Module PdmContext.Pipelines

Expand source code
from PdmContext.ContextGeneration import ContextGenerator


class ContextAndClustering():

    def __init__(self,Clustring_object,context_generator_object: ContextGenerator):
        """
                This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator

                **Parameters**:

                **Clustring_object**: The clustering technique to use to cluster the created context from Context generator
                The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)


                 **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object
                """


        self.clustering=Clustring_object
        self.Contexter=context_generator_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.clustering.add_sample_to_cluster(contextTemp)

        return contextTemp


class ContextAndClusteringAndDatabase():

    def __init__(self,context_generator_object: ContextGenerator,Clustring_object,databaseStore_object):
        """
                This class build a pipeline of Cntext generator and Clustering technique running immediately in the
                results of Context Generator, and storing context results to database

                **Parameters**:

                **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object

                **Clustring_object**: The clustering technique to use to cluster the created context from Context generator
                The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)
                databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)


                **databaseStore_object**: An object of database connection from PdmContext.utils.dbconnector
                """


        self.clustering=Clustring_object
        self.Contexter=context_generator_object
        self.database=databaseStore_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.clustering.add_sample_to_cluster(contextTemp)
            self.database.insert_record(timestamp,name,contextTemp)
        return contextTemp

class ContextAndDatabase():

    def __init__(self,context_generator_object: ContextGenerator,databaseStore_object):
        """
                This class build a pipeline of Cntext generator and storing results to database

                **Parameters**:

                **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object

                **databaseStore_object**: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)

                """


        self.Contexter=context_generator_object
        self.database=databaseStore_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.database.insert_record(timestamp,name,contextTemp)
        return contextTemp

Classes

class ContextAndClustering (Clustring_object, context_generator_object: ContextGenerator)

This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator

Parameters:

Clustring_object: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)

context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object

Expand source code
class ContextAndClustering():

    def __init__(self,Clustring_object,context_generator_object: ContextGenerator):
        """
                This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator

                **Parameters**:

                **Clustring_object**: The clustering technique to use to cluster the created context from Context generator
                The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)


                 **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object
                """


        self.clustering=Clustring_object
        self.Contexter=context_generator_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.clustering.add_sample_to_cluster(contextTemp)

        return contextTemp

Methods

def collect_data(self, timestamp, source, name, value=None, type='Univariate')

Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

Parameters:

timestamp: The timestamp of the arrived value

source: The source of the arrived value

name: The name (or identifier) of the arrived value

value: The value (float), in case this is None the arrived data is considered as event

type: the type of the data can be one of "isolated","configuration" when no value is passed

return: PdmContext.utils.structure.Context object when the data name match to the target name or None.

Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
    """
    Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

    **Parameters**:

    **timestamp**:  The timestamp of the arrived value

    **source**: The source of the arrived value

    **name**: The name (or identifier) of the arrived value

    **value**: The value (float), in case this is None the arrived data is considered as event

    **type**: the type of the data can be one of "isolated","configuration" when no value is passed

    **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
    """

    contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
    if contextTemp is not None:
        self.clustering.add_sample_to_cluster(contextTemp)

    return contextTemp
class ContextAndClusteringAndDatabase (context_generator_object: ContextGenerator, Clustring_object, databaseStore_object)

This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator, and storing context results to database

Parameters:

context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object

Clustring_object: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context) databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)

databaseStore_object: An object of database connection from PdmContext.utils.dbconnector

Expand source code
class ContextAndClusteringAndDatabase():

    def __init__(self,context_generator_object: ContextGenerator,Clustring_object,databaseStore_object):
        """
                This class build a pipeline of Cntext generator and Clustering technique running immediately in the
                results of Context Generator, and storing context results to database

                **Parameters**:

                **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object

                **Clustring_object**: The clustering technique to use to cluster the created context from Context generator
                The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)
                databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)


                **databaseStore_object**: An object of database connection from PdmContext.utils.dbconnector
                """


        self.clustering=Clustring_object
        self.Contexter=context_generator_object
        self.database=databaseStore_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.clustering.add_sample_to_cluster(contextTemp)
            self.database.insert_record(timestamp,name,contextTemp)
        return contextTemp

Methods

def collect_data(self, timestamp, source, name, value=None, type='Univariate')

Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

Parameters:

timestamp: The timestamp of the arrived value

source: The source of the arrived value

name: The name (or identifier) of the arrived value

value: The value (float), in case this is None the arrived data is considered as event

type: the type of the data can be one of "isolated","configuration" when no value is passed

return: PdmContext.utils.structure.Context object when the data name match to the target name or None.

Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
    """
    Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

    **Parameters**:

    **timestamp**:  The timestamp of the arrived value

    **source**: The source of the arrived value

    **name**: The name (or identifier) of the arrived value

    **value**: The value (float), in case this is None the arrived data is considered as event

    **type**: the type of the data can be one of "isolated","configuration" when no value is passed

    **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
    """

    contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
    if contextTemp is not None:
        self.clustering.add_sample_to_cluster(contextTemp)
        self.database.insert_record(timestamp,name,contextTemp)
    return contextTemp
class ContextAndDatabase (context_generator_object: ContextGenerator, databaseStore_object)

This class build a pipeline of Cntext generator and storing results to database

Parameters:

context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object

databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)

Expand source code
class ContextAndDatabase():

    def __init__(self,context_generator_object: ContextGenerator,databaseStore_object):
        """
                This class build a pipeline of Cntext generator and storing results to database

                **Parameters**:

                **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object

                **databaseStore_object**: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)

                """


        self.Contexter=context_generator_object
        self.database=databaseStore_object


    def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
        """
        Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

        **Parameters**:

        **timestamp**:  The timestamp of the arrived value

        **source**: The source of the arrived value

        **name**: The name (or identifier) of the arrived value

        **value**: The value (float), in case this is None the arrived data is considered as event

        **type**: the type of the data can be one of "isolated","configuration" when no value is passed

        **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
        """

        contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
        if contextTemp is not None:
            self.database.insert_record(timestamp,name,contextTemp)
        return contextTemp

Methods

def collect_data(self, timestamp, source, name, value=None, type='Univariate')

Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

Parameters:

timestamp: The timestamp of the arrived value

source: The source of the arrived value

name: The name (or identifier) of the arrived value

value: The value (float), in case this is None the arrived data is considered as event

type: the type of the data can be one of "isolated","configuration" when no value is passed

return: PdmContext.utils.structure.Context object when the data name match to the target name or None.

Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
    """
    Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique

    **Parameters**:

    **timestamp**:  The timestamp of the arrived value

    **source**: The source of the arrived value

    **name**: The name (or identifier) of the arrived value

    **value**: The value (float), in case this is None the arrived data is considered as event

    **type**: the type of the data can be one of "isolated","configuration" when no value is passed

    **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
    """

    contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
    if contextTemp is not None:
        self.database.insert_record(timestamp,name,contextTemp)
    return contextTemp