lisa.analysis.frequency.FrequencyAnalysis#

class lisa.analysis.frequency.FrequencyAnalysis(trace, proxy=None)[source]#

Bases: TraceAnalysisBase

Support for plotting Frequency Analysis data

Parameters:

trace (lisa.trace.Trace) – input Trace object

Attributes

name

Name of the analysis class.

Properties

logger inherited

Convenience short-hand for self.get_logger().

Methods

df_cpu_frequency()

Same as df_cpus_frequency() but for a single CPU.

df_cpu_frequency_residency()

Get per-CPU frequency residency, i.e. amount of time CPU cpu spent at each frequency.

df_cpu_frequency_transition_rate()

Compute frequency transition rate of a given CPU.

df_cpu_frequency_transitions()

Compute number of frequency transitions of a given CPU.

df_cpus_frequency()

Similar to trace.df_event('cpu_frequency'), with userspace@cpu_frequency_devlib support.

df_domain_frequency_residency()

Get per-frequency-domain frequency residency, i.e. amount of time each domain at each frequency.

df_peripheral_clock_effective_rate()

Dataframe of peripheral clock frequencies.

get_average_cpu_frequency()

Get the average frequency for a given CPU.

plot_cpu_frequencies()

Plot frequency for the specified CPU.

plot_cpu_frequency_residency()

Plot per-CPU frequency residency.

plot_cpu_frequency_transitions()

Plot frequency transitions count of the specified CPU.

plot_domain_frequencies()

Plot frequency trend for all frequency domains.

plot_domain_frequency_residency()

Plot the frequency residency for all frequency domains.

plot_domain_frequency_transitions()

Plot frequency transitions count for all frequency domains.

plot_peripheral_frequency()

Plot frequency for the specified peripheral clock frequency.

cache() inherited

Decorator to enable caching of the output of dataframe getter function in the trace cache.

call_on_trace() inherited

Call a method of a subclass on a given trace.

df_method() inherited

Dataframe function decorator.

get_all_events() inherited

Returns the set of all events used by any of the methods.

get_analysis_classes() inherited

get_default_plot_path() inherited

Return the default path to use to save plots for the analysis.

get_df_methods() inherited

get_logger() inherited

Provides a logging.Logger named after cls.

get_plot_methods() inherited

log_locals() inherited

Debugging aid: log the local variables of the calling function.

plot_method() inherited

Plot function decorator.

save_plot() inherited

Save a holoviews element or matplotlib.figure.Figure as an image file.

Attributes#

FrequencyAnalysis.name = 'frequency'#

Name of the analysis class.

Properties#

property FrequencyAnalysis.logger#

Inherited property, see lisa.utils.Loggable.logger

Convenience short-hand for self.get_logger().

Methods#

FrequencyAnalysis.df_cpu_frequency(cpu, *, df_fmt=None, **kwargs)[source]#

Called on Trace instances as trace.ana.frequency.df_cpu_frequency()

Same as df_cpus_frequency() but for a single CPU.

Parameters:

cpu (int) – CPU ID to get the frequency of.

Variable keyword arguments:

Forwarded to df_cpus_frequency().

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.df_cpu_frequency_residency(cpu, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_cpu_frequency_residency()

Get per-CPU frequency residency, i.e. amount of time CPU cpu spent at each frequency.

Parameters:

cpu (int) – CPU ID

Returns:

A pandas.DataFrame with:

  • A total_time column (the total time spent at a frequency)

  • A active_time column (the non-idle time spent at a frequency)

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

  • cpu_idle

FrequencyAnalysis.df_cpu_frequency_transition_rate(cpu, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_cpu_frequency_transition_rate()

Compute frequency transition rate of a given CPU.

Parameters:

cpu (int) – a CPU ID

Returns:

A pandas.DataFrame with:

  • A transitions column (the number of frequency transitions per second)

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.df_cpu_frequency_transitions(cpu, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_cpu_frequency_transitions()

Compute number of frequency transitions of a given CPU.

Parameters:

cpu (int) – a CPU ID

Returns:

A pandas.DataFrame with:

  • A transitions column (the number of frequency transitions)

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.df_cpus_frequency(signals_init=True, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_cpus_frequency()

Similar to trace.df_event('cpu_frequency'), with userspace@cpu_frequency_devlib support.

Parameters:

signals_init – If True, and initial value for signals will be provided. This includes initial value taken outside window boundaries and devlib-provided events.

The userspace@cpu_frequency_devlib user event is merged in the dataframe if it provides earlier values for a CPU.

