Visualization: Creating figures
===============================
The Visualization module provides plotting functionality for creating figures from experiment and analysis results.
This includes ``plotter`` and ``drawer`` classes to plot data in :class:`.CurveAnalysis` and its subclasses.
Plotters define the kind of figures to plot, while drawers are backends that enable them to be visualized.
How much you will interact directly with the visualization module depends on your use case:
- **Running library experiments as-is:** You won't need to interact with the visualization module.
- **Running library experiments with custom styling for figures**: You will be setting figure options through the plotter.
- **Making plots using a plotting library other than Matplotlib**: You will need to define a custom drawer class.
- **Writing your own analysis class**: If you want to use the the default plotter and drawer settings,
you don't need to interact with the visualization module. Optionally, you can customize
your plotter and drawer.
Plotters inherit from :class:`.BasePlotter` and define a type of figure that may be generated from
experiment or analysis data. For example, the results from :class:`.CurveAnalysis` --- or any other
experiment where results are plotted against a single parameter (i.e., :math:`x`) --- can be plotted
using the :class:`.CurvePlotter` class, which plots X-Y-like values.
These plotter classes act as a bridge (from the common bridge pattern in software development) between
analysis classes (or even users) and plotting backends such as Matplotlib. Drawers are the backends, with
a common interface defined in :class:`.BaseDrawer`. Though Matplotlib is the only officially supported
plotting backend in Qiskit Experiments through :class:`.MplDrawer`, custom drawers can be
implemented by users to use alternative backends. As long as the backend is a subclass of
:class:`.BaseDrawer`, and implements all the necessary functionality, all plotters should be able to
generate figures with the alternative backend.
Generating and customizing a figure using a plotter
---------------------------------------------------
First, we display the default figure from a :class:`.Rabi` experiment as a starting point:
.. note::
This tutorial requires the :mod:`qiskit_dynamics`, :external+qiskit_aer:doc:`qiskit-aer <index>`, and
:external+qiskit_ibm_runtime:doc:`qiskit-ibm-runtime <index>` packages to run simulations. You can install them
with ``python -m pip install qiskit-dynamics qiskit-aer qiskit-ibm-runtime``.
.. jupyter-execute::
:hide-code:
import warnings
warnings.filterwarnings(
"ignore",
message=".*Due to the deprecation of Qiskit Pulse.*",
category=DeprecationWarning,
)
warnings.filterwarnings(
"ignore",
message=".*The entire Qiskit Pulse package is being deprecated.*",
category=DeprecationWarning,
)
.. jupyter-execute::
import numpy as np
from qiskit import pulse
from qiskit.circuit import Parameter
from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend
from qiskit_experiments.data_processing import DataProcessor, nodes
from qiskit_experiments.library import Rabi
with pulse.build() as sched:
pulse.play(
pulse.Gaussian(160, Parameter("amp"), sigma=40),
pulse.DriveChannel(0)
)
seed = 100
backend = SingleTransmonTestBackend(seed=seed)
rabi = Rabi(
physical_qubits=(0,),
backend=backend,
schedule=sched,
amplitudes=np.linspace(-0.1, 0.1, 21),
)
rabi_data = rabi.run().block_for_results()
rabi_data.figure(0)
This is the default figure generated by :class:`.OscillationAnalysis`, the data analysis
class for the Rabi experiment. The fitted cosine is shown as a blue line, with the
individual measurements from the experiment shown as data points with error bars corresponding
to their uncertainties. We are also given a small fit report in the caption showing the
``rabi_rate``.
The plotter that generated the figure can be accessed through the analysis instance,
and customizing the figure can be done by setting the plotter's options. We now modify
the color, symbols, and size of our plot, as well as change the axis labels for the amplitude units:
.. jupyter-execute::
# Retrieve the plotter from the analysis instance
plotter = rabi.analysis.plotter
# Change the x-axis unit values
plotter.set_figure_options(
xval_unit="arb.",
xval_unit_scale=False # Don't scale the unit with SI prefixes
)
# Change the color and symbol for the cosine
plotter.figure_options.series_params.update(
{"cos": {"symbol": "x", "color": "r"}}
)
# Set figsize directly so we don't overwrite the entire style
plotter.options.style["figsize"] = (6,4)
# Generate the new figure
plotter.figure()
Plotters have two sets of options that customize their behavior and figure content:
``options``, which have class-specific parameters that define how an instance behaves,
and ``figure_options``, which have figure-specific parameters that control aspects of the
figure itself, such as axis labels and series colors.
