def register_class(cls):
"""Register a new class implementation
Should be used when the class has
complex helper methods and does not
expect to use the default constructor
"""
# Check the argspec of the class
Run.register_class(cls)
def register_class(cls):
"""Register a new class implementation
Should be used when the class has
complex helper methods and does not
expect to use the default constructor
:param cls: The class to be registered for
enabling the parsing of an event in trace
:type cls: :mod:`trappy.base.Base`
"""
# Check the argspec of the class
Run.register_class(cls)
def register_dynamic(class_name, unique_word, scope="all", parse_raw=False):
"""Create a Dynamic Type and register
it with the trappy Framework"""
dyn_class = DynamicTypeFactory(
class_name, (Base, ), {
"__init__": default_init,
"unique_word": unique_word,
"name": _get_name(class_name),
"parse_raw": parse_raw
})
Run.register_class(dyn_class, scope)
return dyn_class
def register_class(cls):
"""Register a new class implementation
Should be used when the class has
complex helper methods and does not
expect to use the default constructor
:param cls: The class to be registered for
enabling the parsing of an event in trace
:type cls: :mod:`trappy.base.Base`
"""
# Check the argspec of the class
Run.register_class(cls)
def register_dynamic(class_name, unique_word, scope="all",
parse_raw=False):
"""Create a Dynamic Type and register
it with the TRAPpy Framework
:param class_name: The name of the class to be registered
(Should be in CamelCase)
:type class_name: str
:param unique_word: The unique_word to be matched in the
trace
:type unique_word: str
:param scope: Registry Scope (Can be used to constrain
the parsing of events and group them together)
:type scope: str
:param parse_raw: If, true, raw trace output (-R flag)
will be used
:type parse_raw: bool
For example if a new unique word :code:`my_unique_word` has
to be registered with TRAPpy:
::
import trappy
custom_class = trappy.register_dynamic("MyEvent", "my_unique_word")
run = trappy.Run("/path/to/trace_file")
# New data member created in the run object
run.my_event
.. note:: The name of the member is :code:`my_event` from **MyEvent**
:return: A class object of type :mod:`trappy.base.Base`
"""
dyn_class = DynamicTypeFactory(
class_name, (Base,), {
"__init__": default_init,
"unique_word": unique_word,
"name": _get_name(class_name),
"parse_raw" : parse_raw
}
)
Run.register_class(dyn_class, scope)
return dyn_class
def register_dynamic(class_name, unique_word, scope="all", parse_raw=False):
"""Create a Dynamic Type and register
it with the TRAPpy Framework
:param class_name: The name of the class to be registered
(Should be in CamelCase)
:type class_name: str
:param unique_word: The unique_word to be matched in the
trace
:type unique_word: str
:param scope: Registry Scope (Can be used to constrain
the parsing of events and group them together)
:type scope: str
:param parse_raw: If, true, raw trace output (-R flag)
will be used
:type parse_raw: bool
For example if a new unique word :code:`my_unique_word` has
to be registered with TRAPpy:
::
import trappy
custom_class = trappy.register_dynamic("MyEvent", "my_unique_word")
run = trappy.Run("/path/to/trace_file")
# New data member created in the run object
run.my_event
.. note:: The name of the member is :code:`my_event` from **MyEvent**
:return: A class object of type :mod:`trappy.base.Base`
"""
dyn_class = DynamicTypeFactory(
class_name, (Base, ), {
"__init__": default_init,
"unique_word": unique_word,
"name": _get_name(class_name),
"parse_raw": parse_raw
})
Run.register_class(dyn_class, scope)
return dyn_class
def get_all_freqs(self, mapping_label):
"""Get a :mod:`pandas.DataFrame` with the maximum frequencies allowed by the governor
:param mapping_label: A dictionary that maps cpumasks to name
of the cpu.
