def test_thread_pool_params_caller(dummy_1, dummy_2):
params = (
dummy_1.voltage_1,
dummy_1.voltage_2,
dummy_2.voltage_1,
dummy_2.voltage_2,
)
with ThreadPoolParamsCaller(*params) as pool_caller:
output1 = pool_caller()
output2 = pool_caller()
for output in (output1, output2):
params_per_thread_id = defaultdict(set)
for param, thread_id in output:
assert thread_id is not None
params_per_thread_id[thread_id].add(param)
assert len(params_per_thread_id) == 2
expected_params_per_thread = {
frozenset([dummy_1.voltage_1, dummy_1.voltage_2]),
frozenset([dummy_2.voltage_1, dummy_2.voltage_2]),
}
assert {
frozenset(value) for value in params_per_thread_id.values()
} == expected_params_per_thread
def do2d(
param_set1: _BaseParameter,
start1: float,
stop1: float,
num_points1: int,
delay1: float,
param_set2: _BaseParameter,
start2: float,
stop2: float,
num_points2: int,
delay2: float,
*param_meas: ParamMeasT,
set_before_sweep: Optional[bool] = True,
enter_actions: ActionsT = (),
exit_actions: ActionsT = (),
before_inner_actions: ActionsT = (),
after_inner_actions: ActionsT = (),
write_period: Optional[float] = None,
measurement_name: str = "",
exp: Optional[Experiment] = None,
flush_columns: bool = False,
do_plot: Optional[bool] = None,
use_threads: Optional[bool] = None,
additional_setpoints: Sequence[ParamMeasT] = tuple(),
show_progress: Optional[None] = None,
log_info: Optional[str] = None,
) -> AxesTupleListWithDataSet:
"""
Perform a 1D scan of ``param_set1`` from ``start1`` to ``stop1`` in
``num_points1`` and ``param_set2`` from ``start2`` to ``stop2`` in
``num_points2`` measuring param_meas at each step.
Args:
param_set1: The QCoDeS parameter to sweep over in the outer loop
start1: Starting point of sweep in outer loop
stop1: End point of sweep in the outer loop
num_points1: Number of points to measure in the outer loop
delay1: Delay after setting parameter in the outer loop
param_set2: The QCoDeS parameter to sweep over in the inner loop
start2: Starting point of sweep in inner loop
stop2: End point of sweep in the inner loop
num_points2: Number of points to measure in the inner loop
delay2: Delay after setting parameter before measurement is performed
*param_meas: Parameter(s) to measure at each step or functions that
will be called at each step. The function should take no arguments.
The parameters and functions are called in the order they are
supplied.
set_before_sweep: if True the outer parameter is set to its first value
before the inner parameter is swept to its next value.
enter_actions: A list of functions taking no arguments that will be
called before the measurements start
exit_actions: A list of functions taking no arguments that will be
called after the measurements ends
before_inner_actions: Actions executed before each run of the inner loop
after_inner_actions: Actions executed after each run of the inner loop
write_period: The time after which the data is actually written to the
database.
measurement_name: Name of the measurement. This will be passed down to
the dataset produced by the measurement. If not given, a default
value of 'results' is used for the dataset.
exp: The experiment to use for this measurement.
flush_columns: The data is written after a column is finished
independent of the passed time and write period.
additional_setpoints: A list of setpoint parameters to be registered in
the measurement but not scanned.
do_plot: should png and pdf versions of the images be saved after the
run. If None the setting will be read from ``qcodesrc.json``
use_threads: If True measurements from each instrument will be done on
separate threads. If you are measuring from several instruments
this may give a significant speedup.
show_progress: should a progress bar be displayed during the
measurement. If None the setting will be read from ``qcodesrc.json`
log_info: Message that is logged during the measurement. If None a default
message is used.
Returns:
The QCoDeS dataset.