To see the residual plot, set ``plot_residuals=True`` in the analysis options:
.. jupyter-execute::
# Set to ``True`` analysis option for residual plot
rabi.analysis.set_options(plot_residuals=True)
# Run experiment
rabi_data = rabi.run().block_for_results()
rabi_data.figure(0)
This option works for experiments without subplots in their figures.
Here is a more complicated experiment in which we customize the figure of a DRAG
experiment before it's run, so that we don't need to regenerate the figure like in
the previous example. First, we run the experiment without customizing the options
to see what the default figure looks like:
.. jupyter-execute::
from qiskit_experiments.library import RoughDrag
from qiskit_experiments.visualization import PlotStyle
from qiskit_experiments.test.mock_iq_helpers import MockIQDragHelper as DragHelper
from qiskit_experiments.test.mock_iq_backend import MockIQBackend
from qiskit.circuit import Parameter
from qiskit import pulse
from qiskit.pulse import DriveChannel, Drag
beta = Parameter("beta")
with pulse.build(name="xp") as xp:
pulse.play(pulse.Drag(64, 0.66, 16, beta), pulse.DriveChannel(0))
drag_experiment_helper = DragHelper(gate_name="Drag(xp)")
backend = MockIQBackend(drag_experiment_helper, rng_seed=seed)
drag = RoughDrag((0,), xp, backend=backend)
drag_data = drag.run().block_for_results()
drag_data.figure(0)
Now we specify the figure options before running the experiment for a second time:
.. jupyter-execute::
drag = RoughDrag((0,), xp, backend=backend)
# Set plotter options
plotter = drag.analysis.plotter
# Update series parameters
plotter.figure_options.series_params.update(
{
"nrep=1": {
"color": (27/255, 158/255, 119/255),
"symbol": "^",
},
"nrep=3": {
"color": (217/255, 95/255, 2/255),
"symbol": "s",
},
"nrep=5": {
"color": (117/255, 112/255, 179/255),
"symbol": "o",
},
}
)
# Set figure options
plotter.set_figure_options(
xval_unit="arb.",
xval_unit_scale=False,
figure_title="Rough DRAG Experiment on Qubit 0",
)
# Set style parameters
plotter.options.style["symbol_size"] = 10
plotter.options.style["legend_loc"] = "upper center"
drag_data = drag.run().block_for_results()
drag_data.figure(0)
As can be seen in the figure, the different series generated by the experiment
were styled differently according to the ``series_params`` attribute of ``figure_options``.
By default, the supported figure options are ``xlabel``, ``ylabel``, ``xlim``, ``ylim``,
``xval_unit``, ``yval_unit``, ``xval_unit_scale``, ``yval_unit_scale``, ``xscale``, ``yscale``,
``figure_title``, and ``series_params``; see :class:`.MplDrawer` for details on how to set these
options. The following T1 experiment provides examples to options that have not been demonstrated
until now in this tutorial:
.. jupyter-execute::
from qiskit_experiments.library import T1
from qiskit_aer import AerSimulator
from qiskit_ibm_runtime.fake_provider import FakePerth
backend = AerSimulator.from_backend(FakePerth())
t1 = T1(physical_qubits=(0,),
delays=np.linspace(0, 300e-6, 30),
backend=backend
)
plotter = t1.analysis.plotter
plotter.set_figure_options(
ylabel="Prob to measure 1",
xlim=(50e-6, 250e-6),
yscale="log"
)
t1_data = t1.run().block_for_results()
t1_data.figure(0)
Customizing plotting in your experiment
---------------------------------------
Plotters are easily integrated into custom analysis classes. To add a plotter instance
to such a class, we define a new ``plotter`` property, pass it relevant data in the
analysis class's ``_run_analysis`` method, and return the generated figure alongside our
analysis results. We use the :class:`.IQPlotter` class to illustrate how this is done for an
arbitrary analysis class.
