python类single()的实例源码

def field_directions(field):
    """ Scene the shows the directions of a vector field.

    Parameters
    ----------
    field: array (X, Y, N, 3)
        the vector field to plot where N is the number of peaks.

    Returns
    ----------
    actors: list of vtkActor
        the scene actors.
    """
    actors = []
    for x in range(field.shape[0]):
        for y in range(field.shape[1]):
            line = numpy.zeros((2, 3), dtype=numpy.single)
            for vector in field[x, y]:
                line[1] = vector
                actors.append(pvtk.line(line, 0, linewidth=2))
                actors[-1].SetPosition((x, y, 0))
    return actors

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def __init__(self, size=10, dtype=np.single):
        self.center = np.zeros([size, size], dtype)
        self.water = None
        self.sediment = None
        self.scour = None
        self.flowrate = None
        self.sedimentpct = None
        self.sedimentpct = None
        self.capacity = None
        self.avalanced = None
        self.minx = None
        self.miny = None
        self.maxx = None
        self.maxy = None
        self.zscale = 1
        self.maxrss = 0.0
        self.sequence = [0, 1, 2, 3]
        self.watermax = 1.0
        self.flowratemax = 1.0
        self.scourmax = 1.0
        self.sedmax = 1.0
        self.scourmin = 1.0

def load(path, dtype=numpy.single):
    """ Load an image.

    Parameters
    ----------
    path: str
        the path to the data to be loaded.
    dtype: str
       type to which the data will be cast. Passing 'None' will not cast.

    Returns
    -------
    image: Image
        the loaded image.
    """
    # Load the image
    loader = get_loader(path)
    image = loader.load(path)

    # Cast the image if requested
    if dtype:
        image.data = image.data.astype(dtype)

    return image

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def SGD(obj, t=0, lr=1e-2, l2reg=1e-2, momentum=0.0, _=0.0, nesterov=0):

    obj = regularize(obj, l2reg)
    lrW = lr #schedule(t, lr, l2reg)

    if not hasattr(obj, 'V'): obj.V = np.zeros_like(obj.W)

    nescale(obj.V, np.single(momentum)       )
    newtadd(obj.V, np.single(lrW     ), obj.G)

    if not nesterov: newtadd(obj.W,                   -1, obj.V)
    else           : newtadd(obj.W, np.single(-momentum), obj.V); newtadd(obj.W, np.single(-lrW), obj.G)

    #obj.V *= np.single(momentum)
    #obj.V -= np.single(lrW) * obj.G

    #if not nesterov: obj.W += obj.V
    #else           : obj.W += np.single(momentum) * obj.V - np.single(lrW) * obj.G

def ADADELTA(obj, t=0, lr=1e-0, l2reg=1e-2, rho=0.95, eps=1e-8):

    obj = regularize(obj, l2reg)

    if not hasattr(obj, 'V'): obj.V = np.zeros_like(obj.W)
    if not hasattr(obj, 'D'): obj.D = np.zeros_like(obj.W)

    nescale(obj.V, np.single(    rho)                   )
    newsadd(obj.V, np.single(1.0-rho), obj.G            )
    nescale(obj.G, nedivsr(obj.D, np.single(eps), obj.V)) # must be careful later with G
    nescale(obj.D, np.single(    rho)                   )
    newsadd(obj.D, np.single(1.0-rho), obj.G            )
    newtadd(obj.W,                 -1, obj.G            )

    #obj.V  = np.single(rho) * obj.V + np.single(1.0 - rho) * obj.G * obj.G
    #D      = np.sqrt((obj.D + eps) / (obj.V + eps)) * obj.G
    #obj.D  = np.single(rho) * obj.D + np.single(1.0 - rho) * D * D
    #obj.W -= D

def ADAM(obj, t=0, lr=1e-3, l2reg=1e-2, beta1=0.9, beta2=0.999, eps=1e-8):

    obj = regularize(obj, l2reg)
    lrW = lr #schedule(t, lr, l2reg)

    if not hasattr(obj, 'M'): obj.M = np.zeros_like(obj.W)
    if not hasattr(obj, 'V'): obj.V = np.zeros_like(obj.W)

    nescale(obj.M, np.single(    beta1)                                       )
    newtadd(obj.M, np.single(1.0-beta1), obj.G                                )
    nescale(obj.V, np.single(    beta2)                                       )
    newsadd(obj.V, np.single(1.0-beta2), obj.G                                )
    newtadd(obj.W, np.single(   -lrW  ), nesrdiv(obj.M, np.single(eps), obj.V))

    #obj.M  = np.single(beta1) * obj.M + np.single(1.0 - beta1) * obj.G
    #obj.V  = np.single(beta2) * obj.V + np.single(1.0 - beta2) * obj.G * obj.G
    #obj.W -= np.single(lrW) * obj.M / (np.sqrt(obj.V) + np.single(eps))

