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import numpy as np
import h5py
class DetectorModule:
# Overridden in subclasses:
image_dims = ()
detector_data_size = 0
# Set by write_file:
ntrains = 100
firsttrain = 10000
chunksize = 32
output_parts = [
'detector',
'header',
'image',
'trailer',
]
def __init__(self, device_id, frames_per_train=64, raw=True,
channel_name='xtdf', legacy_name=None):
self.device_id = device_id
self._frames_per_train = frames_per_train
if not raw:
# Raw data has an extra dimension, used in AGIPD to separate data
# and gain. This dimension is removed by the calibration process.
self.image_dims = self.image_dims[1:]
self.raw = raw
self.channel_name = channel_name
self.legacy_name = legacy_name
def write_control(self, f):
"""Write the CONTROL and RUN data, and the relevant parts of INDEX"""
pass
@property
def image_keys(self):
if self.raw:
return [
('data', 'u2', self.image_dims),
('length', 'u4', (1,)),
('status', 'u2', (1,)),
]
else:
return [
('data', 'f4', self.image_dims),
('mask', 'u4', self.image_dims),
('gain', 'u1', self.image_dims),
('length', 'u4', (1,)),
('status', 'u2', (1,)),
]
@property
def other_keys(self):
return [
('detector/data', 'u1', (self.detector_data_size,)),
('header/dataId', 'u8', ()),
('header/linkId', 'u8', ()),
('header/magicNumberBegin', 'i1', (8,)),
('header/majorTrainFormatVersion', 'u4', ()),
('header/minorTrainFormatVersion', 'u4', ()),
('header/pulseCount', 'u8', ()),
('header/reserved', 'u1', (16,)),
('trailer/checksum', 'i1', (16,)),
('trailer/magicNumberEnd', 'i1', (8,)),
('trailer/status', 'u8', ()),
]
@property
def frames_per_train(self):
if np.ndim(self._frames_per_train) == 0:
return np.full(self.ntrains, self._frames_per_train, np.uint64)
return self._frames_per_train
def write_instrument(self, f):
"""Write the INSTRUMENT data, and the relevant parts of INDEX"""
trainids = np.arange(self.firsttrain, self.firsttrain + self.ntrains)
ntrains_pad = self.ntrains
if ntrains_pad % self.chunksize:
ntrains_pad += + self.chunksize - (ntrains_pad % self.chunksize)
inst_source = f'{self.device_id}:{self.channel_name}'
# INDEX
for part in self.output_parts:
i_first = f.create_dataset(f'INDEX/{inst_source}/{part}/first',
(self.ntrains,), 'u8', maxshape=(None,))
i_count = f.create_dataset(f'INDEX/{inst_source}/{part}/count',
(self.ntrains,), 'u8', maxshape=(None,))
if part == 'image':
# First first is always 0
i_first[1:] = np.cumsum(self.frames_per_train)[:-1]
i_count[:] = self.frames_per_train
else:
i_first[:] = np.arange(self.ntrains)
i_count[:] = 1
# INSTRUMENT (image)
nframes = self.frames_per_train.sum()
tid_index = np.repeat(trainids, self.frames_per_train.astype(np.intp))
pid_index = np.concatenate([
np.arange(0, n, dtype='u8') for n in self.frames_per_train
])
if self.raw:
# Raw data have an extra dimension (length 1) and an unlimited max
# for the first dimension.
ds = f.create_dataset(f'INSTRUMENT/{inst_source}/image/trainId',
(nframes, 1), 'u8', maxshape=(None, 1))
ds[:, 0] = tid_index
pid = f.create_dataset(f'INSTRUMENT/{inst_source}/image/pulseId',
(nframes, 1), 'u8', maxshape=(None, 1))
pid[:, 0] = pid_index
cid = f.create_dataset(f'INSTRUMENT/{inst_source}/image/cellId',
(nframes, 1), 'u2', maxshape=(None, 1))
cid[:, 0] = pid_index # Cell IDs mirror pulse IDs for now
else:
# Corrected data drops the extra dimension, and maxshape==shape.
f.create_dataset(
f'INSTRUMENT/{inst_source}/image/trainId',
(nframes,), 'u8', chunks=True, data=tid_index
)
f.create_dataset(
f'INSTRUMENT/{inst_source}/image/pulseId',
(nframes,), 'u8', chunks=True, data=pid_index
)
f.create_dataset( # Cell IDs mirror pulse IDs for now
f'INSTRUMENT/{inst_source}/image/cellId',
(nframes,), 'u2', chunks=True, data=pid_index
)
max_len = None if self.raw else nframes
for (key, datatype, dims) in self.image_keys:
f.create_dataset(f'INSTRUMENT/{inst_source}/image/{key}',
(nframes,) + dims, datatype, maxshape=((max_len,) + dims))
# INSTRUMENT (other parts)
for part in ['detector', 'header', 'trailer']:
ds = f.create_dataset(f'INSTRUMENT/{inst_source}/{part}/trainId',
(ntrains_pad,), 'u8', maxshape=(None,))
ds[:self.ntrains] = trainids
for (key, datatype, dims) in self.other_keys:
f.create_dataset(f'INSTRUMENT/{inst_source}/{key}',
(ntrains_pad,) + dims, datatype, maxshape=((None,) + dims))
if self.legacy_name is not None:
# The legacy source name for corrected data is the same as for
# raw data, which for these detectors always has the xtdf channel.
f[f'INDEX/{self.legacy_name}:xtdf'] = h5py.SoftLink(
f'/INDEX/{inst_source}')
f[f'INSTRUMENT/{self.legacy_name}:xtdf'] = h5py.SoftLink(
f'/INSTRUMENT/{inst_source}')
def datasource_ids(self):
for part in self.output_parts:
yield f'INSTRUMENT/{self.device_id}:{self.channel_name}/{part}'
if self.legacy_name is not None:
for part in self.output_parts:
yield f'INSTRUMENT/{self.legacy_name}:xtdf/{part}'
class AGIPDModule(DetectorModule):
image_dims = (2, 512, 128)
detector_data_size = 5408
class LPDModule(DetectorModule):
image_dims = (1, 256, 256)
detector_data_size = 416
class DSSCModule(DetectorModule):
image_dims = (1, 128, 512)
detector_data_size = 416
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