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46
Data/PAR_POPC-test.toml
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Data/PAR_POPC-test.toml
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[metadata]
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data_file = "./Data/POPC_sub_logstep_3.dat"
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title = "POPC-test"
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save_folder = "POPC-test"
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q_min = 0.05 # (nm^-1)
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q_max = 7.0 # (nm^-1)
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model = "SDP_POPC_RecBuf"
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temperature = 500 # (none=0)
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binning = 10
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error = 1 # (0..1)
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plot = 0 # (0-1)
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parameters = [
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{ symbol = "Norm", initial_value = 1e5, fixed = true, prior = nan, min = 1e-2, max = 1e+5 }, # Normalization
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{ symbol = "nv", initial_value = 8, fixed = false, prior = nan, min = 3, max = 13 }, # Vesicle concencentration x10^6 (nm^-3)
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# Vesicle's parameters
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{ symbol = "Rm", initial_value = 23, fixed = false, prior = nan, min = 20, max = 28 }, # Radius of the vesicle (nm)
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{ symbol = "Z", initial_value = 6, fixed = false, prior = nan, min = 5.1, max = 17 }, # Radius polydispersity
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# Head-group Positions & Widths
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{ symbol = "n_TR", initial_value = 0.0, fixed = true, prior = nan, min = 0.00, max = 0.14 }, # Tris fraction
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{ symbol = "d_TR", initial_value = 1.0, fixed = true, prior = nan, min = 0.4, max = 1.6 }, # Tris width (nm)
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{ symbol = "s_TR", initial_value = 0.29, fixed = true, prior = nan, min = 0.20, max = 0.35 }, # Tris position (nm)
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{ symbol = "d_Chol", initial_value = 0.22, fixed = false, prior = 0.05, min = 0.164, max = 0.273 }, # CholCH3 position (nm)
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{ symbol = "s_Chol", initial_value = 0.3, fixed = false, prior = 0.05, min = 0.225, max = 0.375 }, # CholCH3 width (nm)
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{ symbol = "d_PCN", initial_value = 0.41, fixed = false, prior = 0.05, min = 0.308, max = 0.512 }, # PCN position (nm)
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{ symbol = "s_PCN", initial_value = 0.25, fixed = false, prior = nan, min = 0.23, max = 0.37 }, # PCN width (nm)
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{ symbol = "d_CG", initial_value = 0.22, fixed = false, prior = nan, min = 0.18, max = 0.26 }, # CG position (nm)
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{ symbol = "s_CG", initial_value = 0.25, fixed = false, prior = nan, min = 0.23, max = 0.37 }, # CG width (nm)
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# Acyl chain Positions & Widths
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{ symbol = "A_L", initial_value = 0.654, fixed = false, prior = 0.02, min = 0.589, max = 0.719 }, # Area per lipid (nm²)
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{ symbol = "s_D_C", initial_value = 0.08, fixed = false, prior = nan, min = 0.05, max = 0.15 }, # HC excess std (nm)
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{ symbol = "s_CH2", initial_value = 0.244, fixed = false, prior = 0.05, min = 0.183, max = 0.305 }, # HC smearing (nm)
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{ symbol = "d_CH", initial_value = 0.90, fixed = true, prior = nan, min = 0.5, max = 0.95 }, # CH position (nm)
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{ symbol = "s_CH", initial_value = 0.305, fixed = true, prior = nan, min = 0.15, max = 0.4 }, # CH width (nm)
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{ symbol = "s_CH3", initial_value = 0.30, fixed = false, prior = nan, min = 0.23, max = 0.37 }, # CH3 width (nm)
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# Area & Volumes
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{ symbol = "r_PCN", initial_value = 0.27, fixed = false, prior = 0.05, min = 0.203, max = 0.338 }, # V_PCN/V_HL
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{ symbol = "r_CG", initial_value = 0.48, fixed = false, prior = 0.05, min = 0.360, max = 0.600 }, # V_GG/V_HL
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{ symbol = "r12", initial_value = 0.81, fixed = true, prior = nan, min = 0.770, max = 0.851 }, # V_CH/V_CH2
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{ symbol = "r32", initial_value = 1.93, fixed = false, prior = 0.05, min = 1.448, max = 2.415 }, # V_CH3/V_CH2
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{ symbol = "T", initial_value = 37, fixed = true, prior = nan, min = 32, max = 42 }, # Temperature (°C)
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{ symbol = "V_BW", initial_value = 0.030, fixed = false, prior = nan, min = 0.0288, max = 0.0303 }, # Bound-water volume (nm³)
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#
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{ symbol = "Con", initial_value = 2.2e-4, fixed = false, prior = nan, min = 0.7e-4, max = 3.7e-4 } # Constant (nm⁻¹)
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]
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355
PLUV.jl
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PLUV.jl
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module PLUV
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import SpecialFunctions: erf
