Ejemplo n.º 1
0
def main():
    """
    NAME
        customize_criteria.py
        NB:  This program has been deprecated - use demag_gui or thellier_gui
           to customize acceptance criteria - OR pandas from within a jupyter notebook

    DESCRIPTION
        Allows user to specify acceptance criteria, saves them in pmag_criteria.txt

    SYNTAX
        customize_criteria.py [-h][command line options]

    OPTIONS
        -h prints help message and quits
        -f IFILE, reads in existing criteria
        -F OFILE, writes to pmag_criteria format file

    DEFAULTS
         IFILE: pmag_criteria.txt
         OFILE: pmag_criteria.txt
  
    OUTPUT
        creates a pmag_criteria.txt formatted output file
    """
    infile, critout = "", "pmag_criteria.txt"
    # parse command line options
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        infile = sys.argv[ind + 1]
        crit_data, file_type = pmag.magic_read(infile)
        if file_type != 'pmag_criteria':
            print('bad input file')
            print(main.__doc__)
            sys.exit()
        print("Acceptance criteria read in from ", infile)
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        critout = sys.argv[ind + 1]
    Dcrit, Icrit, nocrit = 0, 0, 0
    custom = '1'
    crit = input(
        " [0] Use no acceptance criteria?\n [1] Use default criteria\n [2] customize criteria \n "
    )
    if crit == '0':
        print('Very very loose criteria saved in ', critout)
        crit_data = pmag.default_criteria(1)
        pmag.magic_write(critout, crit_data, 'pmag_criteria')
        sys.exit()
    crit_data = pmag.default_criteria(0)
    if crit == '1':
        print('Default criteria saved in ', critout)
        pmag.magic_write(critout, crit_data, 'pmag_criteria')
        sys.exit()
    CritRec = crit_data[0]
    crit_keys = list(CritRec.keys())
    crit_keys.sort()
    print(
        "Enter new threshold value.\n Return to keep default.\n Leave blank to not use as a criterion\n "
    )
    for key in crit_keys:
        if key != 'pmag_criteria_code' and key != 'er_citation_names' and key != 'criteria_definition' and CritRec[
                key] != "":
            print(key, CritRec[key])
            new = input('new value: ')
            if new != "": CritRec[key] = (new)
    pmag.magic_write(critout, [CritRec], 'pmag_criteria')
    print("Criteria saved in pmag_criteria.txt")
Ejemplo n.º 2
0
def main():
    """
    NAME
        thellier_magic_redo.py

    DESCRIPTION
        Calculates paleointensity parameters for thellier-thellier type data using bounds
        stored in the "redo" file

    SYNTAX
        thellier_magic_redo [command line options]

    OPTIONS
        -h prints help message
        -usr USER:   identify user, default is ""
        -fcr CRIT, set criteria for grading
        -f IN: specify input file, default is magic_measurements.txt
        -fre REDO: specify redo file, default is "thellier_redo"
        -F OUT: specify output file, default is thellier_specimens.txt
        -leg:  attaches "Recalculated from original measurements; supercedes published results. " to comment field
        -CR PERC TYPE: apply a blanket cooling rate correction if none supplied in the er_samples.txt file 
            PERC should be a percentage of original (say reduce to 90%)
            TYPE should be one of the following:
               EG (for educated guess); PS (based on pilots); TRM (based on comparison of two TRMs) 
        -ANI:  perform anisotropy correction
        -fsa SAMPFILE: er_samples.txt file with cooling rate correction information, default is NO CORRECTION
        -Fcr  CRout: specify pmag_specimen format file for cooling rate corrected data
        -fan ANIFILE: specify rmag_anisotropy format file, default is rmag_anisotropy.txt 
        -Fac  ACout: specify pmag_specimen format file for anisotropy corrected data
                 default is AC_specimens.txt
        -fnl NLTFILE: specify magic_measurments format file, default is magic_measurements.txt
        -Fnl NLTout: specify pmag_specimen format file for non-linear trm corrected data
                 default is NLT_specimens.txt
        -z use z component differenences for pTRM calculation

    INPUT
        a thellier_redo file is Specimen_name Tmin Tmax (where Tmin and Tmax are in Centigrade)
    """
    dir_path = '.'
    critout = ""
    version_num = pmag.get_version()
    field, first_save = -1, 1
    spec, recnum, start, end = 0, 0, 0, 0
    crfrac = 0
    NltRecs, PmagSpecs, AniSpecRecs, NltSpecRecs, CRSpecs = [], [], [], [], []
    meas_file, pmag_file, mk_file = "magic_measurements.txt", "thellier_specimens.txt", "thellier_redo"
    anis_file = "rmag_anisotropy.txt"
    anisout, nltout = "AC_specimens.txt", "NLT_specimens.txt"
    crout = "CR_specimens.txt"
    nlt_file = ""
    samp_file = ""
    comment, user = "", "unknown"
    anis, nltrm = 0, 0
    jackknife = 0  # maybe in future can do jackknife
    args = sys.argv
    Zdiff = 0
    if '-WD' in args:
        ind = args.index('-WD')
        dir_path = args[ind + 1]
    if "-h" in args:
        print main.__doc__
        sys.exit()
    if "-usr" in args:
        ind = args.index("-usr")
        user = sys.argv[ind + 1]
    if "-leg" in args:
        comment = "Recalculated from original measurements; supercedes published results. "
    cool = 0
    if "-CR" in args:
        cool = 1
        ind = args.index("-CR")
        crfrac = .01 * float(sys.argv[ind + 1])
        crtype = 'DA-CR-' + sys.argv[ind + 2]
    if "-Fcr" in args:
        ind = args.index("-Fcr")
        crout = sys.argv[ind + 1]
    if "-f" in args:
        ind = args.index("-f")
        meas_file = sys.argv[ind + 1]
    if "-F" in args:
        ind = args.index("-F")
        pmag_file = sys.argv[ind + 1]
    if "-fre" in args:
        ind = args.index("-fre")
        mk_file = args[ind + 1]
    if "-fsa" in args:
        ind = args.index("-fsa")
        samp_file = dir_path + '/' + args[ind + 1]
        Samps, file_type = pmag.magic_read(samp_file)
        SampCRs = pmag.get_dictitem(
            Samps, 'cooling_rate_corr', '',
            'F')  # get samples cooling rate corrections
        cool = 1
        if file_type != 'er_samples':
            print 'not a valid er_samples.txt file'
            sys.exit()
    #
    #
    if "-ANI" in args:
        anis = 1
        ind = args.index("-ANI")
        if "-Fac" in args:
            ind = args.index("-Fac")
            anisout = args[ind + 1]
        if "-fan" in args:
            ind = args.index("-fan")
            anis_file = args[ind + 1]
    #
    if "-NLT" in args:
        if "-Fnl" in args:
            ind = args.index("-Fnl")
            nltout = args[ind + 1]
        if "-fnl" in args:
            ind = args.index("-fnl")
            nlt_file = args[ind + 1]
    if "-z" in args: Zdiff = 1
    if '-fcr' in sys.argv:
        ind = args.index("-fcr")
        critout = sys.argv[ind + 1]
#
#  start reading in data:
#
    meas_file = dir_path + "/" + meas_file
    mk_file = dir_path + "/" + mk_file
    accept = pmag.default_criteria(1)[0]  # set criteria to none
    if critout != "":
        critout = dir_path + "/" + critout
        crit_data, file_type = pmag.magic_read(critout)
        if file_type != 'pmag_criteria':
            print 'bad pmag_criteria file, using no acceptance criteria'
        print "Acceptance criteria read in from ", critout
        for critrec in crit_data:
            if 'sample_int_sigma_uT' in critrec.keys(
            ):  # accommodate Shaar's new criterion
                critrec['sample_int_sigma'] = '%10.3e' % (
                    eval(critrec['sample_int_sigma_uT']) * 1e-6)
            for key in critrec.keys():
                if key not in accept.keys() and critrec[key] != '':
                    accept[key] = critrec[key]
    meas_data, file_type = pmag.magic_read(meas_file)
    if file_type != 'magic_measurements':
        print file_type
        print file_type, "This is not a valid magic_measurements file "
        sys.exit()
    try:
        mk_f = open(mk_file, 'rU')
    except:
        print "Bad redo file"
        sys.exit()
    mkspec = []
    speclist = []
    for line in mk_f.readlines():
        tmp = line.split()
        mkspec.append(tmp)
        speclist.append(tmp[0])
    if anis == 1:
        anis_file = dir_path + "/" + anis_file
        anis_data, file_type = pmag.magic_read(anis_file)
        if file_type != 'rmag_anisotropy':
            print file_type
            print file_type, "This is not a valid rmag_anisotropy file "
            sys.exit()
    if nlt_file == "":
        nlt_data = pmag.get_dictitem(
            meas_data, 'magic_method_codes', 'LP-TRM',
            'has')  # look for trm acquisition data in the meas_data file
    else:
        nlt_file = dir_path + "/" + nlt_file
        nlt_data, file_type = pmag.magic_read(nlt_file)
    if len(nlt_data) > 0:
        nltrm = 1


#
# sort the specimen names and step through one by one
#
    sids = pmag.get_specs(meas_data)
    #
    print 'Processing ', len(speclist), ' specimens - please wait '
    while spec < len(speclist):
        s = speclist[spec]
        recnum = 0
        datablock = []
        PmagSpecRec = {}
        PmagSpecRec["er_analyst_mail_names"] = user
        PmagSpecRec["er_citation_names"] = "This study"
        PmagSpecRec["magic_software_packages"] = version_num
        methcodes, inst_code = [], ""
        #
        # find the data from the meas_data file for this specimen
        #
        datablock = pmag.get_dictitem(meas_data, 'er_specimen_name', s, 'T')
        datablock = pmag.get_dictitem(
            datablock, 'magic_method_codes', 'LP-PI-TRM',
            'has')  #pick out the thellier experiment data
        if len(datablock) > 0:
            for rec in datablock:
                if "magic_instrument_codes" not in rec.keys():
                    rec["magic_instrument_codes"] = "unknown"
    #
    #  collect info for the PmagSpecRec dictionary
    #
            rec = datablock[0]
            PmagSpecRec["er_specimen_name"] = s
            PmagSpecRec["er_sample_name"] = rec["er_sample_name"]
            PmagSpecRec["er_site_name"] = rec["er_site_name"]
            PmagSpecRec["er_location_name"] = rec["er_location_name"]
            PmagSpecRec["measurement_step_unit"] = "K"
            PmagSpecRec["specimen_correction"] = 'u'
            if "er_expedition_name" in rec.keys():
                PmagSpecRec["er_expedition_name"] = rec["er_expedition_name"]
            if "magic_instrument_codes" not in rec.keys():
                PmagSpecRec["magic_instrument_codes"] = "unknown"
            else:
                PmagSpecRec["magic_instrument_codes"] = rec[
                    "magic_instrument_codes"]
            if "magic_experiment_name" not in rec.keys():
                rec["magic_experiment_name"] = ""
            else:
                PmagSpecRec["magic_experiment_names"] = rec[
                    "magic_experiment_name"]
            meths = rec["magic_experiment_name"].split(":")
            for meth in meths:
                if meth.strip() not in methcodes and "LP-" in meth:
                    methcodes.append(meth.strip())
    #
    # sort out the data into first_Z, first_I, ptrm_check, ptrm_tail
    #
            araiblock, field = pmag.sortarai(datablock, s, Zdiff)
            first_Z = araiblock[0]
            first_I = araiblock[1]
            ptrm_check = araiblock[2]
            ptrm_tail = araiblock[3]
            if len(first_I) < 3 or len(first_Z) < 4:
                spec += 1
                print 'skipping specimen ', s
            else:
                #
                # get start, end
                #
                for redospec in mkspec:
                    if redospec[0] == s:
                        b, e = float(redospec[1]), float(redospec[2])
                        break
                if e > float(first_Z[-1][0]): e = float(first_Z[-1][0])
                for recnum in range(len(first_Z)):
                    if first_Z[recnum][0] == b: start = recnum
                    if first_Z[recnum][0] == e: end = recnum
                nsteps = end - start
                if nsteps > 2:
                    zijdblock, units = pmag.find_dmag_rec(s, meas_data)
                    pars, errcode = pmag.PintPars(datablock, araiblock,
                                                  zijdblock, start, end,
                                                  accept)
                    if 'specimen_scat' in pars.keys():
                        PmagSpecRec['specimen_scat'] = pars['specimen_scat']
                    if 'specimen_frac' in pars.keys():
                        PmagSpecRec['specimen_frac'] = '%5.3f' % (
                            pars['specimen_frac'])
                    if 'specimen_gmax' in pars.keys():
                        PmagSpecRec['specimen_gmax'] = '%5.3f' % (
                            pars['specimen_gmax'])
                    pars['measurement_step_unit'] = units
                    pars["specimen_lab_field_dc"] = field
                    pars["specimen_int"] = -1 * field * pars["specimen_b"]
                    PmagSpecRec["measurement_step_min"] = '%8.3e' % (
                        pars["measurement_step_min"])
                    PmagSpecRec["measurement_step_max"] = '%8.3e' % (
                        pars["measurement_step_max"])
                    PmagSpecRec["specimen_int_n"] = '%i' % (
                        pars["specimen_int_n"])
                    PmagSpecRec["specimen_lab_field_dc"] = '%8.3e' % (
                        pars["specimen_lab_field_dc"])
                    PmagSpecRec["specimen_int"] = '%9.4e ' % (
                        pars["specimen_int"])
                    PmagSpecRec["specimen_b"] = '%5.3f ' % (pars["specimen_b"])
                    PmagSpecRec["specimen_q"] = '%5.1f ' % (pars["specimen_q"])
                    PmagSpecRec["specimen_f"] = '%5.3f ' % (pars["specimen_f"])
                    PmagSpecRec["specimen_fvds"] = '%5.3f' % (
                        pars["specimen_fvds"])
                    PmagSpecRec["specimen_b_beta"] = '%5.3f' % (
                        pars["specimen_b_beta"])
                    PmagSpecRec["specimen_int_mad"] = '%7.1f' % (
                        pars["specimen_int_mad"])
                    PmagSpecRec["specimen_Z"] = '%7.1f' % (pars["specimen_Z"])
                    PmagSpecRec["specimen_gamma"] = '%7.1f' % (
                        pars["specimen_gamma"])
                    if pars["method_codes"] != "" and pars[
                            "method_codes"] not in methcodes:
                        methcodes.append(pars["method_codes"])
                    PmagSpecRec["specimen_dec"] = '%7.1f' % (
                        pars["specimen_dec"])
                    PmagSpecRec["specimen_inc"] = '%7.1f' % (
                        pars["specimen_inc"])
                    PmagSpecRec["specimen_tilt_correction"] = '-1'
                    PmagSpecRec["specimen_direction_type"] = 'l'
                    PmagSpecRec[
                        "direction_type"] = 'l'  # this is redudant, but helpful - won't be imported
                    PmagSpecRec["specimen_dang"] = '%7.1f ' % (
                        pars["specimen_dang"])
                    PmagSpecRec["specimen_drats"] = '%7.1f ' % (
                        pars["specimen_drats"])
                    PmagSpecRec["specimen_drat"] = '%7.1f ' % (
                        pars["specimen_drat"])
                    PmagSpecRec["specimen_int_ptrm_n"] = '%i ' % (
                        pars["specimen_int_ptrm_n"])
                    PmagSpecRec["specimen_rsc"] = '%6.4f ' % (
                        pars["specimen_rsc"])
                    PmagSpecRec["specimen_md"] = '%i ' % (int(
                        pars["specimen_md"]))
                    if PmagSpecRec["specimen_md"] == '-1':
                        PmagSpecRec["specimen_md"] = ""
                    PmagSpecRec["specimen_b_sigma"] = '%5.3f ' % (
                        pars["specimen_b_sigma"])
                    if "IE-TT" not in methcodes: methcodes.append("IE-TT")
                    methods = ""
                    for meth in methcodes:
                        methods = methods + meth + ":"
                    PmagSpecRec["magic_method_codes"] = methods.strip(':')
                    PmagSpecRec["magic_software_packages"] = version_num
                    PmagSpecRec["specimen_description"] = comment
                    if critout != "":
                        kill = pmag.grade(PmagSpecRec, accept, 'specimen_int')
                        if len(kill) > 0:
                            Grade = 'F'  # fails
                        else:
                            Grade = 'A'  # passes
                        PmagSpecRec["specimen_grade"] = Grade
                    else:
                        PmagSpecRec["specimen_grade"] = ""  # not graded
                    if nltrm == 0 and anis == 0 and cool != 0:  # apply cooling rate correction
                        SCR = pmag.get_dictitem(
                            SampCRs, 'er_sample_name',
                            PmagSpecRec['er_sample_name'],
                            'T')  # get this samples, cooling rate correction
                        CrSpecRec = pmag.cooling_rate(PmagSpecRec, SCR, crfrac,
                                                      crtype)
                        if CrSpecRec['er_specimen_name'] != 'none':
                            CrSpecs.append(CrSpecRec)
                    PmagSpecs.append(PmagSpecRec)
                    NltSpecRec = ""
                    #
                    # check on non-linear TRM correction
                    #
                    if nltrm == 1:
                        #
                        # find the data from the nlt_data list for this specimen
                        #
                        TRMs, Bs = [], []
                        NltSpecRec = ""
                        NltRecs = pmag.get_dictitem(
                            nlt_data, 'er_specimen_name',
                            PmagSpecRec['er_specimen_name'], 'has'
                        )  # fish out all the NLT data for this specimen
                        if len(NltRecs) > 2:
                            for NltRec in NltRecs:
                                Bs.append(float(NltRec['treatment_dc_field']))
                                TRMs.append(
                                    float(NltRec['measurement_magn_moment']))
                            NLTpars = nlt.NLtrm(
                                Bs, TRMs, float(PmagSpecRec['specimen_int']),
                                float(PmagSpecRec['specimen_lab_field_dc']), 0)
                            if NLTpars['banc'] > 0:
                                NltSpecRec = {}
                                for key in PmagSpecRec.keys():
                                    NltSpecRec[key] = PmagSpecRec[key]
                                NltSpecRec['specimen_int'] = '%9.4e' % (
                                    NLTpars['banc'])
                                NltSpecRec['magic_method_codes'] = PmagSpecRec[
                                    "magic_method_codes"] + ":DA-NL"
                                NltSpecRec["specimen_correction"] = 'c'
                                NltSpecRec['specimen_grade'] = PmagSpecRec[
                                    'specimen_grade']
                                NltSpecRec[
                                    "magic_software_packages"] = version_num
                                print NltSpecRec[
                                    'er_specimen_name'], ' Banc= ', float(
                                        NLTpars['banc']) * 1e6
                                if anis == 0 and cool != 0:
                                    SCR = pmag.get_dictitem(
                                        SampCRs, 'er_sample_name',
                                        NltSpecRec['er_sample_name'], 'T'
                                    )  # get this samples, cooling rate correction
                                    CrSpecRec = pmag.cooling_rate(
                                        NltSpecRec, SCR, crfrac, crtype)
                                    if CrSpecRec['er_specimen_name'] != 'none':
                                        CrSpecs.append(CrSpecRec)
                                NltSpecRecs.append(NltSpecRec)
    #
    # check on anisotropy correction
                        if anis == 1:
                            if NltSpecRec != "":
                                Spc = NltSpecRec
                            else:  # find uncorrected data
                                Spc = PmagSpecRec
                            AniSpecs = pmag.get_dictitem(
                                anis_data, 'er_specimen_name',
                                PmagSpecRec['er_specimen_name'], 'T')
                            if len(AniSpecs) > 0:
                                AniSpec = AniSpecs[0]
                                AniSpecRec = pmag.doaniscorr(Spc, AniSpec)
                                AniSpecRec['specimen_grade'] = PmagSpecRec[
                                    'specimen_grade']
                                AniSpecRec[
                                    "magic_instrument_codes"] = PmagSpecRec[
                                        'magic_instrument_codes']
                                AniSpecRec["specimen_correction"] = 'c'
                                AniSpecRec[
                                    "magic_software_packages"] = version_num
                                if cool != 0:
                                    SCR = pmag.get_dictitem(
                                        SampCRs, 'er_sample_name',
                                        AniSpecRec['er_sample_name'], 'T'
                                    )  # get this samples, cooling rate correction
                                    CrSpecRec = pmag.cooling_rate(
                                        AniSpecRec, SCR, crfrac, crtype)
                                    if CrSpecRec['er_specimen_name'] != 'none':
                                        CrSpecs.append(CrSpecRec)
                                AniSpecRecs.append(AniSpecRec)
                    elif anis == 1:
                        AniSpecs = pmag.get_dictitem(
                            anis_data, 'er_specimen_name',
                            PmagSpecRec['er_specimen_name'], 'T')
                        if len(AniSpecs) > 0:
                            AniSpec = AniSpecs[0]
                            AniSpecRec = pmag.doaniscorr(PmagSpecRec, AniSpec)
                            AniSpecRec['specimen_grade'] = PmagSpecRec[
                                'specimen_grade']
                            AniSpecRec["magic_instrument_codes"] = PmagSpecRec[
                                "magic_instrument_codes"]
                            AniSpecRec["specimen_correction"] = 'c'
                            AniSpecRec["magic_software_packages"] = version_num
                            if crfrac != 0:
                                CrSpecRec = {}
                                for key in AniSpecRec.keys():
                                    CrSpecRec[key] = AniSpecRec[key]
                                inten = frac * float(CrSpecRec['specimen_int'])
                                CrSpecRec["specimen_int"] = '%9.4e ' % (
                                    inten
                                )  # adjust specimen intensity by cooling rate correction
                                CrSpecRec['magic_method_codes'] = CrSpecRec[
                                    'magic_method_codes'] + ':DA-CR-' + crtype
                                CRSpecs.append(CrSpecRec)
                            AniSpecRecs.append(AniSpecRec)
                spec += 1
        else:
            print "skipping ", s
            spec += 1
    pmag_file = dir_path + '/' + pmag_file
    pmag.magic_write(pmag_file, PmagSpecs, 'pmag_specimens')
    print 'uncorrected thellier data saved in: ', pmag_file
    if anis == 1 and len(AniSpecRecs) > 0:
        anisout = dir_path + '/' + anisout
        pmag.magic_write(anisout, AniSpecRecs, 'pmag_specimens')
        print 'anisotropy corrected data saved in: ', anisout
    if nltrm == 1 and len(NltSpecRecs) > 0:
        nltout = dir_path + '/' + nltout
        pmag.magic_write(nltout, NltSpecRecs, 'pmag_specimens')
        print 'non-linear TRM corrected data saved in: ', nltout
    if crfrac != 0:
        crout = dir_path + '/' + crout
        pmag.magic_write(crout, CRSpecs, 'pmag_specimens')
        print 'cooling rate corrected data saved in: ', crout
Ejemplo n.º 3
0
def main():
    """
    NAME
        thellier_magic.py

    DESCRIPTION
        plots Thellier-Thellier, allowing interactive setting of bounds
        and customizing of selection criteria.  Saves and reads interpretations
        from a pmag_specimen formatted table, default: thellier_specimens.txt

    SYNTAX
        thellier_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f MEAS, set magic_measurements input file
        -fsp PRIOR, set pmag_specimen prior interpretations file
        -fan ANIS, set rmag_anisotropy file for doing the anisotropy corrections
        -fcr CRIT, set criteria file for grading.
        -fmt [svg,png,jpg], format for images - default is svg
        -sav,  saves plots with out review (default format)
        -spc SPEC, plots single specimen SPEC, saves plot with specified format
            with optional -b bounds adn quits
        -b BEG END: sets  bounds for calculation
           BEG: starting step for slope calculation
           END: ending step for slope calculation
        -z use only z component difference for pTRM calculation

    DEFAULTS
        MEAS: magic_measurements.txt
        REDO: thellier_redo
        CRIT: NONE
        PRIOR: NONE

