# Repository: https://gitlab.com/quantify-os/quantify-core
# Licensed according to the LICENCE file on the main branch
"""Module containing the MeasurementControl."""
from __future__ import annotations
import itertools
import signal
import tempfile
import threading
import time
import types
from collections.abc import Iterable
from itertools import chain
from pathlib import Path
from typing import Any, Dict, Optional
import adaptive
import numpy as np
import xarray as xr
from filelock import FileLock
from qcodes import validators as vals
from qcodes.instrument import Instrument, InstrumentChannel
from qcodes.parameters import InstrumentRefParameter, ManualParameter
from quantify_core.data.experiment import QuantifyExperiment
from quantify_core.data.handling import (
DATASET_NAME,
_is_uniformly_spaced_array,
create_exp_folder,
grow_dataset,
initialize_dataset,
snapshot,
trim_dataset,
)
from quantify_core.measurement.types import Gettable, Settable, is_batched
from quantify_core.utilities.general import call_if_has_method
# Intended for plotting monitors that run in separate processes
_DATASET_LOCKS_DIR = Path(tempfile.gettempdir())
[docs]class MeasurementControl(Instrument): # pylint: disable=too-many-instance-attributes
"""
Instrument responsible for controlling the data acquisition loop.
MeasurementControl (MC) is based on the notion that every experiment consists of
the following steps:
1. Set some parameter(s) (settable_pars)
2. Measure some other parameter(s) (gettable_pars)
3. Store the data.
Example:
.. code-block:: python
meas_ctrl.settables(mw_source1.freq)
meas_ctrl.setpoints(np.arange(5e9, 5.2e9, 100e3))
meas_ctrl.gettables(pulsar_QRM.signal)
dataset = meas_ctrl.run(name='Frequency sweep')
MC exists to enforce structure on experiments. Enforcing this structure allows:
- Standardization of data storage.
- Providing basic real-time visualization.
MC imposes minimal constraints and allows:
- Iterative loops, experiments in which setpoints are processed step by step.
- Batched loops, experiments in which setpoints are processed in batches.
- Adaptive loops, setpoints are determined based on measured values.
"""
[docs] def __init__(self, name: str):
"""
Creates an instance of the Measurement Control.
Parameters
----------
name
name of this instrument.
"""
super().__init__(name=name)
# Parameters are attributes included in logging and which the user can change.
self.lazy_set = ManualParameter(
vals=vals.Bool(),
initial_value=False,
name="lazy_set",
instrument=self,
)
"""If set to ``True``, only set any settable if the setpoint differs
from the previous setpoint. Note that this parameter is overridden by the
``lazy_set`` argument passed to the :meth:`.run` and :meth:`.run_adaptive`
methods."""
self.verbose = ManualParameter(
vals=vals.Bool(),
initial_value=True,
instrument=self,
name="verbose",
)
"""If set to ``True``, prints to ``std_out`` during experiments."""
self.on_progress_callback = ManualParameter(
vals=vals.Callable(),
instrument=self,
name="on_progress_callback",
)
"""A callback to communicate progress. This should be a callable accepting
floats between 0 and 100 indicating the percentage done."""
self.instr_plotmon = InstrumentRefParameter(
vals=vals.MultiType(vals.Strings(), vals.Enum(None)),
instrument=self,
name="instr_plotmon",
)
"""Instrument responsible for live plotting. Can be set to ``None`` to disable
live plotting."""
self.update_interval = ManualParameter(
initial_value=0.5,
vals=vals.Numbers(min_value=0.1),
instrument=self,
name="update_interval",
)
"""Interval for updates during the data acquisition loop, every time more than
:attr:`.update_interval` time has elapsed when acquiring new data points, data
is written to file (and the live monitoring detects updated)."""
