Python numpy.longlong() Examples
The following are 30
code examples of numpy.longlong().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
numpy
, or try the search function
.
Example #1
Source File: svmlight_format.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _open_and_load(f, dtype, multilabel, zero_based, query_id, offset=0, length=-1): if hasattr(f, "read"): actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id, offset, length) else: with closing(_gen_open(f)) as f: actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id, offset, length) # convert from array.array, give data the right dtype if not multilabel: labels = np.frombuffer(labels, np.float64) data = np.frombuffer(data, actual_dtype) indices = np.frombuffer(ind, np.longlong) indptr = np.frombuffer(indptr, dtype=np.longlong) # never empty query = np.frombuffer(query, np.int64) data = np.asarray(data, dtype=dtype) # no-op for float{32,64} return data, indices, indptr, labels, query
Example #2
Source File: histograms.py From pySINDy with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #3
Source File: histograms.py From coffeegrindsize with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #4
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #5
Source File: main.py From ssds with MIT License | 6 votes |
def concatenateNeighborLists(meshPaths): neighbor = [] for path in meshPaths: mesh = om.MFnMesh(path) _, indices = mesh.getTriangles() offset = len(neighbor) neighbor = neighbor + [set() for v in xrange(mesh.numVertices)] for l in xrange(len(indices) / 3): i0 = indices[l * 3 + 0] + offset i1 = indices[l * 3 + 1] + offset i2 = indices[l * 3 + 2] + offset neighbor[i0].add(i1) neighbor[i0].add(i2) neighbor[i1].add(i0) neighbor[i1].add(i2) neighbor[i2].add(i0) neighbor[i2].add(i1) maxlen = 0 for i in xrange(len(neighbor)): maxlen = max(maxlen, len(neighbor[i])) retval = -np.ones([len(neighbor), maxlen], dtype = np.longlong) for i in xrange(len(neighbor)): retval[i, 0:len(neighbor[i])] = list(neighbor[i]) return retval
Example #6
Source File: histograms.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #7
Source File: histograms.py From Carnets with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #8
Source File: histograms.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #9
Source File: histograms.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #10
Source File: histograms.py From lambda-packs with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #11
Source File: histogram.py From mars with Apache License 2.0 | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: # pragma: no cover return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #12
Source File: histograms.py From twitter-stock-recommendation with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #13
Source File: test_numerictypes.py From coffeegrindsize with MIT License | 5 votes |
def test_platform_dependent_aliases(self): if np.int64 is np.int_: assert_('int64' in np.int_.__doc__) elif np.int64 is np.longlong: assert_('int64' in np.longlong.__doc__)
Example #14
Source File: test_return.py From hope with GNU General Public License v3.0 | 5 votes |
def test_return_scalar(dtype): def fkt(a): return a ao, ah = random(dtype, []) ro, rh = fkt(ao), hope.jit(fkt)(ah) assert check(ro, rh) # TODO: fix for np.ulonglong, np.longlong and uint64
Example #15
Source File: test_return.py From hope with GNU General Public License v3.0 | 5 votes |
def test_return_arrayscalar(dtype): def fkt(a): return a[2] ao, ah = random(dtype, [10]) ro, rh = fkt(ao), hope.jit(fkt)(ah) assert check(ro, rh) # TODO: fix for np.ulonglong, np.longlong and uint64
Example #16
Source File: qtemporal.py From qPython with Apache License 2.0 | 5 votes |
def _to_qtimestamp(dt): t_dt = type(dt) if t_dt == numpy.int64: return dt elif t_dt == numpy.datetime64: return (dt - _EPOCH_TIMESTAMP).astype(longlong) if not dt == _NUMPY_NULL[QTIMESTAMP] else _QTIMESTAMP_NULL else: raise ValueError('Cannot convert %s of type %s to q value.' % (dt, type(dt)))
Example #17
Source File: test_return.py From hope with GNU General Public License v3.0 | 5 votes |
def test_return_arrcrt_zeros(a, b, c): d = np.zeros(3) d[:] = 2 d[0] = 1 return d # TODO: fix for np.ulonglong, np.longlong and uint64