    OUTPUT
        figures:
            ALL:  numbers refer to temperature steps in command line window
            1) Arai plot:  closed circles are zero-field first/infield
                           open circles are infield first/zero-field
                           triangles are pTRM checks
                           squares are pTRM tail checks
                           VDS is vector difference sum
                           diamonds are bounds for interpretation
            2) Zijderveld plot:  closed (open) symbols are X-Y (X-Z) planes
                                 X rotated to NRM direction
            3) (De/Re)Magnetization diagram:
                           circles are NRM remaining
                           squares are pTRM gained
            4) equal area projections:
               green triangles are pTRM gained direction
                           red (purple) circles are lower(upper) hemisphere of ZI step directions
                           blue (cyan) squares are lower(upper) hemisphere IZ step directions
            5) Optional:  TRM acquisition
            6) Optional: TDS normalization
        command line window:
            list is: temperature step numbers, temperatures (C), Dec, Inc, Int (units of magic_measuements)
                     list of possible commands: type letter followed by return to select option
                     saving of plots creates .svg format files with specimen_name, plot type as name
    """
    #
    #   initializations
    #
    meas_file, critout, inspec = "magic_measurements.txt", "", "thellier_specimens.txt"
    first = 1
    inlt = 0
    version_num = pmag.get_version()
    TDinit, Tinit, field, first_save = 0, 0, -1, 1
    user, comment, AniSpec, locname = "", '', "", ""
    ans, specimen, recnum, start, end = 0, 0, 0, 0, 0
    plots, pmag_out, samp_file, style = 0, "", "", "svg"
    verbose = pmagplotlib.verbose
    fmt = '.' + style
    #
    # default acceptance criteria
    #
    accept = pmag.default_criteria(0)[0]  # set the default criteria
    #
    # parse command line options
    #
    Zdiff, anis = 0, 0
    spc, BEG, END = "", "", ""
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        meas_file = sys.argv[ind + 1]
    if '-fsp' in sys.argv:
        ind = sys.argv.index('-fsp')
        inspec = sys.argv[ind + 1]
    if '-fan' in sys.argv:
        ind = sys.argv.index('-fan')
        anisfile = sys.argv[ind + 1]
        anis = 1
        anis_data, file_type = pmag.magic_read(anisfile)
        if verbose:
            print("Anisotropy data read in from ", anisfile)
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = '.' + sys.argv[ind + 1]
    if '-dpi' in sys.argv:
        ind = sys.argv.index('-dpi')
        dpi = '.' + sys.argv[ind + 1]
    else:
        dpi = 100
    if '-sav' in sys.argv:
        plots = 1
        verbose = 0
    if '-z' in sys.argv:
        Zdiff = 1
    if '-spc' in sys.argv:
        ind = sys.argv.index('-spc')
        spc = sys.argv[ind + 1]
        if '-b' in sys.argv:
            ind = sys.argv.index('-b')
            BEG = int(sys.argv[ind + 1])
            END = int(sys.argv[ind + 2])
    if '-fcr' in sys.argv:
        ind = sys.argv.index('-fcr')
        critout = sys.argv[ind + 1]
        crit_data, file_type = pmag.magic_read(critout)
        if file_type != 'pmag_criteria':
            if verbose:
                print('bad pmag_criteria file, using no acceptance criteria')
            accept = pmag.default_criteria(1)[0]
        else:
            if verbose:
                print("Acceptance criteria read in from ", critout)
            accept = {
                'pmag_criteria_code': 'ACCEPTANCE',
                'er_citation_names': 'This study'
            }
            for critrec in crit_data:
                if 'sample_int_sigma_uT' in critrec.keys(
                ):  # accommodate Shaar's new criterion
                    critrec['sample_int_sigma'] = '%10.3e' % (
                        eval(critrec['sample_int_sigma_uT']) * 1e-6)
                for key in critrec.keys():
                    if key not in accept.keys() and critrec[key] != '':
                        accept[key] = critrec[key]
    try:
        open(inspec, 'rU')
        PriorRecs, file_type = pmag.magic_read(inspec)
        if file_type != 'pmag_specimens':
            print(file_type)
            print(file_type, inspec, " is not a valid pmag_specimens file ")
            sys.exit()
        for rec in PriorRecs:
            if 'magic_software_packages' not in rec.keys():
                rec['magic_software_packages'] = ""
    except IOError:
        PriorRecs = []
        if verbose:
            print("starting new specimen interpretation file: ", inspec)
    meas_data, file_type = pmag.magic_read(meas_file)
    if file_type != 'magic_measurements':
        print(file_type)
        print(file_type, "This is not a valid magic_measurements file ")
        sys.exit()
    backup = 0
    # define figure numbers for arai, zijderveld and
    #   de-,re-magization diagrams
    AZD = {}
    AZD['deremag'], AZD['zijd'], AZD['arai'], AZD['eqarea'] = 1, 2, 3, 4
    pmagplotlib.plot_init(AZD['arai'], 5, 5)
    pmagplotlib.plot_init(AZD['zijd'], 5, 5)
    pmagplotlib.plot_init(AZD['deremag'], 5, 5)
    pmagplotlib.plot_init(AZD['eqarea'], 5, 5)
    #
    #
    #
    # get list of unique specimen names
    #
    CurrRec = []
    sids = pmag.get_specs(meas_data)
    # get plots for specimen s - default is just to step through arai diagrams
    #
    if spc != "":
        specimen = sids.index(spc)
    while specimen < len(sids):
        methcodes = []

        if verbose:
            print(sids[specimen], specimen + 1, 'of ', len(sids))
        MeasRecs = []
        s = sids[specimen]
        datablock, trmblock, tdsrecs = [], [], []
        PmagSpecRec = {}
        if first == 0:
            for key in keys:
                # make sure all new records have same set of keys
                PmagSpecRec[key] = ""
        PmagSpecRec["er_analyst_mail_names"] = user
        PmagSpecRec["specimen_correction"] = 'u'
        #
        # find the data from the meas_data file for this specimen
        #
        for rec in meas_data:
            if rec["er_specimen_name"] == s:
                MeasRecs.append(rec)
                if "magic_method_codes" not in rec.keys():
                    rec["magic_method_codes"] = ""
                methods = rec["magic_method_codes"].split(":")
                meths = []
                for meth in methods:
                    meths.append(meth.strip())  # take off annoying spaces
                methods = ""
                for meth in meths:
                    if meth.strip() not in methcodes and "LP-" in meth:
                        methcodes.append(meth.strip())
                    methods = methods + meth + ":"
                methods = methods[:-1]
                rec["magic_method_codes"] = methods
                if "LP-PI-TRM" in meths:
                    datablock.append(rec)
                if "LP-TRM" in meths:
                    trmblock.append(rec)
                if "LP-TRM-TD" in meths:
                    tdsrecs.append(rec)
        if len(trmblock) > 2 and inspec != "":
            if Tinit == 0:
                Tinit = 1
                AZD['TRM'] = 5
                pmagplotlib.plot_init(AZD['TRM'], 5, 5)
        elif Tinit == 1:  # clear the TRM figure if not needed
            pmagplotlib.clearFIG(AZD['TRM'])
        if len(tdsrecs) > 2:
            if TDinit == 0:
                TDinit = 1
                AZD['TDS'] = 6
                pmagplotlib.plot_init(AZD['TDS'], 5, 5)
        elif TDinit == 1:  # clear the TDS figure if not needed
            pmagplotlib.clearFIG(AZD['TDS'])
        if len(datablock) < 4:
            if backup == 0:
                specimen += 1
                if verbose:
                    print('skipping specimen - moving forward ', s)
            else:
                specimen -= 1
                if verbose:
                    print('skipping specimen - moving backward ', s)
    #
    #  collect info for the PmagSpecRec dictionary
    #
        else:
            rec = datablock[0]
            PmagSpecRec["er_citation_names"] = "This study"
            PmagSpecRec["er_specimen_name"] = s
            PmagSpecRec["er_sample_name"] = rec["er_sample_name"]
            PmagSpecRec["er_site_name"] = rec["er_site_name"]
            PmagSpecRec["er_location_name"] = rec["er_location_name"]
            locname = rec['er_location_name'].replace('/', '-')
            if "er_expedition_name" in rec.keys():
                PmagSpecRec["er_expedition_name"] = rec["er_expedition_name"]
            if "magic_instrument_codes" not in rec.keys():
                rec["magic_instrument_codes"] = ""
            PmagSpecRec["magic_instrument_codes"] = rec[
                "magic_instrument_codes"]
            PmagSpecRec["measurement_step_unit"] = "K"
            if "magic_experiment_name" not in rec.keys():
                rec["magic_experiment_name"] = ""
            else:
                PmagSpecRec["magic_experiment_names"] = rec[
                    "magic_experiment_name"]

            meths = rec["magic_method_codes"].split()
            # sort data into types
            araiblock, field = pmag.sortarai(datablock, s, Zdiff)
            first_Z = araiblock[0]
            GammaChecks = araiblock[5]
            if len(first_Z) < 3:
                if backup == 0:
                    specimen += 1
                    if verbose:
                        print('skipping specimen - moving forward ', s)
                else:
                    specimen -= 1
                    if verbose:
                        print('skipping specimen - moving backward ', s)
            else:
                backup = 0
                zijdblock, units = pmag.find_dmag_rec(s, meas_data)
                recnum = 0
                if verbose:
                    print("index step Dec   Inc  Int       Gamma")
                    for plotrec in zijdblock:
                        if GammaChecks != "":
                            gamma = ""
                            for g in GammaChecks:
                                if g[0] == plotrec[0] - 273:
                                    gamma = g[1]
                                    break
                        if gamma != "":
                            print('%i     %i %7.1f %7.1f %8.3e %7.1f' %
                                  (recnum, plotrec[0] - 273, plotrec[1],
                                   plotrec[2], plotrec[3], gamma))
                        else:
                            print('%i     %i %7.1f %7.1f %8.3e ' %
                                  (recnum, plotrec[0] - 273, plotrec[1],
                                   plotrec[2], plotrec[3]))
                        recnum += 1
                pmagplotlib.plot_arai_zij(AZD, araiblock, zijdblock, s,
                                          units[0])
                if verbose:
                    pmagplotlib.draw_figs(AZD)
                if len(tdsrecs) > 2:  # a TDS experiment
                    tdsblock = []  # make a list for the TDS  data
                    Mkeys = [
                        'measurement_magnitude', 'measurement_magn_moment',
                        'measurement_magn_volume', 'measuruement_magn_mass'
                    ]
                    mkey, k = "", 0
                    # find which type of intensity
                    while mkey == "" and k < len(Mkeys) - 1:
                        key = Mkeys[k]
                        if key in tdsrecs[0].keys() and tdsrecs[0][key] != "":
                            mkey = key
                        k += 1
                    if mkey == "":
                        break  # get outta here
                    Tnorm = ""
                    for tdrec in tdsrecs:
                        meths = tdrec['magic_method_codes'].split(":")
                        for meth in meths:
                            # strip off potential nasty spaces
                            meth.replace(" ", "")
                        if 'LT-T-I' in meths and Tnorm == "":  # found first total TRM
                            # normalize by total TRM
                            Tnorm = float(tdrec[mkey])
                            # put in the zero step
                            tdsblock.append([273, zijdblock[0][3] / Tnorm, 1.])
                        # found a LP-TRM-TD demag step, now need complementary LT-T-Z from zijdblock
                        if 'LT-T-Z' in meths and Tnorm != "":
                            step = float(tdrec['treatment_temp'])
                            Tint = ""
                            if mkey != "":
                                Tint = float(tdrec[mkey])
                            if Tint != "":
                                for zrec in zijdblock:
                                    if zrec[0] == step:  # found matching
                                        tdsblock.append([
                                            step, zrec[3] / Tnorm, Tint / Tnorm
                                        ])
                                        break
                    if len(tdsblock) > 2:
                        pmagplotlib.plot_tds(AZD['TDS'], tdsblock,
                                             s + ':LP-PI-TDS:')
                        if verbose:
                            pmagplotlib(draw_figs(AZD))
                    else:
                        print("Something wrong here")
                if anis == 1:  # look up anisotropy data for this specimen
                    AniSpec = ""
                    for aspec in anis_data:
                        if aspec["er_specimen_name"] == PmagSpecRec[
                                "er_specimen_name"]:
                            AniSpec = aspec
                            if verbose:
                                print('Found anisotropy record...')
                            break
                if inspec != "":
                    if verbose:
                        print('Looking up saved interpretation....')
                    found = 0
                    for k in range(len(PriorRecs)):
                        try:
                            if PriorRecs[k]["er_specimen_name"] == s:
                                found = 1
                                CurrRec.append(PriorRecs[k])
                                for j in range(len(zijdblock)):
                                    if float(zijdblock[j][0]) == float(
                                            PriorRecs[k]
                                        ["measurement_step_min"]):
                                        start = j
                                    if float(zijdblock[j][0]) == float(
                                            PriorRecs[k]
                                        ["measurement_step_max"]):
                                        end = j
                                pars, errcode = pmag.PintPars(
                                    datablock, araiblock, zijdblock, start,
                                    end, accept)
                                pars['measurement_step_unit'] = "K"
                                pars['experiment_type'] = 'LP-PI-TRM'
                                # put in CurrRec, take out of PriorRecs
                                del PriorRecs[k]
                                if errcode != 1:
                                    pars["specimen_lab_field_dc"] = field
                                    pars["specimen_int"] = -1 * \
                                        field*pars["specimen_b"]
                                    pars["er_specimen_name"] = s
                                    if verbose:
                                        print('Saved interpretation: ')
                                    pars, kill = pmag.scoreit(
                                        pars, PmagSpecRec, accept, '', verbose)
                                    pmagplotlib.plot_b(AZD, araiblock,
                                                       zijdblock, pars)
                                    if verbose:
                                        pmagplotlib.draw_figs(AZD)
                                    if len(trmblock) > 2:
                                        blab = field
                                        best = pars["specimen_int"]
                                        Bs, TRMs = [], []
                                        for trec in trmblock:
                                            Bs.append(
                                                float(
                                                    trec['treatment_dc_field'])
                                            )
                                            TRMs.append(
                                                float(trec[
                                                    'measurement_magn_moment'])
                                            )
                                        # calculate best fit parameters through TRM acquisition data, and get new banc
                                        NLpars = nlt.NLtrm(
                                            Bs, TRMs, best, blab, 0)
                                        Mp, Bp = [], []
                                        for k in range(int(max(Bs) * 1e6)):
                                            Bp.append(float(k) * 1e-6)
                                            # predicted NRM for this field
                                            npred = nlt.TRM(
                                                Bp[-1], NLpars['xopt'][0],
                                                NLpars['xopt'][1])
                                            Mp.append(npred)
                                        pmagplotlib.plot_trm(
                                            AZD['TRM'], Bs, TRMs, Bp, Mp,
                                            NLpars,
                                            trec['magic_experiment_name'])
                                        PmagSpecRec['specimen_int'] = NLpars[
                                            'banc']
                                        if verbose:
                                            print('Banc= ',
                                                  float(NLpars['banc']) * 1e6)
                                            pmagplotlib.draw_figs(AZD)
                                    mpars = pmag.domean(
                                        araiblock[1], start, end, 'DE-BFL')
                                    if verbose:
                                        print(
                                            'pTRM direction= ',
                                            '%7.1f' % (mpars['specimen_dec']),
                                            ' %7.1f' % (mpars['specimen_inc']),
                                            ' MAD:',
                                            '%7.1f' % (mpars['specimen_mad']))
                                    if AniSpec != "":
                                        CpTRM = pmag.Dir_anis_corr([
                                            mpars['specimen_dec'],
                                            mpars['specimen_inc']
                                        ], AniSpec)
                                        AniSpecRec = pmag.doaniscorr(
                                            PmagSpecRec, AniSpec)
                                        if verbose:
                                            print(
                                                'Anisotropy corrected TRM direction= ',
                                                '%7.1f' % (CpTRM[0]),
                                                ' %7.1f' % (CpTRM[1]))
                                            print(
                                                'Anisotropy corrected intensity= ',
                                                float(
                                                    AniSpecRec['specimen_int'])
                                                * 1e6)
                                else:
                                    print('error on specimen ', s)
                        except:
                            pass
                    if verbose and found == 0:
                        print('    None found :(  ')
                if spc != "":
                    if BEG != "":
                        pars, errcode = pmag.PintPars(datablock, araiblock,
                                                      zijdblock, BEG, END,
                                                      accept)
                        pars['measurement_step_unit'] = "K"
                        pars["specimen_lab_field_dc"] = field
                        pars["specimen_int"] = -1 * field * pars["specimen_b"]
                        pars["er_specimen_name"] = s
                        pars['specimen_grade'] = ''  # ungraded
                        pmagplotlib.plot_b(AZD, araiblock, zijdblock, pars)
                        if verbose:
                            pmagplotlib.draw_figs(AZD)
                        if len(trmblock) > 2:
                            if inlt == 0:
                                inlt = 1
                            blab = field
                            best = pars["specimen_int"]
                            Bs, TRMs = [], []
                            for trec in trmblock:
                                Bs.append(float(trec['treatment_dc_field']))
                                TRMs.append(
                                    float(trec['measurement_magn_moment']))
                            # calculate best fit parameters through TRM acquisition data, and get new banc
                            NLpars = nlt.NLtrm(Bs, TRMs, best, blab, 0)
                            #
                            Mp, Bp = [], []
                            for k in range(int(max(Bs) * 1e6)):
                                Bp.append(float(k) * 1e-6)
                                # predicted NRM for this field
                                npred = nlt.TRM(Bp[-1], NLpars['xopt'][0],
                                                NLpars['xopt'][1])
                    files = {}
                    for key in AZD.keys():
                        files[key] = s + '_' + key + fmt
                    pmagplotlib.save_plots(AZD, files, dpi=dpi)
                    sys.exit()
                if verbose:
                    ans = 'b'
                    while ans != "":
                        print("""
               s[a]ve plot, set [b]ounds for calculation, [d]elete current interpretation, [p]revious, [s]ample, [q]uit:
               """)
                        ans = input('Return for next specimen \n')
                        if ans == "":
                            specimen += 1
                        if ans == "d":
                            save_redo(PriorRecs, inspec)
                            CurrRec = []
                            pmagplotlib.plot_arai_zij(AZD, araiblock,
                                                      zijdblock, s, units[0])
                            if verbose:
                                pmagplotlib.draw_figs(AZD)
                        if ans == 'a':
                            files = {}
                            for key in AZD.keys():
                                files[key] = "LO:_"+locname+'_SI:_'+PmagSpecRec['er_site_name'] + \
                                    '_SA:_' + \
                                    PmagSpecRec['er_sample_name'] + \
                                    '_SP:_'+s+'_CO:_s_TY:_'+key+fmt
                            pmagplotlib.save_plots(AZD, files)
                            ans = ""
                        if ans == 'q':
                            print("Good bye")
                            sys.exit()
                        if ans == 'p':
                            specimen = specimen - 1
                            backup = 1
                            ans = ""
                        if ans == 's':
                            keepon = 1
                            spec = input(
                                'Enter desired specimen name (or first part there of): '
                            )
                            while keepon == 1:
                                try:
                                    specimen = sids.index(spec)
                                    keepon = 0
                                except:
                                    tmplist = []
                                    for qq in range(len(sids)):
                                        if spec in sids[qq]:
                                            tmplist.append(sids[qq])
                                    print(specimen,
                                          " not found, but this was: ")
                                    print(tmplist)
                                    spec = input('Select one or try again\n ')
                            ans = ""
                        if ans == 'b':
                            if end == 0 or end >= len(zijdblock):
                                end = len(zijdblock) - 1
                            GoOn = 0
                            while GoOn == 0:
                                answer = input(
                                    'Enter index of first point for calculation: ['
                                    + str(start) + ']  ')
                                try:
                                    start = int(answer)
                                    answer = input(
                                        'Enter index  of last point for calculation: ['
                                        + str(end) + ']  ')
                                    end = int(answer)
                                    if start >= 0 and start < len(
                                            zijdblock
                                    ) - 2 and end > 0 and end < len(
                                            zijdblock) or start >= end:
                                        GoOn = 1
                                    else:
                                        print("Bad endpoints - try again! ")
                                        start, end = 0, len(zijdblock)
                                except ValueError:
                                    print("Bad endpoints - try again! ")
                                    start, end = 0, len(zijdblock)
                            s = sids[specimen]
                            pars, errcode = pmag.PintPars(
                                datablock, araiblock, zijdblock, start, end,
                                accept)
                            pars['measurement_step_unit'] = "K"
                            pars["specimen_lab_field_dc"] = field
                            pars["specimen_int"] = -1 * field * pars[
                                "specimen_b"]
                            pars["er_specimen_name"] = s
                            pars, kill = pmag.scoreit(pars, PmagSpecRec,
                                                      accept, '', 0)
                            PmagSpecRec['specimen_scat'] = pars[
                                'specimen_scat']
                            PmagSpecRec['specimen_frac'] = '%5.3f' % (
                                pars['specimen_frac'])
                            PmagSpecRec['specimen_gmax'] = '%5.3f' % (
                                pars['specimen_gmax'])
                            PmagSpecRec["measurement_step_min"] = '%8.3e' % (
                                pars["measurement_step_min"])
                            PmagSpecRec["measurement_step_max"] = '%8.3e' % (
                                pars["measurement_step_max"])
                            PmagSpecRec["measurement_step_unit"] = "K"
                            PmagSpecRec["specimen_int_n"] = '%i' % (
                                pars["specimen_int_n"])
                            PmagSpecRec["specimen_lab_field_dc"] = '%8.3e' % (
                                pars["specimen_lab_field_dc"])
                            PmagSpecRec["specimen_int"] = '%9.4e ' % (
                                pars["specimen_int"])
                            PmagSpecRec["specimen_b"] = '%5.3f ' % (
                                pars["specimen_b"])
                            PmagSpecRec["specimen_q"] = '%5.1f ' % (
                                pars["specimen_q"])
                            PmagSpecRec["specimen_f"] = '%5.3f ' % (
                                pars["specimen_f"])
                            PmagSpecRec["specimen_fvds"] = '%5.3f' % (
                                pars["specimen_fvds"])
                            PmagSpecRec["specimen_b_beta"] = '%5.3f' % (
                                pars["specimen_b_beta"])
                            PmagSpecRec["specimen_int_mad"] = '%7.1f' % (
                                pars["specimen_int_mad"])
                            PmagSpecRec["specimen_Z"] = '%7.1f' % (
                                pars["specimen_Z"])
                            PmagSpecRec["specimen_gamma"] = '%7.1f' % (
                                pars["specimen_gamma"])
                            PmagSpecRec["specimen_grade"] = pars[
                                "specimen_grade"]
                            if pars["method_codes"] != "":
                                tmpcodes = pars["method_codes"].split(":")
                                for t in tmpcodes:
                                    if t.strip() not in methcodes:
                                        methcodes.append(t.strip())
                            PmagSpecRec["specimen_dec"] = '%7.1f' % (
                                pars["specimen_dec"])
                            PmagSpecRec["specimen_inc"] = '%7.1f' % (
                                pars["specimen_inc"])
                            PmagSpecRec["specimen_tilt_correction"] = '-1'
                            PmagSpecRec["specimen_direction_type"] = 'l'
                            # this is redundant, but helpful - won't be imported
                            PmagSpecRec["direction_type"] = 'l'
                            PmagSpecRec["specimen_int_dang"] = '%7.1f ' % (
                                pars["specimen_int_dang"])
                            PmagSpecRec["specimen_drats"] = '%7.1f ' % (
                                pars["specimen_drats"])
                            PmagSpecRec["specimen_drat"] = '%7.1f ' % (
                                pars["specimen_drat"])
                            PmagSpecRec["specimen_int_ptrm_n"] = '%i ' % (
                                pars["specimen_int_ptrm_n"])
                            PmagSpecRec["specimen_rsc"] = '%6.4f ' % (
                                pars["specimen_rsc"])
                            PmagSpecRec["specimen_md"] = '%i ' % (int(
                                pars["specimen_md"]))
                            if PmagSpecRec["specimen_md"] == '-1':
                                PmagSpecRec["specimen_md"] = ""
                            PmagSpecRec["specimen_b_sigma"] = '%5.3f ' % (
                                pars["specimen_b_sigma"])
                            if "IE-TT" not in methcodes:
                                methcodes.append("IE-TT")
                            methods = ""
                            for meth in methcodes:
                                methods = methods + meth + ":"
                            PmagSpecRec["magic_method_codes"] = methods[:-1]
                            PmagSpecRec["specimen_description"] = comment
                            PmagSpecRec[
                                "magic_software_packages"] = version_num
                            pmagplotlib.plot_arai_zij(AZD, araiblock,
                                                      zijdblock, s, units[0])
                            pmagplotlib.plot_b(AZD, araiblock, zijdblock, pars)
                            if verbose:
                                pmagplotlib.draw_figs(AZD)
                            if len(trmblock) > 2:
                                blab = field
                                best = pars["specimen_int"]
                                Bs, TRMs = [], []
                                for trec in trmblock:
                                    Bs.append(float(
                                        trec['treatment_dc_field']))
                                    TRMs.append(
                                        float(trec['measurement_magn_moment']))
                                # calculate best fit parameters through TRM acquisition data, and get new banc
                                NLpars = nlt.NLtrm(Bs, TRMs, best, blab, 0)
                                Mp, Bp = [], []
                                for k in range(int(max(Bs) * 1e6)):
                                    Bp.append(float(k) * 1e-6)
                                    # predicted NRM for this field
                                    npred = nlt.TRM(Bp[-1], NLpars['xopt'][0],
                                                    NLpars['xopt'][1])
                                    Mp.append(npred)
                                pmagplotlib.plot_trm(
                                    AZD['TRM'], Bs, TRMs, Bp, Mp, NLpars,
                                    trec['magic_experiment_name'])
                                if verbose:
                                    print(
                                        'Non-linear TRM corrected intensity= ',
                                        float(NLpars['banc']) * 1e6)
                            if verbose:
                                pmagplotlib.draw_figs(AZD)
                            pars["specimen_lab_field_dc"] = field
                            pars["specimen_int"] = -1 * field * pars[
                                "specimen_b"]
                            pars, kill = pmag.scoreit(pars, PmagSpecRec,
                                                      accept, '', verbose)
                            saveit = input(
                                "Save this interpretation? [y]/n \n")
                            if saveit != 'n':
                                # put back an interpretation
                                PriorRecs.append(PmagSpecRec)
                                specimen += 1
                                save_redo(PriorRecs, inspec)
                            ans = ""
                elif plots == 1:
                    specimen += 1
                    if fmt != ".pmag":
                        files = {}
                        for key in AZD.keys():
                            files[key] = "LO:_"+locname+'_SI:_'+PmagSpecRec['er_site_name']+'_SA:_' + \
                                PmagSpecRec['er_sample_name'] + \
                                '_SP:_'+s+'_CO:_s_TY:_'+key+'_'+fmt
                        if pmagplotlib.isServer:
                            black = '#000000'
                            purple = '#800080'
                            titles = {}
                            titles['deremag'] = 'DeReMag Plot'
                            titles['zijd'] = 'Zijderveld Plot'
                            titles['arai'] = 'Arai Plot'
                            AZD = pmagplotlib.add_borders(
                                AZD, titles, black, purple)
                        pmagplotlib.save_plots(AZD, files, dpi=dpi)
    #                   pmagplotlib.combineFigs(s,files,3)
                    else:  # save in pmag format
                        script = "grep " + s + " output.mag | thellier -mfsi"
                        script = script + ' %8.4e' % (field)
                        min = '%i' % ((pars["measurement_step_min"] - 273))
                        Max = '%i' % ((pars["measurement_step_max"] - 273))
                        script = script + " " + min + " " + Max
                        script = script + " |plotxy;cat mypost >>thellier.ps\n"
                        pltf.write(script)
                        pmag.domagicmag(outf, MeasRecs)
        if len(CurrRec) > 0:
            for rec in CurrRec:
                PriorRecs.append(rec)
        CurrRec = []
    if plots != 1 and verbose:
        ans = input(" Save last plot? 1/[0] ")
        if ans == "1":
            if fmt != ".pmag":
                files = {}
                for key in AZD.keys():
                    files[key] = s + '_' + key + fmt
                pmagplotlib.save_plots(AZD, files, dpi=dpi)
        else:
            print("\n Good bye\n")
            sys.exit()
        if len(CurrRec) > 0:
            PriorRecs.append(CurrRec)  # put back an interpretation
        if len(PriorRecs) > 0:
            save_redo(PriorRecs, inspec)
            print('Updated interpretations saved in ', inspec)
    if verbose:
        print("Good bye")
Ejemplo n.º 4
0
def main():
    """
    NAME
        thellier_magic.py
    
    DESCRIPTION
        plots Thellier-Thellier, allowing interactive setting of bounds
        and customizing of selection criteria.  Saves and reads interpretations
        from a pmag_specimen formatted table, default: thellier_specimens.txt

    SYNTAX 
        thellier_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f MEAS, set magic_measurements input file
        -fsp PRIOR, set pmag_specimen prior interpretations file
        -fan ANIS, set rmag_anisotropy file for doing the anisotropy corrections
        -fcr CRIT, set criteria file for grading.  
        -fmt [svg,png,jpg], format for images - default is svg
        -sav,  saves plots with out review (default format)
        -spc SPEC, plots single specimen SPEC, saves plot with specified format
            with optional -b bounds adn quits
        -b BEG END: sets  bounds for calculation
           BEG: starting step for slope calculation
           END: ending step for slope calculation
        -z use only z component difference for pTRM calculation
        
    DEFAULTS
        MEAS: magic_measurements.txt
        REDO: thellier_redo
        CRIT: NONE
        PRIOR: NONE
  
    OUTPUT 
        figures:
            ALL:  numbers refer to temperature steps in command line window
            1) Arai plot:  closed circles are zero-field first/infield
                           open circles are infield first/zero-field
                           triangles are pTRM checks
                           squares are pTRM tail checks
                           VDS is vector difference sum
                           diamonds are bounds for interpretation
            2) Zijderveld plot:  closed (open) symbols are X-Y (X-Z) planes
                                 X rotated to NRM direction
            3) (De/Re)Magnetization diagram:
                           circles are NRM remaining
                           squares are pTRM gained
            4) equal area projections:
 			   green triangles are pTRM gained direction
                           red (purple) circles are lower(upper) hemisphere of ZI step directions 
                           blue (cyan) squares are lower(upper) hemisphere IZ step directions 
            5) Optional:  TRM acquisition
            6) Optional: TDS normalization
        command line window:
            list is: temperature step numbers, temperatures (C), Dec, Inc, Int (units of magic_measuements)
                     list of possible commands: type letter followed by return to select option
                     saving of plots creates .svg format files with specimen_name, plot type as name
    """ 
#
#   initializations
#
    meas_file,critout,inspec="magic_measurements.txt","","thellier_specimens.txt"
    first=1
    inlt=0
    version_num=pmag.get_version()
    TDinit,Tinit,field,first_save=0,0,-1,1
    user,comment,AniSpec,locname="",'',"",""
    ans,specimen,recnum,start,end=0,0,0,0,0
    plots,pmag_out,samp_file,style=0,"","","svg"
    verbose=pmagplotlib.verbose 
    fmt='.'+style
#
# default acceptance criteria
#
    accept=pmag.default_criteria(0)[0] # set the default criteria
#
# parse command line options
#
    Zdiff,anis=0,0
    spc,BEG,END="","",""
    if '-h' in sys.argv:
        print main.__doc__
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        meas_file=sys.argv[ind+1]
    if '-fsp' in sys.argv:
        ind=sys.argv.index('-fsp')
        inspec=sys.argv[ind+1]
    if '-fan' in sys.argv:
        ind=sys.argv.index('-fan')
        anisfile=sys.argv[ind+1]
        anis=1
        anis_data,file_type=pmag.magic_read(anisfile)
        if verbose: print "Anisotropy data read in from ", anisfile
    if '-fmt' in sys.argv:
        ind=sys.argv.index('-fmt')
        fmt='.'+sys.argv[ind+1]
    if '-dpi' in sys.argv:
        ind=sys.argv.index('-dpi')
        dpi='.'+sys.argv[ind+1]
    else: dpi=100
    if '-sav' in sys.argv: 
        plots=1
        verbose=0
    if '-z' in sys.argv: Zdiff=1
    if '-spc' in sys.argv:
        ind=sys.argv.index('-spc')
        spc=sys.argv[ind+1]
        if '-b' in sys.argv:
            ind=sys.argv.index('-b')
            BEG=int(sys.argv[ind+1])
            END=int(sys.argv[ind+2])
    if '-fcr' in sys.argv:
        ind=sys.argv.index('-fcr')
        critout=sys.argv[ind+1]
        crit_data,file_type=pmag.magic_read(critout)
        if file_type!='pmag_criteria':
            if verbose: print 'bad pmag_criteria file, using no acceptance criteria'
            accept=pmag.default_criteria(1)[0]
        else:
            if verbose: print "Acceptance criteria read in from ", critout
            accept={'pmag_criteria_code':'ACCEPTANCE','er_citation_names':'This study'}
            for critrec in crit_data:
                if 'sample_int_sigma_uT' in critrec.keys(): # accommodate Shaar's new criterion
                    critrec['sample_int_sigma']='%10.3e'%(eval(critrec['sample_int_sigma_uT'])*1e-6)
                for key in critrec.keys():
                    if key not in accept.keys() and critrec[key]!='':
                        accept[key]=critrec[key]
    try:
        open(inspec,'rU')
        PriorRecs,file_type=pmag.magic_read(inspec)
        if file_type != 'pmag_specimens':
            print file_type
            print file_type,inspec," is not a valid pmag_specimens file " 
            sys.exit()
        for rec in PriorRecs:
            if 'magic_software_packages' not in rec.keys():rec['magic_software_packages']=""
    except IOError:
        PriorRecs=[]
        if verbose:print "starting new specimen interpretation file: ",inspec
    meas_data,file_type=pmag.magic_read(meas_file)
    if file_type != 'magic_measurements':
        print file_type
        print file_type,"This is not a valid magic_measurements file " 
        sys.exit()
    backup=0
    # define figure numbers for arai, zijderveld and 
    #   de-,re-magization diagrams
    AZD={}
    AZD['deremag'], AZD['zijd'],AZD['arai'],AZD['eqarea']=1,2,3,4
    pmagplotlib.plot_init(AZD['arai'],5,5)
    pmagplotlib.plot_init(AZD['zijd'],5,5)
    pmagplotlib.plot_init(AZD['deremag'],5,5)
    pmagplotlib.plot_init(AZD['eqarea'],5,5)
    #
    #
    #
    # get list of unique specimen names
    #
    CurrRec=[]
    sids=pmag.get_specs(meas_data)
    # get plots for specimen s - default is just to step through arai diagrams
    #
    if spc!="": specimen =sids.index(spc)
    while specimen < len(sids):
        methcodes=[]
       
        if verbose:
            print sids[specimen],specimen+1, 'of ', len(sids)
        MeasRecs=[]
        s=sids[specimen]
        datablock,trmblock,tdsrecs=[],[],[]
        PmagSpecRec={}
        if first==0:
           for key in keys:PmagSpecRec[key]="" # make sure all new records have same set of keys
        PmagSpecRec["er_analyst_mail_names"]=user
        PmagSpecRec["specimen_correction"]='u'
    #
    # find the data from the meas_data file for this specimen
    #
        for rec in meas_data:
            if rec["er_specimen_name"]==s:
                MeasRecs.append(rec)
                if "magic_method_codes" not in rec.keys():
                    rec["magic_method_codes"]=""
                methods=rec["magic_method_codes"].split(":")
                meths=[]
                for meth in methods:
                    meths.append(meth.strip()) # take off annoying spaces
                methods=""
                for meth in meths:
                    if meth.strip() not in methcodes and "LP-" in meth:methcodes.append(meth.strip())
                    methods=methods+meth+":"
                methods=methods[:-1]
                rec["magic_method_codes"]=methods 
                if "LP-PI-TRM" in meths: datablock.append(rec)
                if "LP-TRM" in meths: trmblock.append(rec)
                if "LP-TRM-TD" in meths: tdsrecs.append(rec)
        if len(trmblock)>2 and inspec!="":
            if Tinit==0:
                Tinit=1
                AZD['TRM']=5
                pmagplotlib.plot_init(AZD['TRM'],5,5)
        elif Tinit==1: # clear the TRM figure if not needed
            pmagplotlib.clearFIG(AZD['TRM'])
        if len(tdsrecs)>2:
            if TDinit==0:
                TDinit=1
                AZD['TDS']=6
                pmagplotlib.plot_init(AZD['TDS'],5,5)
        elif TDinit==1: # clear the TDS figure if not needed
            pmagplotlib.clearFIG(AZD['TDS'])
        if len(datablock) <4:
           if backup==0:
               specimen+=1
               if verbose:
                   print 'skipping specimen - moving forward ', s
           else:
               specimen-=1
               if verbose:
                   print 'skipping specimen - moving backward ', s
    #
    #  collect info for the PmagSpecRec dictionary
    #
        else:
           rec=datablock[0]
           PmagSpecRec["er_citation_names"]="This study"
           PmagSpecRec["er_specimen_name"]=s
           PmagSpecRec["er_sample_name"]=rec["er_sample_name"]
           PmagSpecRec["er_site_name"]=rec["er_site_name"]
           PmagSpecRec["er_location_name"]=rec["er_location_name"]
           locname=rec['er_location_name'].replace('/','-')
           if "er_expedition_name" in rec.keys():PmagSpecRec["er_expedition_name"]=rec["er_expedition_name"]
           if "magic_instrument_codes" not in rec.keys():rec["magic_instrument_codes"]=""
           PmagSpecRec["magic_instrument_codes"]=rec["magic_instrument_codes"]
           PmagSpecRec["measurement_step_unit"]="K"
           if "magic_experiment_name" not in rec.keys():
               rec["magic_experiment_name"]=""
           else:
               PmagSpecRec["magic_experiment_names"]=rec["magic_experiment_name"]
    
           meths=rec["magic_method_codes"].split()
       # sort data into types
           araiblock,field=pmag.sortarai(datablock,s,Zdiff)
           first_Z=araiblock[0]
           GammaChecks=araiblock[5]
           if len(first_Z)<3:
               if backup==0:
                   specimen+=1
                   if verbose:
                       print 'skipping specimen - moving forward ', s
               else:
                   specimen-=1
                   if verbose:
                       print 'skipping specimen - moving backward ', s
           else:
               backup=0
               zijdblock,units=pmag.find_dmag_rec(s,meas_data)
               recnum=0
               if verbose:
                   print "index step Dec   Inc  Int       Gamma"
                   for plotrec in zijdblock:
                       if GammaChecks!="":
                           gamma=""
                           for g in GammaChecks:
                               if g[0]==plotrec[0]-273:
                                   gamma=g[1]
                                   break
                       if gamma!="":
                           print '%i     %i %7.1f %7.1f %8.3e %7.1f' % (recnum,plotrec[0]-273,plotrec[1],plotrec[2],plotrec[3],gamma)
                       else:
                           print '%i     %i %7.1f %7.1f %8.3e ' % (recnum,plotrec[0]-273,plotrec[1],plotrec[2],plotrec[3])
                       recnum += 1
               pmagplotlib.plotAZ(AZD,araiblock,zijdblock,s,units[0])
               if verbose:pmagplotlib.drawFIGS(AZD)
               if len(tdsrecs)>2: # a TDS experiment
                   tdsblock=[] # make a list for the TDS  data
                   Mkeys=['measurement_magnitude','measurement_magn_moment','measurement_magn_volume','measuruement_magn_mass']
                   mkey,k="",0
                   while mkey=="" and k<len(Mkeys)-1: # find which type of intensity
                       key= Mkeys[k]
                       if key in tdsrecs[0].keys() and tdsrecs[0][key]!="": mkey=key
                       k+=1
                   if mkey=="":break # get outta here
                   Tnorm=""
                   for tdrec in tdsrecs:
                       meths=tdrec['magic_method_codes'].split(":")
                       for meth in meths: meth.replace(" ","") # strip off potential nasty spaces
                       if  'LT-T-I' in meths and Tnorm=="": # found first total TRM 
                           Tnorm=float(tdrec[mkey]) # normalize by total TRM 
                           tdsblock.append([273,zijdblock[0][3]/Tnorm,1.]) # put in the zero step
                       if  'LT-T-Z' in meths and Tnorm!="": # found a LP-TRM-TD demag step, now need complementary LT-T-Z from zijdblock
                           step=float(tdrec['treatment_temp'])
                           Tint=""
                           if mkey!="":
                               Tint=float(tdrec[mkey])
                           if Tint!="":
                               for zrec in zijdblock:
                                   if zrec[0]==step:  # found matching
                                       tdsblock.append([step,zrec[3]/Tnorm,Tint/Tnorm])
                                       break
                   if len(tdsblock)>2: 
                       pmagplotlib.plotTDS(AZD['TDS'],tdsblock,s+':LP-PI-TDS:')
                       if verbose:pmagplotlib(drawFIGS(AZD)) 
                   else: 
                       print "Something wrong here"
               if anis==1:   # look up anisotropy data for this specimen
                   AniSpec=""
                   for aspec in anis_data:
                       if aspec["er_specimen_name"]==PmagSpecRec["er_specimen_name"]:
                           AniSpec=aspec
                           if verbose: print 'Found anisotropy record...'
                           break
               if inspec !="":
                   if verbose: print 'Looking up saved interpretation....'
                   found = 0
                   for k in range(len(PriorRecs)):
                       try:
                         if PriorRecs[k]["er_specimen_name"]==s:
                           found =1
                           CurrRec.append(PriorRecs[k])
                           for j in range(len(zijdblock)):
                               if float(zijdblock[j][0])==float(PriorRecs[k]["measurement_step_min"]):start=j
                               if float(zijdblock[j][0])==float(PriorRecs[k]["measurement_step_max"]):end=j
                           pars,errcode=pmag.PintPars(datablock,araiblock,zijdblock,start,end,accept)
                           pars['measurement_step_unit']="K"
                           pars['experiment_type']='LP-PI-TRM'
                           del PriorRecs[k]  # put in CurrRec, take out of PriorRecs
                           if errcode!=1:
                               pars["specimen_lab_field_dc"]=field
                               pars["specimen_int"]=-1*field*pars["specimen_b"]
                               pars["er_specimen_name"]=s
                               if verbose:
                                   print 'Saved interpretation: '
                               pars,kill=pmag.scoreit(pars,PmagSpecRec,accept,'',verbose)
                               pmagplotlib.plotB(AZD,araiblock,zijdblock,pars)
                               if verbose:pmagplotlib.drawFIGS(AZD)
                               if len(trmblock)>2:
                                   blab=field
                                   best=pars["specimen_int"]
                                   Bs,TRMs=[],[]
                                   for trec in trmblock:
                                       Bs.append(float(trec['treatment_dc_field']))
                                       TRMs.append(float(trec['measurement_magn_moment']))
                                   NLpars=nlt.NLtrm(Bs,TRMs,best,blab,0) # calculate best fit parameters through TRM acquisition data, and get new banc
                                   Mp,Bp=[],[]
                                   for k in  range(int(max(Bs)*1e6)):
                                       Bp.append(float(k)*1e-6)
                                       npred=nlt.TRM(Bp[-1],NLpars['xopt'][0],NLpars['xopt'][1]) # predicted NRM for this field
                                       Mp.append(npred)
                                   pmagplotlib.plotTRM(AZD['TRM'],Bs,TRMs,Bp,Mp,NLpars,trec['magic_experiment_name'])
                                   PmagSpecRec['specimen_int']=NLpars['banc'] 
                                   if verbose:
                                       print 'Banc= ',float(NLpars['banc'])*1e6
                                       pmagplotlib.drawFIGS(AZD)
                               mpars=pmag.domean(araiblock[1],start,end,'DE-BFL')
                               if verbose:
                                       print 'pTRM direction= ','%7.1f'%(mpars['specimen_dec']),' %7.1f'%(mpars['specimen_inc']),' MAD:','%7.1f'%(mpars['specimen_mad'])
                               if AniSpec!="":
                                   CpTRM=pmag.Dir_anis_corr([mpars['specimen_dec'],mpars['specimen_inc']],AniSpec)
                                   AniSpecRec=pmag.doaniscorr(PmagSpecRec,AniSpec)
                                   if verbose:
                                       print 'Anisotropy corrected TRM direction= ','%7.1f'%(CpTRM[0]),' %7.1f'%(CpTRM[1])
                                       print 'Anisotropy corrected intensity= ',float(AniSpecRec['specimen_int'])*1e6
                           else:
                               print 'error on specimen ',s
                       except:
                         pass
                   if verbose and found==0: print  '    None found :(  ' 
               if spc!="":
                   if BEG!="": 
                       pars,errcode=pmag.PintPars(datablock,araiblock,zijdblock,BEG,END,accept)
                       pars['measurement_step_unit']="K"
                       pars["specimen_lab_field_dc"]=field
                       pars["specimen_int"]=-1*field*pars["specimen_b"]
                       pars["er_specimen_name"]=s
                       pars['specimen_grade']='' # ungraded
                       pmagplotlib.plotB(AZD,araiblock,zijdblock,pars)
                       if verbose:pmagplotlib.drawFIGS(AZD)
                       if len(trmblock)>2:
                           if inlt==0:
                               inlt=1
                           blab=field
                           best=pars["specimen_int"]
                           Bs,TRMs=[],[]
                           for trec in trmblock:
                               Bs.append(float(trec['treatment_dc_field']))
                               TRMs.append(float(trec['measurement_magn_moment']))
                           NLpars=nlt.NLtrm(Bs,TRMs,best,blab,0) # calculate best fit parameters through TRM acquisition data, and get new banc
    #
                           Mp,Bp=[],[]
                           for k in  range(int(max(Bs)*1e6)):
                               Bp.append(float(k)*1e-6)
                               npred=nlt.TRM(Bp[-1],NLpars['xopt'][0],NLpars['xopt'][1]) # predicted NRM for this field
                   files={}
                   for key in AZD.keys():
                       files[key]=s+'_'+key+fmt 
                   pmagplotlib.saveP(AZD,files,dpi=dpi)
                   sys.exit()
               if verbose:
                   ans='b'
                   while ans != "":
                       print """
               s[a]ve plot, set [b]ounds for calculation, [d]elete current interpretation, [p]revious, [s]ample, [q]uit:
               """
                       ans=raw_input('Return for next specimen \n')
                       if ans=="": 
                           specimen +=1
                       if ans=="d": 
                           save_redo(PriorRecs,inspec)
                           CurrRec=[]
                           pmagplotlib.plotAZ(AZD,araiblock,zijdblock,s,units[0])
                           if verbose:pmagplotlib.drawFIGS(AZD)
                       if ans=='a':
                           files={}
                           for key in AZD.keys():
                               files[key]="LO:_"+locname+'_SI:_'+PmagSpecRec['er_site_name']+'_SA:_'+PmagSpecRec['er_sample_name']+'_SP:_'+s+'_CO:_s_TY:_'+key+fmt
                           pmagplotlib.saveP(AZD,files)
                           ans=""
                       if ans=='q':
                           print "Good bye"
                           sys.exit()
                       if ans=='p':
                           specimen =specimen -1
                           backup = 1
                           ans=""
                       if ans=='s':
                           keepon=1
                           spec=raw_input('Enter desired specimen name (or first part there of): ')
                           while keepon==1:
                               try:
                                   specimen =sids.index(spec)
                                   keepon=0
                               except:
                                   tmplist=[]
                                   for qq in range(len(sids)):
                                       if spec in sids[qq]:tmplist.append(sids[qq])
                                   print specimen," not found, but this was: "
                                   print tmplist
                                   spec=raw_input('Select one or try again\n ')
                           ans=""
                       if  ans=='b':
                           if end==0 or end >=len(zijdblock):end=len(zijdblock)-1
                           GoOn=0
                           while GoOn==0:
                               answer=raw_input('Enter index of first point for calculation: ['+str(start)+']  ')
                               try:
                                   start=int(answer)
                                   answer=raw_input('Enter index  of last point for calculation: ['+str(end)+']  ')
                                   end=int(answer)
                                   if start >=0 and start <len(zijdblock)-2 and end >0 and end <len(zijdblock) or start>=end:
                                       GoOn=1
                                   else:
                                       print "Bad endpoints - try again! "
                                       start,end=0,len(zijdblock)
                               except ValueError:
                                   print "Bad endpoints - try again! "
                                   start,end=0,len(zijdblock)
                           s=sids[specimen] 
                           pars,errcode=pmag.PintPars(datablock,araiblock,zijdblock,start,end,accept)
                           pars['measurement_step_unit']="K"
                           pars["specimen_lab_field_dc"]=field
                           pars["specimen_int"]=-1*field*pars["specimen_b"]
                           pars["er_specimen_name"]=s
                           pars,kill=pmag.scoreit(pars,PmagSpecRec,accept,'',0)
                           PmagSpecRec['specimen_scat']=pars['specimen_scat']
                           PmagSpecRec['specimen_frac']='%5.3f'%(pars['specimen_frac'])
                           PmagSpecRec['specimen_gmax']='%5.3f'%(pars['specimen_gmax'])
                           PmagSpecRec["measurement_step_min"]='%8.3e' % (pars["measurement_step_min"])
                           PmagSpecRec["measurement_step_max"]='%8.3e' % (pars["measurement_step_max"])
                           PmagSpecRec["measurement_step_unit"]="K"
                           PmagSpecRec["specimen_int_n"]='%i'%(pars["specimen_int_n"])
                           PmagSpecRec["specimen_lab_field_dc"]='%8.3e'%(pars["specimen_lab_field_dc"])
                           PmagSpecRec["specimen_int"]='%9.4e '%(pars["specimen_int"])
                           PmagSpecRec["specimen_b"]='%5.3f '%(pars["specimen_b"])
                           PmagSpecRec["specimen_q"]='%5.1f '%(pars["specimen_q"])
                           PmagSpecRec["specimen_f"]='%5.3f '%(pars["specimen_f"])
                           PmagSpecRec["specimen_fvds"]='%5.3f'%(pars["specimen_fvds"])
                           PmagSpecRec["specimen_b_beta"]='%5.3f'%(pars["specimen_b_beta"])
                           PmagSpecRec["specimen_int_mad"]='%7.1f'%(pars["specimen_int_mad"])
                           PmagSpecRec["specimen_Z"]='%7.1f'%(pars["specimen_Z"])
                           PmagSpecRec["specimen_gamma"]='%7.1f'%(pars["specimen_gamma"])
                           PmagSpecRec["specimen_grade"]=pars["specimen_grade"]
                           if pars["method_codes"]!="":
                               tmpcodes=pars["method_codes"].split(":")
                               for t in tmpcodes:
                                   if t.strip() not in methcodes:methcodes.append(t.strip())
                           PmagSpecRec["specimen_dec"]='%7.1f'%(pars["specimen_dec"])
                           PmagSpecRec["specimen_inc"]='%7.1f'%(pars["specimen_inc"])
                           PmagSpecRec["specimen_tilt_correction"]='-1'
                           PmagSpecRec["specimen_direction_type"]='l'
                           PmagSpecRec["direction_type"]='l' # this is redundant, but helpful - won't be imported
                           PmagSpecRec["specimen_int_dang"]='%7.1f '%(pars["specimen_int_dang"])
                           PmagSpecRec["specimen_drats"]='%7.1f '%(pars["specimen_drats"])
                           PmagSpecRec["specimen_drat"]='%7.1f '%(pars["specimen_drat"])
                           PmagSpecRec["specimen_int_ptrm_n"]='%i '%(pars["specimen_int_ptrm_n"])
                           PmagSpecRec["specimen_rsc"]='%6.4f '%(pars["specimen_rsc"])
                           PmagSpecRec["specimen_md"]='%i '%(int(pars["specimen_md"]))
                           if PmagSpecRec["specimen_md"]=='-1':PmagSpecRec["specimen_md"]=""
                           PmagSpecRec["specimen_b_sigma"]='%5.3f '%(pars["specimen_b_sigma"])
                           if "IE-TT" not in  methcodes:methcodes.append("IE-TT")
                           methods=""
                           for meth in methcodes:
                               methods=methods+meth+":"
                           PmagSpecRec["magic_method_codes"]=methods[:-1]
                           PmagSpecRec["specimen_description"]=comment
                           PmagSpecRec["magic_software_packages"]=version_num
                           pmagplotlib.plotAZ(AZD,araiblock,zijdblock,s,units[0])
                           pmagplotlib.plotB(AZD,araiblock,zijdblock,pars)
                           if verbose:pmagplotlib.drawFIGS(AZD)
                           if len(trmblock)>2:
                               blab=field
                               best=pars["specimen_int"]
                               Bs,TRMs=[],[]
                               for trec in trmblock:
                                   Bs.append(float(trec['treatment_dc_field']))
                                   TRMs.append(float(trec['measurement_magn_moment']))
                               NLpars=nlt.NLtrm(Bs,TRMs,best,blab,0) # calculate best fit parameters through TRM acquisition data, and get new banc
                               Mp,Bp=[],[]
                               for k in  range(int(max(Bs)*1e6)):
                                   Bp.append(float(k)*1e-6)
                                   npred=nlt.TRM(Bp[-1],NLpars['xopt'][0],NLpars['xopt'][1]) # predicted NRM for this field
                                   Mp.append(npred)
                               pmagplotlib.plotTRM(AZD['TRM'],Bs,TRMs,Bp,Mp,NLpars,trec['magic_experiment_name'])
                               if verbose:
                                   print 'Non-linear TRM corrected intensity= ',float(NLpars['banc'])*1e6
                           if verbose:pmagplotlib.drawFIGS(AZD)
                           pars["specimen_lab_field_dc"]=field
                           pars["specimen_int"]=-1*field*pars["specimen_b"]
                           pars,kill=pmag.scoreit(pars,PmagSpecRec,accept,'',verbose)
                           saveit=raw_input("Save this interpretation? [y]/n \n")
                           if saveit!='n':
                               PriorRecs.append(PmagSpecRec) # put back an interpretation
                               specimen+=1
                               save_redo(PriorRecs,inspec)
                           ans=""
               elif plots==1:
                   specimen+=1
                   if fmt != ".pmag":
                       files={}
                       for key in AZD.keys():
                           files[key]="LO:_"+locname+'_SI:_'+PmagSpecRec['er_site_name']+'_SA:_'+PmagSpecRec['er_sample_name']+'_SP:_'+s+'_CO:_s_TY:_'+key+'_'+fmt
                       if pmagplotlib.isServer:
                           black     = '#000000'
                           purple    = '#800080'
                           titles={}
                           titles['deremag']='DeReMag Plot'
                           titles['zijd']='Zijderveld Plot'
                           titles['arai']='Arai Plot'
                           AZD = pmagplotlib.addBorders(AZD,titles,black,purple)
                       pmagplotlib.saveP(AZD,files,dpi=dpi)
    #                   pmagplotlib.combineFigs(s,files,3)
                   else:  # save in pmag format 
                       script="grep "+s+" output.mag | thellier -mfsi"
                       script=script+' %8.4e'%(field)
                       min='%i'%((pars["measurement_step_min"]-273))
                       Max='%i'%((pars["measurement_step_max"]-273))
                       script=script+" "+min+" "+Max
                       script=script+" |plotxy;cat mypost >>thellier.ps\n"
                       pltf.write(script)
                       pmag.domagicmag(outf,MeasRecs)
        if len(CurrRec)>0:
            for rec in CurrRec:
                PriorRecs.append(rec)
        CurrRec=[]
    if plots!=1 and verbose:
        ans=raw_input(" Save last plot? 1/[0] ")
        if ans=="1":
            if fmt != ".pmag":
                files={}
                for key in AZD.keys():
                    files[key]=s+'_'+key+fmt
                pmagplotlib.saveP(AZD,files,dpi=dpi)
        else:
            print "\n Good bye\n"
            sys.exit()
        if len(CurrRec)>0:PriorRecs.append(CurrRec) # put back an interpretation
        if len(PriorRecs)>0:
            save_redo(PriorRecs,inspec)
            print 'Updated interpretations saved in ',inspec
    if verbose:
        print "Good bye"
Ejemplo n.º 5
0
def main():
    """
    NAME
        customize_criteria.py

    DESCRIPTION
        Allows user to specify acceptance criteria, saves them in pmag_criteria.txt

    SYNTAX
        customize_criteria.py [-h][command line options]

    OPTIONS
        -h prints help message and quits
        -f IFILE, reads in existing criteria
        -F OFILE, writes to pmag_criteria format file

    DEFAULTS
         IFILE: pmag_criteria.txt
         OFILE: pmag_criteria.txt
  
    OUTPUT
        creates a pmag_criteria.txt formatted output file
    """
    infile,critout="","pmag_criteria.txt"
# parse command line options
    if  '-h' in sys.argv:
        print main.__doc__
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        infile=sys.argv[ind+1]
        crit_data,file_type=pmag.magic_read(infile)
        if file_type!='pmag_criteria':
            print 'bad input file'
            print main.__doc__
            sys.exit()
        print "Acceptance criteria read in from ", infile
    if '-F' in sys.argv:
        ind=sys.argv.index('-F')
        critout=sys.argv[ind+1]
    Dcrit,Icrit,nocrit=0,0,0
    custom='1'
    crit=raw_input(" [0] Use no acceptance criteria?\n [1] Use default criteria\n [2] customize criteria \n ")
    if crit=='0':
        print 'Very very loose criteria saved in ',critout
        crit_data=pmag.default_criteria(1)
        pmag.magic_write(critout,crit_data,'pmag_criteria')
        sys.exit()
    crit_data=pmag.default_criteria(0)
    if crit=='1':
        print 'Default criteria saved in ',critout
        pmag.magic_write(critout,crit_data,'pmag_criteria')
        sys.exit()
    CritRec=crit_data[0]
    crit_keys=CritRec.keys()
    crit_keys.sort()
    print "Enter new threshold value.\n Return to keep default.\n Leave blank to not use as a criterion\n "
    for key in crit_keys:
        if key!='pmag_criteria_code' and key!='er_citation_names' and key!='criteria_definition' and CritRec[key]!="":
            print key, CritRec[key]
            new=raw_input('new value: ')
            if new != "": CritRec[key]=(new)
    pmag.magic_write(critout,[CritRec],'pmag_criteria')
    print "Criteria saved in pmag_criteria.txt"
Ejemplo n.º 6
0
def main():
    """
    NAME
        thellier_magic_redo.py

    DESCRIPTION
        Calculates paleointensity parameters for thellier-thellier type data using bounds
        stored in the "redo" file

    SYNTAX
        thellier_magic_redo [command line options]

    OPTIONS
        -h prints help message
        -usr USER:   identify user, default is ""
        -fcr CRIT, set criteria for grading
        -f IN: specify input file, default is magic_measurements.txt
        -fre REDO: specify redo file, default is "thellier_redo"
        -F OUT: specify output file, default is thellier_specimens.txt
        -leg:  attaches "Recalculated from original measurements; supercedes published results. " to comment field
        -CR PERC TYPE: apply a blanket cooling rate correction if none supplied in the er_samples.txt file
            PERC should be a percentage of original (say reduce to 90%)
            TYPE should be one of the following:
               EG (for educated guess); PS (based on pilots); TRM (based on comparison of two TRMs)
        -ANI:  perform anisotropy correction
        -fsa SAMPFILE: er_samples.txt file with cooling rate correction information, default is NO CORRECTION
        -Fcr  CRout: specify pmag_specimen format file for cooling rate corrected data
        -fan ANIFILE: specify rmag_anisotropy format file, default is rmag_anisotropy.txt
        -Fac  ACout: specify pmag_specimen format file for anisotropy corrected data
                 default is AC_specimens.txt
        -fnl NLTFILE: specify magic_measurments format file, default is magic_measurements.txt
        -Fnl NLTout: specify pmag_specimen format file for non-linear trm corrected data
                 default is NLT_specimens.txt
        -z use z component differenences for pTRM calculation

    INPUT
        a thellier_redo file is Specimen_name Tmin Tmax (where Tmin and Tmax are in Centigrade)
    """
    dir_path='.'
    critout=""
    version_num=pmag.get_version()
    field,first_save=-1,1
    spec,recnum,start,end=0,0,0,0
    crfrac=0
    NltRecs,PmagSpecs,AniSpecRecs,NltSpecRecs,CRSpecs=[],[],[],[],[]
    meas_file,pmag_file,mk_file="magic_measurements.txt","thellier_specimens.txt","thellier_redo"
    anis_file="rmag_anisotropy.txt"
    anisout,nltout="AC_specimens.txt","NLT_specimens.txt"
    crout="CR_specimens.txt"
    nlt_file=""
    samp_file=""
    comment,user="","unknown"
    anis,nltrm=0,0
    jackknife=0 # maybe in future can do jackknife
    args=sys.argv
    Zdiff=0
    if '-WD' in args:
        ind=args.index('-WD')
        dir_path=args[ind+1]
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    if "-usr" in args:
        ind=args.index("-usr")
        user=sys.argv[ind+1]
    if "-leg" in args: comment="Recalculated from original measurements; supercedes published results. "
    cool=0
    if "-CR" in args:
        cool=1
        ind=args.index("-CR")
        crfrac=.01*float(sys.argv[ind+1])
        crtype='DA-CR-'+sys.argv[ind+2]
    if "-Fcr" in args:
        ind=args.index("-Fcr")
        crout=sys.argv[ind+1]
    if "-f" in args:
        ind=args.index("-f")
        meas_file=sys.argv[ind+1]
    if "-F" in args:
        ind=args.index("-F")
        pmag_file=sys.argv[ind+1]
    if "-fre" in args:
        ind=args.index("-fre")
        mk_file=args[ind+1]
    if "-fsa" in args:
        ind=args.index("-fsa")
        samp_file=dir_path+'/'+args[ind+1]
        Samps,file_type=pmag.magic_read(samp_file)
        SampCRs=pmag.get_dictitem(Samps,'cooling_rate_corr','','F') # get samples cooling rate corrections
        cool=1
        if file_type!='er_samples':
            print('not a valid er_samples.txt file')
            sys.exit()
    #
    #
    if "-ANI" in args:
        anis=1
        ind=args.index("-ANI")
        if "-Fac" in args:
            ind=args.index("-Fac")
            anisout=args[ind+1]
        if "-fan" in args:
            ind=args.index("-fan")
            anis_file=args[ind+1]
    #
    if "-NLT" in args:
        if "-Fnl" in args:
            ind=args.index("-Fnl")
            nltout=args[ind+1]
        if "-fnl" in args:
            ind=args.index("-fnl")
            nlt_file=args[ind+1]
    if "-z" in args: Zdiff=1
    if '-fcr' in sys.argv:
        ind=args.index("-fcr")
        critout=sys.argv[ind+1]
#
#  start reading in data:
#
    meas_file=dir_path+"/"+meas_file
    mk_file=dir_path+"/"+mk_file
    accept=pmag.default_criteria(1)[0] # set criteria to none
    if critout!="":
        critout=dir_path+"/"+critout
        crit_data,file_type=pmag.magic_read(critout)
        if file_type!='pmag_criteria':
            print('bad pmag_criteria file, using no acceptance criteria')
        print("Acceptance criteria read in from ", critout)
        for critrec in crit_data:
            if 'sample_int_sigma_uT' in list(critrec.keys()): # accommodate Shaar's new criterion
                    critrec['sample_int_sigma']='%10.3e'%(eval(critrec['sample_int_sigma_uT'])*1e-6)
            for key in list(critrec.keys()):
                if key not in list(accept.keys()) and critrec[key]!='':
                    accept[key]=critrec[key]
    meas_data,file_type=pmag.magic_read(meas_file)
    if file_type != 'magic_measurements':
        print(file_type)
        print(file_type,"This is not a valid magic_measurements file ")
        sys.exit()
    try:
        mk_f=open(mk_file,'r')
    except:
        print("Bad redo file")
        sys.exit()
    mkspec=[]
    speclist=[]
    for line in mk_f.readlines():
        tmp=line.split()
        mkspec.append(tmp)
        speclist.append(tmp[0])
    if anis==1:
        anis_file=dir_path+"/"+anis_file
        anis_data,file_type=pmag.magic_read(anis_file)
        if file_type != 'rmag_anisotropy':
            print(file_type)
            print(file_type,"This is not a valid rmag_anisotropy file ")
            sys.exit()
    if nlt_file=="":
        nlt_data=pmag.get_dictitem(meas_data,'magic_method_codes','LP-TRM','has')  # look for trm acquisition data in the meas_data file
    else:
        nlt_file=dir_path+"/"+nlt_file
        nlt_data,file_type=pmag.magic_read(nlt_file)
    if len(nlt_data)>0:
        nltrm=1
#
# sort the specimen names and step through one by one
#
    sids=pmag.get_specs(meas_data)
#
    print('Processing ',len(speclist),' specimens - please wait ')
    while spec < len(speclist):
        s=speclist[spec]
        recnum=0
        datablock=[]
        PmagSpecRec={}
        PmagSpecRec["er_analyst_mail_names"]=user
        PmagSpecRec["er_citation_names"]="This study"
        PmagSpecRec["magic_software_packages"]=version_num
        methcodes,inst_code=[],""
    #
    # find the data from the meas_data file for this specimen
    #
        datablock=pmag.get_dictitem(meas_data,'er_specimen_name',s,'T')
        datablock=pmag.get_dictitem(datablock,'magic_method_codes','LP-PI-TRM','has') #pick out the thellier experiment data
        if len(datablock)>0:
            for rec in datablock:
                if "magic_instrument_codes" not in list(rec.keys()): rec["magic_instrument_codes"]="unknown"
    #
    #  collect info for the PmagSpecRec dictionary
    #
            rec=datablock[0]
            PmagSpecRec["er_specimen_name"]=s
            PmagSpecRec["er_sample_name"]=rec["er_sample_name"]
            PmagSpecRec["er_site_name"]=rec["er_site_name"]
            PmagSpecRec["er_location_name"]=rec["er_location_name"]
            PmagSpecRec["measurement_step_unit"]="K"
            PmagSpecRec["specimen_correction"]='u'
            if "er_expedition_name" in list(rec.keys()):PmagSpecRec["er_expedition_name"]=rec["er_expedition_name"]
            if "magic_instrument_codes" not in list(rec.keys()):
                PmagSpecRec["magic_instrument_codes"]="unknown"
            else:
                PmagSpecRec["magic_instrument_codes"]=rec["magic_instrument_codes"]
            if "magic_experiment_name" not in list(rec.keys()):
                rec["magic_experiment_name"]=""
            else:
                PmagSpecRec["magic_experiment_names"]=rec["magic_experiment_name"]
            meths=rec["magic_experiment_name"].split(":")
            for meth in meths:
                if meth.strip() not in methcodes and "LP-" in meth:methcodes.append(meth.strip())
    #
    # sort out the data into first_Z, first_I, ptrm_check, ptrm_tail
    #
            araiblock,field=pmag.sortarai(datablock,s,Zdiff)
            first_Z=araiblock[0]
            first_I=araiblock[1]
            ptrm_check=araiblock[2]
            ptrm_tail=araiblock[3]
            if len(first_I)<3 or len(first_Z)<4:
                spec+=1
                print('skipping specimen ', s)
            else:
    #
    # get start, end
    #
                for redospec in mkspec:
                    if redospec[0]==s:
                        b,e=float(redospec[1]),float(redospec[2])
                        break
                if e > float(first_Z[-1][0]):e=float(first_Z[-1][0])
                for recnum in range(len(first_Z)):
                    if first_Z[recnum][0]==b:start=recnum
                    if first_Z[recnum][0]==e:end=recnum
                nsteps=end-start
                if nsteps>2:
                    zijdblock,units=pmag.find_dmag_rec(s,meas_data)
                    pars,errcode=pmag.PintPars(datablock,araiblock,zijdblock,start,end,accept)
                    if 'specimen_scat' in list(pars.keys()): PmagSpecRec['specimen_scat']=pars['specimen_scat']
                    if 'specimen_frac' in list(pars.keys()): PmagSpecRec['specimen_frac']='%5.3f'%(pars['specimen_frac'])
                    if 'specimen_gmax' in list(pars.keys()): PmagSpecRec['specimen_gmax']='%5.3f'%(pars['specimen_gmax'])
                    pars['measurement_step_unit']=units
                    pars["specimen_lab_field_dc"]=field
                    pars["specimen_int"]=-1*field*pars["specimen_b"]
                    PmagSpecRec["measurement_step_min"]='%8.3e' % (pars["measurement_step_min"])
                    PmagSpecRec["measurement_step_max"]='%8.3e' % (pars["measurement_step_max"])
                    PmagSpecRec["specimen_int_n"]='%i'%(pars["specimen_int_n"])
                    PmagSpecRec["specimen_lab_field_dc"]='%8.3e'%(pars["specimen_lab_field_dc"])
                    PmagSpecRec["specimen_int"]='%9.4e '%(pars["specimen_int"])
                    PmagSpecRec["specimen_b"]='%5.3f '%(pars["specimen_b"])
                    PmagSpecRec["specimen_q"]='%5.1f '%(pars["specimen_q"])
                    PmagSpecRec["specimen_f"]='%5.3f '%(pars["specimen_f"])
                    PmagSpecRec["specimen_fvds"]='%5.3f'%(pars["specimen_fvds"])
                    PmagSpecRec["specimen_b_beta"]='%5.3f'%(pars["specimen_b_beta"])
                    PmagSpecRec["specimen_int_mad"]='%7.1f'%(pars["specimen_int_mad"])
                    PmagSpecRec["specimen_gamma"]='%7.1f'%(pars["specimen_gamma"])
                    if pars["magic_method_codes"]!="" and pars["magic_method_codes"] not in methcodes: methcodes.append(pars["magic_method_codes"])
                    PmagSpecRec["specimen_dec"]='%7.1f'%(pars["specimen_dec"])
                    PmagSpecRec["specimen_inc"]='%7.1f'%(pars["specimen_inc"])
                    PmagSpecRec["specimen_tilt_correction"]='-1'
                    PmagSpecRec["specimen_direction_type"]='l'
                    PmagSpecRec["direction_type"]='l' # this is redudant, but helpful - won't be imported
                    PmagSpecRec["specimen_dang"]='%7.1f '%(pars["specimen_dang"])
                    PmagSpecRec["specimen_drats"]='%7.1f '%(pars["specimen_drats"])
                    PmagSpecRec["specimen_drat"]='%7.1f '%(pars["specimen_drat"])
                    PmagSpecRec["specimen_int_ptrm_n"]='%i '%(pars["specimen_int_ptrm_n"])
                    PmagSpecRec["specimen_rsc"]='%6.4f '%(pars["specimen_rsc"])
                    PmagSpecRec["specimen_md"]='%i '%(int(pars["specimen_md"]))
                    if PmagSpecRec["specimen_md"]=='-1':PmagSpecRec["specimen_md"]=""
                    PmagSpecRec["specimen_b_sigma"]='%5.3f '%(pars["specimen_b_sigma"])
                    if "IE-TT" not in  methcodes:methcodes.append("IE-TT")
                    methods=""
                    for meth in methcodes:
                        methods=methods+meth+":"
                    PmagSpecRec["magic_method_codes"]=methods.strip(':')
                    PmagSpecRec["magic_software_packages"]=version_num
                    PmagSpecRec["specimen_description"]=comment
                    if critout!="":
                        kill=pmag.grade(PmagSpecRec,accept,'specimen_int')
                        if len(kill)>0:
                            Grade='F' # fails
                        else:
                            Grade='A' # passes
                        PmagSpecRec["specimen_grade"]=Grade
                    else:
                        PmagSpecRec["specimen_grade"]="" # not graded
                    if nltrm==0 and anis==0 and cool!=0: # apply cooling rate correction
                        SCR=pmag.get_dictitem(SampCRs,'er_sample_name',PmagSpecRec['er_sample_name'],'T') # get this samples, cooling rate correction
                        CrSpecRec=pmag.cooling_rate(PmagSpecRec,SCR,crfrac,crtype)
                        if CrSpecRec['er_specimen_name']!='none':CrSpecs.append(CrSpecRec)
                    PmagSpecs.append(PmagSpecRec)
                    NltSpecRec=""
    #
    # check on non-linear TRM correction
    #
                    if nltrm==1:
    #
    # find the data from the nlt_data list for this specimen
    #
                        TRMs,Bs=[],[]
                        NltSpecRec=""
                        NltRecs=pmag.get_dictitem(nlt_data,'er_specimen_name',PmagSpecRec['er_specimen_name'],'has') # fish out all the NLT data for this specimen
                        if len(NltRecs) > 2:
                            for NltRec in NltRecs:
                                Bs.append(float(NltRec['treatment_dc_field']))
                                TRMs.append(float(NltRec['measurement_magn_moment']))
                            NLTpars=nlt.NLtrm(Bs,TRMs,float(PmagSpecRec['specimen_int']),float(PmagSpecRec['specimen_lab_field_dc']),0)
                            if NLTpars['banc']>0:
                                NltSpecRec={}
                                for key in list(PmagSpecRec.keys()):
                                    NltSpecRec[key]=PmagSpecRec[key]
                                NltSpecRec['specimen_int']='%9.4e'%(NLTpars['banc'])
                                NltSpecRec['magic_method_codes']=PmagSpecRec["magic_method_codes"]+":DA-NL"
                                NltSpecRec["specimen_correction"]='c'
                                NltSpecRec['specimen_grade']=PmagSpecRec['specimen_grade']
                                NltSpecRec["magic_software_packages"]=version_num
                                print(NltSpecRec['er_specimen_name'],  ' Banc= ',float(NLTpars['banc'])*1e6)
                                if anis==0 and cool!=0:
                                    SCR=pmag.get_dictitem(SampCRs,'er_sample_name',NltSpecRec['er_sample_name'],'T') # get this samples, cooling rate correction
                                    CrSpecRec=pmag.cooling_rate(NltSpecRec,SCR,crfrac,crtype)
                                    if CrSpecRec['er_specimen_name']!='none':CrSpecs.append(CrSpecRec)
                                NltSpecRecs.append(NltSpecRec)
    #
    # check on anisotropy correction
                        if anis==1:
                            if NltSpecRec!="":
                                Spc=NltSpecRec
                            else: # find uncorrected data
                                Spc=PmagSpecRec
                            AniSpecs=pmag.get_dictitem(anis_data,'er_specimen_name',PmagSpecRec['er_specimen_name'],'T')
                            if len(AniSpecs)>0:
                                    AniSpec=AniSpecs[0]
                                    AniSpecRec=pmag.doaniscorr(Spc,AniSpec)
                                    AniSpecRec['specimen_grade']=PmagSpecRec['specimen_grade']
                                    AniSpecRec["magic_instrument_codes"]=PmagSpecRec['magic_instrument_codes']
                                    AniSpecRec["specimen_correction"]='c'
                                    AniSpecRec["magic_software_packages"]=version_num
                                    if cool!=0:
                                        SCR=pmag.get_dictitem(SampCRs,'er_sample_name',AniSpecRec['er_sample_name'],'T') # get this samples, cooling rate correction
                                        CrSpecRec=pmag.cooling_rate(AniSpecRec,SCR,crfrac,crtype)
                                        if CrSpecRec['er_specimen_name']!='none':CrSpecs.append(CrSpecRec)
                                    AniSpecRecs.append(AniSpecRec)
                    elif anis==1:
                        AniSpecs=pmag.get_dictitem(anis_data,'er_specimen_name',PmagSpecRec['er_specimen_name'],'T')
                        if len(AniSpecs)>0:
                                AniSpec=AniSpecs[0]
                                AniSpecRec=pmag.doaniscorr(PmagSpecRec,AniSpec)
                                AniSpecRec['specimen_grade']=PmagSpecRec['specimen_grade']
                                AniSpecRec["magic_instrument_codes"]=PmagSpecRec["magic_instrument_codes"]
                                AniSpecRec["specimen_correction"]='c'
                                AniSpecRec["magic_software_packages"]=version_num
                                if crfrac!=0:
                                    CrSpecRec={}
                                    for key in list(AniSpecRec.keys()):CrSpecRec[key]=AniSpecRec[key]
                                    inten=frac*float(CrSpecRec['specimen_int'])
                                    CrSpecRec["specimen_int"]='%9.4e '%(inten) # adjust specimen intensity by cooling rate correction
                                    CrSpecRec['magic_method_codes'] = CrSpecRec['magic_method_codes']+':DA-CR-'+crtype
                                    CRSpecs.append(CrSpecRec)
                                AniSpecRecs.append(AniSpecRec)
                spec +=1
        else:
            print("skipping ",s)
            spec+=1
    pmag_file=dir_path+'/'+pmag_file
    pmag.magic_write(pmag_file,PmagSpecs,'pmag_specimens')
    print('uncorrected thellier data saved in: ',pmag_file)
    if anis==1 and len(AniSpecRecs)>0:
        anisout=dir_path+'/'+anisout
        pmag.magic_write(anisout,AniSpecRecs,'pmag_specimens')
        print('anisotropy corrected data saved in: ',anisout)
    if nltrm==1 and len(NltSpecRecs)>0:
        nltout=dir_path+'/'+nltout
        pmag.magic_write(nltout,NltSpecRecs,'pmag_specimens')
        print('non-linear TRM corrected data saved in: ',nltout)
    if crfrac!=0:
        crout=dir_path+'/'+crout
        pmag.magic_write(crout,CRSpecs,'pmag_specimens')
        print('cooling rate corrected data saved in: ',crout)
Ejemplo n.º 7
0
def main():
    """
    NAME
        thellier_magic.py

    DESCRIPTION
        plots Thellier-Thellier data in version 3.0 format
        Reads saved interpretations from a specimen formatted table, default: specimens.txt

    SYNTAX
        thellier_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f MEAS, set measurements input file, default is 'measurements.txt'
        -fsp PRIOR, set specimens.txt prior interpretations file, default is 'specimens.txt'
        -fcr CRIT, set criteria file for grading.  # not yet implemented
        -fmt [svg,png,jpg], format for images - default is svg
        -sav,  saves plots with out review (in format specified by -fmt key or default)
        -spc SPEC, plots single specimen SPEC, saves plot with specified format
            with optional -b bounds and quits
        -b BEG END: sets  bounds for calculation
           BEG: starting step number for slope calculation
           END: ending step number for slope calculation
        -z use only z component difference for pTRM calculation

