class SignalCompare(object):
"""
:param data: TRAPpy FTrace Object
:type data: :mod:`trappy.ftrace.FTrace`
:param sig_a: The first signal
:type sig_a: str
:param sig_b: The first signal
:type sig_b: str
:param config: A dictionary of variables, classes
and functions that can be used in the statements
:type config: dict
:param method: The method to be used for reindexing data
This can be one of the standard :mod:`pandas.DataFrame`
methods (eg. pad, bfill, nearest). The default is pad
or use the last valid observation.
:type method: str
:param limit: The number of indices a value will be propagated
when reindexing. The default is None
:type limit: int
:param fill: Whether to fill the NaNs in the data.
The default value is True.
:type fill: bool
.. note::
Both the signals must have the same pivots. For example:
- Signal A has a pivot as :code:`"cpu"` which means that
the trappy event (:mod:`trappy.base.Base`) has a pivot
parameter which is equal to :code:`"cpu"`. Then the signal B
should also have :code:`"cpu"` as it's pivot.
- Signal A and B can both have undefined or None
as their pivots
"""
def __init__(self, data, sig_a, sig_b, **kwargs):
self._parser = Parser(data,
config=kwargs.pop("config", None),
**kwargs)
self._a = sig_a
self._b = sig_b
self._pivot_vals, self._pivot = self._get_signal_pivots()
# Concatenate the indices by doing any operation (say add)
self._a_data = self._parser.solve(sig_a)
self._b_data = self._parser.solve(sig_b)
def _get_signal_pivots(self):
"""Internal function to check pivot conditions and
return an intersection of pivot on the signals"""
sig_a_info = self._parser.inspect(self._a)
sig_b_info = self._parser.inspect(self._b)
if sig_a_info["pivot"] != sig_b_info["pivot"]:
raise RuntimeError("The pivot column for both signals" +
"should be same (%s,%s)" %
(sig_a_info["pivot"], sig_b_info["pivot"]))
if sig_a_info["pivot"]:
pivot_vals = set(sig_a_info["pivot_values"]).intersection(
sig_b_info["pivot_values"])
pivoted = sig_a_info["pivot"]
else:
pivot_vals = [StatConf.GRAMMAR_DEFAULT_PIVOT]
pivoted = False
return pivot_vals, pivoted
def conditional_compare(self, condition, **kwargs):
"""Conditionally compare two signals
The conditional comparison of signals has two components:
- **Value Coefficient** :math:`\\alpha_{v}` which measures the difference in values of
of the two signals when the condition is true:
.. math::
\\alpha_{v} = \\frac{area\_under\_curve(S_A\ |\ C(t)\ is\ true)}
{area\_under\_curve(S_B\ |\ C(t)\ is\ true)} \\\\
\\alpha_{v} = \\frac{\int S_A(\{t\ |\ C(t)\})dt}{\int S_B(\{t\ |\ C(t)\})dt}
- **Time Coefficient** :math:`\\alpha_{t}` which measures the time during which the
condition holds true.
.. math::
\\alpha_{t} = \\frac{T_{valid}}{T_{total}}
:param condition: A condition that returns a truth value and obeys the grammar syntax
::
"event_x:sig_a > event_x:sig_b"
:type condition: str
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for area and time
summation calculation
:type step: str
Consider the two signals A and B as follows:
.. code::
A = [0, 0, 0, 3, 3, 0, 0, 0]
B = [0, 0, 2, 2, 2, 2, 1, 1]
.. code::
A = xxxx
3 *xxxx*xxxx+ B = ----
| |
2 *----*----*----+
| | |
1 | | *----*----+
| | |
0 *x-x-*x-x-+xxxx+ +xxxx*xxxx+
0 1 2 3 4 5 6 7
The condition:
.. math::
A > B
is valid between T=3 and T=5. Therefore,
.. math::
\\alpha_v=1.5 \\\\
\\alpha_t=\\frac{2}{7}
:returns: There are two cases:
- **Pivoted Signals**
::
{
"pivot_name" : {
"pval_1" : (v1,t1),
"pval_2" : (v2, t2)
}
}
- **Non Pivoted Signals**
The tuple of :math:`(\\alpha_v, \\alpha_t)`
"""
if self._pivot:
result = {self._pivot: {}}
mask = self._parser.solve(condition)
step = kwargs.get("step", "post")
for pivot_val in self._pivot_vals:
a_piv = self._a_data[pivot_val]
b_piv = self._b_data[pivot_val]
area = area_under_curve(a_piv[mask[pivot_val]], **kwargs)
try:
area /= area_under_curve(b_piv[mask[pivot_val]], **kwargs)
except ZeroDivisionError:
area = float("nan")
duration = min(a_piv.last_valid_index(), b_piv.last_valid_index())
duration -= max(a_piv.first_valid_index(),
b_piv.first_valid_index())
duration = interval_sum(mask[pivot_val], step=step) / duration
if self._pivot:
result[self._pivot][pivot_val] = area, duration
else:
result = area, duration
return result
def get_overshoot(self, **kwargs):
"""Special case for :func:`conditional_compare`
where the condition is:
::
"sig_a > sig_b"
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for calculation of area
and time summation
:type step: str
.. seealso::
:func:`conditional_compare`
"""
condition = " ".join([self._a, ">", self._b])
return self.conditional_compare(condition, **kwargs)
def get_undershoot(self, **kwargs):
"""Special case for :func:`conditional_compare`
where the condition is:
::
"sig_a < sig_b"
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for calculation of area
and time summation
:type step: str
.. seealso::
:func:`conditional_compare`
"""
condition = " ".join([self._a, "<", self._b])
return self.conditional_compare(condition, **kwargs)
class SignalCompare(object):
"""
:param data: TRAPpy FTrace Object
:type data: :mod:`trappy.ftrace.FTrace`
:param sig_a: The first signal
:type sig_a: str
:param sig_b: The first signal
:type sig_b: str
:param config: A dictionary of variables, classes
and functions that can be used in the statements
:type config: dict
:param method: The method to be used for reindexing data
This can be one of the standard :mod:`pandas.DataFrame`
methods (eg. pad, bfill, nearest). The default is pad
or use the last valid observation.
