# Repository: https://gitlab.com/quantify-os/quantify-scheduler
# Licensed according to the LICENCE file on the main branch
"""Module containing the core concepts of the scheduler."""
from __future__ import annotations
import dataclasses
import json
import warnings
from abc import ABC
from collections import UserDict
from copy import copy
from itertools import chain
from typing import TYPE_CHECKING, Any, Hashable, Literal
from uuid import uuid4
import numpy as np
import pandas as pd
from quantify_scheduler import enums, json_utils, resources
from quantify_scheduler.helpers.collections import make_hash
from quantify_scheduler.helpers.importers import export_python_object_to_path_string
from quantify_scheduler.json_utils import JSONSchemaValMixin
from quantify_scheduler.operations.control_flow_library import (
ConditionalOperation,
LoopOperation,
)
from quantify_scheduler.operations.operation import Operation
if TYPE_CHECKING:
import plotly.graph_objects as go
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from quantify_scheduler.operations.control_flow_library import ControlFlowSpec
from quantify_scheduler.resources import Resource
"""
An ordered dictionary type hint,
which makes it clear and obvious
that order is significant and used by the logic.
Note: dict is ordered from Python version 3.7.
Note: collections.OrderedDict can be slow in some cases.
"""
[docs]
class ScheduleBase(JSONSchemaValMixin, UserDict, ABC):
"""
Interface to be used for :class:`~.Schedule`.
The :class:`~.ScheduleBase` is a data structure that is at
the core of the Quantify-scheduler and describes when what operations are applied
where.
The :class:`~.ScheduleBase` is a collection of
:class:`quantify_scheduler.operations.operation.Operation` objects and timing
constraints that define relations between the operations.
The schedule data structure is based on a dictionary.
This dictionary contains:
- operation_dict - a hash table containing the unique
:class:`quantify_scheduler.operations.operation.Operation` s added to the
schedule.
- schedulables - an ordered dictionary of all timing constraints added
between operations; when multiple schedulables have the same
absolute time, the order defined in the dictionary decides precedence.
The :class:`~.Schedule` provides an API to create schedules.
The :class:`~.CompiledSchedule` represents a schedule after
it has been compiled for execution on a backend.
The :class:`~.Schedule` contains information on the
:attr:`~.ScheduleBase.operations` and
:attr:`~.ScheduleBase.schedulables`.
The :attr:`~.ScheduleBase.operations` is a dictionary of all
unique operations used in the schedule and contain the information on *what*
operation to apply *where*.
The :attr:`~.ScheduleBase.schedulables` is a dictionary of
Schedulables describing timing constraints between operations, i.e. when to apply
an operation.
**JSON schema of a valid Schedule**
.. jsonschema:: https://gitlab.com/quantify-os/quantify-scheduler/-/raw/main/quantify_scheduler/schemas/schedule.json
""" # noqa: E501
@property
[docs]
def name(self) -> str:
"""Returns the name of the schedule."""
return self["name"]
@property
[docs]
def repetitions(self) -> int:
"""
Returns the amount of times this Schedule will be repeated.
Returns
-------
:
The repetitions count.
"""
return self["repetitions"]
@repetitions.setter
def repetitions(self, value: int) -> None:
if value <= 0:
raise ValueError(
f"Attempting to set repetitions for the schedule. "
f"Must be a positive number. Got {value}."
)
self["repetitions"] = int(value)
@property
[docs]
def operations(self) -> dict[str, Operation | Schedule]:
"""
A dictionary of all unique operations used in the schedule.
This specifies information on *what* operation to apply *where*.
The keys correspond to the :attr:`~.Operation.hash` and values are instances
of :class:`quantify_scheduler.operations.operation.Operation`.
"""
return self["operation_dict"]
@property
[docs]
def schedulables(self) -> DictOrdered[str, Any]:
"""
Ordered dictionary of schedulables describing timing and order of operations.
A schedulable uses timing constraints to constrain the operation in time by
specifying the time (:code:`"rel_time"`) between a reference operation and the
added operation. The time can be specified with respect to a reference point
(:code:`"ref_pt"') on the reference operation (:code:`"ref_op"`) and a reference
point on the next added operation (:code:`"ref_pt_new"').
A reference point can be either the "start", "center", or "end" of an
operation. The reference operation (:code:`"ref_op"`) is specified using its
label property.
