# Execution and results

#### Python Code

In order to run the model, it was used Anaconda "Jupyter Notebook" program with Python3 version.

Please, follow the next mandatory steps:

* Before to run the model, it is necessary to verify if the Input File document is saved in the same folder where the Python code is stored.
* The model must be run step by step in the same order in which the model was developed, installing first the available libraries on Python (NumPy, Pandas).
* The next steps in the Python Code import the Tables (A, B and C) from the RULA Input File, in order to use this information to find the final score per each section: Section A = Arms and Wrist, Section B = Neck, Trunk and Legs and the final solution: Section C = Total Assessment
* In the last part of the code, the final result per each section will be export to the RULA Input File.

Observations:

* The order in which the Input File was fulfilled, does not affect the result of the code.
* If after running the model, this generates a message of error on Python, please go back to the Input File sheet and check that the scores are within the scoring scale range corresponding to each position/adjustment.

### Expected Outcomes

After exporting the final result of the Python Code into the RULA Input File, this will be stored in the last sheet of the document with the name "OutputsRULA".

The table with the results will be organised in the following way (Figure 26):

![](https://2247033703-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lh1dC9-F-cM6usakky7%2F-McA3iJDU8AvxRX4cE33%2F-McAExwxbfJd3sTakCW9%2Foutput_RULA.jpg?alt=media\&token=14ceb63f-6a73-490f-9551-89f42248ffe6)

The range of Level of MSD Risk from the RULA methodology, is given by the following table (Figure 27):

![](https://2247033703-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lh1dC9-F-cM6usakky7%2F-McA3iJDU8AvxRX4cE33%2F-McAF2o_ILNa2C1E_EyA%2FStandard-score-from-NIOSH-for-the-comfort-level-of-RULA-analysis-Source-Hashim-et%5B1%5D.ppm?alt=media\&token=114a2fcd-7ee6-4627-bbbf-92eed879b0dd)

###
