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')