python类single()的实例源码

def fromFile(filename):
        if filename == '-':
            filename = sys.stdin
        g=Grid()
        g.center=np.loadtxt(filename,np.single)
        return g

def _sort(self, expfact):
        # keep unique vertices only by creating a set and sort first on x then on y coordinate
        # using rather slow python sort but couldn;t wrap my head around np.lexsort
        verts = sorted(list({ tuple(t) for t in self.center[::] }))
        x = set(c[0] for c in verts)
        y = set(c[1] for c in verts)
        nx = len(x)
        ny = len(y)
        self.minx = min(x)
        self.maxx = max(x)
        self.miny = min(y)
        self.maxy = max(y)
        xscale = (self.maxx-self.minx)/(nx-1)
        yscale = (self.maxy-self.miny)/(ny-1)
        # note: a purely flat plane cannot be scaled 
        if (yscale != 0.0) and (abs(xscale/yscale) - 1.0 > 1e-3):
            raise ValueError("Mesh spacing not square %d x %d  %.4f x %4.f"%(nx,ny,xscale,yscale))
        self.zscale = 1.0
        if abs(yscale) > 1e-6 :
            self.zscale = 1.0/yscale

        # keep just the z-values and null any ofsset
        # we might catch a reshape error that will occur if nx*ny != # of vertices (if we are not dealing with a heightfield but with a mesh with duplicate x,y coords, like an axis aligned cube
        self.center = np.array([c[2] for c in verts],dtype=np.single).reshape(nx,ny)
        self.center = (self.center-np.amin(self.center))*self.zscale
        if self.rainmap is not None:
            rmscale = np.max(self.center)
            self.rainmap = expfact + (1-expfact)*(self.center/rmscale)

def fromBlenderMesh(me, vg, expfact):
        g = Grid()
        g.center = np.asarray(list(tuple(v.co) for v in me.vertices), dtype=np.single )
        g.rainmap = None
        if vg is not None:
            for v in me.vertices:
                vg.add([v.index],0.0,'ADD')
            g.rainmap=np.asarray(list( (v.co[0], v.co[1], vg.weight(v.index)) for v in me.vertices), dtype=np.single )
        g._sort(expfact)
        return g

def _set_spacing(self, spacing):
        """ Set the image spacing.

        Parameters
        ----------
        spacing: uplet
            the image spacing.
        """
        self._spacing = numpy.asarray(spacing, dtype=numpy.single)

def _default_spacing(self):
        """ Return the default image spacing.
        """
        dim = self._get_ndim()
        return numpy.ones(dim, dtype=numpy.single)

def get_scales(min_scale=0.2, max_scale=0.9,num_layers=6):
    """ Following the ssd arxiv paper, regarding the calculation of scales & ratios

    Parameters
    ----------
    min_scale : float
    max_scales: float
    num_layers: int
        number of layers that will have a detection head
    anchor_ratios: list
    first_layer_ratios: list

    return
    ------
    sizes : list
        list of scale sizes per feature layer
    ratios : list
        list of anchor_ratios per feature layer
    """

    # this code follows the original implementation of wei liu
    # for more, look at ssd/score_ssd_pascal.py:310 in the original caffe implementation
    min_ratio = int(min_scale * 100)
    max_ratio = int(max_scale * 100)
    step = int(np.floor((max_ratio - min_ratio) / (num_layers - 2)))
    min_sizes = []
    max_sizes = []
    for ratio in xrange(min_ratio, max_ratio + 1, step):
        min_sizes.append(ratio / 100.)
        max_sizes.append((ratio + step) / 100.)
    min_sizes = [int(100*min_scale / 2.0) / 100.0] + min_sizes
    max_sizes = [min_scale] + max_sizes

    # convert it back to this implementation's notation:
    scales = []
    for layer_idx in range(num_layers):
        scales.append([min_sizes[layer_idx], np.single(np.sqrt(min_sizes[layer_idx] * max_sizes[layer_idx]))])
    return scales

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