Added POP compatibility in water mass analysis and new examples folde…

…r with basic WMT usage

ocean/AMOC/watermassanalysis/examples/basic_surface_wmt/README.md

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+# Basic surface water mass transformation for MPAS runs
+
+The core surface water mass transformation (WMT) calculations are located in [`watermasstools.py`](https://github.com/MPAS-Dev/MPAS-QuickViz/blob/master/ocean/AMOC/watermassanalysis/modules/watermasstools.py). These calculations contain one additional dependency beyond the `e3sm_unified` environment
+
+   - [`fastjmd95`](https://github.com/xgcm/fastjmd95) -- A numba-accelerated package for the Jackett and McDougall (1995) equation of state
+
+### Serial usage
+
+The command line executable module `basic_surface_wmt.py` is a postprocessing wrapper around the core WMT functions. This module is set up to accomplish two tasks
+
+   1. Build a basic coordinates file `python basic_surface_wmt.py -c [MESHNAME]`
+   2. Process a single monthly results file `python basic_surface_wmt.py -f [FILENUMBER] [MESHNAME]`
+
+Here `MESHNAME` is either `LR` (`EC30to60E2r2`) or `HR` (`oRRS18to6v3`). Both options use the CORE-II E3SMv2 G-cases with the `20210421_sim7` tag. Different runs can be specified in `parameters.yaml` (small changes to `python basic_surface_wmt.py` will probably also be required).
+
+### Parallel usage
+
+The workflow is set up to process single monthly files such that each serial task can be distributed across multiple cpus on a single node. I use GNU Parallel for this. The general workflow is as follows
+
+```
+#!/bin/bash
+#SBATCH --job-name=JOB_NAME
+#SBATCH --qos=regular
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=64
+#SBATCH --cpus-per-task=1
+#SBATCH --constraint=cpu
+#SBATCH --exclusive
+#SBATCH --output=JOB_NAME.o%j
+#SBATCH --time=1:00:00
+#SBATCH --account=ACCOUNT
+
+source $HOME/.bashrc
+module load parallel
+conda activate ENV_NAME
+
+meshName=LR
+savePath="/path/to/${meshName}"
+mkdir -p "${savePath}/monthly_files"
+mkdir -p "${savePath}/concatenated"
+
+# Calculate coords file (-c loads the coords file)
+python basic_surface_wmt.py -p "${savePath}" -c "${meshName}"
+
+# We use GNU Parallel to run the serial monthly processing across all cpus on our node
+PARALLEL_OPTS="-N 1 --delay .2 -j $SLURM_NTASKS --joblog parallel-${SLURM_JOBID}.log"
+
+# Here GNU Parallel distributes the individual SRUN tasks, so for SRUN
+SRUN_OPTS="-N 1 -n 1 --exclusive"
+
+# Process the monthly files, $(seq 0 119) does the first 10 years
+parallel $PARALLEL_OPTS srun $SRUN_OPTS \
+    python basic_surface_wmt.py -p "${savePath}" -f {} "${meshName}" ::: $(seq 0 119)
+
+# Concatenate monthly files
+ncrcat -h ${savePath}/monthly_files/${meshName}_WMT1D* \
+    ${savePath}/concatenated/${meshName}_WMT1D_years1-10.nc
+ncrcat -h ${savePath}/monthly_files/${meshName}_WMT2D* \
+    ${savePath}/concatenated/${meshName}_WMT2D_years1-10.nc
+```
+
+### Visualization
+
+A basic visualization example can be found in `basic_surface_wmt.ipynb`.