# Add experiment_data submodule to allow user to save custom metadata
experiment_data = InstrumentChannel(self, "experiment_data")
self.add_submodule("experiment_data", experiment_data)
self._soft_avg_validator = vals.Ints(1, int(1e8)).validate
# variables that are set before the start of any experiment.
self._settable_pars = []
self._setpoints = []
self._setpoints_input = []
self._gettable_pars = []
# variables used for book keeping during acquisition loop.
self._soft_avg = 1
self._nr_acquired_values = 0
self._loop_count = 0
self._begintime = time.time()
self._last_upd = time.time()
self._batch_size_last = None
self._dataarray_cache: Optional[Dict[str, Any]] = None
# variables used for persistence, plotting and data handling
self._dataset = None
self._exp_folder: Path = None
self._experiment = None
self._plotmon_name = ""
# attributes named as if they are python attributes, e.g. dset.drid_2d == True
self._plot_info = {
"grid_2d": False,
"grid_2d_uniformly_spaced": False,
"1d_2_settables_uniformly_spaced": False,
}
# properly handling KeyboardInterrupts
self._thread_data = threading.local()
# counter for KeyboardInterrupts to allow forced interrupt
self._thread_data.events_num = 0
def __repr__full__(self):
str_out = super().__repr__() + "\n"
# hasattr is necessary in case the instrument was closed
if hasattr(self, "_settable_pars"):
settable_names = [p.name for p in self._settable_pars]
str_out += f" settables: {settable_names}\n"
if hasattr(self, "_gettable_pars"):
gettable_names = [p.name for p in self._gettable_pars]
str_out += f" gettables: {gettable_names}\n"
if hasattr(self, "_setpoints_input") and self._setpoints_input is not None:
input_shapes = [
np.asarray(points).shape for points in self._setpoints_input
]
str_out += f" setpoints_grid input shapes: {input_shapes}\n"
if hasattr(self, "_setpoints") and self._setpoints is not None:
str_out += f" setpoints shape: {np.asarray(self._setpoints).shape}\n"
return str_out
def __repr__(self):
"""
Returns a string containing a summary of this object regarding settables,
gettables and setpoints.
Intended, for example, to give a more useful representation in interactive
shells.
"""
return self.__repr__full__()
[docs] def show(self):
"""Print short representation of the object to stdout."""
print(self.__repr__full__())
[docs] def set_experiment_data(
self, experiment_data: Dict[str, Any], overwrite: bool = True
):
"""
Populates the experiment_data submodule with experiment_data parameters
Parameters
-----------
experiment_data:
Dict specifying the names of the experiment_data parameters and their
values. Follows the format:
.. code-block:: python
{
"parameter_name": {
"value": 10.2
"label": "parameter label"
"unit": "Hz"
}
}
overwrite:
If True, clear all previously saved experiment_data parameters and save new
ones.
If False, keep all previously saved experiment_data parameters and change
their values if necessary
"""
if overwrite:
self.clear_experiment_data()
for name, parameter in experiment_data.items():
if name not in self.experiment_data.parameters:
self.experiment_data.add_parameter(
name=name, parameter_class=ManualParameter
)
self.experiment_data.parameters[name](parameter.get("value"))
self.experiment_data.parameters[name].label = parameter.get("label", name)
self.experiment_data.parameters[name].unit = parameter.get("unit", "")
[docs] def clear_experiment_data(self):
"""
Remove all experiment_data parameters from the experiment_data submodule
"""
self.experiment_data.parameters = {}
############################################
# Methods used to control the measurements #
############################################
def _reset(self, save_data=True):
"""
Resets all experiment specific variables for a new run.
"""
self._nr_acquired_values = 0
self._loop_count = 0
self._begintime = time.time()
self._batch_size_last = None
# Reset KeyboardInterrupt counter
self._thread_data.events_num = 0
# Assign handler to interrupt signal
signal.signal(signal.SIGINT, self._interrupt_handler)
self._save_data = save_data
self._dataarray_cache = None
def _reset_post(self):
"""
Resets specific variables that can change before `.run()`.
"""
self._plot_info = {
"grid_2d": False,
"grid_2d_uniformly_spaced": False,
"1d_2_settables_uniformly_spaced": False,
}
# Reset to default interrupt handler
signal.signal(signal.SIGINT, signal.default_int_handler)
def _init(self, name):
"""
Initializes MC, such as creating the Dataset, experiment folder and such.
