Python numpy.float_() Examples

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))) 

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() 

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])) 

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) 

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) 

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_)) 

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() 

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() 

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() 

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() 

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() 

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() 

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)) 

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() 

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() 

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 

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)) 

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) 

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) 

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) 

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]) 

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) 

def test_float(self):
        vals = nan_to_num(1.0)
        assert_all(vals == 1.0)
        assert_equal(type(vals), np.float_) 

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)) 

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]) 

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)) 

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)) 

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) 

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']) 

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_