StatisticalTestEWMA¶

class StatisticalTestEWMA(self: visp._visp.core.StatisticalTestEWMA, alpha: float = 0.1f)¶

Bases: StatisticalTestAbstract

Class that permits to perform Exponentially Weighted Moving Average mean drft tests.

The EWMA test is designed to detect drift in the mean \(\mu\) of an observed signal \(s(t)\) .

The test signal \(w(t)\) is computed as follow:

\(w(0) = \mu\)

\(w(t) = \alpha s(t) + ( 1 - \alpha ) * w(t-1)\)

Be \(\sigma\) the standard deviation of the input signal \(s(t)\) .

A downward alarm is raised if: \(w(t) <= \mu - 3 * \sigma * \sqrt{ \frac{\alpha}{2 - \alpha}}\)

An upward alarm is raised if: \(w(t) >= \mu + 3 * \sigma * \sqrt{ \frac{\alpha}{2 - \alpha}}\)

To detect only downward drifts of the input signal \(s(t)\) use testDownwardMeanDrift() .To detect only upward drifts in \(s(t)\) use testUpwardMeanDrift() . To detect both, downward and upward drifts use testDownUpwardMeanDrift() .

Construct a new vpStatisticalTestEWMA object.

Parameters:

alpha: The forgetting factor.

Methods

`__init__`	Construct a new vpStatisticalTestEWMA object.
`getAlpha`	Get the forgetting factor of the algorithm.
`getWt`	Get the current value of the test signal.
`init`	Overloaded function.
`setAlpha`	Set the forgetting factor.

Inherited Methods

`testDownUpwardMeanDrift`	Test if a downward or an upward mean drift occurred according to the new value of the signal.
`MEAN_DRIFT_BOTH`
`getStdev`	Get the standard deviation used as reference.
`MEAN_DRIFT_NONE`
`testDownwardMeanDrift`	Test if a downward mean drift occurred according to the new value of the signal.
`MEAN_DRIFT_DOWNWARD`
`getLimits`	Get the upper and lower limits of the test signal.
`vpMeanDriftTypeFromString`	Cast a string into a vpMeanDriftType .
`MEAN_DRIFT_UNKNOWN`
`MeanDriftType`	Enum that indicates if a drift of the mean occurred.
`testUpwardMeanDrift`	Test if an upward mean drift occurred according to the new value of the signal.
`print`
`getAvailableMeanDriftType`	Get the list of available vpMeanDriftType objects that are handled.
`getMean`	Get the mean used as reference.
`setNbSamplesForStat`	Set the number of samples required to compute the mean and standard deviation of the signal and allocate the memory accordingly.
`MEAN_DRIFT_COUNT`
`setMinStdev`	Set the minimum value of the standard deviation that is expected.
`MEAN_DRIFT_UPWARD`
`vpMeanDriftTypeToString`

Operators

`__doc__`
`__init__`	Construct a new vpStatisticalTestEWMA object.
`__module__`

Attributes

`MEAN_DRIFT_BOTH`
`MEAN_DRIFT_COUNT`
`MEAN_DRIFT_DOWNWARD`
`MEAN_DRIFT_NONE`
`MEAN_DRIFT_UNKNOWN`
`MEAN_DRIFT_UPWARD`
`__annotations__`

class MeanDriftType(self, value: int)¶

Bases: pybind11_object

Enum that indicates if a drift of the mean occurred.

Values:

MEAN_DRIFT_NONE: No mean drift occurred
MEAN_DRIFT_DOWNWARD: A downward drift of the mean occurred.
MEAN_DRIFT_UPWARD: An upward drift of the mean occurred.
MEAN_DRIFT_BOTH: Both an aupward and a downward drifts occurred.
MEAN_DRIFT_COUNT
MEAN_DRIFT_UNKNOWN

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

__init__(self: visp._visp.core.StatisticalTestEWMA, alpha: float = 0.1f)¶

Construct a new vpStatisticalTestEWMA object.