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######################################################################################################
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######################################################################################################
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######################################################################################################
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############### POPC/POPG 95/5 mol/mol LUVs ###############
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############### Reconstitution buffer: TRIS 20 mM, EDTA 2 mM ###############
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############### Buffer used for PLUV exp. in DESY in 2017: ###############
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######################################################################################################
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######################################################################################################
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######################################################################################################
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const parameter_names = [
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:norm, :nv, :rm, :z, :n_tr, :d_tr, :s_tr, :d_chol, :s_chol, :d_pcn, :s_pcn, :d_cg, :s_cg,
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:a_l, :s_d_c, :s_ch2, :d_ch, :s_ch, :s_ch3, :r_pcn, :r_cg, :r12, :r32, :t, :v_bw, :con
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]
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############### GLOBAL VARIABLES ###############
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# POPG molar ratio
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const CONST_x_PG = 0.05
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# ############## POPC and POPG ###############
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# 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine
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# 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol
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# number of chain groups
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const CONST_n_CH = 2
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const CONST_n_CH2 = 28
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const CONST_n_CH3 = 2
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#X-ray scattering length chain groups (nm)
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const CONST_b_CH = 1.97256E-05
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const CONST_b_CH2 = 2.25435E-05
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const CONST_b_CH3 = 2.53615E-05
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### POPC
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# Lipid-head volume
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const CONST_V_HL_PC = 0.331 # 0.320
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# X-ray scattering length of head groups (nm)
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const CONST_b_PC = 2.73340E-04
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const CONST_b_CG = 1.88802E-04
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const CONST_b_PCN = 1.97256E-04
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const CONST_b_Chol = 7.60844E-05
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# Lipid-volume temperature-dependencies a0 + a1*T (nm^3)
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const CONST_a0_V_POPC = 1.22810311835285
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const CONST_a1_V_POPC = 0.000934915795086395
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### POPG
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# Lipid-head volume
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const CONST_V_HL_PG = 0.289
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# X-ray Scattering length of head groups (nm)
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const CONST_b_PG = 2.47979E-04
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const CONST_b_PG1 = 1.32443E-04
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const CONST_b_PG2 = 1.15536E-04
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# Lipid-volume temperature-dependencies a0 + a1*T (nm^3)
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const CONST_a0_V_POPG = 1.17881068602663
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const CONST_a1_V_POPG = 0.00108364914520327
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############### Other variables ###############
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### Water
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# V_PW = 24.5e-3 #(nm^3) Perkins 2001 (Hydration shell in proteins)
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# polynome coefficient for T-dependency of bulk-water-molecule volume (V_HW)
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# Units in degree Celsius
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const CONST_p0_VW = 0.0299218
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const CONST_p1_VW = -2.25941e-06
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const CONST_p2_VW = 2.5675e-07
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const CONST_p3_VW = -1.69661e-09
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const CONST_p4_VW = 6.52029e-12
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# polynome coefficient for T-dependency of bulk-water molar concentration (Cw)
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#Units in degree Celsius
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const CONST_p0_Cw = 55.5052
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const CONST_p1_Cw = 0.00131894
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const CONST_p2_Cw = -0.000334396
