def main(): """ NAME pyscu_draw.py DESCRIPTION plot the data calculated in pyscu_calc.py INPUT ouput files from pyscu_calc.py You must open the *_main.txt interactive data entry using Easygui (http://easygui.sourceforge.net/) """ print( '\nThis program uses the PmagPy and pySCu softwares utilities\n\tTauxe et al. 2016, G3, http://dx.doi.org/10.1002/2016GC006307\n\tCalvín et al. 2017, C&G, http://dx.doi.org/10.1016/j.cageo.2017.07.002' ) if '-h' in sys.argv: print(main.__doc__) sys.exit() infile = eg.fileopenbox(msg="Open File", title="Control: fileopenbox", default='') outfile = infile[:-9] infile_m = infile[:-8] + 'mat.txt' infile_ref = infile[:-8] + 'Ref.txt' infile_inter = infile[:-8] + 'inter.txt' infile_sci = infile[:-8] + 'SCIs.txt' if path.exists(infile_ref): Ref = 'true' else: Ref = 'false' if path.exists(infile_m): matrix = 'true' else: matrix = 'false' if path.exists(infile_inter): inter = 'true' else: inter = 'false' if path.exists(infile_sci): SCIs = 'true' else: SCIs = 'false' if Ref == 'false': campos = [ 'Dec', 'Inc', 'Eta', 'Dec_Eta', 'Inc_Eta', 'Zeta', 'Dec_Zeta', 'Inc_Zeta' ] ref = [] ref = eg.multenterbox( msg= "I can't found the file with the reference direction \n Please, input the Kent parameters of it", title='Interactive entry of the remagnetization direction', fields=campos, values=(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)) if Ref == 'false' and ref != None: print(ref) ref[0] = float(ref[0]) ref[1] = float(ref[1]) ref[2] = float(ref[2]) ref[3] = float(ref[3]) ref[4] = float(ref[4]) ref[5] = float(ref[5]) ref[6] = float(ref[6]) ref[7] = float(ref[7]) iRef = 'true' print(ref) ans_mat = eg.boolbox(msg='Do you want to plot te A matrix?', title='Control: boolbox', choices=('Yes', 'No')) preS = eg.buttonbox( msg= 'Doy you want plot the SCI solutions (s), the intersections (i) or none (n)', title='Control: buttonbox', choices=('s', 'i', 'n')) if matrix == 'false' and ans_mat == True: print("\nTake care, I don't found the file", infile_m, ' whit the A/N matriz data') if inter == 'false' and preS == 'i': print("\nTake care, I don't found the file", infile_inter, ' whit the intersections directions') if SCIs == 'false' and preS == 's': print("\nTake care, I don't found the file", infile_sci, ' whit the SCIs directions') out_name_bbc = outfile + '_bbc.svg' out_name_bfd = outfile + '_bfd.svg' out_name_atbc = outfile + '_atbc.svg' out_name_mat = outfile + '_mat.svg' print('\nPlease, wait a moment') print('\nPlots will be saved as', out_name_bbc, ', ', out_name_bfd, '...\n') #Saving the data in different list site, sc, geo, tilt, bfd = scu.getInFile_main(infile) #main file n = len(site) if Ref == 'true': #reference direction reader = csv.reader(open(infile_ref), delimiter=' ') dat_Ref = list(reader) ref = [ float(dat_Ref[1][1]), float(dat_Ref[1][2]), float(dat_Ref[1][3]), float(dat_Ref[1][5]), float(dat_Ref[1][6]), float(dat_Ref[1][4]), float(dat_Ref[1][7]), float(dat_Ref[1][8]), float(dat_Ref[1][11]) ] if inter == 'true' and preS == 'i': #intersections directions reader = csv.reader(open(infile_inter), delimiter=' ') dat_inter_h = list(reader) dat_inter = dat_inter_h[1:] if SCIs == 'true' and preS == 's': #intersections directions reader = csv.reader(open(infile_sci), delimiter=' ') dat_SCIs_h = list(reader) dat_SCIs = dat_SCIs_h[1:] if matrix == 'true' and ans_mat == True: #A/n values X, Y, Z, minA, maxA = scu.getInFile_mat(infile_m) #Drawing... plt.figure(num=1, figsize=(6, 6), facecolor='white') #Plotting the BBC directions, the SCs and the reference pmagpl.plotNET(1) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'BBC', fontsize=13) plt.scatter(0.8, 0.74, color='r', marker='s', s=30) plt.text(0.70, 0.85, 'n=' + str(n), fontsize=13) for dato in sc: #The SCs scu.smallcirc(dato, 