"""
# needs to be calculated here because we need the settables' `.batched`
if self._setpoints is None:
self._setpoints = grid_setpoints(self._setpoints_input, self._settable_pars)
# initialize an empty dataset
self._dataset = initialize_dataset(
self._settable_pars, self._setpoints, self._gettable_pars
)
self._dataset.attrs["name"] = name
# cannot add it as a separate (nested) dict so make it flat.
self._dataset.attrs.update(self._plot_info)
tuid = self._dataset.attrs["tuid"]
self._experiment = QuantifyExperiment(tuid=tuid)
if self._save_data:
self._exp_folder = Path(create_exp_folder(tuid=tuid, name=name))
self._safe_write_dataset() # Write the empty dataset
snap = snapshot(update=False, clean=True) # Save a snapshot of all
self._experiment.save_snapshot(snap)
else:
self._exp_folder = None
if self.instr_plotmon():
# Tell plotmon to start monitoring the new dataset
self.instr_plotmon.get_instr().update(tuid=tuid)
[docs] def run(
self,
name: str = "",
soft_avg: int = 1,
lazy_set: Optional[bool] = None,
save_data: bool = True,
) -> xr.Dataset:
"""
Starts a data acquisition loop.
Parameters
----------
name
Name of the measurement. It is included in the name of the data files.
soft_avg
Number of software averages to be performed by the measurement control.
E.g. if `soft_avg=3` the full dataset will be measured 3 times and the
measured values will be averaged element-wise, the averaged dataset is then
returned.
lazy_set
If ``True`` and a setpoint equals the previous setpoint, the ``.set`` method
of the settable will not be called for that iteration.
If this argument is ``None``, the ``.lazy_set()`` ManualParameter is used
instead (which by default is ``False``).
.. warning:: This feature is not available yet when running in batched mode.
save_data
If ``True`` that the measurement data is stored.
"""
lazy_set = lazy_set if lazy_set is not None else self.lazy_set()
self._soft_avg_validator(soft_avg) # validate first
self._soft_avg = soft_avg
self._reset(save_data=save_data)
self._init(name)
self._prepare_settables()
try:
if self._get_is_batched():
if self.verbose():
print("Starting batched measurement...")
self._run_batched()
else:
if self.verbose():
print("Starting iterative measurement...")
self._run_iterative(lazy_set)
except KeyboardInterrupt:
print("\nInterrupt signaled, exiting gracefully...")
if self._save_data:
self._safe_write_dataset() # Wrap up experiment and store data
self._finish()
self._reset_post()
self._check_interrupt() # Propagate interruption
return self._dataset
[docs] def run_adaptive(self, name, params, lazy_set: Optional[bool] = None) -> xr.Dataset:
"""
Starts a data acquisition loop using an adaptive function.
.. warning ::
The functionality of this mode can be complex - it is recommended to read
the relevant long form documentation.
Parameters
----------
name
Name of the measurement. This name is included in the name of the data
files.
params
Key value parameters describe the adaptive function to use, and any further
parameters for that function.
lazy_set
If ``True`` and a setpoint equals the previous setpoint, the ``.set`` method
of the settable will not be called for that iteration.
If this argument is ``None``, the ``.lazy_set()`` ManualParameter is used
instead (which by default is ``False``).
"""
lazy_set = lazy_set if lazy_set is not None else self.lazy_set()
def measure(vec) -> float:
"""
This function executes the measurement and is passed to the adaptive
function (often a minimization algorithm) to be evaluated many times.
Although the measure function acquires (and stores) all gettable parameters,
only the first value is returned to match the function signature for a valid
measurement function.
"""
if len(self._dataset["y0"]) == self._nr_acquired_values:
self._dataset = grow_dataset(self._dataset)
# 1D sweeps return single values, wrap in a list
if np.isscalar(vec):
vec = [vec]
self._iterative_set_and_get(vec, self._nr_acquired_values, lazy_set)
# only y0 is returned so as to match the function signature for a valid
# measurement function.
ret = self._dataset["y0"].values[self._nr_acquired_values]
self._nr_acquired_values += 1
self._update(".")
