Dark Theme
Using of themes for the plots¶
For working with different color themes, you can import color_schemas into the Notebook and overwrite the default theme.
In [1]:
Copied!
# Loading packages and default data
from spectrafit.plugins import notebook as nb
import pandas as pd
df = pd.read_csv(
"https://raw.githubusercontent.com/Anselmoo/spectrafit/main/Examples/data.csv"
)
# Loading packages and default data from spectrafit.plugins import notebook as nb import pandas as pd df = pd.read_csv( "https://raw.githubusercontent.com/Anselmoo/spectrafit/main/Examples/data.csv" )
Loading of the dark color theme¶
In [2]:
Copied!
from spectrafit.plugins import color_schemas as cs
spn = nb.SpectraFitNotebook(
df=df,
x_column="Energy",
y_column="Noisy",
color=cs.DraculaColor(),
font=cs.DraculaFont(),
)
from spectrafit.plugins import color_schemas as cs spn = nb.SpectraFitNotebook( df=df, x_column="Energy", y_column="Noisy", color=cs.DraculaColor(), font=cs.DraculaFont(), )
Define the fitting model as usual¶
In [3]:
Copied!
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 fitting and plot the results in the dark theme¶
In [4]:
Copied!
spn.solver_model(initial_model=initial_model)
spn.solver_model(initial_model=initial_model)
## Warning: uncertainties could not be estimated:
