def __init__(self, h5file, mpmt_positions_file, is_distributed, transforms=None, collapse_arrays=False):
"""
Args:
h5_path ... path to h5 dataset file
is_distributed ... whether running in multiprocessing mode
transforms ... transforms to apply
collapse_arrays ... whether to collapse arrays in return
"""
super().__init__(h5file, is_distributed)
self.mpmt_positions = np.load(mpmt_positions_file)['mpmt_image_positions']
self.data_size = np.max(self.mpmt_positions, axis=0) + 1
self.barrel_rows = [row for row in range(self.data_size[0]) if
np.count_nonzero(self.mpmt_positions[:,0] == row) == self.data_size[1]]
n_channels = pmts_per_mpmt
self.data_size = np.insert(self.data_size, 0, n_channels)
self.collapse_arrays = collapse_arrays
self.transforms = du.get_transformations(transformations, transforms)
def __init__(self,
h5file,
mpmt_positions_file,
is_distributed,
transforms=None,
collapse_arrays=False,
pad=False):
"""
Args:
h5_path ... path to h5 dataset file
is_distributed ... whether running in multiprocessing mode
transforms ... transforms to apply
collapse_arrays ... whether to collapse arrays in return
"""
super().__init__(h5file, is_distributed)
self.mpmt_positions = np.load(
mpmt_positions_file)['mpmt_image_positions']
self.data_size = np.max(self.mpmt_positions, axis=0) + 1
self.barrel_rows = [
row for row in range(self.data_size[0]) if np.count_nonzero(
self.mpmt_positions[:, 0] == row) == self.data_size[1]
]
n_channels = pmts_per_mpmt
self.data_size = np.insert(self.data_size, 0, n_channels)
self.collapse_arrays = collapse_arrays
self.transforms = du.get_transformations(self, transforms)
self.pad = pad
################
# self.transforms = transforms
# self.transforms_funcs = {
# 'horizontal_flip': self.horizontal_flip,
# 'vertical_flip': self.vertical_flip,
# 'rotation180': self.rotation180
# }
self.horizontal_flip_mpmt_map = [
0, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 12, 17, 16, 15, 14, 13, 18
]
self.vertical_flip_mpmt_map = [
6, 5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 15, 14, 13, 12, 17, 16, 18
]
def __init__(self,
h5file,
geometry_file,
is_distributed,
use_orientations=False,
transforms=None,
max_points=None):
super().__init__(h5file, is_distributed)
geo_file = np.load(geometry_file, 'r')
geo_positions = torch.from_numpy(geo_file["position"]).float()
geo_orientations = torch.from_numpy(geo_file["orientation"]).float()
self.mpmt_positions = geo_positions[18::19, :].T
self.mpmt_orientations = geo_orientations[18::19, :].T
mpmt_y = np.abs(self.mpmt_positions[1, :])
self.barrel_mpmts = np.where(mpmt_y < mpmt_y.max() - 10)[0].astype(
np.int16)
self.use_orientations = use_orientations
self.max_points = max_points
self.transforms = du.get_transformations(transformations, transforms)
def __init__(self,
h5file,
geometry_file,
is_distributed,
use_times=True,
use_orientations=False,
n_points=4000,
transforms=None):
super().__init__(h5file, is_distributed)
geo_file = np.load(geometry_file, 'r')
self.geo_positions = geo_file["position"].astype(np.float32)
self.geo_orientations = geo_file["orientation"].astype(np.float32)
self.use_orientations = use_orientations
self.use_times = use_times
self.n_points = n_points
self.transforms = du.get_transformations(transformations, transforms)
self.channels = 4
if use_orientations:
self.channels += 3
if use_times:
self.channels += 1