self._check_interrupt()
return ret
def subroutine():
self._prepare_settables()
self._prepare_gettables()
adaptive_function = params.get("adaptive_function")
af_pars_copy = dict(params)
# if the adaptive function is part of the python adaptive library
if isinstance(adaptive_function, type) and issubclass(
adaptive_function, adaptive.learner.BaseLearner
):
goal = af_pars_copy["goal"]
unusued_pars = ["adaptive_function", "goal"]
for unusued_par in unusued_pars:
af_pars_copy.pop(unusued_par, None)
learner = adaptive_function(measure, **af_pars_copy)
adaptive.runner.simple(learner, goal)
# any adaptive function with the right signature
elif isinstance(adaptive_function, types.FunctionType):
unused_pars = ["adaptive_function"]
for unused_par in unused_pars:
af_pars_copy.pop(unused_par, None)
adaptive_function(measure, **af_pars_copy)
self._reset()
self.setpoints(
np.empty((64, len(self._settable_pars)))
) # block out some space in the dataset
self._init(name)
try:
print("Running adaptively...")
subroutine()
except KeyboardInterrupt:
print("\nInterrupt signaled, exiting gracefully...")
self._finish()
self._dataset = trim_dataset(self._dataset)
self._safe_write_dataset() # Wrap up experiment and store data
self._check_interrupt() # Propagate interruption
return self._dataset
def _run_iterative(self, lazy_set: bool = False):
while self._get_fracdone() < 1.0:
self._prepare_gettables()
self._dataarray_cache = {}
for row in self._setpoints:
self._iterative_set_and_get(row, self._curr_setpoint_idx(), lazy_set)
self._nr_acquired_values += 1
self._update()
self._check_interrupt()
self._dataarray_cache = None
self._loop_count += 1
def _run_batched(self): # pylint: disable=too-many-locals
batch_size = self._get_batch_size()
where_batched = self._get_where_batched()
where_iterative = self._get_where_iterative()
batched_settables = self._get_batched_settables()
iterative_settables = self._get_iterative_settables()
if self.verbose():
print(
"Iterative settable(s) [outer loop(s)]:\n\t",
", ".join(par.name for par in iterative_settables) or "--- (None) ---",
"\nBatched settable(s):\n\t",
", ".join(par.name for par in batched_settables),
f"\nBatch size limit: {batch_size:d}\n",
)
while self._get_fracdone() < 1.0:
setpoint_idx = self._curr_setpoint_idx()
self._batch_size_last = batch_size
slice_len = setpoint_idx + self._batch_size_last
for i, spar in enumerate(iterative_settables):
# Here ensure that all setpoints of each iterative settable are the same
# within each batch
val, iterator = next(
itertools.groupby(
self._setpoints[setpoint_idx:slice_len, where_iterative[i]]
)
)
spar.set(val)
# We also determine the size of each next batch
self._batch_size_last = min(self._batch_size_last, len(tuple(iterator)))
slice_len = setpoint_idx + self._batch_size_last
for i, spar in enumerate(batched_settables):
pnts = self._setpoints[setpoint_idx:slice_len, where_batched[i]]
spar.set(pnts)
# Update for `print_progress`
self._batch_size_last = min(self._batch_size_last, len(pnts))
self._prepare_gettables()
y_off = 0
for gpar in self._gettable_pars:
new_data = gpar.get() # can return (N, M)
# if we get a simple array, shape it to (1, M)
if len(np.shape(new_data)) == 1:
new_data = new_data.reshape(1, (len(new_data)))
for row in new_data:
yi_name = f"y{y_off}"
slice_len = setpoint_idx + len(row) # the slice we will be updating
old_vals = self._dataset[yi_name].values[setpoint_idx:slice_len]
old_vals[
np.isnan(old_vals)
] = 0 # will be full of NaNs on the first iteration, change to 0
self._dataset[yi_name].values[
setpoint_idx:slice_len
] = self._build_data(row, old_vals)
y_off += 1
self._nr_acquired_values += np.shape(new_data)[1]
self._update()
self._check_interrupt()
def _build_data(self, new_data, old_data):
if self._soft_avg == 1:
return old_data + new_data
return (new_data + old_data * self._loop_count) / (1 + self._loop_count)
def _iterative_set_and_get(
self, setpoints: np.ndarray, idx: int, lazy_set: bool = False
):
"""
Processes one row of setpoints. Sets all settables, gets all gettables, encodes
new data in dataset.
If lazy_set==True and any setpoint equals the corresponding previous setpoint,
that setpoint is not set in its corresponding settable.
