quantify_scheduler.visualization.pulse_diagram

Functions for drawing pulse diagrams

Module Contents

Functions

_populate_port_mapping(→ None)

Dynamically add up to 8 ports to the port_map dictionary.

validate_operation_data(operation_data, port_map, ...)

Validates if the pulse/acquisition information is valid for visualization.

pulse_diagram_plotly(→ plotly.graph_objects.Figure)

Produce a plotly visualization of the pulses used in the schedule.

sample_schedule(→ Tuple[numpy.ndarray, Dict[str, ...)

Sample a schedule at discrete points in time.

pulse_diagram_matplotlib(...)

Plots a schedule using matplotlib.

get_window_operations(→ List[Tuple[float, float, ...)

Return a list of all WindowOperations with start and end time.

plot_window_operations(...)

Plot the window operations in a schedule.

plot_acquisition_operations(→ List[Any])

Plot the acquisition operations in a schedule.

Attributes

logger

logger[source]
_populate_port_mapping(schedule, portmap: Dict[str, int], ports_length) → None[source]

Dynamically add up to 8 ports to the port_map dictionary.

validate_operation_data(operation_data, port_map, schedulable, operation)[source]

Validates if the pulse/acquisition information is valid for visualization.

pulse_diagram_plotly(schedule: Union[quantify_scheduler.Schedule, quantify_scheduler.CompiledSchedule], port_list: Optional[List[str]] = None, fig_ch_height: float = 300, fig_width: float = 1000, modulation: Literal[off, if, clock] = 'off', modulation_if: float = 0.0, sampling_rate: float = 1000000000.0) → plotly.graph_objects.Figure[source]

Produce a plotly visualization of the pulses used in the schedule.

Parameters

  • schedule – The schedule to render.

  • port_list – A list of ports to show. if set to None will use the first 8 ports it encounters in the sequence.

  • fig_ch_height – Height for each channel subplot in px.

  • fig_width – Width for the figure in px.

  • 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.

Returns

the plot

Return type

plotly.graph_objects.Figure

sample_schedule(schedule: quantify_scheduler.Schedule, port_list: Optional[List[str]] = None, modulation: Literal[off, if, clock] = 'off', modulation_if: float = 0.0, sampling_rate: float = 1000000000.0) → Tuple[numpy.ndarray, Dict[str, numpy.ndarray]][source]

Sample a schedule at discrete points in time.

Parameters

  • schedule – The schedule to render.

  • port_list – A list of ports to show. if set to None will use the first 8 ports it encounters in the sequence.

  • 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.

Returns

  • timestamps – Sample times.

  • waveforms – Dictionary with the data samples for each port.

pulse_diagram_matplotlib(schedule: Union[quantify_scheduler.Schedule, quantify_scheduler.CompiledSchedule], port_list: Optional[List[str]] = None, sampling_rate: float = 1000000000.0, modulation: Literal[off, if, clock] = 'off', modulation_if: float = 0.0, ax: Optional[matplotlib.axes.Axes] = None) → Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Plots a schedule using matplotlib.

Parameters

  • schedule – The schedule to plot.

  • port_list – A list of ports to show. if set to None will use the first 8 ports it encounters in the sequence.

  • 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.

  • ax – Axis onto which to plot.

Returns

  • fig – The matplotlib figure.

  • ax – The matplotlib ax.

get_window_operations(schedule: quantify_scheduler.Schedule) → List[Tuple[float, float, quantify_scheduler.Operation]][source]

Return a list of all WindowOperations with start and end time.

Parameters

schedule – Schedule to use.

Returns

List of all window operations in the schedule.

plot_window_operations(schedule: quantify_scheduler.Schedule, ax: Optional[matplotlib.axes.Axes] = None, time_scale_factor: float = 1) → Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Plot the window operations in a schedule.

Parameters

  • schedule – Schedule from which to plot window operations.

  • ax – Axis handle to use for plotting.

  • time_scale_factor – Used to scale the independent data before using as data for the x-axis of the plot.

Returns

  • fig – The matplotlib figure.

  • ax – The matplotlib ax.

plot_acquisition_operations(schedule: quantify_scheduler.Schedule, ax: Optional[matplotlib.axes.Axes] = None, **kwargs) → List[Any][source]

Plot the acquisition operations in a schedule.

Parameters

  • schedule – Schedule from which to plot window operations.

  • ax – Axis handle to use for plotting.

  • kwargs – Passed to matplotlib plotting routine

Returns

List of handles