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16
config.txt
16
config.txt
@ -1,11 +1,14 @@
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# Simulation part
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num_walker=20000
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# Motion model part
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delta=161e3
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eta=0.2
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delta=126e3
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eta=0.0
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# Distribution part
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# this tau value is overwritten if sim is run with test.py
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tau=1e-1
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angle1=2
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angle2=30
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probability1=0.98
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# Spectrum part
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dwell_time=1e-6
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num_acq=4096
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@ -14,8 +17,11 @@ techo_stop=40e-6
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techo_steps=5
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# STE part
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tevo_start=1e-6
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tevo_stop=60e-6
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tevo_steps=121
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tevo_stop=120e-6
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tevo_steps=8
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tmix_start=1e-5
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tmix_stop=1e1
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tmix_steps=61
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tmix_steps=31
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tau=0.01
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tau=0.01
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tau=0.01
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13
test.py
13
test.py
@ -17,6 +17,8 @@ def run_sims(taus, ste: bool = True, spectrum: bool = False, exec_file: str = '.
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arguments += ['BimodalAngle']
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# arguments += ['-TAU', '1']
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with pathlib.Path(config_file).open('a') as f:
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f.write(f'tau={tau}\n')
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@ -69,7 +71,7 @@ def post_process_spectrum(taus, apod, tpulse):
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def post_process_ste(taus):
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tevo = np.linspace(1e-6, 120e-6, num=121)
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tevo = np.linspace(1e-6, 120e-6, num=8)
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for i, tau in enumerate(taus):
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try:
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@ -83,7 +85,7 @@ def post_process_ste(taus):
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fig_cc_raw, ax_cc_raw = plt.subplots()
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fig_ss_raw, ax_ss_raw = plt.subplots()
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ax_cc_raw.semilogx(t_mix, raw_data_cc[:, 1:], '.')
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# ax_cc_raw.semilogx(t_mix, raw_data_cc[:, 1:], '.')
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ax_ss_raw.semilogx(t_mix, raw_data_ss[:, 1:], '.')
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scaled_cc = (raw_data_cc[:, 1:]-raw_data_cc[-1, 1:])/(raw_data_cc[0, 1:]-raw_data_cc[-1, 1:])
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@ -113,9 +115,11 @@ def post_process_ste(taus):
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0.1
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]
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res = curve_fit(ste, t_mix, raw_data_cc[:, j+1], p0, bounds=[(0, 0, 0, 0), (np.inf, np.inf, 1, 1)])
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res = curve_fit(ste, t_mix, raw_data_cc[:, j+1], p0, bounds=[(0, 0, 0., 0), (np.inf, np.inf, 1, 1)])
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m0, tauc, beta, finfty = res[0]
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# print(f'Cos-Cos-Fit for {tevo[j]}: tau_c = {tauc:.6e}, beta={beta:.4e}, amplitude = {m0: .4e}, f_infty={finfty:.4e}')
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l = ax_cc_raw.semilogx(t_mix, raw_data_cc[:, j+1], 'x', label=f'{tevo[j]}')
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ax_cc_raw.semilogx(t_mix, ste(t_mix, *res[0]), linestyle='--', color = l[0].get_color())
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tau_cc_fit.append(res[0][1])
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beta_cc_fit.append(res[0][2])
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@ -139,7 +143,7 @@ def post_process_ste(taus):
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p0 = [
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raw_data_cc[0, 1],
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t_mix[np.argmin(np.abs(scaled_ss[:, j]-np.exp(-1)))],
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1,
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0.5,
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0.1
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]
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@ -165,6 +169,7 @@ def post_process_ste(taus):
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ax_beta.plot(tevo[1:], beta_plus_fit, 'C2-')
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ax_finfty.plot(tevo[1:], finfty_plus_fit, 'C2-')
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ax_cc_raw.legend()
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plt.show()
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# np.savetxt('cc_tauc.dat', list(zip(tevo[1:], tau_cc_fit)))
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