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const CONST_p3_Cw = 9.10861e-07
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const CONST_b_HW = 2.8179E-05
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const CONST_d_shl = 0.31 # (nm) Perkins 2001 (Hydration shell in proteins)
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# Composition of the Reconstitution buffer (M)
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const CONST_ctris = 0.02
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const CONST_cEDTA = 0.002
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### Extra molecules
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# TRIS buffer
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const CONST_b_tris = 1.860E-04
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const CONST_V_tris = 0.15147 # (nm^3)
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# EDTA
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const CONST_b_EDTA = 4.340E-04
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const CONST_V_EDTA = 0.56430 # (nm^3)
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##################################################################################################################
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##################################################################################################################
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#########################################################
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#@njit(parallel=True)
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function PDF_normal(x, mu, sig)
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return exp(-(x - mu)^2 / (2 * sig^2)) / (sig * sqrt(2 * pi))
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end
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#########################################################
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#@njit(parallel=True)
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function PDF_lognormal(x, mu_x, sig_x)
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# https://en.wikipedia.org/wiki/Log-normal_distribution
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mu = log(mu_x^2 / sqrt(mu_x^2 + sig_x^2))
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sig = sqrt(log(1 + sig_x^2 / mu_x^2))
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return exp(-(log(x) - mu)^2 / (2 * sig^2)) / (x * sig * sqrt(2 * pi))
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end
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#########################################################
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function lipid_volume(T)
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return (1 - CONST_x_PG) * (CONST_a0_V_POPC + T * CONST_a1_V_POPC) + CONST_x_PG * (CONST_a0_V_POPG + T * CONST_a1_V_POPG)
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end
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#########################################################
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function water_volume(T)
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return CONST_p0_VW + CONST_p1_VW * T + CONST_p2_VW * T^2 + CONST_p3_VW * T^3 + CONST_p4_VW * T^4
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end
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#########################################################
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#@njit(parallel=True)
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function FTreal_erf(q, mu, d, sig)
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""" FTreal_erf(q, mu, d, sig) """
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return sin(q * d / 2.0) / (q * d / 2.0) * exp(-(q * sig)^2 / 2.0) * cos(q * mu)
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end
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#########################################################
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#@njit(parallel=True)
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function FTreal_gauss(q, mu, sig)
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""" FTreal_gauss(q, mu, sig) """
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return exp(-(q * sig)^2 / 2.0) * cos(q * mu)
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end
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#########################################################
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function Slab(x, mu, L, sig)
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return 0.5 * (erf((x - (mu - L / 2.0)) / (sqrt(2) * sig)) - erf((x - (mu + L / 2)) / (sqrt(2) * sig)))
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end
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#########################################################
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function Gauss(x, V, mu, sig, A_L)
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return V * PDF_normal(x, mu, sig) / A_L
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end
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#################################### j0^2 MOMENTS (SCHULZ PDF) #########################################
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#################################### (checked!) #########################################
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#@njit(parallel=True)
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function mu4(q, Z, a)
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return a^2 * (Z + 2) * (Z + 1) * (1 - (1 + 4 * q^2 * a^2)^(-(Z + 3) / 2.0) * cos((Z + 3) * atan(2 * q * a))) / (2 * q^2)
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end
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##################################################################################################################
