Basic Functionality

This article explains the various classes of Quartic SDK along with their methods and available attributes.

GraphQL Client

GraphQLClient

Class refers to the Graphql client which is used as the interface between the user querying the Quartic AI Platform and their use case.

This class has the following parameters for initialization:

  • url (mandatory): Refers to the host URL the user connects to for making API calls.

  • username (optional): Required for Basic Authentication.

  • password (optional): Required for Basic Authentication.

  • token (optional): Required for OAuth2.0 Authentication.

  • ssl (optional): Required when the host needs to be verified for SSL.

  • timeout (optional): Required if the user wants to specify timeout for API calls.

Here’s an example:

client = GraphqlClient(url='https://stag.quartic.ai', username="username", password="password")

The two methods are Basic Authentication and OAuth2.0:

Basic Authentication:

The user must pass the username and password along with the hostname in the GraphqlClient to ensure the successive API calls are authenticated via Basic Authentication.

client = GraphqlClient(host="https://test.quartic.ai", username="username", password="password")

OAuth2.0

The user must pass the OAuth token along with the hostname to ensure that all the successive API calls are authenticated via OAuth2.0. For detailed information on fetching tokens, please refer to the Quartic Knowledge Base.

client = GraphqlClient(url="https://test.quartic.ai", oauth_token="9865c994212e495690c2db3fc6cbdfea")

The available methods are as follows:

.version

This method returns the current version of the package.

client.version() # Returns 2.1.0 as of the time of writing this document

.execute_query

This method executes the GraphQL query.

  • query_params (required): User needs to pass the string in the format given below:

query='''
    query MyQuery {
      Site {
        id
        name
      }
    }'''

  • variables_params (optional): User can pass a dictionary of variables which are defined in the query in the format below.

query = '''
    mutation($file: Upload!, $edge_connector: Int!, $date_format: DateTime!) {
        uploadTelemetryCsv(
            file: $file,
            fileName: "123",
            edgeConnector: $edge_connector,
            dateFormat: $date_format
            )
            {
            taskId
            status
        }
    }
'''

variables = {
    'file': open('<path/to/file>', 'rb'),
    'edge_connector': 'edgeConnector Id',
    'date_format': 'DatTime format'
}

client.execute_query(query, variables) # Returns a json response on a success.

API Client

Class refers to the API client which is used as the interface between the user querying the Quartic AI Platform and their use case.

This class has the following parameters for initialization:

  • host (mandatory): Refers to the host the user connects to for making API calls.

  • username (optional): Required for Basic Authentication.

  • password (optional): Required for Basic Authentication.

  • oauth_token (optional): Required for OAuth2.0 Authentication.

  • verify_ssl (optional): Required when the host needs to be verified for SSL.

  • cert_path (optional): Required for verification of SSL certificates for HTTPS requests.

Here’s an example:

client = APIClient(host="https://test.quartic.ai", username="username", password="password")

It can be noted that the class provides two methods of authentication to the user.

The user must pass the username and password along with the hostname in the APIClient to ensure all the successive API calls are authenticated via Basic Authentication

client = APIClient(host="https://test.quartic.ai", username="username", password="password")

The user must pass the OAuth token along with the hostname to ensure that all the successive API calls are authenticated via OAuth2.0. For the detailed information on fetching tokens, please refer to the Global Settings article in the Quartic Knowledge Base.

client = APIClient(host="https://test.quartic.ai", oauth_token="9865c994212e495690c2db3fc6cbdfea")

This method returns the current version of the package.

client.version() # Returns 0.0.0 as of the time of writing this document

This method returns the list of assets that the authenticated user has access to. The list of assets are an object of type EntityList. More details on the class is provided below.

The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the Assets accordingly. Filter conditions can be like

{"created_at__gt": "2020-04-05 17:59:50.466338+00:00", "status": 1} {"created_at__lt": "2021-04-05 17:59:50.466338", "status": 1}

Note : timestamps to be passed according to ISO 8610 format in query_params

client_assets = client.assets()

This method returns the list of context frames which are created using the assets that the user has access to. The list of context frames are an object of type EntityList. More details on the class is provided below.

