def run(run_start=None, sleep=0):
if sleep != 0:
raise NotImplementedError("A sleep time is not implemented for this "
"example.")
# Make the data
ramp = common.stepped_ramp(start, stop, step, points_per_step)
deadbanded_ramp = common.apply_deadband(ramp, deadband_size)
rs = np.random.RandomState(5)
point_det_data = rs.randn(num_exposures)
# Create Event Descriptors
data_keys1 = {'point_det': dict(source='PV:ES:PointDet', dtype='number')}
data_keys2 = {
'Tsam': dict(source='PV:ES:Tsam', dtype='number'),
'Troom': dict(source='PV:ES:Troom', dtype='number')
}
ev_desc1_uid = insert_descriptor(run_start=run_start,
data_keys=data_keys1,
time=common.get_time(),
uid=str(uuid.uuid4()))
ev_desc2_uid = insert_descriptor(run_start=run_start,
data_keys=data_keys2,
time=common.get_time(),
uid=str(uuid.uuid4()))
print('event descriptor 1 uid = {0!s}'.format(ev_desc1_uid))
print('event descriptor 2 uid = {0!s}'.format(ev_desc2_uid))
# Create Events.
events = []
# Point Detector Events
base_time = common.get_time()
for i in range(num_exposures):
time = float(i + 0.5 * rs.randn()) + base_time
data = {'point_det': (point_det_data[i], time)}
data = {'point_det': point_det_data[i]}
timestamps = {'point_det': time}
event_uid = insert_event(descriptor=ev_desc1_uid,
seq_num=i,
time=time,
data=data,
uid=str(uuid.uuid4()),
timestamps=timestamps)
event, = find_events(uid=event_uid)
events.append(event)
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time) + base_time
data = {'Tsam': temp, 'Troom': temp + 10}
timestamps = {'Tsam': time, 'Troom': time}
event_uid = insert_event(descriptor=ev_desc2_uid,
time=time,
data=data,
seq_num=i,
uid=str(uuid.uuid4()),
timestamps=timestamps)
event, = find_events(uid=event_uid)
events.append(event)
return events
def run(run_start_uid=None, sleep=0):
if sleep != 0:
raise NotImplementedError("A sleep time is not implemented for this "
"example.")
# Make the data
ramp = common.stepped_ramp(start, stop, step, points_per_step)
deadbanded_ramp = common.apply_deadband(ramp, deadband_size)
rs = np.random.RandomState(5)
point_det_data = rs.randn(num_exposures) + np.arange(num_exposures)
# Create Event Descriptors
data_keys1 = {'point_det': dict(source='PV:ES:PointDet', dtype='number')}
data_keys2 = {'Tsam': dict(source='PV:ES:Tsam', dtype='number')}
ev_desc1_uid = insert_descriptor(run_start=run_start_uid,
data_keys=data_keys1,
time=common.get_time(),
uid=str(uuid.uuid4()),
name='primary')
ev_desc2_uid = insert_descriptor(run_start=run_start_uid,
data_keys=data_keys2,
time=common.get_time(),
uid=str(uuid.uuid4()),
name='baseline')
# Create Events.
events = []
# Point Detector Events
base_time = common.get_time()
for i in range(num_exposures):
time = float(2 * i + 0.5 * rs.randn()) + base_time
data = {'point_det': point_det_data[i]}
timestamps = {'point_det': time}
event_dict = dict(descriptor=ev_desc1_uid, seq_num=i,
time=time, data=data, timestamps=timestamps,
uid=str(uuid.uuid4()))
event_uid = insert_event(**event_dict)
# grab the actual event from metadatastore
event, = find_events(uid=event_uid)
events.append(event)
assert event['data'] == event_dict['data']
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time) + base_time
data = {'Tsam': temp}
timestamps = {'Tsam': time}
event_dict = dict(descriptor=ev_desc2_uid, time=time,
data=data, timestamps=timestamps, seq_num=i,
uid=str(uuid.uuid4()))
event_uid = insert_event(**event_dict)
event, = find_events(uid=event_uid)
events.append(event)
assert event['data'] == event_dict['data']
return events
def run(run_start=None, sleep=0):
if sleep != 0:
raise NotImplementedError("A sleep time is not implemented for this "
"example.")
