions.py b/numpy/core/_exceptions.py index 5e0105beb..227c08cd6 100644 --- a/numpy/core/_exceptions.py +++ b/numpy/core/_exceptions.py @@ -5,8 +5,6 @@ in python where it's easier. By putting the formatting in `__str__`, we also avoid paying the cost for users who silence the exceptions. """ -from numpy.core.overrides import set_module - def _unpack_tuple(tup): if len(tup) == 1: diff --git a/numpy/core/overrides.py b/numpy/core/overrides.py index 46097f39c..9f91adc83 100644 --- a/numpy/core/overrides.py +++ b/numpy/core/overrides.py @@ -1,7 +1,6 @@ """Implementation of __array_function__ overrides from NEP-18.""" import collections import functools -import os from numpy.core._multiarray_umath import ( add_docstring, implement_array_function, _get_implementing_args) diff --git a/numpy/core/records.py b/numpy/core/records.py index 42aca5b60..2adcdae61 100644 --- a/numpy/core/records.py +++ b/numpy/core/records.py @@ -610,9 +610,7 @@ def fromarrays(arrayList, dtype=None, shape=None, formats=None, # and determine the formats. formats = [] for obj in arrayList: - if not isinstance(obj, ndarray): - raise ValueError("item in the array list must be an ndarray.") - formats.append(obj.dtype.str) + formats.append(obj.dtype) if dtype is not None: descr = sb.dtype(dtype) @@ -768,7 +766,7 @@ def fromfile(fd, dtype=None, shape=None, offset=0, formats=None, >>> r.shape (10,) """ - + if dtype is None and formats is None: raise TypeError("fromfile() needs a 'dtype' or 'formats' argument") diff --git a/numpy/core/shape_base.py b/numpy/core/shape_base.py index e43519689..0eac772e8 100644 --- a/numpy/core/shape_base.py +++ b/numpy/core/shape_base.py @@ -5,7 +5,6 @@ __all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'block', 'hstack', import functools import operator -import types import warnings from . import numeric as _nx diff --git a/numpy/core/src/npymath/ieee754.c.src b/numpy/core/src/npymath/ieee754.c.src index d960838c8..3f66b24a4 100644 --- a/numpy/core/src/npymath/ieee754.c.src +++ b/numpy/core/src/npymath/ieee754.c.src @@ -681,7 +681,8 @@ void npy_set_floatstatus_invalid(void) fp_raise_xcp(FP_INVALID); } -#elif defined(_MSC_VER) || (defined(__osf__) && defined(__alpha)) +#elif defined(_MSC_VER) || (defined(__osf__) && defined(__alpha)) || \ + defined (__UCLIBC__) || (defined(__arc__) && defined(__GLIBC__)) /* * By using a volatile floating point value, diff --git a/numpy/core/tests/test_records.py b/numpy/core/tests/test_records.py index f0ec38029..878ef2ac9 100644 --- a/numpy/core/tests/test_records.py +++ b/numpy/core/tests/test_records.py @@ -437,6 +437,13 @@ class TestRecord(object): arr = np.zeros((3,), dtype=[('x', int), ('y', int)]) assert_raises(ValueError, lambda: arr[['nofield']]) + def test_fromarrays_nested_structured_arrays(self): + arrays = [ + np.arange(10), + np.ones(10, dtype=[('a', '<u2'), ('b', '<f4')]), + ] + arr = np.rec.fromarrays(arrays) # ValueError? + def test_find_duplicate(): l1 = [1, 2, 3, 4, 5, 6] diff --git a/numpy/ctypeslib.py b/numpy/ctypeslib.py index 02c3bd211..1967a85b6 100644 --- a/numpy/ctypeslib.py +++ b/numpy/ctypeslib.py @@ -51,7 +51,7 @@ Then, we're ready to call ``foo_func``: """ from __future__ import division, absolute_import, print_function -__all__ = ['load_library', 'ndpointer', 'test', 'ctypes_load_library', +__all__ = ['load_library', 'ndpointer', 'ctypes_load_library', 'c_intp', 'as_ctypes', 'as_array'] import os diff --git a/numpy/distutils/_shell_utils.py b/numpy/distutils/_shell_utils.py index 5ef874900..82abd5f4e 100644 --- a/numpy/distutils/_shell_utils.py +++ b/numpy/distutils/_shell_utils.py @@ -24,12 +24,12 @@ class CommandLineParser: @staticmethod def join(argv): """ Join a list of arguments into a command line string """ - raise NotImplemented + raise NotImplementedError @staticmethod def split(cmd): """ Split a command line string into a list of arguments """ - raise NotImplemented + raise NotImplementedError class WindowsParser: diff --git a/numpy/distutils/command/build_ext.py b/numpy/distutils/command/build_ext.py index ab9d585a5..ef54fb25e 100644 --- a/numpy/distutils/command/build_ext.py +++ b/numpy/distutils/command/build_ext.py @@ -281,8 +281,8 @@ class build_ext (old_build_ext): runtime_lib = os.path.join(self.extra_dll_dir, fn) copy_file(runtime_lib, shared_lib_dir) - def swig_sources(self, sources): - # Do nothing. Swig sources have beed handled in build_src command. + def swig_sources(self, sources, extensions=None): + # Do nothing. Swig sources have been handled in build_src command. return sources def build_extension(self, ext): diff --git a/numpy/distutils/system_info.py b/numpy/distutils/system_info.py index 242494331..4d923ad26 100644 --- a/numpy/distutils/system_info.py +++ b/numpy/distutils/system_info.py @@ -906,7 +906,6 @@ class fftw_info(system_info): == len(ver_param['includes']): dict_append(info, include_dirs=[d]) flag = 1 - incl_dirs = [d] break if flag: dict_append(info, define_macros=ver_param['macros']) @@ -1058,9 +1057,9 @@ class mkl_info(system_info): for d in paths: dirs = glob(os.path.join(d, 'mkl', '*')) dirs += glob(os.path.join(d, 'mkl*')) - for d in dirs: - if os.path.isdir(os.path.join(d, 'lib')): - return d + for sub_dir in dirs: + if