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
|
Source code in src/sdk/python/rtdip_sdk/queries/time_series/time_weighted_average.py
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77 | 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
Returns:
DataFrame: A dataframe containing the time weighted averages.
"""
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().