56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
import numpy as np
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from scipy.optimize import fsolve
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def antiparallel2gap(K, phi, undudict):
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gLH = K2gap(K, undudict['K-value_LH'])
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if phi >= 0.0:
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gLV = K2gap(K, undudict['K-value_apLV+'])
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gC = K2gap(K, undudict['K-value_45+'])
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dgLV = gLV - gLH
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dgC = gC - gLH - dgLV/2
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else:
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gLV = K2gap(K, undudict['K-value_apLV-'])
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gC = K2gap(K, undudict['K-value_45-'])
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dgLV = gLV - gLH
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dgC = gC - gLH - dgLV/2
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return gLH + dgLV * np.sin(0.5 * phi)**2 + dgC * np.sin(phi)**2
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def K2gap(Kval, fitparam):
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g2K_func = np.poly1d(fitparam[::-1])
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tau_init = 1.0
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k_log = float(np.log(Kval))
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return float(fsolve(k_log - g2K_func, tau_init))
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## TODO: implement the proper model:
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#def __antiparallel2gap(K, phi, undudict):
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# if phi >= 0.0:
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# fullpol2Kfit_app()
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# else:
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# return gLH + dgLV * np.sin(0.5 * phi)**2 + dgC * np.sin(phi)**2
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#
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#def fullpol2Kfit_apm(shiftlist, amp1, amp2, amp3, KLH, dKLV, dK45):
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# gLH = K2gap(K, undudict['K-value_LH'])
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# gLV = K2gap(K, undudict['K-value_LV-'])
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# gC = K2gap(K, undudict['K-value_C-'])
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# dgLV = gLV - gLH
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# dgC = gC - gLH - dgLV/2
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# return KLH + dKLV * np.sin((0.5 * shiftlist)*ratio)**2 + dK45 * np.sin(shiftlist*ratio)**2 + amp1 * np.sin(2*shiftlist*ratio) + amp2 * np.sin((2.0 * shiftlist)*ratio)**2 + amp3 * np.cos(6*shiftlist*ratio)
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#
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#def fullpol2Kfit_app(shiftlist, amp1, amp2, amp3, KLH, dKLV, dK45):
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# gLH = K2gap(K, undudict['K-value_LH'])
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# return KLH + dKLV * np.sin((0.5 * shiftlist)*ratio)**2 + dK45 * np.sin(shiftlist*ratio)**2 + amp1 * np.sin(2*shiftlist*ratio) + amp2 * np.sin((2.0 * shiftlist)*ratio)**2 + amp3 * np.cos(8*shiftlist*ratio)
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## reference
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#def fullpol2Kfit_apm(shiftlist, amp1, amp2, amp3, KLH, dKLV, dK45):
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# return KLH + dKLV * np.sin((0.5 * shiftlist)*ratio)**2 + dK45 * np.sin(shiftlist*ratio)**2 + amp1 * np.sin(2*shiftlist*ratio) + amp2 * np.sin((2.0 * shiftlist)*ratio)**2 + amp3 * np.cos(6*shiftlist*ratio)
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#
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#def fullpol2Kfit_app(shiftlist, amp1, amp2, amp3, KLH, dKLV, dK45):
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# return KLH + dKLV * np.sin((0.5 * shiftlist)*ratio)**2 + dK45 * np.sin(shiftlist*ratio)**2 + amp1 * np.sin(2*shiftlist*ratio) + amp2 * np.sin((2.0 * shiftlist)*ratio)**2 + amp3 * np.cos(8*shiftlist*ratio)
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