Python numpy.float_() Examples
The following are 30 code examples for showing how to use numpy.float_(). These examples are extracted from open source projects. 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.
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Example 1
Project: torch-fenics Author: barkm File: numpy_fenics.py License: GNU General Public License v3.0 | 7 votes |
def fenics_to_numpy(fenics_var): """Convert FEniCS variable to numpy array""" if isinstance(fenics_var, (fenics.Constant, fenics_adjoint.Constant)): return fenics_var.values() if isinstance(fenics_var, (fenics.Function, fenics_adjoint.Constant)): np_array = fenics_var.vector().get_local() n_sub = fenics_var.function_space().num_sub_spaces() # Reshape if function is multi-component if n_sub != 0: np_array = np.reshape(np_array, (len(np_array) // n_sub, n_sub)) return np_array if isinstance(fenics_var, fenics.GenericVector): return fenics_var.get_local() if isinstance(fenics_var, fenics_adjoint.AdjFloat): return np.array(float(fenics_var), dtype=np.float_) raise ValueError('Cannot convert ' + str(type(fenics_var)))
Example 2
Project: recruit Author: Frank-qlu File: testutils.py License: Apache License 2.0 | 6 votes |
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8): """ Returns true if all components of a and b are equal to given tolerances. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. The relative error rtol should be positive and << 1.0 The absolute error atol comes into play for those elements of b that are very small or zero; it says how small a must be also. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y)) return d.ravel()
Example 3
Project: recruit Author: Frank-qlu File: test_linalg.py License: Apache License 2.0 | 6 votes |
def test_nan(self): # nans should be passed through, not converted to infs ps = [None, 1, -1, 2, -2, 'fro'] p_pos = [None, 1, 2, 'fro'] A = np.ones((2, 2)) A[0,1] = np.nan for p in ps: c = linalg.cond(A, p) assert_(isinstance(c, np.float_)) assert_(np.isnan(c)) A = np.ones((3, 2, 2)) A[1,0,1] = np.nan for p in ps: c = linalg.cond(A, p) assert_(np.isnan(c[1])) if p in p_pos: assert_(c[0] > 1e15) assert_(c[2] > 1e15) else: assert_(not np.isnan(c[0])) assert_(not np.isnan(c[2]))
Example 4
Project: recruit Author: Frank-qlu File: test_constructors.py License: Apache License 2.0 | 6 votes |
def test_fromValue(self, datetime_series): nans = Series(np.NaN, index=datetime_series.index) assert nans.dtype == np.float_ assert len(nans) == len(datetime_series) strings = Series('foo', index=datetime_series.index) assert strings.dtype == np.object_ assert len(strings) == len(datetime_series) d = datetime.now() dates = Series(d, index=datetime_series.index) assert dates.dtype == 'M8[ns]' assert len(dates) == len(datetime_series) # GH12336 # Test construction of categorical series from value categorical = Series(0, index=datetime_series.index, dtype="category") expected = Series(0, index=datetime_series.index).astype("category") assert categorical.dtype == 'category' assert len(categorical) == len(datetime_series) tm.assert_series_equal(categorical, expected)
Example 5
Project: mars Author: mars-project File: test_arithmetic.py License: Apache License 2.0 | 6 votes |
def testFrexp(self): t1 = ones((3, 4, 5), chunk_size=2) t2 = empty((3, 4, 5), dtype=np.float_, chunk_size=2) op_type = type(t1.op) o1, o2 = frexp(t1) self.assertIs(o1.op, o2.op) self.assertNotEqual(o1.dtype, o2.dtype) o1, o2 = frexp(t1, t1) self.assertIs(o1, t1) self.assertIsNot(o1.inputs[0], t1) self.assertIsInstance(o1.inputs[0].op, op_type) self.assertIsNot(o2.inputs[0], t1) o1, o2 = frexp(t1, t2, where=t1 > 0) op_type = type(t2.op) self.assertIs(o1, t2) self.assertIsNot(o1.inputs[0], t1) self.assertIsInstance(o1.inputs[0].op, op_type) self.assertIsNot(o2.inputs[0], t1)
Example 6
Project: mars Author: mars-project File: test_session.py License: Apache License 2.0 | 6 votes |
def testArrayProtocol(self): arr = mt.ones((10, 20)) result = np.asarray(arr) np.testing.assert_array_equal(result, np.ones((10, 20))) arr2 = mt.ones((10, 20)) result = np.asarray(arr2, mt.bool_) np.testing.assert_array_equal(result, np.ones((10, 20), dtype=np.bool_)) arr3 = mt.ones((10, 20)).sum() result = np.asarray(arr3) np.testing.assert_array_equal(result, np.asarray(200)) arr4 = mt.ones((10, 20)).sum() result = np.asarray(arr4, dtype=np.float_) np.testing.assert_array_equal(result, np.asarray(200, dtype=np.float_))
Example 7
Project: lambda-packs Author: ryfeus File: testutils.py License: MIT License | 6 votes |