1) for dato in geo: #The BBC directions scu.plot_di_mean(dato[0], dato[1], dato[2], color='r', marker='s', markersize=8, label='Geo', legend='no', zorder=3) #You can change the marker (+, ., o, *, p, s, x, D, h, ^), the color (b, g, r, c, m, y, k, w) or the size as you prefere if Ref == 'true' or iRef == 'true': #The reference scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize=13) plt.scatter(0.45, -1, color='m', marker='*', s=100) plt.title('Before Bedding Correction', fontsize=20) plt.savefig(out_name_bbc) #Plotting the ATBC directions, the SCs and the reference plt.figure(num=2, figsize=(6, 6), facecolor='white') pmagpl.plotNET(2) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'ATBC', fontsize=13) plt.scatter(0.8, 0.745, color='g', marker='^', s=40) plt.text(0.70, 0.85, 'n=' + str(n), fontsize=13) for dato in sc: scu.smallcirc(dato, 1) if Ref == 'true' or iRef == 'true': scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize=13) plt.scatter(0.45, -1, color='m', marker='*', s=100) for dato in tilt: scu.plot_di_mean(dato[0], dato[1], dato[2], color='g', marker='^', markersize=9, label='Tilt', legend='no', zorder=3) plt.savefig(out_name_atbc) #Plotting the BFD directions, the SCs and the reference plt.figure(num=3, figsize=(6, 6), facecolor='white') pmagpl.plotNET(3) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'BFD', fontsize=13) plt.scatter(0.8, 0.74, color='b', marker='o', s=30) plt.text(0.70, 0.85, 'n=' + str(n), fontsize=13) for dato in sc: scu.smallcirc(dato, 1) for dato in bfd: scu.plot_di_mean(dato[0], dato[1], dato[2], color='b', marker='o', markersize=5, label='BFD', legend='no', zorder=3) if Ref == 'true' or iRef == 'true': #Ploting the reference and the leyend scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize=13) plt.scatter(0.45, -1, color='m', marker='*', s=100) plt.savefig(out_name_bfd) #Plotting the A/n contour plot and/or the intersections if (ans_mat == True and matrix == 'true') or ( preS == 'i' and inter == 'true') or (preS == 's' and SCIs == 'true'): plt.figure(num=4, figsize=(6, 6), facecolor='white') pmagpl.plotNET(4) pylab.figtext(.02, .045, 'pySCu v3.1') fig4 = 'true' else: fig4 = 'false' if ans_mat == True and matrix == 'true': #plotting the A/n contour plot max_z = max(Z) max_z_s = max_z + (5 - max_z % 5) + 0.1 min_z = min(Z) min_z_s = min_z - (min_z % 5) levels5 = np.arange(min_z_s, max_z_s, 5) levels1 = np.arange(min_z_s, max_z_s, 1) CS = plt.tricontourf( X, Y, Z, vmin=min_z, vmax=max_z, cmap='Blues', levels=levels1 ) #Other colormaps (as 'rainbow') are possibles. Change 'Blues' for the choosed colormap cbar = plt.colorbar(CS, orientation='horizontal', pad=0.05) CS2 = plt.tricontour(X, Y, Z, colors='k', linewidths=.5, levels=levels5) #plt.clabel(CS2,levels=levels5, inline=1, fmt='%1.0f', fontsize=10) cbar.ax.set_xlabel('A/n value' + ' (' + str(round(minA, 1)) + '-' + str(round(maxA, 1)) + ')') #cbar.add_lines(CS2) plt.axis((-1.35, 1.35, -1.35, 1.35)) else: for dato in sc: scu.smallcirc(dato, 1) if preS == 'i' and inter == 'true': #plotting the intersections text_i = 'SCs intersec. (n=' + str(len(dat_inter)) + ')' plt.text(-0.3, -1.2, text_i, fontsize=12) plt.scatter(-0.38, -1.12, color='k', marker='.', s=50) for dato in dat_inter: scu.plot_di_mean(float(dato[0]), float(dato[1]), 0., color='k', marker='.', markersize=1, label='Intersections', legend='no') if preS == 's' and SCIs == 'true': #plotting the SCIs text_s = 'SCIs solutions (n=' + str(len(dat_SCIs)) + ')' plt.text(-0.3, -1.2, text_s, fontsize=12) plt.scatter(-0.38, -1.12, color='k', marker='.', s=50) for dato in dat_SCIs: scu.plot_di_mean(float(dato[0]), float(dato[1]), 0., color='k', marker='.', markersize=1, label='SCIs', legend='no') if fig4 == 'true' and (Ref == 'true' or iRef == 'true'): #Plotting the reference and the leyend scu.plotCONF(ref) plt.text(0.6, 0.83, 'Reference', fontsize=13) plt.scatter(0.93, 0.73, color='m', marker='*', s=100) text_rat = 'mr/mp=' + str(ref[8]) + ';' plt.text(-1.37, -1.2, text_rat, fontsize=12) if fig4 == 'true': plt.savefig(out_name_mat) plt.show()