Each item in the list represents a timing constraint and is a dictionary with
the following keys:
.. code-block::
['label', 'rel_time', 'ref_op', 'ref_pt_new', 'ref_pt', 'operation_id']
The label is used as a unique identifier that can be used as a reference for
other operations, the operation_id refers to the hash of an
operation in :attr:`~.ScheduleBase.operations`.
.. note::
timing constraints are not intended to be modified directly.
Instead use the :meth:`~.Schedule.add`
"""
return self["schedulables"]
@property
[docs]
def resources(self) -> dict[str, Resource]:
"""
A dictionary containing resources.
Keys are names (str), values are instances of
:class:`~quantify_scheduler.resources.Resource`.
"""
return self["resource_dict"]
def __hash__(self) -> int:
return make_hash(self.data)
@property
[docs]
def hash(self) -> str:
"""A hash based on the contents of the Schedule."""
return str(hash(self))
def __repr__(self) -> str:
"""Return a string representation of this instance."""
return (
f'{self.__class__.__name__} "{self["name"]}" containing '
f'({len(self["operation_dict"])}) '
f"{len(self.schedulables)} (unique) operations."
)
[docs]
def to_json(self) -> str:
"""
Convert the Schedule data structure to a JSON string.
Returns
-------
:
The json string result.
"""
return json.dumps(self, cls=json_utils.SchedulerJSONEncoder)
@classmethod
[docs]
def from_json(cls, data: str) -> Schedule:
"""
Convert the JSON data to a Schedule.
Parameters
----------
data
The JSON data.
Returns
-------
:
The Schedule object.
"""
return json_utils.SchedulerJSONDecoder().decode(data)
[docs]
def plot_circuit_diagram(
self,
figsize: tuple[int, int] = None,
ax: Axes | None = None,
plot_backend: Literal["mpl"] = "mpl",
) -> tuple[Figure, Axes | list[Axes]]:
"""
Create a circuit diagram visualization of the schedule using the specified plotting backend.
The circuit diagram visualization depicts the schedule at the quantum circuit
layer. Because quantify-scheduler uses a hybrid gate-pulse paradigm, operations
for which no information is specified at the gate level are visualized using an
icon (e.g., a stylized wavy pulse) depending on the information specified at
the quantum device layer.
Alias of :func:`quantify_scheduler.schedules._visualization.circuit_diagram.circuit_diagram_matplotlib`.
Parameters
----------
schedule
the schedule to render.
figsize
matplotlib figsize.
ax
Axis handle to use for plotting.
plot_backend
Plotting backend to use, currently only 'mpl' is supported
Returns
-------
fig
matplotlib figure object.
ax
matplotlib axis object.
Each gate, pulse, measurement, and any other operation are plotted in the order
of execution, but no timing information is provided.
.. admonition:: Example
:class: tip
.. jupyter-execute::
from quantify_scheduler import Schedule
from quantify_scheduler.operations.gate_library import Reset, X90, CZ, Rxy, Measure
sched = Schedule(f"Bell experiment on q0-q1")
sched.add(Reset("q0", "q1"))
sched.add(X90("q0"))
sched.add(X90("q1"), ref_pt="start", rel_time=0)
sched.add(CZ(qC="q0", qT="q1"))
sched.add(Rxy(theta=45, phi=0, qubit="q0") )
sched.add(Measure("q0", acq_index=0))
sched.add(Measure("q1", acq_index=0), ref_pt="start")
sched.plot_circuit_diagram();
.. note::
Gates that are started simultaneously on the same qubit will overlap.
.. jupyter-execute::
from quantify_scheduler import Schedule
from quantify_scheduler.operations.gate_library import X90, Measure
sched = Schedule(f"overlapping gates")
sched.add(X90("q0"))
sched.add(Measure("q0"), ref_pt="start", rel_time=0)
sched.plot_circuit_diagram();
.. note::
If the pulse's port address was not found then the pulse will be plotted on the
'other' timeline.
