# Login to Cannon
ssh <username>@login.rc.fas.harvard.edu

# Go to a compute node on the test partition:
salloc --partition test --nodes=1 --cpus-per-task=2 --mem=12GB --time=00:30:00

# Check Python modules available on Cannon:
module spider python

# Get detailed information on specific module, e.g.: 
module spider python/3.10.13-fasrc01

# Load the latest (usually also the default) Python module: 
module load python

# Check current location & change if desired for this training:
pwd
cd <desired-location>

# Clone FASRC User Codes repository: https://github.com/fasrc/User_Codes/tree/master
SSH - git clone git@github.com:fasrc/User_Codes.git  
HTTPS - git clone https://github.com/fasrc/User_Codes.git

# Create a training folder & go to that folder:
mkdir python-training
cd python-training

# Copy Python folders from the User Codes directory: 
cp -r ../User_Codes/Languages/Python .

# Check Python version
python --version

# Invoke Python interpreter & write a program
#def square(x):
#      """square a number""" 
#return x ** 2

#for N in range(1, 4):
#       print(N, "squared is", square(N))

#exit()
       
# Run a Python program
python myscript.py

# Go to Example1 folder
cd Python/Example1

# Submit job
sbatch run.sbatch

# Go to a compute node
salloc -p test --nodes=1 --cpus-per-task=2 --mem=12GB --time=01:00:00

# Create a vanilla mamba environment on a desired location
module load python
mamba create --prefix=/n/holylabs/LABS/<desired-folder>/multiproc_env python=3.11 -y

# Create a vanilla mamba environment on default $HOME
module load python
mamba create --name multiproc_env python=3.11 -y

# Activate the environment based on the location
mamba activate /n/holylabs/LABS/<desired-folder>/multiproc_env

#OR
mamba activate multiproc_env

# Install relevant packages
mamba install numpy pandas matplotlib -y
pip install jupyterlab swifter

# Deactivate environment
mamba deactivate

# Go to Example2 folder
cd ../Python/Example2

# Submit job
sbatch run.sbatch