def test_overwrite(self):
io = MatchIO([1])
with self.assertRaises(FileExistsError):
new_data(location='somewhere', io=io)
data = new_data(location='somewhere', io=io, overwrite=True,)
self.assertEqual(data.location, 'somewhere')
def create_data_set(name: str,
base_folder: str,
subfolder: str = None,
formatter: Formatter = None):
"""Create empty ``DataSet`` within main data folder.
Uses ``new_data``, and handles location formatting.
Args:
name: ``DataSet`` name, used as DataSet folder name.
base_folder: Base folder for DataSet. Should be a pre-existing
``DataSet`` folder. If None, a new folder is created in main data
folder.
subfolder: Adds subfolder within base_folder for ``DataSet``.
Should not be used without explicitly setting ``base_folder``.
formatter: Formatter to use for data storage (e.g. GNUPlotFormat).
Returns:
New empty ``DataSet``.
"""
location_string = '{base_folder}/'
if subfolder is not None:
location_string += '{subfolder}/'
location_string += '#{{counter}}_{{name}}_{{time}}'
location = qc.data.location.FormatLocation(fmt=location_string.format(
base_folder=base_folder, name=name, subfolder=subfolder))
data_set = new_data(location=location, name=name, formatter=formatter)
return data_set
def test_writing_unsupported_types_to_hdf5(self):
"""
Tests writing of
- unsuported list type attr
- nested dataset
"""
some_dict = {}
some_dict['list_of_ints'] = list(np.arange(5))
some_dict['list_of_floats'] = list(np.arange(5.1))
some_dict['weird_dict'] = {'a': 5}
data1 = new_data(formatter=self.formatter,
location=self.loc_provider,
name='test_missing_attr')
some_dict['nested_dataset'] = data1
some_dict['list_of_dataset'] = [data1, data1]
fp = self.loc_provider(io=DataSet.default_io,
record={'name': 'test_dict_writing'}) + '.hdf5'
F = h5py.File(fp, mode='a')
self.formatter.write_dict_to_hdf5(some_dict, F)
new_dict = {}
self.formatter.read_dict_from_hdf5(new_dict, F)
# objects are not identical but the string representation should be
self.assertEqual(str(some_dict['nested_dataset']),
new_dict['nested_dataset'])
self.assertEqual(str(some_dict['list_of_dataset']),
new_dict['list_of_dataset'])
F['weird_dict'].attrs['list_type'] = 'unsuported_list_type'
with self.assertRaises(NotImplementedError):
self.formatter.read_dict_from_hdf5(new_dict, F)
def test_full_write(self):
formatter = GNUPlotFormat()
location = self.locations[0]
data = DataSet1D(name="test_full_write", location=location)
formatter.write(data, data.io, data.location)
with open(location + '/x_set.dat') as f:
self.assertEqual(f.read(), file_1d())
# check that we can add comment lines randomly into the file
# as long as it's after the first three lines, which are comments
# with well-defined meaning,
# and that we can un-quote the labels
lines = file_1d().split('\n')
lines[1] = lines[1].replace('"', '')
lines[3:3] = ['# this data is awesome!']
lines[6:6] = ['# the next point is my favorite.']
with open(location + '/x_set.dat', 'w') as f:
f.write('\n'.join(lines))
# normally this would be just done by data2 = load_data(location)
# but we want to work directly with the Formatter interface here
data2 = DataSet(location=location)
formatter.read(data2)
self.checkArraysEqual(data2.x_set, data.x_set)
self.checkArraysEqual(data2.y, data.y)
# data has been saved
self.assertEqual(data.y.last_saved_index, 4)
# data2 has been read back in, should show the same
# last_saved_index
self.assertEqual(data2.y.last_saved_index, 4)
# while we're here, check some errors on bad reads
# first: trying to read into a dataset that already has the
# wrong size
x = DataArray(name='x_set', label='X', preset_data=(1., 2.))