Example #18
Source File: qtemporal.py From qPython with Apache License 2.0 | 5 votes |
def _to_qtimespan(dt): t_dt = type(dt) if t_dt == numpy.int64: return dt elif t_dt == numpy.timedelta64: return dt.astype(longlong) if not dt == _NUMPY_NULL[QTIMESPAN] else _QTIMESTAMP_NULL else: raise ValueError('Cannot convert %s of type %s to q value.' % (dt, type(dt)))
Example #19
Source File: test_histograms.py From coffeegrindsize with MIT License | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #20
Source File: test_histograms.py From pySINDy with MIT License | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #21
Source File: test_histograms.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #22
Source File: test_histograms.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #23
Source File: array.py From khiva-python with Mozilla Public License 2.0 | 5 votes |
def get_dims(self): """ Gets the dimensions of the KHIVA array. :return: The dimensions of the KHIVA array. """ c_array_n = (ctypes.c_longlong * 4)(*(np.zeros(4)).astype(np.longlong)) error_code = ctypes.c_int(0) error_message = ctypes.create_string_buffer(KHIVA_ERROR_LENGTH) KhivaLibrary().c_khiva_library.get_dims(ctypes.pointer(self.arr_reference), ctypes.pointer(c_array_n), ctypes.pointer(error_code), error_message) return np.array(c_array_n)
Example #24
Source File: test_json.py From eliot with Apache License 2.0 | 5 votes |
def test_numpy(self): """NumPy objects get serialized to readable JSON.""" l = [ np.float32(12.5), np.float64(2.0), np.float16(0.5), np.bool(True), np.bool(False), np.bool_(True), np.unicode_("hello"), np.byte(12), np.short(12), np.intc(-13), np.int_(0), np.longlong(100), np.intp(7), np.ubyte(12), np.ushort(12), np.uintc(13), np.ulonglong(100), np.uintp(7), np.int8(1), np.int16(3), np.int32(4), np.int64(5), np.uint8(1), np.uint16(3), np.uint32(4), np.uint64(5), ] l2 = [l, np.array([1, 2, 3])] roundtripped = loads(dumps(l2, cls=EliotJSONEncoder)) self.assertEqual([l, [1, 2, 3]], roundtripped)
Example #25
Source File: histograms.py From recruit with Apache License 2.0 | 5 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #26
Source File: test_numerictypes.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_platform_dependent_aliases(self): if np.int64 is np.int_: assert_('int64' in np.int_.__doc__) elif np.int64 is np.longlong: assert_('int64' in np.longlong.__doc__)
Example #27
Source File: test_histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #28
Source File: test_decomp.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_eigvals_banded(self): """Compare eigenvalues of eigvals_banded with those of linalg.eig.""" w_sym = eigvals_banded(self.bandmat_sym) w_sym = w_sym.real assert_array_almost_equal(sort(w_sym), self.w_sym_lin) w_herm = eigvals_banded(self.bandmat_herm) w_herm = w_herm.real assert_array_almost_equal(sort(w_herm), self.w_herm_lin) # extracting eigenvalues with respect to an index range ind1 = 2 ind2 = np.longlong(6) w_sym_ind = eigvals_banded(self.bandmat_sym, select='i', select_range=(ind1, ind2)) assert_array_almost_equal(sort(w_sym_ind), self.w_sym_lin[ind1:ind2+1]) w_herm_ind = eigvals_banded(self.bandmat_herm, select='i', select_range=(ind1, ind2)) assert_array_almost_equal(sort(w_herm_ind), self.w_herm_lin[ind1:ind2+1]) # extracting eigenvalues with respect to a value range v_lower = self.w_sym_lin[ind1] - 1.0e-5 v_upper = self.w_sym_lin[ind2] + 1.0e-5 w_sym_val = eigvals_banded(self.bandmat_sym, select='v', select_range=(v_lower, v_upper)) assert_array_almost_equal(sort(w_sym_val), self.w_sym_lin[ind1:ind2+1]) v_lower = self.w_herm_lin[ind1] - 1.0e-5 v_upper = self.w_herm_lin[ind2] + 1.0e-5 w_herm_val = eigvals_banded(self.bandmat_herm, select='v', select_range=(v_lower, v_upper)) assert_array_almost_equal(sort(w_herm_val), self.w_herm_lin[ind1:ind2+1]) w_sym = eigvals_banded(self.bandmat_sym, check_finite=False) w_sym = w_sym.real assert_array_almost_equal(sort(w_sym), self.w_sym_lin)
Example #29
Source File: test_histograms.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong)
Example #30
Source File: lax_numpy_test.py From trax with Apache License 2.0 | 5 votes |
def testLongLong(self): self.assertAllClose( onp.int64(7), npe.jit(lambda x: x)(onp.longlong(7)), check_dtypes=True)