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf)

def test_floats_from_string(self, level=rlevel):
        # Ticket #640, floats from string
        fsingle = np.single('1.234')
        fdouble = np.double('1.234')
        flongdouble = np.longdouble('1.234')
        assert_almost_equal(fsingle, 1.234)
        assert_almost_equal(fdouble, 1.234)
        assert_almost_equal(flongdouble, 1.234)

def test_compress_small_type(self, level=rlevel):
        # Ticket #789, changeset 5217.
        # compress with out argument segfaulted if cannot cast safely
        import numpy as np
        a = np.array([[1, 2], [3, 4]])
        b = np.zeros((2, 1), dtype=np.single)
        try:
            a.compress([True, False], axis=1, out=b)
            raise AssertionError("compress with an out which cannot be "
                                 "safely casted should not return "
                                 "successfully")
        except TypeError:
            pass

def test_trace_subclass(self):
        # The class would need to overwrite trace to ensure single-element
        # output also has the right subclass.
        class MyArray(np.ndarray):
            pass

        b = np.arange(8).reshape((2, 2, 2)).view(MyArray)
        t = b.trace()
        assert isinstance(t, MyArray)

def test_export_record(self):
        dt = [('a', 'b'),
              ('b', 'h'),
              ('c', 'i'),
              ('d', 'l'),
              ('dx', 'q'),
              ('e', 'B'),
              ('f', 'H'),
              ('g', 'I'),
              ('h', 'L'),
              ('hx', 'Q'),
              ('i', np.single),
              ('j', np.double),
              ('k', np.longdouble),
              ('ix', np.csingle),
              ('jx', np.cdouble),
              ('kx', np.clongdouble),
              ('l', 'S4'),
              ('m', 'U4'),
              ('n', 'V3'),
              ('o', '?'),
              ('p', np.half),
              ]
        x = np.array(
                [(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    asbytes('aaaa'), 'bbbb', asbytes('   '), True, 1.0)],
                dtype=dt)
        y = memoryview(x)
        assert_equal(y.shape, (1,))
        assert_equal(y.ndim, 1)
        assert_equal(y.suboffsets, EMPTY)

        sz = sum([np.dtype(b).itemsize for a, b in dt])
        if np.dtype('l').itemsize == 4:
            assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
        else:
            assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
        # Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides
        if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
            assert_equal(y.strides, (sz,))
        assert_equal(y.itemsize, sz)

def test_singleton(self):
        ftype = finfo(single)
        ftype2 = finfo(single)
        assert_equal(id(ftype), id(ftype2))

def get_real_dtype(dtype):
    return {single: single, double: double,
            csingle: single, cdouble: double}[dtype]

def get_complex_dtype(dtype):
    return {single: csingle, double: cdouble,
            csingle: csingle, cdouble: cdouble}[dtype]

def get_rtol(dtype):
    # Choose a safe rtol
    if dtype in (single, csingle):
        return 1e-5
    else:
        return 1e-11

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            assert_equal(linalg.solve(x, x).dtype, dtype)
        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def test_0_size(self):
        class ArraySubclass(np.ndarray):
            pass
        # Test system of 0x0 matrices
        a = np.arange(8).reshape(2, 2, 2)
        b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass)

        expected = linalg.solve(a, b)[:, 0:0, :]
        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, :])
        assert_array_equal(result, expected)
        assert_(isinstance(result, ArraySubclass))

        # Test errors for non-square and only b's dimension being 0
        assert_raises(linalg.LinAlgError, linalg.solve, a[:, 0:0, 0:1], b)
        assert_raises(ValueError, linalg.solve, a, b[:, 0:0, :])

        # Test broadcasting error
        b = np.arange(6).reshape(1, 3, 2)  # broadcasting error
        assert_raises(ValueError, linalg.solve, a, b)
        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])

        # Test zero "single equations" with 0x0 matrices.
        b = np.arange(2).reshape(1, 2).view(ArraySubclass)
        expected = linalg.solve(a, b)[:, 0:0]
        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0])
        assert_array_equal(result, expected)
        assert_(isinstance(result, ArraySubclass))

        b = np.arange(3).reshape(1, 3)
        assert_raises(ValueError, linalg.solve, a, b)
        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
        assert_raises(ValueError, linalg.solve, a[:, 0:0, 0:0], b)

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            assert_equal(linalg.eigvals(x).dtype, dtype)
            x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
            assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, dtype)
            assert_equal(v.dtype, dtype)

            x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, get_complex_dtype(dtype))
            assert_equal(v.dtype, get_complex_dtype(dtype))

        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            u, s, vh = linalg.svd(x)
            assert_equal(u.dtype, dtype)
            assert_equal(s.dtype, get_real_dtype(dtype))
            assert_equal(vh.dtype, dtype)
            s = linalg.svd(x, compute_uv=False)
            assert_equal(s.dtype, get_real_dtype(dtype))

        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            assert_equal(np.linalg.det(x).dtype, dtype)
            ph, s = np.linalg.slogdet(x)
            assert_equal(s.dtype, get_real_dtype(dtype))
            assert_equal(ph.dtype, dtype)
        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            w, v = np.linalg.eigh(x)
            assert_equal(w.dtype, get_real_dtype(dtype))
            assert_equal(v.dtype, dtype)
        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype

def __init__(self):
        super(EmptyDataSource, self).__init__()
        self._data = np.zeros((2, 2), dtype=np.single)
        self._data[0, 0] = -1.0
        self._data[1, 1] = 1.0
        self._xlines = [0, 1]
        self._ilines = [0, 1]
        self._samples = []  # changed to allow dims() calculate len(samples)

def init_water_and_sediment(self):
        if self.water is None:
            self.water = np.zeros(self.center.shape, dtype=np.single)
        if self.sediment is None:
            self.sediment = np.zeros(self.center.shape, dtype=np.single)
        if self.scour is None:
            self.scour = np.zeros(self.center.shape, dtype=np.single)
        if self.flowrate is None:
            self.flowrate = np.zeros(self.center.shape, dtype=np.single)
        if self.sedimentpct is None:
            self.sedimentpct = np.zeros(self.center.shape, dtype=np.single)
        if self.capacity is None:
            self.capacity = np.zeros(self.center.shape, dtype=np.single)
        if self.avalanced is None:
            self.avalanced = np.zeros(self.center.shape, dtype=np.single)