| Name | llwp JSON |
| Version |
2.0.20
JSON |
| download |
| home_page | None |
| Summary | LLWP is a fast, efficient and easy solution for exploring and assigning spectra - relying on Loomis-Wood plots. |
| upload_time | 2025-07-10 09:25:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | None |
| keywords |
llwp
loomis-wood plots
spectroscopy
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# LLWP - Luis' Loomis-Wood Program
LLWP allows you to efficiently and confidently assign (typically rotational or rovibrational) spectra by relying on Loomis-Wood plots.
A quickstart guide is given down below. For more information see its [website](https://llwp.astro.uni-koeln.de).
If you want to acknowledge LLWP, please cite the paper [LLWP - A new Loomis-Wood software at the example of Acetone-13C1](https://doi.org/10.1016/j.jms.2022.111674).
## Quickstart Guide
The preferred way to install LLWP is via Python's package manager pip.
Run the following command in a terminal to install LLWP:
```bash
pip install llwp
```
After installing LLWP via pip you can run it from any terminal by simply running
```bash
llwp
```
To see and assign your first series
1. open your spectrum and prediction files via drag and drop or *Files > Add Files*
2. specify the correct reference series in the *Reference Series* window
3. choose the fitfunction under *Fit > Choose Fit Function*
4. select the area around the experimental peak with the mouse to fit the data
### ASAP Mode
To start the [ASAP](https://doi.org/10.1016/j.jms.2015.02.014) mode of LLWP run
```bash
lasap
```
To see and assign your first cross-correlation peaks
1. open your spectrum, \*.egy, and \*.cat file via drag and drop or *Files > Add Files*
2. specify the correct energy levels in the *ASAP Settings* window
3. specify the correct unit conversion factor for the \*.cat file in the *Units Cat File* field (e.g. 3.335641e-05 for \*.cat file in MHz and \*.egy file in wavenumbers)
3. press *Calculate Cross Correlation*
3. choose the fitfunction under *Fit > Choose Fit Function*
4. select the area around the experimental peak with the mouse to fit the data
Raw data
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"description": "# LLWP - Luis' Loomis-Wood Program\n\nLLWP allows you to efficiently and confidently assign (typically rotational or rovibrational) spectra by relying on Loomis-Wood plots.\n\nA quickstart guide is given down below. For more information see its [website](https://llwp.astro.uni-koeln.de).\n\nIf you want to acknowledge LLWP, please cite the paper [LLWP - A new Loomis-Wood software at the example of Acetone-13C1](https://doi.org/10.1016/j.jms.2022.111674).\n\n\n## Quickstart Guide\n\nThe preferred way to install LLWP is via Python's package manager pip.\nRun the following command in a terminal to install LLWP:\n\n```bash\npip install llwp\n```\n\nAfter installing LLWP via pip you can run it from any terminal by simply running\n\n```bash\nllwp\n```\n\nTo see and assign your first series\n\n1. open your spectrum and prediction files via drag and drop or *Files > Add Files*\n2. specify the correct reference series in the *Reference Series* window\n3. choose the fitfunction under *Fit > Choose Fit Function*\n4. select the area around the experimental peak with the mouse to fit the data\n\n### ASAP Mode\n\nTo start the [ASAP](https://doi.org/10.1016/j.jms.2015.02.014) mode of LLWP run\n\n```bash\nlasap\n```\n\nTo see and assign your first cross-correlation peaks\n\n1. open your spectrum, \\*.egy, and \\*.cat file via drag and drop or *Files > Add Files*\n2. specify the correct energy levels in the *ASAP Settings* window\n3. specify the correct unit conversion factor for the \\*.cat file in the *Units Cat File* field (e.g. 3.335641e-05 for \\*.cat file in MHz and \\*.egy file in wavenumbers)\n3. press *Calculate Cross Correlation*\n3. choose the fitfunction under *Fit > Choose Fit Function*\n4. select the area around the experimental peak with the mouse to fit the data\n",
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