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##################################################################################################################
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################################ SYMMETRIC VESICLE FOR X-RAY SLDs #########################################
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######################################## SDP MODELLING #########################################
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#################################### SEPARATED FORM FACTOR #########################################
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##################################################################################################################
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##################################################################################################################
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#########################################################
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######### Symmetric POPC bilayer ########################
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######### liposomes and proteoliposomes #################
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#########################################################
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##################
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function intensity(P::NamedTuple, q)
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return intensity(Float64[P...], q)
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end
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function intensity(P::AbstractArray{Float64}, q)
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Norm, nv, Rm, Z, n_TR, d_TR, s_TR, d_Chol, s_Chol, d_PCN, s_PCN, d_CG, s_CG,
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A_L, s_D_C, s_CH2, d_CH, s_CH, s_CH3, r_PCN, r_CG, r12, r32, T, V_BW, Con = P
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# [Example 1], fixed parameters
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# Norm 1e5 # Normalization
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# n_TR 0.0 # Tris fraction
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# d_TR 1.0 # Tris width (nm)
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# s_TR 0.29 # Tris position (nm)
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# d_CH 0.90 # CH position (nm)
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# s_CH 0.305 # CH width (nm)
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# r12 0.81 # V_CH/V_CH2
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# T 37 # Temperature (°C)
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# [example 1] fixed
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Cw = CONST_p0_Cw + CONST_p1_Cw * T + CONST_p2_Cw * T^2 + CONST_p3_Cw * T^3
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xtris = CONST_ctris / Cw # mole fraction of free TRIS in bulk
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xEDTA = CONST_cEDTA / Cw # mole fraction of free EDTA in bulk
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# Volumes
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# [example 1] fixed
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V_L = lipid_volume(T)
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V_HW = water_volume(T)
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V_HC = V_L - ((1 - CONST_x_PG) * CONST_V_HL_PC + CONST_x_PG * CONST_V_HL_PG)
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# Calculation of mean D_C
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D_C = V_HC / A_L
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# Quasi-molecular volumes
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# [example 1] r12 fixed
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V_CH2 = V_HC / (CONST_n_CH2 + CONST_n_CH * r12 + CONST_n_CH3 * r32) # Volume of CH2 groups
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V_CH = V_CH2 * r12 # Volume of CH groups
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V_CH3 = V_CH2 * r32 # Volume of CH3 groups
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V_CG = CONST_V_HL_PC * r_CG # Volume of CG group
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V_PCN = CONST_V_HL_PC * r_PCN # Volume of PCN group
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V_Chol = CONST_V_HL_PC * (1 - r_PCN - r_CG) # Volume of CholCH3 group
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# CONST
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CONST_V_PG1 = CONST_V_HL_PG * 0.16 # Kucerka 2012
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CONST_V_PG2 = CONST_V_HL_PG * (1 - 0.51 - 0.16) # Kucerka 2012
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############### X-ray scattering lengths (nm)
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# [example 1] rho_sol fixed
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rho_sol = (CONST_b_HW + xtris * CONST_b_tris + xEDTA * CONST_b_EDTA) / V_HW
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drho_Chol = ((1 - CONST_x_PG) * CONST_b_Chol / V_Chol + CONST_x_PG * CONST_b_PG2 / CONST_V_PG2) - rho_sol
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drho_PCN = ((1 - CONST_x_PG) * CONST_b_PCN / V_PCN + CONST_x_PG * CONST_b_PG1 / CONST_V_PG1) - rho_sol
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drho_CG = CONST_b_CG / V_CG - rho_sol
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drho_TR = CONST_b_tris / CONST_V_tris - rho_sol
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drho_CH = CONST_b_CH / V_CH - rho_sol
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drho_CH2 = CONST_b_CH2 / V_CH2 - rho_sol
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drho_CH3 = CONST_b_CH3 / V_CH3 - rho_sol
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drho_HW = CONST_b_HW / V_BW - rho_sol
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############### D_C polydispersity
