def __init__(self, bounds=None):
from mitsuba.core import ScalarBoundingBox2f
if bounds is None:
bounds = ScalarBoundingBox2f(-1, 1)
self._bounds = bounds
def test_sample_position(variant_packet_rgb, filter_type, wrap_mode):
from mitsuba.core.xml import load_string
from mitsuba.python.chi2 import ChiSquareTest, PlanarDomain
from mitsuba.core import ScalarBoundingBox2f
bitmap = load_string("""
<texture type="bitmap" version="2.0.0">
<string name="filename" value="resources/data/common/textures/carrot.png"/>
<string name="filter_type" value="%s"/>
<string name="wrap_mode" value="%s"/>
</texture>""" % (filter_type, wrap_mode)).expand()[0]
chi2 = ChiSquareTest(domain=PlanarDomain(
ScalarBoundingBox2f([0, 0], [1, 1])),
sample_func=lambda s: bitmap.sample_position(s)[0],
pdf_func=lambda p: bitmap.pdf_position(p),
sample_dim=2,
res=201,
ires=8)
assert chi2.run()
def test04_chi2(variant_packet_rgb, warp, attempt):
# Chi^2 test to make sure that the warping schemes are producing
# the right distribution. Test for all supported variants of
# N-dimensional warps (N = 0..3)
from mitsuba.core import ScalarBoundingBox2f
from mitsuba.python.chi2 import ChiSquareTest, PlanarDomain
cls = getattr(mitsuba.core, warp)
ndim = int(warp[-1]) + 2
np.random.seed(all_warps.index(warp) * 10 + attempt)
shape = np.random.randint(2, 8, ndim)
param_res = [sorted(np.random.rand(s)) for s in shape[:-2]]
if attempt == 9:
values = np.ones(shape)
else:
values = np.random.rand(*shape) * 10
instance = cls(values, param_res)
for j in range(10 if ndim > 2 else 1):
param = [
ek.lerp(param_res[i][0], param_res[i][-1], np.random.rand())
for i in range(0, ndim - 2)
]
chi2 = ChiSquareTest(
domain=PlanarDomain(ScalarBoundingBox2f(0, 1)),
sample_func=lambda p: instance.sample(p, param=param)[0],
pdf_func=lambda p: instance.eval(p, param=param),
sample_dim=2,
res=31,
sample_count=100000)
assert chi2.run(test_count=11 * len(all_warps))
def bounds(self):
from mitsuba.core import ScalarBoundingBox2f
return ScalarBoundingBox2f([-ek.pi, -1], [ek.pi, 1])
def __init__(self, bounds=[-1.0, 1.0]):
from mitsuba.core import ScalarBoundingBox2f
self._bounds = ScalarBoundingBox2f(min=(bounds[0], -0.5),
max=(bounds[1], 0.5))