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 = 0.01 * 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
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(): 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 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"
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 '-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) 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) # 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) 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"
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"