def translate_types(translation_table):
"""Translate types for rwa files.
"""
# reduce the table
_table = dict(translation_table)
_any_update = True
while _any_update:
_any_update = False
for _former_type, _current_type in list(_table.items()):
try:
_more_current_type = _table[_current_type]
except KeyError:
pass
else:
_table[_former_type] = _more_current_type
_any_update = True
# load and register
for _former_type, _current_type in _table.items():
_module_name = _current_type.split('.')
_type_name = _module_name.pop()
_module_name = '.'.join(_module_name)
_module = importlib.import_module(_module_name)
_type = getattr(_module, _type_name)
try:
_storable = rwa.hdf5.hdf5_service.byStorableType(_current_type)
except:
_storable = rwa.default_storable(_type, storable_type=_current_type)
_storable_handler = _storable.handlers[0]
else:
_storable_handler = copy.copy(_storable.asVersion())
_storable_handler._poke = None # peek only
_storable = rwa.Storable(_type, key=_former_type, handlers=_storable_handler)
rwa.hdf5_storable(_storable, agnostic=True)
space_columns = self.scaler.columns
n_centroids = int(1. / self.avg_probability)
centroids = pd.DataFrame(np.random.rand(n_centroids, len(space_columns)), columns=space_columns)
centroids *= self.upper_bound - self.lower_bound
centroids += self.lower_bound
# make the Voronoi tessellation
Voronoi.tessellate(self, centroids)
#
if not allow_empty_cells:
cells = CellStats(points, self)
#cells.cell_index = cells.cell_index(centroids, **kwargs)
ok = 0 < cells.location_count
self.cell_centers = None
Voronoi.tessellate(self, centroids[ok])
__all__ = ['setup', 'RandomMesh']
import sys
if sys.version_info[0] < 3:
import rwa
import tramway.core.hdf5 as rules
random_mesh_exposes = rules.voronoi_exposes + list(RandomMesh.__slots__)
rwa.hdf5_storable(
rwa.default_storable(RandomMesh, exposes=random_mesh_exposes),
agnostic=True)
__all__.append('random_mesh_exposes')
t0, t1 = ts.min(), ts.max()
if self.start_time is not None:
t0 = self.start_time
duration, shift = self.duration, self.shift
if isinstance(duration, int):
dt = np.median(np.diff(np.unique(ts)))
duration *= dt
shift *= dt
dt /= 10.
else:
dt = 1e-7 # precision down to a microsecond (quantum < microsecond)
nsegments = np.round((t1 - t0 - duration) / shift) + 1.
t1 = t0 + (nsegments - 1.) * shift + duration
t0s = np.arange(t0, t1 - duration + dt, shift)
t1s = t0s + duration
self.time_lattice = np.stack((t0s, t1s), axis=-1)
return TimeLattice.cell_index(self, points, *args, **kwargs)
import sys
if sys.version_info[0] < 3:
import rwa
sliding_window_exposes = hdf5.time_lattice_exposes + list(
SlidingWindow.__slots__) #['duration', 'shift']
rwa.hdf5_storable(rwa.default_storable(SlidingWindow,
exposes=sliding_window_exposes),
agnostic=True)
__all__.append('sliding_window_exposes')
dict(
args=("-c", "--location-count"),
kwargs=dict(
type=int,
default=80,
help="average number of locations per cell",
),
translate=True,
),
),
)),
}
__all__ = ["setup", "HexagonalMesh"]
import sys
if sys.version_info[0] < 3:
import rwa
import tramway.core.hdf5 as rules
hexagonal_mesh_exposes = rules.voronoi_exposes + list(
HexagonalMesh.__slots__)
rwa.hdf5_storable(
rwa.default_storable(HexagonalMesh, exposes=hexagonal_mesh_exposes),
agnostic=True,
)
__all__.append("hexagonal_mesh_exposes")
(
"avg_location_count",
dict(
args=("-c", "--location-count"),
kwargs=dict(
type=int,
default=80,
help="average number of locations per cell",
),
translate=True,
),
),
)),
}
__all__ = ["setup", "KohonenMesh"]
import sys
if sys.version_info[0] < 3:
import rwa
import tramway.core.hdf5 as rules
kohonen_mesh_exposes = rules.voronoi_exposes + list(KohonenMesh.__slots__)
rwa.hdf5_storable(rwa.default_storable(KohonenMesh,
exposes=kohonen_mesh_exposes),
agnostic=True)
__all__.append("kohonen_mesh_exposes")