To ensure that we have an interface similar to existing analysis classes, we make our plotter
accessible as an ``analysis.plotter`` property and analysis.options.plotter option.
The code below accomplishes this for our example ``MyIQAnalysis`` analysis class. We
set the drawer to :class:`.MplDrawer` to use :mod:`matplotlib` by default. The plotter property of our
analysis class makes it easier to access the plotter instance; i.e., using ``self.plotter``
and ``analysis.plotter``. We set default options and figure options in
``_default_options``, but you can still override them as we did above.
The ``MyIQAnalysis`` class accepts single-shot level 1 IQ data, which consists of an
in-phase and quadrature measurement for each shot and circuit. ``_run_analysis`` is
passed an :class:`.ExperimentData` instance which contains IQ data as a list of dictionaries
(one per circuit) where their "memory" entries are lists of IQ values (one per shot).
Each dictionary has a "metadata" entry, with the name of a prepared state: "0", "1",
or "2". These are our series names.
Our goal is to create a figure that displays the single-shot IQ values of each
prepared-state (one per circuit). We process the "memory" data passed to the
analysis class and set the points and centroid series data in the plotter.
This is accomplished in the code below, where we also train a discriminator
to label the IQ points as one of the three prepared states. :class:`.IQPlotter` supports
plotting a discriminator as optional supplementary data, which will show predicted
series over the axis area.
.. jupyter-input::
with pulse.build(name="xp") as xp:
pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0))
x_plus = xp
drag = RoughDrag(1, x_plus)
expdata = drag.run(backend)
from qiskit_experiments.framework import BaseAnalysis, Options
from qiskit_experiments.visualization import (
BasePlotter,
IQPlotter,
MplDrawer,
PlotStyle,
)
class MYIQAnalysis(BaseAnalysis):
@classmethod
def _default_options(cls) -> Options:
options = super()._default_options()
# We create the plotter and create an option for it.
options.plotter = IQPlotter(MplDrawer())
options.plotter.set_figure_options(
xlabel="In-phase",
ylabel="Quadrature",
figure_title="My IQ Analysis Figure",
series_params={
"0": {"label": "|0>"},
"1": {"label": "|1>"},
"2": {"label": "|2>"},
},
)
return options
@property
def plotter(self) -> BasePlotter:
return self.options.plotter
def _run_analysis(self, experiment_data):
data = experiment_data.data()
analysis_results = []
for datum in data:
# Analysis code
analysis_results.append(self._analysis_result(datum))
# Plotting code
series_name = datum["metadata"]["name"]
points = datum["memory"]
centroid = np.mean(points, axis=0)
self.plotter.set_series_data(
series_name,
points=points,
centroid=centroid,
)
# Add discriminator to IQPlotter
discriminator = self._train_discriminator(data)
self.plotter.set_supplementary_data(discriminator=discriminator)
return analysis_results, [self.plotter.figure()]
If we run the above analysis on some appropriate experiment data, as previously
described, our class will generate a figure showing IQ points and their centroids.
Creating your own plotter
-------------------------
You can create a custom figure plotter by subclassing :class:`.BasePlotter` and overriding
:meth:`~.BasePlotter.expected_series_data_keys`,
:meth:`~.BasePlotter.expected_supplementary_data_keys`, and
:meth:`~.BasePlotter._plot_figure`.
The first two methods allow you to define a list of supported data-keys
as strings, which identify the different data to plot. The third method,
:meth:`~.BasePlotter._plot_figure`, must contain your code to generate a figure by calling methods
on the plotter's drawer instance (self.drawer). When ``plotter.figure()`` is called
by an analysis class, the plotter calls ``_plot_figure()`` and then returns your figure
object which is added to the experiment data instance. It is also good practice to
set default values for figure options, such as axis labels. You can do this by
overriding the :meth:`~.BasePlotter._default_figure_options` method in your plotter subclass.
See also
--------
API documentation: :doc:`Visualization Module </apidocs/visualization>`