.. note ::
Note: some lines in this function are redundant depending on mode (sweep vs
adaptive). Specifically:
- in sweep, the x dimensions are already filled
- in adaptive, soft_avg is always 1
"""
assert self._dataset is not None
# set all individual setparams
for setpar_idx, (spar, spt) in enumerate(zip(self._settable_pars, setpoints)):
xi_name = f"x{setpar_idx}"
if self._dataarray_cache is None:
xi_dataarray_values = self._dataset[xi_name].values
else:
if not xi_name in self._dataarray_cache:
self._dataarray_cache[xi_name] = self._dataset[xi_name].values
xi_dataarray_values = self._dataarray_cache[xi_name]
xi_dataarray_values[idx] = spt
prev_spt = xi_dataarray_values[idx - 1] if idx else None
# if lazy_set==True and the setpoint equals the previous setpoint, do not
# set the setpoint.
if not (lazy_set and spt == prev_spt):
spar.set(spt)
# get all data points
y_offset = 0
for gpar in self._gettable_pars:
new_data = gpar.get()
# if the gettable returned a float, cast to list
if np.isscalar(new_data):
new_data = [new_data]
# iterate through the data list, each element is different y for these
# x coordinates
for val in new_data:
yi_name = f"y{y_offset}"
if self._dataarray_cache is None:
yi_dataarray_values = self._dataset[yi_name].values
else:
if not yi_name in self._dataarray_cache:
self._dataarray_cache[yi_name] = self._dataset[yi_name].values
yi_dataarray_values = self._dataarray_cache[yi_name]
old_val = yi_dataarray_values[idx]
if self._soft_avg == 1 or np.isnan(old_val):
yi_dataarray_values[idx] = val
else:
averaged = (val + old_val * self._loop_count) / (
1 + self._loop_count
)
yi_dataarray_values[idx] = averaged
y_offset += 1
############################################
# Methods used to control the measurements #
############################################
def _interrupt_handler(self, signal, frame):
"""
A proper handler for signal.SIGINT (a.k.a. KeyboardInterrupt).
Used to avoid affecting any other code, e.g. instruments drivers, and be able
safely stop the MC after an iteration finishes.
"""
self._thread_data.events_num += 1
if self._thread_data.events_num >= 5:
raise KeyboardInterrupt
print(
f"\n\n[!!!] {self._thread_data.events_num} interruption(s) signaled. "
"Stopping after this iteration/batch.\n"
f"[Send {5 - self._thread_data.events_num} more interruptions to force"
f"stop (not safe!)].\n"
)
def _check_interrupt(self):
"""
Verifies if the user has signaled the interruption of the experiment.
Intended to be used after each iteration or after each batch of data.
"""
if self._thread_data.events_num >= 1:
# It is safe to raise the KeyboardInterrupt here because we are guaranteed
# To be running MC code. The exception can be handled in a try-except
raise KeyboardInterrupt("Measurement interrupted")
def _update(self, print_message: str = None):
"""
Do any updates to/from external systems, such as saving, plotting, etc.
"""
update = (
time.time() - self._last_upd > self.update_interval()
or self._nr_acquired_values == self._get_max_setpoints()
)
if update:
self.print_progress(print_message)
if self._save_data:
self._safe_write_dataset()
self._last_upd = time.time()
def _prepare_gettables(self) -> None:
"""
Call prepare() on the Gettable, if prepare() exists
"""
for getpar in self._gettable_pars:
call_if_has_method(getpar, "prepare")
def _prepare_settables(self) -> None:
"""
Call prepare() on all Settable, if prepare() exists
"""
for setpar in self._settable_pars:
call_if_has_method(setpar, "prepare")
def _finish(self) -> None:
"""
Call finish() on all Settables and Gettables, if finish() exists
"""
for par in self._gettable_pars + self._settable_pars:
call_if_has_method(par, "finish")
def _get_batched_mask(self):
return tuple(is_batched(spar) for spar in self._settable_pars)
def _get_where_batched(self):
# Indices to select correct entries in results data
return np.where(self._get_batched_mask())[0]
def _get_where_iterative(self):
return np.where(tuple(not m for m in self._get_batched_mask()))[0]
def _get_iterative_settables(self):
return tuple(spar for spar in self._settable_pars if not is_batched(spar))
def _get_batched_settables(self):
return tuple(spar for spar in self._settable_pars if is_batched(spar))
def _get_batch_size(self):
# np.inf is not supported by the JSON schema, but we keep the code robust
min_with_inf = min(
getattr(par, "batch_size", np.inf)
for par in chain.from_iterable((self._settable_pars, self._gettable_pars))
)
return min(min_with_inf, len(self._setpoints))
def _get_is_batched(self) -> bool:
if any(
is_batched(gpar) for gpar in chain(self._gettable_pars, self._settable_pars)
):
if not all(is_batched(gpar) for gpar in self._gettable_pars):
raise RuntimeError(
"Control mismatch; all Gettables must have batched Control Mode, "
"i.e. all gettables must have `.batched=True`."
