def fromFile(filename):
if filename == '-':
filename = sys.stdin
g=Grid()
g.center=np.loadtxt(filename,np.single)
return g
python类single()的实例源码
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