Python script and example yaml configuration for running IMP modeling job

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

From the downloaded and extracted master branch. Run the following to execute the example job:

imp_msstudio_init-master\driver_imp_init\imp_model>run_py_impjob.bat

For linux platform there is a python script available: imp_job_run.py

The driver script runs with python 3.x and depends on the Python Modeling Interface (PMI)

initial setup for PMI
Anaconda3\Library\bin\conda config --add channels salilab
Anaconda3\Library\bin\conda install imp scikit-learn matplotlib

https://integrativemodeling.org/tutorials/rnapolii_stalk/
Anaconda3\Library\bin\conda install numpy scipy


bring up Anaconda Prompt and run : activate base
you can see envs available with: conda info --envs
for example: this shows us that base is c:\apps\Anaconda3

Give examples

A step by step series of examples that tell you how to get a development env running

The readme_setup_cedar.txt is a good place to start. This explains how to setup the example on Compute Canada's Cedar cluster.

Taken from the readme_setup_cedar.txt:


#### get the setup script and call it with the correct username as the first arg: 
~~~
curl -LOk https://gist.githubusercontent.com/pellst/4853822ea5ca74785af61d0ad39cf84d/raw/uoc_mss_prep_step1.sh
chmod 755 uoc_mss_prep_step1.sh
~~~

#### run the script uoc_mss_prep_step1.sh in order to get the sample folders setup
~~~
uoc_mss_prep_step1.sh tpells
~~~

#### in the folder scratch/imp/imp_msstudio_init-master/mss_out/imp_model, the following shell scripts are now available
~~~
            uoc_mss_prep_step1.sh
            uoc_mss_prep_step2.sh
            uoc_mss_prep_step3.sh
~~~
			
#### we can continue on to step2 to setup anaconda			
uoc_mss_prep_step2.sh


#### once anaconda has been setup we can bring in the imp module and others needed for the job run
uoc_mss_prep_step3.sh

#### the next step is to review the following:
#### located in scratch/imp/imp_msstudio_init-master/mss_out/imp_model
ConfigImp.yaml
mjob_run_cedar.sh

sampling_frame: 1000

#Runbook info.

#!/bin/bash
# example slurm job script setup to run on for example Cedar
#SBATCH --job-name=SLURM_imp
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16
#SBATCH --cpus-per-task=1
#SBATCH --mem=32G
#SBATCH --time=0-01:00:00
#SBATCH --account=sponsername 

#in order to run the job we call this and give a unit number to be used for naming the folder that is setup eg: 12 here

./mjob_run_cedar.sh 12

The wrapper script mjob_run_cedar.sh essentially performs a slurm job scheduler call to launch: srun python prep_hyperp_imp_v2ux.py and this in turn runs the prep_hyperp_imp_v2ux.py driver script. The configuration of the driver script is accomplished with the ConfigImp.yaml There are assumptions that have been made and while a basic modeling run has been anticipated. The prep_hyperp_imp_v2ux.py script can be customised further in order to fit the specific modeling scenario.

finished

End with an example of getting some data out of the system or using it for a little demo

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Add additional notes about how to deploy this on a live system

• Python - The language used
• IMP - The integrated modeling platform

Please read [CONTRIBUTING.md] for details on our code of conduct, and the process for submitting pull requests to us.

We use SemVer for versioning. For the versions available, see the tags on this repository.

• MassSpecStudio Development Team - Initial work - University of Calgary

See also the list of contributors who participated in this project.

This project is licensed under the MIT License - see the LICENSE.md file for details

• MassSpecStudio Development Team
• [IMP dataset source] (https://integrativemodeling.org/tutorials/rnapolii_stalk/)
• Inspiration
• etc