SpectraFit¶
SpectraFit is a tool for quick data fitting based on the regular expression of distribution and linear functions via command line or Jupyter Notebook. Furthermore, it can be also used as a module in existing python code. A previous version of SpectraFit was used for the following publication:
Now, it is completely rewritten and is more flexible. It is supporting all common ASCII-data formats and it 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
Installation¶
via pip:
pip install spectrafit
# with support for Jupyter Notebook
pip install spectrafit[jupyter]
# with all upcomming features
pip install spectrafit[all]
# Upgrade
pip install spectrafit --upgrade
via conda, see also conda-forge:
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:
- The reference data, which should be fitted.
- The input file, which contains the initial model.
As model files json, toml, and yaml are supported. By making use of the python **kwargs feature, the input file can call most of the following functions of LMFIT. LMFIT is the workhorse for the fit optimization, which is macro wrapper based on:
In case of SpectraFit, we have further extend the package by:
- Pandas
- statsmodels
- numdifftools
- Matplotlib in combination with Seaborn
spectrafit data_file.txt -i input_file.json
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:
spectrafit-jupyter
or via Docker Image:
docker pull ghcr.io/anselmoo/spectrafit:latest
docker run -it -p 8888:8888 spectrafit:latest
or just:
docker run -p 8888:8888 ghcr.io/anselmoo/spectrafit:latest
Next define your initial model and the reference data:
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 for the full usage of SpectraFit.