Python numpy.float_() Examples
The following are 30
code examples of numpy.float_().
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: numpy_fenics.py From torch-fenics with 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
Source File: testutils.py From recruit with 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
Source File: test_linalg.py From recruit with 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
Source File: test_constructors.py From recruit with 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
Source File: test_arithmetic.py From mars with 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
Source File: test_session.py From mars with 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
Source File: testutils.py From lambda-packs with 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
Source File: testutils.py From lambda-packs with 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
Source File: testutils.py From lambda-packs with 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
Source File: testutils.py From lambda-packs with 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
Source File: testutils.py From auto-alt-text-lambda-api with 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
Source File: testutils.py From auto-alt-text-lambda-api with 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
Source File: test_indexing.py From auto-alt-text-lambda-api with 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
Source File: testutils.py From vnpy_crypto with 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
Source File: testutils.py From vnpy_crypto with 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
Source File: ed_quantum.py From nn_physical_concepts with 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
Source File: solution_classes.py From risk-slim with 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
Source File: solution_classes.py From risk-slim with 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
Source File: solution_classes.py From risk-slim with 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
Source File: setup_functions.py From risk-slim with 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
Source File: data.py From MnemonicReader with 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
Source File: numpy_fenics.py From torch-fenics with 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
Source File: test_type_check.py From recruit with 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
Source File: test_old_ma.py From recruit with 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
Source File: test_extras.py From recruit with 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
Source File: test_indexing.py From recruit with 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
Source File: test_indexing.py From recruit with 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
Source File: test_base.py From recruit with 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
Source File: test_apply.py From recruit with 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
Source File: test_alter_index.py From recruit with 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_