ACCESS Model Output Post-Processor (ACCESS-MOPPeR) v2

ACCESS-MOPPeR is a CMORisation tool designed to post-process ACCESS model output and produce CMIP-compliant datasets. This version represents a significant rewrite focusing on usability, flexibility, and integration with modern Python workflows.

Key Features

• Python API for integration into notebooks and scripts
• Batch processing system for HPC environments with PBS
• Real-time monitoring with web-based dashboard
• Flexible CMORisation of individual variables
• Dask-enabled for scalable parallel processing
• Cross-platform compatibility (not limited to NCI Gadi)
• CMIP6 and CMIP7 FastTrack support

Installation

ACCESS-MOPPeR requires Python >= 3.11. Install with:

pip install numpy pandas xarray netCDF4 cftime dask pyyaml tqdm requests streamlit
pip install .

Quick Start

Interactive Usage (Python API)

import glob
from access_mopper import ACCESS_ESM_CMORiser

# Select input files
files = glob.glob("/path/to/model/output/*mon.nc")

# Create CMORiser instance
cmoriser = ACCESS_ESM_CMORiser(
    input_paths=files,
    compound_name="Amon.pr",  # table.variable format
    experiment_id="historical",
    source_id="ACCESS-ESM1-5",
    variant_label="r1i1p1f1",
    grid_label="gn",
    activity_id="CMIP",
    output_path="/path/to/output"
)

# Run CMORisation
cmoriser.run()
cmoriser.write()

Batch Processing (HPC/PBS)

For large-scale processing on HPC systems:

1. Create a configuration file (batch_config.yml):

variables:
  - Amon.pr
  - Omon.tos
  - Amon.ts

experiment_id: piControl
source_id: ACCESS-ESM1-5
variant_label: r1i1p1f1
grid_label: gn

input_folder: "/g/data/project/model/output"
output_folder: "/scratch/project/cmor_output"

file_patterns:
  Amon.pr: "output[0-4][0-9][0-9]/atmosphere/netCDF/*mon.nc"
  Omon.tos: "output[0-4][0-9][0-9]/ocean/*temp*.nc"
  Amon.ts: "output[0-4][0-9][0-9]/atmosphere/netCDF/*mon.nc"

# PBS configuration
queue: normal
cpus_per_node: 16
mem: 32GB
walltime: "02:00:00"
scheduler_options: "#PBS -P your_project"
storage: "gdata/project+scratch/project"

worker_init: |
  module load conda
  conda activate your_environment

2. Submit batch job:

mopper-cmorise batch_config.yml

3. Monitor progress at http://localhost:8501

Batch Processing Features

The batch processing system provides:

• Parallel execution: Each variable processed as a separate PBS job
• Real-time monitoring: Web dashboard showing job status and progress
• Automatic tracking: SQLite database maintains job history and status
• Error handling: Failed jobs can be easily identified and resubmitted
• Resource optimization: Configurable CPU, memory, and storage requirements
• Environment management: Automatic setup of conda/module environments

Monitoring Tools

• Streamlit Dashboard: Real-time web interface at http://localhost:8501
• Command line: Use standard PBS commands (qstat, qdel)
• Database: SQLite tracking at {output_folder}/cmor_tasks.db
• Log files: Individual stdout/stderr for each job

File Organization

work_directory/
├── batch_config.yml          # Your configuration
├── cmor_job_scripts/          # Generated PBS scripts and logs
│   ├── cmor_Amon_pr.sh       # PBS script
│   ├── cmor_Amon_pr.py       # Python processing script
│   ├── cmor_Amon_pr.out      # Job output
│   └── cmor_Amon_pr.err      # Job errors
└── output_folder/
    ├── cmor_tasks.db         # Progress tracking
    └── [CMORised files]      # Final output

Documentation

• Getting Started: docs/source/getting_started.rst
• Example Configuration: src/access_mopper/examples/batch_config.yml
• API Reference: [Coming soon]

Current Limitations

• Alpha version: Intended for evaluation only
• Ocean variables: Limited support in current release
• Variable mapping: Under review for CMIP6/CMIP7 compliance

Support

• Issues: Submit via GitHub Issues
• Questions: Contact ACCESS-NRI support
• Contributions: Welcome via Pull Requests

License

ACCESS-MOPPeR is licensed under the Apache-2.0 License.

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Background: ACCESS-MOPPeR v2 is a complete rewrite using modern Python libraries (xarray, dask) instead of CMOR, providing improved flexibility and integration with contemporary data science workflows.