def main(): """ NAME eqarea_magic.py DESCRIPTION makes equal area projections from declination/inclination data SYNTAX eqarea_magic.py [command line options] INPUT takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic,default='pmag_results.txt' supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web] -obj OBJ: specify level of plot [all, sit, sam, spc], default is all -crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted default is geographic, unspecified assumed geographic -fmt [svg,png,jpg] format for output plots -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors -c plot as colour contour -sav save plot and quit quietly NOTE all: entire file; sit: site; sam: sample; spc: specimen """ FIG = {} # plot dictionary FIG['eqarea'] = 1 # eqarea is figure 1 in_file, plot_key, coord, crd = 'pmag_results.txt', 'all', "0", 'g' plotE, contour = 0, 0 dir_path = '.' fmt = 'svg' verbose = pmagplotlib.verbose if '-h' in sys.argv: print main.__doc__ sys.exit() if '-WD' in sys.argv: ind = sys.argv.index('-WD') dir_path = sys.argv[ind + 1] pmagplotlib.plot_init(FIG['eqarea'], 5, 5) if '-f' in sys.argv: ind = sys.argv.index("-f") in_file = dir_path + "/" + sys.argv[ind + 1] if '-obj' in sys.argv: ind = sys.argv.index('-obj') plot_by = sys.argv[ind + 1] if plot_by == 'all': plot_key = 'all' if plot_by == 'sit': plot_key = 'er_site_name' if plot_by == 'sam': plot_key = 'er_sample_name' if plot_by == 'spc': plot_key = 'er_specimen_name' if '-c' in sys.argv: contour = 1 plots = 0 if '-sav' in sys.argv: plots = 1 verbose = 0 if '-ell' in sys.argv: plotE = 1 ind = sys.argv.index('-ell') ell_type = sys.argv[ind + 1] if ell_type == 'F': dist = 'F' if ell_type == 'K': dist = 'K' if ell_type == 'B': dist = 'B' if ell_type == 'Be': dist = 'BE' if ell_type == 'Bv': dist = 'BV' FIG['bdirs'] = 2 pmagplotlib.plot_init(FIG['bdirs'], 5, 5) if '-crd' in sys.argv: ind = sys.argv.index("-crd") crd = sys.argv[ind + 1] if crd == 's': coord = "-1" if crd == 'g': coord = "0" if crd == 't': coord = "100" if '-fmt' in sys.argv: ind = sys.argv.index("-fmt") fmt = sys.argv[ind + 1] Dec_keys = ['site_dec', 'sample_dec', 'specimen_dec', 'measurement_dec', 'average_dec', 'none'] Inc_keys = ['site_inc', 'sample_inc', 'specimen_inc', 'measurement_inc', 'average_inc', 'none'] Tilt_keys = ['tilt_correction', 'site_tilt_correction', 'sample_tilt_correction', 'specimen_tilt_correction', 'none'] Dir_type_keys = ['', 'site_direction_type', 'sample_direction_type', 'specimen_direction_type'] Name_keys = ['er_specimen_name', 'er_sample_name', 'er_site_name', 'pmag_result_name'] data, file_type = pmag.magic_read(in_file) if file_type == 'pmag_results' and plot_key != "all": plot_key = plot_key + 's' # need plural for results table if verbose: print len(data), ' records read from ', in_file # # # find desired dec,inc data: # dir_type_key = '' # # get plotlist if not plotting all records # plotlist = [] if plot_key != "all": plots = pmag.get_dictitem(data, plot_key, '', 'F') for rec in plots: if rec[plot_key] not in plotlist: plotlist.append(rec[plot_key]) plotlist.sort() else: plotlist.append('All') for plot in plotlist: if verbose: print plot DIblock = [] GCblock = [] SLblock, SPblock = [], [] title = plot mode = 1 dec_key, inc_key, tilt_key, name_key, k = "", "", "", "", 0 if plot != "All": odata = pmag.get_dictitem(data, plot_key, plot, 'T') else: odata = data # data for this obj for dec_key in Dec_keys: Decs = pmag.get_dictitem(odata, dec_key, '', 