)
if not any(is_batched(spar) for spar in self._settable_pars):
raise RuntimeError(
"Control mismatch; At least one settable must have "
"`settable.batched=True`, if the gettables are batched."
)
return True
return False
def _get_max_setpoints(self) -> int:
"""
The total number of setpoints to examine
"""
return len(self._setpoints) * self._soft_avg
def _curr_setpoint_idx(self) -> int:
"""
Current position through the sweep
Returns
-------
int
setpoint_idx
"""
acquired = self._nr_acquired_values
setpoint_idx = acquired % len(self._setpoints)
self._loop_count = acquired // len(self._setpoints)
return setpoint_idx
def _get_fracdone(self) -> float:
"""
Returns the fraction of the experiment that is completed.
"""
return self._nr_acquired_values / self._get_max_setpoints()
[docs] def print_progress(self, progress_message: str = None):
"""
Prints the provided `progress_messages` or a default one; and calls the
callback specified by `on_progress_callback`.
Printing can be suppressed with `.verbose(False)`.
"""
# There are no points initialized, progress does not make sense
if self._get_max_setpoints() == 0:
raise ValueError("No setpoints available, progress cannot be defined")
progress_percent = self._get_fracdone() * 100
elapsed_time = time.time() - self._begintime
if self.verbose() and progress_message is None:
t_left = (
round((100.0 - progress_percent) / progress_percent * elapsed_time, 1)
if progress_percent != 0
else None
)
progress_message = (
f"\r{int(progress_percent):#3d}% completed | elapsed time: "
f"{int(round(elapsed_time, 1)):#6d}s"
)
if t_left is not None:
progress_message += f" | time left: {int(round(t_left, 1)):#6d}s"
if self._batch_size_last is not None:
progress_message += f" last batch size: {self._batch_size_last:#6d}"
progress_message += " "
print(progress_message, end="" if progress_percent < 100 else "\n")
if self.on_progress_callback() is not None:
self.on_progress_callback()(progress_percent)
if self.verbose() and progress_message is not None:
print(progress_message, end="")
def _safe_write_dataset(self):
"""
Uses a lock when writing the file to stay safe for multiprocessing.
Locking files are written into a temporary dir to avoid polluting
the experiment container.
"""
# Multiprocess safe
lockfile = (
_DATASET_LOCKS_DIR / f"{self._dataset.attrs['tuid']}-{DATASET_NAME}.lock"
)
with FileLock(lockfile, 5):
self._experiment.write_dataset(self._dataset)
####################################
# Non-parameter get/set functions #
####################################
[docs] def settables(self, settable_pars):
"""
Define the settable parameters for the acquisition loop.
The :class:`.Settable` helper class defines the
requirements for a Settable object.
Parameters
---------
settable_pars
parameter(s) to be set during the acquisition loop, accepts a list or tuple
of multiple Settable objects or a single Settable object.
"""
# for native nD compatibility we treat this like a list of settables.
if not isinstance(settable_pars, (list, tuple)):
settable_pars = [settable_pars]
self._settable_pars = []
for settable in settable_pars:
self._settable_pars.append(Settable(settable))
[docs] def setpoints(self, setpoints: np.ndarray):
"""
Set setpoints that determine values to be set in acquisition loop.
.. tip::
Use :func:`~numpy.column_stack` to reshape multiple
1D arrays when setting multiple settables.
Parameters
----------
setpoints :
An array that defines the values to loop over in the experiment.
