Tutorial: Adding a simple function

Before you begin

1. Install Python 3.9.x in your environment.
2. Install IoT Functions in your local environment. Follow the instructions in the IoT Functions readme.
   Tip: Alternatively, to install IoT Functions and its dependencies, you can follow the steps in the Before you begin section of Tutorial: Adding a custom function. Clone the HelloWorld starter package and import the starter package into PyCharm. Install the requirements from PyCharm.
3. Complete Tutorial: Adding expressions to add a distance metric to the robot device type in Maximo Monitor.

About this task

You are testing a simple function in your local environment to make sure that the calculation works and that the syntax is correct.

In the script, you create a sample device type, generate some sample data, and apply the simple function to the sample data to create a new adjusted_distance metric.

Steps

Complete these steps to apply a calculation to sample data using a simple function.

Step 1: Define your calculation

Define adjusted_distance as follows:

adjusted_distance = distance * 0.9

Step 2: Save your credentials to a file

Set credentials to connect to Maximo Monitor.

1. Download the credentials_as.json file.
2. Replace the variables with your data and then save the file to your local machine.

Step 3: Write a simple function in Python

Write a script, save_simple_function_locally.py, that adjusts the distance traveled by robots. In the script, connect to Maximo Monitor using your credentials and save the function to a local model store. See the inline comments for instructions.

#Import packages and libraries

import datetime as dt
import json
import pandas as pd
import numpy as np
from sqlalchemy import Column, Integer, String, Float, DateTime, Boolean, func
import iotfunctions.bif as bif
from iotfunctions.metadata import EntityType, LocalEntityType
from iotfunctions.db import Database
from iotfunctions.dbtables import FileModelStore


#Connect to Maximo Monitor
with open('credentials_as.json', encoding='utf-8') as F:
    credentials = json.loads(F.read())
db_schema = None
db = Database(credentials=credentials)

#Write the function

def f(df, parameters = None):
    adjusted_distance = df['distance'] * 0.9
    return adjusted_distance

#Save the function to a local model store
model_store = FileModelStore()
model_store.store_model('adjusted_distance', f)

Step 4: Save the function to a local file model store

Run the script save_simple_function_locally.py from your local environment. A file KPI_MODEL_STOREadjusted_distance is created in the same directory where you stored your script.

Step 5: Test your simple function in your local environment

Create a script called test_my_simple_function_locally.py. In the script, complete these steps:

1. Import Python libraries and packages:

    import datetime as dt
    import json
    import pandas as pd
    import numpy as np
    from sqlalchemy import Column, Integer, String, Float, DateTime, Boolean, func
    import iotfunctions.bif as bif
    from iotfunctions.metadata import EntityType, LocalEntityType
    from iotfunctions.db import Database
    from iotfunctions.dbtables import FileModelStore

2. Connect to the Maximo Monitor:

    with open('credentials_as.json', encoding='utf-8') as F:
        credentials = json.loads(F.read())
    db_schema = None
    model_store = FileModelStore()
    db = Database(credentials=credentials, model_store=model_store)
   				

3. Create a sample device type and pass the simple function to it using the built-in PythonFunction from the bif module that you imported from IoT Functions.

   sample_entity_type = bif.PythonFunction(
       function_code = 'adjusted_distance',
       input_items = ['distance'],
       output_item = 'adjusted_distance',
       parameters = {}
           )

4. Run the execute_local_test method of the sample device type to test the function in your local environment. Save the results to a csv file.

   sample_entity_type.execute_local_test(db=db, generate_days=1, to_csv=True)

The script creates a new adjusted_distance data item. Review the results in the .csv file. If successful, you are ready add the simple function using the Maximo Monitor UI.

Step 6: Apply the simple function to the Robot device type

1. On the Data tab, click Creat metric.
2. Select the PythonFunction function from the catalog.
3. Set the scope and click Next.
4. Select distance as an input item.
5. Enter or paste in the simple function in the function_code field. Enter:

   def f(df, parameters = None):
    adjusted_distance = df['distance'] * 0.9
    return adjusted_distance

6. Click Next
7. Specify adjusted_distance as an output data item.
8. Specify the data type as Number.
9. Click Create
10. In the Data item list, select adjusted_distance.
11. View the new adjusted_distance metric data.

Wait up to 5 minutes for Maximo Monitor to evaluate the simple function against the sample data.