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Time Weighted Average

get(connection, parameters_dict)

A function that receives a dataframe of raw tag data and performs a time weighted averages, returning the results.

This function requires the input of a pandas dataframe acquired via the rtdip.functions.raw() method and the user to input a dictionary of parameters. (See Attributes table below)

Pi data points will either have step enabled (True) or step disabled (False). You can specify whether you want step to be fetched by "Pi" or you can set the step parameter to True/False in the dictionary below.

Parameters:

Name Type Description Default
connection object

Connection chosen by the user (Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect)

 required
parameters_dict dict

A dictionary of parameters (see Attributes table below)

 required

Attributes:

Name Type Description
business_unit str

Business unit
region str

Region
asset str

Asset
data_security_level str

Level of data security
data_type str

Type of the data (float, integer, double, string)
tag_names list

List of tagname or tagnames
start_date str

Start date (Either a utc date in the format YYYY-MM-DD or a utc datetime in the format YYYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
end_date str

End date (Either a utc date in the format YYYY-MM-DD or a utc datetime in the format YYYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
window_size_mins int

(deprecated) Window size in minutes. Please use time_interval_rate and time_interval_unit below instead.
time_interval_rate str

The time interval rate (numeric input)
time_interval_unit str

The time interval unit (second, minute, day, hour)
window_length int

Add longer window time in days for the start or end of specified date to cater for edge cases.
include_bad_data bool

Include "Bad" data points with True or remove "Bad" data points with False
step str

data points with step "enabled" or "disabled". The options for step are "true", "false" or "metadata". "metadata" will retrieve the step value from the metadata table.
pivot bool

Pivot the data on timestamp column with True or do not pivot the data with False
limit optional int

The number of rows to be returned
offset optional int

The number of rows to skip before returning rows
case_insensitivity_tag_search optional bool

Search for tags using case insensitivity with True or case sensitivity with False

Returns:

Name Type Description
DataFrame DataFrame

A dataframe containing the time weighted averages.

Warning

Setting case_insensitivity_tag_search to True will result in a longer query time.

Source code in src/sdk/python/rtdip_sdk/queries/time_series/time_weighted_average.py 
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def get(connection: object, parameters_dict: dict) -> pd.DataFrame:
    """
    A function that receives a dataframe of raw tag data and performs a time weighted averages, returning the results.

    This function requires the input of a pandas dataframe acquired via the rtdip.functions.raw() method and the user to input a dictionary of parameters. (See Attributes table below)

    Pi data points will either have step enabled (True) or step disabled (False). You can specify whether you want step to be fetched by "Pi" or you can set the step parameter to True/False in the dictionary below.

    Args:
        connection: Connection chosen by the user (Databricks SQL Connect, PYODBC SQL Connect, TURBODBC SQL Connect)
        parameters_dict (dict): A dictionary of parameters (see Attributes table below)

    Attributes:
        business_unit (str): Business unit
        region (str): Region
        asset (str): Asset
        data_security_level (str): Level of data security
        data_type (str): Type of the data (float, integer, double, string)
        tag_names (list): List of tagname or tagnames
        start_date (str): Start date (Either a utc date in the format YYYY-MM-DD or a utc datetime in the format YYYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
        end_date (str): End date (Either a utc date in the format YYYY-MM-DD or a utc datetime in the format YYYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
        window_size_mins (int): (deprecated) Window size in minutes. Please use time_interval_rate and time_interval_unit below instead.
        time_interval_rate (str): The time interval rate (numeric input)
        time_interval_unit (str): The time interval unit (second, minute, day, hour)
        window_length (int): Add longer window time in days for the start or end of specified date to cater for edge cases.
        include_bad_data (bool): Include "Bad" data points with True or remove "Bad" data points with False
        step (str): data points with step "enabled" or "disabled". The options for step are "true", "false" or "metadata". "metadata" will retrieve the step value from the metadata table.
        pivot (bool): Pivot the data on timestamp column with True or do not pivot the data with False
        limit (optional int): The number of rows to be returned
        offset (optional int): The number of rows to skip before returning rows
        case_insensitivity_tag_search (optional bool): Search for tags using case insensitivity with True or case sensitivity with False

    Returns:
        DataFrame: A dataframe containing the time weighted averages.

    !!! warning
        Setting `case_insensitivity_tag_search` to True will result in a longer query time.
    """
    if isinstance(parameters_dict["tag_names"], list) is False:
        raise ValueError("tag_names must be a list")

    if "window_size_mins" in parameters_dict:
        logging.warning(
            "Parameter window_size_mins is deprecated and will be removed in v1.0.0. Please use time_interval_rate and time_interval_unit instead."
        )
        parameters_dict["time_interval_rate"] = str(parameters_dict["window_size_mins"])
        parameters_dict["time_interval_unit"] = "minute"

    try:
        query = _query_builder(parameters_dict, "time_weighted_average")

        try:
            cursor = connection.cursor()
            cursor.execute(query)
            df = cursor.fetch_all()
            cursor.close()
            connection.close()
            return df
        except Exception as e:
            logging.exception("error returning dataframe")
            raise e

    except Exception as e:
        logging.exception("error with time weighted average function")
        raise e

Example

from rtdip_sdk.authentication.azure import DefaultAuth
from rtdip_sdk.connectors import DatabricksSQLConnection
from rtdip_sdk.queries import time_weighted_average

auth = DefaultAuth().authenticate()
token = auth.get_token("2ff814a6-3304-4ab8-85cb-cd0e6f879c1d/.default").token
connection = DatabricksSQLConnection("{server_hostname}", "{http_path}", token)

parameters = {
    "business_unit": "{business_unit}",
    "region": "{region}", 
    "asset": "{asset_name}", 
    "data_security_level": "{security_level}", 
    "data_type": "float",
    "tag_names": ["{tag_name_1}", "{tag_name_2}"],
    "start_date": "2023-01-01",
    "end_date": "2023-01-31",
    "time_interval_rate": "15",
    "time_interval_unit": "minute",
    "window_length": 1,
    "include_bad_data": True,
    "step": "true"
}
x = time_weighted_average.get(connection, parameters)
print(x)

This example is using DefaultAuth() and DatabricksSQLConnection() to authenticate and connect. You can find other ways to authenticate here. The alternative built in connection methods are either by PYODBCSQLConnection(), TURBODBCSQLConnection() or SparkConnection().

Note

See Samples Repository for full list of examples.

Note

server_hostname and http_path can be found on the SQL Warehouses Page.