def __init__(self):
super(Experiment, self).__init__()
# Experiment name
self.name = None
# Sweep control
self.sweeper = Sweeper()
# This holds the experiment graph
self.graph = None
# This holds a reference to a matplotlib server instance
# for plotting, if there is one.
self.matplot_server_thread = None
# If this is True, don't close the plot server thread so that
# we might push additional plots after run_sweeps is complete.
self.leave_plot_server_open = False
# Also keep references to all of the plot filters
self.plotters = [] # Standard pipeline plotters using streams
self.extra_plotters = [
] # Plotters using streams, but not the pipeline
self.manual_plotters = [
] # Plotters using neither streams nor the pipeline
self.manual_plotter_callbacks = [
] # These are called at the end of run
self._extra_plots_to_streams = {}
# Furthermore, keep references to all of the file writers.
# If multiple writers request acces to the same filename, they
# should share the same file object and write in separate
# hdf5 groups.
self.writers = []
self.buffers = []
# ExpProgressBar object to display progress bars
self.progressbar = None
# indicates whether the instruments are already connected
self.instrs_connected = False
# Things we can't metaclass
self.output_connectors = {}
for oc in self._output_connectors.keys():
a = OutputConnector(name=oc,
data_name=oc,
unit=self._output_connectors[oc].data_unit,
parent=self)
a.parent = self
self.output_connectors[oc] = a
setattr(self, oc, a)
# Some instruments don't clean up well after themselves, reconstruct them on a
# per instance basis
for n in self._instruments.keys():
new_cls = type(self._instruments[n])
new_inst = new_cls(
resource_name=self._instruments[n].resource_name,
name=self._instruments[n].name)
setattr(self, n, new_inst)
self._instruments[n] = new_inst
# Create the asyncio measurement loop
self.loop = asyncio.get_event_loop()
# Based on the logging level, infer whether we want asyncio debug
do_debug = logger.getEffectiveLevel() <= logging.DEBUG
self.loop.set_debug(do_debug)
# Run the stream init
self.init_streams()
def __init__(self):
super(Experiment, self).__init__()
# Experiment name
self.name = None
# Sweep control
self.sweeper = Sweeper()
# This holds the experiment graph
self.graph = None
# Should we show the dashboard?
self.dashboard = False
# Create and use plots?
self.do_plotting = False
# Unique ID for this experiment
self.uuid = str(uuid.uuid4())
# Disconnect at the end of experiment?
self.keep_instruments_connected = False
# Also keep references to all of the plot filters
self.plotters = [] # Standard pipeline plotters using streams
self.extra_plotters = [
] # Plotters using streams, but not the pipeline
self.manual_plotters = [
] # Plotters using neither streams nor the pipeline
self.manual_plotter_callbacks = [
] # These are called at the end of run
self._extra_plots_to_streams = {}
# Keep track of additional DataStreams created for manual plotters, etc.
self.extra_streams = []
# Furthermore, keep references to all of the file writers and buffers.
self.writers = []
self.buffers = []
# ExpProgressBar object to display progress bars
self.progressbar = None
# indicates whether the instruments are already connected
self.instrs_connected = False
# indicates whether this is the first (or only) experiment in a series (e.g. for pulse calibrations)
self.first_exp = True
# add date to data files?
self.add_date = False
# save channel library
self.save_chanddb = False
# Things we can't metaclass
self.output_connectors = {}
for oc in self._output_connectors.keys():
a = OutputConnector(
name=oc,
data_name=oc,
unit=self._output_connectors[oc].data_unit,
dtype=self._output_connectors[oc].descriptor.dtype,
parent=self)
a.parent = self
self.output_connectors[oc] = a
setattr(self, oc, a)
# Some instruments don't clean up well after themselves, reconstruct them on a
# per instance basis. These instruments contain a wide variety of complex behaviors
# and rely on other classes and data structures, so we avoid copying them and
# run through the constructor instead.
self._instruments_instance = {}
for n in self._instruments.keys():
new_cls = type(self._instruments[n])
new_inst = new_cls(
resource_name=self._instruments[n].resource_name,
name=self._instruments[n].name)
setattr(self, n, new_inst)
self._instruments_instance[n] = new_inst
self._instruments = self._instruments_instance
# We don't want to add parameters to the base class, so do the same here.
# These aren't very complicated objects, so we'll throw caution to the wind and
# try copying them directly.
self._parameters_instance = {}
for n, v in self._parameters.items():
new_inst = copy.deepcopy(v)
setattr(self, n, new_inst)
self._parameters_instance[n] = new_inst
self._parameters = self._parameters_instance
# Based on the logging level, infer whether we want asyncio debug
do_debug = logger.getEffectiveLevel() <= logging.DEBUG
# Run the stream init
self.init_streams()