""" # noqa: E501
# NB imported here to avoid circular import
if plot_backend == "mpl":
import quantify_scheduler.schedules._visualization.circuit_diagram as cd
return cd.circuit_diagram_matplotlib(schedule=self, figsize=figsize, ax=ax)
raise ValueError(
f"plot_backend must be equal to 'mpl', value given: {repr(plot_backend)}"
)
[docs]
def plot_pulse_diagram(
self,
port_list: list[str] | None = None,
sampling_rate: float = 1e9,
modulation: Literal["off", "if", "clock"] = "off",
modulation_if: float = 0.0,
plot_backend: Literal["mpl", "plotly"] = "mpl",
x_range: tuple[float, float] = (-np.inf, np.inf),
combine_waveforms_on_same_port: bool = False,
**backend_kwargs,
) -> tuple[Figure, Axes] | go.Figure:
"""
Create a visualization of all the pulses in a schedule using the specified plotting backend.
The pulse diagram visualizes the schedule at the quantum device layer.
For this visualization to work, all operations need to have the information
present (e.g., pulse info) to represent these on the quantum-circuit level and
requires the absolute timing to have been determined.
This information is typically added when the quantum-device level compilation is
performed.
Alias of
:func:`quantify_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_matplotlib`
and
:func:`quantify_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_plotly`.
Parameters
----------
port_list :
A list of ports to show. If ``None`` (default) the first 8 ports encountered in the sequence are used.
modulation :
Determines if modulation is included in the visualization.
modulation_if :
Modulation frequency used when modulation is set to "if".
sampling_rate :
The time resolution used to sample the schedule in Hz.
plot_backend:
Plotting library to use, can either be 'mpl' or 'plotly'.
x_range:
The range of the x-axis that is plotted, given as a tuple (left limit, right
limit). This can be used to reduce memory usage when plotting a small section of
a long pulse sequence. By default (-np.inf, np.inf).
combine_waveforms_on_same_port:
By default False. If True, combines all waveforms on the same port into one
single waveform. The resulting waveform is the sum of all waveforms on that
port (small inaccuracies may occur due to floating point approximation). If
False, the waveforms are shown individually.
backend_kwargs:
Keyword arguments to be passed on to the plotting backend. The arguments
that can be used for either backend can be found in the documentation of
:func:`quantify_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_matplotlib`
and
:func:`quantify_scheduler.schedules._visualization.pulse_diagram.pulse_diagram_plotly`.
Returns
-------
Union[Tuple[Figure, Axes], :class:`!plotly.graph_objects.Figure`]
the plot
.. admonition:: Example
:class: tip
A simple plot with matplotlib can be created as follows:
.. jupyter-execute::
from quantify_scheduler.backends.graph_compilation import SerialCompiler
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.operations.pulse_library import (
DRAGPulse, SquarePulse, RampPulse, VoltageOffset,
)
from quantify_scheduler.resources import ClockResource
schedule = Schedule("Multiple waveforms")
schedule.add(DRAGPulse(G_amp=0.2, D_amp=0.2, phase=0, duration=4e-6, port="P", clock="C"))
schedule.add(RampPulse(amp=0.2, offset=0.0, duration=6e-6, port="P"))
schedule.add(SquarePulse(amp=0.1, duration=4e-6, port="Q"), ref_pt='start')
schedule.add_resource(ClockResource(name="C", freq=4e9))
quantum_device = QuantumDevice("quantum_device")
device_compiler = SerialCompiler("Device compiler", quantum_device)
compiled_schedule = device_compiler.compile(schedule)
_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6)
The backend can be changed to the plotly backend by specifying the
``plot_backend=plotly`` argument. With the plotly backend, pulse
diagrams include a separate plot for each port/clock
combination:
.. jupyter-execute::
_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, plot_backend='plotly')
The same can be achieved in the default ``plot_backend`` (``matplotlib``)
by passing the keyword argument ``multiple_subplots=True``:
.. jupyter-execute::
_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, multiple_subplots=True)
By default, waveforms overlapping in time on the same port are shown separately:
.. jupyter-execute::
schedule = Schedule("Overlapping waveforms")