y = DataArray(name='y',
label='Y',
preset_data=(3., 4.),
set_arrays=(x, ))
data3 = new_data(arrays=(x, y), location=location + 'XX')
# initially give it a different location so we can make it without
# error, then change back to the location we want.
data3.location = location
with LogCapture() as logs:
formatter.read(data3)
self.assertTrue('ValueError' in logs.value, logs.value)
# no problem reading again if only data has changed, it gets
# overwritten with the disk copy
data2.x_set[2] = 42
data2.y[2] = 99
formatter.read(data2)
self.assertEqual(data2.x_set[2], 3)
self.assertEqual(data2.y[2], 5)
def test_snapshot(self):
data = new_data(location=False)
expected_snap = {
'__class__': 'qcodes.data.data_set.DataSet',
'location': False,
'arrays': {},
'formatter': 'qcodes.data.gnuplot_format.GNUPlotFormat',
}
snap = strip_qc(data.snapshot())
# handle io separately so we don't need to figure out our path
self.assertIn('DiskIO', snap['io'])
del snap['io']
self.assertEqual(snap, expected_snap)
# even though we removed io from the snapshot, it's still in .metadata
self.assertIn('io', data.metadata)
# then do the same transformations to metadata to check it too
del data.metadata['io']
strip_qc(data.metadata)
self.assertEqual(data.metadata, expected_snap)
# location is False so read_metadata should be a noop
data.metadata = {'food': 'Fried chicken'}
data.read_metadata()
self.assertEqual(data.metadata, {'food': 'Fried chicken'})
# snapshot should never delete things from metadata, only add or update
data.metadata['location'] = 'Idaho'
snap = strip_qc(data.snapshot())
expected_snap['food'] = 'Fried chicken'
del snap['io']
self.assertEqual(snap, expected_snap)
def test_default_parameter(self):
# Test whether the default_array function works
m = DataSet2D()
# test we can run with default arguments
name = m.default_parameter_name()
# test with paramname
name = m.default_parameter_name(paramname='z')
self.assertEqual(name, 'z')
# test we can get the array instead of the name
array = m.default_parameter_array(paramname='z')
self.assertEqual(array, m.z)
# first non-setpoint array
array = m.default_parameter_array()
self.assertEqual(array, m.z)
# test with metadata
m.metadata = dict({'default_parameter_name': 'x_set'})
name = m.default_parameter_name()
self.assertEqual(name, 'x_set')
# test the fallback: no name matches, no non-setpoint array
x = DataArray(name='x',
label='X',
preset_data=(1., 2., 3., 4., 5.),
is_setpoint=True)
m = new_data(arrays=(x, ), name='onlysetpoint')
name = m.default_parameter_name(paramname='dummy')
self.assertEqual(name, 'x_set')
def DataSetCombined(location=None, name=None):
# Complex DataSet with two 1D and two 2D arrays
x = DataArray(name='x',
label='X!',
preset_data=(16., 17.),
is_setpoint=True)
y1 = DataArray(name='y1',
label='Y1 value',
preset_data=(18., 19.),
set_arrays=(x, ))
y2 = DataArray(name='y2',
label='Y2 value',
preset_data=(20., 21.),
set_arrays=(x, ))
yset = DataArray(name='y',
label='Y',
preset_data=(22., 23., 24.),
is_setpoint=True)
yset.nest(2, 0, x)
z1 = DataArray(name='z1',
label='Z1',
preset_data=((25., 26., 27.), (28., 29., 30.)),
set_arrays=(x, yset))
z2 = DataArray(name='z2',
label='Z2',
preset_data=((31., 32., 33.), (34., 35., 36.)),
set_arrays=(x, yset))
return new_data(arrays=(x, y1, y2, yset, z1, z2),
location=location,
name=name)
def test_default_parameter(self):
loc_fmt = 'data/{date}/#{counter}_{name}_{date}_{time}'
rcd = {'name': 'test_default_parameter'}
loc_provider = FormatLocation(fmt=loc_fmt, record=rcd)
# Test whether the default_array function works
m = DataSet2D(location=loc_provider)
# test we can run with default arguments
name = m.default_parameter_name()
# test with paramname
name = m.default_parameter_name(paramname='z')
self.assertEqual(name, 'z')
# test we can get the array instead of the name
array = m.default_parameter_array(paramname='z')
self.assertEqual(array, m.z)
# first non-setpoint array
array = m.default_parameter_array()
self.assertEqual(array, m.z)
# test with metadata
m.metadata = dict({'default_parameter_name': 'x_set'})
name = m.default_parameter_name()
self.assertEqual(name, 'x_set')
# test the fallback: no name matches, no non-setpoint array
x = DataArray(name='x',
label='X',
preset_data=(1., 2., 3., 4., 5.),
is_setpoint=True)
m = new_data(arrays=(x, ), name='onlysetpoint')
name = m.default_parameter_name(paramname='dummy')
self.assertEqual(name, 'x_set')
def test_location_functions(self):
def my_location(io, record):
return 'data/{}'.format((record or {}).get('name') or 'LOOP!')