os.path.isdir(os.path.join(sub_dir, 'lib')): + return sub_dir return None def __init__(self): @@ -1743,6 +1742,8 @@ class blas_info(system_info): res = "blas" except distutils.ccompiler.CompileError: res = None + except distutils.ccompiler.LinkError: + res = None finally: shutil.rmtree(tmpdir) return res diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py index b236cc449..630767dc5 100644 --- a/numpy/lib/arraypad.py +++ b/numpy/lib/arraypad.py @@ -957,12 +957,12 @@ def _as_pairs(x, ndim, as_index=False): # Public functions -def _pad_dispatcher(array, pad_width, mode, **kwargs): +def _pad_dispatcher(array, pad_width, mode=None, **kwargs): return (array,) @array_function_dispatch(_pad_dispatcher, module='numpy') -def pad(array, pad_width, mode, **kwargs): +def pad(array, pad_width, mode='constant', **kwargs): """ Pads an array. @@ -977,10 +977,10 @@ def pad(array, pad_width, mode, **kwargs): ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. - mode : str or function + mode : str or function, optional One of the following string values or a user supplied function. - 'constant' + 'constant' (default) Pads with a constant value. 'edge' Pads with the edge values of array. diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index b61a64b8e..fb40ef927 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -31,7 +31,6 @@ from numpy.core.overrides import set_module from numpy.core import overrides from numpy.core.function_base import add_newdoc from numpy.lib.twodim_base import diag -from .utils import deprecate from numpy.core.multiarray import ( _insert, add_docstring, bincount, normalize_axis_index, _monotonicity, interp as compiled_interp, interp_complex as compiled_interp_complex diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 1e43fdb34..d702859fa 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -23,8 +23,7 @@ from ._iotools import ( ) from numpy.compat import ( - asbytes, asstr, asunicode, asbytes_nested, bytes, basestring, unicode, - os_fspath, os_PathLike, pickle + asbytes, asstr, asunicode, bytes, basestring, os_fspath, os_PathLike, pickle ) if sys.version_info[0] >= 3: @@ -1123,7 +1122,6 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, if type(x) is bytes: return conv(x) return conv(x.encode("latin1")) - import functools converters[i] = functools.partial(tobytes_first, conv=conv) else: converters[i] = conv @@ -1974,7 +1972,6 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, if type(x) is bytes: return conv(x) return conv(x.encode("latin1")) - import functools user_conv = functools.partial(tobytes_first, conv=conv) else: user_conv = conv diff --git a/numpy/lib/tests/test_arraypad.py b/numpy/lib/tests/test_arraypad.py index a9030e737..b7393294a 100644 --- a/numpy/lib/tests/test_arraypad.py +++ b/numpy/lib/tests/test_arraypad.py @@ -1249,10 +1249,9 @@ def test_kwargs(mode): np.pad([1, 2, 3], 1, mode, **{key: value}) -def test_missing_mode(): - match = "missing 1 required positional argument: 'mode'" - with pytest.raises(TypeError, match=match): - np.pad(np.ones((5, 6)), 4) +def test_constant_zero_default(): + arr = np.array([1, 1]) + assert_array_equal(np.pad(arr, 2), [0, 0, 1, 1, 0, 0]) @pytest.mark.parametrize("mode", _all_modes.keys()) diff --git a/numpy/ma/core.py b/numpy/ma/core.py index b8fd76860..1f5bc2a51 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -44,7 +44,6 @@ from numpy.compat import ( getargspec, formatargspec, long, basestring, unicode, bytes ) from numpy import expand_dims -from numpy.core.multiarray import normalize_axis_index from numpy.core.numeric import normalize_axis_tuple from numpy.core._internal import recursive from numpy.compat import pickle @@ -1198,7 +1197,6 @@ exp = _MaskedUnaryOperation(umath.exp) conjugate = _MaskedUnaryOperation(umath.conjugate) sin = _MaskedUnaryOperation(umath.sin) cos = _MaskedUnaryOperation(umath.cos) -tan = _MaskedUnaryOperation(umath.tan) arctan = _MaskedUnaryOperation(umath.arctan) arcsinh = _MaskedUnaryOperation(umath.arcsinh) sinh = _MaskedUnaryOperation(umath.sinh) diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index 2e3b84e1c..a51d83578 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -390,7 +390,6 @@ def apply_along_axis(func1d, axis, arr, *args, **kwargs): i[axis] = slice(None, None) outshape = np.asarray(arr.shape).take(indlist) i.put(indlist, ind) - j = i.copy() res = func1d(arr[tuple(i.tolist())], *args, **kwargs) # if res is a number, then we have a smaller output array asscalar = np.isscalar(res) diff --git a/numpy/ma/mrecords.py b/numpy/ma/mrecords.py index 6779be746..9bcb3947d 100644 --- a/numpy/ma/mrecords.py +++ b/numpy/ma/mrecords.py @@ -19,7 +19,6 @@ import sys import warnings import numpy as np -import numpy.core.numerictypes as ntypes from numpy.compat import basestring from numpy import ( bool_, dtype, ndarray, recarray, array as narray diff --git a/numpy/ma/timer_comparison.py b/numpy/ma/timer_comparison.py index 68104ed0a..4ad635e38 100644 --- a/numpy/ma/timer_comparison.py +++ b/numpy/ma/timer_comparison.py @@ -430,11 +430,10 @@ if __name__ == '__main__': setup_cur = "import numpy.ma.core as module\n" + setup_base (nrepeat, nloop) = (10, 10) - if 1: - for i in range(1, 8): - func = 'tester.test_%i()' % i - cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10) - cur = np.sort(cur) - print("#%i" % i + 50*'.') - print(eval("ModuleTester.test_%i.