{"id":42581207,"url":"https://github.com/mtzgroup/chemcloud-client","last_synced_at":"2026-01-28T22:09:08.105Z","repository":{"id":40451962,"uuid":"327454983","full_name":"mtzgroup/chemcloud-client","owner":"mtzgroup","description":"Python client for TeraChem Cloud","archived":false,"fork":false,"pushed_at":"2025-06-19T03:13:55.000Z","size":7807,"stargazers_count":13,"open_issues_count":5,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-05T03:29:48.811Z","etag":null,"topics":["cloud-computing","quantum-chemistry"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mtzgroup.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-01-06T23:43:30.000Z","updated_at":"2025-11-06T11:41:57.000Z","dependencies_parsed_at":"2023-01-31T05:31:29.526Z","dependency_job_id":"8cf7838a-ceed-450a-8da6-5318be4ff316","html_url":"https://github.com/mtzgroup/chemcloud-client","commit_stats":{"total_commits":54,"total_committers":2,"mean_commits":27.0,"dds":0.01851851851851849,"last_synced_commit":"606beda63121480fb0444db52e568c4d7fb94d69"},"previous_names":["mtzgroup/tccloud","coltonbh/terachem-cloud-pyclient"],"tags_count":37,"template":false,"template_full_name":null,"purl":"pkg:github/mtzgroup/chemcloud-client","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mtzgroup%2Fchemcloud-client","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mtzgroup%2Fchemcloud-client/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mtzgroup%2Fchemcloud-client/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mtzgroup%2Fchemcloud-client/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mtzgroup","download_url":"https://codeload.github.com/mtzgroup/chemcloud-client/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mtzgroup%2Fchemcloud-client/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28853227,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-28T15:15:36.453Z","status":"ssl_error","status_checked_at":"2026-01-28T15:15:13.020Z","response_time":57,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cloud-computing","quantum-chemistry"],"created_at":"2026-01-28T22:09:07.619Z","updated_at":"2026-01-28T22:09:08.098Z","avatar_url":"https://github.com/mtzgroup.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Actions status](https://github.com/mtzgroup/chemcloud-client/workflows/Tests/badge.svg)](https://github.com/mtzgroup/chemcloud-client/actions/workflows/test.yaml)\n[![image](https://img.shields.io/pypi/v/chemcloud.svg?color=%2334D058\u0026label=pypi%20package)](https://pypi.python.org/pypi/chemcloud)\n[![image](https://img.shields.io/pypi/pyversions/chemcloud.svg)](https://pypi.python.org/pypi/chemcloud)\n[![downloads](https://static.pepy.tech/badge/chemcloud/month)](https://pepy.tech/project/chemcloud)\n[![Actions status](https://github.com/mtzgroup/chemcloud-client/workflows/Basic%20Code%20Quality/badge.svg)](https://github.com/mtzgroup/chemcloud-client/actions/workflows/basic-code-quality.yaml)\n[![image](https://img.shields.io/pypi/l/chemcloud.svg?color=%2334D058)](https://pypi.python.org/pypi/chemcloud)\n\n# chemcloud - A Python Client for ChemCloud\n\n`chemcloud` is a python client for the [ChemCloud Server](https://github.com/mtzgroup/chemcloud-server). The client provides a simple yet powerful interface to perform computational chemistry calculations at scale using nothing but modern Python and an internet connection.\n\n**Documentation**: \u003chttps://mtzgroup.github.io/chemcloud-client\u003e\n\n`chemcloud` works in harmony with a suite of other quantum chemistry tools for fast, structured, and interoperable quantum chemistry.\n\n## The QC Suite of Programs\n\n- [qcio](https://github.com/coltonbh/qcio) - Elegant and intuitive data structures for quantum chemistry, featuring seamless Jupyter Notebook visualizations. [Documentation](https://qcio.coltonhicks.com)\n- [qcparse](https://github.com/coltonbh/qcparse) - A library for efficient parsing of quantum chemistry data into structured `qcio` objects.\n- [qcop](https://github.com/coltonbh/qcop) - A package for operating quantum chemistry programs using `qcio` standardized data structures. Compatible with `TeraChem`, `psi4`, `QChem`, `NWChem`, `ORCA`, `Molpro`, `geomeTRIC`, and many more, featuring seamless Jupyter Notebook visualizations.\n- [BigChem](https://github.com/mtzgroup/bigchem) - A distributed application for running quantum chemistry calculations at scale across clusters of computers or the cloud. Bring multi-node scaling to your favorite quantum chemistry program, featuring seamless Jupyter Notebook visualizations.\n- `ChemCloud` - A [web application](https://github.com/mtzgroup/chemcloud-server) and associated [Python client](https://github.com/mtzgroup/chemcloud-client) for exposing a BigChem cluster securely over the internet, featuring seamless Jupyter Notebook visualizations.