"""
if do_plot is None:
do_plot = config.dataset.dond_plot
if show_progress is None:
show_progress = config.dataset.dond_show_progress
meas = Measurement(name=measurement_name, exp=exp)
if log_info is not None:
meas._extra_log_info = log_info
else:
meas._extra_log_info = "Using 'qcodes.utils.dataset.doNd.do2d'"
all_setpoint_params = (
param_set1,
param_set2,
) + tuple(s for s in additional_setpoints)
measured_parameters = tuple(
param for param in param_meas if isinstance(param, _BaseParameter)
)
try:
loop_shape = (num_points1, num_points2) + tuple(1 for _ in additional_setpoints)
shapes: Shapes = detect_shape_of_measurement(measured_parameters, loop_shape)
except TypeError:
LOG.exception(
f"Could not detect shape of {measured_parameters} "
f"falling back to unknown shape."
)
shapes = None
_register_parameters(meas, all_setpoint_params)
_register_parameters(meas, param_meas, setpoints=all_setpoint_params, shapes=shapes)
_set_write_period(meas, write_period)
_register_actions(meas, enter_actions, exit_actions)
if use_threads is None:
use_threads = config.dataset.use_threads
param_meas_caller = (
ThreadPoolParamsCaller(*param_meas)
if use_threads
else SequentialParamsCaller(*param_meas)
)
with _catch_keyboard_interrupts() as interrupted, meas.run() as datasaver, param_meas_caller as call_param_meas:
dataset = datasaver.dataset
additional_setpoints_data = process_params_meas(additional_setpoints)
setpoints1 = np.linspace(start1, stop1, num_points1)
for set_point1 in tqdm(setpoints1, disable=not show_progress):
if set_before_sweep:
param_set2.set(start2)
param_set1.set(set_point1)
for action in before_inner_actions:
action()
time.sleep(delay1)
setpoints2 = np.linspace(start2, stop2, num_points2)
# flush to prevent unflushed print's to visually interrupt tqdm bar
# updates
sys.stdout.flush()
sys.stderr.flush()
for set_point2 in tqdm(setpoints2, disable=not show_progress, leave=False):
# skip first inner set point if `set_before_sweep`
if set_point2 == start2 and set_before_sweep:
pass
else:
param_set2.set(set_point2)
time.sleep(delay2)
datasaver.add_result(
(param_set1, set_point1),
(param_set2, set_point2),
*call_param_meas(),
*additional_setpoints_data,
)
for action in after_inner_actions:
action()
if flush_columns:
datasaver.flush_data_to_database()
return _handle_plotting(dataset, do_plot, interrupted())
def dond(
*params: Union[AbstractSweep, Union[ParamMeasT, Sequence[ParamMeasT]]],
write_period: Optional[float] = None,
measurement_name: str = "",
exp: Optional[Experiment] = None,
enter_actions: ActionsT = (),
exit_actions: ActionsT = (),
do_plot: Optional[bool] = None,
show_progress: Optional[bool] = None,
use_threads: Optional[bool] = None,
additional_setpoints: Sequence[ParamMeasT] = tuple(),
log_info: Optional[str] = None,
) -> Union[AxesTupleListWithDataSet, MultiAxesTupleListWithDataSet]:
"""
Perform n-dimentional scan from slowest (first) to the fastest (last), to
measure m measurement parameters. The dimensions should be specified
as sweep objects, and after them the parameters to measure should be passed.
Args:
*params: Instances of n sweep classes and m measurement parameters,
e.g. if linear sweep is considered:
.. code-block::
LinSweep(param_set_1, start_1, stop_1, num_points_1, delay_1), ...,
LinSweep(param_set_n, start_n, stop_n, num_points_n, delay_n),
param_meas_1, param_meas_2, ..., param_meas_m
If multiple DataSets creation is needed, measurement parameters should
be grouped, so one dataset will be created for each group. e.g.:
.. code-block::
LinSweep(param_set_1, start_1, stop_1, num_points_1, delay_1), ...,
LinSweep(param_set_n, start_n, stop_n, num_points_n, delay_n),
[param_meas_1, param_meas_2], ..., [param_meas_m]
write_period: The time after which the data is actually written to the
database.
measurement_name: Name of the measurement. This will be passed down to
the dataset produced by the measurement. If not given, a default
value of 'results' is used for the dataset.
exp: The experiment to use for this measurement.
enter_actions: A list of functions taking no arguments that will be
called before the measurements start.
exit_actions: A list of functions taking no arguments that will be
called after the measurements ends.
do_plot: should png and pdf versions of the images be saved and plots
are shown after the run. If None the setting will be read from
``qcodesrc.json``
show_progress: should a progress bar be displayed during the
measurement. If None the setting will be read from ``qcodesrc.json`
use_threads: If True, measurements from each instrument will be done on
separate threads. If you are measuring from several instruments
this may give a significant speedup.
additional_setpoints: A list of setpoint parameters to be registered in
the measurement but not scanned/swept-over.
log_info: Message that is logged during the measurement. If None a default
message is used.