def my_location2(io, record):
name = (record or {}).get('name') or 'loop?'
return 'data/{}/folder'.format(name)
DataSet.location_provider = my_location
self.assertEqual(new_data().location, 'data/LOOP!')
self.assertEqual(new_data(name='cheese').location, 'data/cheese')
data = new_data(location=my_location2)
self.assertEqual(data.location, 'data/loop?/folder')
data = new_data(location=my_location2, name='iceCream')
self.assertEqual(data.location, 'data/iceCream/folder')
def get_data_set(self, data_manager=USE_MP, *args, **kwargs):
"""
Return the data set for this loop.
If no data set has been created yet, a new one will be created and
returned. Note that all arguments can only be provided when the
`DataSet` is first created; giving these during `run` when
`get_data_set` has already been called on its own is an error.
data_manager: a DataManager instance (omit to use default,
False to store locally)
kwargs are passed along to data_set.new_data. The key ones are:
location: the location of the DataSet, a string whose meaning
depends on formatter and io, or False to only keep in memory.
May be a callable to provide automatic locations. If omitted, will
use the default DataSet.location_provider
name: if location is default or another provider function, name is
a string to add to location to make it more readable/meaningful
to users
formatter: knows how to read and write the file format
default can be set in DataSet.default_formatter
io: knows how to connect to the storage (disk vs cloud etc)
write_period: how often to save to storage during the loop.
default 5 sec, use None to write only at the end
returns:
a DataSet object that we can use to plot
"""
if self.data_set is None:
if data_manager is False:
data_mode = DataMode.LOCAL
else:
warnings.warn("Multiprocessing is in beta, use at own risk",
UserWarning)
data_mode = DataMode.PUSH_TO_SERVER
data_set = new_data(arrays=self.containers(),
mode=data_mode,
data_manager=data_manager,
*args,
**kwargs)
self.data_set = data_set
else:
has_args = len(kwargs) or len(args)
uses_data_manager = (self.data_set.mode != DataMode.LOCAL)
if has_args or (uses_data_manager != data_manager):
raise RuntimeError(
'The DataSet for this loop already exists. '
'You can only provide DataSet attributes, such as '
'data_manager, location, name, formatter, io, '
'write_period, when the DataSet is first created.')
return self.data_set
def test_dataset_with_missing_attrs(self):
data1 = new_data(formatter=self.formatter,
location=self.loc_provider,
name='test_missing_attr')
arr = DataArray(array_id='arr', preset_data=np.linspace(0, 10, 21))
data1.add_array(arr)
data1.write()
# data2 = DataSet(location=data1.location, formatter=self.formatter)
# data2.read()
data2 = load_data(location=data1.location, formatter=self.formatter)
# cannot use the check arrays equal as I expect the attributes
# to not be equal
np.testing.assert_array_equal(data2.arrays['arr'], data1.arrays['arr'])
def DataSet2D(location=None, name=None):
# DataSet with one 2D array with 4 x 6 points
yy, xx = numpy.meshgrid(range(4), range(6))
zz = xx**2 + yy**2
# outer setpoint should be 1D
xx = xx[:, 0]
x = DataArray(name='x', label='X', preset_data=xx, is_setpoint=True)
y = DataArray(name='y',
label='Y',
preset_data=yy,
set_arrays=(x, ),
is_setpoint=True)
z = DataArray(name='z', label='Z', preset_data=zz, set_arrays=(x, y))
return new_data(arrays=(x, y, z), location=location, name=name)
def DataSet1D(location=None, name=None):
# DataSet with one 1D array with 5 points
# TODO: since y lists x as a set_array, it should automatically
# set is_setpoint=True for x, shouldn't it? Any reason we woundn't
# want that?
x = DataArray(name='x',
label='X',
preset_data=(1., 2., 3., 4., 5.),
is_setpoint=True)
y = DataArray(name='y',
label='Y',
preset_data=(3., 4., 5., 6., 7.),
set_arrays=(x, ))
return new_data(arrays=(x, y), location=location, name=name)
def get_data_set(self, *args, **kwargs):
"""
Return the data set for this loop.