__doc__" % i)) - print("core_current : %.3f - %.3f" % (cur[0], cur[1])) + for i in range(1, 8): + func = 'tester.test_%i()' % i + cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10) + cur = np.sort(cur) + print("#%i" % i + 50*'.') + print(eval("ModuleTester.test_%i.__doc__" % i)) + print("core_current : %.3f - %.3f" % (cur[0], cur[1])) diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index 0eef90177..836f47363 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -583,15 +583,7 @@ def chebadd(c1, c2): array([4., 4., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def chebsub(c1, c2): @@ -635,16 +627,7 @@ def chebsub(c1, c2): array([ 2., 0., -2.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def chebmulx(c): @@ -843,6 +826,9 @@ def chebpow(c, pow, maxpower=16): array([15.5, 22. , 16. , ..., 12.5, 12. , 8. ]) """ + # note: this is more efficient than `pu._pow(chebmul, c1, c2)`, as it + # avoids converting between z and c series repeatedly + # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) @@ -927,14 +913,10 @@ def chebder(c, m=1, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1050,10 +1032,8 @@ def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = c.astype(np.double) if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -1062,8 +1042,6 @@ def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1423,9 +1401,7 @@ def chebvander(x, deg): the converted `x`. """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1669,81 +1645,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): -------- """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = chebvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = chebvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim > 0: - if c.ndim == 2: - cc = np.zeros((lmax + 1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax + 1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(chebvander, x, y, deg, rcond, full, w) def chebcompanion(c): @@ -1949,9 +1851,9 @@ def chebgauss(deg): .. math:: w_i = \\pi / n """ - ideg = int(deg) - if ideg != deg or ideg < 1: - raise ValueError("deg must be a non-negative integer") + ideg = pu._deprecate_as_int(deg, "deg") + if ideg <= 0: + raise ValueError("deg must be a positive integer") x = np.cos(np.pi * np.arange(1, 2*ideg, 2) / (2.0*ideg)) w = np.ones(ideg)*(np.pi/ideg) diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index caa47001a..dcd0a2b4d 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -326,15 +326,7 @@ def hermadd(c1, c2): array([2., 4., 6., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def hermsub(c1, c2): @@ -374,16 +366,7 @@ def hermsub(c1, c2): array([0., 0., 0., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def hermmulx(c): @@ -587,24 +570,7 @@ def hermpow(c, pow, maxpower=16): array([81., 52., 82., 12., 9.]) """ - # c is a trimmed copy - [c] = pu.as_series([c]) - power = int(pow) - if power != pow or power < 0: - raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower: - raise ValueError("Power is too large") - elif power == 0: - return np.array([1], dtype=c.dtype) - elif power == 1: - return c - else: - # This can be made more efficient by using powers of two - # in the usual way. - prd = c - for i in range(2, power + 1): - prd = hermmul(prd, c) - return prd + return pu._pow(hermmul, c, pow, maxpower) def hermder(c, m=1, scl=1, axis=0): @@ -665,14 +631,10 @@ def hermder(c, m=1, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -782,10 +744,8 @@ def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = c.astype(np.double) if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -794,8 +754,6 @@ def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1169,9 +1127,7 @@ def hermvander(x, deg): [ 1., 2., 2., -4.]]) """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1419,81 +1375,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): array([1.0218, 1.9986, 2.9999]) # may vary """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = hermvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = hermvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim > 0: - if c.ndim == 2: - cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax+1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(hermvander, x, y, deg, rcond, full, w) def hermcompanion(c): @@ -1679,9 +1561,9 @@ def hermgauss(deg): the right value when integrating 1. """ - ideg = int(deg) - if ideg != deg or ideg < 1: - raise ValueError("deg must be a non-negative integer") + ideg = pu._deprecate_as_int(deg, "deg") + if ideg <= 0: + raise ValueError("deg must be a positive integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index 228396457..48e5eab20 100644 --- a/numpy/polynomial/hermite_e.py +++ b/numpy/polynomial/hermite_e.py @@ -327,15 +327,7 @@ def hermeadd(c1, c2): array([2., 4., 6., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def hermesub(c1, c2): @@ -375,16 +367,7 @@ def hermesub(c1, c2): array([0., 0., 0., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def hermemulx(c): @@ -582,24 +565,7 @@ def hermepow(c, pow, maxpower=16): array([23., 28., 46., 12., 9.]) """ - # c