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# Number of integration points
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N = 201
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# Parameter dependent integration bounds
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# HC_min, HC_max = D_C-3*s_D_C, D_C+3*s_D_C
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#
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# Hardcoded integration bounds for POPC, fixed T = 37, A_L in [0.598, 0.719]
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HC_min, HC_max = V_HC / 0.719 - 0.5, V_HC / 0.589 + 0.5
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# Minimum and maximum samples (offset by 1/2 of the integration step w.r.t integration bounds for the midpoint rule)
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integration_step = (HC_max - HC_min) / N
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HC_first, HC_last = HC_min + 0.5 * integration_step, HC_max - 0.5 * integration_step
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HC_array = range(HC_first, HC_last, length=N)
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Normal = PDF_normal.(HC_array, D_C, s_D_C)
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############### c-prefactors
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c_Chol = ((1 - CONST_x_PG) * V_Chol + CONST_x_PG * CONST_V_PG2) / A_L
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c_PCN = ((1 - CONST_x_PG) * V_PCN + CONST_x_PG * CONST_V_PG1) / A_L
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c_CG = V_CG / A_L
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c_TR = CONST_V_tris * n_TR / A_L
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c_CH = V_CH * CONST_n_CH / V_HC * HC_array
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c_CH3 = V_CH3 * CONST_n_CH3 / V_HC * HC_array
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d_comp = d_CG + d_PCN + d_Chol + CONST_d_shl
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############### calculating scattering amplitude -----------------------------------------------
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Am = zeros(size(q, 1), N)
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for i in 1:N
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# Adding hydrocarbon-chain envelope
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Am[:, i] += 2 * drho_CH2 * HC_array[i] * FTreal_erf.(q, 0, 2 * HC_array[i], s_CH2)
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# Adding CH and CH3 groups
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Am[:, i] += 2 * (drho_CH - drho_CH2) * c_CH[i] * FTreal_gauss.(q, d_CH, s_CH)
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Am[:, i] += 2 * (drho_CH3 - drho_CH2) * c_CH3[i] * FTreal_gauss.(q, 0, s_CH3)
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# Adding hydration-water envelope
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Am[:, i] += 4 * drho_HW * d_comp * FTreal_erf.(q, (HC_array[i] + d_comp / 2.0), d_comp, s_CH2)
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# Adding CG, PCN and CholCH3 groups
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Am[:, i] += 2 * (drho_TR - drho_HW) * c_TR * FTreal_gauss.(q, (HC_array[i] + d_TR / 2.0), s_TR)
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Am[:, i] += 2 * (drho_CG - drho_HW) * c_CG * FTreal_gauss.(q, (HC_array[i] + d_CG / 2.0), s_CG)
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Am[:, i] += 2 * (drho_PCN - drho_HW) * c_PCN * FTreal_gauss.(q, (HC_array[i] + d_CG + d_PCN / 2.0), s_PCN)
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Am[:, i] += 2 * (drho_Chol - drho_HW) * c_Chol * FTreal_gauss.(q, (HC_array[i] + d_CG + d_PCN + d_Chol / 2.0), s_Chol)
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############### Ensemble average
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# multiply each columns of Am by Normal and sum along the columns,
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# then multiply by integration step
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I = vec(sum(Am .^ 2 .* Normal'; dims=2)) * integration_step
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alp = Rm / (Z + 1)
|
||||
return @. (Norm * nv * 1e-6) * I * (16 * pi^2 * mu4(q, Z, alp)) + Con * (0.99 * (1.0 / (1 + exp(-8 * (q - 1.0)))) + 0.01)
|
||||
end
|
||||
end
|
||||
|
||||
##################
|
||||
function negative_water(P::NamedTuple)
|
||||
return negative_water([P...])
|
||||
end
|
||||
|
||||
function negative_water(P::AbstractArray{Float64})
|
||||
|
||||
_, _, _, _, n_TR, d_TR, s_TR, d_Chol, s_Chol, d_PCN, s_PCN, d_CG, s_CG,
|
||||
A_L, _, s_CH2, _, _, _, r_PCN, r_CG, _, _, T, _, _ = P
|
||||
|
||||
# Volumes
|
||||
V_L = lipid_volume(T)
|
||||
V_HC = V_L - ((1 - CONST_x_PG) * CONST_V_HL_PC + CONST_x_PG * CONST_V_HL_PG)
|
||||
|
||||
# Calculation of mean D_C
|
||||
D_C = V_HC / A_L
|
||||
|
||||
# Quasi-molecular volumes
|
||||
V_CG = CONST_V_HL_PC * r_CG # Volume of CG group
|
||||
V_PCN = CONST_V_HL_PC * r_PCN # Volume of PCN group
|
||||
V_Chol = CONST_V_HL_PC * (1 - r_PCN - r_CG) # Volume of CholCH3 group
|
||||
|
||||
check = 0
|
||||
z_array = range(0.0, 4.0, length=81)
|
||||
|
||||
CG = Gauss.(z_array, V_CG, D_C + d_CG / 2.0, s_CG, A_L)
|
||||
PCN = Gauss.(z_array, V_PCN, D_C + d_CG + d_PCN / 2.0, s_PCN, A_L)
|
||||
Chol = Gauss.(z_array, V_Chol, D_C + d_CG + d_PCN + d_Chol / 2.0, s_Chol, A_L)
|
||||
TRIS = Gauss.(z_array, n_TR * CONST_V_tris, D_C + d_TR / 2.0, s_TR, A_L)
|
||||
BW = Slab.(z_array, D_C + (d_CG + d_PCN + d_Chol + CONST_d_shl) / 2.0, d_CG + d_PCN + d_Chol + CONST_d_shl, s_CH2) - CG - PCN - Chol - TRIS
|
||||
|
||||
for i in (BW)
|
||||
if i < -1e-3
|
||||
check += 1
|
||||
end
|
||||
end
|
||||
|
||||
return check
|
||||
end
|
||||
|
||||
function get_free_parameters_idx(metadata::Dict)
|
||||
m = metadata["parameters"]
|
||||
return filter(i -> !m[i]["fixed"], eachindex(m))
|
||||
end
|
||||
|
||||
function reduce_to_free_parameters(metadata::Dict, params_init::NamedTuple, lb, ub, q)
|
||||
default_params = Float64[params_init...]
