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diff --git a/python-spectrafit.spec b/python-spectrafit.spec new file mode 100644 index 0000000..5ba9c7b --- /dev/null +++ b/python-spectrafit.spec @@ -0,0 +1,827 @@ +%global _empty_manifest_terminate_build 0 +Name: python-spectrafit +Version: 0.18.2 +Release: 1 +Summary: Fast fitting of 2D- and 3D-Spectra with established routines +License: BSD-3-Clause +URL: https://pypi.org/project/spectrafit/ +Source0: https://mirrors.aliyun.com/pypi/web/packages/87/da/6d40aaa3743fc7faa85a67fb79a283f9393f9cde04893eccd7e0c71f748e/spectrafit-0.18.2.tar.gz +BuildArch: noarch + +Requires: python3-PyYAML +Requires: python3-art +Requires: python3-dash-bootstrap-components +Requires: python3-dash-bootstrap-templates +Requires: python3-dtale +Requires: python3-emcee +Requires: python3-ipywidgets +Requires: python3-itables +Requires: python3-jupyter-dash +Requires: python3-jupyterlab +Requires: python3-kaleido +Requires: python3-lmfit +Requires: python3-networkx[all] +Requires: python3-numdifftools +Requires: python3-numpy +Requires: python3-openpyxl +Requires: python3-pandas +Requires: python3-plotly +Requires: python3-pydantic +Requires: python3-pydot +Requires: python3-scikit-learn +Requires: python3-seaborn +Requires: python3-tabulate +Requires: python3-tomli +Requires: python3-tomli-w +Requires: python3-tqdm + +%description +[](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) + +<p align="center"> +<img src="https://github.com/Anselmoo/spectrafit/blob/c5f7ee05e5610fb8ef4e237a88f62977b6f832e5/docs/images/spectrafit_synopsis.png?raw=true"> +</p> + +# SpectraFit + +`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: + +```terminal +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): + +```terminal +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/) + +```terminal +spectrafit data_file.txt -i input_file.json +``` + +```terminal +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: + +```terminal +spectrafit-jupyter +``` + +or via Docker Image: + +```terminal +docker pull ghcr.io/anselmoo/spectrafit:latest +docker run -it -p 8888:8888 spectrafit:latest +``` + +or just: + +```terminal +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`. + + + +%package -n python3-spectrafit +Summary: Fast fitting of 2D- and 3D-Spectra with established routines +Provides: python-spectrafit +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-spectrafit +[](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) + +<p align="center"> +<img src="https://github.com/Anselmoo/spectrafit/blob/c5f7ee05e5610fb8ef4e237a88f62977b6f832e5/docs/images/spectrafit_synopsis.png?raw=true"> +</p> + +# SpectraFit + +`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: + +```terminal +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): + +```terminal +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/) + +```terminal +spectrafit data_file.txt -i input_file.json +``` + +```terminal +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: + +```terminal +spectrafit-jupyter +``` + +or via Docker Image: + +```terminal +docker pull ghcr.io/anselmoo/spectrafit:latest +docker run -it -p 8888:8888 spectrafit:latest +``` + +or just: + +```terminal +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`. + + + +%package help +Summary: Development documents and examples for spectrafit +Provides: python3-spectrafit-doc +%description help +[](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) + +<p align="center"> +<img src="https://github.com/Anselmoo/spectrafit/blob/c5f7ee05e5610fb8ef4e237a88f62977b6f832e5/docs/images/spectrafit_synopsis.png?raw=true"> +</p> + +# SpectraFit + +`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: + +```terminal +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): + +```terminal +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/) + +```terminal +spectrafit data_file.txt -i input_file.json +``` + +```terminal +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: + +```terminal +spectrafit-jupyter +``` + +or via Docker Image: + +```terminal +docker pull ghcr.io/anselmoo/spectrafit:latest +docker run -it -p 8888:8888 spectrafit:latest +``` + +or just: + +```terminal +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`. + + + +%prep +%autosetup -n spectrafit-0.18.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-spectrafit -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.18.2-1 +- Package Spec generated |