    OUTPUT
        figures:
            ALL:  numbers refer to temperature steps in command line window
            1) Arai plot:  closed circles are zero-field first/infield
                           open circles are infield first/zero-field
                           triangles are pTRM checks
                           squares are pTRM tail checks
                           VDS is vector difference sum
                           diamonds are bounds for interpretation
            2) Zijderveld plot:  closed (open) symbols are X-Y (X-Z) planes
                                 X rotated to NRM direction
            3) (De/Re)Magnetization diagram:
                           circles are NRM remaining
                           squares are pTRM gained
            4) equal area projections:
               green triangles are pTRM gained direction
                           red (purple) circles are lower(upper) hemisphere of ZI step directions
                           blue (cyan) squares are lower(upper) hemisphere IZ step directions
            5) Optional:  TRM acquisition
            6) Optional: TDS normalization
        command line window:
            list is: temperature step numbers, temperatures (C), Dec, Inc, Int (units of measuements)
                     list of possible commands: type letter followed by return to select option
                     saving of plots creates image files with specimen, plot type as name
    """
    #
    #   initializations
    #
    version_num = pmag.get_version()
    verbose = pmagplotlib.verbose
    #
    # default acceptance criteria
    #
    accept = pmag.default_criteria(0)[0]  # set the default criteria
    #
    # parse command line options
    #
    plots, fmt, Zdiff = 0, 'svg', 0
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
    meas_file = pmag.get_named_arg_from_sys("-f",
                                            default_val="measurements.txt")
    spec_file = pmag.get_named_arg_from_sys("-fsp",
                                            default_val="specimens.txt")
    crit_file = pmag.get_named_arg_from_sys("-fcr", default_val="criteria.txt")
    spec_file = os.path.join(dir_path, spec_file)
    meas_file = os.path.join(dir_path, meas_file)
    crit_file = os.path.join(dir_path, crit_file)
    fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
    if '-sav' in sys.argv: plots, verbose = 1, 0
    if '-z' in sys.argv: Zdiff = 1
    specimen = pmag.get_named_arg_from_sys("-spc", default_val="")
    if '-b' in sys.argv:
        ind = sys.argv.index('-b')
        start = int(sys.argv[ind + 1])
        end = int(sys.argv[ind + 2])
    else:
        start, end = "", ""
    fnames = {
        'measurements': meas_file,
        'specimens': spec_file,
        'criteria': crit_file
    }
    contribution = nb.Contribution(
        dir_path,
        custom_filenames=fnames,
        read_tables=['measurements', 'specimens', 'criteria'])
    #
    #   import  prior interpretations  from specimen file
    #
    specimen_cols = [
        'analysts', 'aniso_ftest', 'aniso_ftest12', 'aniso_ftest23', 'aniso_s',
        'aniso_s_mean', 'aniso_s_n_measurements', 'aniso_s_sigma',
        'aniso_s_unit', 'aniso_tilt_correction', 'aniso_type', 'aniso_v1',
        'aniso_v2', 'aniso_v3', 'citations', 'description', 'dir_alpha95',
        'dir_comp', 'dir_dec', 'dir_inc', 'dir_mad_free', 'dir_n_measurements',
        'dir_tilt_correction', 'experiments', 'geologic_classes',
        'geologic_types', 'hyst_bc', 'hyst_bcr', 'hyst_mr_moment',
        'hyst_ms_moment', 'int_abs', 'int_b', 'int_b_beta', 'int_b_sigma',
        'int_corr', 'int_dang', 'int_drats', 'int_f', 'int_fvds', 'int_gamma',
        'int_mad_free', 'int_md', 'int_n_measurements', 'int_n_ptrm', 'int_q',
        'int_rsc', 'int_treat_dc_field', 'lithologies', 'meas_step_max',
        'meas_step_min', 'meas_step_unit', 'method_codes', 'sample',
        'software_packages', 'specimen'
    ]
    if 'specimens' in contribution.tables:
        spec_container = contribution.tables['specimens']
        prior_spec_data = spec_container.get_records_for_code(
            'LP-PI-TRM',
            strict_match=False)  # look up all prior intensity interpretations
    else:
        spec_container, prior_spec_data = None, []
    backup = 0
    #
    Mkeys = ['magn_moment', 'magn_volume', 'magn_mass']
    #
    #   create measurement dataframe
    #
    meas_container = contribution.tables['measurements']
    meas_data = meas_container.df
    #
    meas_data['method_codes'] = meas_data['method_codes'].str.replace(
        " ", "")  # get rid of nasty spaces
    meas_data = meas_data[meas_data['method_codes'].str.contains(
        'LP-PI-TRM|LP-TRM|LP-TRM-TD') ==
                          True]  # fish out zero field steps for plotting
    intensity_types = [
        col_name for col_name in meas_data.columns if col_name in Mkeys
    ]
    int_key = intensity_types[
        0]  # plot first intensity method found - normalized to initial value anyway - doesn't matter which used
    meas_data = meas_data[meas_data[int_key].notnull(
    )]  # get all the non-null intensity records of the same type
    if 'flag' not in meas_data.columns:
        meas_data['flag'] = 'g'  # set the default flag to good
    meas_data = meas_data[meas_data['flag'].str.contains('g') ==
                          True]  # only the 'good' measurements
    thel_data = meas_data[meas_data['method_codes'].str.contains('LP-PI-TRM')
                          == True]  # get all the Thellier data
    trm_data = meas_data[meas_data['method_codes'].str.contains('LP-TRM') ==
                         True]  # get all the TRM acquisition data
    td_data = meas_data[meas_data['method_codes'].str.contains('LP-TRM-TD') ==
                        True]  # get all the TD data
    anis_data = meas_data[meas_data['method_codes'].str.contains('LP-AN') ==
                          True]  # get all the anisotropy data
    #
    # get list of unique specimen names from measurement data
    #
    specimen_names = meas_data.specimen.unique(
    )  # this is a Series of all the specimen names
    specimen_names = specimen_names.tolist()  # turns it into a list
    specimen_names.sort()  # sorts by specimen name
    #
    # set up new DataFrame for this sessions specimen interpretations
    #
    spec_container = nb.MagicDataFrame(dtype='specimens',
                                       columns=specimen_cols)
    current_spec_data = spec_container.df  # this is for interpretations from this session
    if specimen == "":  # do all specimens
        k = 0
    else:
        k = specimen_names.index(specimen)  # just do this one
    # define figure numbers for arai, zijderveld and
    #   de-,re-magnetization diagrams
    AZD = {}
    AZD['deremag'], AZD['zijd'], AZD['arai'], AZD['eqarea'] = 1, 2, 3, 4
    pmagplotlib.plot_init(AZD['arai'], 5, 5)
    pmagplotlib.plot_init(AZD['zijd'], 5, 5)
    pmagplotlib.plot_init(AZD['deremag'], 5, 5)
    pmagplotlib.plot_init(AZD['eqarea'], 5, 5)
    if len(trm_data) > 0:
        AZD['TRM'] = 5
        pmagplotlib.plot_init(AZD['TRM'], 5, 5)
    if len(td_data) > 0:
        AZD['TDS'] = 6
        pmagplotlib.plot_init(AZD['TDS'], 5, 5)
    #
    while k < len(specimen_names):
        this_specimen = specimen_names[
            k]  # set the current specimen for plotting
        if verbose and this_specimen != "":
            print(this_specimen, k + 1, 'out of ', len(specimen_names))
        #
        #    set up datablocks
        #
        thelblock = thel_data[thel_data['specimen'].str.contains(this_specimen)
                              == True]  # fish out this specimen
        trmblock = trm_data[trm_data['specimen'].str.contains(this_specimen) ==
                            True]  # fish out this specimen
        tdsrecs = td_data[td_data['specimen'].str.contains(this_specimen) ==
                          True]  # fish out this specimen
        anisblock = anis_data[anis_data['specimen'].str.contains(this_specimen)
                              == True]  # fish out the anisotropy data
        prior_specimen_interpretations = prior_spec_data[
            prior_spec_data['specimen'].str.contains(
                this_specimen) == True]  # fish out prior interpretation
        #
        # sort data into types
        #
        araiblock, field = pmag.sortarai(thelblock,
                                         this_specimen,
                                         Zdiff,
                                         version=3)
        first_Z = araiblock[0]
        GammaChecks = araiblock[5]
        if len(first_Z) < 3:
            if backup == 0:
                k += 1
                if verbose:
                    print('skipping specimen - moving forward ', this_specimen)
            else:
                k -= 1
                if verbose:
                    print('skipping specimen - moving backward ',
                          this_specimen)
        else:
            backup = 0
            zijdblock, units = pmag.find_dmag_rec(this_specimen,
                                                  thelblock,
                                                  version=3)
            if start == "" and len(prior_specimen_interpretations) > 0:
                if verbose: print('Looking up saved interpretation....')
                #
                # get prior interpretation steps
                #
                beg_int = pd.to_numeric(prior_specimen_interpretations.
                                        meas_step_min.values).tolist()[0]
                end_int = pd.to_numeric(prior_specimen_interpretations.
                                        meas_step_max.values).tolist()[0]
            else:
                beg_int, end_int = "", ""
            recnum = 0
            if verbose: print("index step Dec   Inc  Int       Gamma")
            for plotrec in zijdblock:
                if plotrec[0] == beg_int:
                    start = recnum  # while we are at it, collect these bounds
                if plotrec[0] == end_int: end = recnum
                if verbose:
                    if GammaChecks != "":
                        gamma = ""
                        for g in GammaChecks:
                            if g[0] == plotrec[0] - 273:
                                gamma = g[1]
                                break
                    if gamma != "":
                        print('%i     %i %7.1f %7.1f %8.3e %7.1f' %
                              (recnum, plotrec[0] - 273, plotrec[1],
                               plotrec[2], plotrec[3], gamma))
                    else:
                        print('%i     %i %7.1f %7.1f %8.3e ' %
                              (recnum, plotrec[0] - 273, plotrec[1],
                               plotrec[2], plotrec[3]))
                recnum += 1
            for fig in list(AZD.keys()):
                pmagplotlib.clearFIG(AZD[fig])  # clear all figures
            pmagplotlib.plotAZ(AZD, araiblock, zijdblock, this_specimen,
                               units[0])
            if verbose: pmagplotlib.drawFIGS(AZD)
            pars, errcode = pmag.PintPars(thelblock,
                                          araiblock,
                                          zijdblock,
                                          start,
                                          end,
                                          accept,
                                          version=3)
            pars['measurement_step_unit'] = "K"
            pars['experiment_type'] = 'LP-PI-TRM'
            #
            # work on saving interpretations stuff later
            #
            if errcode != 1:  # no problem in PintPars
                pars["specimen_lab_field_dc"] = field
                pars["specimen_int"] = -1 * field * pars["specimen_b"]
                pars["er_specimen_name"] = this_specimen
                #pars,kill=pmag.scoreit(pars,this_specimen_interpretation,accept,'',verbose) # deal with this later
                pars["specimen_grade"] = 'None'
                pars['measurement_step_min'] = pars['meas_step_min']
                pars['measurement_step_max'] = pars['meas_step_max']
                if pars['measurement_step_unit'] == 'K':
                    outstr = "specimen     Tmin  Tmax  N  lab_field  B_anc  b  q  f(coe)  Fvds  beta  MAD  Dang  Drats  Nptrm  Grade  R  MD%  sigma  Gamma_max \n"
                    pars_out = (this_specimen, (pars["meas_step_min"] - 273),
                                (pars["meas_step_max"] -
                                 273), (pars["specimen_int_n"]),
                                1e6 * (pars["specimen_lab_field_dc"]),
                                1e6 * (pars["specimen_int"]),
                                pars["specimen_b"], pars["specimen_q"],
                                pars["specimen_f"], pars["specimen_fvds"],
                                pars["specimen_b_beta"], pars["int_mad_free"],
                                pars["int_dang"], pars["int_drats"],
                                pars["int_n_ptrm"], pars["specimen_grade"],
                                np.sqrt(pars["specimen_rsc"]),
                                int(pars["int_md"]), pars["specimen_b_sigma"],
                                pars['specimen_gamma'])
                    outstring = '%s %4.0f %4.0f %i %4.1f %4.1f %5.3f %5.1f %5.3f %5.3f %5.3f  %7.1f %7.1f %7.1f %s %s %6.3f %i %5.3f %7.1f' % pars_out + '\n'
                elif pars['measurement_step_unit'] == 'J':
                    outstr = "specimen     Wmin  Wmax  N  lab_field  B_anc  b  q  f(coe)  Fvds  beta  MAD  Dang  Drats  Nptrm  Grade  R  MD%  sigma  ThetaMax DeltaMax GammaMax\n"
                    pars_out = (
                        this_specimen, (pars["meas_step_min"]),
                        (pars["meas_step_max"]), (pars["specimen_int_n"]),
                        1e6 * (pars["specimen_lab_field_dc"]),
                        1e6 * (pars["specimen_int"]), pars["specimen_b"],
                        pars["specimen_q"], pars["specimen_f"],
                        pars["specimen_fvds"], pars["specimen_b_beta"],
                        pars["specimen_int_mad"], pars["specimen_int_dang"],
                        pars["specimen_drats"], pars["specimen_int_ptrm_n"],
                        pars["specimen_grade"], np.sqrt(pars["specimen_rsc"]),
                        int(pars["specimen_md"]), pars["specimen_b_sigma"],
                        pars["specimen_theta"], pars["specimen_delta"],
                        pars["specimen_gamma"])
                    outstring = '%s %4.0f %4.0f %i %4.1f %4.1f %5.3f %5.1f %5.3f %5.3f %5.3f  %7.1f %7.1f %7.1f %s %s %6.3f %i %5.3f %7.1f %7.1f %7.1f' % pars_out + '\n'
                print(outstr)
                print(outstring)
                pmagplotlib.plotB(AZD, araiblock, zijdblock, pars)
                mpars = pmag.domean(araiblock[1], start, end, 'DE-BFL')
                if verbose:
                    pmagplotlib.drawFIGS(AZD)
                    print('pTRM direction= ',
                          '%7.1f' % (mpars['specimen_dec']),
                          ' %7.1f' % (mpars['specimen_inc']), ' MAD:',
                          '%7.1f' % (mpars['specimen_mad']))
            if len(anisblock) > 0:  # this specimen has anisotropy data
                if verbose:
                    print('Found anisotropy record... but ignoring for now ')
            if plots == 1:
                if fmt != "pmag":
                    files = {}
                    for key in list(AZD.keys()):
                        files[
                            key] = 'SP:_' + this_specimen + '_TY:_' + key + '_' + '.' + fmt
                    if pmagplotlib.isServer:
                        black = '#000000'
                        purple = '#800080'
                        titles = {}
                        titles['deremag'] = 'DeReMag Plot'
                        titles['zijd'] = 'Zijderveld Plot'
                        titles['arai'] = 'Arai Plot'
                        titles['TRM'] = 'TRM Acquisition data'
                        AZD = pmagplotlib.addBorders(AZD, titles, black,
                                                     purple)
                    pmagplotlib.saveP(AZD, files)
                else:  # save in pmag format
                    print('pmag format no longer supported')
                    #script="grep "+this_specimen+" output.mag | thellier -mfsi"
                    #script=script+' %8.4e'%(field)
                    #min='%i'%((pars["measurement_step_min"]-273))
                    #Max='%i'%((pars["measurement_step_max"]-273))
                    #script=script+" "+min+" "+Max
                    #script=script+" |plotxy;cat mypost >>thellier.ps\n"
                    #pltf.write(script)
                    #pmag.domagicmag(outf,MeasRecs)
            if specimen != "": sys.exit()  # syonara
            if verbose:
                ans = input('Return for next specimen, q to quit:  ')
                if ans == 'q': sys.exit()
            k += 1  # moving on
Ejemplo n.º 8
0
def main():
    """
    NAME
	specimens_results_magic.py

    DESCRIPTION
	combines pmag_specimens.txt file with age, location, acceptance criteria and
	outputs pmag_results table along with other MagIC tables necessary for uploading to the database

    SYNTAX
	specimens_results_magic.py [command line options]

    OPTIONS
	-h prints help message and quits
	-usr USER:   identify user, default is ""
	-f: specimen input magic_measurements format file, default is "magic_measurements.txt"
	-fsp: specimen input pmag_specimens format file, default is "pmag_specimens.txt"
	-fsm: sample input er_samples format file, default is "er_samples.txt"
	-fsi: specimen input er_sites format file, default is "er_sites.txt"
	-fla: specify a file with paleolatitudes for calculating VADMs, default is not to calculate VADMS
               format is:  site_name paleolatitude (space delimited file)
	-fa AGES: specify er_ages format file with age information
	-crd [s,g,t,b]:   specify coordinate system
	    (s, specimen, g geographic, t, tilt corrected, b, geographic and tilt corrected)
	    Default is to assume geographic
	    NB: only the tilt corrected data will appear on the results table, if both g and t are selected.
        -cor [AC:CR:NL]: colon delimited list of required data adjustments for all specimens 
            included in intensity calculations (anisotropy, cooling rate, non-linear TRM)
            unless specified, corrections will not be applied
        -pri [TRM:ARM] colon delimited list of priorities for anisotropy correction (-cor must also be set to include AC). default is TRM, then ARM 
	-age MIN MAX UNITS:   specify age boundaries and units
	-exc:  use exiting selection criteria (in pmag_criteria.txt file), default is default criteria
	-C: no acceptance criteria
	-aD:  average directions per sample, default is NOT
	-aI:  average multiple specimen intensities per sample, default is by site 
	-aC:  average all components together, default is NOT
	-pol:  calculate polarity averages
	-sam:  save sample level vgps and v[a]dms, default is by site
	-xSi:  skip the site level intensity calculation
	-p: plot directions and look at intensities by site, default is NOT
	    -fmt: specify output for saved images, default is svg (only if -p set)
	-lat: use present latitude for calculating VADMs, default is not to calculate VADMs
	-xD: skip directions
	-xI: skip intensities
    OUPUT
	writes pmag_samples, pmag_sites, pmag_results tables
    """
    # set defaults
    Comps = []  # list of components
    version_num = pmag.get_version()
    args = sys.argv
    DefaultAge = ["none"]
    skipdirs, coord, excrit, custom, vgps, average, Iaverage, plotsites, opt = 1, 0, 0, 0, 0, 0, 0, 0, 0
    get_model_lat = 0  # this skips VADM calculation altogether, when get_model_lat=1, uses present day
    fmt = 'svg'
    dir_path = "."
    model_lat_file = ""
    Caverage = 0
    infile = 'pmag_specimens.txt'
    measfile = "magic_measurements.txt"
    sampfile = "er_samples.txt"
    sitefile = "er_sites.txt"
    agefile = "er_ages.txt"
    specout = "er_specimens.txt"
    sampout = "pmag_samples.txt"
    siteout = "pmag_sites.txt"
    resout = "pmag_results.txt"
    critout = "pmag_criteria.txt"
    instout = "magic_instruments.txt"
    sigcutoff, OBJ = "", ""
    noDir, noInt = 0, 0
    polarity = 0
    coords = ['0']
    Dcrit, Icrit, nocrit = 0, 0, 0
    corrections = []
    nocorrection = ['DA-NL', 'DA-AC', 'DA-CR']
    priorities = ['DA-AC-ARM',
                  'DA-AC-TRM']  # priorities for anisotropy correction
    # get command line stuff
    if "-h" in args:
        print main.__doc__
        sys.exit()
    if '-WD' in args:
        ind = args.index("-WD")
        dir_path = args[ind + 1]
    if '-cor' in args:
        ind = args.index('-cor')
        cors = args[ind + 1].split(':')  # list of required data adjustments
        for cor in cors:
            nocorrection.remove('DA-' + cor)
            corrections.append('DA-' + cor)
    if '-pri' in args:
        ind = args.index('-pri')
        priorities = args[ind + 1].split(
            ':')  # list of required data adjustments
        for p in priorities:
            p = 'DA-AC-' + p
    if '-f' in args:
        ind = args.index("-f")
        measfile = args[ind + 1]
    if '-fsp' in args:
        ind = args.index("-fsp")
        infile = args[ind + 1]
    if '-fsi' in args:
        ind = args.index("-fsi")
        sitefile = args[ind + 1]
    if "-crd" in args:
        ind = args.index("-crd")
        coord = args[ind + 1]
        if coord == 's': coords = ['-1']
        if coord == 'g': coords = ['0']
        if coord == 't': coords = ['100']
        if coord == 'b': coords = ['0', '100']
    if "-usr" in args:
        ind = args.index("-usr")
        user = sys.argv[ind + 1]
    else:
        user = ""
    if "-C" in args: Dcrit, Icrit, nocrit = 1, 1, 1  # no selection criteria
    if "-sam" in args: vgps = 1  # save sample level VGPS/VADMs
    if "-xSi" in args:
        nositeints = 1  # skip site level intensity
    else:
        nositeints = 0
    if "-age" in args:
        ind = args.index("-age")
        DefaultAge[0] = args[ind + 1]
        DefaultAge.append(args[ind + 2])
        DefaultAge.append(args[ind + 3])
    Daverage, Iaverage, Caverage = 0, 0, 0
    if "-aD" in args: Daverage = 1  # average by sample directions
    if "-aI" in args: Iaverage = 1  # average by sample intensities
    if "-aC" in args:
        Caverage = 1  # average all components together ???  why???
    if "-pol" in args: polarity = 1  # calculate averages by polarity
    if '-xD' in args: noDir = 1
    if '-xI' in args:
        noInt = 1
    elif "-fla" in args:
        if '-lat' in args:
            print "you should set a paleolatitude file OR use present day lat - not both"
            sys.exit()
        ind = args.index("-fla")
        model_lat_file = dir_path + '/' + args[ind + 1]
        get_model_lat = 2
        mlat = open(model_lat_file, 'rU')
        ModelLats = []
        for line in mlat.readlines():
            ModelLat = {}
            tmp = line.split()
            ModelLat["er_site_name"] = tmp[0]
            ModelLat["site_model_lat"] = tmp[1]
            ModelLat["er_sample_name"] = tmp[0]
            ModelLat["sample_lat"] = tmp[1]
            ModelLats.append(ModelLat)
        get_model_lat = 2
    elif '-lat' in args:
        get_model_lat = 1
    if "-p" in args:
        plotsites = 1
        if "-fmt" in args:
            ind = args.index("-fmt")
            fmt = args[ind + 1]
        if noDir == 0:  # plot by site - set up plot window
            import pmagplotlib
            EQ = {}
            EQ['eqarea'] = 1
            pmagplotlib.plot_init(
                EQ['eqarea'], 5, 5)  # define figure 1 as equal area projection
            pmagplotlib.plotNET(
                EQ['eqarea']
            )  # I don't know why this has to be here, but otherwise the first plot never plots...
            pmagplotlib.drawFIGS(EQ)
    if '-WD' in args:
        infile = dir_path + '/' + infile
        measfile = dir_path + '/' + measfile
        instout = dir_path + '/' + instout
        sampfile = dir_path + '/' + sampfile
        sitefile = dir_path + '/' + sitefile
        agefile = dir_path + '/' + agefile
        specout = dir_path + '/' + specout
        sampout = dir_path + '/' + sampout
        siteout = dir_path + '/' + siteout
        resout = dir_path + '/' + resout
        critout = dir_path + '/' + critout
    if "-exc" in args:  # use existing pmag_criteria file
        if "-C" in args:
            print 'you can not use both existing and no criteria - choose either -exc OR -C OR neither (for default)'
            sys.exit()
        crit_data, file_type = pmag.magic_read(critout)
        print "Acceptance criteria read in from ", critout
    else:  # use default criteria (if nocrit set, then get really loose criteria as default)
        crit_data = pmag.default_criteria(nocrit)
        if nocrit == 0:
            print "Acceptance criteria are defaults"
        else:
            print "No acceptance criteria used "
    accept = {}
    for critrec in crit_data:
        for key in critrec.keys():
            # need to migrate specimen_dang to specimen_int_dang for intensity data using old format
            if 'IE-SPEC' in critrec.keys() and 'specimen_dang' in critrec.keys(
            ) and 'specimen_int_dang' not in critrec.keys():
                critrec['specimen_int_dang'] = critrec['specimen_dang']
                del critrec['specimen_dang']
# need to get rid of ron shaars sample_int_sigma_uT
            if 'sample_int_sigma_uT' in critrec.keys():
                critrec['sample_int_sigma'] = '%10.3e' % (
                    eval(critrec['sample_int_sigma_uT']) * 1e-6)
            if key not in accept.keys() and critrec[key] != '':
                accept[key] = critrec[key]
    #
    #
    if "-exc" not in args and "-C" not in args:
        print "args", args
        pmag.magic_write(critout, [accept], 'pmag_criteria')
        print "\n Pmag Criteria stored in ", critout, '\n'
#
# now we're done slow dancing
#
    SiteNFO, file_type = pmag.magic_read(
        sitefile)  # read in site data - has the lats and lons
    SampNFO, file_type = pmag.magic_read(
        sampfile)  # read in site data - has the lats and lons
    height_nfo = pmag.get_dictitem(SiteNFO, 'site_height', '',
                                   'F')  # find all the sites with height info.
    if agefile != "":
        AgeNFO, file_type = pmag.magic_read(
            agefile)  # read in the age information
    Data, file_type = pmag.magic_read(
        infile)  # read in specimen interpretations
    IntData = pmag.get_dictitem(Data, 'specimen_int', '',
                                'F')  # retrieve specimens with intensity data
    comment, orient = "", []
    samples, sites = [], []
    for rec in Data:  # run through the data filling in missing keys and finding all components, coordinates available
        # fill in missing fields, collect unique sample and site names
        if 'er_sample_name' not in rec.keys():
            rec['er_sample_name'] = ""
        elif rec['er_sample_name'] not in samples:
            samples.append(rec['er_sample_name'])
        if 'er_site_name' not in rec.keys():
            rec['er_site_name'] = ""
        elif rec['er_site_name'] not in sites:
            sites.append(rec['er_site_name'])
        if 'specimen_int' not in rec.keys(): rec['specimen_int'] = ''
        if 'specimen_comp_name' not in rec.keys(
        ) or rec['specimen_comp_name'] == "":
            rec['specimen_comp_name'] = 'A'
        if rec['specimen_comp_name'] not in Comps:
            Comps.append(rec['specimen_comp_name'])
        rec['specimen_tilt_correction'] = rec[
            'specimen_tilt_correction'].strip('\n')
        if "specimen_tilt_correction" not in rec.keys():
            rec["specimen_tilt_correction"] = "-1"  # assume sample coordinates
        if rec["specimen_tilt_correction"] not in orient:
            orient.append(rec["specimen_tilt_correction"]
                          )  # collect available coordinate systems
        if "specimen_direction_type" not in rec.keys():
            rec["specimen_direction_type"] = 'l'  # assume direction is line - not plane
        if "specimen_dec" not in rec.keys():
            rec["specimen_direction_type"] = ''  # if no declination, set direction type to blank
        if "specimen_n" not in rec.keys(): rec["specimen_n"] = ''  # put in n
        if "specimen_alpha95" not in rec.keys():
            rec["specimen_alpha95"] = ''  # put in alpha95
        if "magic_method_codes" not in rec.keys():
            rec["magic_method_codes"] = ''
    #
    # start parsing data into SpecDirs, SpecPlanes, SpecInts
    SpecInts, SpecDirs, SpecPlanes = [], [], []
    samples.sort()  # get sorted list of samples and sites
    sites.sort()
    if noInt == 0:  # don't skip intensities
        IntData = pmag.get_dictitem(
            Data, 'specimen_int', '',
            'F')  # retrieve specimens with intensity data
        if nocrit == 0:  # use selection criteria
            for rec in IntData:  # do selection criteria
                kill = pmag.grade(rec, accept, 'specimen_int')
                if len(kill) == 0:
                    SpecInts.append(
                        rec
                    )  # intensity record to be included in sample, site calculations
        else:
            SpecInts = IntData[:]  # take everything - no selection criteria
# check for required data adjustments
        if len(corrections) > 0 and len(SpecInts) > 0:
            for cor in corrections:
                SpecInts = pmag.get_dictitem(
                    SpecInts, 'magic_method_codes', cor,
                    'has')  # only take specimens with the required corrections
        if len(nocorrection) > 0 and len(SpecInts) > 0:
            for cor in nocorrection:
                SpecInts = pmag.get_dictitem(
                    SpecInts, 'magic_method_codes', cor, 'not'
                )  # exclude the corrections not specified for inclusion
# take top priority specimen of its name in remaining specimens (only one per customer)
        PrioritySpecInts = []
        specimens = pmag.get_specs(SpecInts)  # get list of uniq specimen names
        for spec in specimens:
            ThisSpecRecs = pmag.get_dictitem(
                SpecInts, 'er_specimen_name', spec,
                'T')  # all the records for this specimen
            if len(ThisSpecRecs) == 1:
                PrioritySpecInts.append(ThisSpecRecs[0])
            elif len(ThisSpecRecs) > 1:  # more than one
                prec = []
                for p in priorities:
                    ThisSpecRecs = pmag.get_dictitem(
                        SpecInts, 'magic_method_codes', p,
                        'has')  # all the records for this specimen
                    if len(ThisSpecRecs) > 0: prec.append(ThisSpecRecs[0])
                PrioritySpecInts.append(prec[0])  # take the best one
        SpecInts = PrioritySpecInts  # this has the first specimen record
    if noDir == 0:  # don't skip directions
        AllDirs = pmag.get_dictitem(
            Data, 'specimen_direction_type', '',
            'F')  # retrieve specimens with directed lines and planes
        Ns = pmag.get_dictitem(
            AllDirs, 'specimen_n', '',
            'F')  # get all specimens with specimen_n information
        if nocrit != 1:  # use selection criteria
            for rec in Ns:  # look through everything with specimen_n for "good" data
                kill = pmag.grade(rec, accept, 'specimen_dir')
                if len(kill) == 0:  # nothing killed it
                    SpecDirs.append(rec)
        else:  # no criteria
            SpecDirs = AllDirs[:]  # take them all
# SpecDirs is now the list of all specimen directions (lines and planes) that pass muster
#
    PmagSamps, SampDirs = [], [
    ]  # list of all sample data and list of those that pass the DE-SAMP criteria
    PmagSites, PmagResults = [], [
    ]  # list of all site data and selected results
    SampInts = []
    for samp in samples:  # run through the sample names
        if Daverage == 1:  #  average by sample if desired
            SampDir = pmag.get_dictitem(
                SpecDirs, 'er_sample_name', samp,
                'T')  # get all the directional data for this sample
            if len(SampDir) > 0:  # there are some directions
                for coord in coords:  # step through desired coordinate systems
                    CoordDir = pmag.get_dictitem(
                        SampDir, 'specimen_tilt_correction', coord,
                        'T')  # get all the directions for this sample
                    if len(CoordDir
                           ) > 0:  # there are some with this coordinate system
                        if Caverage == 0:  # look component by component
                            for comp in Comps:
                                CompDir = pmag.get_dictitem(
                                    CoordDir, 'specimen_comp_name', comp, 'T'
                                )  # get all directions from this component
                                if len(CompDir) > 0:  # there are some
                                    PmagSampRec = pmag.lnpbykey(
                                        CompDir, 'sample', 'specimen'
                                    )  # get a sample average from all specimens
                                    PmagSampRec["er_location_name"] = CompDir[0][
                                        'er_location_name']  # decorate the sample record
                                    PmagSampRec["er_site_name"] = CompDir[0][
                                        'er_site_name']
                                    PmagSampRec["er_sample_name"] = samp
                                    PmagSampRec[
                                        "er_citation_names"] = "This study"
                                    PmagSampRec["er_analyst_mail_names"] = user
                                    PmagSampRec[
                                        'magic_software_packages'] = version_num
                                    if nocrit != 1:
                                        PmagSampRec[
                                            'pmag_criteria_codes'] = "ACCEPT"
                                    if agefile != "":
                                        PmagSampRec = pmag.get_age(
                                            PmagSampRec, "er_site_name",
                                            "sample_inferred_", AgeNFO,
                                            DefaultAge)
                                    site_height = pmag.get_dictitem(
                                        height_nfo, 'er_site_name',
                                        PmagSampRec['er_site_name'], 'T')
                                    if len(site_height) > 0:
                                        PmagSampRec[
                                            "sample_height"] = site_height[0][
                                                'site_height']  # add in height if available
                                    PmagSampRec['sample_comp_name'] = comp
                                    PmagSampRec[
                                        'sample_tilt_correction'] = coord
                                    PmagSampRec[
                                        'er_specimen_names'] = pmag.get_list(
                                            CompDir, 'er_specimen_name'
                                        )  # get a list of the specimen names used
                                    PmagSampRec[
                                        'magic_method_codes'] = pmag.get_list(
                                            CompDir, 'magic_method_codes'
                                        )  # get a list of the methods used
                                    if nocrit != 1:  # apply selection criteria
                                        kill = pmag.grade(
                                            PmagSampRec, accept, 'sample_dir')
                                    else:
                                        kill = []
                                    if len(kill) == 0:
                                        SampDirs.append(PmagSampRec)
                                        if vgps == 1:  # if sample level VGP info desired, do that now
                                            PmagResRec = pmag.getsampVGP(
                                                PmagSampRec, SiteNFO)
                                            if PmagResRec != "":
                                                PmagResults.append(PmagResRec)
                                        PmagSamps.append(PmagSampRec)
                        if Caverage == 1:  # average all components together  basically same as above
                            PmagSampRec = pmag.lnpbykey(
                                CoordDir, 'sample', 'specimen')
                            PmagSampRec["er_location_name"] = CoordDir[0][
                                'er_location_name']
                            PmagSampRec["er_site_name"] = CoordDir[0][
                                'er_site_name']
                            PmagSampRec["er_sample_name"] = samp
                            PmagSampRec["er_citation_names"] = "This study"
                            PmagSampRec["er_analyst_mail_names"] = user
                            PmagSampRec[
                                'magic_software_packages'] = version_num
                            if nocrit != 1:
                                PmagSampRec['pmag_criteria_codes'] = ""
                            if agefile != "":
                                PmagSampRec = pmag.get_age(
                                    PmagSampRec, "er_site_name",
                                    "sample_inferred_", AgeNFO, DefaultAge)
                            site_height = pmag.get_dictitem(
                                height_nfo, 'er_site_name', site, 'T')
                            if len(site_height) > 0:
                                PmagSampRec["sample_height"] = site_height[0][
                                    'site_height']  # add in height if available
                            PmagSampRec['sample_tilt_correction'] = coord
                            PmagSampRec['sample_comp_name'] = pmag.get_list(
                                CoordDir,
                                'specimen_comp_name')  # get components used
                            PmagSampRec['er_specimen_names'] = pmag.get_list(
                                CoordDir, 'er_specimen_name'
                            )  # get specimne names averaged
                            PmagSampRec['magic_method_codes'] = pmag.get_list(
                                CoordDir,
                                'magic_method_codes')  # assemble method codes
                            if nocrit != 1:  # apply selection criteria
                                kill = pmag.grade(PmagSampRec, accept,
                                                  'sample_dir')
                                if len(kill) == 0:  # passes the mustard
                                    SampDirs.append(PmagSampRec)
                                    if vgps == 1:
                                        PmagResRec = pmag.getsampVGP(
                                            PmagSampRec, SiteNFO)
                                        if PmagResRec != "":
                                            PmagResults.append(PmagResRec)
                            else:  # take everything
                                SampDirs.append(PmagSampRec)
                                if vgps == 1:
                                    PmagResRec = pmag.getsampVGP(
                                        PmagSampRec, SiteNFO)
                                    if PmagResRec != "":
                                        PmagResults.append(PmagResRec)
                            PmagSamps.append(PmagSampRec)
        if Iaverage == 1:  #  average by sample if desired
            SampI = pmag.get_dictitem(
                SpecInts, 'er_sample_name', samp,
                'T')  # get all the intensity data for this sample
            if len(SampI) > 0:  # there are some
                PmagSampRec = pmag.average_int(
                    SampI, 'specimen', 'sample')  # get average intensity stuff
                PmagSampRec[
                    "sample_description"] = "sample intensity"  # decorate sample record
                PmagSampRec["sample_direction_type"] = ""
                PmagSampRec['er_site_name'] = SampI[0]["er_site_name"]
                PmagSampRec['er_sample_name'] = samp
                PmagSampRec['er_location_name'] = SampI[0]["er_location_name"]
                PmagSampRec["er_citation_names"] = "This study"
                PmagSampRec["er_analyst_mail_names"] = user
                if agefile != "":
                    PmagSampRec = pmag.get_age(PmagSampRec, "er_site_name",
                                               "sample_inferred_", AgeNFO,
                                               DefaultAge)
                site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
                                                PmagSampRec['er_site_name'],
                                                'T')
                if len(site_height) > 0:
                    PmagSampRec["sample_height"] = site_height[0][
                        'site_height']  # add in height if available
                PmagSampRec['er_specimen_names'] = pmag.get_list(
                    SampI, 'er_specimen_name')
                PmagSampRec['magic_method_codes'] = pmag.get_list(
                    SampI, 'magic_method_codes')
                if nocrit != 1:  # apply criteria!
                    kill = pmag.grade(PmagSampRec, accept, 'sample_int')
                    if len(kill) == 0:
                        PmagSampRec['pmag_criteria_codes'] = "ACCEPT"
                        SampInts.append(PmagSampRec)
                        PmagSamps.append(PmagSampRec)
                    else:
                        PmagSampRec = {}  # sample rejected
                else:  # no criteria
                    SampInts.append(PmagSampRec)
                    PmagSamps.append(PmagSampRec)
                    PmagSampRec['pmag_criteria_codes'] = ""
                if vgps == 1 and get_model_lat != 0 and PmagSampRec != {}:  #
                    if get_model_lat == 1:  # use sample latitude
                        PmagResRec = pmag.getsampVDM(PmagSampRec, SampNFO)
                        del (PmagResRec['model_lat']
                             )  # get rid of the model lat key
                    elif get_model_lat == 2:  # use model latitude
                        PmagResRec = pmag.getsampVDM(PmagSampRec, ModelLats)
                        if PmagResRec != {}:
                            PmagResRec['magic_method_codes'] = PmagResRec[
                                'magic_method_codes'] + ":IE-MLAT"
                    if PmagResRec != {}:
                        PmagResRec['er_specimen_names'] = PmagSampRec[
                            'er_specimen_names']
                        PmagResRec['er_sample_names'] = PmagSampRec[
                            'er_sample_name']
                        PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
                        PmagResRec['average_int_sigma_perc'] = PmagSampRec[
                            'sample_int_sigma_perc']
                        PmagResRec['average_int_sigma'] = PmagSampRec[
                            'sample_int_sigma']
                        PmagResRec['average_int_n'] = PmagSampRec[
                            'sample_int_n']
                        PmagResRec['vadm_n'] = PmagSampRec['sample_int_n']
                        PmagResRec['data_type'] = 'i'
                        PmagResults.append(PmagResRec)
    if len(PmagSamps) > 0:
        TmpSamps, keylist = pmag.fillkeys(
            PmagSamps)  # fill in missing keys from different types of records
        pmag.magic_write(sampout, TmpSamps,
                         'pmag_samples')  # save in sample output file
        print ' sample averages written to ', sampout