If no data set has been created yet, a new one will be created and
returned. Note that all arguments can only be provided when the
`DataSet` is first created; giving these during `run` when
`get_data_set` has already been called on its own is an error.
Args:
data_manager: a DataManager instance (omit to use default,
False to store locally)
print_data_set (True): print data_set
kwargs are passed along to data_set.new_data. The key ones are:
Args:
location: the location of the DataSet, a string whose meaning
depends on formatter and io, or False to only keep in memory.
May be a callable to provide automatic locations. If omitted, will
use the default DataSet.location_provider
name: if location is default or another provider function, name is
a string to add to location to make it more readable/meaningful
to users
formatter: knows how to read and write the file format
default can be set in DataSet.default_formatter
io: knows how to connect to the storage (disk vs cloud etc)
write_period: how often to save to storage during the loop.
default 5 sec, use None to write only at the end
returns:
a DataSet object that we can use to plot
"""
if self.data_set is None:
data_set = new_data(arrays=self.containers(), *args, **kwargs)
data_set.add_metadata({'measurement_type': 'Loop'})
self.data_set = data_set
else:
has_args = len(kwargs) or len(args)
if has_args:
raise RuntimeError(
'The DataSet for this loop already exists. '
'You can only provide DataSet attributes, such as '
'data_manager, location, name, formatter, io, '
'write_period, when the DataSet is first created.')
return self.data_set
def test_complete(self):
array = DataArray(name='y', shape=(5,))
array.init_data()
data = new_data(arrays=(array,), location=False)
self.syncing_array = array
self.sync_index = 0
data.sync = self.mock_sync
bf = DataSet.background_functions
bf['fail'] = self.failing_func
bf['log'] = self.logging_func
with LogCapture() as logs:
# grab info and warnings but not debug messages
logging.getLogger().setLevel(logging.INFO)
data.complete(delay=0.001)
logs = logs.value
expected_logs = [
'waiting for DataSet <False> to complete',
'DataSet: 0% complete',
'RuntimeError: it is called failing_func for a reason!',
'background at index 1',
'DataSet: 20% complete',
'RuntimeError: it is called failing_func for a reason!',
'background function fail failed twice in a row, removing it',
'background at index 2',
'DataSet: 40% complete',
'background at index 3',
'DataSet: 60% complete',
'background at index 4',
'DataSet: 80% complete',
'background at index 5',
'DataSet <False> is complete'
]
log_index = 0
for line in expected_logs:
self.assertIn(line, logs, logs)
try:
log_index_new = logs.index(line, log_index)
except ValueError:
raise ValueError('line {} not found after {} in: \n {}'.format(
line, log_index, logs))
self.assertTrue(log_index_new >= log_index, logs)
log_index = log_index_new + len(line) + 1 # +1 for \n
self.assertEqual(log_index, len(logs), logs)
def save_single_raw_file(xdata, my_raw_data, rawdatacounter, maincounter):
xarr = DataArray(preset_data=xdata,
is_setpoint=True,
name='time',
label='Time',
unit='s')
yarr = DataArray(preset_data=my_raw_data,
set_arrays=(xarr, ),
name='demodulated_signal',
label='Demodulated Signal',
unit='V')
name = '{0:06d}'.format(rawdatacounter)
locstring = '{}{:03}_raw{}'.format(CURRENT_EXPERIMENT['exp_folder'],
maincounter, sep)
rawdataset = new_data(location=locstring, arrays=[xarr, yarr])
rawdataset.formatter.number_format = '{:g}'
rawdataset.formatter.extension = '.raw'
rawdataset.finalize(filename=name, write_metadata=False)
def test_fraction_complete(self):
empty_data = new_data(arrays=(), location=False)
self.assertEqual(empty_data.fraction_complete(), 0.0)
data = DataSetCombined(location=False)
self.assertEqual(data.fraction_complete(), 1.0)
# alter only the measured arrays, check that only these are used
# to calculate fraction_complete
data.y1.modified_range = (0, 0) # 1 of 2
data.y2.modified_range = (0, 0) # 1 of 2
data.z1.modified_range = (0, 2) # 3 of 6
data.z2.modified_range = (0, 2) # 3 of 6
self.assertEqual(data.fraction_complete(), 0.5)
# mark more things complete using last_saved_index and synced_index
data.y1.last_saved_index = 1 # 2 of 2
data.z1.synced_index = 5 # 6 of 6
self.assertEqual(data.fraction_complete(), 0.75)
def dictionary_to_dataset(data_dictionary: dict) -> DataSet:
""" Convert dictionary to DataSet.