is a trimmed copy - [c] = pu.as_series([c]) - power = int(pow) - if power != pow or power < 0: - raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower: - raise ValueError("Power is too large") - elif power == 0: - return np.array([1], dtype=c.dtype) - elif power == 1: - return c - else: - # This can be made more efficient by using powers of two - # in the usual way. - prd = c - for i in range(2, power + 1): - prd = hermemul(prd, c) - return prd + return pu._pow(hermemul, c, pow, maxpower) def hermeder(c, m=1, scl=1, axis=0): @@ -660,14 +626,10 @@ def hermeder(c, m=1, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -777,10 +739,8 @@ def hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = c.astype(np.double) if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -789,8 +749,6 @@ def hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1163,9 +1121,7 @@ def hermevander(x, deg): [ 1., 1., 0., -2.]]) """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1413,81 +1369,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): array([ 1.01690445, 1.99951418, 2.99948696]) # may vary """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = hermevander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = hermevander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim > 0: - if c.ndim == 2: - cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax+1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(hermevander, x, y, deg, rcond, full, w) def hermecompanion(c): @@ -1674,9 +1556,9 @@ def hermegauss(deg): the right value when integrating 1. """ - ideg = int(deg) - if ideg != deg or ideg < 1: - raise ValueError("deg must be a non-negative integer") + ideg = pu._deprecate_as_int(deg, "deg") + if ideg <= 0: + raise ValueError("deg must be a positive integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index dec469c17..41d15142a 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -324,15 +324,7 @@ def lagadd(c1, c2): """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def lagsub(c1, c2): @@ -372,16 +364,7 @@ def lagsub(c1, c2): array([0., 0., 0., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def lagmulx(c): @@ -584,24 +567,7 @@ def lagpow(c, pow, maxpower=16): array([ 14., -16., 56., -72., 54.]) """ - # c is a trimmed copy - [c] = pu.as_series([c]) - power = int(pow) - if power != pow or power < 0: - raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower: - raise ValueError("Power is too large") - elif power == 0: - return np.array([1], dtype=c.dtype) - elif power == 1: - return c - else: - # This can be made more efficient by using powers of two - # in the usual way. - prd = c - for i in range(2, power + 1): - prd = lagmul(prd, c) - return prd + return pu._pow(lagmul, c, pow, maxpower) def lagder(c, m=1, scl=1, axis=0): @@ -662,14 +628,11 @@ def lagder(c, m=1, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) - cnt, iaxis = [int(t) for t in [m, axis]] - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -782,10 +745,8 @@ def lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = c.astype(np.double) if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -794,8 +755,6 @@ def lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1169,9 +1128,7 @@ def lagvander(x, deg): [ 1. , -1. , -1. , -0.33333333]]) """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1418,81 +1375,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): array([ 0.96971004, 2.00193749, 3.00288744]) # may vary """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = lagvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = lagvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim > 0: - if c.ndim == 2: - cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax+1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(lagvander, x, y, deg, rcond, full, w) def lagcompanion(c): @@ -1634,9 +1517,9 @@ def laggauss(deg): the right value when integrating 1. """ - ideg = int(deg) - if ideg != deg or ideg < 1: - raise ValueError("deg must be a non-negative integer") + ideg = pu._deprecate_as_int(deg, "deg") + if ideg <= 0: + raise ValueError("deg must be a positive integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py index 5f8ce53a3..d56e2ae2c 100644 --- a/numpy/polynomial/legendre.py +++ b/numpy/polynomial/legendre.py @@ -356,15 +356,7 @@ def legadd(c1, c2): array([4., 4., 4.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def legsub(c1, c2): @@ -408,16 +400,7 @@ def legsub(c1, c2): array([ 2., 0., -2.