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.df_domain_frequency_residency(cpu, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_domain_frequency_residency()

Get per-frequency-domain frequency residency, i.e. amount of time each domain at each frequency.

Parameters:

cpu (int) – Any CPU of the domain to analyse

Returns:

A pandas.DataFrame with:

  • A total_time column (the total time spent at a frequency)

  • A active_time column (the non-idle time spent at a frequency)

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

  • cpu_idle

FrequencyAnalysis.df_peripheral_clock_effective_rate(clk_name, *, df_fmt=None)[source]#

Called on Trace instances as trace.ana.frequency.df_peripheral_clock_effective_rate()

Dataframe of peripheral clock frequencies.

Added by lisa.analysis.base.TraceAnalysisBase.df_method():

Parameters:

df_fmt (str or None) –

Format of dataframe to return. One of:

Returns:

The return type is determined by the dataframe format chosen for the trace object.

Required trace events:

  • clk_set_rate

  • clk_enable

  • clk_disable

FrequencyAnalysis.get_average_cpu_frequency(cpu)[source]#

Called on Trace instances as trace.ana.frequency.get_average_cpu_frequency()

Get the average frequency for a given CPU

Parameters:

cpu (int) – The CPU to analyse

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.plot_cpu_frequencies(cpu: CPU, average: bool = True, overutilized: bool = True, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

average=True, cpu=0, overutilized=True

Called on Trace instances as trace.ana.frequency.plot_cpu_frequencies()

Plot frequency for the specified CPU

Parameters:

  • cpu – The CPU for which to plot frequencies

  • average (bool) – If True, add a horizontal line which is the frequency average.

  • overutilized (bool) – If True, add the overutilized state as an overlay.

If sched_overutilized events are available, the plots will also show the intervals of time where the system was overutilized.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.plot_cpu_frequency_residency(cpu: CPU, pct: bool = False, domain_label: bool = False, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

cpu=0, domain_label=False, pct=False

Called on Trace instances as trace.ana.frequency.plot_cpu_frequency_residency()

Plot per-CPU frequency residency.

Parameters:

  • cpu (int) – The CPU to generate the plot for

  • pct (bool) – Plot residencies in percentage

  • domain_label (bool) – If True, the labels will mention all CPUs in the domain, rather than the CPU passed.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

  • cpu_idle

FrequencyAnalysis.plot_cpu_frequency_transitions(cpu: CPU, pct: bool = False, domain_label: bool = False, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

cpu=0, domain_label=False, pct=False

Called on Trace instances as trace.ana.frequency.plot_cpu_frequency_transitions()

Plot frequency transitions count of the specified CPU

Parameters:

  • cpu (int) – The CPU to genererate the plot for

  • pct (bool) – Plot frequency transitions in percentage

  • domain_label (bool) – If True, the labels will mention all CPUs in the domain, rather than the CPU passed.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.plot_domain_frequencies(*, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

Called on Trace instances as trace.ana.frequency.plot_domain_frequencies()

Plot frequency trend for all frequency domains.

If sched_overutilized events are available, the plots will also show the intervals of time where the cluster was overutilized.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.plot_domain_frequency_residency(pct: bool = False, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

pct=False

Called on Trace instances as trace.ana.frequency.plot_domain_frequency_residency()

Plot the frequency residency for all frequency domains.

Parameters:

pct (bool) – Plot residencies in percentage

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

  • cpu_idle

FrequencyAnalysis.plot_domain_frequency_transitions(pct: bool = False, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

pct=False

Called on Trace instances as trace.ana.frequency.plot_domain_frequency_transitions()

Plot frequency transitions count for all frequency domains

Parameters:

pct (bool) – Plot frequency transitions in percentage

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • cpu_frequency or userspace@cpu_frequency_devlib

FrequencyAnalysis.plot_peripheral_frequency(clk_name: str, average: bool = True, *, filepath=None, output='holoviews', img_format=None, always_save=False, backend=None, _compat_render=False, link_dataframes=None, cursor_delta=None, width=None, height=None, rc_params=None, axis=None, interactive=None, colors: Sequence[str] = None, linestyles: Sequence[str] = None, markers: Sequence[str] = None, **kwargs)[source]#

average=True, clk_name=aplclk

Called on Trace instances as trace.ana.frequency.plot_peripheral_frequency()

Plot frequency for the specified peripheral clock frequency

Parameters:

  • clk_name (str) – The clock name for which to plot frequency

  • average (bool) – If True, add a horizontal line which is the frequency average.

Added by lisa.analysis.base.AnalysisHelpers.plot_method():

Returns:

The return type is determined by the output parameter.