schedule.add(VoltageOffset(offset_path_I=0.25, offset_path_Q=0.0, port="Q"))
schedule.add(SquarePulse(amp=0.1, duration=4e-6, port="Q"), rel_time=2e-6)
schedule.add(VoltageOffset(offset_path_I=0.0, offset_path_Q=0.0, port="Q"), ref_pt="start", rel_time=2e-6)
compiled_schedule = device_compiler.compile(schedule)
_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6)
This behaviour can be changed with the parameter ``combine_waveforms_on_same_port``:
.. jupyter-execute::
_ = compiled_schedule.plot_pulse_diagram(sampling_rate=20e6, combine_waveforms_on_same_port=True)
""" # noqa: E501
# NB imported here to avoid circular import
from quantify_scheduler.schedules._visualization.pulse_diagram import (
sample_schedule,
)
sampled_pulses_and_acqs = sample_schedule(
self,
sampling_rate=sampling_rate,
port_list=port_list,
modulation=modulation,
modulation_if=modulation_if,
x_range=x_range,
combine_waveforms_on_same_port=combine_waveforms_on_same_port,
)
if plot_backend == "mpl":
# NB imported here to avoid circular import
from quantify_scheduler.schedules._visualization.pulse_diagram import (
pulse_diagram_matplotlib,
)
return pulse_diagram_matplotlib(
sampled_pulses_and_acqs=sampled_pulses_and_acqs,
title=self["name"],
**backend_kwargs,
)
if plot_backend == "plotly":
# NB imported here to avoid circular import
from quantify_scheduler.schedules._visualization.pulse_diagram import (
pulse_diagram_plotly,
)
return pulse_diagram_plotly(
sampled_pulses_and_acqs=sampled_pulses_and_acqs,
title=self["name"],
**backend_kwargs,
)
raise ValueError(
f"plot_backend must be equal to either 'mpl' or 'plotly', "
f"value given: {repr(plot_backend)}"
)
@classmethod
[docs]
def _generate_timing_table_list(
cls,
operation: Operation | ScheduleBase,
time_offset: float,
timing_table_list: list,
operation_id: str | None,
) -> None:
if isinstance(operation, ScheduleBase):
for schedulable in operation.schedulables.values():
if "abs_time" not in schedulable:
# when this exception is encountered
raise ValueError(
"Absolute time has not been determined yet. "
"Please compile your schedule."
)
cls._generate_timing_table_list(
operation.operations[schedulable["operation_id"]],
time_offset + schedulable["abs_time"],
timing_table_list,
schedulable["operation_id"],
)
elif isinstance(operation, LoopOperation):
for i in range(operation.data["control_flow_info"]["repetitions"]):
cls._generate_timing_table_list(
operation.body,
time_offset + i * operation.body.duration,
timing_table_list,
operation_id,
)
elif isinstance(operation, ConditionalOperation):
cls._generate_timing_table_list(
operation.body,
time_offset,
timing_table_list,
operation_id,
)
else:
for i, op_info in chain(
enumerate(operation["pulse_info"]),
enumerate(operation["acquisition_info"]),
):
t0 = time_offset + op_info["t0"]
df_row = {
"waveform_op_id": str(operation) + f"_acq_{i}",
"port": op_info["port"],
"clock": op_info["clock"],
"abs_time": t0,
"duration": op_info["duration"],
"is_acquisition": "acq_channel" in op_info or "bin_mode" in op_info,
"operation": str(operation),
"wf_idx": i,
"operation_hash": operation_id,
}
timing_table_list.append(pd.DataFrame(df_row, index=range(1)))
@property
[docs]
def timing_table(self) -> pd.io.formats.style.Styler:
"""
A styled pandas dataframe containing the absolute timing of pulses and acquisitions in a schedule.
This table is constructed based on the ``abs_time`` key in the
:attr:`~quantify_scheduler.schedules.schedule.ScheduleBase.schedulables`.
This requires the timing to have been determined.
The table consists of the following columns:
- `operation`: a ``repr`` of :class:`~quantify_scheduler.operations.operation.Operation` corresponding to the pulse/acquisition.
- `waveform_op_id`: an id corresponding to each pulse/acquisition inside an :class:`~quantify_scheduler.operations.operation.Operation`.
- `port`: the port the pulse/acquisition is to be played/acquired on.
- `clock`: the clock used to (de)modulate the pulse/acquisition.
- `abs_time`: the absolute time the pulse/acquisition is scheduled to start.
- `duration`: the duration of the pulse/acquisition that is scheduled.
- `is_acquisition`: whether the pulse/acquisition is an acquisition or not (type ``numpy.bool_``).
- `wf_idx`: the waveform index of the pulse/acquisition belonging to the Operation.
- `operation_hash`: the unique hash corresponding to the :class:`~.Schedulable` that the pulse/acquisition belongs to.