|
||||
free_idx = get_free_parameters_idx(metadata)
|
||||
P_reduced = view(default_params, free_idx)
|
||||
lb_reduced = lb[free_idx]
|
||||
ub_reduced = ub[free_idx]
|
||||
function reduced(P::AbstractArray{Float64})
|
||||
P_reduced .= P
|
||||
return intensity(default_params, q)
|
||||
end
|
||||
return reduced, P_reduced, lb_reduced, ub_reduced
|
||||
end
|
||||
|
||||
function reduce_to_free_parameters(metadata::Dict, params::Vector{Float64})
|
||||
free_idx = get_free_parameters_idx(metadata)
|
||||
return params[free_idx]
|
||||
end
|
||||
|
||||
end # module PLUV
|
||||
|
||||
# vim ts=4,sts=4,sw=4
|
||||
86
Utils.jl
Normal file
86
Utils.jl
Normal file
|
|
@ -0,0 +1,86 @@
|
|||
module Utils
|
||||
|
||||
import TOML: parsefile
|
||||
import DelimitedFiles: readdlm
|
||||
import DSP
|
||||
|
||||
function load_init_params(filename::String)
|
||||
meta = parsefile(filename)["metadata"]
|
||||
params = NamedTuple(Dict(Symbol(p["symbol"]) => p["initial_value"] for p in meta["parameters"]))
|
||||
lower_bounds = [p["min"] for p in meta["parameters"]]
|
||||
upper_bounds = [p["max"] for p in meta["parameters"]]
|
||||
return (meta=meta, params=params, lower_bounds=lower_bounds, upper_bounds=upper_bounds)
|
||||
end
|
||||
|
||||
function select_columns(QIE_array, qmin, qmax, bin, err_mul)
|
||||
#""Select q / I(q) / err(q) data - Change I(q) value from mm^-1 to nm^-1'
|
||||
|
||||
Q = Float64[]
|
||||
IDATA = Float64[]
|
||||
ERR = Float64[]
|
||||
|
||||
for i in axes(QIE_array, 1)
|
||||
# keep only the values of q within bounds
|
||||
if qmin <= QIE_array[i, 1] <= qmax
|
||||
# skip all but the (k*bin)-th values
|
||||
if i % bin == 0
|
||||
if QIE_array[i, 2] > 0
|
||||
push!(Q, QIE_array[i, 1])
|
||||
push!(IDATA, 1e-6 * QIE_array[i, 2])
|
||||
if err_mul == 0
|
||||
push!(ERR, 0.01 * 1e-6 * QIE_array[i, 2])
|
||||
elseif QIE_array[i, 3] * err_mul > 0.01 * QIE_array[i, 2]
|
||||
push!(ERR, 1e-6 * QIE_array[i, 3] * err_mul)
|
||||
else
|
||||
push!(ERR, 0.01 * 1e-6 * QIE_array[i, 2])
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
return (Q, IDATA, ERR)
|
||||
end
|
||||
|
||||
"""
|
||||
compute_logscale_weights(q)
|
||||
|
||||
Compute the weights W, which are proportional to the distance in the log space
|
||||
"""
|
||||
function compute_logscale_weights(q)
|
||||
diff_log_q = diff(log10.(q))
|
||||
left_diff_log_q = view(diff_log_q, [firstindex(diff_log_q); eachindex(diff_log_q)])
|
||||
right_diff_log_q = view(diff_log_q, [eachindex(diff_log_q); lastindex(diff_log_q)])
|
||||
|
||||
return left_diff_log_q + right_diff_log_q
|
||||
end
|
||||
|
||||
