| Name | pyckett JSON |
| Version |
0.1.13
JSON |
| download |
| home_page | None |
| Summary | A wrapper around Pickett's SPFIT and SPCAT |
| upload_time | 2024-04-16 14:23:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | None |
| keywords |
pickett
spectroscopy
spfit
spcat
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# Pyckett
Pyckett is a python wrapper around the SPFIT/SPCAT package (*H. M. Pickett, "The Fitting and Prediction of Vibration-Rotation Spectra with Spin Interactions," **J. Mol. Spectrosc. 148,** 371-377 (1991)*).
Install the package with pip by using the following command
```
pip install pyckett
```
If SPFIT and SPCAT cannot be executed via the commands *spfit* and *spcat* (if they are not in your PATH or have different names) point to them by setting the *PYCKETT_SPFIT_PATH* and *PYCKETT_SPCAT_PATH* environment variables to their full paths.
# Documentation
Currently, the documentation is limited to the docstrings of the main library.
To easily access the docstrings you can run
```
pip install pdoc
pdoc pyckett
```
after having installed Pyckett.
The CLI tools provide help texts via the -h or --help flags.
# CLI Tools
Pyckett provides a set of command line utilities which perform common steps of an iterative fitting process.
*pyckett_add* helps adding new parameters to the fit, *pyckett_omit* is used for evaluating which parameters can be omitted.
*pyckett_separatefits* can be used to separate a global fit into separate fits for each state.
*pyckett_uncertainties* evaluates the uncertainties of the parameters.
*pyckett_partitionfunction* calculates the partition function for different temperatures.
*pyckett_auto* automatically builds up the Hamiltonian step by step.
*pyckett_fit* and *pyckett_cat* are shortcuts to SPFIT and SPCAT, respectively.
See the respective help functions (by adding *--help* after the command) to see their syntax.
# Examples
You can read files from the SPFIT/SPCAT universe with the following syntax
```python
var_dict = pyckett.parvar_to_dict(r"path/to/your/project/molecule.var")
par_dict = pyckett.parvar_to_dict(r"path/to/your/project/molecule.par")
int_dict = pyckett.int_to_dict(r"path/to/your/project/molecule.int")
lin_df = pyckett.lin_to_df(r"path/to/your/project/molecule.lin")
cat_df = pyckett.cat_to_df(r"path/to/your/project/molecule.cat")
egy_df = pyckett.egy_to_df(r"path/to/your/project/molecule.egy")
erham_df = pyckett.erhamlines_to_df(r"path/to/your/project/molecule.in")
```
## Finalize cat file
This function merges the cat and lin dataframes, sums up duplicate values in the cat file and allows to translate quantum numbers:
```python
fin_cat_df, fin_lin_df = pyckett.finalize(cat_df, lin_df, qn_tdict, qn)
```
## Find candidates for double-resonance measurements
This function finds possible transition arrangements for double-resonance measurements.
Input two cat dataframes with the transitions that are in the range of your probe and pump source.
```python
results_df = pyckett.get_dr_candidates(cat_df1, cat_df2)
```
## Check Crossings
```python
pyckett.check_crossings(egy_df, [1], range(10))
```
## Plot Mixing Coefficients
```python
pyckett.mixing_coefficient(egy_df, "qn4 == 1 and qn2 < 20 and qn1 < 20 and qn1==qn2+qn3")
```
Raw data
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"description": "# Pyckett\r\n\r\nPyckett is a python wrapper around the SPFIT/SPCAT package (*H. M. Pickett, \"The Fitting and Prediction of Vibration-Rotation Spectra with Spin Interactions,\" **J. Mol. Spectrosc. 148,** 371-377 (1991)*).\r\n\r\nInstall the package with pip by using the following command\r\n\r\n```\r\npip install pyckett\r\n```\r\n\r\nIf SPFIT and SPCAT cannot be executed via the commands *spfit* and *spcat* (if they are not in your PATH or have different names) point to them by setting the *PYCKETT_SPFIT_PATH* and *PYCKETT_SPCAT_PATH* environment variables to their full paths.\r\n\r\n# Documentation\r\n\r\nCurrently, the documentation is limited to the docstrings of the main library.\r\nTo easily access the docstrings you can run\r\n```\r\npip install pdoc\r\npdoc pyckett\r\n```\r\nafter having installed Pyckett.\r\n\r\nThe CLI tools provide help texts via the -h or --help flags.\r\n\r\n# CLI Tools\r\n\r\nPyckett provides a set of command line utilities which perform common steps of an iterative fitting process.\r\n\r\n*pyckett_add* helps adding new parameters to the fit, *pyckett_omit* is used for evaluating which parameters can be omitted.\r\n\r\n*pyckett_separatefits* can be used to separate a global fit into separate fits for each state.\r\n\r\n*pyckett_uncertainties* evaluates the uncertainties of the parameters.\r\n\r\n*pyckett_partitionfunction* calculates the partition function for different temperatures.\r\n\r\n*pyckett_auto* automatically builds up the Hamiltonian step by step.\r\n\r\n*pyckett_fit* and *pyckett_cat* are shortcuts to SPFIT and SPCAT, respectively.\r\n\r\n\r\nSee the respective help functions (by adding *--help* after the command) to see their syntax.\r\n\r\n# Examples\r\n\r\nYou can read files from the SPFIT/SPCAT universe with the following syntax\r\n\r\n```python\r\nvar_dict = pyckett.parvar_to_dict(r\"path/to/your/project/molecule.var\")\r\npar_dict = pyckett.parvar_to_dict(r\"path/to/your/project/molecule.par\")\r\nint_dict = pyckett.int_to_dict(r\"path/to/your/project/molecule.int\")\r\nlin_df = pyckett.lin_to_df(r\"path/to/your/project/molecule.lin\")\r\ncat_df = pyckett.cat_to_df(r\"path/to/your/project/molecule.cat\")\r\negy_df = pyckett.egy_to_df(r\"path/to/your/project/molecule.egy\")\r\n\r\nerham_df = pyckett.erhamlines_to_df(r\"path/to/your/project/molecule.in\")\r\n```\r\n\r\n## Finalize cat file\r\n\r\nThis function merges the cat and lin dataframes, sums up duplicate values in the cat file and allows to translate quantum numbers:\r\n\r\n```python\r\nfin_cat_df, fin_lin_df = pyckett.finalize(cat_df, lin_df, qn_tdict, qn)\r\n```\r\n\r\n## Find candidates for double-resonance measurements\r\n\r\nThis function finds possible transition arrangements for double-resonance measurements.\r\nInput two cat dataframes with the transitions that are in the range of your probe and pump source.\r\n\r\n```python\r\nresults_df = pyckett.get_dr_candidates(cat_df1, cat_df2)\r\n```\r\n\r\n## Check Crossings\r\n\r\n```python\r\npyckett.check_crossings(egy_df, [1], range(10))\r\n```\r\n\r\n## Plot Mixing Coefficients\r\n\r\n```python\r\npyckett.mixing_coefficient(egy_df, \"qn4 == 1 and qn2 < 20 and qn1 < 20 and qn1==qn2+qn3\")\r\n```\r\n",
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