Time Weighted Average

Time Weighted Averages provide an unbiased average when working with irregularly sampled data. The RTDIP SDK requires the following parameters to perform time weighted average queries:

Window Size Mins - (deprecated) Time Interval Rate - The time interval rate Time Interval Unit - The time interval unit (second, minute, day, hour) Window Length - Adds a longer window time for the start or end of specified date to cater for edge cases Step - Data points with step "enabled" or "disabled". The options for step are "true", "false" or "metadata" as string types. For "metadata", the query requires that the TagName has a step column configured correctly in the meta data table

Prerequisites

Ensure you have installed the RTDIP SDK as specified in the Getting Started section.

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().

Parameters

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 ["tag_1", "tag_2"]
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

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)