How to Run a Python Script from

Author: Guy Shepherd

One of the most common requirements is to run a Python script before or after a projection, perhaps to run an existing Python script as part of an end-to-end modelling activity, prepare some data for a projection task, or process some results of a projection task.

This is easily accomplished in either as a group projection task or by developing a small reusable component.

In this example I will show how to run an existing Python-based Black Scholes model from a group projection task, passing in arguments to define the vector size to use, and retrieving the completion message & run time from the Python script.

Environmental Configuration

The example illustrated here uses out-of-the-box installations of both Model Development Studio and Python:

  • Model Development Studio (Standard, Professional or Enterprise) v7.0 or later
  • Python 3.5 or later

Black Scholes Model

The Black Scholes Python script is shown below.  This is saved as in the Python folder of the project.

import time
from datetime import datetime
import numpy as np
from scipy.stats import norm
import sys

# S = current stock price
# K = option strike price
# P = theoretical put premium
# s = standard deviation of returns
# r = risk free rate (assumed zero for this example)
# t = time until option matures

if len (sys.argv) == 1 :
    print('Using default vector size: ',xx,yy)
    print('Using custom vector size: ',xx,yy)

nparray_S = np.random.rand(xx,yy)
nparray_K = np.random.rand(xx,yy)

s = 0.12                            
r = 0

def bsvector():
    nparray_SoverK = nparray_S / nparray_K
    nparray_lnSoverK = np.log(nparray_SoverK)
    nparray_tproj = np.arange(1,721)
    nparray_tproj2d = nparray_tproj.reshape(720, 1)
    nparray_tproj2d = np.repeat(nparray_tproj2d, nparray_S.shape[1], axis = 1)
    nparray_d1topright = nparray_tproj2d / 12 * (r+(s**2)/2)
    nparray_d1bot = np.sqrt(nparray_tproj2d / 12) * s
    nparray_d2right = nparray_d1bot.copy()
    nparray_d1 = (nparray_lnSoverK + nparray_d1topright) / nparray_d1bot
    nparray_d2 = nparray_d1 - nparray_d2right
    nparray_Nnegd1 = norm.cdf(-nparray_d1)
    nparray_Nnegd2 = norm.cdf(-nparray_d2)

    nparray_Pleft = nparray_Nnegd2 * nparray_K
    nparray_Pright = nparray_Nnegd1 * nparray_S
    nparray_P = nparray_Pleft - nparray_Pright

starttime =
endtime =

print("Black-Scholes - Vector Approach")
print("Duration: ",endtime-starttime)

print("Start:    ", starttime.strftime("%H:%M:%S"))
print("End:      ", endtime.strftime("%H:%M:%S"))

The script accepts two command line arguments (xx and yy) which define the dimensions of the vector.  If no arguments are provided, then it defaults to 720 and 5000 respectively.  The script returns the dimensions of the vector used and the elapsed time of calculation.  While the script has been designed to run as part of a projection task, it can also be run standalone in Python or any Python development IDE (IDLE, PyCharm, etc).

Example Project

The example project can be downloaded directly from the Software Alliance website.  It is available in the Additional Components section of the Customer Portal and is called PythonBlog2Project.mozip.

Open the example project:

  1. Download the mozip from the Software Alliance website
  2. Restore the mozip to a suitable location using Model Development Studio
  3. Navigate to the Tasks view of the project and click on the RunPython group projection task to display the code as shown below:
Sub Run()
    '   This group projection task runs a vectorised Black Scholes Python script, 
    '   captures the elapsed time of the calculation, and presents this back to the 
    '   user in the progress window.

    '   Note: This sample requires Python v3.5 or later to be installed.  
    '   We have assumed Python 3.9 is installed in c:\python39, but this can be 
    '   changed below as required.
    '   create a process instance
    Dim p As System.Diagnostics.Process = New System.Diagnostics.Process
    '   define the dimensions of the vector to be created in the Python script
    Dim xx As Integer = 720, yy As Integer = 500
    '   allow redirection
    p.StartInfo.UseShellExecute = False
    '   allows us to capture the output from the script
    p.StartInfo.RedirectStandardOutput = True           

    '   path / Python binary name                                                            
    p.StartInfo.FileName = "c:\python39\python.exe"         

    '   arguments passed to Python script to define vector size                                                        
    p.StartInfo.Arguments = Project.Workspace & "Python\ " & xx & " " & yy

    '   start the process

    '   capture the output from the script
    Dim output As String = p.StandardOutput.ReadToEnd()     

    '   present the output in the Progress Window
End Sub

Tip: Before attempting to run the projection, make sure to edit the location of Python binary if needed, from C:\Python39 (see Line 24 above).

Run the Projection.  You should see the following displayed in the Progress Window.

Now try changing the vector dimensions (xx and yy) in the group projection task – to say 720 and 1000 respectively and rerun to make sure they are correctly passed to the Python script. I advise not changing yy to anything greater than 50,000 or you are likely to run out of memory.

What’s Going On?

The key elements of the group projection code are as follows.

12Instantiate an instance of the standard .NET system.diagnostics.process object.  This is akin to “shelling out” to a command prompt, but with significantly more flexibility.
15Define the dimensions of the vector
18-21Configure the process object to allow redirection of output
24Define location of the Python binary
27Define the arguments to pass to Python.  This includes the script and the parameters, in this case the dimensions of the vector
31Start the Python process
34Capture the directed output from the Python process
37Pass the output from the Python process to the Progress Window

Possible Enhancements

There are a number of obvious extensions to this basic projection task.  For example, there is nothing to handle errors, either within the projection code or the Python script.  A first step might be to capture any errors raised by the Python script and show these in the Progress Window.  This can be done relatively easily by capturing additional properties of the process instance.

Another enhancement might be to expose the standard deviation and risk-free rate parameters in the Python script allowing them to be controlled by the projection.  This would require a subtle change to the Python logic to process another command line argument.

Finally, it might be useful to read the vector calculated by the Python script back into as a table for downstream use in another task.

In the next article I will cover how to reverse the use case and call a projection from Python.

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