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_event_descriptor(run_start=run_start,
data_keys=data_keys1, time=common.get_time(),
uid=str(uuid.uuid4()))
ev_desc2_uid = insert_event_descriptor(run_start=run_start,
data_keys=data_keys2, time=common.get_time(),
uid=str(uuid.uuid4()))
print('event descriptor 1 uid = %s' % ev_desc1_uid)
print('event descriptor 2 uid = %s' % 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=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 = insert_event_descriptor(run_start=run_start,
data_keys=data_keys1,
time=0.,
uid=str(uuid.uuid4()))
ev_desc2 = insert_event_descriptor(run_start=run_start,
data_keys=data_keys2,
time=0.,
uid=str(uuid.uuid4()))
# Create Events.
events = []
# Point Detector Events
for i in range(num_exposures):
time = float(i + 0.01 * rs.randn())
data = {'point_det': (point_det_data[i], time)}
event = insert_event(event_descriptor=ev_desc1,
seq_num=i,
time=time,
data=data,
uid=str(uuid.uuid4()))
events.append(event)
# Temperature Events
for i, (time, temp) in enumerate(zip(*deadbanded_ramp)):
time = float(time)
data = {'Tsam': (temp, time), 'Troom': (temp + 10, time)}
event = insert_event(event_descriptor=ev_desc2,
time=time,
data=data,
seq_num=i,
uid=str(uuid.uuid4()))
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_event_descriptor(run_start=run_start_uid,
data_keys=data_keys1, time=common.get_time())
ev_desc2_uid = insert_event_descriptor(run_start=run_start_uid,
data_keys=data_keys2, time=common.get_time())
# 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)
event_uid = insert_event(**event_dict)
# grab the actual event from metadatastore
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}
timestamps = {'Tsam': time}
event_dict = dict(descriptor=ev_desc2_uid, time=time,
data=data, timestamps=timestamps, seq_num=i)
event_uid = insert_event(**event_dict)
event, = find_events(uid=event_uid)
events.append(event)
#todo insert run stop if run_start_uid is not None
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')}
ev_desc = insert_event_descriptor(run_start=run_start,
data_keys=data_keys, time=0.)
# 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)
event, = find_events(uid=event_uid)
events.append(event)
return events
def test_data_key():
rs1_uid = insert_run_start(time=100., scan_id=1,
owner='nedbrainard', beamline_id='example',
beamline_config=insert_beamline_config(
{}, time=0.))
rs2_uid = insert_run_start(time=200., scan_id=2,
owner='nedbrainard', beamline_id='example',
beamline_config=insert_beamline_config(
{}, time=0.))
rs1, = find_run_starts(uid=rs1_uid)
rs2, = find_run_starts(uid=rs2_uid)
data_keys = {'fork': {'source': '_', 'dtype': 'number'},
'spoon': {'source': '_', 'dtype': 'number'}}
evd1_uid = insert_event_descriptor(run_start=rs1_uid, data_keys=data_keys,
time=100.)
insert_event_descriptor(run_start=rs2_uid, data_keys=data_keys, time=200.)
result1 = db.find_headers(data_key='fork')
result2 = db.find_headers(data_key='fork', start_time=150)
assert_equal(len(result1), 2)
assert_equal(len(result2), 1)
actual = result2[0].run_start_uid
assert_equal(actual, str(rs2.uid))
def hdf_data_io():
"""
Save data to db and run test when data is retrieved.
"""
blc = insert_beamline_config({'cfg1': 1}, 0.0)
run_start_uid = insert_run_start(time=0., scan_id=1, beamline_id='csx',
uid=str(uuid.uuid4()),
beamline_config=blc)
# 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_event_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 setup(self, n):
self.obj = range(n)
self.bcfg = mdsc.insert_beamline_config(time=1315315135.5135,
config_params={'param1': 1})
self.data_keys = {'linear_motor': {'source': 'PV:pv1',
'shape': None,
'dtype': 'number'},
'scalar_detector': {'source': 'PV:pv2',
'shape': None,
'dtype': 'number'},
'Tsam': {'source': 'PV:pv3',
'dtype': 'number',
'shape': None}}
self.custom = {'custom_key': 'value'}
self.scan_id = 1903
self.run_start = mdsc.insert_run_start(scan_id=int(self.scan_id),
owner='benchmark_script',
beamline_id='benchmark_b',
time=1315315135.5135,
beamline_config=self.bcfg,
custom=self.custom)
self.e_desc = mdsc.insert_event_descriptor(data_keys=self.data_keys,
time=1315315135.5135,
run_start=self.run_start)