'F') # get all records with this dec_key not blank if len(Decs) > 0: break for inc_key in Inc_keys: Incs = pmag.get_dictitem(Decs, inc_key, '', 'F') # get all records with this inc_key not blank if len(Incs) > 0: break for tilt_key in Tilt_keys: if tilt_key in Incs[0].keys(): break # find the tilt_key for these records if tilt_key == 'none': # no tilt key in data, need to fix this with fake data which will be unknown tilt tilt_key = 'tilt_correction' for rec in Incs: rec[tilt_key] = '' cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T') # get all records matching specified coordinate system if coord == '0': # geographic udata = pmag.get_dictitem(Incs, tilt_key, '', 'T') # get all the blank records - assume geographic if len(cdata) == 0: crd = '' if len(udata) > 0: for d in udata: cdata.append(d) crd = crd + 'u' for name_key in Name_keys: Names = pmag.get_dictitem(cdata, name_key, '', 'F') # get all records with this name_key not blank if len(Names) > 0: break for dir_type_key in Dir_type_keys: Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F') # get all records with this direction type if len(Dirs) > 0: break if dir_type_key == "": dir_type_key = 'direction_type' locations, site, sample, specimen = "", "", "", "" for rec in cdata: # pick out the data if 'er_location_name' in rec.keys() and rec['er_location_name'] != "" and rec[ 'er_location_name'] not in locations: locations = locations + rec['er_location_name'].replace("/", "") + "_" if 'er_location_names' in rec.keys() and rec['er_location_names'] != "": locs = rec['er_location_names'].split(':') for loc in locs: if loc not in locations: locations = locations + loc.replace("/", "") + '_' if plot_key == 'er_site_name' or plot_key == 'er_sample_name' or plot_key == 'er_specimen_name': site = rec['er_site_name'] if plot_key == 'er_sample_name' or plot_key == 'er_specimen_name': sample = rec['er_sample_name'] if plot_key == 'er_specimen_name': specimen = rec['er_specimen_name'] if plot_key == 'er_site_names' or plot_key == 'er_sample_names' or plot_key == 'er_specimen_names': site = rec['er_site_names'] if plot_key == 'er_sample_names' or plot_key == 'er_specimen_names': sample = rec['er_sample_names'] if plot_key == 'er_specimen_names': specimen = rec['er_specimen_names'] if dir_type_key not in rec.keys() or rec[dir_type_key] == "": rec[dir_type_key] = 'l' if 'magic_method_codes' not in rec.keys(): rec['magic_method_codes'] = "" DIblock.append([float(rec[dec_key]), float(rec[inc_key])]) SLblock.append([rec[name_key], rec['magic_method_codes']]) if rec[tilt_key] == coord and rec[dir_type_key] != 'l' and rec[dec_key] != "" and rec[inc_key] != "": GCblock.append([float(rec[dec_key]), float(rec[inc_key])]) SPblock.append([rec[name_key], rec['magic_method_codes']]) if len(DIblock) == 0 and len(GCblock) == 0: if verbose: print "no records for plotting" sys.exit() if verbose: for k in range(len(SLblock)): print '%s %s %7.1f %7.1f' % (SLblock[k][0], SLblock[k][1], DIblock[k][0], DIblock[k][1]) for k in range(len(SPblock)): print '%s %s %7.1f %7.1f' % (SPblock[k][0], SPblock[k][1], GCblock[k][0], GCblock[k][1]) if len(DIblock) > 0: if contour == 0: pmagplotlib.plotEQ(FIG['eqarea'], DIblock, title) else: pmagplotlib.plotEQcont(FIG['eqarea'], DIblock) else: pmagplotlib.plotNET(FIG['eqarea']) if len(GCblock) > 0: for rec in GCblock: pmagplotlib.plotC(FIG['eqarea'], rec, 90., 'g') if plotE == 1: ppars = pmag.doprinc(DIblock) # get principal directions nDIs, rDIs, npars, rpars = [], [], [], [] for rec in DIblock: angle = pmag.angle([rec[0], rec[1]], [ppars['dec'], ppars['inc']]) if angle > 90.: rDIs.append(rec) else: nDIs.append(rec) if dist == 'B': # do on whole dataset etitle = "Bingham confidence ellipse" bpars = pmag.dobingham(DIblock) for key in bpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (bpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist == 'F': etitle = "Fisher confidence cone" if len(nDIs) > 2: fpars = pmag.fisher_mean(nDIs) for key in fpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] npars.append(fpars['inc'] - isign * 90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec'] + 90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs) > 2: fpars = pmag.fisher_mean(rDIs) if verbose: print "mode ", mode for key in fpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] rpars.append(fpars['inc'] - isign * 90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec'] + 90.) # Beta dec rpars.append(0.) #Beta inc if dist == 'K': etitle = "Kent confidence ellipse" if len(nDIs) > 3: kpars = pmag.dokent(nDIs, len(nDIs)) if verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs) > 3: kpars = pmag.dokent(rDIs, len(rDIs)) if verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if dist == 'BE': if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) Bkpars = pmag.dokent(BnDIs, 1.) if verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) Bkpars = pmag.dokent(BrDIs, 1.) if verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle = "Bootstrapped confidence ellipse" elif dist == 'BV': sym = {'lower': ['o', 'c'], 'upper': ['o', 'g'], 'size': 3, 'edgecolor': 'face'} if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, sym) else: pmagplotlib.plotEQ(FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors') if dist == 'B': if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0) elif len(nDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0) if len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0) elif len(rDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0) if verbose: pmagplotlib.drawFIGS(FIG) # files = {} locations = locations[:-1] for key in FIG.keys(): filename = 'LO:_' + locations + '_SI:_' + site + '_SA:_' + sample + '_SP:_' + specimen + '_CO:_' + crd + '_TY:_' + key + '_.' + fmt files[key] = filename if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles = {} titles['eq'] = 'Equal Area Plot' FIG = pmagplotlib.addBorders(FIG, titles, black, purple) pmagplotlib.saveP(FIG, files) elif verbose: ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG, files) if plots: pmagplotlib.saveP(FIG, files)
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"
def main(): """ NAME eqarea_magic.py DESCRIPTION makes equal area projections from declination/inclination data SYNTAX eqarea_magic.py [command line options] INPUT takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic,default='pmag_results.txt' supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web] -obj OBJ: specify level of plot [all, sit, sam, spc], default is all -crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted default is geographic -fmt [svg,png,jpg] format for output plots -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors -c plot as colour contour NOTE all: entire file; sit: site; sam: sample; spc: specimen """ FIG={} # plot dictionary FIG['eq']=1 # eqarea is figure 1 in_file,plot_key,coord,crd='pmag_results.txt','all',"-1",'g' fmt,dist,mode='svg','F',1 plotE,contour=0,0 dir_path='.' if '-h' in sys.argv: print main.