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# chemcloud - A Python Client for ChemCloud
`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 using nothing but modern Python and an internet connection.
**Documentation**: <https://mtzgroup.github.io/chemcloud-client>
`chemcloud` works in harmony with a suite of other quantum chemistry tools for fast, structured, and interoperable quantum chemistry.
## The QC Suite of Programs
- [qcio](https://github.com/coltonbh/qcio) - Elegant and intuitive data structures for quantum chemistry, featuring seamless Jupyter Notebook visualizations. [Documentation](https://qcio.coltonhicks.com)
- [qcparse](https://github.com/coltonbh/qcparse) - A library for efficient parsing of quantum chemistry data into structured `qcio` objects.
- [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.
- [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.
- `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.
## Installation
```sh
pip install chemcloud
```
## Quickstart
- Create a ChemCloud account at [https://chemcloud.mtzlab.com/signup](https://chemcloud.mtzlab.com/signup) (or the address of the ChemCloud Server you want to communicate with).
- Instantiate a client
- Configure client (only required the very first time you use `CCClient`)
```python
from chemcloud import CCClient
client = CCClient()
client.configure() # only run this the very first time you use CCClient
# See supported compute engines on the ChemCloud Server
client.supported_engines
['psi4', 'terachem', ...]
# Test connection to ChemCloud
client.hello_world("Colton")
'Welcome to ChemCloud, Colton'
```
- Run calculations just like you would with `qcop` except calling `client.compute` instead of `qcop.compute`. Rather than getting back an `Output` object directly, `client.compute` returns a `FutureOutput` object which can be used to get the output of the computation once it is complete.
```python
from qcio import Structure, ProgramInput
from chemcloud import CCClient
client = CCClient()
# Create the structure
h2o = Structure.open("h2o.xyz")
# Define the program input
prog_input = ProgramInput(
structure=h2o,
calctype="energy",
model={"method": "hf", "basis": "sto-3g"},
keywords={"purify": "no", "restricted": False},
)
# Submit the calculation to the server
future_output = client.compute("terachem", prog_input, collect_files=True)
# Status can be checked at any time
future_result.status
# Get the output (blocking)
output = future_output.get()
# Inspect the output
output.input_data # Input data used by the QC program
output.success # Whether the calculation succeeded
output.results # All structured results from the calculation
output.stdout # Stdout log from the calculation
output.pstdout # Shortcut to print out the stdout in human readable format
output.files # Any files returned by the calculation
output.provenance # Provenance information about the calculation
output.extras # Any extra information not in the schema
output.traceback # Stack trace if calculation failed
output.ptraceback # Shortcut to print out the traceback in human readable format
```
## Examples
Examples of various computations can be found in the [examples directory](https://github.com/mtzgroup/chemcloud-client/tree/main/examples).
## ✨ Visualization ✨
Visualize all your results with a single line of code!
First install the visualization module:
```sh
pip install qcio[view]
```
or if your shell requires `''` around arguments with brackets:
```sh
pip install 'qcio[view]'
```
Then 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`.
Seamless visualizations for `ProgramOutput` objects make results analysis easy!
Single point calculations display their results in a table.
If 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.
## Support
If you have any issues with `chemcloud` or would like to request a feature, please open an [issue](https://github.com/mtzgroup/chemcloud-client/issues).
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"description": "[](https://pypi.python.org/pypi/chemcloud)\n[](https://pypi.python.org/pypi/chemcloud)\n[](https://pypi.python.org/pypi/chemcloud)\n[](https://github.com/mtzgroup/chemcloud-client/actions)\n[](https://github.com/mtzgroup/chemcloud-client/actions)\n[](https://github.com/charliermarsh/ruff)\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 using nothing but modern Python and an internet connection.\n\n**Documentation**: <https://mtzgroup.github.io/chemcloud-client>\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\n- Create a ChemCloud account at [https://chemcloud.mtzlab.com/signup](https://chemcloud.mtzlab.com/signup) (or the address of the ChemCloud Server you want to communicate with).\n- Instantiate a client\n- Configure client (only required the very first time you use `CCClient`)\n\n```python\nfrom chemcloud import CCClient\n\nclient = CCClient()\nclient.configure() # only run this the very first time you use CCClient\n# See supported compute engines on the ChemCloud Server\nclient.supported_engines\n['psi4', 'terachem', ...]\n# Test connection to ChemCloud\nclient.hello_world(\"Colton\")\n'Welcome to ChemCloud, Colton'\n```\n\n- Run calculations just like you would with `qcop` except calling `client.compute` instead of `qcop.compute`. 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