Source code for quantify_core.analysis.cosine_analysis

# Repository:
# Licensed according to the LICENCE file on the main branch
Module containing an education example of an analysis subclass.

See :ref:`analysis-framework-tutorial` that guides you through the process of building
this analysis.

import matplotlib.pyplot as plt

import quantify_core.analysis.base_analysis as ba
from quantify_core.analysis.fitting_models import CosineModel
from quantify_core.visualization import mpl_plotting as qpl
from quantify_core.visualization.SI_utilities import (

[docs] class CosineAnalysis(ba.BaseAnalysis): """ Exemplary analysis subclass that fits a cosine to a dataset. """
[docs] def process_data(self): """ In some cases, you might need to process the data, e.g., reshape, filter etc., before starting the analysis. This is the method where it should be done. See :meth:`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.process_data` for an implementation example. """ # pylint: disable=line-too-long
[docs] def run_fitting(self): """ Fits a :class:`~quantify_core.analysis.fitting_models.CosineModel` to the data. """ # create a fitting model based on a cosine function model = CosineModel() guess = model.guess(self.dataset.y0.values, x=self.dataset.x0.values) result = self.dataset.y0.values, x=self.dataset.x0.values, params=guess ) self.fit_results.update({"cosine": result})
[docs] def create_figures(self): """ Creates a figure with the data and the fit. """ fig, ax = plt.subplots() fig_id = "cos_fit" self.figs_mpl.update({fig_id: fig}) self.axs_mpl.update({fig_id: ax}) self.dataset.y0.plot(ax=ax, x="x0", marker="o", linestyle="") qpl.plot_fit(ax, self.fit_results["cosine"]) qpl.plot_textbox(ax, ba.wrap_text(self.quantities_of_interest["fit_msg"])) adjust_axeslabels_SI(ax) qpl.set_suptitle_from_dataset(fig, self.dataset, "x0-y0") ax.legend()
[docs] def analyze_fit_results(self): """ Checks fit success and populates :code:`quantities_of_interest`. """ fit_result = self.fit_results["cosine"] fit_warning = ba.check_lmfit(fit_result) # If there is a problem with the fit, display an error message in the text box. # Otherwise, display the parameters as normal. if fit_warning is None: self.quantities_of_interest["fit_success"] = True unit = self.dataset.y0.units text_msg = "Summary\n" text_msg += format_value_string( r"$f$", fit_result.params["frequency"], end_char="\n", unit="Hz" ) text_msg += format_value_string( r"$A$", fit_result.params["amplitude"], unit=unit ) else: text_msg = fit_warning self.quantities_of_interest["fit_success"] = False # save values and fit uncertainty for parameter_name in ["frequency", "amplitude"]: self.quantities_of_interest[parameter_name] = ba.lmfit_par_to_ufloat( fit_result.params[parameter_name] ) self.quantities_of_interest["fit_msg"] = text_msg