The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the ContextFrame Occurrences accordingly. Filter conditions can be like {"start_ef_occurrence": "A3412", "stop_ef_occurrence": "C7415"}

client_context_frames = client.context_frames()

The method parameters as included in v2.0.0 are as follows:

asset_id (mandatory): The asset_id of the asset whose tags are to be returned.

query_params (optional): User can pass a dictionary of conditions and condition values to filter the tags accordingly. Filter conditions can be like |{"tag_type": 1, "edge_connector": 674}

tags_of_asset_id_1 = client.tags(1)

This method returns the list of all the data sources which the user has access to. The list of data sources are an object of type EntityList. More details on the class are provided below.

The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the Assets accordingly. Filter conditions can be like

    {"created_at__gt": "2020-04-05 17:59:50.466338+00:00", "connector_protocol": 206}

    {"created_at__lt": "2020-04-05 17:59:50", "connector_protocol": 206}

    {"updated_at__lt": "2020-04-05", "connector_protocol": 206}

    Note : timestamps to be passed according to ISO 8610 format in query_params

edge_connectors = client.edge_connectors()

This method lists the ML models and its parameters and requires the following parameters to be called:

  • is_active (optional): If is_active is true, list_models will list the models which are active in the Quartic AI Platform.

  • ml_node (optional): If ml_node (numeric field) is specified, list_models will list all the custom models that are deployed into a specific ml node.

This method is used to fetch list of all product belongs to a particular client. The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the Products accordingly. Filter conditions can be like

    {"created_at__gt": "2020-04-05 14:19:38"}

    Note : timestamps to be passed according to ISO 8610 format in query_params

products = client.products()

This method is used to fetch all sites available for a user’s client. The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the Products accordingly. Filter conditions can be like

    {"start__gt": "2021-04-05 14:19:38", "stop__lt": "2021-04-06 11:19:38}

sites = client.sites()

Entity

Entity is the parent term by which all the objects accessible in the SDK are referred to. The available methods are as follows:

An object of this class is initialiazed with two parameters. These are automatically created and depend on the user’s query through the client.

  • body_json (mandatory): This is the json object which is used to create the related Entity object.

  • api_helper (mandatory): This is the APIHelper object which contains information about the authentication and is used for making API calls.

The available methods are as follows:

get

The attribute value of the object is returned for the given name.

  • name (mandatory): Refers to the attribute name whose value is to be returned.

Asset

This refers to the asset entity which contains the details of the asset. Asset contains all the properties of the base entity defined above. When one prints the name of the asset object, the class returns the name along with the ID of the asset and the template as Asset: {asset_name}_{asset_id}. The available attributes in the class are:

  • id: The ID of the asset

  • name : The name of the asset

  • edge_connectors: The datasource IDs whose tags belong to this asset

  • last_overhaul_date: The last overhaul date of the asset in epoch

  • onboarded_at: The onboarded at time of the asset in epoch

  • created_at: The created at time of the asset in epoch

  • status: The streaming status of the asset. They are given by:

Asset streaming status

Integer

Constant

0

INIT

1

ACTIVE

2

PARTIAL_STREAMING

3

INACTIVE

4

UNASSIGNED_TAGS

The available methods are as follows:

.get_tags

The method returns all the tags present in the given asset in the form of EntityList where each object refers to Tag.

The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the tags accordingly. Filter conditions can be like

{"tag_type": 1, "edge_connector": 674}

.batches

The method returns all the batches present in the given asset in the form of EntityList where each object refers to Batch.

The method parameters as included in v2.0.0 are as follows:

  • query_params (optional): User can pass a dictionary of conditions and condition values to filter the batches accordingly. Filter conditions can be like

    {"created_at__gt": "2021-04-05 14:19:38.303717+00:00"}

    Note : timestamps to be passed according to ISO 8610 format in query_params

.data

The method returns the tag data iterator for all the tags present in the asset for the set start_time and stop_time. It can be used to iterate through the data in batches of 200,000 datapoints. More details are provided under the TagDataIterator subsection.

The method parameters are as follows:

  • start_time (mandatory): (epoch) This refers to the start_time for fetching the data of the asset.

  • stop_time (mandatory): (epoch) This refers to the stop_time for fetching the data of the asset.

  • granularity (optional): This refers to the granularity at which data is required. If the granularity provided, the method returns the data in the tag for the given time range with the lower of the closest possible granularity: Raw (granularity of the datasource), 5s, 30s, 60s, 300s, 1200s, 3600s, 10800s, 21600s, 43200s or 86400s. The default granularity is Raw.

  • return_type (optional): The user can pass either pd, which will return the pandas dataframe iterator, or json which will return json object on return. This value takes the pd value as default.

  • transformations (optional): The user is supposed to pass the list of interpolations and aggregations here. Further details on transformations is provided towards the end of this documentation.

Tag

This refers to the tag entity which contains the details of the tag. Tag contains all the properties of the base Entity defined above. When one prints the name of the tag object, it returns the name alongwith the ID of the tag, with the template as Tag: {tag_name}_{tag_id}

The available attributes in this class are:

  • id: Tag ID

  • name: Tag Name

  • tag_type: The tag types:- 1.Raw, 2.Soft, 3.Aggregation, 4.Bitwise, 5.Writeback

  • tag_data_type: The tag data types. They are given by

Tag Data Types

Integer

Constant

0

DOUBLE

1

STRING

2

BOOLEAN

3

INT

4

LONG

5

FLOAT

6

SPECTRAL

  • short_name: Tag short name

  • edge_connector: The data source ID

  • tag_process_type: The tag process types. They are given by:

Tag Process Type

Integer

Constant

1

PROCESS_VARIABLE

2

CONDITION_VARIABLE

3

PROCESS_ALARM

4

PROCESS_EVENT

5

ANOMALY_SCORE

6

PREDICTED_VARIABLE

7

OTHERS

8

WORKFLOW

9

INFLUENCING_SCORE

  • category: Intelligence Categories. They are given by:

Tag Intelligence Categories

Integer

Constant

1

Energy

2

Throughput

3

Reliability

4

Quality

5

Safety

6

Environment

  • uom_name: The name of measurement unit.

  • asset: ID of the asset

  • created_by: The user ID, who created this tag

  • value_table: The key value pair where key is the integer while the value is the string

The available methods are as follows:

.data

The method returns the tagdata iterator for the selected tag for the set start_time and stop_time, which can be used to iterate through the data in batches of 200,000 datapoints. More details under the TagDataIterator subsection. The method parameters are as follows:

  • start_time (mandatory): (epoch) Refers to the start_time for fetching the data of the asset.

  • stop_time (mandatory): (epoch) Refers to the stop_time for fetching the data of the asset.

  • granularity (optional): This refers to the granularity at which data is required. If the granularity provided, the method returns the data in the tag for the given time range with the lower of the closest possible granularity: Raw (granularity of the datasource), 5s, 30s, 60s, 300s, 1200s, 3600s, 10800s, 21600s, 43200s or 86400s. The default granularity is Raw.

  • return_type (optional): The user can pass either pd, which will return the pandas dataframe iterator, or json which will return json object on return. This value takes the pd value as default.

  • wavelengths (optional): This parameter is only valid for spectral type tags.User can pass a dict with key as “wavelengths” and value as the list of wavelength values for which user wants to fetch the data. By default for a given spectral tag, data for all of its available wavelengths will be fetched. By passing this paramter user can choose to fetch for the specified wavelengths.

  • transformations (optional): The user is supposed to pass the list of interpolations and aggregations here. Further details on transformations is provided towards the end of this documentation.

.wavelengths

The method returns the list of all the wavelengths for the given spectral tag entity, and throws an error otherwise. The method parameters are as follows:

  • start_time (optional): (epoch) Refers to the start_time for evaluating the wavelengths for the spectral tag. In case this is not provided, this defaults to the time, when the given spectral tag is created at.

  • stop_time (optional): (epoch) Refers to the stop_time for evaluating the wavelengths for the spectral tag. In case this is not provided, this defaults to the current datetime value

Batch

This refers to the batch entity which contains the details of the tag. Tag contains all the properties of the base Entity defined above. When one prints the name of the batch object, it returns the name alongwith the ID of the batch and the template as Batch: {batch_name}_{batch_id}

The available attributes in this class are:

  • id: Batch ID

  • batch_name: Batch Name

  • start: Batch start time in epoch

  • stop: Batch stop time in epoch

  • notes: List of notes regarding the batch

  • is_questionable: Whether the batch is questionable

EdgeConnector

This refers to the datasource entity which contains the details of the datasource. Datasource contains all the properties of the base Entity defined above. When one prints the name of the datasource object, it returns the ID of the datasource and the template as datasource: {datasource_name}_{datasource_id}