#
#create site averages from specimens or samples as specified
#
    for site in sites:
        if Daverage == 0:
            key, dirlist = 'specimen', SpecDirs  # if specimen averages at site level desired
        if Daverage == 1:
            key, dirlist = 'sample', SampDirs  # if sample averages at site level desired
        tmp = pmag.get_dictitem(dirlist, 'er_site_name', site,
                                'T')  # get all the sites with  directions
        tmp1 = pmag.get_dictitem(
            tmp, key + '_tilt_correction', coords[-1],
            'T')  # use only the last coordinate if Caverage==0
        sd = pmag.get_dictitem(
            SiteNFO, 'er_site_name', site,
            'T')  # fish out site information (lat/lon, etc.)
        if len(sd) > 0:
            sitedat = sd[0]
            if Caverage == 0:  # do component wise averaging
                for comp in Comps:
                    siteD = pmag.get_dictitem(tmp1, key + '_comp_name', comp,
                                              'T')  # get all components comp
                    if len(
                            siteD
                    ) > 0:  # there are some for this site and component name
                        PmagSiteRec = pmag.lnpbykey(
                            siteD, 'site', key)  # get an average for this site
                        PmagSiteRec[
                            'site_comp_name'] = comp  # decorate the site record
                        PmagSiteRec["er_location_name"] = siteD[0][
                            'er_location_name']
                        PmagSiteRec["er_site_name"] = siteD[0]['er_site_name']
                        PmagSiteRec['site_tilt_correction'] = coords[-1]
                        PmagSiteRec['site_comp_name'] = pmag.get_list(
                            siteD, key + '_comp_name')
                        if Daverage == 1:
                            PmagSiteRec['er_sample_names'] = pmag.get_list(
                                siteD, 'er_sample_name')
                        else:
                            PmagSiteRec['er_specimen_names'] = pmag.get_list(
                                siteD, 'er_specimen_name')


# determine the demagnetization code (DC3,4 or 5) for this site
                        AFnum = len(
                            pmag.get_dictitem(siteD, 'magic_method_codes',
                                              'LP-DIR-AF', 'has'))
                        Tnum = len(
                            pmag.get_dictitem(siteD, 'magic_method_codes',
                                              'LP-DIR-T', 'has'))
                        DC = 3
                        if AFnum > 0: DC += 1
                        if Tnum > 0: DC += 1
                        PmagSiteRec['magic_method_codes'] = pmag.get_list(
                            siteD,
                            'magic_method_codes') + ':' + 'LP-DC' + str(DC)
                        PmagSiteRec['magic_method_codes'].strip(":")
                        if plotsites == 1:
                            print PmagSiteRec['er_site_name']
                            pmagplotlib.plotSITE(EQ['eqarea'], PmagSiteRec,
                                                 siteD,
                                                 key)  # plot and list the data
                            pmagplotlib.drawFIGS(EQ)
                        PmagSites.append(PmagSiteRec)
            else:  # last component only
                siteD = tmp1[:]  # get the last orientation system specified
                if len(siteD) > 0:  # there are some
                    PmagSiteRec = pmag.lnpbykey(
                        siteD, 'site', key)  # get the average for this site
                    PmagSiteRec["er_location_name"] = siteD[0][
                        'er_location_name']  # decorate the record
                    PmagSiteRec["er_site_name"] = siteD[0]['er_site_name']
                    PmagSiteRec['site_comp_name'] = comp
                    PmagSiteRec['site_tilt_correction'] = coords[-1]
                    PmagSiteRec['site_comp_name'] = pmag.get_list(
                        siteD, key + '_comp_name')
                    PmagSiteRec['er_specimen_names'] = pmag.get_list(
                        siteD, 'er_specimen_name')
                    PmagSiteRec['er_sample_names'] = pmag.get_list(
                        siteD, 'er_sample_name')
                    AFnum = len(
                        pmag.get_dictitem(siteD, 'magic_method_codes',
                                          'LP-DIR-AF', 'has'))
                    Tnum = len(
                        pmag.get_dictitem(siteD, 'magic_method_codes',
                                          'LP-DIR-T', 'has'))
                    DC = 3
                    if AFnum > 0: DC += 1
                    if Tnum > 0: DC += 1
                    PmagSiteRec['magic_method_codes'] = pmag.get_list(
                        siteD, 'magic_method_codes') + ':' + 'LP-DC' + str(DC)
                    PmagSiteRec['magic_method_codes'].strip(":")
                    if Daverage == 0:
                        PmagSiteRec['site_comp_name'] = pmag.get_list(
                            siteD, key + '_comp_name')
                    if plotsites == 1:
                        pmagplotlib.plotSITE(EQ['eqarea'], PmagSiteRec, siteD,
                                             key)
                        pmagplotlib.drawFIGS(EQ)
                    PmagSites.append(PmagSiteRec)
        else:
            print 'site information not found in er_sites for site, ', site, ' site will be skipped'
    for PmagSiteRec in PmagSites:  # now decorate each dictionary some more, and calculate VGPs etc. for results table
        PmagSiteRec["er_citation_names"] = "This study"
        PmagSiteRec["er_analyst_mail_names"] = user
        PmagSiteRec['magic_software_packages'] = version_num
        if agefile != "":
            PmagSiteRec = pmag.get_age(PmagSiteRec, "er_site_name",
                                       "site_inferred_", AgeNFO, DefaultAge)
        PmagSiteRec['pmag_criteria_codes'] = 'ACCEPT'
        if 'site_n_lines' in PmagSiteRec.keys(
        ) and 'site_n_planes' in PmagSiteRec.keys() and PmagSiteRec[
                'site_n_lines'] != "" and PmagSiteRec['site_n_planes'] != "":
            if int(PmagSiteRec["site_n_planes"]) > 0:
                PmagSiteRec["magic_method_codes"] = PmagSiteRec[
                    'magic_method_codes'] + ":DE-FM-LP"
            elif int(PmagSiteRec["site_n_lines"]) > 2:
                PmagSiteRec["magic_method_codes"] = PmagSiteRec[
                    'magic_method_codes'] + ":DE-FM"
            kill = pmag.grade(PmagSiteRec, accept, 'site_dir')
            if len(kill) == 0:
                PmagResRec = {
                }  # set up dictionary for the pmag_results table entry
                PmagResRec['data_type'] = 'i'  # decorate it a bit
                PmagResRec['magic_software_packages'] = version_num
                PmagSiteRec[
                    'site_description'] = 'Site direction included in results table'
                PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
                dec = float(PmagSiteRec["site_dec"])
                inc = float(PmagSiteRec["site_inc"])
                if 'site_alpha95' in PmagSiteRec.keys(
                ) and PmagSiteRec['site_alpha95'] != "":
                    a95 = float(PmagSiteRec["site_alpha95"])
                else:
                    a95 = 180.
                sitedat = pmag.get_dictitem(
                    SiteNFO, 'er_site_name', PmagSiteRec['er_site_name'],
                    'T')[0]  # fish out site information (lat/lon, etc.)
                lat = float(sitedat['site_lat'])
                lon = float(sitedat['site_lon'])
                plong, plat, dp, dm = pmag.dia_vgp(
                    dec, inc, a95, lat, lon)  # get the VGP for this site
                if PmagSiteRec['site_tilt_correction'] == '-1':
                    C = ' (spec coord) '
                if PmagSiteRec['site_tilt_correction'] == '0':
                    C = ' (geog. coord) '
                if PmagSiteRec['site_tilt_correction'] == '100':
                    C = ' (strat. coord) '
                PmagResRec["pmag_result_name"] = "VGP Site: " + PmagSiteRec[
                    "er_site_name"]  # decorate some more
                PmagResRec[
                    "result_description"] = "Site VGP, coord system = " + str(
                        coord) + ' component: ' + comp
                PmagResRec['er_site_names'] = PmagSiteRec['er_site_name']
                PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
                PmagResRec['er_citation_names'] = 'This study'
                PmagResRec['er_analyst_mail_names'] = user
                PmagResRec["er_location_names"] = PmagSiteRec[
                    "er_location_name"]
                if Daverage == 1:
                    PmagResRec["er_sample_names"] = PmagSiteRec[
                        "er_sample_names"]
                else:
                    PmagResRec["er_specimen_names"] = PmagSiteRec[
                        "er_specimen_names"]
                PmagResRec["tilt_correction"] = PmagSiteRec[
                    'site_tilt_correction']
                PmagResRec["pole_comp_name"] = PmagSiteRec['site_comp_name']
                PmagResRec["average_dec"] = PmagSiteRec["site_dec"]
                PmagResRec["average_inc"] = PmagSiteRec["site_inc"]
                PmagResRec["average_alpha95"] = PmagSiteRec["site_alpha95"]
                PmagResRec["average_n"] = PmagSiteRec["site_n"]
                PmagResRec["average_n_lines"] = PmagSiteRec["site_n_lines"]
                PmagResRec["average_n_planes"] = PmagSiteRec["site_n_planes"]
                PmagResRec["vgp_n"] = PmagSiteRec["site_n"]
                PmagResRec["average_k"] = PmagSiteRec["site_k"]
                PmagResRec["average_r"] = PmagSiteRec["site_r"]
                PmagResRec["average_lat"] = '%10.4f ' % (lat)
                PmagResRec["average_lon"] = '%10.4f ' % (lon)
                if agefile != "":
                    PmagResRec = pmag.get_age(PmagResRec, "er_site_names",
                                              "average_", AgeNFO, DefaultAge)
                site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
                                                site, 'T')
                if len(site_height) > 0:
                    PmagResRec["average_height"] = site_height[0][
                        'site_height']
                PmagResRec["vgp_lat"] = '%7.1f ' % (plat)
                PmagResRec["vgp_lon"] = '%7.1f ' % (plong)
                PmagResRec["vgp_dp"] = '%7.1f ' % (dp)
                PmagResRec["vgp_dm"] = '%7.1f ' % (dm)
                PmagResRec["magic_method_codes"] = PmagSiteRec[
                    "magic_method_codes"]
                if PmagSiteRec['site_tilt_correction'] == '0':
                    PmagSiteRec['magic_method_codes'] = PmagSiteRec[
                        'magic_method_codes'] + ":DA-DIR-GEO"
                if PmagSiteRec['site_tilt_correction'] == '100':
                    PmagSiteRec['magic_method_codes'] = PmagSiteRec[
                        'magic_method_codes'] + ":DA-DIR-TILT"
                PmagSiteRec['site_polarity'] = ""
                if polarity == 1:  # assign polarity based on angle of pole lat to spin axis - may want to re-think this sometime
                    angle = pmag.angle([0, 0], [0, (90 - plat)])
                    if angle <= 55.: PmagSiteRec["site_polarity"] = 'n'
                    if angle > 55. and angle < 125.:
                        PmagSiteRec["site_polarity"] = 't'
                    if angle >= 125.: PmagSiteRec["site_polarity"] = 'r'
                PmagResults.append(PmagResRec)
    if polarity == 1:
        crecs = pmag.get_dictitem(PmagSites, 'site_tilt_correction', '100',
                                  'T')  # find the tilt corrected data
        if len(crecs) < 2:
            crecs = pmag.get_dictitem(
                PmagSites, 'site_tilt_correction', '0',
                'T')  # if there aren't any, find the geographic corrected data
        if len(crecs) > 2:  # if there are some,
            comp = pmag.get_list(
                crecs,
                'site_comp_name').split(':')[0]  # find the first component
            crecs = pmag.get_dictitem(
                crecs, 'site_comp_name', comp,
                'T')  # fish out all of the first component
            precs = []
            for rec in crecs:
                precs.append({
                    'dec': rec['site_dec'],
                    'inc': rec['site_inc'],
                    'name': rec['er_site_name'],
                    'loc': rec['er_location_name']
                })
            polpars = pmag.fisher_by_pol(
                precs)  # calculate average by polarity
            for mode in polpars.keys(
            ):  # hunt through all the modes (normal=A, reverse=B, all=ALL)
                PolRes = {}
                PolRes['er_citation_names'] = 'This study'
                PolRes[
                    "pmag_result_name"] = "Polarity Average: Polarity " + mode  #
                PolRes["data_type"] = "a"
                PolRes["average_dec"] = '%7.1f' % (polpars[mode]['dec'])
                PolRes["average_inc"] = '%7.1f' % (polpars[mode]['inc'])
                PolRes["average_n"] = '%i' % (polpars[mode]['n'])
                PolRes["average_r"] = '%5.4f' % (polpars[mode]['r'])
                PolRes["average_k"] = '%6.0f' % (polpars[mode]['k'])
                PolRes["average_alpha95"] = '%7.1f' % (
                    polpars[mode]['alpha95'])
                PolRes['er_site_names'] = polpars[mode]['sites']
                PolRes['er_location_names'] = polpars[mode]['locs']
                PolRes['magic_software_packages'] = version_num
                PmagResults.append(PolRes)

    if noInt != 1 and nositeints != 1:
        for site in sites:  # now do intensities for each site
            if plotsites == 1: print site
            if Iaverage == 0:
                key, intlist = 'specimen', SpecInts  # if using specimen level data
            if Iaverage == 1:
                key, intlist = 'sample', PmagSamps  # if using sample level data
            Ints = pmag.get_dictitem(
                intlist, 'er_site_name', site,
                'T')  # get all the intensities  for this site
            if len(Ints) > 0:  # there are some
                PmagSiteRec = pmag.average_int(
                    Ints, key,
                    'site')  # get average intensity stuff for site table
                PmagResRec = pmag.average_int(
                    Ints, key,
                    'average')  # get average intensity stuff for results table
                if plotsites == 1:  # if site by site examination requested - print this site out to the screen
                    for rec in Ints:
                        print rec['er_' + key + '_name'], ' %7.1f' % (
                            1e6 * float(rec[key + '_int']))
                    if len(Ints) > 1:
                        print 'Average: ', '%7.1f' % (1e6 * float(
                            PmagResRec['average_int'])), 'N: ', len(Ints)
                        print 'Sigma: ', '%7.1f' % (
                            1e6 * float(PmagResRec['average_int_sigma'])
                        ), 'Sigma %: ', PmagResRec['average_int_sigma_perc']
                    raw_input('Press any key to continue\n')
                er_location_name = Ints[0]["er_location_name"]
                PmagSiteRec[
                    "er_location_name"] = er_location_name  # decorate the records
                PmagSiteRec["er_citation_names"] = "This study"
                PmagResRec["er_location_names"] = er_location_name
                PmagResRec["er_citation_names"] = "This study"
                PmagSiteRec["er_analyst_mail_names"] = user
                PmagResRec["er_analyst_mail_names"] = user
                PmagResRec["data_type"] = 'i'
                if Iaverage == 0:
                    PmagSiteRec['er_specimen_names'] = pmag.get_list(
                        Ints, 'er_specimen_name')  # list of all specimens used
                    PmagResRec['er_specimen_names'] = pmag.get_list(
                        Ints, 'er_specimen_name')
                PmagSiteRec['er_sample_names'] = pmag.get_list(
                    Ints, 'er_sample_name')  # list of all samples used
                PmagResRec['er_sample_names'] = pmag.get_list(
                    Ints, 'er_sample_name')
                PmagSiteRec['er_site_name'] = site
                PmagResRec['er_site_names'] = site
                PmagSiteRec['magic_method_codes'] = pmag.get_list(
                    Ints, 'magic_method_codes')
                PmagResRec['magic_method_codes'] = pmag.get_list(
                    Ints, 'magic_method_codes')
                kill = pmag.grade(PmagSiteRec, accept, 'site_int')
                if nocrit == 1 or len(kill) == 0:
                    b, sig = float(PmagResRec['average_int']), ""
                    if (PmagResRec['average_int_sigma']) != "":
                        sig = float(PmagResRec['average_int_sigma'])
                    sdir = pmag.get_dictitem(PmagResults, 'er_site_names',
                                             site,
                                             'T')  # fish out site direction
                    if len(sdir) > 0 and sdir[-1][
                            'average_inc'] != "":  # get the VDM for this record using last average inclination (hope it is the right one!)
                        inc = float(sdir[0]['average_inc'])  #
                        mlat = pmag.magnetic_lat(
                            inc)  # get magnetic latitude using dipole formula
                        PmagResRec["vdm"] = '%8.3e ' % (pmag.b_vdm(
                            b, mlat))  # get VDM with magnetic latitude
                        PmagResRec["vdm_n"] = PmagResRec['average_int_n']
                        if 'average_int_sigma' in PmagResRec.keys(
                        ) and PmagResRec['average_int_sigma'] != "":
                            vdm_sig = pmag.b_vdm(
                                float(PmagResRec['average_int_sigma']), mlat)
                            PmagResRec["vdm_sigma"] = '%8.3e ' % (vdm_sig)
                        else:
                            PmagResRec["vdm_sigma"] = ""
                    mlat = ""  # define a model latitude
                    if get_model_lat == 1:  # use present site latitude
                        mlats = pmag.get_dictitem(SiteNFO, 'er_site_name',
                                                  site, 'T')
                        if len(mlats) > 0: mlat = mlats[0]['site_lat']
                    elif get_model_lat == 2:  # use a model latitude from some plate reconstruction model (or something)
                        mlats = pmag.get_dictitem(ModelLats, 'er_site_name',
                                                  site, 'T')
                        if len(mlats) > 0:
                            PmagResRec['model_lat'] = mlats[0][
                                'site_model_lat']
                        mlat = PmagResRec['model_lat']
                    if mlat != "":
                        PmagResRec["vadm"] = '%8.3e ' % (
                            pmag.b_vdm(b, float(mlat))
                        )  # get the VADM using the desired latitude
                        if sig != "":
                            vdm_sig = pmag.b_vdm(
                                float(PmagResRec['average_int_sigma']),
                                float(mlat))
                            PmagResRec["vadm_sigma"] = '%8.3e ' % (vdm_sig)
                            PmagResRec["vadm_n"] = PmagResRec['average_int_n']
                        else:
                            PmagResRec["vadm_sigma"] = ""
                    sitedat = pmag.get_dictitem(
                        SiteNFO, 'er_site_name', PmagSiteRec['er_site_name'],
                        'T')  # fish out site information (lat/lon, etc.)
                    if len(sitedat) > 0:
                        sitedat = sitedat[0]
                        PmagResRec['average_lat'] = sitedat['site_lat']
                        PmagResRec['average_lon'] = sitedat['site_lon']
                    else:
                        PmagResRec['average_lon'] = 'UNKNOWN'
                        PmagResRec['average_lon'] = 'UNKNOWN'
                    PmagResRec['magic_software_packages'] = version_num
                    PmagResRec["pmag_result_name"] = "V[A]DM: Site " + site
                    PmagResRec["result_description"] = "V[A]DM of site"
                    PmagResRec["pmag_criteria_codes"] = "ACCEPT"
                    if agefile != "":
                        PmagResRec = pmag.get_age(PmagResRec, "er_site_names",
                                                  "average_", AgeNFO,
                                                  DefaultAge)
                    site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
                                                    site, 'T')
                    if len(site_height) > 0:
                        PmagResRec["average_height"] = site_height[0][
                            'site_height']
                    PmagSites.append(PmagSiteRec)
                    PmagResults.append(PmagResRec)
    if len(PmagSites) > 0:
        Tmp, keylist = pmag.fillkeys(PmagSites)
        pmag.magic_write(siteout, Tmp, 'pmag_sites')
        print ' sites written to ', siteout
    else:
        print "No Site level table"
    if len(PmagResults) > 0:
        TmpRes, keylist = pmag.fillkeys(PmagResults)
        pmag.magic_write(resout, TmpRes, 'pmag_results')
        print ' results written to ', resout
    else:
        print "No Results level table"
Ejemplo n.º 9
0
def main():
    """
    NAME
        thellier_magic.py