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def legmulx(c): @@ -624,24 +607,7 @@ def legpow(c, pow, maxpower=16): -------- """ - # c is a trimmed copy - [c] = pu.as_series([c]) - power = int(pow) - if power != pow or power < 0: - raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower: - raise ValueError("Power is too large") - elif power == 0: - return np.array([1], dtype=c.dtype) - elif power == 1: - return c - else: - # This can be made more efficient by using powers of two - # in the usual way. - prd = c - for i in range(2, power + 1): - prd = legmul(prd, c) - return prd + return pu._pow(legmul, c, pow, maxpower) def legder(c, m=1, scl=1, axis=0): @@ -707,14 +673,10 @@ def legder(c, m=1, scl=1, axis=0): c = np.array(c, ndmin=1, copy=1) if c.dtype.char in '?bBhHiIlLqQpP': c = c.astype(np.double) - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -830,10 +792,8 @@ def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = c.astype(np.double) if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -842,8 +802,6 @@ def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1204,9 +1162,7 @@ def legvander(x, deg): the converted `x`. """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1452,81 +1408,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): -------- """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = legvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = legvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim > 0: - if c.ndim == 2: - cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax+1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(legvander, x, y, deg, rcond, full, w) def legcompanion(c): @@ -1665,9 +1547,9 @@ def leggauss(deg): the right value when integrating 1. """ - ideg = int(deg) - if ideg != deg or ideg < 1: - raise ValueError("deg must be a non-negative integer") + ideg = pu._deprecate_as_int(deg, "deg") + if ideg <= 0: + raise ValueError("deg must be a positive integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index f63d9dd74..e3eb7ec5a 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -224,15 +224,7 @@ def polyadd(c1, c2): 28.0 """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] += c2 - ret = c1 - else: - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._add(c1, c2) def polysub(c1, c2): @@ -269,16 +261,7 @@ def polysub(c1, c2): array([ 2., 0., -2.]) """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2): - c1[:c2.size] -= c2 - ret = c1 - else: - c2 = -c2 - c2[:c1.size] += c1 - ret = c2 - return pu.trimseq(ret) + return pu._sub(c1, c2) def polymulx(c): @@ -451,24 +434,9 @@ def polypow(c, pow, maxpower=None): array([ 1., 4., 10., 12., 9.]) """ - # c is a trimmed copy - [c] = pu.as_series([c]) - power = int(pow) - if power != pow or power < 0: - raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower: - raise ValueError("Power is too large") - elif power == 0: - return np.array([1], dtype=c.dtype) - elif power == 1: - return c - else: - # This can be made more efficient by using powers of two - # in the usual way. - prd = c - for i in range(2, power + 1): - prd = np.convolve(prd, c) - return prd + # note: this is more efficient than `pu._pow(polymul, c1, c2)`, as it + # avoids calling `as_series` repeatedly + return pu._pow(np.convolve, c, pow, maxpower) def polyder(c, m=1, scl=1, axis=0): @@ -528,14 +496,10 @@ def polyder(c, m=1, scl=1, axis=0): # astype fails with NA c = c + 0.0 cdt = c.dtype - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of derivation must be integer") + cnt = pu._deprecate_as_int(m, "the order of derivation") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of derivation must be non-negative") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -642,10 +606,8 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): cdt = c.dtype if not np.iterable(k): k = [k] - cnt, iaxis = [int(t) for t in [m, axis]] - - if cnt != m: - raise ValueError("The order of integration must be integer") + cnt = pu._deprecate_as_int(m, "the order of integration") + iaxis = pu._deprecate_as_int(axis, "the axis") if cnt < 0: raise ValueError("The order of integration must be non-negative") if len(k) > cnt: @@ -654,8 +616,6 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): raise ValueError("lbnd must be a scalar.") if np.ndim(scl) != 0: raise ValueError("scl must be a scalar.") - if iaxis != axis: - raise ValueError("The axis must be integer") iaxis = normalize_axis_index(iaxis, c.ndim) if cnt == 0: @@ -1112,9 +1072,7 @@ def polyvander(x, deg): polyvander2d, polyvander3d """ - ideg = int(deg) - if ideg != deg: - raise ValueError("deg must be integer") + ideg = pu._deprecate_as_int(deg, "deg") if ideg < 0: raise ValueError("deg must be non-negative") @@ -1375,81 +1333,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): 0.50443316, 0.28853036]), 1.1324274851176597e-014] """ - x = np.asarray(x) + 0.0 - y = np.asarray(y) + 0.0 - deg = np.asarray(deg) - - # check arguments. - if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: - raise TypeError("deg must be an int or non-empty 1-D array of int") - if deg.min() < 0: - raise ValueError("expected deg >= 0") - if x.ndim != 1: - raise TypeError("expected 1D vector for x") - if x.size == 0: - raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2: - raise TypeError("expected 1D or 2D array for y") - if len(x) != len(y): - raise TypeError("expected x and y to have same length") - - if deg.ndim == 0: - lmax = deg - order = lmax + 1 - van = polyvander(x, lmax) - else: - deg = np.sort(deg) - lmax = deg[-1] - order = len(deg) - van = polyvander(x, lmax)[:, deg] - - # set up the least squares matrices in transposed form - lhs = van.T - rhs = y.T - if w is not None: - w = np.asarray(w) + 0.0 - if w.ndim != 1: - raise TypeError("expected 1D vector for w") - if len(x) != len(w): - raise TypeError("expected x and w to have same length") - # apply weights. Don't use inplace operations as they - # can cause problems with