Args:
data_dictionary: data to convert
Returns:
DataSet with converted data.
"""
dataset = new_data()
dataset.metadata.update(data_dictionary['metadata'])
for array_key, array_dict in data_dictionary['arrays'].items():
data_array = _dictionary_to_data_array(array_dict)
dataset.add_array(data_array)
for array_key, array_dict in data_dictionary['arrays'].items():
set_arrays_names = array_dict['set_arrays']
set_arrays = tuple([dataset.arrays[name] for name in set_arrays_names])
dataset.arrays[array_key].set_arrays = set_arrays
return dataset
def scan_inside_awg(
name,
label,
):
data = dig.get()
pulse_length = sweep_loop1['para1'] if 'para1' in sweep_loop1 else 1
data = np.array([data])
pulse_length = np.array([pulse_length])
data_array = DataArray(
preset_data=data,
name='digitizer',
)
pulse_array = DataArray(preset_data=pulse_length,
name=name + '_set',
is_setpoint=True)
set_array = DataArray(preset_data=np.array([1]),
name='none_set',
array_id='pulse_length_set',
is_setpoint=True)
data_set = new_data(
arrays=[set_array, pulse_array, data_array],
location=data_location,
loc_record={
'name': experiment_name,
'label': sweep_type
},
io=data_IO,
)
return data_set
def _make_data_set(measured_data_list, measurement_list, measurement_unit,
location, loc_record, preset_data, setpoints):
""" Generic code to make the data set for makeDataSet1D, makeDataSet1DPlain, makeDataSet2D, makeDataSet2DPlain
Warnings logged:
1. When the shape of the measured data is not matching the shape of the setpoint array. When the
measured data is a list, each list item must match the shape of the setpoint array.
Raises:
ValueError: When the number of measurements names in the measurement_list does not match the number of
measurements.
If len(measurement_list) > len(measured_data_list) we would otherwise get an
IndexError later on. When len(measurement_list) < len(measured_data_list) now a ValueError is
raised because a part of the measure data is not stored silently otherwise.
TypeError: When a measurement name in the measurement list has an invalid type.
Returns:
The resulting data set and the measure names list.
"""
data_set = new_data(arrays=(), location=location, loc_record=loc_record)
if len(setpoints) > 1:
set_arrays = (setpoints[0], setpoints[1])
else:
set_arrays = (setpoints[0], )
if measured_data_list is not None:
if len(measurement_list) != len(measured_data_list):
raise ValueError(
f'The number of measurement names {len(measurement_list)} does not match the number '
f'of measurements {len(measured_data_list)}')
measure_names = []
measure_units = []
for parameter in measurement_list:
if isinstance(parameter, str):
# parameter is a str
measure_names += [parameter]
measure_units += [measurement_unit]
elif isinstance(parameter, qcodes.Parameter):
# parameter is a Parameter
measure_names += [parameter.name]
measure_units += [parameter.unit]
else:
raise TypeError(
'Type of measurement names must be str or qcodes.Parameter')
for idm, mname in enumerate(measure_names):
preset_data_array = DataArray(name=mname,
array_id=mname,
label=mname,
unit=measure_units[idm],
preset_data=np.copy(preset_data),
set_arrays=set_arrays)
data_set.add_array(preset_data_array)
if measured_data_list is not None and measured_data_list[
idm] is not None:
measured_array = np.array(measured_data_list[idm])
if measured_array.shape != preset_data.shape:
logger.warning(
f'Shape of measured data {preset_data.shape} does not match '
f'setpoint shape {measured_array.shape}')
getattr(data_set, mname).ndarray = measured_array
if len(setpoints) > 1:
data_set.add_array(setpoints[1])
data_set.add_array(setpoints[0])
else:
data_set.add_array(setpoints[0])
return data_set, measure_names
data3 = DataArray(preset_data=data, name='digitizer3')
data1.ndarray
##
arrays = LP.containers()
arrays2 = []
arrays3 = [
data1,
]
arrays4 = [data1, data2, data3]
data_set_2 = new_data(
arrays=arrays3,
location=None,
loc_record={
'name': 'T1',
'label': 'Vread_sweep'
},
io=NewIO,
)
data_set_2.save_metadata()