:type method: str
:param limit: The number of indices a value will be propagated
when reindexing. The default is None
:type limit: int
:param fill: Whether to fill the NaNs in the data.
The default value is True.
:type fill: bool
.. note::
Both the signals must have the same pivots. For example:
- Signal A has a pivot as :code:`"cpu"` which means that
the trappy event (:mod:`trappy.base.Base`) has a pivot
parameter which is equal to :code:`"cpu"`. Then the signal B
should also have :code:`"cpu"` as it's pivot.
- Signal A and B can both have undefined or None
as their pivots
"""
def __init__(self, data, sig_a, sig_b, **kwargs):
self._parser = Parser(
data,
config=kwargs.pop(
"config",
None),
**kwargs)
self._a = sig_a
self._b = sig_b
self._pivot_vals, self._pivot = self._get_signal_pivots()
# Concatenate the indices by doing any operation (say add)
self._a_data = self._parser.solve(sig_a)
self._b_data = self._parser.solve(sig_b)
def _get_signal_pivots(self):
"""Internal function to check pivot conditions and
return an intersection of pivot on the signals"""
sig_a_info = self._parser.inspect(self._a)
sig_b_info = self._parser.inspect(self._b)
if sig_a_info["pivot"] != sig_b_info["pivot"]:
raise RuntimeError("The pivot column for both signals" +
"should be same (%s,%s)"
% (sig_a_info["pivot"], sig_b_info["pivot"]))
if sig_a_info["pivot"]:
pivot_vals = set(
sig_a_info["pivot_values"]).intersection(sig_b_info["pivot_values"])
pivoted = sig_a_info["pivot"]
else:
pivot_vals = [StatConf.GRAMMAR_DEFAULT_PIVOT]
pivoted = False
return pivot_vals, pivoted
def conditional_compare(self, condition, **kwargs):
"""Conditionally compare two signals
The conditional comparison of signals has two components:
- **Value Coefficient** :math:`\\alpha_{v}` which measures the difference in values of
of the two signals when the condition is true:
.. math::
\\alpha_{v} = \\frac{area\_under\_curve(S_A\ |\ C(t)\ is\ true)}
{area\_under\_curve(S_B\ |\ C(t)\ is\ true)} \\\\
\\alpha_{v} = \\frac{\int S_A(\{t\ |\ C(t)\})dt}{\int S_B(\{t\ |\ C(t)\})dt}
- **Time Coefficient** :math:`\\alpha_{t}` which measures the time during which the
condition holds true.
.. math::
\\alpha_{t} = \\frac{T_{valid}}{T_{total}}
:param condition: A condition that returns a truth value and obeys the grammar syntax
::
"event_x:sig_a > event_x:sig_b"
:type condition: str
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for area and time
summation calculation
:type step: str
Consider the two signals A and B as follows:
.. code::
A = [0, 0, 0, 3, 3, 0, 0, 0]
B = [0, 0, 2, 2, 2, 2, 1, 1]
.. code::
A = xxxx
3 *xxxx*xxxx+ B = ----
| |
2 *----*----*----+
| | |
1 | | *----*----+
| | |
0 *x-x-*x-x-+xxxx+ +xxxx*xxxx+
0 1 2 3 4 5 6 7
The condition:
.. math::
A > B
is valid between T=3 and T=5. Therefore,
.. math::
\\alpha_v=1.5 \\\\
\\alpha_t=\\frac{2}{7}
:returns: There are two cases:
- **Pivoted Signals**
::
{
"pivot_name" : {
"pval_1" : (v1,t1),
"pval_2" : (v2, t2)
}
}
- **Non Pivoted Signals**
The tuple of :math:`(\\alpha_v, \\alpha_t)`
"""
if self._pivot:
result = {self._pivot: {}}
mask = self._parser.solve(condition)
step = kwargs.get("step", "post")
for pivot_val in self._pivot_vals:
a_piv = self._a_data[pivot_val]
b_piv = self._b_data[pivot_val]
area = area_under_curve(a_piv[mask[pivot_val]], **kwargs)
try:
area /= area_under_curve(b_piv[mask[pivot_val]], **kwargs)
except ZeroDivisionError:
area = float("nan")
duration = min(a_piv.last_valid_index(), b_piv.last_valid_index())
duration -= max(a_piv.first_valid_index(),
b_piv.first_valid_index())
duration = interval_sum(mask[pivot_val], step=step) / duration
if self._pivot:
result[self._pivot][pivot_val] = area, duration
else:
result = area, duration
return result
def get_overshoot(self, **kwargs):
"""Special case for :func:`conditional_compare`
where the condition is:
::
"sig_a > sig_b"
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for calculation of area
and time summation
:type step: str
.. seealso::
:func:`conditional_compare`
"""
condition = " ".join([self._a, ">", self._b])
return self.conditional_compare(condition, **kwargs)
def get_undershoot(self, **kwargs):
"""Special case for :func:`conditional_compare`
where the condition is:
::
"sig_a < sig_b"
:param method: The method for area calculation. This can
be any of the integration methods supported in `numpy`
or `rect`
:type param: str
:param step: The step behaviour for calculation of area
and time summation
:type step: str
.. seealso::
:func:`conditional_compare`
"""
condition = " ".join([self._a, "<", self._b])
return self.conditional_compare(condition, **kwargs)
