def ui_imagearray(header, index=0):
"Return image array from the header object and event index."
e = blank_events(header)[index]
fill_event(e)
nm = [k for k in e.data if k.endswith('image_lightfield')][0]
rv = e.data[nm][0]
return rv
def ui_imagearray(header, index=0):
"Return image array from the header object and event index."
e = blank_events(header)[index]
fill_event(e)
nm = [k for k in e.data if k.endswith('image_lightfield')][0]
rv = e.data[nm][0]
return rv
def export(headers, filename):
"""
Parameters
----------
headers : a Header or a list of Headers
objects retruned by the Data Broker
filename : string
path to a new or existing HDF5 file
"""
with h5py.File(filename) as f:
for header in headers:
assert isinstance(header, MutableMapping)
header = copy.deepcopy(header)
try:
descriptors = header.pop('event_descriptors')
except KeyError:
warnings.warn(
"Header with uid {header.uid} contains no "
"data.".format(header), UserWarning)
continue
top_group_name = repr(header).replace(' ', '_')[1:-1]
group = f.create_group(top_group_name)
_safe_attrs_assignment(group, header)
for i, descriptor in enumerate(descriptors):
desc_group = group.create_group('Event_Stream_{0}'.format(i))
data_keys = descriptor.pop('data_keys')
_safe_attrs_assignment(desc_group, descriptor)
events = list(find_events(descriptor=descriptor))
event_times = [e['time'] for e in events]
desc_group.create_dataset('event_times', event_times)
data_group = desc_group.create_group('data')
ts_group = desc_group.create_group('timestamps')
[fill_event(e) for e in events]
for key, value in data_keys.items():
print('data key = %s' % key)
timestamps = [e['timestamps'][key] for e in events]
ts_group.create_dataset(key, data=timestamps)
data = [e['data'][key] for e in events]
dataset = data_group.create_dataset(key, data=data)
def export(headers, filename):
"""
Parameters
----------
headers : a Header or a list of Headers
objects retruned by the Data Broker
filename : string
path to a new or existing HDF5 file
"""
with h5py.File(filename) as f:
for header in headers:
assert isinstance(header, MutableMapping)
header = copy.deepcopy(header)
try:
descriptors = header.pop('event_descriptors')
except KeyError:
warnings.warn("Header with uid {header.uid} contains no "
"data.".format(header), UserWarning)
continue
top_group_name = repr(header).replace(' ', '_')[1:-1]
group = f.create_group(top_group_name)
_safe_attrs_assignment(group, header)
for i, descriptor in enumerate(descriptors):
desc_group = group.create_group('Event_Stream_{0}'.format(i))
data_keys = descriptor.pop('data_keys')
_safe_attrs_assignment(desc_group, descriptor)
events = list(find_events(descriptor=descriptor))
event_times = [e['time'] for e in events]
desc_group.create_dataset('event_times', event_times)
data_group = desc_group.create_group('data')
ts_group = desc_group.create_group('timestamps')
[fill_event(e) for e in events]
for key, value in data_keys.items():
print('data key = %s' % key)
timestamps = [e['timestamps'][key] for e in events]
ts_group.create_dataset(key, data=timestamps)
data = [e['data'][key] for e in events]
dataset = data_group.create_dataset(key, data=data)
def run(run_start=None, sleep=0):
# Make the data
rs = np.random.RandomState(5)
# set up the data keys entry
data_keys1 = {'linear_motor': dict(source='PV:ES:sam_x', dtype='number'),
'img': dict(source='CCD', shape=(5, 5), dtype='array',
external='FILESTORE:'),
'total_img_sum': dict(source='CCD:sum', dtype='number'),
'img_x_max': dict(source='CCD:xmax', dtype='number'),
'img_y_max': dict(source='CCD:ymax', dtype='number'),
'img_sum_x': dict(source='CCD:xsum', dtype='array',
shape=(5,), external='FILESTORE:'),
'img_sum_y': dict(source='CCD:ysum', dtype='array',
shape=(5,), external='FILESTORE:')
}
data_keys2 = {'Tsam': dict(source='PV:ES:Tsam', dtype='number')}
# save the first event descriptor
e_desc1 = insert_event_descriptor(
run_start=run_start, data_keys=data_keys1, time=0.,
uid=str(uuid.uuid4()))
e_desc2 = insert_event_descriptor(
run_start=run_start, data_keys=data_keys2, time=0.,
uid=str(uuid.uuid4()))
# number of motor positions to fake
num1 = 20
# number of temperatures to record per motor position
num2 = 10
events = []
for idx1, i in enumerate(range(num1)):
img = next(frame_generator)
img_sum = float(img.sum())
img_sum_x = img.sum(axis=0)
img_sum_y = img.sum(axis=1)
img_x_max = float(img_sum_x.argmax())
img_y_max = float(img_sum_y.argmax())
fsid_img = save_ndarray(img)
fsid_x = save_ndarray(img_sum_x)
fsid_y = save_ndarray(img_sum_y)
# Put in actual ndarray data, as broker would do.
data1 = {'linear_motor': (i, noisy(i)),
'total_img_sum': (img_sum, noisy(i)),
'img': (fsid_img, noisy(i)),
'img_sum_x': (fsid_x, noisy(i)),
'img_sum_y': (fsid_y, noisy(i)),
'img_x_max': (img_x_max, noisy(i)),
'img_y_max': (img_y_max, noisy(i))
}
event = insert_event(event_descriptor=e_desc1, seq_num=idx1,
time=noisy(i), data=data1, uid=str(uuid.uuid4()))
fill_event(event)
events.append(event)
for idx2, i2 in enumerate(range(num2)):
time = noisy(i/num2)
data2 = {'Tsam': (idx1 + np.random.randn()/100, time)}
event = insert_event(event_descriptor=e_desc2, seq_num=idx2+idx1,
time=time, data=data2, uid=str(uuid.uuid4()))
events.append(event)
ttime.sleep(sleep)
return events
def events_generator(header):
"Return a generator of Events. Large (nonscalar) data is lazy-loaded."
for e in blank_events(header):
fill_event(e)
yield e
def events_generator(header):
"Return a generator of Events. Large (nonscalar) data is lazy-loaded."
for e in blank_events(header):
fill_event(e)
yield e
def fill_all_events(*args, **kwargs):
events = func(*args, **kwargs)
for event in events:
fill_event(event)
return events