function load_config(filename::String)
|
||||
meta, params_init, lower_bounds, upper_bounds = load_init_params(filename)
|
||||
f = open(meta["data_file"], "r")
|
||||
qie_data = readdlm(f, '\t')
|
||||
close(f)
|
||||
return (meta=meta,
|
||||
params_init=params_init,
|
||||
lower_bounds=lower_bounds,
|
||||
upper_bounds=upper_bounds,
|
||||
qie_deta=qie_data)
|
||||
end
|
||||
|
||||
function lowpass_filter(i; σ=1)
|
||||
half_w = 10
|
||||
x = range(-5, 5; length=2half_w + 1)
|
||||
i_padded = view(i, [fill(firstindex(i), half_w); eachindex(i); fill(lastindex(i), half_w)])
|
||||
k = exp.(-0.5 * ((x / σ) .^ 2)) / (2 * σ * sqrt(2π))
|
||||
k ./= sum(k)
|
||||
@show sum(k)
|
||||
r = DSP.conv(i_padded, k)[2half_w+1:end-2half_w]
|
||||
@show size(r)
|
||||
return r
|
||||
end
|
||||
|
||||
function chi2(I_data, I_model, err)
|
||||
return sum((I_data .- I_model ./ err) .^ 2)
|
||||
end
|
||||
|
||||
end # module Utils
|
||||
121
test.jl
Normal file
121
test.jl
Normal file
|
|
@ -0,0 +1,121 @@
|
|||
import GLMakie as M
|
||||
import Metaheuristics as MH
|
||||
import DelimitedFiles: readdlm
|
||||
|
||||
include("PLUV.jl")
|
||||
include("Utils.jl")
|
||||
|
||||
|
||||
# filtered_idx = d[:, 2] .> 0.0
|
||||
# @views q, I, err = d[filtered_idx, 1], d[filtered_idx, 2], d[filtered_idx, 3]
|
||||
|
||||
meta, params_init, lower_bounds, upper_bounds, qie_data = Utils.load_config("Data/PAR_POPC-test.toml")
|
||||
|
||||
best_5k = begin
|
||||
f_χ2 = open("POPC-test-5k/Results_collection-X2.dat", "r")
|
||||
|
||||
d_χ2 = readdlm(f_χ2)
|
||||
close(f_χ2)
|
||||
|
||||
idx_min_χ2 = argmin(d_χ2)[1]
|
||||
@show idx_min_χ2
|
||||
|
||||
f_params = open("POPC-test-5k/Results_collection.dat", "r")
|
||||
for i in 1:(idx_min_χ2-1)
|
||||
readline(f_params)
|
||||
end
|
||||
best_params = map(x -> parse(Float64, x), split(readline(f_params), ' '))
|
||||
@show best_params
|
||||
|
||||
PLUV.reduce_to_free_parameters(meta, best_params)
|
||||
end
|
||||
|
||||
mean_5k = begin
|
||||
f_params = open("POPC-test-5k/Results_collection.dat", "r")
|
||||
params_pop = readdlm(f_params)
|
||||
N_trials, _ = size(params_pop)
|
||||
|
||||
mean_params = vec(sum(params_pop; dims=1) / N_trials)
|
||||
|
||||
PLUV.reduce_to_free_parameters(meta, mean_params)
|
||||
end
|
||||
|
||||
# best_5k = PLUV.reduce_to_free_parameters(meta, [100000.0000000, 7.5004085, 23.5074449, 9.8664991, 0.0000000, 1.0000000, 0.2900000, 0.2203793,
|
||||
# 0.2990402, 0.4122816, 0.3266636, 0.2276763, 0.2481895, 0.6642123, 0.1203572, 0.2629861, 0.9000000, 0.3050000,
|
||||
# 0.2945315, 0.2762371, 0.4777401, 0.8100000, 2.0149998, 37.0000000, 0.0299139, 0.0002171])
|