.. admonition:: Example
.. jupyter-execute::
:hide-code:
from quantify_scheduler.backends import SerialCompiler
from quantify_scheduler.device_under_test.quantum_device import QuantumDevice
from quantify_scheduler.device_under_test.transmon_element import BasicTransmonElement
from quantify_scheduler.operations.gate_library import (
Measure,
Reset,
X,
Y,
)
from quantify_scheduler.schedules.schedule import Schedule
from quantify_scheduler.schemas.examples import utils
compiler = SerialCompiler("compiler")
q0 = BasicTransmonElement("q0")
q4 = BasicTransmonElement("q4")
for qubit in [q0, q4]:
qubit.rxy.amp180(0.115)
qubit.rxy.motzoi(0.1)
qubit.clock_freqs.f01(7.3e9)
qubit.clock_freqs.f12(7.0e9)
qubit.clock_freqs.readout(8.0e9)
qubit.measure.acq_delay(100e-9)
quantum_device = QuantumDevice(name="quantum_device0")
quantum_device.add_element(q0)
quantum_device.add_element(q4)
device_config = quantum_device.generate_device_config()
hardware_config = utils.load_json_example_scheme(
"qblox_hardware_config_transmon.json"
)
hardware_config["hardware_options"].pop("distortion_corrections")
quantum_device.hardware_config(hardware_config)
compiler = SerialCompiler("compiler")
compiler.quantum_device = quantum_device
.. jupyter-execute::
schedule = Schedule("demo timing table")
schedule.add(Reset("q0", "q4"))
schedule.add(X("q0"))
schedule.add(Y("q4"))
schedule.add(Measure("q0", acq_index=0))
schedule.add(Measure("q4", acq_index=0))
compiled_schedule = compiler.compile(schedule)
compiled_schedule.timing_table
Parameters
----------
schedule
a schedule for which the absolute timing has been determined.
Returns
-------
:
styled_timing_table, a pandas Styler containing a dataframe with
an overview of the timing of the pulses and acquisitions present in the
schedule. The dataframe can be accessed through the .data attribute of
the Styler.
Raises
------
ValueError
When the absolute timing has not been determined during compilation.
""" # noqa: E501
timing_table_list = []
self._generate_timing_table_list(self, 0, timing_table_list, None)
timing_table = pd.concat(timing_table_list, ignore_index=True)
timing_table = timing_table.sort_values(by="abs_time")
# apply a style so that time is easy to read.
# this works under the assumption that we are using timings on the order of
# nanoseconds.
styled_timing_table = timing_table.style.format(
{
"abs_time": lambda val: f"{val*1e9:,.1f} ns",
"duration": lambda val: f"{val*1e9:,.1f} ns",
}
)
return styled_timing_table
[docs]
def get_schedule_duration(self) -> float:
"""
Return the duration of the schedule.
Returns
-------
schedule_duration : float
Duration of current schedule
"""
schedule_duration = 0
# find last timestamp
for schedulable in self.schedulables.values():
timestamp = schedulable["abs_time"]
operation_id = schedulable["operation_id"]
operation = self["operation_dict"][operation_id]
tmp_time = timestamp + operation.duration
# keep track of longest found schedule
schedule_duration = max(tmp_time, schedule_duration)
schedule_duration *= self.repetitions
return schedule_duration
@property
[docs]
def duration(self) -> float | None:
"""
Determine the cached duration of the schedule.
Will return None if get_schedule_duration() has not been called before.
"""
return self.get("duration", None)
def __getstate__(self) -> dict[str, Any]:
data = copy(self.data)
# For serialization, we need to keep the order
# of keys in the serialized data too.
data["schedulables"] = list(data["schedulables"].items())
return {
"deserialization_type": export_python_object_to_path_string(self.__class__),
"data": data,
}
def __setstate__(self, state: dict[str, Any]) -> None:
# Logic to allow legacy (old serialized, saved) and current serialization.
data = (
state["data"]
if ("deserialization_type" in state) and ("data" in state)
else state
)
if isinstance(data["schedulables"], list):
# Schedulables can be a list of pair of key values to store
# the order of schedulables too in the serialized data.
data["schedulables"] = {k: v for k, v in data["schedulables"]}
self.data = data
[docs]
class Schedule(ScheduleBase):
"""
A modifiable schedule.
