def plot_convergence_main(self, RES: pd.DataFrame): """Plot alpha and frequency versus pass number, as well as convergence of delta (in %). Args: RES (pd.DataFrame): Dictionary of capacitance matrices versus pass number, organized as pandas table. """ if self._pinfo: eprd = epr.DistributedAnalysis(self._pinfo) epr.toolbox.plotting.mpl_dpi(110) return _plot_q3d_convergence_main(eprd, RES)
def distributed_analysis(self): """Returns class containing info on Hamiltonian parameters from HFSS simulation. Returns: DistributedAnalysis: A class from pyEPR which does DISTRIBUTED ANALYSIS of layout and microwave results. It is the main computation class & interface with HFSS. This class defines a DistributedAnalysis object which calculates and saves Hamiltonian parameters from an HFSS simulation. It allows one to calculate dissipation. """ if self.pinfo: return epr.DistributedAnalysis(self.pinfo)
if 1: path_to_project = r'Z:\akshay_koottandavida\3. Pair-Coherent States\HFSS\pcs_straddling_regime' pinfo = epr.ProjectInfo(project_path=path_to_project, project_name='straddling_regime_transmon', design_name='2. stradling_tmon_prev_sample') pinfo.junctions['j1'] = { 'Lj_variable': 'LJ_wig', 'rect': 'wigner_qubit', 'line': 'Polyline1', 'length': epr.parse_units('200um') } pinfo.validate_junction_info() eprh = epr.DistributedAnalysis(pinfo) eprh.do_EPR_analysis() epra = epr.QuantumAnalysis(eprh.data_filename) # Analyze epra.analyze_all_variations(cos_trunc=6, fock_trunc=7, return_ef=True) #epra.plot_hamiltonian_results(); #%% import matplotlib.pyplot as plt chi_ef = [epra.results[str(i)]['chi_ef'] for i in range(11)] freq = [epra.results[str(i)]['f_ND'][2] for i in range(11)] plt.plot(freq, chi_ef, 'o')