||||
#
|
||||
# best_5k = PLUV.reduce_to_free_parameters(meta, [100000.0000000, 7.6975592, 23.4041912, 9.7275630, 0.0000000, 1.0000000, 0.2900000, 0.2219937, 0.3000114,
|
||||
# 0.4158804, 0.3278631, 0.2296156, 0.2475607, 0.6664143, 0.1191859, 0.2618609, 0.9000000, 0.3050000, 0.2963982, 0.2770345, 0.4762528,
|
||||
# 0.8100000, 1.9706651, 37.0000000, 0.0299179, 0.0002167])
|
||||
|
||||
|
||||
q, I_data, err = Utils.select_columns(qie_data, meta["q_min"], meta["q_max"], meta["binning"], meta["binning"])
|
||||
q_all, I_all, err_all = Utils.select_columns(qie_data, meta["q_min"], meta["q_max"], 1, meta["binning"])
|
||||
|
||||
I_data_lp = Utils.lowpass_filter(I_data; σ=2)
|
||||
I_all_lp = Utils.lowpass_filter(I_all; σ=2)
|
||||
|
||||
w = Utils.compute_logscale_weights(q)
|
||||
w_all = Utils.compute_logscale_weights(q_all)
|
||||
|
||||
I_init = PLUV.intensity(params_init, q)
|
||||
|
||||
intensity_reduced, P_reduced, lb_reduced, ub_reduced = PLUV.reduce_to_free_parameters(meta, params_init, lower_bounds, upper_bounds, q)
|
||||
|
||||
bounds = MH.boxconstraints(lb=lb_reduced, ub=ub_reduced)
|
||||
|
||||
function obj(P)
|
||||
I_model = intensity_reduced(P)
|
||||
return Utils.chi2(I_data, I_model, err)
|
||||
end
|
||||
|
||||
information = MH.Information(f_optimum=0.0)
|
||||
options = MH.Options(f_calls_limit=1_000_000, f_tol=1e-5);
|
||||
algorithm = MH.ECA(information=information, options=options)
|
||||
|
||||
I_best_5k = intensity_reduced(best_5k)
|
||||
I_mean_5k = intensity_reduced(mean_5k)
|
||||
|
||||
if false
|
||||
result = MH.optimize(obj, bounds, algorithm)
|
||||
|
||||
P_best = MH.minimizer(result)
|
||||
I_best = intensity_reduced(P_best)
|
||||
end
|
||||
|
||||
if true
|
||||
fig = M.Figure()
|
||||
ax = M.Axis(fig[1, 1]; xscale=log10, yscale=log10)
|
||||
|
||||
M.lines!(ax, q, I_best, label="MH best (julia)")
|
||||
M.scatter!(ax, q, I_data, label="data")
|
||||
M.lines!(ax, q, I_best_5k, label="TSA best (5k)")
|
||||
M.lines!(ax, q, I_mean_5k, label="TSA mean (5k)")
|
||||
|
||||
M.axislegend()
|
||||
|
||||
display(fig)
|
||||
end
|
||||
|
||||
|
||||
if false
|
||||
fig = M.Figure()
|
||||
ax = M.Axis(fig[1, 1]; xscale=log10, yscale=log10)
|
||||
|
||||
M.scatter!(ax, q, I)
|
||||
M.lines!(ax, q_all, I_all)
|
||||
M.lines!(ax, q_all, I_all_lp)
|
||||
#M.lines!(ax, q, I_init)
|
||||
|
||||
display(fig)
|
||||
end
|
||||
|
||||
if false
|
||||
fig = M.Figure()
|
||||
ax = M.Axis(fig[1, 1]; xscale=log10)
|
||||
|
||||
#M.scatter!(ax, q, w)
|
||||
M.scatter!(ax, q_all, w_all)
|
||||
|
||||
display(fig)
|
||||
end
|
||||
Loading…
Reference in a new issue