def __init__(self, rt60_opt, absc_opt, room_dim):
"""
rt60_opt: "" or "a,b", higher priority than absc_opt
absc_opt: tuple like (a,b)
room_dim: str like "a,b;c,d;e,d"
"""
self.rt60_opt = rt60_opt
if not rt60_opt:
self.absc = UniformSampler(absc_opt)
else:
rt60_r = str2tuple(rt60_opt)
self.rt60 = UniformSampler(rt60_r)
dim_range = [str2tuple(t) for t in room_dim.split(";")]
if len(dim_range) != 3:
raise RuntimeError(f"Wrong format with --room-dim={room_dim}")
self.dim_sampler = [UniformSampler(c) for c in dim_range]
class RoomGenerator(object):
"""
Room generator
"""
def __init__(self, rt60_opt, absc_opt, room_dim):
"""
rt60_opt: "" or "a,b", higher priority than absc_opt
absc_opt: tuple like (a,b)
room_dim: str like "a,b;c,d;e,d"
"""
self.rt60_opt = rt60_opt
if not rt60_opt:
self.absc = UniformSampler(absc_opt)
else:
rt60_r = str2tuple(rt60_opt)
self.rt60 = UniformSampler(rt60_r)
dim_range = [str2tuple(t) for t in room_dim.split(";")]
if len(dim_range) != 3:
raise RuntimeError(
"Wrong format with --room-dim={}".format(room_dim))
self.dim_sampler = [UniformSampler(c) for c in dim_range]
def generate(self, v=340):
# (l, w, h)
(l, w, h) = (s.sample() for s in self.dim_sampler)
if self.rt60_opt:
# no reflection is ok
if self.rt60.max == 0:
return Room(l, w, h, rt60=0)
else:
# check rt60 here
S, V = l * w * h, (l * w + l * h + w * h) * 2
# sabine formula
rt60_min = 24 * V * np.log(10) / (v * S)
if rt60_min >= self.rt60.max:
return None
# raise RuntimeError(
# "Configuration error in rt60: {}, minimum {:.2f} "
# "required with ({:.2f}x{:.2f}x{:.2f})".format(
# self.rt60_opt, rt60_min, l, w, h))
else:
rt60 = random.uniform(rt60_min, self.rt60.max)
return Room(l, w, h, rt60=rt60)
else:
absc = self.absc.sample()
return Room(l, w, h, refl=np.sqrt(1 - absc))