# Compose event data list for 1mil events in setup.
# See params in event insert test to see how many of these are used
func = np.cos
num = EVENT_COUNT
start = 0
stop = 10
sleep_time = .1
self.data = list()
for idx, i in enumerate(np.linspace(start, stop, num)):
self.data.append({'linear_motor': [i, 1315315135.5135],
'Tsam': [i + 5, 1315315135.5135],
'scalar_detector': [func(i) +
np.random.randn() / 100,
1315315135.5135]})
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
begin_runs = db.begin_run_event.find()
for br in begin_runs:
the_run_start = insert_run_start(
time=br["time"],
beamline_id=br["beamline_id"],
beamline_config=the_bc,
owner=br["owner"],
scan_id=br["scan_id"],
custom=br.get("custom", {}),
uid=br["uid"],
)
event_descs = db.event_descriptor.find({"begin_run_id": br["_id"]})
max_time = 0.0
for e_desc in event_descs:
the_e_desc = insert_event_descriptor(
run_start=the_run_start, data_keys=e_desc["data_keys"], time=e_desc["time"], uid=e_desc["uid"]
)
events = db.event.find({"descriptor_id": e_desc["_id"]})
for ev in events:
if ev["time"] > max_time:
max_time = ev["time"]
insert_event(
event_descriptor=the_e_desc, time=ev["time"], data=ev["data"], seq_num=ev["seq_num"], uid=ev["uid"]
)
insert_run_stop(run_start=the_run_start, time=float(max_time), exit_status="success", reason=None, uid=None)
run_start_mapping = dict()
run_starts = db.run_start.find()
for rs in run_starts:
time = rs.pop("time")
beamline_id = rs.pop("beamline_id")
def _start_scan(self, run_start=None, detectors=None,
data=None, positioners=None, **kwargs):
dets = detectors
triggers = [det for det in dets if isinstance(det, Detector)]
# creation of the event descriptor should be delayed until the first
# event comes in. Set it to None for now
event_descriptor = None
# provide header for formatted list of positioners and detectors in
# INFO channel
names = list()
for pos in positioners + dets:
names.extend(pos.describe().keys())
self.logger.info(self._demunge_names(names))
seq_num = 0
while self._scan_state is True:
self.logger.debug(
'self._scan_state is True in self._start_scan')
posvals = self._move_positioners(positioners=positioners, **kwargs)
self.logger.debug('moved positioners')
# if we're done iterating over positions, get outta Dodge
if posvals is None:
break
# Trigger detector acquisision
acq_status = [trig.acquire() for trig in triggers]
while any([not stat.done for stat in acq_status]):
time.sleep(0.05)
time.sleep(0.05)
# Read detector values
tmp_detvals = {}
for det in dets + positioners:
tmp_detvals.update(det.read())
detvals = mds.format_events(tmp_detvals)
# pass data onto Demuxer for distribution
self.logger.info(self._demunge_values(detvals, names))
# grab the current time as a timestamp that describes when the
# event data was bundled together
bundle_time = time.time()
# actually insert the event into metadataStore
try:
self.logger.debug(
'inserting event %d------------------', seq_num)
event = mds.insert_event(event_descriptor=event_descriptor,
time=bundle_time, data=detvals,
seq_num=seq_num)
except mds.EventDescriptorIsNoneError:
# the time when the event descriptor was created
self.logger.debug(
'event_descriptor has not been created. '
'creating it now...')
evdesc_creation_time = time.time()
data_key_info = _get_info(
positioners=positioners,
detectors=dets, data=detvals)
event_descriptor = mds.insert_event_descriptor(
run_start=run_start, time=evdesc_creation_time,
data_keys=mds.format_data_keys(data_key_info))
self.logger.debug(
'event_descriptor: %s', vars(event_descriptor))
# insert the event again. this time it better damn well work
self.logger.debug(
'inserting event %d------------------', seq_num)
event = mds.insert_event(event_descriptor=event_descriptor,
time=bundle_time, data=detvals,
seq_num=seq_num)
self.logger.debug('event %d--------', seq_num)
self.logger.debug('%s', vars(event))
seq_num += 1
# update the 'data' object from detvals dict
for k, v in detvals.items():
data[k].append(v)
if not positioners:
break
self._scan_state = False
return
beamline_cfg_mapping[bc['_id']] = the_bc
begin_runs = db.begin_run_event.find()
for br in begin_runs:
the_run_start = insert_run_start(time=br['time'],
beamline_id=br['beamline_id'],
beamline_config=the_bc,
owner=br['owner'],
scan_id=br['scan_id'],
custom=br.get('custom', {}),
uid=br['uid'])
event_descs = db.event_descriptor.find({'begin_run_id': br['_id']})
max_time = 0.0
for e_desc in event_descs:
the_e_desc = insert_event_descriptor(run_start=the_run_start,
data_keys=e_desc['data_keys'],
time=e_desc['time'],
uid=e_desc['uid'])
events = db.event.find({'descriptor_id': e_desc['_id']})
for ev in events:
if ev['time'] > max_time:
max_time = ev['time']
insert_event(event_descriptor=the_e_desc,
time=ev['time'],
data=ev['data'],
seq_num=ev['seq_num'],
uid=ev['uid'])
insert_run_stop(run_start=the_run_start,
time=float(max_time),
exit_status='success',
reason=None,
uid=None)
def time_single_descriptor(self, n):
for _ in self.obj:
mdsc.insert_event_descriptor(data_keys=self.data_keys,
time=1315315135.5135,
run_start=self.run_start)
"Tsam": {"source": "PV:pv3", "dtype": "number", "shape": None},
}
try:
last_hdr = next(find_last())
scan_id = int(last_hdr.scan_id) + 1
except (IndexError, TypeError):
scan_id = 1
custom = {"plotx": "linear_motor", "ploty": "scalar_detector"}
# Create a BeginRunEvent that serves as entry point for a run
rs = insert_run_start(scan_id=scan_id, beamline_id="csx", time=time.time(), beamline_config=b_config, custom=custom)
# Create an EventDescriptor that indicates the data
# keys and serves as header for set of Event(s)
e_desc = insert_event_descriptor(data_keys=data_keys, time=time.time(), run_start=rs)
func = np.cos
num = 1000
start = 0
stop = 10
sleep_time = 0.1
for idx, i in enumerate(np.linspace(start, stop, num)):
data = {
"linear_motor": [i, time.time()],
"Tsam": [i + 5, time.time()],
"scalar_detector": [func(i) + np.random.randn() / 100, time.time()],
}
e = insert_event(event_descriptor=e_desc, seq_num=idx, time=time.time(), data=data)
last_run = next(find_last())
try:
if str(last_run.id) != str(rs.id):
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)
# 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
descriptor1_uid = insert_event_descriptor(
run_start=run_start_uid, data_keys=data_keys1, time=0.,
uid=str(uuid.uuid4()))
descriptor2_uid = insert_event_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
