Interpolate Function
get(connection, parameters_dict)
An RTDIP interpolation function that is intertwined with the RTDIP Resampling function.
The Interpolation function will forward fill or backward fill the resampled data depending users specified interpolation method.
This function requires the user to input a dictionary of parameters. (See Attributes table 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 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 |
str
|
The resampling rate (numeric input) |
sample_unit |
str
|
The resampling unit (second, minute, day, hour) |
agg_method |
str
|
Aggregation Method (first, last, avg, min, max) |
interpolation_method |
str
|
Optional. Interpolation method (forward_fill, backward_fill) |
include_bad_data |
bool
|
Include "Bad" data points with True or remove "Bad" data points with False |
Returns:
Name | Type | Description |
---|---|---|
DataFrame |
pd.DataFrame
|
A resampled and interpolated dataframe. |
Source code in src/sdk/python/rtdip_sdk/queries/time_series/interpolate.py
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|
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", #options:["float", "double", "integer", "string"]
"tag_names": ["tag_1", "tag_2"], #list of tags
"start_date": "2023-01-01", #start_date can be a date in the format "YYYY-MM-DD" or a 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": "2023-01-31", #end_date can be a date in the format "YYYY-MM-DD" or a datetime in the format "YYYY-MM-DDTHH:MM:SS" or specify the timezone offset in the format "YYYY-MM-DDTHH:MM:SS+zz:zz"
"sample_rate": "1", #numeric input
"sample_unit": "hour", #options: ["second", "minute", "day", "hour"]
"agg_method": "first", #options: ["first", "last", "avg", "min", "max"]
"interpolation_method": "forward_fill", #options: ["forward_fill", "backward_fill"]
"include_bad_data": True, #options: [True, False]
}
x = interpolate.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
server_hostname
and http_path
can be found on the SQL Warehouses Page.