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https://github.com/anselmoo/spectrafit
📊📈🔬 SpectraFit is a command-line and Jupyter-notebook tool for quick data-fitting based on the regular expression of distribution functions.
https://github.com/anselmoo/spectrafit
console-application curve-fitting data-analysis data-analysis-python data-science data-visualization fitting juypter-notebook python science science-research scientific-plotting spectral-analysis spectroscopy
Last synced: 4 days ago
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📊📈🔬 SpectraFit is a command-line and Jupyter-notebook tool for quick data-fitting based on the regular expression of distribution functions.
- Host: GitHub
- URL: https://github.com/anselmoo/spectrafit
- Owner: Anselmoo
- License: bsd-3-clause
- Created: 2021-07-20T16:29:27.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-10T19:54:00.000Z (11 days ago)
- Last Synced: 2025-01-11T17:13:19.828Z (10 days ago)
- Topics: console-application, curve-fitting, data-analysis, data-analysis-python, data-science, data-visualization, fitting, juypter-notebook, python, science, science-research, scientific-plotting, spectral-analysis, spectroscopy
- Language: Python
- Homepage: https://anselmoo.github.io/spectrafit/
- Size: 456 MB
- Stars: 26
- Watchers: 5
- Forks: 7
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
README
[](https://github.com/Anselmoo/spectrafit/actions/workflows/python-ci.yml)
[](https://codecov.io/gh/Anselmoo/spectrafit)
[](https://pypi.org/project/spectrafit/)
[](https://github.com/conda-forge/spectrafit-feedstock)
[](https://pypi.org/project/spectrafit/)
[](https://results.pre-commit.ci/latest/github/Anselmoo/spectrafit/main)
[](https://pubs.acs.org/doi/full/10.1021/acsomega.3c09262)
# SpectraFit
---
> Data Analysis Tool for All Kinds of Spectra
`SpectraFit` is a Python tool for quick data fitting based on the regular
expression of distribution and linear functions via the command line (CMD) or
[Jupyter Notebook](https://jupyter.org) It is designed to be easy to use and
supports all common ASCII data formats. SpectraFit runs on **Linux**,
**Windows**, and **MacOS**.
## Scope
- Fitting of 2D data, also with multiple columns as _global fitting_
- Using established and advanced solver methods
- Extensibility of the fitting function
- Guarantee traceability of the fitting results
- Saving all results in a _SQL-like-format_ (`CSV`) for publications
- Saving all results in a _NoSQL-like-format_ (`JSON`) for project management
- Having an API interface for Graph-databases
`SpectraFit` is a tool designed for researchers and scientists who require
immediate data fitting to a model. It proves to be especially beneficial for
individuals working with vast datasets or who need to conduct numerous fits
within a limited time frame. `SpectraFit's` adaptability to various platforms
and data formats makes it a versatile tool that caters to a broad spectrum of
scientific applications.
## Installation
via pip:
```bash
pip install spectrafit
# with support for Jupyter Notebook
pip install spectrafit[jupyter]
# with support for the dashboard in the Jupyter Notebook
pip install spectrafit[jupyter-dash]
# with support to visualize pkl-files as graph
pip install spectrafit[graph]
# with all upcomming features
pip install spectrafit[all]
# Upgrade
pip install spectrafit --upgrade
```
via conda, see also
[conda-forge](https://github.com/conda-forge/spectrafit-feedstock):
```bash
conda install -c conda-forge spectrafit
# with support for Jupyter Notebook
conda install -c conda-forge spectrafit-jupyter
# with all upcomming features
conda install -c conda-forge spectrafit-all
```
## Usage
`SpectraFit` needs as command line tool only two things:
1. The reference data, which should be fitted.
2. The input file, which contains the initial model.
As model files [json](https://en.wikipedia.org/wiki/JSON),
[toml](https://en.wikipedia.org/wiki/TOML), and
[yaml](https://en.wikipedia.org/wiki/YAML) are supported. By making use of the
python `**kwargs` feature, the input file can call most of the following
functions of [LMFIT](https://lmfit.github.io/lmfit-py/index.html). LMFIT is the
workhorse for the fit optimization, which is macro wrapper based on:
1. [NumPy](https://www.numpy.org/)
2. [SciPy](https://www.scipy.org/)
3. [uncertainties](https://pythonhosted.org/uncertainties/)
In case of `SpectraFit`, we have further extend the package by:
1. [Pandas](https://pandas.pydata.org/)
2. [statsmodels](https://www.statsmodels.org/stable/index.html)
3. [numdifftools](https://github.com/pbrod/numdifftools)
4. [Matplotlib](https://matplotlib.org/) in combination with
[Seaborn](https://seaborn.pydata.org/)
```bash
spectrafit data_file.txt -i input_file.json
```
```bash
usage: spectrafit [-h] [-o OUTFILE] [-i INPUT] [-ov] [-e0 ENERGY_START]
[-e1 ENERGY_STOP] [-s SMOOTH] [-sh SHIFT] [-c COLUMN COLUMN]
[-sep { ,,,;,:,|, ,s+}] [-dec {.,,}] [-hd HEADER]
[-g {0,1,2}] [-auto] [-np] [-v] [-vb {0,1,2}]
infile
Fast Fitting Program for ascii txt files.