The available attributes in this class are:

  • id: Datasource ID

  • created_at: Time of creation of data source in epoch

  • edge_device: ID of the edge node

  • connector_protocol: The different datasource types are as follows:

Data Source connector protocol

Integer

Constant

200

ABDF1

201

OPTO22

202

OPCDA

203

OSIPI

204

MODBUS

205

MQTT

206

OPCUA

207

SQL

  • last_streamed_on: Last streamed on epoch

  • update_interval: Update interval in ms

  • name: Name of the datasource

  • stream_status: The stream status for the datasource are as follows:

Data Source streaming status

Integer

Constant

0

INIT

1

ACTIVE

2

PARTIAL_STREAMING

3

INACTIVE

4

UNASSIGNED_TAGS

  • created_by: ID of the user who created the datasource

  • config: Configurations of the data source

  • parent: In case of query datasource, this refers to the ID of the parent datasource

The available methods are as follows:

.get_tags

The method returns all the tags present in the given datasource in the form of EntityList where each object refers to Tag.

.data

The method returns the tag data iterator for all the tags present in the datasource for the set start_time and stop_time. It can be used to iterate through the data in batches of 200,000 datapoints. More details are provided under the TagDataIterator subsection.

The method parameters are as follows:

  • start_time (mandatory): (epoch) This refers to the start_time for fetching the data of the datasource.

  • stop_time (mandatory): (epoch) This refers to the stop_time for fetching the data of the data ource.

  • granularity (optional): This refers to the granularity at which data is required. If the granularity provided, the method returns the data in the tag for the given time range with the lower of the closest possible granularity: Raw (granularity of the datasource), 5s, 30s, 60s, 300s, 1200s, 3600s, 10800s, 21600s, 43200s or 86400s. The default granularity is Raw.

  • return_type (optional): The user can pass either pd, which will return the pandas dataframe iterator, or json which will return json object on return. This value takes the pd value as default.

  • transformations (optional): The user must pass the list of interpolations and aggregations here. Further details on transformations is provided towards the end of this documentation.

.historical_data

The method returns the historical tag data iterator for all the tags present in the datasource for the set start_time and stop_time. It can be used to iterate through the ddata in custom batches as decided by the user. More details are provided under the HistoricalTagDataIterator subsection.

The method parameters are as follows:

  • start_time (mandatory): (epoch) This refers to the start_time for fetching the data of the datasource.

  • stop_time (mandatory): (epoch) This refers to the stop_time for fetching the data of the data source.

  • batch_size (optional): This refers to the number of rows in each page while iterating through the historical data

  • max_records (optional): This refers to the maximum number of records that are to be fetched in the API call

  • tags (optional): (EntityList) This is the entitylist of tags. This is an optional value, and will take all the data source tags by default

  • return_type (optional): The user can pass either pd, which will return the pandas dataframe iterator, or json which will return json object on return. This value takes the pd value as default.

ContextFrame

This refers to the context frame entity which contains the details of the tag. ContextFrame contains all the properties of the base Entity defined above. When one prints the name of the ContextFrame object, it returns the ID of the ContextFrame and the template as ContextFrame: {context_frame_id}

The available attributes in this class are:

  • id: ContextFrame ID

  • name: ContextFrame name

  • description: ContextFrame description

  • pu_or_wc: ID of the Process Unit/Work cell

The available methods are as follows:

  • occurrences: The method returns all the occurrences of the given ContextFrame in the form of EntityList where each object refers to ContextFrameOccurrence.

    The method parameters as included in v2.0.0 are as follows:

    • query_params (optional): User can pass a dictionary of conditions and condition values to filter the ContextFrame Occurrences accordingly. Filter conditions can be like {"start_ef_occurrence": "A3412", "stop_ef_occurrence": "C7415"}

ContextFrameOccurrence

This refers to the context frame occurrence entity which contains the details of the tag. ContextFrameOccurrence contains all the properties of the base Entity defined above. When one prints the name of the ContextFrame object, it returns a random unique integer denoting the occurrence, with the template as ContextFrameOccurrence: {random_integer}

The available attributes in this class are:

