def parse(
vmf: VMF, pack: PackList
) -> Tuple[int, Dict[Tuple[str, str, int], VacObject], Dict[str, str], ]:
"""Parse out the cube objects from the map.
The return value is the number of objects, a dict of objects, and the
filenames of the script generated for each group.
The dict is (group, model, skin) -> object.
"""
cube_objects: Dict[Tuple[str, str, int], VacObject] = {}
vac_objects: Dict[str, List[VacObject]] = defaultdict(list)
for i, ent in enumerate(vmf.by_class['comp_vactube_object']):
offset = Vec.from_str(ent['origin']) - Vec.from_str(ent['offset'])
obj = VacObject(
f'obj_{i:x}',
ent['group'],
ent['model'],
ent['cube_model'],
offset,
srctools.conv_int(ent['weight']),
srctools.conv_int(ent['tv_skin']),
srctools.conv_int(ent['cube_skin']),
srctools.conv_int(ent['skin']),
)
vac_objects[obj.group].append(obj)
# Convert the ent into a precache ent, stripping the other keyvalues.
ent.keys = {'model': ent['model']}
make_precache_prop(ent)
if obj.model_drop:
cube_objects[obj.group,
obj.model_drop.replace('\\', '/'),
obj.skin_drop, ] = obj
# Generate and pack the vactube object scripts.
# Each group is the same, so it can be shared among them all.
codes = {}
for group in sorted(vac_objects):
code = []
for i, obj in enumerate(vac_objects[group]):
if obj.model_drop:
model_code = f'"{obj.model_drop}"'
else:
model_code = 'null'
code.append(
f'{obj.id} <- obj("{obj.model_vac}", {obj.skin_vac}, '
f'{model_code}, {obj.weight}, "{obj.offset}", {obj.skin_tv});')
codes[group] = pack.inject_vscript('\n'.join(code))
return len(vac_objects), cube_objects, codes
def run_transformations(
vmf: VMF,
filesys: FileSystem,
pack: PackList,
bsp: BSP,
game: Game,
studiomdl_loc: Path=None,
) -> None:
"""Run all transformations."""
context = Context(filesys, vmf, pack, bsp, game, studiomdl_loc=studiomdl_loc)
for func_name, func in sorted(
TRANSFORMS.items(),
key=lambda tup: tup[1].priority,
):
LOGGER.info('Running "{}"...', func_name)
func(context)
if context._ent_code:
LOGGER.info('Injecting VScript code...')
for ent, code in context._ent_code.items():
init_scripts = ent['vscripts'].split()
init_scripts.append(pack.inject_vscript(code.replace('`', '"')))
ent['vscripts'] = ' '.join(init_scripts)
if context._io_remaps:
LOGGER.info('Remapping outputs...')
for ent in vmf.entities:
todo = ent.outputs[:]
# Recursively convert only up to 500 times.
# Arbitrary limit, should be sufficient.
for _ in range(500):
if not todo:
break
deferred = []
for out in todo:
try:
remaps, should_remove = context._io_remaps[
out.target.casefold(),
out.input.casefold(),
]
except KeyError:
continue
if should_remove:
ent.outputs.remove(out)
for rep_out in remaps:
new_out = Output(
out.output,
rep_out.target,
rep_out.input,
rep_out.params or out.params,
out.delay + rep_out.delay,
only_once=rep_out.only_once and out.only_once,
)
ent.outputs.append(new_out)
deferred.append(new_out)
todo = deferred
else:
LOGGER.error(
'Entity "{}" ({}) has infinite loop when expanding '
' compiler outputs to real ones! Final output list: \n{}',
ent['targetname'], ent['classname'],
'\n'.join(['* {}\n'.format(out) for out in ent.outputs])
)
def parse(
vmf: VMF, pack: PackList
) -> Tuple[int, Dict[Tuple[str, str, int], VacObject], Dict[str, str], ]:
"""Parse out the cube objects from the map.
The return value is the number of objects, a dict of objects, and the
filenames of the script generated for each group.
The dict is (group, model, skin) -> object.
"""
cube_objects: Dict[Tuple[str, str, int], VacObject] = {}
vac_objects: Dict[str, List[VacObject]] = defaultdict(list)
for i, ent in enumerate(vmf.by_class['comp_vactube_object']):
offset = Vec.from_str(ent['origin']) - Vec.from_str(ent['offset'])
obj = VacObject(
f'obj_{i:x}',
ent['group'],
ent['model'],
ent['cube_model'],
offset,
srctools.conv_int(ent['weight']),
srctools.conv_int(ent['tv_skin']),
srctools.conv_int(ent['cube_skin']),
srctools.conv_int(ent['skin']),
)
vac_objects[obj.group].append(obj)
# Convert the ent into a precache ent, stripping the other keyvalues.
mdl_name = ent['model']
ent.keys = {'model': mdl_name}
make_precache_prop(ent)
pack.pack_file(mdl_name, FileType.MODEL, skinset={obj.skin_vac})
if obj.model_drop:
cube_objects[obj.group,
obj.model_drop.replace('\\', '/'),
obj.skin_drop, ] = obj
# Generate and pack the vactube object scripts.
# Each group is the same, so it can be shared among them all.
codes = {}
for group, objects in sorted(vac_objects.items(), key=lambda t: t[0]):
# First, see if there's a common multiple among the weights, allowing
# us to simplify.
multiple = objects[0].weight
for obj in objects[1:]:
multiple = math.gcd(multiple, obj.weight)
if multiple > 1:
LOGGER.info('Group "{}" has common factor of {}, simplifying.',
group, multiple)
code = []
for i, obj in enumerate(objects):
obj.weight /= multiple
if obj.model_drop:
model_code = f'"{obj.model_drop}"'
else:
model_code = 'null'
code.append(
f'{obj.id} <- obj("{obj.model_vac}", {obj.skin_vac}, '
f'{model_code}, {obj.weight}, "{obj.offset}", {obj.skin_tv});')
codes[group] = pack.inject_vscript('\n'.join(code))
return len(vac_objects), cube_objects, codes