positional arguments:
infile Filename of the spectra data
optional arguments:
-h, --help show this help message and exit
-o OUTFILE, --outfile OUTFILE
Filename for the export, default to set to
'spectrafit_results'.
-i INPUT, --input INPUT
Filename for the input parameter, default to set to
'fitting_input.toml'.Supported fileformats are:
'*.json', '*.yml', '*.yaml', and '*.toml'
-ov, --oversampling Oversampling the spectra by using factor of 5;
default to False.
-e0 ENERGY_START, --energy_start ENERGY_START
Starting energy in eV; default to start of energy.
-e1 ENERGY_STOP, --energy_stop ENERGY_STOP
Ending energy in eV; default to end of energy.
-s SMOOTH, --smooth SMOOTH
Number of smooth points for lmfit; default to 0.
-sh SHIFT, --shift SHIFT
Constant applied energy shift; default to 0.0.
-c COLUMN COLUMN, --column COLUMN COLUMN
Selected columns for the energy- and intensity-values;
default to '0' for energy (x-axis) and '1' for intensity
(y-axis). In case of working with header, the column
should be set to the column names as 'str'; default
to 0 and 1.
-sep { ,,,;,:,|, ,s+}, --separator { ,,,;,:,|, ,s+}
Redefine the type of separator; default to ' '.
-dec {.,,}, --decimal {.,,}
Type of decimal separator; default to '.'.
-hd HEADER, --header HEADER
Selected the header for the dataframe; default to None.
-cm COMMENT, --comment COMMENT
Lines with comment characters like '#' should not be
parsed; default to None.
-g {0,1,2}, --global_ {0,1,2}
Perform a global fit over the complete dataframe. The
options are '0' for classic fit (default). The
option '1' for global fitting with auto-definition
of the peaks depending on the column size and '2'
for self-defined global fitting routines.
-auto, --autopeak Auto detection of peaks in the spectra based on `SciPy`.
The position, height, and width are used as estimation
for the `Gaussian` models.The default option is 'False'
for manual peak definition.
-np, --noplot No plotting the spectra and the fit of `SpectraFit`.
-v, --version Display the current version of `SpectraFit`.
-vb {0,1,2}, --verbose {0,1,2}
Display the initial configuration parameters and fit
results, as a table '1', as a dictionary '2', or not in
the terminal '0'. The default option is set to 1 for
table `printout`.
```
### Jupyter Notebook
Open the `Jupyter Notebook` and run the following code:
```bash
spectrafit-jupyter
```
or via Docker Image for `` with `amd64` and `arm64`:
```bash
docker pull ghcr.io/anselmoo/spectrafit-:latest
docker run -it -p 8888:8888 spectrafit-:latest
```
or just:
```bash
docker run -p 8888:8888 ghcr.io/anselmoo/spectrafit-:latest
```
Next define your initial model and the reference data:
```python
from spectrafit.plugins.notebook import SpectraFitNotebook
import pandas as pd
df = pd.read_csv(
"https://raw.githubusercontent.com/Anselmoo/spectrafit/main/Examples/data.csv"
)
initial_model = [
{
"pseudovoigt": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 1},
"center": {"max": 2, "min": -2, "vary": True, "value": 0},
"fwhmg": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
"fwhml": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
}
},
{
"pseudovoigt": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 1},
"center": {"max": 2, "min": -2, "vary": True, "value": 1},
"fwhmg": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
"fwhml": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
}
},
{
"pseudovoigt": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 1},
"center": {"max": 2, "min": -2, "vary": True, "value": 1},
"fwhmg": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
"fwhml": {"max": 0.4, "min": 0.1, "vary": True, "value": 0.21},
}
},
]
spf = SpectraFitNotebook(df=df, x_column="Energy", y_column="Noisy")
spf.solver_model(initial_model)
```
Which results in the following output:
## Documentation
Please see the [extended documentation](https://anselmoo.github.io/spectrafit/)
for the full usage of `SpectraFit`.
The documentation is generated by
.