test_location = '2017-08-18/20-40-19_T1_Vread_sweep'
data_set_3 = DataSet(
location=test_location,
io=NewIO,
)
data_set_3.read()
AWGpara_array = data_set_3.arrays['AWGpara_set'].ndarray
def test_mode_error(self):
with self.assertRaises(ValueError):
new_data(mode=DataMode.PUSH_TO_SERVER, data_manager=False)
print('loop.data_set: %s' % LP.data_set)
NewIO = DiskIO(base_location = 'C:\\Users\\LocalAdmin\\Documents')
formatter = HDF5FormatMetadata()
OldIO = DiskIO(base_location = 'D:\\文献\\QuTech\\QTlab\\xiaotest\\testIO')
## get_data_set should contain parameter like io, location, formatter and others
data = LP.get_data_set(location=None, loc_record = {'name':'T1', 'label':'Vread_sweep'},
io = NewIO, formatter = formatter)
#data = LP.get_data_set(data_manager=False, location=None, loc_record = {'name':'T1', 'label':'T_load_sweep'})
print('loop.data_set: %s' % LP.data_set)
#%%
DS = new_data(location = 'aaaaaaaaaa',io = NewIO)
def live_plotting():
for para in data.arrays:
DS.arrays[para] = data.arrays[para]
return DS
DS = live_plotting()
#def add_T1exp_metadata(data):
#
# data.metadata['Parameters'] = {'Nrep': 10, 't_empty': 2, 't_load': 2.4, 't_read': 2.2}
# data.write(write_metadata=True)
#
#
#add_T1exp_metadata(data)
#datatata = LP.run(background=False)
#%%
def __enter__(self):
"""Operation when entering a loop"""
self.is_context_manager = True
# Encapsulate everything in a try/except to ensure that the context
# manager is properly exited.
try:
if Measurement.running_measurement is None:
# Register current measurement as active primary measurement
Measurement.running_measurement = self
Measurement.measurement_thread = threading.current_thread()
# Initialize dataset
self.dataset = new_data(name=self.name)
self.dataset.active = True
self._initialize_metadata(self.dataset)
with self.timings.record(['dataset', 'save_metadata']):
self.dataset.save_metadata()
if hasattr(self.dataset, 'save_config'):
self.dataset.save_config()
# Initialize attributes
self.loop_shape = ()
self.loop_indices = ()
self.action_indices = (0,)
self.data_arrays = {}
self.set_arrays = {}
self.log(f'Measurement started {self.dataset.location}')
print(f'Measurement started {self.dataset.location}')
else:
if threading.current_thread() is not Measurement.measurement_thread:
raise RuntimeError(
"Cannot run a measurement while another measurement "
"is already running in a different thread."
)
# Primary measurement is already running. Add this measurement as
# a data_group of the primary measurement
msmt = Measurement.running_measurement
msmt.data_groups[msmt.action_indices] = self
data_groups = [
(key, getattr(val, 'name', 'None')) for key, val in msmt.data_groups.items()
]
msmt.dataset.add_metadata({'data_groups': data_groups})
msmt.action_indices += (0,)
# Nested measurement attributes should mimic the primary measurement
self.loop_shape = msmt.loop_shape
self.loop_indices = msmt.loop_indices
self.action_indices = msmt.action_indices
self.data_arrays = msmt.data_arrays
self.set_arrays = msmt.set_arrays
self.timings = msmt.timings
# Perform measurement thread check, and set user namespace variables
if self.force_cell_thread and Measurement.running_measurement is self:
# Raise an error if force_cell_thread is True and the code is run
# directly from an IPython cell/prompt but not from a separate thread
is_main_thread = threading.current_thread() == threading.main_thread()
if is_main_thread and directly_executed_from_cell():
raise RuntimeError(
"Measurement must be created in dedicated thread. "
"Otherwise specify force_thread=False"
)
# Register the Measurement and data as variables in the user namespace
# Usually as variable names are 'msmt' and 'data' respectively
from IPython import get_ipython
shell = get_ipython()
shell.user_ns[self._default_measurement_name] = self
shell.user_ns[self._default_dataset_name] = self.dataset
return self
except:
# An error has occured, ensure running_measurement is cleared
if Measurement.running_measurement is self:
Measurement.running_measurement = None
raise