Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

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 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 BasePlotter and define a type of figure that may be generated from experiment or analysis data. For example, the results from CurveAnalysis — or any other experiment where results are plotted against a single parameter (i.e., \(x\)) — can be plotted using the 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 BaseDrawer. Though Matplotlib is the only officially supported plotting backend in Qiskit Experiments through MplDrawer, custom drawers can be implemented by users to use alternative backends. As long as the backend is a subclass of 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 Rabi experiment as a starting point:

Note

This tutorial requires the qiskit_dynamics, qiskit-aer, and qiskit-ibm-runtime packages to run simulations. You can install them with python -m pip install qiskit-dynamics qiskit-aer qiskit-ibm-runtime.

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)
../_images/visualization_1_0.png

This is the default figure generated by 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:

# 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()

<Figure size 600x400 with 1 Axes>

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:

# 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)
../_images/visualization_3_0.png

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:

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)
../_images/visualization_4_0.png

Now we specify the figure options before running the experiment for a second time:

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)
../_images/visualization_5_0.png

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 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:

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)
../_images/visualization_6_0.png

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 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 MplDrawer to use 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 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. IQPlotter supports plotting a discriminator as optional supplementary data, which will show predicted series over the axis area.

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 BasePlotter and overriding expected_series_data_keys(), expected_supplementary_data_keys(), and _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, _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 _default_figure_options() method in your plotter subclass.

See also

API documentation: Visualization Module