Interpolate

Interpolate - takes resampling one step further to estimate the values of unknown data points that fall between existing, known data points. In addition to the resampling parameters, interpolation also requires:

Interpolation Method - Forward Fill, Backward Fill or Linear

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 of the data
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 date in the format YY-MM-DD or a datetime in the format YYY-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 date in the format YY-MM-DD or a datetime in the format YYY-MM-DDTHH:MM:SS or specify the timezone offset in the format YYYY-MM-DDTHH:MM:SS+zz:zz)
sample_rate	int	(deprecated) Please use time_interval_rate instead. See below.
sample_unit	str	(deprecated) Please use time_interval_unit instead. See below.
time_interval_rate	str	The time interval rate (numeric input)
time_interval_unit	str	The time interval unit (second, minute, day, hour)
agg_method	str	Aggregation Method (first, last, avg, min, max)
interpolation_method	str	Interpolation method (forward_fill, backward_fill, linear)
include_bad_data	bool	Include "Bad" data points with True or remove "Bad" data points with False

Example

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

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",
    "agg_method": "first",
    "interpolation_method": "forward_fill",
    "include_bad_data": True,
}
x = interpolate.get(connection, parameters)
print(x)