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8): """ Returns true if all components of a and b are equal to given tolerances. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. The relative error rtol should be positive and << 1.0 The absolute error atol comes into play for those elements of b that are very small or zero; it says how small a must be also. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y)) return d.ravel()
Example 8
Project: lambda-packs Author: ryfeus File: testutils.py License: MIT License | 6 votes |
def almost(a, b, decimal=6, fill_value=True): """ Returns True if a and b are equal up to decimal places. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal) return d.ravel()
Example 9
Project: lambda-packs Author: ryfeus File: testutils.py License: MIT License | 6 votes |
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8): """ Returns true if all components of a and b are equal to given tolerances. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. The relative error rtol should be positive and << 1.0 The absolute error atol comes into play for those elements of b that are very small or zero; it says how small a must be also. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y)) return d.ravel()
Example 10
Project: lambda-packs Author: ryfeus File: testutils.py License: MIT License | 6 votes |
def almost(a, b, decimal=6, fill_value=True): """ Returns True if a and b are equal up to decimal places. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal) return d.ravel()
Example 11
Project: auto-alt-text-lambda-api Author: abhisuri97 File: testutils.py License: MIT License | 6 votes |
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8): """ Returns true if all components of a and b are equal to given tolerances. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. The relative error rtol should be positive and << 1.0 The absolute error atol comes into play for those elements of b that are very small or zero; it says how small a must be also. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y)) return d.ravel()
Example 12
Project: auto-alt-text-lambda-api Author: abhisuri97 File: testutils.py License: MIT License | 6 votes |
def almost(a, b, decimal=6, fill_value=True): """ Returns True if a and b are equal up to decimal places. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal) return d.ravel()
Example 13
Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_indexing.py License: MIT License | 6 votes |
def test_empty_tuple_index(self): # Empty tuple index creates a view a = np.array([1, 2, 3]) assert_equal(a[()], a) assert_(a[()].base is a) a = np.array(0) assert_(isinstance(a[()], np.int_)) # Regression, it needs to fall through integer and fancy indexing # cases, so need the with statement to ignore the non-integer error. with warnings.catch_warnings(): warnings.filterwarnings('ignore', '', DeprecationWarning) a = np.array([1.]) assert_(isinstance(a[0.], np.float_)) a = np.array([np.array(1)], dtype=object) assert_(isinstance(a[0.], np.ndarray))
Example 14
Project: vnpy_crypto Author: birforce File: testutils.py License: MIT License | 6 votes |
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8): """ Returns true if all components of a and b are equal to given tolerances. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. The relative error rtol should be positive and << 1.0 The absolute error atol comes into play for those elements of b that are very small or zero; it says how small a must be also. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y)) return d.ravel()
Example 15
Project: vnpy_crypto Author: birforce File: testutils.py License: MIT License | 6 votes |
def almost(a, b, decimal=6, fill_value=True): """ Returns True if a and b are equal up to decimal places. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal) return d.ravel()
Example 16
Project: nn_physical_concepts Author: eth-nn-physics File: ed_quantum.py License: Apache License 2.0 | 5 votes |
def create_data(qubit_num, measurement_num, sample_num, file_name=None): measurements = np.empty([sample_num, measurement_num], dtype=np.float_) states = np.empty([sample_num, 2**qubit_num], dtype=np.complex_) projectors = [random_state(qubit_num) for _ in range(measurement_num)] for i in range(sample_num): sample = random_state(qubit_num) states[i] = sample measurements[i] = np.array([projection(p, sample) for p in projectors]) result = (measurements, states, projectors) if file_name is not None: f = gzip.open(io.data_path + file_name + ".plk.gz", 'wb') cPickle.dump(result, f, protocol=2) f.close() return result
Example 17