Operations :class:`quantify_scheduler.operations.operation.Operation` can be added
using the :meth:`~.Schedule.add` method, allowing precise
specification *when* to perform an operation using timing constraints.
When adding an operation, it is not required to specify how to represent this
:class:`quantify_scheduler.operations.operation.Operation` on all layers.
Instead, this information can be added later during
:ref:`compilation <sec-compilation>`.
This allows the user to effortlessly mix the gate- and pulse-level descriptions as
required for many (calibration) experiments.
Parameters
----------
name
The name of the schedule
repetitions
The amount of times the schedule will be repeated, by default 1
data
A dictionary containing a pre-existing schedule, by default None
"""
[docs]
schema_filename = "schedule.json"
def __init__(
self, name: str, repetitions: int = 1, data: dict = None
) -> None: # noqa: E501
# validate the input data to ensure it is valid schedule data
super().__init__()
# ensure keys exist
self["operation_dict"] = {}
self["resource_dict"] = {}
self["name"] = name
self["repetitions"] = repetitions
# Note the order of schedulables is important.
# If two schedulables have the same absolute time,
# the order is determined by the order of their keys.
self["schedulables"] = {}
# This is used to define baseband pulses and is expected to always be present
# in any schedule.
self.add_resource(
resources.BasebandClockResource(resources.BasebandClockResource.IDENTITY)
)
# This is used to define operations on marker and digital channels.
self.add_resource(
resources.DigitalClockResource(resources.DigitalClockResource.IDENTITY)
)
if data is not None:
self.data.update(data)
[docs]
def add_resources(self, resources_list: list) -> None:
"""Add wrapper for adding multiple resources."""
for resource in resources_list:
self.add_resource(resource)
[docs]
def add_resource(self, resource: Resource) -> None:
"""Add a resource such as a channel or qubit to the schedule."""
if not resources.Resource.is_valid(resource):
raise ValueError(
f"Attempting to add resource to schedule. "
f"Resource '{resource}' is not valid."
)
if resource.name in self["resource_dict"]:
raise ValueError(f"Key {resource.name} is already present")
self["resource_dict"][resource.name] = resource
[docs]
def add(
self,
operation: Operation | Schedule,
rel_time: float = 0,
ref_op: Schedulable | str | None = None,
ref_pt: Literal["start", "center", "end"] | None = None,
ref_pt_new: Literal["start", "center", "end"] | None = None,
label: str | None = None,
control_flow: ControlFlowSpec | None = None,
) -> Schedulable:
"""
Add an operation or a subschedule to the schedule.
Parameters
----------
operation
The operation to add to the schedule, or another schedule to add
as a subschedule.
rel_time
relative time between the reference operation and the added operation.
the time is the time between the "ref_pt" in the reference operation and
"ref_pt_new" of the operation that is added.
ref_op
reference schedulable. If set to :code:`None`, will default
to the last added operation.
ref_pt
reference point in reference operation must be one of
:code:`"start"`, :code:`"center"`, :code:`"end"`, or :code:`None`; in case
of :code:`None`,
:func:`~quantify_scheduler.compilation.determine_absolute_timing` assumes
:code:`"end"`.
ref_pt_new
reference point in added operation must be one of
:code:`"start"`, :code:`"center"`, :code:`"end"`, or :code:`None`; in case
of :code:`None`,
:func:`~quantify_scheduler.compilation.determine_absolute_timing` assumes
:code:`"start"`.
label
a unique string that can be used as an identifier when adding operations.
if set to `None`, a random hash will be generated instead.
control_flow
Virtual operation describing if the operation should be subject to control
flow (loop, conditional, ...). See
:ref:`control flow reference documentation <sec-control-flow>`
for a detailed explanation.
Returns
-------
:
Returns the schedulable created in the schedule.
"""
if label is None:
label = str(uuid4())
self._validate_add_arguments(operation, label, control_flow)
# ensure the schedulable name is unique
if label in self.schedulables:
raise ValueError(f"Schedulable name '{label}' must be unique.")