# Make the data
ramp = common.stepped_ramp(start, stop, step, points_per_step)
deadbanded_ramp = common.apply_deadband(ramp, deadband_size)
rs = np.random.RandomState(5)
point_det_data = rs.randn(num_exposures)
# Create Event Descriptors
data_keys1 = {'point_det': dict(source='PV:ES:PointDet',
dtype='number')}
data_keys2 = {'Tsam': dict(source='PV:ES:Tsam', dtype='number'),
'Troom': dict(source='PV:ES:Troom', dtype='number')}
ev_desc1_uid = insert_descriptor(run_start=run_start,
data_keys=data_keys1, time=common.get_time(),
uid=str(uuid.uuid4()))
ev_desc2_uid = insert_descriptor(run_start=run_start,
data_keys=data_keys2, time=common.get_time(),
uid=str(uuid.uuid4()))
print('event descriptor 1 uid = {0!s}'.format(ev_desc1_uid))
print('event descriptor 2 uid = {0!s}'.format(ev_desc2_uid))
# Create Events.
events = []
# Point Detector Events
base_time = common.get_time()
for i in range(num_exposures):
time = float(i + 0.5 * rs.randn()) + base_time
data = {'point_det': (point_det_data[i], time)}
data = {'point_det': point_det_data[i]}
timestamps = {'point_det': time}
event_uid = insert_event(descriptor=ev_desc1_uid, seq_num=i, time=time,
data=data, uid=str(uuid.uuid4()),
timestamps=timestamps)
event, = find_events(uid=event_uid)
events.append(event)
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time) + base_time
data = {'Tsam': temp,
'Troom': temp + 10}
timestamps = {'Tsam': time,
'Troom': time}
event_uid = insert_event(descriptor=ev_desc2_uid, time=time,
data=data, seq_num=i, uid=str(uuid.uuid4()),
timestamps=timestamps)
event, = find_events(uid=event_uid)
events.append(event)
return events
def test_data_key():
rs1_uid = insert_run_start(time=100.0, scan_id=1, owner="nedbrainard", beamline_id="example", uid=str(uuid.uuid4()))
rs2_uid = insert_run_start(time=200.0, scan_id=2, owner="nedbrainard", beamline_id="example", uid=str(uuid.uuid4()))
rs1, = find_run_starts(uid=rs1_uid)
rs2, = find_run_starts(uid=rs2_uid)
data_keys = {"fork": {"source": "_", "dtype": "number"}, "spoon": {"source": "_", "dtype": "number"}}
insert_descriptor(run_start=rs1_uid, data_keys=data_keys, time=100.0, uid=str(uuid.uuid4()))
insert_descriptor(run_start=rs2_uid, data_keys=data_keys, time=200.0, uid=str(uuid.uuid4()))
result1 = db(data_key="fork")
result2 = db(data_key="fork", start_time=150)
assert len(result1) == 2
assert len(result2) == 1
actual = result2[0]["start"]["uid"]
assert actual == str(rs2.uid)
def run(run_start=None, sleep=0):
if sleep != 0:
raise NotImplementedError("A sleep time is not implemented for this "
"example.")
# Make the data
ramp = common.stepped_ramp(start, stop, step, points_per_step)
deadbanded_ramp = common.apply_deadband(ramp, deadband_size)
# Create Event Descriptors
data_keys = {'Tsam': dict(source='PV:ES:Tsam', dtype='number'),
'point_det': dict(source='PV:ES:point_det', dtype='number')}
ev_desc = insert_descriptor(run_start=run_start,
data_keys=data_keys, time=0.,
uid=str(uuid.uuid4()))
# Create Events.
events = []
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time)
point_det = np.random.randn()
data = {'Tsam': temp, 'point_det': point_det}
timestamps = {'Tsam': time, 'point_det': time}
event_uid = insert_event(descriptor=ev_desc, time=time, data=data,
seq_num=i, timestamps=timestamps,
uid=str(uuid.uuid4()))
event, = find_events(uid=event_uid)
events.append(event)
return events
def _insert_descriptor(name, doc):
"""Rearrange the dict for unpacking it into the MDS API."""
