def test_stack_detector_data_missing(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000,
devices=[('*/DET/*', 'image.data')])
# Three variants of missing data:
# 1. Source missing
del data['FXE_DET_LPD1M-1/DET/3CH0:xtdf']
# 2. Key missing
del data['FXE_DET_LPD1M-1/DET/7CH0:xtdf']['image.data']
# 3. Empty array
missing = [
'FXE_DET_LPD1M-1/DET/{}CH0:xtdf'.format(m) for m in (1, 5, 9, 15)
]
for module in missing:
data[module]['image.data'] = np.zeros((0, 1, 256, 256),
dtype=np.uint16)
comb = stack_detector_data(data, 'image.data', fillvalue=22)
assert comb.shape == (128, 1, 16, 256, 256)
assert not (comb[:, :, 0] == 22).any() # Control
assert (comb[:, :, 3] == 22).all() # Source missing
assert (comb[:, :, 7] == 22).all() # Key missing
assert (comb[:, :, 5] == 22).all() # Empty array
# default fillvalue for int is 0
comb = stack_detector_data(data, 'image.data')
assert (comb[:, :, 3] == 0).all()
with pytest.raises(ValueError):
comb = stack_detector_data(data, 'image.data', fillvalue=np.nan)
def _get_modules_file(self, data, src):
"""Override.
In the file, the data is separated into arrays of different
modules. The layout of data for each module is:
- calibrated, (memory cells, x, y)
- raw, (memory cells, 2, x, y)
- calibrated, "image.data", (memory cells, modules, y, x)
- raw, "image.data", (memory cell, 2, modules, y, x)
[:, 0, ...] -> data
[:, 1, ...] -> gain
-> (memory cells, modules, y, x)
"""
modules_data = stack_detector_data(data[src],
src.split(' ')[1],
real_array=False)
dtype = modules_data.dtype
if dtype == _IMAGE_DTYPE:
return modules_data
if dtype == _RAW_IMAGE_DTYPE:
return modules_data[:, 0, ...]
raise AssemblingError(f"Unknown detector data type: {dtype}!")
def test_stack_detector_data(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000,
devices=[('*/DET/*', 'image.data')])
comb = stack_detector_data(data, 'image.data')
assert comb.shape == (128, 1, 16, 256, 256)
def test_stack_detector_data_stackview(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000, devices=[('*/DET/*', 'image.data')])
# Three variants of missing data:
# 1. Source missing
del data['FXE_DET_LPD1M-1/DET/3CH0:xtdf']
# 2. Key missing
del data['FXE_DET_LPD1M-1/DET/7CH0:xtdf']['image.data']
# 3. Empty array
missing = ['FXE_DET_LPD1M-1/DET/{}CH0:xtdf'.format(m) for m in (1, 5, 9, 15)]
for module in missing:
data[module]['image.data'] = np.zeros((0, 1, 256, 256), dtype=np.uint16)
comb = stack_detector_data(data, 'image.data', fillvalue=22, real_array=False)
assert comb.shape == (128, 1, 16, 256, 256)
assert not (comb[:, :, 0] == 22).any() # Control
assert (comb[:, :, 3] == 22).all() # Source missing
assert (comb[:, :, 7] == 22).all() # Key missing
assert (comb[:, :, 5] == 22).all() # Empty array
# Slice across all modules
pulse = comb[0, 0]
assert pulse.shape == (16, 256, 256)
assert not (pulse[0] == 22).any()
assert (pulse[3] == 22).all()
assert (pulse[7] == 22).all()
assert (pulse[5] == 22).all()
pulse_arr = pulse.asarray()
assert pulse_arr.shape == (16, 256, 256)
assert pulse_arr.max() == 22
assert pulse_arr.min() == 0
def agipd_frame(mock_run):
run = RunDirectory(mock_run)
print(run.instrument_sources)
print(run.keys_for_source('SPB_DET_AGIPD1M-1/DET/10CH0:xtdf'))
tid, train_data = run.select('*/DET/*', 'image.data').train_from_index(0)
ary = stack_detector_data(train_data, 'image.data')
return ary[0]
def test_stack_detector_data_jungfrau(mock_jungfrau_run):
run = RunDirectory(mock_jungfrau_run)
_, data = run.select('*JF4M/DET/*', 'data.adc').train_from_index(0)
comb = stack_detector_data(data,
'data.adc',
modules=8,
pattern=r'/DET/JNGFR(\d+)',
starts_at=1)
assert comb.shape == (16, 8, 512, 1024)
def test_stack_detector_data_type_error(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000, devices=[('*/DET/*', 'image.data')])
module = 'FXE_DET_LPD1M-1/DET/3CH0:xtdf'
data[module]['image.data'] = data[module]['image.data'].astype(np.float32)
with pytest.raises(ValueError) as excinfo:
comb = stack_detector_data(data, 'image.data')
assert "dtype('float32')" in str(excinfo.value)
def test_stack_detector_data_wrong_shape(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000, devices=[('*/DET/*', 'image.data')])
misshaped = ['FXE_DET_LPD1M-1/DET/{}CH0:xtdf'.format(m) for m in (0, 15)]
for module in misshaped:
data[module]['image.data'] = np.zeros((128, 1, 512, 128), dtype=np.uint16)
with pytest.raises(ValueError) as excinfo:
comb = stack_detector_data(data, 'image.data')
assert '(128, 1, 512, 128)' in str(excinfo.value)
def test_stack_detector_data_extra_mods(mock_fxe_raw_run):
test_run = RunDirectory(mock_fxe_raw_run)
tid, data = test_run.train_from_id(10000, devices=[('*/DET/*', 'image.data')])
data.setdefault(
'FXE_DET_LPD1M-1/DET/16CH0:xtdf',
{'image.data': np.zeros((128, 1, 256, 256), dtype=np.uint16)},
)
with pytest.raises(IndexError) as excinfo:
comb = stack_detector_data(data, 'image.data')
assert "16" in str(excinfo.value)
def _get_modules_file(self, data, src):
"""Override.
- calibrated, "image.data", (memory cells, modules, y, x)
- raw, "image.data", (memory cell, 1, modules, y, x)
-> (memory cells, modules, y, x)
"""
modules_data = stack_detector_data(
data[src], src.split(' ')[1], real_array=False)
dtype = modules_data.dtype
if dtype == _IMAGE_DTYPE:
return modules_data
if dtype == _RAW_IMAGE_DTYPE:
return modules_data.squeeze(axis=1)
raise AssemblingError(f"Unknown detector data type: {dtype}!")
def get_train_stack(self):
"""Get a 4D array representing detector data in a train
(pulses, modules, slow_scan, fast_scan)
"""
tid = self.sb_train_id.value()
if tid == self._cached_train_stack[0]:
return self._cached_train_stack[1]
self.main_widget.log.info('Reading train #: %s', tid)
_, data = self.rundir.select('*/DET/*',
'image.data').train_from_id(tid)
img = stack_detector_data(data, 'image.data')
# Probaply raw data with gain dimension - take the data dim
if len(img.shape) == 5:
img = img[:, 0] # TODO: confirm if first gain dim is data
arr = np.clip(img, 0, None)
self._cached_train_stack = (tid, arr)
return arr