    DESCRIPTION
        plots Thellier-Thellier data in version 3.0 format
        Reads saved interpretations from a specimen formatted table, default: specimens.txt

    SYNTAX
        thellier_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f MEAS, set measurements input file, default is 'measurements.txt'
        -fsp PRIOR, set specimens.txt prior interpretations file, default is 'specimens.txt'
        -fcr CRIT, set criteria file for grading.  # not yet implemented
        -fmt [svg,png,jpg], format for images - default is svg
        -sav,  saves plots with out review (in format specified by -fmt key or default)
        -spc SPEC, plots single specimen SPEC, saves plot with specified format
            with optional -b bounds and quits
        -b BEG END: sets  bounds for calculation
           BEG: starting step number for slope calculation
           END: ending step number for slope calculation
        -z use only z component difference for pTRM calculation

    OUTPUT
        figures:
            ALL:  numbers refer to temperature steps in command line window
            1) Arai plot:  closed circles are zero-field first/infield
                           open circles are infield first/zero-field
                           triangles are pTRM checks
                           squares are pTRM tail checks
                           VDS is vector difference sum
                           diamonds are bounds for interpretation
            2) Zijderveld plot:  closed (open) symbols are X-Y (X-Z) planes
                                 X rotated to NRM direction
            3) (De/Re)Magnetization diagram:
                           circles are NRM remaining
                           squares are pTRM gained
            4) equal area projections:
               green triangles are pTRM gained direction
                           red (purple) circles are lower(upper) hemisphere of ZI step directions
                           blue (cyan) squares are lower(upper) hemisphere IZ step directions
            5) Optional:  TRM acquisition
            6) Optional: TDS normalization
        command line window:
            list is: temperature step numbers, temperatures (C), Dec, Inc, Int (units of measuements)
                     list of possible commands: type letter followed by return to select option
                     saving of plots creates image files with specimen, plot type as name
    """
#
#   initializations
#
    version_num=pmag.get_version()
    verbose=pmagplotlib.verbose
#
# default acceptance criteria
#
    accept=pmag.default_criteria(0)[0] # set the default criteria
#
# parse command line options
#
    plots,fmt,Zdiff=0,'svg',0
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
    meas_file = pmag.get_named_arg_from_sys("-f", default_val="measurements.txt")
    spec_file=pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt")
    crit_file=pmag.get_named_arg_from_sys("-fcr", default_val="criteria.txt")
    spec_file=os.path.join(dir_path,spec_file)
    meas_file=os.path.join(dir_path,meas_file)
    crit_file=os.path.join(dir_path,crit_file)
    fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
    if '-sav' in sys.argv: plots,verbose=1,0
    if '-z' in sys.argv: Zdiff=1
    specimen=pmag.get_named_arg_from_sys("-spc",default_val="")
    if '-b' in sys.argv:
        ind=sys.argv.index('-b')
        start=int(sys.argv[ind+1])
        end=int(sys.argv[ind+2])
    else:
        start,end="",""
    fnames = {'measurements': meas_file, 'specimens': spec_file, 'criteria': crit_file}
    contribution = nb.Contribution(dir_path, custom_filenames=fnames, read_tables=['measurements', 'specimens', 'criteria'])
#
#   import  prior interpretations  from specimen file
#
    specimen_cols = ['analysts', 'aniso_ftest', 'aniso_ftest12', 'aniso_ftest23', 'aniso_s', 'aniso_s_mean', 'aniso_s_n_measurements', 'aniso_s_sigma', 'aniso_s_unit', 'aniso_tilt_correction', 'aniso_type', 'aniso_v1', 'aniso_v2', 'aniso_v3', 'citations', 'description', 'dir_alpha95', 'dir_comp', 'dir_dec', 'dir_inc', 'dir_mad_free', 'dir_n_measurements', 'dir_tilt_correction', 'experiments', 'geologic_classes', 'geologic_types', 'hyst_bc', 'hyst_bcr', 'hyst_mr_moment', 'hyst_ms_moment', 'int_abs', 'int_b', 'int_b_beta', 'int_b_sigma', 'int_corr', 'int_dang', 'int_drats', 'int_f', 'int_fvds', 'int_gamma', 'int_mad_free', 'int_md', 'int_n_measurements', 'int_n_ptrm', 'int_q', 'int_rsc', 'int_treat_dc_field', 'lithologies', 'meas_step_max', 'meas_step_min', 'meas_step_unit', 'method_codes', 'sample', 'software_packages', 'specimen']
    if 'specimens' in contribution.tables:
        spec_container = contribution.tables['specimens']
        prior_spec_data=spec_container.get_records_for_code('LP-PI-TRM',strict_match=False) # look up all prior intensity interpretations
    else:
           spec_container, prior_spec_data = None, []
    backup=0
    #
    Mkeys = ['magn_moment', 'magn_volume', 'magn_mass']
#
#   create measurement dataframe
#
    meas_container = contribution.tables['measurements']
    meas_data = meas_container.df
#
    meas_data['method_codes']=meas_data['method_codes'].str.replace(" ","") # get rid of nasty spaces
    meas_data= meas_data[meas_data['method_codes'].str.contains('LP-PI-TRM|LP-TRM|LP-TRM-TD')==True] # fish out zero field steps for plotting
    intensity_types = [col_name for col_name in meas_data.columns if col_name in Mkeys]
    int_key = intensity_types[0] # plot first intensity method found - normalized to initial value anyway - doesn't matter which used
    meas_data = meas_data[meas_data[int_key].notnull()] # get all the non-null intensity records of the same type
    if 'flag' not in meas_data.columns: meas_data['flag'] = 'g' # set the default flag to good
    meas_data = meas_data[meas_data['flag'].str.contains('g')==True] # only the 'good' measurements
    thel_data = meas_data[meas_data['method_codes'].str.contains('LP-PI-TRM')==True] # get all the Thellier data
    trm_data = meas_data[meas_data['method_codes'].str.contains('LP-TRM')==True] # get all the TRM acquisition data
    td_data = meas_data[meas_data['method_codes'].str.contains('LP-TRM-TD')==True] # get all the TD data
    anis_data = meas_data[meas_data['method_codes'].str.contains('LP-AN')==True] # get all the anisotropy data
#
# get list of unique specimen names from measurement data
#
    specimen_names= meas_data.specimen.unique() # this is a Series of all the specimen names
    specimen_names= specimen_names.tolist() # turns it into a list
    specimen_names.sort() # sorts by specimen name
#
# set up new DataFrame for this sessions specimen interpretations
#
    spec_container = nb.MagicDataFrame(dtype='specimens', columns=specimen_cols)
    current_spec_data = spec_container.df # this is for interpretations from this session
    if specimen=="": # do all specimens
        k = 0
    else:
        k=specimen_names.index(specimen) # just do this one
    # define figure numbers for arai, zijderveld and
    #   de-,re-magnetization diagrams
    AZD={}
    AZD['deremag'], AZD['zijd'],AZD['arai'],AZD['eqarea']=1,2,3,4
    pmagplotlib.plot_init(AZD['arai'],5,5)
    pmagplotlib.plot_init(AZD['zijd'],5,5)
    pmagplotlib.plot_init(AZD['deremag'],5,5)
    pmagplotlib.plot_init(AZD['eqarea'],5,5)
    if len(trm_data)>0:
        AZD['TRM']=5
        pmagplotlib.plot_init(AZD['TRM'],5,5)
    if len(td_data)>0:
        AZD['TDS']=6
        pmagplotlib.plot_init(AZD['TDS'],5,5)
    #
    while k < len(specimen_names):
        this_specimen=specimen_names[k] # set the current specimen for plotting
        if verbose and  this_specimen!="":print(this_specimen, k+1 , 'out of ',len(specimen_names))
#
#    set up datablocks
#
        thelblock= thel_data[thel_data['specimen'].str.contains(this_specimen)==True] # fish out this specimen
        trmblock= trm_data[trm_data['specimen'].str.contains(this_specimen)==True] # fish out this specimen
        tdsrecs= td_data[td_data['specimen'].str.contains(this_specimen)==True] # fish out this specimen
        anisblock= anis_data[anis_data['specimen'].str.contains(this_specimen)==True] # fish out the anisotropy data
        prior_specimen_interpretations= prior_spec_data[prior_spec_data['specimen'].str.contains(this_specimen)==True] # fish out prior interpretation
#
# sort data into types
#
        araiblock,field=pmag.sortarai(thelblock,this_specimen,Zdiff,version=3)
        first_Z=araiblock[0]
        GammaChecks=araiblock[5]
        if len(first_Z)<3:
           if backup==0:
                   k+=1
                   if verbose:
                       print('skipping specimen - moving forward ', this_specimen)
           else:
                   k-=1
                   if verbose:
                       print('skipping specimen - moving backward ', this_specimen)
        else:
               backup=0
               zijdblock,units=pmag.find_dmag_rec(this_specimen,thelblock,version=3)
               if start=="" and len(prior_specimen_interpretations)>0:
                   if verbose: print('Looking up saved interpretation....')
#
# get prior interpretation steps
#
                   beg_int=pd.to_numeric(prior_specimen_interpretations.meas_step_min.values).tolist()[0]
                   end_int=pd.to_numeric(prior_specimen_interpretations.meas_step_max.values).tolist()[0]
               else: beg_int,end_int="",""
               recnum=0
               if verbose: print("index step Dec   Inc  Int       Gamma")
               for plotrec in zijdblock:
                   if plotrec[0]==beg_int:start=recnum # while we are at it, collect these bounds
                   if plotrec[0]==end_int:end=recnum
                   if verbose:
                       if GammaChecks!="":
                           gamma=""
                           for g in GammaChecks:
                               if g[0]==plotrec[0]-273:
                                   gamma=g[1]
                                   break
                       if gamma!="":
                           print('%i     %i %7.1f %7.1f %8.3e %7.1f' % (recnum,plotrec[0]-273,plotrec[1],plotrec[2],plotrec[3],gamma))
                       else:
                           print('%i     %i %7.1f %7.1f %8.3e ' % (recnum,plotrec[0]-273,plotrec[1],plotrec[2],plotrec[3]))
                   recnum += 1
               for fig in list(AZD.keys()):pmagplotlib.clearFIG(AZD[fig]) # clear all figures
               pmagplotlib.plotAZ(AZD,araiblock,zijdblock,this_specimen,units[0])
               if verbose:pmagplotlib.drawFIGS(AZD)
               pars,errcode=pmag.PintPars(thelblock,araiblock,zijdblock,start,end,accept,version=3)
               pars['measurement_step_unit']="K"
               pars['experiment_type']='LP-PI-TRM'
#
# work on saving interpretations stuff later
#
               if errcode!=1: # no problem in PintPars
                    pars["specimen_lab_field_dc"]=field
                    pars["specimen_int"]=-1*field*pars["specimen_b"]
                    pars["er_specimen_name"]=this_specimen
                    #pars,kill=pmag.scoreit(pars,this_specimen_interpretation,accept,'',verbose) # deal with this later
                    pars["specimen_grade"]='None'
                    pars['measurement_step_min']=pars['meas_step_min']
                    pars['measurement_step_max']=pars['meas_step_max']
                    if pars['measurement_step_unit']=='K':
                        outstr= "specimen     Tmin  Tmax  N  lab_field  B_anc  b  q  f(coe)  Fvds  beta  MAD  Dang  Drats  Nptrm  Grade  R  MD%  sigma  Gamma_max \n"
                        pars_out= (this_specimen,(pars["meas_step_min"]-273),(pars["meas_step_max"]-273),(pars["specimen_int_n"]),1e6*(pars["specimen_lab_field_dc"]),1e6*(pars["specimen_int"]),pars["specimen_b"],pars["specimen_q"],pars["specimen_f"],pars["specimen_fvds"],pars["specimen_b_beta"],pars["int_mad_free"],pars["int_dang"],pars["int_drats"],pars["int_n_ptrm"],pars["specimen_grade"],np.sqrt(pars["specimen_rsc"]),int(pars["int_md"]), pars["specimen_b_sigma"],pars['specimen_gamma'])
                        outstring= '%s %4.0f %4.0f %i %4.1f %4.1f %5.3f %5.1f %5.3f %5.3f %5.3f  %7.1f %7.1f %7.1f %s %s %6.3f %i %5.3f %7.1f' % pars_out +'\n'
                    elif pars['measurement_step_unit']=='J':
                        outstr= "specimen     Wmin  Wmax  N  lab_field  B_anc  b  q  f(coe)  Fvds  beta  MAD  Dang  Drats  Nptrm  Grade  R  MD%  sigma  ThetaMax DeltaMax GammaMax\n"
                        pars_out= (this_specimen,(pars["meas_step_min"]),(pars["meas_step_max"]),(pars["specimen_int_n"]),1e6*(pars["specimen_lab_field_dc"]),1e6*(pars["specimen_int"]),pars["specimen_b"],pars["specimen_q"],pars["specimen_f"],pars["specimen_fvds"],pars["specimen_b_beta"],pars["specimen_int_mad"],pars["specimen_int_dang"],pars["specimen_drats"],pars["specimen_int_ptrm_n"],pars["specimen_grade"],np.sqrt(pars["specimen_rsc"]),int(pars["specimen_md"]), pars["specimen_b_sigma"],pars["specimen_theta"],pars["specimen_delta"],pars["specimen_gamma"])
                        outstring= '%s %4.0f %4.0f %i %4.1f %4.1f %5.3f %5.1f %5.3f %5.3f %5.3f  %7.1f %7.1f %7.1f %s %s %6.3f %i %5.3f %7.1f %7.1f %7.1f' % pars_out +'\n'
                    print(outstr)
                    print(outstring)
                    pmagplotlib.plotB(AZD,araiblock,zijdblock,pars)
                    mpars=pmag.domean(araiblock[1],start,end,'DE-BFL')
                    if verbose:
                        pmagplotlib.drawFIGS(AZD)
                        print('pTRM direction= ','%7.1f'%(mpars['specimen_dec']),' %7.1f'%(mpars['specimen_inc']),' MAD:','%7.1f'%(mpars['specimen_mad']))
               if len(anisblock)>0:  # this specimen has anisotropy data
                           if verbose: print('Found anisotropy record... but ignoring for now ')
               if plots==1:
                   if fmt != "pmag":
                       files={}
                       for key in list(AZD.keys()):
                           files[key]='SP:_'+this_specimen+'_TY:_'+key+'_'+'.'+fmt
                       if pmagplotlib.isServer:
                           black     = '#000000'
                           purple    = '#800080'
                           titles={}
                           titles['deremag']='DeReMag Plot'
                           titles['zijd']='Zijderveld Plot'
                           titles['arai']='Arai Plot'
                           titles['TRM']='TRM Acquisition data'
                           AZD = pmagplotlib.addBorders(AZD,titles,black,purple)
                       pmagplotlib.saveP(AZD,files)
                   else:  # save in pmag format
                       print('pmag format no longer supported')
                       #script="grep "+this_specimen+" output.mag | thellier -mfsi"
                       #script=script+' %8.4e'%(field)
                       #min='%i'%((pars["measurement_step_min"]-273))
                       #Max='%i'%((pars["measurement_step_max"]-273))
                       #script=script+" "+min+" "+Max
                       #script=script+" |plotxy;cat mypost >>thellier.ps\n"
                       #pltf.write(script)
                       #pmag.domagicmag(outf,MeasRecs)
               if specimen!="": sys.exit() # syonara
               if verbose:
                   ans=input('Return for next specimen, q to quit:  ')
                   if ans=='q':sys.exit()
               k+=1 # moving on
Ejemplo n.º 10
0
def main():
    """
    NAME
    specimens_results_magic.py

    DESCRIPTION
    combines pmag_specimens.txt file with age, location, acceptance criteria and
    outputs pmag_results table along with other MagIC tables necessary for uploading to the database

    SYNTAX
    specimens_results_magic.py [command line options]

