Objectives

In this Exercise using Monitor you will learn how to :

• Add a Python Function to check if the ambient temperature is greater than the maximum allowed ambient temperature constant.

The ambient temperature where the hopper asset is located should not exceed a temperature above 27 degrees. The food being packaged has a shorter shelf life when it is packaged at temperatures higher than that. A remote operational support teams monitor monitors the packaging hoppers to ensure they are all operating within the required operating ranges and without anomalies.

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Before you begin:
- You have completed the pre-requisites required for all exercises - You have completed the prior exercise in this lab.

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Create a Python Function to Monitor

1. In Monitor, select Setup Assets tab, search for Sample_Packaging_Hopper_Type_yourinitials . Click on your Device Type and click on Setup Asset Type button.
   

2. Click Data tab, + button to add a new calculated metric from the catalog. Click + button and enter Puthon in the search field of the Monitor catalog.

3. Click PythonFucntion option and theSelect button to add the function to your Asset Type.
    

4. Set the calculation data item inputs. Select ambient_temp from the Data Item field
    

5. Past the code below into the function_code This will assign a value of one to this metric each time the metric value for ambient_temp is equal to or above 27.

   def f(df, parameters=None): import numpy as np return np.where(df['ambient_temp'] >= 27, 1, 0)

6. Click the next button
    

7. Configure the function calculation schedule. Click the schedule slider to edit the schedule. Enter 7 in the field calculating the last and changet time to days instead of minutes. Also set the output name to ambient_temp_over_max

8. Set the Output Type to string then set it back to number then click on Createbutton.
    

!!! note If you encounter the error below, you likely hit a bug. Carefully redo the step 8.

1. Wait 5 minutes and make sure your function doesn't have any errors and causes the pipeline analysis to stop. If it does you will see an error. Click on the Analysis Stopped error
     Read the message to trouble shoot. If you need more information download the log file to trouble shoot the error further.

2. Click on the calculated metric max_temp_yourinitials and the data table tab to see the time series data added to your Device Type. Sort the data by clicking on ambient_temp_over_max. Note how there are now 1's and 0's for the times when the ambient temperature was above max_temp_yourinitials for each packaging hopper.
    

3. Go back and now update the PythonFucntion you created in step 3. Modify the code to reference the constant you created in the previous exercise. Expand the Calculated metrics and click the ambient_temp_over_max. Click on the open function icon on the top right to modify the code.
   

4. Add a another pythonfunction to evaluate the the ambient_temp_over_max and set a new calculated metric named ambient_temp_over_max_status of type string to exceeds ornormal when the value is 1 or 0 respectively.

   ``` def f(df, parameters=None): import numpy as np return np.where(df['ambient_temp_over_max'] == 1, "Exceeds", "Normal")

   ```

Next steps

You now have a new calculated metric that will have a value of 1 each time the ambient temperature exceeds 27 degress Celcius or 0 if it is less than or equal to 27 degrees.

In the next exercise you wil create a PythonExpression to calculate the temperature_deviation between the ambient_temp and the ambient_temp_over_max.