# ensure that reference schedulable exists in current schedule
if (
ref_op is not None
and (ref_op not in self.schedulables)
and (not any([ref_op is op for op in self.schedulables.values()]))
):
raise ValueError(
f"Reference schedulable '{ref_op}' does not exist in this schedule. Please "
"ensure that `ref_op` corresponds to a label, for example\n\n"
" schedule.add(operationA, label='my_label')\n"
" schedule.add(operationB, ref_op='my_label')\n\n"
"or a schedulable that has been added to the schedule, for example\n\n"
" my_operation = schedule.add(operationA)\n"
" schedule.add(operationB, ref_op=my_operation)."
)
if control_flow is not None:
return self._add(
control_flow.create_operation(operation),
rel_time,
ref_op,
ref_pt,
ref_pt_new,
label,
)
else:
return self._add(operation, rel_time, ref_op, ref_pt, ref_pt_new, label)
[docs]
def _add(
self,
operation: Operation | Schedule,
rel_time: float = 0,
ref_op: Schedulable | str | None = None,
ref_pt: Literal["start", "center", "end"] | None = None,
ref_pt_new: Literal["start", "center", "end"] | None = None,
label: str | None = None,
) -> Schedulable:
operation_id = operation.hash
self["operation_dict"][operation_id] = operation
element = Schedulable(name=label, operation_id=operation_id)
element.add_timing_constraint(
rel_time=rel_time,
ref_schedulable=ref_op,
ref_pt=ref_pt,
ref_pt_new=ref_pt_new,
)
self.schedulables.update({label: element})
return element
[docs]
def _validate_add_arguments(
self,
operation: Operation | Schedule,
label: str,
control_flow: Operation | None,
) -> None:
if not isinstance(operation, (Operation, Schedule)):
raise ValueError(
f"Attempting to add operation to schedule. "
f"The provided object '{operation=}' is not"
" an instance of Operation or Schedule"
)
if control_flow is not None:
warnings.warn(
"Using the `control_flow` argument in `Schedule.add` is deprecated, and "
"will be removed from the public interface in quantify-scheduler >= 0.23.0. "
"Please add control flow operations directly to the schedule instead.",
FutureWarning,
)
# ensure the schedulable name is unique
if label in self.schedulables:
raise ValueError(f"Schedulable name '{label}' must be unique.")
[docs]
class Schedulable(JSONSchemaValMixin, UserDict):
"""
A representation of an element on a schedule.
All elements on a schedule are schedulables. A schedulable contains all
information regarding the timing of this element as well as the operation
being executed by this element. This operation is currently represented by
an operation ID.
Schedulables can contain an arbitrary number of timing constraints to
determine the timing. Multiple different constraints are currently resolved
by delaying the element until after all timing constraints have been met, to
aid compatibility. To specify an exact timing between two schedulables,
please ensure to only specify exactly one timing constraint.
Parameters
----------
name
The name of this schedulable, by which it can be referenced by other
schedulables. Separate schedulables cannot share the same name.
operation_id
Reference to the operation which is to be executed by this schedulable.
"""
[docs]
schema_filename = "schedulable.json"
def __init__(
self, name: str, operation_id: str, control_flow: Operation | None = None
) -> None:
super().__init__()
self["name"] = name
self["operation_id"] = operation_id
self["timing_constraints"] = []
# the next lines are to prevent breaking the existing API
self["label"] = name
if control_flow is not None:
self["control_flow"] = control_flow
[docs]
def add_timing_constraint(
self,
rel_time: float = 0,
ref_schedulable: Schedulable | str | None = None,
ref_pt: Literal["start", "center", "end"] | None = None,
ref_pt_new: Literal["start", "center", "end"] | None = None,
) -> None:
"""
Add timing constraint.
A timing constraint constrains the operation in time by specifying the time
(:code:`"rel_time"`) between a reference schedulable and the added schedulable.
The time can be specified with respect to the "start", "center", or "end" of
the operations.
The reference schedulable (:code:`"ref_schedulable"`) is specified using its
name property.
See also :attr:`~.ScheduleBase.schedulables`.
Parameters
----------
rel_time
relative time between the reference schedulable and the added schedulable.
the time is the time between the "ref_pt" in the reference operation and
"ref_pt_new" of the operation that is added.
ref_schedulable
name of the reference schedulable. If set to :code:`None`, will default
to the last added operation.
ref_pt
reference point in reference operation must be one of
:code:`"start"`, :code:`"center"`, :code:`"end"`, or :code:`None`; in case
of :code:`None`,
:meth:`~quantify_scheduler.compilation.determine_absolute_timing` assumes
:code:`"end"`.
ref_pt_new
reference point in added operation must be one of
:code:`"start"`, :code:`"center"`, :code:`"end"`, or :code:`None`; in case
of :code:`None`,
:meth:`~quantify_scheduler.compilation.determine_absolute_timing` assumes
:code:`"start"`.