Returns:
A tuple of QCoDeS DataSet, Matplotlib axis, Matplotlib colorbar. If
more than one group of measurement parameters is supplied, the output
will be a tuple of tuple(QCoDeS DataSet), tuple(Matplotlib axis),
tuple(Matplotlib colorbar), in which each element of each sub-tuple
belongs to one group, and the order of elements is the order of
the supplied groups.
"""
if do_plot is None:
do_plot = config.dataset.dond_plot
if show_progress is None:
show_progress = config.dataset.dond_show_progress
sweep_instances, params_meas = _parse_dond_arguments(*params)
nested_setpoints = _make_nested_setpoints(sweep_instances)
all_setpoint_params = tuple(sweep.param for sweep in sweep_instances) + tuple(
s for s in additional_setpoints
)
(
all_meas_parameters,
grouped_parameters,
measured_parameters,
) = _extract_paramters_by_type_and_group(measurement_name, params_meas)
try:
loop_shape = tuple(sweep.num_points for sweep in sweep_instances) + tuple(
1 for _ in additional_setpoints
)
shapes: Shapes = detect_shape_of_measurement(measured_parameters, loop_shape)
except TypeError:
LOG.exception(
f"Could not detect shape of {measured_parameters} "
f"falling back to unknown shape."
)
shapes = None
meas_list = _create_measurements(
all_setpoint_params,
enter_actions,
exit_actions,
exp,
grouped_parameters,
shapes,
write_period,
log_info,
)
post_delays: List[float] = []
params_set: List[_BaseParameter] = []
post_actions: List[ActionsT] = []
for sweep in sweep_instances:
post_delays.append(sweep.delay)
params_set.append(sweep.param)
post_actions.append(sweep.post_actions)
datasets = []
plots_axes = []
plots_colorbar = []
if use_threads is None:
use_threads = config.dataset.use_threads
params_meas_caller = (
ThreadPoolParamsCaller(*all_meas_parameters)
if use_threads
else SequentialParamsCaller(*all_meas_parameters)
)
try:
with _catch_keyboard_interrupts() as interrupted, ExitStack() as stack, params_meas_caller as call_params_meas:
datasavers = [stack.enter_context(measure.run()) for measure in meas_list]
additional_setpoints_data = process_params_meas(additional_setpoints)
previous_setpoints = np.empty(len(sweep_instances))
for setpoints in tqdm(nested_setpoints, disable=not show_progress):
active_actions, delays = _select_active_actions_delays(
post_actions, post_delays, setpoints, previous_setpoints,
)
previous_setpoints = setpoints
param_set_list = []
param_value_action_delay = zip(
params_set,
setpoints,
active_actions,
delays,
)
for setpoint_param, setpoint, action, delay in param_value_action_delay:
_conditional_parameter_set(setpoint_param, setpoint)
param_set_list.append((setpoint_param, setpoint))
for act in action:
act()
time.sleep(delay)
meas_value_pair = call_params_meas()
for group in grouped_parameters.values():
group["measured_params"] = []
for measured in meas_value_pair:
if measured[0] in group["params"]:
group["measured_params"].append(measured)
for ind, datasaver in enumerate(datasavers):
datasaver.add_result(
*param_set_list,
*grouped_parameters[f"group_{ind}"]["measured_params"],
*additional_setpoints_data,
)
finally:
for datasaver in datasavers:
ds, plot_axis, plot_color = _handle_plotting(
datasaver.dataset, do_plot, interrupted()
)
datasets.append(ds)
plots_axes.append(plot_axis)
plots_colorbar.append(plot_color)
if len(grouped_parameters) == 1:
return datasets[0], plots_axes[0], plots_colorbar[0]
else:
return tuple(datasets), tuple(plots_axes), tuple(plots_colorbar)
def do1d(
param_set: _BaseParameter,
start: float,
stop: float,
num_points: int,
delay: float,
*param_meas: ParamMeasT,
enter_actions: ActionsT = (),
exit_actions: ActionsT = (),
write_period: Optional[float] = None,
measurement_name: str = "",
exp: Optional[Experiment] = None,
do_plot: Optional[bool] = None,
use_threads: Optional[bool] = None,
additional_setpoints: Sequence[ParamMeasT] = tuple(),
show_progress: Optional[None] = None,
log_info: Optional[str] = None,
) -> AxesTupleListWithDataSet:
"""
Perform a 1D scan of ``param_set`` from ``start`` to ``stop`` in
``num_points`` measuring param_meas at each step. In case param_meas is
an ArrayParameter this is effectively a 2d scan.
