def pulse_drawer(data, dt=1, style=None, filename=None,
interp_method=None, scaling=None, channels_to_plot=None,
plot_all=False, plot_range=None, interactive=False,
table=True, label=False, framechange=True,
channels=None):
"""Plot the interpolated envelope of pulse
Args:
data (ScheduleComponent or SamplePulse): Data to plot
dt (float): Time interval of samples
style (PulseStyle or SchedStyle): A style sheet to configure
plot appearance
filename (str): Name required to save pulse image
interp_method (Callable): interpolation function
See `qiskit.visualization.interpolation` for more information
scaling (float): scaling of waveform amplitude
channels_to_plot (list): Deprecated, see `channels`
plot_all (bool): Plot empty channels
plot_range (tuple): A tuple of time range to plot
interactive (bool): When set true show the circuit in a new window
(this depends on the matplotlib backend being used supporting this)
table (bool): Draw event table for supported commands
label (bool): Label individual instructions
framechange (bool): Add framechange indicators
channels (list): A list of channel names to plot
Returns:
matplotlib.figure: A matplotlib figure object for the pulse envelope
Raises:
VisualizationError: when invalid data is given or lack of information
ImportError: when matplotlib is not installed
"""
if channels_to_plot:
warnings.warn('The parameter "channels_to_plot" is being replaced by "channels"',
DeprecationWarning, 3)
channels = channels_to_plot
if not _matplotlib.HAS_MATPLOTLIB:
raise ImportError('Must have Matplotlib installed.')
if isinstance(data, SamplePulse):
drawer = _matplotlib.SamplePulseDrawer(style=style)
image = drawer.draw(data, dt=dt, interp_method=interp_method, scaling=scaling)
elif isinstance(data, (Schedule, Instruction)):
drawer = _matplotlib.ScheduleDrawer(style=style)
image = drawer.draw(data, dt=dt, interp_method=interp_method, scaling=scaling,
plot_range=plot_range, plot_all=plot_all, table=table,
label=label, framechange=framechange, channels=channels)
else:
raise VisualizationError('This data cannot be visualized.')
if filename:
image.savefig(filename, dpi=drawer.style.dpi, bbox_inches='tight')
if get_backend() in ['module://ipykernel.pylab.backend_inline',
'nbAgg']:
_matplotlib.plt.close(image)
if image and interactive:
image.show()
return image
def pulse_drawer(data: Union[Waveform, ScheduleComponent],
dt: int = 1,
style: Union[PulseStyle, SchedStyle] = None,
filename: str = None,
interp_method: Callable = None,
scale: float = None,
channel_scales: Dict[Channel, float] = None,
plot_all: bool = False,
plot_range: Tuple[Union[int, float], Union[int,
float]] = None,
interactive: bool = False,
table: bool = False,
label: bool = False,
framechange: bool = True,
channels: List[Channel] = None,
scaling: float = None,
show_framechange_channels: bool = True):
"""Plot the interpolated envelope of pulse and schedule.
Args:
data: Pulse or schedule object to plot.
dt: Time interval of samples. Pulses are visualized in the unit of
cycle time if not provided.
style: A style sheet to configure plot appearance.
See :mod:`~qiskit.visualization.pulse.qcstyle` for more information.
filename: Name required to save pulse image. The drawer just returns
`matplot.Figure` object if not provided.
interp_method: Interpolation function. Interpolation is disabled in default.
See :mod:`~qiskit.visualization.pulse.interpolation` for more information.
scale: Scaling of waveform amplitude. Pulses are automatically
scaled channel by channel if not provided.
channel_scales: Dictionary of scale factor for specific channels.
Scale of channels not specified here is overwritten by `scale`.
plot_all: When set `True` plot empty channels.
plot_range: A tuple of time range to plot.
interactive: When set `True` show the circuit in a new window.
This depends on the matplotlib backend being used supporting this.
table: When set `True` draw event table for supported commands.
label: When set `True` draw label for individual instructions.
framechange: When set `True` draw framechange indicators.
scaling: Deprecated, see `scale`.
channels: A list of channel names to plot.
All non-empty channels are shown if not provided.
show_framechange_channels: When set `True` plot channels
with only framechange instructions.
Returns:
matplotlib.figure.Figure: A matplotlib figure object for the pulse envelope.
Example:
This example shows how to visualize your pulse schedule.
Pulse names are added to the plot, unimportant channels are removed
and the time window is truncated to draw out U3 pulse sequence of interest.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.test.mock.backends.almaden import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
You are also able to call visualization module from the instance method::
sched.draw(channels=channels, plot_range=(0, 1000), label=True, channel_scales=scales)
To customize the format of the schedule plot, you can setup your style sheet.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.test.mock.backends.almaden import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
# setup style sheet
my_style = qiskit.visualization.SchedStyle(
figsize = (10, 5),
bg_color='w',
d_ch_color = ['#32cd32', '#556b2f'])
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched, style=my_style,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
Raises:
VisualizationError: when invalid data is given
ImportError: when matplotlib is not installed
"""
if scaling is not None:
warnings.warn('The parameter "scaling" is being replaced by "scale"',
DeprecationWarning, 3)
scale = scaling
if not HAS_MATPLOTLIB:
raise ImportError('Must have Matplotlib installed.')
