Resample
Resample enables changing the frequency of time series observations. This is achieved by providing the following parameters:
Sample Rate - (deprecated) Sample Unit - (deprecated) Time Interval Rate - The time interval rate Time Interval Unit - The time interval unit (second, minute, day, hour) Aggregation Method - Aggregations including first, last, avg, min, max
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 |
---|---|---|
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) |
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 TimeSeriesQueryBuilder
auth = DefaultAuth().authenticate()
token = auth.get_token("2ff814a6-3304-4ab8-85cb-cd0e6f879c1d/.default").token
connection = DatabricksSQLConnection("{server_hostname}", "{http_path}", token)
data = (
TimeSeriesQueryBuilder()
.connect(connection)
.source("{tablename_or_path}")
.resample(
tagname_filter=["{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",
)
)
print(data)