    OPTIONS
    -h prints help message and quits
    -usr USER:   identify user, default is ""
    -f: specimen input magic_measurements format file, default is "magic_measurements.txt"
    -fsp: specimen input pmag_specimens format file, default is "pmag_specimens.txt"
    -fsm: sample input er_samples format file, default is "er_samples.txt"
    -fsi: specimen input er_sites format file, default is "er_sites.txt"
    -fla: specify a file with paleolatitudes for calculating VADMs, default is not to calculate VADMS
               format is:  site_name paleolatitude (space delimited file)
    -fa AGES: specify er_ages format file with age information
    -crd [s,g,t,b]:   specify coordinate system
        (s, specimen, g geographic, t, tilt corrected, b, geographic and tilt corrected)
        Default is to assume geographic
        NB: only the tilt corrected data will appear on the results table, if both g and t are selected.
        -cor [AC:CR:NL]: colon delimited list of required data adjustments for all specimens
            included in intensity calculations (anisotropy, cooling rate, non-linear TRM)
            unless specified, corrections will not be applied
        -pri [TRM:ARM] colon delimited list of priorities for anisotropy correction (-cor must also be set to include AC). default is TRM, then ARM
    -age MIN MAX UNITS:   specify age boundaries and units
    -exc:  use exiting selection criteria (in pmag_criteria.txt file), default is default criteria
    -C: no acceptance criteria
    -aD:  average directions per sample, default is NOT
    -aI:  average multiple specimen intensities per sample, default is by site
    -aC:  average all components together, default is NOT
    -pol:  calculate polarity averages
    -sam:  save sample level vgps and v[a]dms, default is by site
    -xSi:  skip the site level intensity calculation
    -p: plot directions and look at intensities by site, default is NOT
        -fmt: specify output for saved images, default is svg (only if -p set)
    -lat: use present latitude for calculating VADMs, default is not to calculate VADMs
    -xD: skip directions
    -xI: skip intensities
    OUPUT
    writes pmag_samples, pmag_sites, pmag_results tables
    """
# set defaults
    Comps=[] # list of components
    version_num=pmag.get_version()
    args=sys.argv
    DefaultAge=["none"]
    skipdirs,coord,excrit,custom,vgps,average,Iaverage,plotsites,opt=1,0,0,0,0,0,0,0,0
    get_model_lat=0 # this skips VADM calculation altogether, when get_model_lat=1, uses present day
    fmt='svg'
    dir_path="."
    model_lat_file=""
    Caverage=0
    infile='pmag_specimens.txt'
    measfile="magic_measurements.txt"
    sampfile="er_samples.txt"
    sitefile="er_sites.txt"
    agefile="er_ages.txt"
    specout="er_specimens.txt"
    sampout="pmag_samples.txt"
    siteout="pmag_sites.txt"
    resout="pmag_results.txt"
    critout="pmag_criteria.txt"
    instout="magic_instruments.txt"
    sigcutoff,OBJ="",""
    noDir,noInt=0,0
    polarity=0
    coords=['0']
    Dcrit,Icrit,nocrit=0,0,0
    corrections=[]
    nocorrection=['DA-NL','DA-AC','DA-CR']
    priorities=['DA-AC-ARM','DA-AC-TRM'] # priorities for anisotropy correction
# get command line stuff
    if "-h" in args:
        print(main.__doc__)
        sys.exit()
    if '-WD' in args:
        ind=args.index("-WD")
        dir_path=args[ind+1]
    if '-cor' in args:
        ind=args.index('-cor')
        cors=args[ind+1].split(':') # list of required data adjustments
        for cor in cors:
            nocorrection.remove('DA-'+cor)
            corrections.append('DA-'+cor)
    if '-pri' in args:
        ind=args.index('-pri')
        priorities=args[ind+1].split(':') # list of required data adjustments
        for p in priorities:
            p='DA-AC-'+p
    if '-f' in args:
        ind=args.index("-f")
        measfile=args[ind+1]
    if '-fsp' in args:
        ind=args.index("-fsp")
        infile=args[ind+1]
    if '-fsi' in args:
        ind=args.index("-fsi")
        sitefile=args[ind+1]
    if "-crd" in args:
        ind=args.index("-crd")
        coord=args[ind+1]
        if coord=='s':coords=['-1']
        if coord=='g':coords=['0']
        if coord=='t':coords=['100']
        if coord=='b':coords=['0','100']
    if "-usr" in args:
        ind=args.index("-usr")
        user=sys.argv[ind+1]
    else: user=""
    if "-C" in args: Dcrit,Icrit,nocrit=1,1,1 # no selection criteria
    if "-sam" in args: vgps=1 # save sample level VGPS/VADMs
    if "-xSi" in args:
        nositeints=1 # skip site level intensity
    else:
        nositeints=0
    if "-age" in args:
        ind=args.index("-age")
        DefaultAge[0]=args[ind+1]
        DefaultAge.append(args[ind+2])
        DefaultAge.append(args[ind+3])
    Daverage,Iaverage,Caverage=0,0,0
    if "-aD" in args: Daverage=1 # average by sample directions
    if "-aI" in args: Iaverage=1 # average by sample intensities
    if "-aC" in args: Caverage=1 # average all components together ???  why???
    if "-pol" in args: polarity=1 # calculate averages by polarity
    if '-xD' in args:noDir=1
    if '-xI' in args:
        noInt=1
    elif "-fla" in args:
        if '-lat' in args:
            print("you should set a paleolatitude file OR use present day lat - not both")
            sys.exit()
        ind=args.index("-fla")
        model_lat_file=dir_path+'/'+args[ind+1]
        get_model_lat=2
        mlat=open(model_lat_file,'r')
        ModelLats=[]
        for line in mlat.readlines():
            ModelLat={}
            tmp=line.split()
            ModelLat["er_site_name"]=tmp[0]
            ModelLat["site_model_lat"]=tmp[1]
            ModelLat["er_sample_name"]=tmp[0]
            ModelLat["sample_lat"]=tmp[1]
            ModelLats.append(ModelLat)
        get_model_lat=2
    elif '-lat' in args:
        get_model_lat=1
    if "-p" in args:
        plotsites=1
        if "-fmt" in args:
            ind=args.index("-fmt")
            fmt=args[ind+1]
        if noDir==0: # plot by site - set up plot window
            import pmagplotlib
            EQ={}
            EQ['eqarea']=1
            pmagplotlib.plot_init(EQ['eqarea'],5,5) # define figure 1 as equal area projection
            pmagplotlib.plotNET(EQ['eqarea']) # I don't know why this has to be here, but otherwise the first plot never plots...
            pmagplotlib.drawFIGS(EQ)
    if '-WD' in args:
        infile=dir_path+'/'+infile
        measfile=dir_path+'/'+measfile
        instout=dir_path+'/'+instout
        sampfile=dir_path+'/'+sampfile
        sitefile=dir_path+'/'+sitefile
        agefile=dir_path+'/'+agefile
        specout=dir_path+'/'+specout
        sampout=dir_path+'/'+sampout
        siteout=dir_path+'/'+siteout
        resout=dir_path+'/'+resout
        critout=dir_path+'/'+critout
    if "-exc" in args: # use existing pmag_criteria file
        if "-C" in args:
            print('you can not use both existing and no criteria - choose either -exc OR -C OR neither (for default)')
            sys.exit()
        crit_data,file_type=pmag.magic_read(critout)
        print("Acceptance criteria read in from ", critout)
    else  : # use default criteria (if nocrit set, then get really loose criteria as default)
        crit_data=pmag.default_criteria(nocrit)
        if nocrit==0:
            print("Acceptance criteria are defaults")
        else:
            print("No acceptance criteria used ")
    accept={}
    for critrec in crit_data:
        for key in list(critrec.keys()):
# need to migrate specimen_dang to specimen_int_dang for intensity data using old format
            if 'IE-SPEC' in list(critrec.keys()) and 'specimen_dang' in list(critrec.keys()) and 'specimen_int_dang' not in list(critrec.keys()):
                critrec['specimen_int_dang']=critrec['specimen_dang']
                del critrec['specimen_dang']
# need to get rid of ron shaars sample_int_sigma_uT
            if 'sample_int_sigma_uT' in list(critrec.keys()):
                critrec['sample_int_sigma']='%10.3e'%(eval(critrec['sample_int_sigma_uT'])*1e-6)
            if key not in list(accept.keys()) and critrec[key]!='':
                accept[key]=critrec[key]
    #
    #
    if "-exc" not in args and "-C" not in args:
        print("args",args)
        pmag.magic_write(critout,[accept],'pmag_criteria')
        print("\n Pmag Criteria stored in ",critout,'\n')
#
# now we're done slow dancing
#
    SiteNFO,file_type=pmag.magic_read(sitefile) # read in site data - has the lats and lons
    SampNFO,file_type=pmag.magic_read(sampfile) # read in site data - has the lats and lons
    height_nfo=pmag.get_dictitem(SiteNFO,'site_height','','F') # find all the sites with height info.
    if agefile !="":AgeNFO,file_type=pmag.magic_read(agefile) # read in the age information
    Data,file_type=pmag.magic_read(infile) # read in specimen interpretations
    IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
    comment,orient="",[]
    samples,sites=[],[]
    for rec in Data: # run through the data filling in missing keys and finding all components, coordinates available
# fill in missing fields, collect unique sample and site names
        if 'er_sample_name' not in list(rec.keys()):
            rec['er_sample_name']=""
        elif rec['er_sample_name'] not in samples:
            samples.append(rec['er_sample_name'])
        if 'er_site_name' not in list(rec.keys()):
            rec['er_site_name']=""
        elif rec['er_site_name'] not in sites:
            sites.append(rec['er_site_name'])
        if 'specimen_int' not in list(rec.keys()):rec['specimen_int']=''
        if 'specimen_comp_name' not in list(rec.keys()) or rec['specimen_comp_name']=="":rec['specimen_comp_name']='A'
        if rec['specimen_comp_name'] not in Comps:Comps.append(rec['specimen_comp_name'])
        rec['specimen_tilt_correction']=rec['specimen_tilt_correction'].strip('\n')
        if "specimen_tilt_correction" not in list(rec.keys()): rec["specimen_tilt_correction"]="-1" # assume sample coordinates
        if rec["specimen_tilt_correction"] not in orient: orient.append(rec["specimen_tilt_correction"])  # collect available coordinate systems
        if "specimen_direction_type" not in list(rec.keys()): rec["specimen_direction_type"]='l'  # assume direction is line - not plane
        if "specimen_dec" not in list(rec.keys()): rec["specimen_direction_type"]=''  # if no declination, set direction type to blank
        if "specimen_n" not in list(rec.keys()): rec["specimen_n"]=''  # put in n
        if "specimen_alpha95" not in list(rec.keys()): rec["specimen_alpha95"]=''  # put in alpha95
        if "magic_method_codes" not in list(rec.keys()): rec["magic_method_codes"]=''
     #
     # start parsing data into SpecDirs, SpecPlanes, SpecInts
    SpecInts,SpecDirs,SpecPlanes=[],[],[]
    samples.sort() # get sorted list of samples and sites
    sites.sort()
    if noInt==0: # don't skip intensities
        IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
        if nocrit==0: # use selection criteria
            for rec in IntData: # do selection criteria
                kill=pmag.grade(rec,accept,'specimen_int')
                if len(kill)==0: SpecInts.append(rec) # intensity record to be included in sample, site calculations
        else:
            SpecInts=IntData[:] # take everything - no selection criteria
    # check for required data adjustments
        if len(corrections)>0 and len(SpecInts)>0:
            for cor in corrections:
                SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'has') # only take specimens with the required corrections
        if len(nocorrection)>0 and len(SpecInts)>0:
            for cor in nocorrection:
                SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'not') # exclude the corrections not specified for inclusion
# take top priority specimen of its name in remaining specimens (only one per customer)
        PrioritySpecInts=[]
        specimens=pmag.get_specs(SpecInts) # get list of uniq specimen names
        for spec in specimens:
            ThisSpecRecs=pmag.get_dictitem(SpecInts,'er_specimen_name',spec,'T') # all the records for this specimen
            if len(ThisSpecRecs)==1:
                PrioritySpecInts.append(ThisSpecRecs[0])
            elif len(ThisSpecRecs)>1: # more than one
                prec=[]
                for p in priorities:
                    ThisSpecRecs=pmag.get_dictitem(SpecInts,'magic_method_codes',p,'has') # all the records for this specimen
                    if len(ThisSpecRecs)>0:prec.append(ThisSpecRecs[0])
                PrioritySpecInts.append(prec[0]) # take the best one
        SpecInts=PrioritySpecInts # this has the first specimen record
    if noDir==0: # don't skip directions
        AllDirs=pmag.get_dictitem(Data,'specimen_direction_type','','F') # retrieve specimens with directed lines and planes
        Ns=pmag.get_dictitem(AllDirs,'specimen_n','','F')  # get all specimens with specimen_n information
        if nocrit!=1: # use selection criteria
            for rec in Ns: # look through everything with specimen_n for "good" data
                kill=pmag.grade(rec,accept,'specimen_dir')
                if len(kill)==0: # nothing killed it
                    SpecDirs.append(rec)
        else: # no criteria
            SpecDirs=AllDirs[:] # take them all
# SpecDirs is now the list of all specimen directions (lines and planes) that pass muster
#
    PmagSamps,SampDirs=[],[] # list of all sample data and list of those that pass the DE-SAMP criteria
    PmagSites,PmagResults=[],[] # list of all site data and selected results
    SampInts=[]
    for samp in samples: # run through the sample names
        if Daverage==1: #  average by sample if desired
           SampDir=pmag.get_dictitem(SpecDirs,'er_sample_name',samp,'T') # get all the directional data for this sample
           if len(SampDir)>0: # there are some directions
               for coord in coords: # step through desired coordinate systems
                   CoordDir=pmag.get_dictitem(SampDir,'specimen_tilt_correction',coord,'T') # get all the directions for this sample
                   if len(CoordDir)>0: # there are some with this coordinate system
                       if Caverage==0: # look component by component
                           for comp in Comps:
                               CompDir=pmag.get_dictitem(CoordDir,'specimen_comp_name',comp,'T') # get all directions from this component
                               if len(CompDir)>0: # there are some
                                   PmagSampRec=pmag.lnpbykey(CompDir,'sample','specimen') # get a sample average from all specimens
                                   PmagSampRec["er_location_name"]=CompDir[0]['er_location_name'] # decorate the sample record
                                   PmagSampRec["er_site_name"]=CompDir[0]['er_site_name']
                                   PmagSampRec["er_sample_name"]=samp
                                   PmagSampRec["er_citation_names"]="This study"
                                   PmagSampRec["er_analyst_mail_names"]=user
                                   PmagSampRec['magic_software_packages']=version_num
                                   if nocrit!=1:PmagSampRec['pmag_criteria_codes']="ACCEPT"
                                   if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
                                   site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
                                   if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
                                   PmagSampRec['sample_comp_name']=comp
                                   PmagSampRec['sample_tilt_correction']=coord
                                   PmagSampRec['er_specimen_names']= pmag.get_list(CompDir,'er_specimen_name') # get a list of the specimen names used
                                   PmagSampRec['magic_method_codes']= pmag.get_list(CompDir,'magic_method_codes') # get a list of the methods used
                                   if nocrit!=1: # apply selection criteria
                                       kill=pmag.grade(PmagSampRec,accept,'sample_dir')
                                   else:
                                       kill=[]
                                   if len(kill)==0:
                                       SampDirs.append(PmagSampRec)
                                       if vgps==1: # if sample level VGP info desired, do that now
                                           PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
                                           if PmagResRec!="":PmagResults.append(PmagResRec)
                                       PmagSamps.append(PmagSampRec)
                       if Caverage==1: # average all components together  basically same as above
                           PmagSampRec=pmag.lnpbykey(CoordDir,'sample','specimen')
                           PmagSampRec["er_location_name"]=CoordDir[0]['er_location_name']
                           PmagSampRec["er_site_name"]=CoordDir[0]['er_site_name']
                           PmagSampRec["er_sample_name"]=samp
                           PmagSampRec["er_citation_names"]="This study"
                           PmagSampRec["er_analyst_mail_names"]=user
                           PmagSampRec['magic_software_packages']=version_num
                           if nocrit!=1:PmagSampRec['pmag_criteria_codes']=""
                           if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
                           site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
                           if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
                           PmagSampRec['sample_tilt_correction']=coord
                           PmagSampRec['sample_comp_name']= pmag.get_list(CoordDir,'specimen_comp_name') # get components used
                           PmagSampRec['er_specimen_names']= pmag.get_list(CoordDir,'er_specimen_name') # get specimne names averaged
                           PmagSampRec['magic_method_codes']= pmag.get_list(CoordDir,'magic_method_codes') # assemble method codes
                           if nocrit!=1: # apply selection criteria
                               kill=pmag.grade(PmagSampRec,accept,'sample_dir')
                               if len(kill)==0: # passes the mustard
                                   SampDirs.append(PmagSampRec)
                                   if vgps==1:
                                       PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
                                       if PmagResRec!="":PmagResults.append(PmagResRec)
                           else: # take everything
                               SampDirs.append(PmagSampRec)
                               if vgps==1:
                                   PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
                                   if PmagResRec!="":PmagResults.append(PmagResRec)
                           PmagSamps.append(PmagSampRec)
        if Iaverage==1: #  average by sample if desired
           SampI=pmag.get_dictitem(SpecInts,'er_sample_name',samp,'T') # get all the intensity data for this sample
           if len(SampI)>0: # there are some
               PmagSampRec=pmag.average_int(SampI,'specimen','sample') # get average intensity stuff
               PmagSampRec["sample_description"]="sample intensity" # decorate sample record
               PmagSampRec["sample_direction_type"]=""
               PmagSampRec['er_site_name']=SampI[0]["er_site_name"]
               PmagSampRec['er_sample_name']=samp
               PmagSampRec['er_location_name']=SampI[0]["er_location_name"]
               PmagSampRec["er_citation_names"]="This study"
               PmagSampRec["er_analyst_mail_names"]=user
               if agefile != "":   PmagSampRec=pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_", AgeNFO,DefaultAge)
               site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
               if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
               PmagSampRec['er_specimen_names']= pmag.get_list(SampI,'er_specimen_name')
               PmagSampRec['magic_method_codes']= pmag.get_list(SampI,'magic_method_codes')
               if nocrit!=1:  # apply criteria!
                   kill=pmag.grade(PmagSampRec,accept,'sample_int')
                   if len(kill)==0:
                       PmagSampRec['pmag_criteria_codes']="ACCEPT"
                       SampInts.append(PmagSampRec)
                       PmagSamps.append(PmagSampRec)
                   else:PmagSampRec={} # sample rejected
               else: # no criteria
                   SampInts.append(PmagSampRec)
                   PmagSamps.append(PmagSampRec)
                   PmagSampRec['pmag_criteria_codes']=""
               if vgps==1 and get_model_lat!=0 and PmagSampRec!={}: #
                   if get_model_lat==1: # use sample latitude
                       PmagResRec=pmag.getsampVDM(PmagSampRec,SampNFO)
                       del(PmagResRec['model_lat']) # get rid of the model lat key
                   elif get_model_lat==2: # use model latitude
                       PmagResRec=pmag.getsampVDM(PmagSampRec,ModelLats)
                       if PmagResRec!={}:PmagResRec['magic_method_codes']=PmagResRec['magic_method_codes']+":IE-MLAT"
                   if PmagResRec!={}:
                          PmagResRec['er_specimen_names']=PmagSampRec['er_specimen_names']
                          PmagResRec['er_sample_names']=PmagSampRec['er_sample_name']
                          PmagResRec['pmag_criteria_codes']='ACCEPT'
                          PmagResRec['average_int_sigma_perc']=PmagSampRec['sample_int_sigma_perc']
                          PmagResRec['average_int_sigma']=PmagSampRec['sample_int_sigma']
                          PmagResRec['average_int_n']=PmagSampRec['sample_int_n']
                          PmagResRec['vadm_n']=PmagSampRec['sample_int_n']
                          PmagResRec['data_type']='i'
                          PmagResults.append(PmagResRec)
    if len(PmagSamps)>0:
        TmpSamps,keylist=pmag.fillkeys(PmagSamps) # fill in missing keys from different types of records
        pmag.magic_write(sampout,TmpSamps,'pmag_samples') # save in sample output file
        print(' sample averages written to ',sampout)

#
#create site averages from specimens or samples as specified
#
    for site in sites:
        if Daverage==0: key,dirlist='specimen',SpecDirs # if specimen averages at site level desired
        if Daverage==1: key,dirlist='sample',SampDirs # if sample averages at site level desired
        tmp=pmag.get_dictitem(dirlist,'er_site_name',site,'T') # get all the sites with  directions
        tmp1=pmag.get_dictitem(tmp,key+'_tilt_correction',coords[-1],'T') # use only the last coordinate if Caverage==0
        sd=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T') # fish out site information (lat/lon, etc.)
        if len(sd)>0:
            sitedat=sd[0]
            if Caverage==0: # do component wise averaging
                for comp in Comps:
                    siteD=pmag.get_dictitem(tmp1,key+'_comp_name',comp,'T') # get all components comp
                    if len(siteD)>0: # there are some for this site and component name
                        PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get an average for this site
                        PmagSiteRec['site_comp_name']=comp # decorate the site record
                        PmagSiteRec["er_location_name"]=siteD[0]['er_location_name']
                        PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
                        PmagSiteRec['site_tilt_correction']=coords[-1]
                        PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
                        if Daverage==1:
                            PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
                        else:
                            PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
        # determine the demagnetization code (DC3,4 or 5) for this site
                        AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
                        Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
                        DC=3
                        if AFnum>0:DC+=1
                        if Tnum>0:DC+=1
                        PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
                        PmagSiteRec['magic_method_codes'].strip(":")
                        if plotsites==1:
                            print(PmagSiteRec['er_site_name'])
                            pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key) # plot and list the data
                            pmagplotlib.drawFIGS(EQ)
                        PmagSites.append(PmagSiteRec)
            else: # last component only
                siteD=tmp1[:] # get the last orientation system specified
                if len(siteD)>0: # there are some
                    PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get the average for this site
                    PmagSiteRec["er_location_name"]=siteD[0]['er_location_name'] # decorate the record
                    PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
                    PmagSiteRec['site_comp_name']=comp
                    PmagSiteRec['site_tilt_correction']=coords[-1]
                    PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
                    PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
                    PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
                    AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
                    Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
                    DC=3
                    if AFnum>0:DC+=1
                    if Tnum>0:DC+=1
                    PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
                    PmagSiteRec['magic_method_codes'].strip(":")
                    if Daverage==0:PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
                    if plotsites==1:
                        pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key)
                        pmagplotlib.drawFIGS(EQ)
                    PmagSites.append(PmagSiteRec)
        else:
            print('site information not found in er_sites for site, ',site,' site will be skipped')
    for PmagSiteRec in PmagSites: # now decorate each dictionary some more, and calculate VGPs etc. for results table
        PmagSiteRec["er_citation_names"]="This study"
        PmagSiteRec["er_analyst_mail_names"]=user
        PmagSiteRec['magic_software_packages']=version_num
        if agefile != "": PmagSiteRec= pmag.get_age(PmagSiteRec,"er_site_name","site_inferred_",AgeNFO,DefaultAge)
        PmagSiteRec['pmag_criteria_codes']='ACCEPT'
        if 'site_n_lines' in list(PmagSiteRec.keys()) and 'site_n_planes' in list(PmagSiteRec.keys()) and PmagSiteRec['site_n_lines']!="" and PmagSiteRec['site_n_planes']!="":
            if int(PmagSiteRec["site_n_planes"])>0:
                PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM-LP"
            elif int(PmagSiteRec["site_n_lines"])>2:
                PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM"
            kill=pmag.grade(PmagSiteRec,accept,'site_dir')
            if len(kill)==0:
                PmagResRec={} # set up dictionary for the pmag_results table entry
                PmagResRec['data_type']='i' # decorate it a bit
                PmagResRec['magic_software_packages']=version_num
                PmagSiteRec['site_description']='Site direction included in results table'
                PmagResRec['pmag_criteria_codes']='ACCEPT'
                dec=float(PmagSiteRec["site_dec"])
                inc=float(PmagSiteRec["site_inc"])
                if 'site_alpha95' in list(PmagSiteRec.keys()) and PmagSiteRec['site_alpha95']!="":
                    a95=float(PmagSiteRec["site_alpha95"])
                else:a95=180.
                sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T')[0] # fish out site information (lat/lon, etc.)
                lat=float(sitedat['site_lat'])
                lon=float(sitedat['site_lon'])
                plong,plat,dp,dm=pmag.dia_vgp(dec,inc,a95,lat,lon) # get the VGP for this site
                if PmagSiteRec['site_tilt_correction']=='-1':C=' (spec coord) '
                if PmagSiteRec['site_tilt_correction']=='0':C=' (geog. coord) '
                if PmagSiteRec['site_tilt_correction']=='100':C=' (strat. coord) '
                PmagResRec["pmag_result_name"]="VGP Site: "+PmagSiteRec["er_site_name"] # decorate some more
                PmagResRec["result_description"]="Site VGP, coord system = "+str(coord)+' component: '+comp
                PmagResRec['er_site_names']=PmagSiteRec['er_site_name']
                PmagResRec['pmag_criteria_codes']='ACCEPT'
                PmagResRec['er_citation_names']='This study'
                PmagResRec['er_analyst_mail_names']=user
                PmagResRec["er_location_names"]=PmagSiteRec["er_location_name"]
                if Daverage==1:
                    PmagResRec["er_sample_names"]=PmagSiteRec["er_sample_names"]
                else:
                    PmagResRec["er_specimen_names"]=PmagSiteRec["er_specimen_names"]
                PmagResRec["tilt_correction"]=PmagSiteRec['site_tilt_correction']
                PmagResRec["pole_comp_name"]=PmagSiteRec['site_comp_name']
                PmagResRec["average_dec"]=PmagSiteRec["site_dec"]
                PmagResRec["average_inc"]=PmagSiteRec["site_inc"]
                PmagResRec["average_alpha95"]=PmagSiteRec["site_alpha95"]
                PmagResRec["average_n"]=PmagSiteRec["site_n"]
                PmagResRec["average_n_lines"]=PmagSiteRec["site_n_lines"]
                PmagResRec["average_n_planes"]=PmagSiteRec["site_n_planes"]
                PmagResRec["vgp_n"]=PmagSiteRec["site_n"]
                PmagResRec["average_k"]=PmagSiteRec["site_k"]
                PmagResRec["average_r"]=PmagSiteRec["site_r"]
                PmagResRec["average_lat"]='%10.4f ' %(lat)
                PmagResRec["average_lon"]='%10.4f ' %(lon)
                if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
                site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
                if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
                PmagResRec["vgp_lat"]='%7.1f ' % (plat)
                PmagResRec["vgp_lon"]='%7.1f ' % (plong)
                PmagResRec["vgp_dp"]='%7.1f ' % (dp)
                PmagResRec["vgp_dm"]='%7.1f ' % (dm)
                PmagResRec["magic_method_codes"]= PmagSiteRec["magic_method_codes"]
                if PmagSiteRec['site_tilt_correction']=='0':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-GEO"
                if PmagSiteRec['site_tilt_correction']=='100':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-TILT"
                PmagSiteRec['site_polarity']=""
                if polarity==1: # assign polarity based on angle of pole lat to spin axis - may want to re-think this sometime
                    angle=pmag.angle([0,0],[0,(90-plat)])
                    if angle <= 55.: PmagSiteRec["site_polarity"]='n'
                    if angle > 55. and angle < 125.: PmagSiteRec["site_polarity"]='t'
                    if angle >= 125.: PmagSiteRec["site_polarity"]='r'
                PmagResults.append(PmagResRec)
    if polarity==1:
        crecs=pmag.get_dictitem(PmagSites,'site_tilt_correction','100','T') # find the tilt corrected data
        if len(crecs)<2:crecs=pmag.get_dictitem(PmagSites,'site_tilt_correction','0','T') # if there aren't any, find the geographic corrected data
        if len(crecs)>2: # if there are some,
            comp=pmag.get_list(crecs,'site_comp_name').split(':')[0] # find the first component
            crecs=pmag.get_dictitem(crecs,'site_comp_name',comp,'T') # fish out all of the first component
            precs=[]
            for rec in crecs:
                precs.append({'dec':rec['site_dec'],'inc':rec['site_inc'],'name':rec['er_site_name'],'loc':rec['er_location_name']})
            polpars=pmag.fisher_by_pol(precs) # calculate average by polarity
            for mode in list(polpars.keys()): # hunt through all the modes (normal=A, reverse=B, all=ALL)
                PolRes={}
                PolRes['er_citation_names']='This study'
                PolRes["pmag_result_name"]="Polarity Average: Polarity "+mode #
                PolRes["data_type"]="a"
                PolRes["average_dec"]='%7.1f'%(polpars[mode]['dec'])
                PolRes["average_inc"]='%7.1f'%(polpars[mode]['inc'])
                PolRes["average_n"]='%i'%(polpars[mode]['n'])
                PolRes["average_r"]='%5.4f'%(polpars[mode]['r'])
                PolRes["average_k"]='%6.0f'%(polpars[mode]['k'])
                PolRes["average_alpha95"]='%7.1f'%(polpars[mode]['alpha95'])
                PolRes['er_site_names']= polpars[mode]['sites']
                PolRes['er_location_names']= polpars[mode]['locs']
                PolRes['magic_software_packages']=version_num
                PmagResults.append(PolRes)

    if noInt!=1 and nositeints!=1:
      for site in sites: # now do intensities for each site
        if plotsites==1:print(site)
        if Iaverage==0: key,intlist='specimen',SpecInts # if using specimen level data
        if Iaverage==1: key,intlist='sample',PmagSamps # if using sample level data
        Ints=pmag.get_dictitem(intlist,'er_site_name',site,'T') # get all the intensities  for this site
        if len(Ints)>0: # there are some
            PmagSiteRec=pmag.average_int(Ints,key,'site') # get average intensity stuff for site table
            PmagResRec=pmag.average_int(Ints,key,'average') # get average intensity stuff for results table
            if plotsites==1: # if site by site examination requested - print this site out to the screen
                for rec in Ints:print(rec['er_'+key+'_name'],' %7.1f'%(1e6*float(rec[key+'_int'])))
                if len(Ints)>1:
                    print('Average: ','%7.1f'%(1e6*float(PmagResRec['average_int'])),'N: ',len(Ints))
                    print('Sigma: ','%7.1f'%(1e6*float(PmagResRec['average_int_sigma'])),'Sigma %: ',PmagResRec['average_int_sigma_perc'])
                input('Press any key to continue\n')
            er_location_name=Ints[0]["er_location_name"]
            PmagSiteRec["er_location_name"]=er_location_name # decorate the records
            PmagSiteRec["er_citation_names"]="This study"
            PmagResRec["er_location_names"]=er_location_name
            PmagResRec["er_citation_names"]="This study"
            PmagSiteRec["er_analyst_mail_names"]=user
            PmagResRec["er_analyst_mail_names"]=user
            PmagResRec["data_type"]='i'
            if Iaverage==0:
                PmagSiteRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name') # list of all specimens used
                PmagResRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name')
            PmagSiteRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name') # list of all samples used
            PmagResRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name')
            PmagSiteRec['er_site_name']= site
            PmagResRec['er_site_names']= site
            PmagSiteRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
            PmagResRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
            kill=pmag.grade(PmagSiteRec,accept,'site_int')
            if nocrit==1 or len(kill)==0:
                b,sig=float(PmagResRec['average_int']),""
                if(PmagResRec['average_int_sigma'])!="":sig=float(PmagResRec['average_int_sigma'])
                sdir=pmag.get_dictitem(PmagResults,'er_site_names',site,'T') # fish out site direction
                if len(sdir)>0 and  sdir[-1]['average_inc']!="": # get the VDM for this record using last average inclination (hope it is the right one!)
                        inc=float(sdir[0]['average_inc']) #
                        mlat=pmag.magnetic_lat(inc) # get magnetic latitude using dipole formula
                        PmagResRec["vdm"]='%8.3e '% (pmag.b_vdm(b,mlat)) # get VDM with magnetic latitude
                        PmagResRec["vdm_n"]=PmagResRec['average_int_n']
                        if 'average_int_sigma' in list(PmagResRec.keys()) and PmagResRec['average_int_sigma']!="":
                            vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),mlat)
                            PmagResRec["vdm_sigma"]='%8.3e '% (vdm_sig)
                        else:
                            PmagResRec["vdm_sigma"]=""
                mlat="" # define a model latitude
                if get_model_lat==1: # use present site latitude
                    mlats=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T')
                    if len(mlats)>0: mlat=mlats[0]['site_lat']
                elif get_model_lat==2: # use a model latitude from some plate reconstruction model (or something)
                    mlats=pmag.get_dictitem(ModelLats,'er_site_name',site,'T')
                    if len(mlats)>0: PmagResRec['model_lat']=mlats[0]['site_model_lat']
                    mlat=PmagResRec['model_lat']
                if mlat!="":
                    PmagResRec["vadm"]='%8.3e '% (pmag.b_vdm(b,float(mlat))) # get the VADM using the desired latitude
                    if sig!="":
                        vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),float(mlat))
                        PmagResRec["vadm_sigma"]='%8.3e '% (vdm_sig)
                        PmagResRec["vadm_n"]=PmagResRec['average_int_n']
                    else:
                        PmagResRec["vadm_sigma"]=""
                sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T') # fish out site information (lat/lon, etc.)
                if len(sitedat)>0:
                    sitedat=sitedat[0]
                    PmagResRec['average_lat']=sitedat['site_lat']
                    PmagResRec['average_lon']=sitedat['site_lon']
                else:
                    PmagResRec['average_lon']='UNKNOWN'
                    PmagResRec['average_lon']='UNKNOWN'
                PmagResRec['magic_software_packages']=version_num
                PmagResRec["pmag_result_name"]="V[A]DM: Site "+site
                PmagResRec["result_description"]="V[A]DM of site"
                PmagResRec["pmag_criteria_codes"]="ACCEPT"
                if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
                site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
                if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
                PmagSites.append(PmagSiteRec)
                PmagResults.append(PmagResRec)
    if len(PmagSites)>0:
        Tmp,keylist=pmag.fillkeys(PmagSites)
        pmag.magic_write(siteout,Tmp,'pmag_sites')
        print(' sites written to ',siteout)
    else: print("No Site level table")
    if len(PmagResults)>0:
        TmpRes,keylist=pmag.fillkeys(PmagResults)
        pmag.magic_write(resout,TmpRes,'pmag_results')
        print(' results written to ',resout)
    else: print("No Results level table")