Project: risk-slim Author: ustunb File: solution_classes.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def __init__(self, obj): if isinstance(obj, SolutionPool): self._P = obj.P self._objvals = obj.objvals self._solutions = obj.solutions elif isinstance(obj, int): assert obj >= 1 self._P = int(obj) self._objvals = np.empty(0) self._solutions = np.empty(shape = (0, self._P)) elif isinstance(obj, dict): assert len(obj) == 2 objvals = np.copy(obj['objvals']).flatten().astype(dtype = np.float_) solutions = np.copy(obj['solutions']) n = objvals.size if solutions.ndim == 2: assert n in solutions.shape if solutions.shape[1] == n and solutions.shape[0] != n: solutions = np.transpose(solutions) elif solutions.ndim == 1: assert n == 1 solutions = np.reshape(solutions, (1, solutions.size)) else: raise ValueError('solutions has more than 2 dimensions') self._P = solutions.shape[1] self._objvals = objvals self._solutions = solutions else: raise ValueError('cannot initialize SolutionPool using %s object' % type(obj))
Example 18
Project: risk-slim Author: ustunb File: solution_classes.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def objvals(self, objvals): if hasattr(objvals, "__len__"): if len(objvals) > 0: self._objvals = np.copy(list(objvals)).flatten().astype(dtype = np.float_) elif len(objvals) == 0: self._objvals = np.empty(0) else: self._objvals = float(objvals)
Example 19
Project: risk-slim Author: ustunb File: solution_classes.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def add(self, new_objvals, new_solutions): if isinstance(new_objvals, (np.ndarray, list)): n = len(new_objvals) self._objvals = np.append(self._objvals, np.array(new_objvals).astype(dtype = np.float_).flatten()) else: n = 1 self._objvals = np.append(self._objvals, float(new_objvals)) new_solutions = np.reshape(new_solutions, (n, self._P)) self._solutions = np.append(self._solutions, new_solutions, axis = 0)
Example 20
Project: risk-slim Author: ustunb File: setup_functions.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def setup_objective_functions(compute_loss, L0_reg_ind, C_0_nnz): get_objval = lambda rho: compute_loss(rho) + np.sum(C_0_nnz * (rho[L0_reg_ind] != 0.0)) get_L0_norm = lambda rho: np.count_nonzero(rho[L0_reg_ind]) get_L0_penalty = lambda rho: np.sum(C_0_nnz * (rho[L0_reg_ind] != 0.0)) get_alpha = lambda rho: np.array(abs(rho[L0_reg_ind]) > 0.0, dtype = np.float_) get_L0_penalty_from_alpha = lambda alpha: np.sum(C_0_nnz * alpha) return (get_objval, get_L0_norm, get_L0_penalty, get_alpha, get_L0_penalty_from_alpha)
Example 21
Project: MnemonicReader Author: HKUST-KnowComp File: data.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def __iter__(self): lengths = np.array( [(-l[0], -l[1], np.random.random()) for l in self.lengths], dtype=[('l1', np.int_), ('l2', np.int_), ('rand', np.float_)] ) indices = np.argsort(lengths, order=('l1', 'l2', 'rand')) batches = [indices[i:i + self.batch_size] for i in range(0, len(indices), self.batch_size)] if self.shuffle: np.random.shuffle(batches) return iter([i for batch in batches for i in batch])
Example 22
Project: torch-fenics Author: barkm File: numpy_fenics.py License: GNU General Public License v3.0 | 5 votes |
def numpy_to_fenics(numpy_array, fenics_var_template): """Convert numpy array to FEniCS variable""" if isinstance(fenics_var_template, (fenics.Constant, fenics_adjoint.Constant)): if numpy_array.shape == (1,): return type(fenics_var_template)(numpy_array[0]) else: return type(fenics_var_template)(numpy_array) if isinstance(fenics_var_template, (fenics.Function, fenics_adjoint.Function)): np_n_sub = numpy_array.shape[-1] np_size = np.prod(numpy_array.shape) function_space = fenics_var_template.function_space() u = type(fenics_var_template)(function_space) fenics_size = u.vector().local_size() fenics_n_sub = function_space.num_sub_spaces() if (fenics_n_sub != 0 and np_n_sub != fenics_n_sub) or np_size != fenics_size: err_msg = 'Cannot convert numpy array to Function:' \ ' Wrong shape {} vs {}'.format(numpy_array.shape, u.vector().get_local().shape) raise ValueError(err_msg) if numpy_array.dtype != np.float_: err_msg = 'The numpy array must be of type {}, ' \ 'but got {}'.format(np.float_, numpy_array.dtype) raise ValueError(err_msg) u.vector().set_local(np.reshape(numpy_array, fenics_size)) u.vector().apply('insert') return u if isinstance(fenics_var_template, fenics_adjoint.AdjFloat): return fenics_adjoint.AdjFloat(numpy_array) err_msg = 'Cannot convert numpy array to {}'.format(fenics_var_template) raise ValueError(err_msg)
Example 23
Project: recruit Author: Frank-qlu File: test_type_check.py License: Apache License 2.0 | 5 votes |
def test_float(self): vals = nan_to_num(1.0) assert_all(vals == 1.0) assert_equal(type(vals), np.float_)
Example 24