The shape of the array has to be either (N,) or (N,1) for a 1D loop;
or (N, M) in the case of an MD loop.
"""
if len(np.shape(setpoints)) == 1:
setpoints = setpoints.reshape((len(setpoints), 1))
elif len(np.shape(setpoints)) == 2:
# used in plotmon to detect need for interpolation in 2d plot
is_uniform = all(
_is_uniformly_spaced_array(setpoints_i) for setpoints_i in setpoints.T
)
self._plot_info["1d_2_settables_uniformly_spaced"] = is_uniform
self._setpoints = setpoints
# `.setpoints()` and `.setpoints_grid()` cannot be used at the same time
self._setpoints_input = None
[docs] def setpoints_grid(self, setpoints: Iterable[np.ndarray]):
"""
Makes a grid from the provided `setpoints` assuming each array element
corresponds to an orthogonal dimension.
The resulting gridded points determine values to be set in the acquisition loop.
The gridding is such that the inner most loop corresponds to the batched
settable with the smallest `.batch_size`.
Parameters
----------
setpoints
The values to loop over in the experiment. The grid is reshaped in the same
order.
.. admonition:: Examples
:class: dropdown, tip
We first prepare some utilities necessarily for the examples.
.. jupyter-execute::
:hide-code:
:hide-output:
from qcodes import Instrument
Instrument.close_all()
.. jupyter-execute::
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from qcodes import ManualParameter, Parameter
import quantify_core.data.handling as dh
from quantify_core.measurement import MeasurementControl
dh.set_datadir(Path.home() / "quantify-data")
meas_ctrl = MeasurementControl("meas_ctrl")
par0 = ManualParameter(name="x0", label="X0", unit="s")
par1 = ManualParameter(name="x1", label="X1", unit="s")
par2 = ManualParameter(name="x2", label="X2", unit="s")
par3 = ManualParameter(name="x3", label="X3", unit="s")
sig = Parameter(name="sig", label="Signal", unit="V", get_cmd=lambda: np.exp(par0()))
.. admonition:: Iterative-only settables
:class: dropdown, tip
.. jupyter-execute::
par0.batched = False
par1.batched = False
par2.batched = False
sig.batched = False
meas_ctrl.settables([par0, par1, par2])
meas_ctrl.setpoints_grid(
[
np.linspace(0, 1, 4),
np.linspace(1, 2, 5),
np.linspace(2, 3, 6),
]
)
meas_ctrl.gettables(sig)
dset = meas_ctrl.run("demo")
list(xr.plot.line(xi, label=name) for name, xi in dset.coords.items())
plt.gca().legend()
.. admonition:: Batched-only settables
:class: dropdown, tip
Note that the settable with lowest `.batch_size` will be correspond to the
innermost loop.
.. jupyter-execute::
par0.batched = True
par1.batch_size = 8
par1.batched = True
par1.batch_size = 8
par2.batched = True
par2.batch_size = 4
sig = Parameter(name="sig", label="Signal", unit="V", get_cmd=lambda: np.exp(par2()))
sig.batched = True
sig.batch_size = 32
meas_ctrl.settables([par0, par1, par2])
meas_ctrl.setpoints_grid(
[
np.linspace(0, 1, 3),
np.linspace(1, 2, 5),
np.linspace(2, 3, 4),
]
)
meas_ctrl.gettables(sig)
dset = meas_ctrl.run("demo")
list(xr.plot.line(xi, label=name) for name, xi in dset.coords.items())
plt.gca().legend()
.. admonition:: Batched and iterative settables
:class: dropdown, tip
Note that the settable with lowest `.batch_size` will be correspond to the
innermost loop. Furthermore, the iterative settables will be the outermost loops.