# Move dynamic keys into 'custom' for MDS API.
# We should change this in MDS to save the time of copying here:
doc = copy.deepcopy(doc)
for key in list(doc):
if key not in known_descriptor_keys:
try:
doc['custom']
except KeyError:
doc['custom'] = {}
doc['custom'][key] = doc.pop(key)
return mds.insert_descriptor(**doc)
def test_data_key():
rs1_uid = insert_run_start(time=100., scan_id=1,
owner='nedbrainard', beamline_id='example',
uid=str(uuid.uuid4()))
rs2_uid = insert_run_start(time=200., scan_id=2,
owner='nedbrainard', beamline_id='example',
uid=str(uuid.uuid4()))
rs1, = find_run_starts(uid=rs1_uid)
rs2, = find_run_starts(uid=rs2_uid)
data_keys = {'fork': {'source': '_', 'dtype': 'number'},
'spoon': {'source': '_', 'dtype': 'number'}}
insert_descriptor(run_start=rs1_uid, data_keys=data_keys,
time=100.,
uid=str(uuid.uuid4()))
insert_descriptor(run_start=rs2_uid, data_keys=data_keys, time=200.,
uid=str(uuid.uuid4()))
result1 = db(data_key='fork')
result2 = db(data_key='fork', start_time=150)
assert len(result1) == 2
assert len(result2) == 1
actual = result2[0]['start']['uid']
assert actual == str(rs2.uid)
def hdf_data_io():
"""
Save data to db and run test when data is retrieved.
"""
run_start_uid = insert_run_start(time=0., scan_id=1, beamline_id='csx',
uid=str(uuid.uuid4()))
# data keys entry
data_keys = {'x_pos': dict(source='MCA:pos_x', dtype='number'),
'y_pos': dict(source='MCA:pos_y', dtype='number'),
'xrf_spectrum': dict(source='MCA:spectrum', dtype='array',
#shape=(5,),
external='FILESTORE:')}
# save the event descriptor
descriptor_uid = insert_descriptor(
run_start=run_start_uid, data_keys=data_keys, time=0.,
uid=str(uuid.uuid4()))
# number of positions to record, basically along a horizontal line
num = 5
events = []
for i in range(num):
v_pos = 0
h_pos = i
spectrum_uid = get_data(v_pos, h_pos)
# Put in actual ndarray data, as broker would do.
data1 = {'xrf_spectrum': spectrum_uid,
'v_pos': v_pos,
'h_pos': h_pos}
timestamps1 = {k: noisy(i) for k in data1}
event_uid = insert_event(descriptor=descriptor_uid, seq_num=i,
time=noisy(i), data=data1,
uid=str(uuid.uuid4()),
timestamps=timestamps1)
event, = find_events(uid=event_uid)
# test on retrieve data for all data sets
events.append(event)
return events
def run(run_start=None, sleep=0):
if sleep != 0:
raise NotImplementedError("A sleep time is not implemented for this " "example.")
# Make the data
ramp = common.stepped_ramp(start, stop, step, points_per_step)
deadbanded_ramp = common.apply_deadband(ramp, deadband_size)
# Create Event Descriptors
data_keys = {
"Tsam": dict(source="PV:ES:Tsam", dtype="number"),
"point_det": dict(source="PV:ES:point_det", dtype="number"),
}
conf = {
"point_det": {
"data_keys": {"exposure_time": {"source": "PS:ES:point_det_exp"}},
"data": {"exposure_time": 5},
"timestamps": {"exposure_time": 0.0},
}
}
ev_desc = insert_descriptor(
run_start=run_start, data_keys=data_keys, time=0.0, uid=str(uuid.uuid4()), configuration=conf
)
# Create Events.
events = []
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time)
point_det = np.random.randn()
data = {"Tsam": temp, "point_det": point_det}
timestamps = {"Tsam": time, "point_det": time}
event_uid = insert_event(
descriptor=ev_desc, time=time, data=data, seq_num=i, timestamps=timestamps, uid=str(uuid.uuid4())
)
event, = find_events(uid=event_uid)
events.append(event)
return events
def hdf_data_io(rows, cols):
"""
Save data to db and run test when data is retrieved.
