<jupyter_start><jupyter_text>frequency 01<jupyter_code>frequencies,schedules = cal.frequency01_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_f01(result_data,frequencies)
cal.df01_calib/cal.f01_default<jupyter_output><empty_output><jupyter_text>amplitude 01<jupyter_code>drive_powers,schedules = cal.amplitude01_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_a01(result_data,drive_powers)
cal.a01_calib<jupyter_output><empty_output><jupyter_text>frequency 12<jupyter_code>frequencies,schedules = cal.frequency12_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_f12(result_data,frequencies)
cal.df12_calib<jupyter_output><empty_output><jupyter_text>amplitude 12<jupyter_code>drive_powers,schedules = cal.amplitude12_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_a12(result_data,drive_powers)
cal.a12_calib<jupyter_output><empty_output><jupyter_text>y 01<jupyter_code>drive_powers,schedules = cal.amplitude_y01_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_a_y01(result_data,drive_powers)
cal.y01_calib<jupyter_output><empty_output><jupyter_text>hadamard 12<jupyter_code>drive_powers,schedules = cal.amplitude_h12_schedules()
result_data = cal.run_schedule(schedules)
#result_state = cal.classify_results(result_data)
plt.scatter(np.real(result_data), np.imag(result_data))
#plt.scatter(np.real(result_data[0,:]),np.imag(result_data[0,:]),s=1,c=result_state[0,:])
plt.axis('equal')
#print(np.unique(result_state,return_counts=True))
cal.fit_results_a_h12(result_data,drive_powers)
cal.h12_calib<jupyter_output><empty_output><jupyter_text>hadamard 02<jupyter_code>drive_powers,schedules = cal.amplitude_h02_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
#plt.scatter(np.real(result_data[0,:]),np.imag(result_data[0,:]),s=1,c=result_state[0,:])
plt.axis('equal')
#print(np.unique(result_state,return_counts=True))
cal.fit_results_a_h02(result_data,drive_powers)
cal.h02_calib<jupyter_output><empty_output><jupyter_text>equal superposition<jupyter_code>drive_powers,schedules = cal.amplitude_equal_superposition_schedules()
result_data = cal.run_schedule(schedules)
plt.scatter(np.real(result_data), np.imag(result_data))
cal.fit_results_a_eq_sup(result_data,drive_powers)<jupyter_output><empty_output>
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