.. jupyter-execute::
par0.batched = False
par1.batched = True
par1.batch_size = 8
par2.batched = False
par3.batched = True
par3.batch_size = 4
sig = Parameter(name="sig", label="Signal", unit="V", get_cmd=lambda: np.exp(par3()))
sig.batched = True
sig.batch_size = 32
meas_ctrl.settables([par0, par1, par2, par3])
meas_ctrl.setpoints_grid(
[
np.linspace(0, 1, 3),
np.linspace(1, 2, 5),
np.linspace(2, 3, 4),
np.linspace(3, 4, 6),
]
)
meas_ctrl.gettables(sig)
dset = meas_ctrl.run("demo")
list(xr.plot.line(xi, label=name) for name, xi in dset.coords.items())
plt.gca().legend()
""" # pylint: disable=line-too-long
self._setpoints = None # assigned later in the `._init()`
self._setpoints_input = setpoints
if len(setpoints) == 2:
self._plot_info["xlen"] = len(setpoints[0])
self._plot_info["ylen"] = len(setpoints[1])
self._plot_info["grid_2d"] = True
is_uniform = all( # used in plotmon to detect need for interpolation
_is_uniformly_spaced_array(setpoints[i]) for i in (0, 1)
)
self._plot_info["grid_2d_uniformly_spaced"] = is_uniform
[docs] def gettables(self, gettable_pars):
"""
Define the parameters to be acquired during the acquisition loop.
The :class:`.Gettable` helper class defines the
requirements for a Gettable object.
Parameters
----------
gettable_pars
parameter(s) to be get during the acquisition loop, accepts:
- list or tuple of multiple Gettable objects
- a single Gettable object
"""
if not isinstance(gettable_pars, (list, tuple)):
gettable_pars = [gettable_pars]
self._gettable_pars = []
for gpar in gettable_pars:
self._gettable_pars.append(Gettable(gpar))
[docs] def measurement_description(self) -> Dict[str, Any]:
"""Return a serializable description of the latest measurement
Users can add additional information to the description manually.
Returns
-------
:
Dictionary with description of the measurement
"""
experiment_description = {
"name": self._dataset.attrs["name"],
"settables": [str(s) for s in self._settable_pars],
}
experiment_description["gettables"] = [str(s) for s in self._gettable_pars]
experiment_description["setpoints_shape"] = self._setpoints.shape
experiment_description["soft_avg"] = self._soft_avg
experiment_description["acquired_dataset"] = {"tuid": self._dataset.tuid}
return experiment_description
[docs]def grid_setpoints(setpoints: Iterable, settables: Iterable = None) -> np.ndarray:
"""
Makes gridded setpoints. If `settables` is provided, the gridding is such that the
inner most loop corresponds to the batched settable with the smallest `.batch_size`.
.. warning ::
Using this method typecasts all values into the same type.
This may lead to validator errors when setting
e.g., a `float` instead of an `int`.
Parameters
----------
setpoints
A list of arrays that defines the values to loop over in the experiment for each
orthogonal dimension. The grid is reshaped in the same order.
settables
A list of settable objects to which the elements in the `setpoints` correspond
to. Used to correctly grid data when mixing batched and iterative settables.
Returns
-------
:class:`~numpy.ndarray`
An array where the first numpy axis correspond to individual setpoints.
"""
if settables is None:
settables = [None] * len(setpoints)
coordinates_names = [f"x{i}" for i, pnts in enumerate(setpoints)]
dataset_coordinates = xr.Dataset(coords=dict(zip(coordinates_names, setpoints)))
coordinates_batched = [
name for name, spar in zip(coordinates_names, settables) if is_batched(spar)
]
coordinates_iterative = sorted(
(
name
for name, spar in zip(coordinates_names, settables)
if not is_batched(spar)
),
reverse=True,
)
if len(coordinates_batched):
batch_sizes = [
getattr(spar, "batch_size", np.inf)
for spar in settables
if is_batched(spar)
]
coordinates_batched_set = set(coordinates_batched)
inner_coord_name = coordinates_batched[np.argmin(batch_sizes)]
coordinates_batched_set.remove(inner_coord_name)
# The inner most coordinate must correspond to the batched settable with
# min `.batch_size`
stack_order = (
coordinates_iterative
+ sorted(coordinates_batched_set, reverse=True)
+ [inner_coord_name]
)
else:
stack_order = coordinates_iterative + sorted(coordinates_batched, reverse=True)
# Internally the xarray's stack mechanism is used to achieve the desired grid
stacked_dset = dataset_coordinates.stack(dim_0=stack_order)
# Return numpy array in the original order
return np.column_stack([stacked_dset[name].values for name in coordinates_names])