"""
run_start_uid = insert_run_start(time=0., scan_id=1, beamline_id='csx',
uid=str(uuid.uuid4()))
# data keys entry
data_keys = {'v_pos': dict(source='MCA:pos_y', dtype='number'),
'h_pos': dict(source='MCA:pos_x', dtype='number'),
'xrf_spectrum': dict(source='MCA:spectrum', dtype='array',
shape=(20, 1, 10),
external='FILESTORE:')}
# save the event descriptor
descriptor_uid = insert_descriptor(
run_start=run_start_uid, data_keys=data_keys, time=0.,
uid=str(uuid.uuid4()))
events = []
for i, (v_pos, h_pos) in enumerate(product(range(rows), range(cols))):
spectrum_uid = get_data(v_pos, h_pos, rows, cols)
# Put in actual ndarray data, as broker would do.
data1 = {'xrf_spectrum': spectrum_uid,
'v_pos': v_pos,
'h_pos': h_pos}
timestamps1 = {k: noisy(i) for k in data1}
event_uid = insert_event(descriptor=descriptor_uid, seq_num=i,
time=noisy(i), data=data1,
uid=str(uuid.uuid4()),
timestamps=timestamps1)
events.append(event_uid)
return run_start_uid, events
scan_id = int(last_hdr.scan_id) + 1
except (IndexError, TypeError):
scan_id = 1
custom = {}
# Create a BeginRunEvent that serves as entry point for a run
run_start = insert_run_start(scan_id=scan_id,
beamline_id='csx',
time=time.time(),
custom=custom,
uid=str(uuid.uuid4()))
# Create an EventDescriptor that indicates the data
# keys and serves as header for set of Event(s)
descriptor = insert_descriptor(data_keys=data_keys,
time=time.time(),
run_start=run_start,
uid=str(uuid.uuid4()))
func = np.cos
num = 1000
start = 0
stop = 10
sleep_time = .1
for idx, i in enumerate(np.linspace(start, stop, num)):
data = {
'linear_motor': i,
'Tsam': i + 5,
'scalar_detector': func(i) + np.random.randn() / 100
}
ts = {k: time.time() for k in data}
def run(run_start_uid=None, sleep=0):
frame_generator = frame_generators.brownian(img_size, step_scale=.5,
I_fluc_function=I_func_gaus,
step_fluc_function=scale_fluc)
# seed data to make deterministic
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
descriptor1_uid = insert_descriptor(
run_start=run_start_uid, data_keys=data_keys1, time=0.,
uid=str(uuid.uuid4()))
descriptor2_uid = insert_descriptor(
run_start=run_start_uid, data_keys=data_keys2, time=0.,
uid=str(uuid.uuid4()))
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,
'total_img_sum': img_sum,
'img': fsid_img,
'img_sum_x': fsid_x,
'img_sum_y': fsid_y,
'img_x_max': img_x_max,
'img_y_max': img_y_max
}
timestamps1 = {k: noisy(i) for k in data1}
event_uid = insert_event(descriptor=descriptor1_uid, seq_num=idx1,
time=noisy(i), data=data1,
timestamps=timestamps1,
uid=str(uuid.uuid4()))
event, = find_events(uid=event_uid)
events.append(event)
for idx2, i2 in enumerate(range(num2)):
time = noisy(i/num2)
data2 = {'Tsam': idx1 + np.random.randn()}
timestamps2 = {'Tsam': time}
event_uid = insert_event(descriptor=descriptor2_uid,
seq_num=idx2+idx1, time=time, data=data2,
uid=str(uuid.uuid4()),
timestamps=timestamps2)
event, = find_events(uid=event_uid)
events.append(event)
ttime.sleep(sleep)
return events
}
try:
last_hdr = next(find_last())
scan_id = int(last_hdr.scan_id)+1
except (IndexError, TypeError):
scan_id = 1
# Create a BeginRunEvent that serves as entry point for a run
run_start = insert_run_start(scan_id=scan_id, beamline_id='csx',
time=time.time(),
uid=str(uuid.uuid4()), function='cos')
# Create an EventDescriptor that indicates the data
# keys and serves as header for set of Event(s)
descriptor = insert_descriptor(data_keys=data_keys, time=time.time(),
run_start=run_start, uid=str(uuid.uuid4()))
func = np.cos
num = 1000
start = 0
stop = 10
sleep_time = .1
for idx, i in enumerate(np.linspace(start, stop, num)):
data = {'linear_motor': i,
'Tsam': i + 5,
'scalar_detector': func(i) + np.random.randn() / 100}
ts = {k: time.time() for k in data}
e = insert_event(descriptor=descriptor, seq_num=idx,
time=time.time(),
timestamps=ts,
