Source code for quantify_core.analysis.optimization_analysis

# Repository:
# Licensed according to the LICENCE file on the main branch
import matplotlib.pyplot as plt
import numpy as np

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

[docs] class OptimizationAnalysis(ba.BaseAnalysis): """ An analysis class which extracts the optimal quantities from an N-dimensional interpolating experiment. """ # Override the run method so that we can add the new optional arguments # pylint: disable=attribute-defined-outside-init, arguments-differ
[docs] def run(self, minimize: bool = True): """ Parameters ---------- minimize Boolean which determines whether to report the minimum or the maximum. True for minimize. False for maximize. Returns ------- :class:`~quantify_core.analysis.optimization_analysis.OptimizationAnalysis`: The instance of this analysis. """ # NB the return type need to be specified manually to avoid circular import self.minimize = minimize return super().run()
[docs] def process_data(self): """ Finds the optimal (minimum or maximum) for y0 and saves the xi and y0 values in the :code:`quantities_of_interest`. """ text_msg = "Summary\n" arg_optimum_function = np.argmin if self.minimize else np.argmax optimum_function = np.min if self.minimize else np.max optimum_text = "minimum" if self.minimize else "maximum" # Go through every y variable and find the optimal point y_variable = "y0" text_msg += "\n" variable_name = self.dataset[y_variable].attrs["long_name"] text_msg += f"{variable_name} {optimum_text}:\n" # Find the optimum for each x coordinate for x_variable in self.dataset.coords: optimum = float( self.dataset[x_variable][ arg_optimum_function(self.dataset[y_variable].values) ].values ) self.quantities_of_interest[ self.dataset[x_variable].attrs["name"] ] = optimum text_msg += format_value_string( self.dataset[x_variable].attrs["long_name"], optimum, end_char="\n", unit=self.dataset[x_variable].units, ) # Find the corresponding optimal y value optimum = float(optimum_function(self.dataset[y_variable].values)) self.quantities_of_interest[self.dataset[y_variable].attrs["name"]] = optimum text_msg += format_value_string( self.dataset[y_variable].attrs["long_name"], optimum, end_char="\n", unit=self.dataset[y_variable].units, ) self.quantities_of_interest["plot_msg"] = text_msg
[docs] def create_figures(self): """ Plot each of the x variables against each of the y variables. """ figs, axs = iteration_plots(self.dataset, self.quantities_of_interest) self.figs_mpl.update(figs) self.axs_mpl.update(axs)
[docs] def iteration_plots(dataset, quantities_of_interest): """ For every x and y variable, plot a graph of that variable vs the iteration index. """ figs = {} axs = {} all_variables = list(dataset.coords.items()) + list(dataset.data_vars.items()) for variable, values in all_variables: variable_name = dataset[variable].attrs["long_name"] fig, ax = plt.subplots() fig_id = f"Line plot {variable_name} vs iteration" ax.plot(values, marker=".", linewidth="0.5", markersize="4.5") adjust_axeslabels_SI(ax) qpl.set_ylabel(variable_name, dataset[variable].units, axis=ax) qpl.set_xlabel("iteration index", axis=ax) qpl.set_suptitle_from_dataset( fig, dataset, f"{variable_name} vs iteration number:" ) qpl.plot_textbox(ax, quantities_of_interest["plot_msg"]) # add the figure and axis to the dicts for saving figs[fig_id] = fig axs[fig_id] = ax return figs, axs