NA. - lhs = lhs * w - rhs = rhs * w - - # set rcond - if rcond is None: - rcond = len(x)*np.finfo(x.dtype).eps - - # Determine the norms of the design matrix columns. - if issubclass(lhs.dtype.type, np.complexfloating): - scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) - else: - scl = np.sqrt(np.square(lhs).sum(1)) - scl[scl == 0] = 1 - - # Solve the least squares problem. - c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond) - c = (c.T/scl).T - - # Expand c to include non-fitted coefficients which are set to zero - if deg.ndim == 1: - if c.ndim == 2: - cc = np.zeros((lmax + 1, c.shape[1]), dtype=c.dtype) - else: - cc = np.zeros(lmax + 1, dtype=c.dtype) - cc[deg] = c - c = cc - - # warn on rank reduction - if rank != order and not full: - msg = "The fit may be poorly conditioned" - warnings.warn(msg, pu.RankWarning, stacklevel=2) - - if full: - return c, [resids, rank, s, rcond] - else: - return c + return pu._fit(polyvander, x, y, deg, rcond, full, w) def polycompanion(c): diff --git a/numpy/polynomial/polyutils.py b/numpy/polynomial/polyutils.py index 1d5390984..5128e35e0 100644 --- a/numpy/polynomial/polyutils.py +++ b/numpy/polynomial/polyutils.py @@ -45,6 +45,8 @@ Functions """ from __future__ import division, absolute_import, print_function +import operator + import numpy as np __all__ = [ @@ -423,11 +425,10 @@ def _vander2d(vander_f, x, y, deg): x, y, deg : See the ``<type>vander2d`` functions for more detail """ - ideg = [int(d) for d in deg] - is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] - if is_valid != [1, 1]: - raise ValueError("degrees must be non-negative integers") - degx, degy = ideg + degx, degy = [ + _deprecate_as_int(d, "degrees") + for d in deg + ] x, y = np.array((x, y), copy=0) + 0.0 vx = vander_f(x, degx) @@ -447,11 +448,10 @@ def _vander3d(vander_f, x, y, z, deg): x, y, z, deg : See the ``<type>vander3d`` functions for more detail """ - ideg = [int(d) for d in deg] - is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)] - if is_valid != [1, 1, 1]: - raise ValueError("degrees must be non-negative integers") - degx, degy, degz = ideg + degx, degy, degz = [ + _deprecate_as_int(d, "degrees") + for d in deg + ] x, y, z = np.array((x, y, z), copy=0) + 0.0 vx = vander_f(x, degx) @@ -573,3 +573,189 @@ def _div(mul_f, c1, c2): rem = rem[:-1] - q*p[:-1] quo[i] = q return quo, trimseq(rem) + + +def _add(c1, c2): + """ Helper function used to implement the ``<type>add`` functions. """ + # c1, c2 are trimmed copies + [c1, c2] = as_series([c1, c2]) + if len(c1) > len(c2): + c1[:c2.size] += c2 + ret = c1 + else: + c2[:c1.size] += c1 + ret = c2 + return trimseq(ret) + + +def _sub(c1, c2): + """ Helper function used to implement the ``<type>sub`` functions. """ + # c1, c2 are trimmed copies + [c1, c2] = as_series([c1, c2]) + if len(c1) > len(c2): + c1[:c2.size] -= c2 + ret = c1 + else: + c2 = -c2 + c2[:c1.size] += c1 + ret = c2 + return trimseq(ret) + + +def _fit(vander_f, x, y, deg, rcond=None, full=False, w=None): + """ + Helper function used to implement the ``<type>fit`` functions. + + Parameters + ---------- + vander_f : function(array_like, int) -> ndarray + The 1d vander function, such as ``polyvander`` + c1, c2 : + See the ``<type>fit`` functions for more detail + """ + x = np.asarray(x) + 0.0 + y = np.asarray(y) + 0.0 + deg = np.asarray(deg) + + # check arguments. + if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0: + raise TypeError("deg must be an int or non-empty 1-D array of int") + if deg.min() < 0: + raise ValueError("expected deg >= 0") + if x.ndim != 1: + raise TypeError("expected 1D vector for x") + if x.size == 0: + raise TypeError("expected non-empty vector for x") + if y.ndim < 1 or y.ndim > 2: + raise TypeError("expected 1D or 2D array for y") + if len(x) != len(y): + raise TypeError("expected x and y to have same length") + + if deg.ndim == 0: + lmax = deg + order = lmax + 1 + van = vander_f(x, lmax) + else: + deg = np.sort(deg) + lmax = deg[-1] + order = len(deg) + van = vander_f(x, lmax)[:, deg] + + # set up the least squares matrices in transposed form + lhs = van.T + rhs = y.T + if w is not None: + w = np.asarray(w) + 0.0 + if w.ndim != 1: + raise TypeError("expected 1D vector for w") + if len(x) != len(w): + raise TypeError("expected x and w to have same length") + # apply weights. Don't use inplace operations as they + # can cause problems with NA. + lhs = lhs * w + rhs = rhs * w + + # set rcond + if rcond is None: + rcond = len(x)*np.finfo(x.dtype).eps + + # Determine the norms of the design matrix columns. + if issubclass(lhs.dtype.type, np.complexfloating): + scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1)) + else: + scl = np.sqrt(np.square(lhs).sum(1)) + scl[scl == 0] = 1 + + # Solve the least squares problem. + c, resids, rank, s = np.linalg.lstsq(lhs.T/scl, rhs.T, rcond) + c = (c.T/scl).T + + # Expand c to include non-fitted coefficients which are set to zero + if deg.ndim > 0: + if c.ndim == 2: + cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype) + else: + cc = np.zeros(lmax+1, dtype=c.dtype) + cc[deg] = c + c = cc + + # warn on rank reduction + if rank != order and not full: + msg = "The fit may be poorly