Default Plot
Regular Usage of the Jupyter-Notebook extension for SpectraFit¶
This notebook shows how to use the Jupyter-Notebook extension for SpectraFit to fit a spectrum. In order to use the extension, you need to install the SpectraFit like the following:
pip install spectrafit[jupyter]
or straight in the notebook:
! pip install spectrafit[jupyter]
Then, you need to enable the extension by running the following command in the terminal:
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from spectrafit.plugins import notebook as nb
from spectrafit.plugins import notebook as nb
Other regular packages are already installed:
numpymatplotlibscipypandas
So that the data / spectra can be loaded and plotted.
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import pandas as pd
import matplotlib
# %matplotlib inline
import pandas as pd import matplotlib # %matplotlib inline
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df = pd.read_csv(
"https://raw.githubusercontent.com/Anselmoo/spectrafit/main/Examples/data.csv"
)
df.plot(x="Energy", y="Noisy", kind="line", label="Spectra")
df = pd.read_csv( "https://raw.githubusercontent.com/Anselmoo/spectrafit/main/Examples/data.csv" ) df.plot(x="Energy", y="Noisy", kind="line", label="Spectra")
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<Axes: xlabel='Energy'>
The spectra aboive shows a pseudo 2p3d spectrum, see also: https://doi.org/10.1021/acs.inorgchem.8b01550
Load the data into the notebook-plugin of SpectraFit¶
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spn = nb.SpectraFitNotebook(df=df, x_column="Energy", y_column="Noisy")
spn = nb.SpectraFitNotebook(df=df, x_column="Energy", y_column="Noisy")
Define the fitting model¶
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initial_model = [
{
"pseudovoigt": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 1},
"center": {"max": 2, "min": -2, "vary": True, "value": 0},
"fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1},
"fwhml": {"max": 0.2, "min": 0.01, "vary": True, "value": 0.1},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 2.0, "min": 0, "vary": True, "value": 2},
"fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.5, "min": 2, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 4.7, "min": 3.7, "vary": True, "value": 3.8},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
]
initial_model = [ { "pseudovoigt": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 1}, "center": {"max": 2, "min": -2, "vary": True, "value": 0}, "fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1}, "fwhml": {"max": 0.2, "min": 0.01, "vary": True, "value": 0.1}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 2.0, "min": 0, "vary": True, "value": 2}, "fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.5, "min": 2, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 4.7, "min": 3.7, "vary": True, "value": 3.8}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, ]
Run the fitting with the proposed initial_model¶
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spn.solver_model(initial_model=initial_model)
spn.solver_model(initial_model=initial_model)
## Warning: uncertainties could not be estimated:
Show the dataframe with the fitted parameters¶
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spn.display_fit_df(mode="interactive")
spn.display_fit_df(mode="interactive")
| energy | intensity | residual | fit | pseudovoigt_1 | gaussian_2 | gaussian_3 | gaussian_4 | gaussian_5 | gaussian_6 |
|---|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) |
Metric Plot of the fitted model¶
SpectraFit also shows the metric of the fitted model. The plot shows the metric of the fitted model for each run. The metric consits of the goodness of fit and the regression metric of the model. By default, the Akaike Information Criterion and Bayesian Information Criteria are used for the bar plot and the Mean Squared Error for the line plot.
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initial_model = [
{
"pseudovoigt": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 1},
"center": {"max": 2, "min": -2, "vary": True, "value": 0},
"fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1},
"fwhml": {"max": 0.2, "min": 0.01, "vary": True, "value": 0.1},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 2.0, "min": 0, "vary": True, "value": 2},
"fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.25, "min": 1.8, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 3.5, "min": 2, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
{
"gaussian": {
"amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3},
"center": {"max": 4.7, "min": 3.7, "vary": True, "value": 3.8},
"fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3},
}
},
]
spn.solver_model(initial_model=initial_model, conf_interval=False)
initial_model = [ { "pseudovoigt": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 1}, "center": {"max": 2, "min": -2, "vary": True, "value": 0}, "fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1}, "fwhml": {"max": 0.2, "min": 0.01, "vary": True, "value": 0.1}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 2.0, "min": 0, "vary": True, "value": 2}, "fwhmg": {"max": 0.3, "min": 0.02, "vary": True, "value": 0.1}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.5, "min": 1.5, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.2}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.25, "min": 1.8, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 3.5, "min": 2, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 4.5, "min": 3, "vary": True, "value": 2.5}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, { "gaussian": { "amplitude": {"max": 2, "min": 0, "vary": True, "value": 0.3}, "center": {"max": 4.7, "min": 3.7, "vary": True, "value": 3.8}, "fwhmg": {"max": 0.4, "min": 0.02, "vary": True, "value": 0.3}, } }, ] spn.solver_model(initial_model=initial_model, conf_interval=False)
## Warning: uncertainties could not be estimated:
Fit Plot¶
Metric Blogs¶