Project: recruit Author: Frank-qlu File: test_old_ma.py License: Apache License 2.0 | 5 votes |
def test_ptp(self): (x, X, XX, m, mx, mX, mXX,) = self.d (n, m) = X.shape assert_equal(mx.ptp(), mx.compressed().ptp()) rows = np.zeros(n, np.float_) cols = np.zeros(m, np.float_) for k in range(m): cols[k] = mX[:, k].compressed().ptp() for k in range(n): rows[k] = mX[k].compressed().ptp() assert_(eq(mX.ptp(0), cols)) assert_(eq(mX.ptp(1), rows))
Example 25
Project: recruit Author: Frank-qlu File: test_extras.py License: Apache License 2.0 | 5 votes |
def test_testAverage2(self): # More tests of average. w1 = [0, 1, 1, 1, 1, 0] w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]] x = arange(6, dtype=np.float_) assert_equal(average(x, axis=0), 2.5) assert_equal(average(x, axis=0, weights=w1), 2.5) y = array([arange(6, dtype=np.float_), 2.0 * arange(6)]) assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.) assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.) assert_equal(average(y, axis=1), [average(x, axis=0), average(x, axis=0) * 2.0]) assert_equal(average(y, None, weights=w2), 20. / 6.) assert_equal(average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.]) assert_equal(average(y, axis=1), [average(x, axis=0), average(x, axis=0) * 2.0]) m1 = zeros(6) m2 = [0, 0, 1, 1, 0, 0] m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]] m4 = ones(6) m5 = [0, 1, 1, 1, 1, 1] assert_equal(average(masked_array(x, m1), axis=0), 2.5) assert_equal(average(masked_array(x, m2), axis=0), 2.5) assert_equal(average(masked_array(x, m4), axis=0).mask, [True]) assert_equal(average(masked_array(x, m5), axis=0), 0.0) assert_equal(count(average(masked_array(x, m4), axis=0)), 0) z = masked_array(y, m3) assert_equal(average(z, None), 20. / 6.) assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5]) assert_equal(average(z, axis=1), [2.5, 5.0]) assert_equal(average(z, axis=0, weights=w2), [0., 1., 99., 99., 4.0, 10.0])
Example 26
Project: recruit Author: Frank-qlu File: test_indexing.py License: Apache License 2.0 | 5 votes |
def test_scalar_return_type(self): # Full scalar indices should return scalars and object # arrays should not call PyArray_Return on their items class Zero(object): # The most basic valid indexing def __index__(self): return 0 z = Zero() class ArrayLike(object): # Simple array, should behave like the array def __array__(self): return np.array(0) a = np.zeros(()) assert_(isinstance(a[()], np.float_)) a = np.zeros(1) assert_(isinstance(a[z], np.float_)) a = np.zeros((1, 1)) assert_(isinstance(a[z, np.array(0)], np.float_)) assert_(isinstance(a[z, ArrayLike()], np.float_)) # And object arrays do not call it too often: b = np.array(0) a = np.array(0, dtype=object) a[()] = b assert_(isinstance(a[()], np.ndarray)) a = np.array([b, None]) assert_(isinstance(a[z], np.ndarray)) a = np.array([[b, None]]) assert_(isinstance(a[z, np.array(0)], np.ndarray)) assert_(isinstance(a[z, ArrayLike()], np.ndarray))
Example 27
Project: recruit Author: Frank-qlu File: test_indexing.py License: Apache License 2.0 | 5 votes |
def test_non_integer_sequence_multiplication(self): # NumPy scalar sequence multiply should not work with non-integers def mult(a, b): return a * b assert_raises(TypeError, mult, [1], np.float_(3)) # following should be OK mult([1], np.int_(3))
Example 28
Project: recruit Author: Frank-qlu File: test_base.py License: Apache License 2.0 | 5 votes |
def test_empty_fancy_raises(self, attr): # pd.DatetimeIndex is excluded, because it overrides getitem and should # be tested separately. empty_farr = np.array([], dtype=np.float_) index = getattr(self, attr) empty_index = index.__class__([]) assert index[[]].identical(empty_index) # np.ndarray only accepts ndarray of int & bool dtypes, so should Index pytest.raises(IndexError, index.__getitem__, empty_farr)
Example 29
Project: recruit Author: Frank-qlu File: test_apply.py License: Apache License 2.0 | 5 votes |
def test_map_int(self): left = Series({'a': 1., 'b': 2., 'c': 3., 'd': 4}) right = Series({1: 11, 2: 22, 3: 33}) assert left.dtype == np.float_ assert issubclass(right.dtype.type, np.integer) merged = left.map(right) assert merged.dtype == np.float_ assert isna(merged['d']) assert not isna(merged['c'])
Example 30
Project: recruit Author: Frank-qlu File: test_alter_index.py License: Apache License 2.0 | 5 votes |
def test_reindex_int(test_data): ts = test_data.ts[::2] int_ts = Series(np.zeros(len(ts), dtype=int), index=ts.index) # this should work fine reindexed_int = int_ts.reindex(test_data.ts.index) # if NaNs introduced assert reindexed_int.dtype == np.float_ # NO NaNs introduced reindexed_int = int_ts.reindex(int_ts.index[::2]) assert reindexed_int.dtype == np.int_