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Preserve mode identities and full-model Lamb shifts in EPR reports #95

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Preserve mode identities and full-model Lamb shifts in EPR reports #95

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31 changes: 12 additions & 19 deletions SQDMetal/PALACE/Eigenmode_Simulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,7 +354,8 @@ def calculate_hamiltonian_parameters_EPR(self, modes_to_compare = [], print_outp
Parameters
----------
modes_to_compare : list
Choose the modes which are to be compared, e.g. modes_to_compare = [1,3].
One-based modes to report, in the requested order; empty selects all modes.
Lamb shifts retain contributions from all simulated modes.
print_output : bool
(defaults to True) print out the results of the EPR calculations.

Expand Down Expand Up @@ -511,7 +512,8 @@ def calculate_hamiltonian_parameters_EPR_from_files(directory: str, config_json_
config_json_path : str
Path for the json configuration file e.g. ``'C:\Two_Transmon\Two_Transmon.json'``.
modes_to_compare : list
Choose the modes which are to be compared, e.g. modes_to_compare = [1,3].
One-based modes to report, in the requested order; empty selects all modes.
Lamb shifts retain contributions from all simulated modes.
print_output : bool
(defaults to True) print out the results of the EPR calculations.

Expand Down Expand Up @@ -555,25 +557,16 @@ def calculate_hamiltonian_parameters_EPR_from_files(directory: str, config_json_
modes_to_compare = np.array(modes_to_compare) #cast to array
if modes_to_compare.size == 0:
modes_to_compare = modes
modes_to_delete = np.setdiff1d(modes, modes_to_compare)

#update number of modes left for comparison
num_of_modes = num_of_modes - modes_to_delete.size

#create labels for the modes
dataframe_label_mode = []
for i, mode in enumerate(modes_to_compare):
for i, mode in enumerate(modes):
dataframe_label_mode.append("mode_" + str(mode))
selected_labels = ["mode_" + str(mode) for mode in modes_to_compare]

dataframe_label_junc = []
for i in range(num_of_junctions):
dataframe_label_junc.append("jj_" + str(i+1))

#delete modes not wanted from participation ratio and frequency vectors
if modes_to_delete.size > 0:
for i,mode in enumerate(modes_to_delete):
participation_ratios = np.delete(participation_ratios, (modes_to_delete[i] - (i+1)), axis=0)
frequencies = np.delete(frequencies, (modes_to_delete[i] - (i+1)), axis=0)

#participation ratio matrix - dimension (num_of_modes, num_of_junctions)
P = np.matrix(np.abs(participation_ratios))
Expand Down Expand Up @@ -626,12 +619,12 @@ def dual_print(*args, **kwargs):
print(*args, **kwargs) # normal notebook output
print(*args, **kwargs, file=output_buffer) # captured copy

freq_df = pd.DataFrame(frequencies / 1e9, dataframe_label_mode, ['Freq (GHz)'])
freq_renorm_df = pd.DataFrame(renormalised_freqs, dataframe_label_mode, ['Freq (GHz)'])
ratios_df = pd.DataFrame(np.abs(participation_ratios), dataframe_label_mode, dataframe_label_junc)
chi_anharm_df = pd.DataFrame(chi_anharm, dataframe_label_mode, dataframe_label_mode)
lamb_shift_df = pd.DataFrame(lamb_shift_freq, dataframe_label_mode, ['Lamb Shift (MHz)'])
delta_df = pd.DataFrame(delta_freq, dataframe_label_mode, dataframe_label_mode)
freq_df = pd.DataFrame(frequencies / 1e9, dataframe_label_mode, ['Freq (GHz)']).loc[selected_labels]
freq_renorm_df = pd.DataFrame(renormalised_freqs, dataframe_label_mode, ['Freq (GHz)']).loc[selected_labels]
ratios_df = pd.DataFrame(np.abs(participation_ratios), dataframe_label_mode, dataframe_label_junc).loc[selected_labels]
chi_anharm_df = pd.DataFrame(chi_anharm, dataframe_label_mode, dataframe_label_mode).loc[selected_labels, selected_labels]
lamb_shift_df = pd.DataFrame(lamb_shift_freq, dataframe_label_mode, ['Lamb Shift (MHz)']).loc[selected_labels]
delta_df = pd.DataFrame(delta_freq, dataframe_label_mode, dataframe_label_mode).loc[selected_labels, selected_labels]
if print_output:
dual_print('Simulation Mode Frequencies:')
dual_print(freq_df)
Expand Down