  • id: ContextFrameOccurrence ID

  • start_ef_occurrence: Start event frame occurrence for the context frame

  • stop_ef_occurrence: Stop event frame occurrence for the context frame

  • is_valid: Whether the context frame occurrence is valid

  • context_frame: Context Frame ID

Model

This refers to Model entity, which contains the details of the model, Model contains all the properties of the base Entity defined above. When one prints the name of the model object, it returns the name alongwith the ID of the model, with the template as Model: {model_name}_{model_id}

The available attributes in this class are:

  • model_id: Unique ID for the model

  • model_name: Name given for the model

  • feature_tags: IDs of tags used as Feature

  • output_tag: ID of the tag in which prediction results are stored

  • target_tag: ID of the tag which is used as the parent for prediction output tag

  • is_spectral_model: Boolean if the model is spectral or not

The available methods are as follows:

.model_instance

This method returns the Model object (created and deployed by extending model base- BaseQuarticModel).

EntityList

This class contains the list of entities, where each entity can be of the type Asset, Tag, ContextFrame,ContextFrameOccurrence, Model and Batch.

The class requires the following parameters for initialization:

  • class_type (mandatory): Refers to the type of objects it will contain. These can be Asset, Tag, ContextFrame, ContextFrameOccurrence, Model or Batch.

  • entities_list (optional): Refers to the list of entities, which will be contained in the list. The types should match the class_type as provided. In case this is not present, it will default to an empty list.

The available methods are as follows:

.get

The method returns the first item in the list whose attribute name has the value value. It requires the following parameters:

  • name (mandatory): Refers to the name of the attribute.

  • value (mandatory): Refers to the value of this attribute.

The usage is as below:

asset_with_id_1 = client_assets.get("id", 1)

.all

The method returns all the entities present in the list.

all_assets = client_assets.all()

.first

This method returns the first element of the EntityList.

first_asset = client_assets.first()

.last

This method returns the last element of the EntityList.

last_asset = client_assets.last()

.add

This method adds the given object into the list. If the object already exists or it belongs to a different class_type, it throws an exception.

client_assets.add(new_asset_entity)

.filter

This method filters the given EntityList to return an updated list that contains only those entities which satisfy all the conditions given in arguments. It works with all primitive attribtues of the Entity in the EntityList, if the specific operation is defined for that data type.

It takes multiple keyword arguments as a parameter (**kwargs) to filter the entities.

For a simple filter operation that includes entities by checking for equality, the format is: entity_attribute=value

For operators other than equality, the format is: entity_attribute__operator=value

You can refer here for a list of possible operators.

filtered_entity_list = client_assets.filter(status='Inactive')
filtered_entity_list = client_assets.filter(country__ne='India')

.exclude

This method filters the given EntityList to return an updated list that doesn’t contain entities which satisfy any of the conditions given in arguments.

It takes the same arguments as .filter above but negates the conditions to exclude them.

updated_entity_list = client_assets.exclude(id=5)
updated_entity_list = client_assets.exclude(created_at__lt=first_asset.created_at)

.data

This method is present only for the Tag type EntityList, and it returns the data present in the given tags. It returns a TagDataIterator instance, and has the following parameters:

  • start_time (mandatory): (epoch) Refers to the start_time for fetching the data of the asset.

  • stop_time (mandatory): (epoch) Refers to the stop_time for fetching the data of the asset.

  • granularity (optional): This refers to the granularity at which data is required. If granularity is provided, the method returns the data in the tag for the given time range with the lower of the closest possible granularity: Raw (granularity of the datasource), 5s, 30s, 60s, 300s, 1200s, 3600s, 10800s, 21600s, 43200s or 86400s. The default granularity is Raw.

  • return_type (optional): The user can pass either pd, which will return the pandas dataframe iterator, or json which will return json object on return. This value takes the pd value as default.

  • transformations (optional): The user must pass the list of interpolations and aggregations here. Further details on transformations is provided towards the end of this documentation.

TagDataIterator

Querying data for any set of tags in any given duration returns an instance of TagDataIterator, which can be used to iterate between the given time range. When the .data of tags/assets is called, the method divides the complete interval between start_time and stop_time into different time_ranges, with each range containing up to 200,000 data points for all the tags. The user can loop through this interval to get all the data points.