"""
# Save as str to help serialization of schedules.
if ref_schedulable is not None:
ref_schedulable = str(ref_schedulable)
timing_constr = {
"rel_time": rel_time,
"ref_schedulable": ref_schedulable,
"ref_pt_new": ref_pt_new,
"ref_pt": ref_pt,
}
self["timing_constraints"].append(timing_constr)
def __hash__(self) -> int:
return make_hash(self.data)
@property
[docs]
def hash(self) -> str:
"""A hash based on the contents of the Operation."""
return str(hash(self))
def __str__(self) -> str:
return str(self["name"])
def __getstate__(self) -> dict[str, Any]:
return {
"deserialization_type": export_python_object_to_path_string(self.__class__),
"data": self.data,
}
def __setstate__(self, state: dict[str, Any]) -> None:
self.data = state["data"]
[docs]
class CompiledSchedule(ScheduleBase):
"""
A schedule that contains compiled instructions ready for execution using the :class:`~.InstrumentCoordinator`.
The :class:`CompiledSchedule` differs from a :class:`.Schedule` in
that it is considered immutable (no new operations or resources can be added), and
that it contains :attr:`~.compiled_instructions`.
.. tip::
A :class:`~.CompiledSchedule` can be obtained by compiling a
:class:`~.Schedule` using :meth:`~quantify_scheduler.backends.graph_compilation.QuantifyCompiler.compile`.
""" # noqa: E501
[docs]
schema_filename = "schedule.json"
def __init__(self, schedule: Schedule) -> None:
# validate the input data to ensure it is valid schedule data
super().__init__()
[docs]
self._hardware_timing_table: pd.DataFrame = pd.DataFrame()
# N.B. this relies on a bit of a dirty monkey patch way of adding these
# properties. Not so nice.
if hasattr(schedule, "_hardware_timing_table"):
self._hardware_timing_table = schedule._hardware_timing_table
if hasattr(schedule, "_hardware_waveform_dict"):
self._hardware_waveform_dict = schedule._hardware_waveform_dict
# ensure keys exist
self["compiled_instructions"] = {}
self.data.update(schedule.data)
@property
[docs]
def compiled_instructions(self) -> dict[str, Resource]:
"""
A dictionary containing compiled instructions.
The contents of this dictionary depend on the backend it was compiled for.
However, we assume that the general format consists of a dictionary in which
the keys are instrument names corresponding to components added to a
:class:`~.InstrumentCoordinator`, and the
values are the instructions for that component.
These values typically contain a combination of sequence files, waveform
definitions, and parameters to configure on the instrument.
"""
return self["compiled_instructions"]
@classmethod
[docs]
def is_valid(cls, object_to_be_validated: Any) -> bool: # noqa: ANN401
"""
Check if the contents of the object_to_be_validated are valid.
Additionally checks if the object_to_be_validated is
an instance of :class:`~.CompiledSchedule`.
"""
valid_schedule = super().is_valid(object_to_be_validated)
if valid_schedule:
return isinstance(object_to_be_validated, CompiledSchedule)
return False
@property
[docs]
def hardware_timing_table(self) -> pd.io.formats.style.Styler:
"""
Return a timing table representing all operations at the Control-hardware layer.
Note that this timing table is typically different from the `.timing_table` in
that it contains more hardware specific information such as channels, clock
cycles and samples and corrections for things such as gain.
This hardware timing table is intended to provide a more
This table is constructed based on the timing_table and modified during
compilation in one of the hardware back ends and optionally added to the
schedule. Not all back ends support this feature.
"""
styled_hardware_timing_table = self._hardware_timing_table.style.format(
{
"abs_time": lambda val: f"{val*1e9:,.1f} ns",
"duration": lambda val: f"{val*1e9:,.1f} ns",
"clock_cycle_start": lambda val: f"{val:,.1f}",
"sample_start": lambda val: f"{val:,.1f}",
}
)
return styled_hardware_timing_table
@property
@dataclasses.dataclass
@dataclasses.dataclass
