Commit 31968a5a authored by Zhang, Yuanpeng's avatar Zhang, Yuanpeng ⭐
Browse files

change peak shape to lorentzian

parent 63784a82

.offsets/NOM_185222.cal

+20697 −20697

File changed.

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cal_config.json

+4 −4
Original line number Diff line number Diff line 
{

    "Diamond": "/SNS/NOM/IPTS-28922/nexus/NOM_172394.nxs.h5",

    "Diamond": "/SNS/NOM/IPTS-30147/nexus/NOM_189201.nxs.h5",

    "Instrument": "NOM",

    "Date": "2022-05-25",

    "SampleEnv": "shifter",

    "Date": "2023-01-10",

    "SampleEnv": "furnace",

    "OutputDir": "/SNS/NOM/shared/autoreduce/calibration",

    "GenShadowMask": "shadow_mask_shifter_172394.in",

    "GenShadowMask": "shadow_mask_shifter_189201.in",

    "DiaLattParam": 3.5671299351,

    "GroupMethod": "KMEANS_ED",

    "SaveInitCalTable": true,

utils/nom_cal.py

+109 −13
Original line number Diff line number Diff line
@@ -570,7 +570,7 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
        if bank == 0:

            chi_s_min = 5.E-3

        else:

            chi_s_min = 5.E-4

            chi_s_min = 5.E-3



        d_norm = list()

        d_fitted = list()
@@ -590,9 +590,9 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
                    OutputWorkspace="dia_calibrant_d_cc_f_fine_fit" + str(bank + 1) + str(key),

                    ParameterTableWorkspace="dia_calibrant_d_cc_f_fine_table" + str(bank + 1) + str(key),

                    WorkspaceIndex=bank,

                    PeakFunctionType="BackToBackExponential",

                    PeakParameterNames="I, A, B, X0, S",

                    PeakParameterValues=f"{height_est}, 100, 100, {c_est}, 0.0005",

                    PeakFunctionType="Lorentzian",

                    PeakParameterNames="Amplitude, PeakCentre, FWHM",

                    PeakParameterValues=f"{height_est}, {c_est}, 0.05",

                    BackgroundType="Linear",

                    BackgroundParameterNames="A0, A1",

                    BackgroundParameterValues="10, 0",
@@ -653,7 +653,7 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 


        PDCalibration(InputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}",

                      TofBinning="300, -0.0001, 16667",

                      PeakFunction="BackToBackExponential",

                      PeakFunction="Lorentzian",

                      BackgroundType="Linear",

                      PeakPositions=center_str,

                      PeakWindow=range_str,
@@ -702,9 +702,9 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
                                OutputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}_{row_i}_fit",

                                ParameterTableWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}_{row_i}_table",

                                WorkspaceIndex=0,

                                PeakFunctionType="BackToBackExponential",

                                PeakParameterNames="I, A, B, X0, S",

                                PeakParameterValues=f"{height_est}, 100., 100., {tof_cent_trial}, 0.0005",

                                PeakFunctionType="Lorentzian",

                                PeakParameterNames="Amplitude, PeakCentre, FWHM",

                                PeakParameterValues=f"{height_est}, {tof_cent_trial}, 100",

                                BackgroundType="Linear",

                                BackgroundParameterNames="A0, A1",

                                BackgroundParameterValues="10, 0",
@@ -730,9 +730,9 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
                            OutputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}_{row_i}_fit",

                            ParameterTableWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}_{row_i}_table",

                            WorkspaceIndex=0,

                            PeakFunctionType="BackToBackExponential",

                            PeakParameterNames="I, A, B, X0, S",

                            PeakParameterValues=f"{height_est}, 100., 100., {tof_cent_opt}, 0.0005",

                            PeakFunctionType="Lorentzian",

                            PeakParameterNames="Amplitude, PeakCentre, FWHM",

                            PeakParameterValues=f"{height_est}, {tof_cent_opt}, 100",

                            BackgroundType="Linear",

                            BackgroundParameterNames="A0, A1",

                            BackgroundParameterValues="10, 0",
@@ -772,6 +772,14 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
            k = float(key.split("-")[1])

            l = float(key.split("-")[2])

            ds_tmp = dia_a / np.sqrt(h**2. + k**2. + l**2.)

            if bank == 3:

                if ds_tmp > 1.1:

                    break

            elif bank == 4:

                if ds_tmp > 0.7:

                    break

            else:

                pass

            center_str.append("{0:8.6F}".format(ds_tmp))

            range_str.append("{0:8.6F}".format(item["dmin"]))

            range_str.append("{0:8.6F}".format(item["dmax"]))
@@ -789,9 +797,9 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 


        if bank == 5:

            range_str = "0.3"

            pf_use = "Gaussian"

            pf_use = "Lorentzian"

        else:

            pf_use = "BackToBackExponential"

            pf_use = "Lorentzian"



        PDCalibration(InputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}",

                      TofBinning="300, -0.0001, 16667",
@@ -845,6 +853,94 @@ for bank in range(mtd["dia_calibrant_d_cc_f_tof"].getNumberHistograms()): 
                d_tmp /= difc_arb

                d_fitted.append(d_tmp)



        if bank in (3, 4):

            center_str = list()

            range_str = list()

            row_count = 0

            peak_list = dict()

            if bank == 4:

                chi_s_min = 5.E-4

                chi_s_min_pd = 5.E-5

            else:

                chi_s_min = 5.E-3

                chi_s_min_pd = 5.E-3



            for key, item in dia_params[f"{bank + 1}"].items():

                h = float(key.split("-")[0])

                k = float(key.split("-")[1])

                l = float(key.split("-")[2])

                ds_tmp = dia_a / np.sqrt(h**2. + k**2. + l**2.)

                if bank == 3:

                    if ds_tmp < 1.1:

                        continue

                elif bank == 4:

                    if ds_tmp < 0.7:

                        continue

                else:

                    pass

                center_str.append("{0:8.6F}".format(ds_tmp))

                range_str.append("{0:8.6F}".format(item["dmin"]))

                range_str.append("{0:8.6F}".format(item["dmax"]))

                peak_list[row_count] = ["{0:8.6F}".format(ds_tmp),

                                        "{0:8.6F}".format(item["dmin"]),

                                        "{0:8.6F}".format(item["dmax"])]

                row_count += 1



            center_str = ",".join(center_str)

            range_str = ",".join(range_str)



            pf_use = "Lorentzian"



            PDCalibration(InputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}",

                          TofBinning="300, -0.0001, 16667",

                          PeakFunction=pf_use,

                          BackgroundType="Linear",

                          PeakPositions=center_str,

                          PeakWindow=range_str,

                          CalibrationParameters="DIFC+TZERO+DIFA",

                          OutputCalibrationTable=f"pdcal_table_{bank + 1}_high_d",

                          DiagnosticWorkspaces=f"pdcal_diag_{bank + 1}_high_d")



            difc_arb = float(mtd["_det_f_table"].row(bank)["DIFC"])

            for row_i in range(mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].rowCount()):

                chi2_tmp = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i)["chi2"]

                int_tmp = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i)["intensity"]

                if row_i > 0:

                    w_current = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i)["width"]

                    w_last = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i - 1)["width"]

                    h_current = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i)["height"]

                    h_last = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i - 1)["height"]

                    extra_cond = abs(w_last - w_current) < 1E-10 and abs(h_current - h_last) < 1E-10

                else:

                    extra_cond = int_tmp < 0 or chi2_tmp / int_tmp > chi_s_min_pd



                if int_tmp < 0 or chi2_tmp / int_tmp > chi_s_min_pd or extra_cond:

                    second_peak_right = "{0:8.6F}".format(

                        2. * float(peak_list[row_i][2]) - float(peak_list[row_i][0])

                    )

                    PDCalibration(InputWorkspace=f"dia_calibrant_d_cc_f_tof_{bank + 1}",

                          TofBinning="300, -0.0001, 16667",

                          PeakFunction=pf_use,

                          BackgroundType="Linear",

                          PeakPositions=(float(peak_list[row_i][0]), float(peak_list[row_i][2])),

                          PeakWindow=f"{peak_list[row_i][1]}, {peak_list[row_i][2]}, {peak_list[row_i][0]}, {second_peak_right}",

                          CalibrationParameters="DIFC+TZERO+DIFA",

                          OutputCalibrationTable=f"pdcal_table_{bank + 1}_{row_i}_high_d",

                          DiagnosticWorkspaces=f"pdcal_diag_{bank + 1}_{row_i}_high_d")



                    chi2_tmp = mtd[f"pdcal_diag_{bank + 1}_{row_i}_high_d_fitparam"].row(0)["chi2"]

                    int_tmp = mtd[f"pdcal_diag_{bank + 1}_{row_i}_high_d_fitparam"].row(0)["intensity"]

                    if int_tmp > 0 and chi2_tmp / int_tmp < chi_s_min:

                        d_norm.append(float(peak_list[row_i][0]))

                        d_tmp = mtd[f"pdcal_diag_{bank + 1}_{row_i}_high_d_fitparam"].row(0)["centre"]

                        d_tmp /= difc_arb

                        d_fitted.append(d_tmp)

                else:

                    d_norm.append(float(peak_list[row_i][0]))

                    d_tmp = mtd[f"pdcal_diag_{bank + 1}_high_d_fitparam"].row(row_i)["centre"]

                    d_tmp /= difc_arb

                    d_fitted.append(d_tmp)



        d_norm.sort()

        d_fitted.sort()

utils/nom_cal_btb.py

0 → 100644
+1179 −0

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utils/nom_cal_lorentzian.py

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+1275 −0

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