\n\n## Installation\n\n```sh\npip install chemcloud\n```\n\n## Quickstart\n\nRun calculations just like you would with `qcop` except calling `chemcloud.compute` instead of `qcop.compute`. You may also pass list of inputs to `chemcloud.compute` to run calculations in parallel. By default `chemcloud.compute` will return `ProgramOutput` objects for all calculations, even those that failed, rather than raising exceptions. Check if calculations were successful by accessing `output.success`.\n\n```python\nfrom qcio import Structure, ProgramInput\nfrom chemcloud import compute\n\n# Create the structure\nh2o = Structure.open(\"h2o.xyz\")\n\n# Define the program input\nprog_input = ProgramInput(\n    structure=h2o,\n    calctype=\"energy\",\n    model={\"method\": \"hf\", \"basis\": \"sto-3g\"},\n    keywords={\"purify\": \"no\", \"restricted\": False},\n)\n\n# Submit the calculation to the server\noutput = compute(\"terachem\", prog_input)\n\n# Inspect the output\noutput.input_data # Input data used by the QC program\noutput.success # Whether the calculation succeeded\noutput.results # All structured results from the calculation\noutput.stdout # Stdout log from the calculation\noutput.pstdout # Shortcut to print out the stdout in human readable format\noutput.files # Any files returned by the calculation\noutput.provenance # Provenance information about the calculation\noutput.extras # Any extra information not in the schema\noutput.traceback # Stack trace if calculation failed\noutput.ptraceback # Shortcut to print out the traceback in human readable format\n```\n\nSubmit thousands of calculations simultaneously and collect results parallel:\n\n```python\nprog_inputs = [prog_input] * 10\noutputs = compute(\"terachem\", prog_inputs)\n\nfor output in outputs:\n    # Process outputs\n    output.save(...)\n```\n\nOr stream results from the server as they complete:\n\n```python\nprog_inputs = [prog_input] * 10\n# Submit the calculation to the server\nfuture = compute(\"terachem\", prog_inputs, return_future=True)\nfor output in future.as_completed():\n    # Outputs returned as they complete\n    output.save(...)\n```\n\nIf you want to use a non-blocking API, pass `return_future=True` to `compute`. Calling `.get()` on the future will return a `ProgramOutput` or list of `ProgramOutput` once the calculations are complete.\n\n```python\nprog_inputs = [prog_input] * 10\n# Submit the calculation to the server\nfuture = compute(\"terachem\", prog_inputs, return_future=True)\n# Check the status of calculations (optional)\nfuture.is_ready\n# Block and retrieve results\noutputs = future.get()\nfor output in outputs:\n    # Process outputs\n    output.save(...)\n```\n\nSave a `future` to disk and then collect results later:\n\n```python\n# Submit the calculation to the server\nfuture = compute(\"terachem\", prog_inputs, return_future=True)\nfuture.save(\"myfuture.json\")\n\n# Later in a different script\nfuture.open(\"myfuture.json\")\noutputs = future.get()\n```\n\n## Examples\n\nMore examples can be found in the [examples directory](https://github.com/mtzgroup/chemcloud-client/tree/main/examples).\n\n## ✨ Visualization ✨\n\nVisualize all your results with a single line of code!\n\nFirst install the visualization module:\n\n```sh\npip install qcio[view]\n```\n\nor if your shell requires `''` around arguments with brackets:\n\n```sh\npip install 'qcio[view]'\n```\n\nThen in a Jupyter notebook import the `qcio` view module and call `view.view(...)` passing it one or any number of `qcio` objects you want to visualizing including `Structure` objects or any `ProgramOutput` object. You may also pass an array of `titles` and/or `subtitles` to add additional information to the molecular structure display. If no titles are passed `qcio` with look for `Structure` identifiers such as a name or SMILES to label the `Structure`.\n\n![Structure Viewer](https://public.coltonhicks.com/assets/qcio/structure_viewer.png)\n\nSeamless visualizations for `ProgramOutput` objects make results analysis easy!\n\n![Optimization Viewer](https://public.coltonhicks.com/assets/qcio/optimization_viewer.png)\n\nSingle point calculations display their results in a table.\n\n![Single Point Viewer](https://public.coltonhicks.com/assets/qcio/single_point_viewer.png)\n\nIf you want to use the HTML generated by the viewer to build your own dashboards use the functions inside of `qcio.view.py` that begin with the word `generate_` to create HTML you can insert into any dashboard.\n\n## Support\n\nIf you have any issues with `chemcloud` or would like to request a feature, please open an [issue](https://github.com/mtzgroup/chemcloud-client/issues).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmtzgroup%2Fchemcloud-client","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmtzgroup%2Fchemcloud-client","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmtzgroup%2Fchemcloud-client/lists"}