Plotting and Post-Processing#

1. Overview of Visualization Utilities#

Command

Target Data Dimensionality

Input

Primary Use Case

plot_2d

1D / Multi-D (with slider controls)

Experiment directory with data.pkl

Standard line plots for 1D sweeps or multi-sweep data with dynamic slice controls.

plot_3d_heat

2D / Time-Domain Traces

Experiment directory with data.pkl

2D color maps (heatmaps) for 2-variable sweeps or time-of-flight evolution over a sweep.

plot_iq

Single-Shot IQ Cloud

Two experiment directories with data.pkl

Complex plane scatter plots for state discrimination (\(\vert{}0\rangle\) vs \(\vert{}1\rangle\)).

plot_spectr

1D Frequency / Power Sweeps

Two experiment directories with data.pkl

Overlaid response curves comparing transmission/dispersion shifts between two states.

2. Command Reference & Usage#

2.1 plot_2d#

Visualizes standard \(Y(X)\) response curves across parameter sweeps and time-domain traces. The target directory must contain data.pkl. For raw or decimated acquisitions, the experiment can be plotted directly after run_yaml. For accumulated data, plot_2d only accepts experiments that include at least one sweep variable; non-swept accumulated data is rejected and the function directs the user to plot_iq. Swept data that must be exported first for downstream plotting should be prepared with the client’s export command.

Behavior#

  • Single Sweep Variable: Generates a 2D line plot mapping the measured IQ magnitude or quadrature components against the swept parameter.

  • Multi-Sweep Variables: Automatically detects additional sweep dimensions and inserts interactive UI sliders, allowing real-time navigation through 1D slices of higher-dimensional datasets.

2.2 plot_3d_heat#

Renders two-dimensional parameter spaces using color intensity maps (heatmaps).

Behavior#

  • Two-Variable Sweeps: Maps two independent variables (\(X, Y\)) against the acquired signal intensity (\(Z\)) on a 2D grid.

  • Time-of-Flight (ToF) Evolution: Visualizes the full time-domain trace over a single sweep parameter.

Note on Data Modes:
Time-of-Flight trace visualization requires acquisition data to be recorded in decimated or raw output mode. In accumulated mode, the time-domain trace is integrated on-chip, collapsing the ToF axis into a single scalar value.

2.3 plot_iq#

Evaluates single-shot state classification by plotting single-point readout shots on the complex plane (\(I\) vs \(Q\)).

Usage Syntax#

python run_plotter.py
# inside the interactive prompt:
plot_iq path/to/exported_state_0/ path/to/exported_state_1/

Input Requirements & Characteristics#

  • State \(\vert{}0\rangle\) Directory (state_0/): Path to an experiment folder containing config.json and data.pkl, acquired with no variables and $output_type: accumulated while the system is prepared in state \(\vert{}0\rangle\).

  • State \(\vert{}1\rangle\) Directory (state_1/): Same as above for state \(\vert{}1\rangle\).

  • Output: A 2D scatter plot on the complex plane illustrating shot distribution, thermal noise spread, and state separation distance.

2.4 plot_spectr#

Compares spectral response curves (e.g., resonator transmission or qubit spectroscopy) between two experiment configurations.

Usage Syntax#

python run_plotter.py
# inside the interactive prompt:
plot_spectr path/to/exported_state_0/ path/to/exported_state_1/

Input Requirements & Characteristics#

  • Prerequisite: Both target directories must contain data.pkl files from experiments executed with the exact same sweep vector (same range, step count, and variable target). Export swept experiments before plotting them.

  • Output: Overlays response curves for state \(\vert{}0\rangle\) and state \(\vert{}1\rangle\), enabling direct measurement of dispersive frequency shifts (\(\chi\)) or readout contrast optimization.