Parameters:

alpha: The forgetting factor.

getAlpha(self) → float¶

Get the forgetting factor of the algorithm.

Returns:: float The forgetting factor.

static getAvailableMeanDriftType(prefix: str = <, sep: str =, suffix: str = >) → str¶

Get the list of available vpMeanDriftType objects that are handled.

Parameters:

prefix: The prefix that should be placed before the list.
sep: The separator between each element of the list.
suffix: The suffix that should terminate the list.

Returns:

std::string The list of handled type of process tests, presented as a string.

getLimits(self, limitDown: float, limitUp: float) → tuple[float, float]¶

Get the upper and lower limits of the test signal.

Parameters:

limitDown: float¶: The lower limit.
limitUp: float¶: The upper limit.

Returns:

A tuple containing:

limitDown: The lower limit.
limitUp: The upper limit.

getMean(self) → float¶

Get the mean used as reference.

Returns:: float The mean.

getStdev(self) → float¶

Get the standard deviation used as reference.

Returns:: float The standard deviation.

getWt(self) → float¶

Get the current value of the test signal.

Returns:: float The current value of the test signal.

init(*args, **kwargs)¶

Overloaded function.

init(self: visp._visp.core.StatisticalTestEWMA, alpha: float) -> None

Initialize the EWMA algorithm.

Parameters:

alpha: The forgetting factor.

init(self: visp._visp.core.StatisticalTestEWMA, alpha: float, mean: float, stdev: float) -> None

Initialize the EWMA algorithm.

Parameters:

alpha: The forgetting factor.
mean: The expected mean of the signal to monitor.
stdev: The expected standard deviation of the signal to monitor.

init(self: visp._visp.core.StatisticalTestAbstract) -> None

(Re)Initialize the algorithm.

static print(type: visp._visp.core.StatisticalTestAbstract.MeanDriftType) → None¶

setAlpha(self, alpha: float) → None¶

Set the forgetting factor.

Parameters:

alpha: float¶: The forgetting factor.

setMinStdev(self, stdevmin: float) → None¶

Set the minimum value of the standard deviation that is expected. The computed standard deviation cannot be lower this value if set.

Parameters:

stdevmin: float¶: The minimum value of the standard deviation that is expected.

setNbSamplesForStat(self, nbSamples: int) → None¶

Set the number of samples required to compute the mean and standard deviation of the signal and allocate the memory accordingly.

Parameters:

nbSamples: int¶: The number of samples we want to use.

testDownUpwardMeanDrift(self, signal: float) → visp._visp.core.StatisticalTestAbstract.MeanDriftType¶

Test if a downward or an upward mean drift occurred according to the new value of the signal.

Note

See testDownwardMeanDrift() testUpwardMeanDrift()

Parameters:

signal: float¶: The new value of the signal.

Returns:

vpMeanDriftType The type of mean drift that occurred.

testDownwardMeanDrift(self, signal: float) → visp._visp.core.StatisticalTestAbstract.MeanDriftType¶

Test if a downward mean drift occurred according to the new value of the signal.

Note

See testUpwardMeanDrift()

Parameters:

signal: float¶: The new value of the signal.

Returns:

vpMeanDriftType The type of mean drift that occurred.

testUpwardMeanDrift(self, signal: float) → visp._visp.core.StatisticalTestAbstract.MeanDriftType¶

Test if an upward mean drift occurred according to the new value of the signal.

Note

See testDownwardMeanDrift()

Parameters:

signal: float¶: The new value of the signal.

Returns:

vpMeanDriftType The type of mean drift that occurred.

static vpMeanDriftTypeFromString(name: str) → visp._visp.core.StatisticalTestAbstract.MeanDriftType¶

Cast a string into a vpMeanDriftType .

Parameters:

name: str¶: The name of the mean drift.

Returns:

vpMeanDriftType The corresponding vpMeanDriftType .

static vpMeanDriftTypeToString(type: visp._visp.core.StatisticalTestAbstract.MeanDriftType) → str¶