if isinstance(data, (SamplePulse, Waveform)):
drawer = _matplotlib.WaveformDrawer(style=style)
image = drawer.draw(data,
dt=dt,
interp_method=interp_method,
scale=scale)
elif isinstance(data, (Schedule, Instruction)):
drawer = _matplotlib.ScheduleDrawer(style=style)
image = drawer.draw(
data,
dt=dt,
interp_method=interp_method,
scale=scale,
channel_scales=channel_scales,
plot_range=plot_range,
plot_all=plot_all,
table=table,
label=label,
framechange=framechange,
channels=channels,
show_framechange_channels=show_framechange_channels)
else:
raise VisualizationError('This data cannot be visualized.')
if filename:
image.savefig(filename, dpi=drawer.style.dpi, bbox_inches='tight')
if get_backend() in ['module://ipykernel.pylab.backend_inline', 'nbAgg']:
_matplotlib.plt.close(image)
if image and interactive:
image.show()
return image
def pulse_drawer(
data: Union[Waveform, Union[Schedule, Instruction]],
dt: int = 1,
style: Union[PulseStyle, SchedStyle] = None,
filename: str = None,
interp_method: Callable = None,
scale: float = None,
channel_scales: Dict[Channel, float] = None,
plot_all: bool = False,
plot_range: Tuple[float, float] = None,
interactive: bool = False,
table: bool = False,
label: bool = False,
framechange: bool = True,
channels: List[Channel] = None,
show_framechange_channels: bool = True,
draw_title: bool = False,
):
"""Deprecated.
Plot the interpolated envelope of pulse and schedule.
Args:
data: Pulse or schedule object to plot.
dt: Time interval of samples. Pulses are visualized in the unit of
cycle time if not provided.
style: A style sheet to configure plot appearance.
See :mod:`~qiskit.visualization.pulse.qcstyle` for more information.
filename: Name required to save pulse image. The drawer just returns
`matplot.Figure` object if not provided.
interp_method: Interpolation function. Interpolation is disabled in default.
See :mod:`~qiskit.visualization.pulse.interpolation` for more information.
scale: Scaling of waveform amplitude. Pulses are automatically
scaled channel by channel if not provided.
channel_scales: Dictionary of scale factor for specific channels.
Scale of channels not specified here is overwritten by `scale`.
plot_all: When set `True` plot empty channels.
plot_range: A tuple of time range to plot.
interactive: When set `True` show the circuit in a new window.
This depends on the matplotlib backend being used supporting this.
table: When set `True` draw event table for supported commands.
label: When set `True` draw label for individual instructions.
framechange: When set `True` draw framechange indicators.
channels: A list of channel names to plot.
All non-empty channels are shown if not provided.
show_framechange_channels: When set `True` plot channels
with only framechange instructions.
draw_title: Add a title to the plot when set to ``True``.
Returns:
matplotlib.figure.Figure: A matplotlib figure object for the pulse envelope.
Example:
This example shows how to visualize your pulse schedule.
Pulse names are added to the plot, unimportant channels are removed
and the time window is truncated to draw out U3 pulse sequence of interest.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.providers.fake_provider import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
You are also able to call visualization module from the instance method::
sched.draw(channels=channels, plot_range=(0, 1000), label=True, channel_scales=scales)
To customize the format of the schedule plot, you can setup your style sheet.
.. jupyter-execute::
import numpy as np
import qiskit
from qiskit import pulse
from qiskit.providers.fake_provider import FakeAlmaden
inst_map = FakeAlmaden().defaults().instruction_schedule_map
sched = pulse.Schedule()
sched += inst_map.get('u3', 0, np.pi, 0, np.pi)
sched += inst_map.get('measure', list(range(20))) << sched.duration
# setup style sheet
my_style = qiskit.visualization.SchedStyle(
figsize = (10, 5),
bg_color='w',
d_ch_color = ['#32cd32', '#556b2f'])
channels = [pulse.DriveChannel(0), pulse.MeasureChannel(0)]
scales = {pulse.DriveChannel(0): 10}
qiskit.visualization.pulse_drawer(sched, style=my_style,
channels=channels,
plot_range=(0, 1000),
label=True,
channel_scales=scales)
Raises:
VisualizationError: when invalid data is given
MissingOptionalLibraryError: when matplotlib is not installed
"""
warnings.warn(
"This legacy pulse drawer is deprecated and will be removed no earlier than "
"3 months after the release date. Use `qiskit.visualization.pulse_drawer_v2` "
"instead. After the legacy drawer is removed, the import path of this module "
"will be dedicated to the v2 drawer. "
"New drawer will provide much more flexibility with richer stylesheets "
"and cleaner visualization.",
DeprecationWarning,
)
if isinstance(data, Waveform):
drawer = _matplotlib.WaveformDrawer(style=style)
image = drawer.draw(data,
dt=dt,
interp_method=interp_method,
scale=scale)
elif isinstance(data, (Schedule, Instruction)):
drawer = _matplotlib.ScheduleDrawer(style=style)
image = drawer.draw(
data,
dt=dt,
interp_method=interp_method,
scale=scale,
channel_scales=channel_scales,
plot_range=plot_range,
plot_all=plot_all,
table=table,
label=label,
framechange=framechange,
channels=channels,
show_framechange_channels=show_framechange_channels,
draw_title=draw_title,
)
else:
raise VisualizationError("This data cannot be visualized.")
if filename:
image.savefig(filename, dpi=drawer.style.dpi, bbox_inches="tight")
matplotlib_close_if_inline(image)
if image and interactive:
image.show()
return image