Parameters:

  • backend (str or None) –

    Holoviews plot library backend to use:

    • bokeh: good support for interactive plots

    • matplotlib: sometimes better static image output, but unpredictable results that more often than not require a fair amount of hacks to get something good.

    • plotly: not supported by LISA but technically available. Since it’s very similar to bokeh feature-wise, bokeh should be preferred.

    Note

    In a notebook, the way to choose which backend should be used to display plots is typically selected with e.g. holoviews.extension('bokeh') at the beginning of the notebook. The backend parameter is more intended for expert use where an object of the given library is required, without depending on the environment.

  • link_dataframes (list(pandas.DataFrame) or None) – Gated by output="ui". List of dataframes to display under the figure, which is dynamically linked with it: clicking on the plot will scroll in the dataframes and vice versa.

  • filepath (str or None) – Path of the file to save the figure in. If None, no file is saved.

  • always_save (bool) – When True, the plot is always saved even if no filepath has explicitly been set. In that case, a default path will be used.

  • img_format (str) – The image format to generate. Defaults to using filepath to guess the type, or “png” if no filepath is given. html and rst are supported in addition to matplotlib image formats.

  • output (str or None) –

    Change the return value of the method:

    • None: Equivalent to holoviews for now. In the future, this will be either holoviews or ui if used in an interactive jupyter notebook.

    • holoviews: a bare holoviews element.

    • render: a backend-specific object, such as matplotlib.figure.Figure if backend='matplotlib'

    • html: HTML document

    • rst: a snippet of reStructuredText

    • ui: Pseudo holoviews figure, enriched with extra controls.

      Note

      No assumption must be made on the return type other than that it can be displayed in a notebook cell output (and with IPython.display.display()). The public API holoviews is implemented in a best-effort approach, so that .options() and .opts() will work, but compositions using e.g. x * y will not work if x is a holoviews element.

      In the midterm, the output type will be changed so that it is a real holoviews object, rather than some sort of proxy.

  • colors (list(str) or None) –

    List of color names to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • linestyles (list(str) or None) –

    List of linestyle to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • markers (list(str) or None) –

    List of marker to use for the plots.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • axis (matplotlib.axes.Axes or numpy.ndarray(matplotlib.axes.Axes) or None) –

    instance of matplotlib.axes.Axes to plot into. If None, a new figure and axis are created and returned.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to compose plot elements: http://holoviews.org/user_guide/Composing_Elements.html

  • rc_params (dict(str, object) or None) –

    Matplotlib rc params dictionary overlaid on existing settings.

    Deprecated since version 2.0: This parameter is deprecated, use holoviews APIs to set matplotlib options.

  • _compat_render (bool) – Internal parameter not to be used. This enables the compatibility mode where render=True by default when matplotlib is the current holoviews backend.

Required trace events:

  • clk_set_rate

  • clk_enable

  • clk_disable

classmethod FrequencyAnalysis.cache(f, fmt='parquet', ignored_params=None)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.cache()

Decorator to enable caching of the output of dataframe getter function in the trace cache.

classmethod FrequencyAnalysis.call_on_trace(meth, trace, meth_kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.call_on_trace()

Call a method of a subclass on a given trace.

classmethod FrequencyAnalysis.df_method(f, index=None)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.df_method()

Dataframe function decorator.

classmethod FrequencyAnalysis.get_all_events()#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_all_events()

Returns the set of all events used by any of the methods.

classmethod FrequencyAnalysis.get_analysis_classes()#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_analysis_classes()

FrequencyAnalysis.get_default_plot_path(**kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_default_plot_path()

Return the default path to use to save plots for the analysis.

classmethod FrequencyAnalysis.get_df_methods(*args, **kwargs)#

Inherited method, see lisa.analysis.base.TraceAnalysisBase.get_df_methods()

classmethod FrequencyAnalysis.get_logger(suffix=None)#

Inherited method, see lisa.utils.Loggable.get_logger()

Provides a logging.Logger named after cls.

classmethod FrequencyAnalysis.get_plot_methods(*args, **kwargs)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.get_plot_methods()

classmethod FrequencyAnalysis.log_locals(var_names=None, level='debug')#

Inherited method, see lisa.utils.Loggable.log_locals()

Debugging aid: log the local variables of the calling function.

classmethod FrequencyAnalysis.plot_method(f)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.plot_method()

Plot function decorator.

FrequencyAnalysis.save_plot(figure, filepath=None, img_format=None, backend=None)#

Inherited method, see lisa.analysis.base.AnalysisHelpers.save_plot()

Save a holoviews element or matplotlib.figure.Figure as an image file.