conditioned" + warnings.warn(msg, RankWarning, stacklevel=2) + + if full: + return c, [resids, rank, s, rcond] + else: + return c + + +def _pow(mul_f, c, pow, maxpower): + """ + Helper function used to implement the ``<type>pow`` functions. + + Parameters + ---------- + vander_f : function(array_like, int) -> ndarray + The 1d vander function, such as ``polyvander`` + pow, maxpower : + See the ``<type>pow`` functions for more detail + mul_f : function(array_like, array_like) -> ndarray + The ``<type>mul`` function, such as ``polymul`` + """ + # c is a trimmed copy + [c] = as_series([c]) + power = int(pow) + if power != pow or power < 0: + raise ValueError("Power must be a non-negative integer.") + elif maxpower is not None and power > maxpower: + raise ValueError("Power is too large") + elif power == 0: + return np.array([1], dtype=c.dtype) + elif power == 1: + return c + else: + # This can be made more efficient by using powers of two + # in the usual way. + prd = c + for i in range(2, power + 1): + prd = mul_f(prd, c) + return prd + + +def _deprecate_as_int(x, desc): + """ + Like `operator.index`, but emits a deprecation warning when passed a float + + Parameters + ---------- + x : int-like, or float with integral value + Value to interpret as an integer + desc : str + description to include in any error message + + Raises + ------ + TypeError : if x is a non-integral float or non-numeric + DeprecationWarning : if x is an integral float + """ + try: + return operator.index(x) + except TypeError: + # Numpy 1.17.0, 2019-03-11 + try: + ix = int(x) + except TypeError: + pass + else: + if ix == x: + warnings.warn( + "In future, this will raise TypeError, as {} will need to " + "be an integer not just an integral float." + .format(desc), + DeprecationWarning, + stacklevel=3 + ) + return ix + + raise TypeError("{} must be an integer".format(desc)) diff --git a/numpy/polynomial/tests/test_chebyshev.py b/numpy/polynomial/tests/test_chebyshev.py index 7fb7492c6..c8d2d6dba 100644 --- a/numpy/polynomial/tests/test_chebyshev.py +++ b/numpy/polynomial/tests/test_chebyshev.py @@ -221,12 +221,12 @@ class TestIntegral(object): def test_chebint(self): # check exceptions - assert_raises(ValueError, cheb.chebint, [0], .5) + assert_raises(TypeError, cheb.chebint, [0], .5) assert_raises(ValueError, cheb.chebint, [0], -1) assert_raises(ValueError, cheb.chebint, [0], 1, [0, 0]) assert_raises(ValueError, cheb.chebint, [0], lbnd=[0]) assert_raises(ValueError, cheb.chebint, [0], scl=[0]) - assert_raises(ValueError, cheb.chebint, [0], axis=.5) + assert_raises(TypeError, cheb.chebint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -323,7 +323,7 @@ class TestDerivative(object): def test_chebder(self): # check exceptions - assert_raises(ValueError, cheb.chebder, [0], .5) + assert_raises(TypeError, cheb.chebder, [0], .5) assert_raises(ValueError, cheb.chebder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/polynomial/tests/test_hermite.py b/numpy/polynomial/tests/test_hermite.py index 1287ef3fe..271c1964b 100644 --- a/numpy/polynomial/tests/test_hermite.py +++ b/numpy/polynomial/tests/test_hermite.py @@ -209,12 +209,12 @@ class TestIntegral(object): def test_hermint(self): # check exceptions - assert_raises(ValueError, herm.hermint, [0], .5) + assert_raises(TypeError, herm.hermint, [0], .5) assert_raises(ValueError, herm.hermint, [0], -1) assert_raises(ValueError, herm.hermint, [0], 1, [0, 0]) assert_raises(ValueError, herm.hermint, [0], lbnd=[0]) assert_raises(ValueError, herm.hermint, [0], scl=[0]) - assert_raises(ValueError, herm.hermint, [0], axis=.5) + assert_raises(TypeError, herm.hermint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -311,7 +311,7 @@ class TestDerivative(object): def test_hermder(self): # check exceptions - assert_raises(ValueError, herm.hermder, [0], .5) + assert_raises(TypeError, herm.hermder, [0], .5) assert_raises(ValueError, herm.hermder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/polynomial/tests/test_hermite_e.py b/numpy/polynomial/tests/test_hermite_e.py index ccb44ad73..434b30e7b 100644 --- a/numpy/polynomial/tests/test_hermite_e.py +++ b/numpy/polynomial/tests/test_hermite_e.py @@ -209,12 +209,12 @@ class TestIntegral(object): def test_hermeint(self): # check exceptions - assert_raises(ValueError, herme.hermeint, [0], .5) + assert_raises(TypeError, herme.hermeint, [0], .5) assert_raises(ValueError, herme.hermeint, [0], -1) assert_raises(ValueError, herme.hermeint, [0], 1, [0, 0]) assert_raises(ValueError, herme.hermeint, [0], lbnd=[0]) assert_raises(ValueError, herme.hermeint, [0], scl=[0]) - assert_raises(ValueError, herme.hermeint, [0], axis=.5) + assert_raises(TypeError, herme.hermeint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -311,7 +311,7 @@ class TestDerivative(object): def test_hermeder(self): # check exceptions - assert_raises(ValueError, herme.hermeder, [0], .5) + assert_raises(TypeError, herme.hermeder, [0], .5) assert_raises(ValueError, herme.hermeder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/polynomial/tests/test_laguerre.py b/numpy/polynomial/tests/test_laguerre.py index 3ababec5e..4b9b28637 100644 --- a/numpy/polynomial/tests/test_laguerre.py +++ b/numpy/polynomial/tests/test_laguerre.py @@ -206,12 +206,12 @@ class TestIntegral(object): def test_lagint(self): # check exceptions - assert_raises(ValueError, lag.lagint, [0], .5) + assert_raises(TypeError, lag.lagint, [0], .5) assert_raises(ValueError, lag.lagint, [0], -1) assert_raises(ValueError, lag.lagint, [0], 1, [0, 0]) assert_raises(ValueError, lag.lagint, [0], lbnd=[0]) assert_raises(ValueError, lag.lagint, [0], scl=[0]) - assert_raises(ValueError, lag.lagint, [0], axis=.5) + assert_raises(TypeError, lag.lagint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -308,7 +308,7 @@ class TestDerivative(object): def test_lagder(self): # check exceptions - assert_raises(ValueError, lag.lagder, [0], .5) + assert_raises(TypeError, lag.lagder, [0], .5) assert_raises(ValueError, lag.lagder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/polynomial/tests/test_legendre.py b/numpy/polynomial/tests/test_legendre.py index a23086d59..917a7e03a 100644 --- a/numpy/polynomial/tests/test_legendre.py +++ b/numpy/polynomial/tests/test_legendre.py @@ -210,12 +210,12 @@ class TestIntegral(object): def test_legint(self): # check exceptions - assert_raises(ValueError, leg.legint, [0], .5) + assert_raises(TypeError, leg.legint, [0], .5) assert_raises(ValueError, leg.legint, [0], -1) assert_raises(ValueError, leg.legint, [0], 1, [0, 0]) assert_raises(ValueError, leg.legint, [0], lbnd=[0]) assert_raises(ValueError, leg.legint, [0], scl=[0]) - assert_raises(ValueError, leg.legint, [0], axis=.5) + assert_raises(TypeError, leg.legint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -312,7 +312,7 @@ class TestDerivative(object): def test_legder(self): # check exceptions - assert_raises(ValueError, leg.legder, [0], .5) + assert_raises(TypeError, leg.legder, [0], .5) assert_raises(ValueError, leg.legder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/polynomial/tests/test_polynomial.py b/numpy/polynomial/tests/test_polynomial.py index 562aa904d..08b73da15 100644 --- a/numpy/polynomial/tests/test_polynomial.py +++ b/numpy/polynomial/tests/test_polynomial.py @@ -291,12 +291,12 @@ class TestIntegral(object): def test_polyint(self): # check exceptions - assert_raises(ValueError, poly.polyint, [0], .5) + assert_raises(TypeError, poly.polyint, [0], .5) assert_raises(ValueError, poly.polyint, [0], -1) assert_raises(ValueError, poly.polyint, [0], 1, [0, 0]) assert_raises(ValueError, poly.polyint, [0], lbnd=[0]) assert_raises(ValueError, poly.polyint, [0], scl=[0]) - assert_raises(ValueError, poly.polyint, [0], axis=.5) + assert_raises(TypeError, poly.polyint, [0], axis=.5) # test integration of zero polynomial for i in range(2, 5): @@ -388,7 +388,7 @@ class TestDerivative(object): def test_polyder(self): # check exceptions - assert_raises(ValueError, poly.polyder, [0], .5) + assert_raises(TypeError, poly.polyder, [0], .5) assert_raises(ValueError, poly.polyder, [0], -1) # check that zeroth derivative does nothing diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 856198f24..5d4dee8af 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -4203,9 +4203,9 @@ cdef class RandomState: Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified - parameters, ngood (ways to make a good selection), nbad (ways to make - a bad selection), and nsample = number of items sampled, which is less - than or equal to the sum ngood + nbad. + parameters, `ngood` (ways to make a good selection), `nbad` (ways to make + a bad selection), and `nsample` (number of items sampled, which is less + than or equal to the sum ``ngood + nbad``). Parameters ---------- @@ -4219,14 +4219,16 @@ cdef class RandomState: size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), - a single value is returned if ``ngood``, ``nbad``, and ``nsample`` + a single value is returned if `ngood`, `nbad`, and `nsample` are all scalars. Otherwise, ``np.broadcast(ngood, nbad, nsample).size`` samples are drawn. Returns ------- out : ndarray or scalar - Drawn samples from the parameterized hypergeometric distribution. + Drawn samples from the parameterized hypergeometric distribution. Each + sample is the number of good items within a randomly selected subset of + size `nsample` taken from a set of `ngood` good items and `nbad` bad items. See Also -------- @@ -4241,11 +4243,11 @@ cdef class RandomState: where :math:`0 \\le x \\le n` and :math:`n-b \\le x \\le g` - for P(x) the probability of x successes, g = ngood, b = nbad, and - n = number of samples. + for P(x) the probability of ``x`` good results in the drawn sample, + g = `ngood`, b = `nbad`, and n = `nsample`. - Consider an urn with black and white marbles in it, ngood of them - black and nbad are white. If you draw nsample balls without + Consider an urn with black and white marbles in it, `ngood` of them + are black and `nbad` are white. If you draw `nsample` balls without replacement, then the hypergeometric distribution describes the distribution of black balls in the drawn sample. diff --git a/runtests.py b/runtests.py index 8ec337ecf..23245aeac 100755 --- a/runtests.py +++ b/runtests.py @@ -259,8 +259,6 @@ def main(argv): ret = subprocess.call(cmd, cwd=os.path.join(ROOT_DIR, 'benchmarks')) sys.exit(ret) - test_dir = os.path.join(ROOT_DIR, 'build', 'test') - if args.build_only: sys.exit(0) else: |