Args:
param_set: The QCoDeS parameter to sweep over
start: Starting point of sweep
stop: End point of sweep
num_points: Number of points in sweep
delay: Delay after setting parameter before measurement is performed
*param_meas: Parameter(s) to measure at each step or functions that
will be called at each step. The function should take no arguments.
The parameters and functions are called in the order they are
supplied.
enter_actions: A list of functions taking no arguments that will be
called before the measurements start
exit_actions: A list of functions taking no arguments that will be
called after the measurements ends
write_period: The time after which the data is actually written to the
database.
additional_setpoints: A list of setpoint parameters to be registered in
the measurement but not scanned.
measurement_name: Name of the measurement. This will be passed down to
the dataset produced by the measurement. If not given, a default
value of 'results' is used for the dataset.
exp: The experiment to use for this measurement.
do_plot: should png and pdf versions of the images be saved after the
run. If None the setting will be read from ``qcodesrc.json`
use_threads: If True measurements from each instrument will be done on
separate threads. If you are measuring from several instruments
this may give a significant speedup.
show_progress: should a progress bar be displayed during the
measurement. If None the setting will be read from ``qcodesrc.json`
log_info: Message that is logged during the measurement. If None a default
message is used.
Returns:
The QCoDeS dataset.
"""
if do_plot is None:
do_plot = config.dataset.dond_plot
if show_progress is None:
show_progress = config.dataset.dond_show_progress
meas = Measurement(name=measurement_name, exp=exp)
if log_info is not None:
meas._extra_log_info = log_info
else:
meas._extra_log_info = "Using 'qcodes.utils.dataset.doNd.do1d'"
all_setpoint_params = (param_set,) + tuple(s for s in additional_setpoints)
measured_parameters = tuple(
param for param in param_meas if isinstance(param, _BaseParameter)
)
try:
loop_shape = (num_points,) + tuple(1 for _ in additional_setpoints)
shapes: Shapes = detect_shape_of_measurement(measured_parameters, loop_shape)
except TypeError:
LOG.exception(
f"Could not detect shape of {measured_parameters} "
f"falling back to unknown shape."
)
shapes = None
_register_parameters(meas, all_setpoint_params)
_register_parameters(meas, param_meas, setpoints=all_setpoint_params, shapes=shapes)
_set_write_period(meas, write_period)
_register_actions(meas, enter_actions, exit_actions)
if use_threads is None:
use_threads = config.dataset.use_threads
param_meas_caller = (
ThreadPoolParamsCaller(*param_meas)
if use_threads
else SequentialParamsCaller(*param_meas)
)
# do1D enforces a simple relationship between measured parameters
# and set parameters. For anything more complicated this should be
# reimplemented from scratch
with _catch_keyboard_interrupts() as interrupted, meas.run() as datasaver, param_meas_caller as call_param_meas:
dataset = datasaver.dataset
additional_setpoints_data = process_params_meas(additional_setpoints)
setpoints = np.linspace(start, stop, num_points)
# flush to prevent unflushed print's to visually interrupt tqdm bar
# updates
sys.stdout.flush()
sys.stderr.flush()
for set_point in tqdm(setpoints, disable=not show_progress):
param_set.set(set_point)
time.sleep(delay)
datasaver.add_result(
(param_set, set_point), *call_param_meas(), *additional_setpoints_data
)
return _handle_plotting(dataset, do_plot, interrupted())