HistoricalTagDataIterator

Querying historical data for any set of tags in any given duration returns an instance of HistoricalTagDataIterator, which can be used to iterate between the given time range. When the .historical_data of data source is called, the class queries via cursor, based upon the entered batch_size

The available methods are as follows:

.get_complete_data_in_range

The method loops through the complete range and returns the final data frame which is required. Note that this is meant to be used only for pandas return of historical data and not for json return. The method has the following parameters:

  • historical_data_iterator (mandatory) This is the historicaldataiterator object whose complete data is to be returned

Transformations:

The tag data iterator is created based upon the multiple transformations that a user might need. The transformations is a list of dictionaries where each dictionary contains the details of interpolation/aggregation to be performed on the data. The different transformations are:

Interpolation:

Interpolation requires the following keys to be present:

  • transformation_type (mandatory): Refers to the type of transformation and must always be interpolation.

  • column (mandatory): Refers to the column which is to be interpolated.

  • method (mandatory): Refers to the interpolation method; the options are: linear, spline, cubic interpolation, and polynomial.

  • order (mandatory): Refers to the order of the interpolation, and is required for all methods except linear.

  • limit_direction (optional): Refers to the direction in which the interpolation takes place. The default value is forward.

Aggregation:

Aggregation requires the following keys to be present:

  • transformation_type (mandatory): Refers to the type of transformation and must always be aggregation.

  • aggregation_column (mandatory): Refers to the column being aggregated.

  • aggregation_dict (mandatory): This dictionary refers to the methods on which the different columns in the dataset are to be aggregated.

Procedure

This refers to the product (Product Harbour) procedures. Procedures are present/created within the Product

The available attributes in this class are:

  • id: Procedure ID

  • name: Procedure name

  • additional_attributes: This field contains additional information and fields of Procedure like receipe_type, formula and receipe_version

  • procedure_state: state among 5 values

  • test_results:

  • site: Site id under which procedure gets created

  • product: ID of the Product under which that procedure gets created

  • created_by: The user ID, who created this procedure

  • created_at: created at date time

  • updated_at: updated at date time

The available methods are as follows:

  • fetch_unit_procedures: The method returns all the UnitProcedures of the given Procedure in the form of EntityList where each object refers to ProcedureStep.

    The method parameters as included in v2.0.0 are as follows:

    • query_params (optional): User can pass a dictionary of conditions and condition values to filter the UnitProcedures accordingly.

ProcedureStep

This refers to the Node/Child(UnitProcedure/Operation/Phase/PhaseStep) added in Procedure Node in the hierarchy.

The available attributes in this class are:

  • id: ProcedureStep ID

  • name: ProcedureStep name

  • step_type: Integer Field denoted the type of node(UnitProcedure/Operation/Phase/PhaseStep) at each step

  • order: Sequence in which we want to add child nodes inside parent(ProcedureStep) node

  • parent: ID of the parent ProcedureStep Node under which that procedure step gets created

  • procedure: ID of the Procedure under which that procedure step gets created

  • step_components: list of components involved

  • created_at: created at date time

  • updated_at: updated at date time

The available methods are as follows:

  • fetch_substep_details: The method returns all the ProcedureStep details like Operation/Phase/PhaseStep in the form of EntityList where each object refers to ProcedureStep.

    The method parameters as included in v2.0.0 are as follows:

    • query_params (optional): User can pass a dictionary of conditions and condition values to filter the ProcedureStep accordingly.

Product

This refers to the Product under which all the Procedure and Procedure Step hierarchy is present or created. This is the root node of the entire hierarchy.

The available attributes in this class are:

  • id: Product ID

  • name: Product name

  • client: Client id under which procedure gets created

  • description: This contains description of the product

  • created_by: The user ID, who created this product

  • created_by_name: The user name, who created this product

  • created_at: created at date time

  • updated_at: updated at date time

The available methods are as follows:

  • get_procedures: The method returns all the Procedures of the given Product in the form of EntityList where each object refers to Procedure Entity.

    The method parameters as included in v2.0.0 are as follows:

    • query_params (optional): User can pass a dictionary of conditions and condition values to filter the Procedures accordingly.

Site

This refers to the User’s Client Site.

The available attributes in this class are:

  • id: Site ID

  • name: Site name

  • country_name: Country name

  • state_name: State name

  • pin_code: PinCode where site belongs

  • address_line_1: Line one of the site address

  • address_line_2: Line two of the site address

  • country: Country Id site belongs to

  • state: State Id site belongs to

  • client: Client Id site belongs to