:type mapping_label: dict
:return: freqs are in MHz
"""
dfr = self.data_frame
return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
Run.register_class(CpuOutPower, "thermal")
class CpuInPower(Base):
"""Process the cpufreq cooling power actor data in a ftrace dump
"""
unique_word = "thermal_power_cpu_get"
"""The unique word that will be matched in a trace line"""
name = "cpu_in_power"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
pivot = "cpus"
"""The Pivot along which the data is orthogonal"""
:param ax: Axis instance
:type ax: :mod:`matplotlib.Axis`
:param title: The title of the plot
:type title: str
"""
temps = self.data_frame["temp"] / 1000
title = normalize_title("Temperature", title)
xlim = (0, temps.max())
plot_hist(temps, ax, title, "C", 30, "Temperature", xlim, "default")
Run.register_class(Thermal, "thermal")
class ThermalGovernor(Base):
"""Process the power allocator data in a ftrace dump"""
unique_word = "thermal_power_allocator:"
"""The unique word that will be matched in a trace line"""
name = "thermal_governor"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
pivot = "thermal_zone_id"
"""The Pivot along which the data is orthogonal"""
def __init__(self):
"""Plot a temperature histogram
:param ax: Axis instance
:type ax: :mod:`matplotlib.Axis`
:param title: The title of the plot
:type title: str
"""
temps = self.data_frame["temp"] / 1000
title = normalize_title("Temperature", title)
xlim = (0, temps.max())
plot_hist(temps, ax, title, "C", 30, "Temperature", xlim, "default")
Run.register_class(Thermal, "thermal")
class ThermalGovernor(Base):
"""Process the power allocator data in a ftrace dump"""
unique_word = "thermal_power_allocator:"
"""The unique word that will be matched in a trace line"""
name = "thermal_governor"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
pivot = "thermal_zone_id"
"""The Pivot along which the data is orthogonal"""
def __init__(self):
_cpu_mask_column = "cpus"
def __init__(self):
super(SchedLoadAvgSchedGroup, self).__init__(
unique_word=self.unique_word,
)
def finalize_object(self):
"""This condition is necessary to force column 'cpus' to be printed
as 8 digits w/ leading 0
"""
if self._cpu_mask_column in self.data_frame.columns:
dfr = self.data_frame[self._cpu_mask_column].apply('{:0>8}'.format)
self.data_frame[self._cpu_mask_column] = dfr
Run.register_class(SchedLoadAvgSchedGroup, "sched")
class SchedLoadAvgTask(Base):
"""Corresponds to Linux kernel trace event sched_load_avg_task"""
unique_word = "sched_load_avg_task:"
"""The unique word that will be matched in a trace line"""
name = "sched_load_avg_task"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
def __init__(self):
super(SchedLoadAvgTask, self).__init__(
unique_word=self.unique_word,
)
"""The Pivot along which the data is orthogonal"""
def __init__(self):
super(PIDController,
self).__init__(unique_word="thermal_power_allocator_pid", )
def plot_controller(self, title="", width=None, height=None, ax=None):
"""Plot a summary of the controller data
:param ax: Axis instance
:type ax: :mod:`matplotlib.Axis`
:param title: The title of the plot
:type title: str
:param width: The width of the plot
:type width: int
:param height: The height of the plot
:type int: int
"""
title = normalize_title("PID", title)
if not ax:
ax = pre_plot_setup(width, height)
self.data_frame[["output", "p", "i", "d"]].plot(ax=ax)
post_plot_setup(ax, title=title)
Run.register_class(PIDController, "thermal")
self).__init__(unique_word="thermal_power_devfreq_get_power:", )
def get_all_freqs(self):
"""Return a :mod:`pandas.DataFrame` with
the frequencies for the devfreq device
The format should be the same as the one for
:code:`CpuInPower().get_all_freqs()`.
.. note:: Frequencies are in MHz.
"""
return pd.DataFrame(self.data_frame["freq"] / 1000000)
Run.register_class(DevfreqInPower, "thermal")
class DevfreqOutPower(Base):
"""Process de devfreq cooling device data regarding power2state in an
ftrace dump"""
name = "devfreq_out_power"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
def __init__(self):
super(DevfreqOutPower,
self).__init__(unique_word="thermal_power_devfreq_limit:", )
def get_all_freqs(self):
"""Return a :mod:`pandas.DataFrame` with
_cpu_mask_column = "cpus"
def __init__(self):
super(SchedLoadAvgSchedGroup, self).__init__(
unique_word=self.unique_word,
)
def finalize_object(self):
"""This condition is necessary to force column 'cpus' to be printed
as 8 digits w/ leading 0
"""
if self._cpu_mask_column in self.data_frame.columns:
dfr = self.data_frame[self._cpu_mask_column].apply('{:0>8}'.format)
self.data_frame[self._cpu_mask_column] = dfr
Run.register_class(SchedLoadAvgSchedGroup, "sched")
class SchedLoadAvgTask(Base):
"""Corresponds to Linux kernel trace event sched_load_avg_task"""
unique_word = "sched_load_avg_task:"
"""The unique word that will be matched in a trace line"""
name = "sched_load_avg_task"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
def __init__(self):
super(SchedLoadAvgTask, self).__init__(
unique_word=self.unique_word,
)
def get_all_freqs(self, mapping_label):
"""Get a :mod:`pandas.DataFrame` with the maximum frequencies allowed by the governor
:param mapping_label: A dictionary that maps cpumasks to name
of the cpu.
:type mapping_label: dict
:return: freqs are in MHz
"""
dfr = self.data_frame
return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
Run.register_class(CpuOutPower, "thermal")
class CpuInPower(Base):
"""Process the cpufreq cooling power actor data in a ftrace dump
"""
unique_word = "thermal_power_cpu_get"
"""The unique word that will be matched in a trace line"""
name = "cpu_in_power"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.run.Run` object"""
pivot = "cpus"
"""The Pivot along which the data is orthogonal"""
def __init__(self):
super(PIDController, self).__init__(
unique_word="thermal_power_allocator_pid",
)
def plot_controller(self, title="", width=None, height=None, ax=None):
"""Plot a summary of the controller data
:param ax: Axis instance
:type ax: :mod:`matplotlib.Axis`
:param title: The title of the plot
:type title: str
:param width: The width of the plot
:type width: int
:param height: The height of the plot
:type int: int
"""
title = normalize_title("PID", title)
if not ax:
ax = pre_plot_setup(width, height)
self.data_frame[["output", "p", "i", "d"]].plot(ax=ax)
post_plot_setup(ax, title=title)
Run.register_class(PIDController, "thermal")