__doc__ sys.exit() if '-WD' in sys.argv: ind=sys.argv.index('-WD') dir_path=sys.argv[ind+1] pmagplotlib.plot_init(FIG['eq'],5,5) if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=dir_path+"/"+sys.argv[ind+1] if '-obj' in sys.argv: ind=sys.argv.index('-obj') plot_by=sys.argv[ind+1] if plot_by=='all':plot_key='all' if plot_by=='sit':plot_key='er_site_name' if plot_by=='sam':plot_key='er_sample_name' if plot_by=='spc':plot_key='er_specimen_name' if '-c' in sys.argv: contour=1 if '-ell' in sys.argv: plotE=1 ind=sys.argv.index('-ell') ell_type=sys.argv[ind+1] if ell_type=='F':dist='F' if ell_type=='K':dist='K' if ell_type=='B':dist='B' if ell_type=='Be':dist='BE' if ell_type=='Bv': dist='BV' FIG['bdirs']=2 pmagplotlib.plot_init(FIG['bdirs'],5,5) if '-crd' in sys.argv: ind=sys.argv.index("-crd") coord=sys.argv[ind+1] if coord=='g':coord="0" if coord=='t':coord="100" if '-fmt' in sys.argv: ind=sys.argv.index("-fmt") fmt=sys.argv[ind+1] Dec_keys=['site_dec','sample_dec','specimen_dec','measurement_dec','average_dec'] Inc_keys=['site_inc','sample_inc','specimen_inc','measurement_inc','average_inc'] Tilt_keys=['tilt_correction','site_tilt_correction','sample_tilt_correction','specimen_tilt_correction'] Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type'] Name_keys=['er_specimen_name','er_sample_name','er_site_name','pmag_result_name'] data,file_type=pmag.magic_read(in_file) if file_type=='pmag_results' and plot_key!="all":plot_key=plot_key+'s' # need plural for results table if pmagplotlib.verbose: print len(data),' records read from ',in_file # # # find desired dec,inc data: # dir_type_key='' # # get plotlist if not plotting all records # plotlist=[] if plot_key!="all": for rec in data: if rec[plot_key] not in plotlist: plotlist.append(rec[plot_key]) plotlist.sort() else: plotlist.append('Whole file') for plot in plotlist: DIblock=[] GCblock=[] SLblock,SPblock=[],[] tilt_key="" mode=1 for rec in data: # find what data are available if plot_key=='all' or rec[plot_key]==plot: if plot_key!="all": title=rec[plot_key] else: title=plot if coord=='-1':title=title+' Specimen Coordinates' if coord=='0':title=title+' Geographic Coordinates' if coord=='100':title=title+' Tilt corrected Coordinates' dec_key,inc_key,tilt_key,name_key,k="","","","",0 while dec_key=="" and k<len(Dec_keys): if Dec_keys[k] in rec.keys() and rec[Dec_keys[k]]!="" and Inc_keys[k] in rec.keys() and rec[Inc_keys[k]]!="": dec_key,inc_key =Dec_keys[k],Inc_keys[k] k+=1 k=0 while tilt_key=="" and k<len(Tilt_keys): if Tilt_keys[k] in rec.keys():tilt_key=Tilt_keys[k] k+=1 k=0 while name_key=="" and k<len(Name_keys): if Name_keys[k] in rec.keys():name_key=Name_keys[k] k+=1 k=1 while dir_type_key=="" and k<len(Dir_type_keys): if Dir_type_keys[k] in rec.keys():dir_type_key=Dir_type_keys[k] k+=1 if dec_key!="":break if tilt_key=="":tilt_key='-1' if dir_type_key=="":dir_type_key='direction_type' for rec in data: # pick out the data if (plot_key=='all' or rec[plot_key]==plot) and rec[dec_key].strip()!="" and rec[inc_key].strip()!="": if dir_type_key not in rec.keys() or rec[dir_type_key]=="":rec[dir_type_key]='l' if tilt_key not in rec.keys():rec[tilt_key]='-1' # assume specimen coordinates unless otherwise specified if coord=='-1': DIblock.append([float(rec[dec_key]),float(rec[inc_key])]) SLblock.append([rec[name_key],rec['magic_method_codes']]) elif rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="": if rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="": DIblock.append([float(rec[dec_key]),float(rec[inc_key])]) SLblock.append([rec[name_key],rec['magic_method_codes']]) elif rec[tilt_key]==coord and rec[dir_type_key]!='l' and rec[dec_key]!="" and rec[inc_key]!="": GCblock.append([float(rec[dec_key]),float(rec[inc_key])]) SPblock.append([rec[name_key],rec['magic_method_codes']]) if len(DIblock)==0 and len(GCblock)==0: if pmagplotlib.verbose: print "no records for plotting" sys.exit() if pmagplotlib.verbose: for k in range(len(SLblock)): print '%s %s %7.1f %7.1f'%(SLblock[k][0],SLblock[k][1],DIblock[k][0],DIblock[k][1]) for k in range(len(SPblock)): print '%s %s %7.1f %7.1f'%(SPblock[k][0],SPblock[k][1],GCblock[k][0],GCblock[k][1]) if len(DIblock)>0: if contour==0: pmagplotlib.plotEQ(FIG['eq'],DIblock,title) else: pmagplotlib.plotEQcont(FIG['eq'],DIblock) else: pmagplotlib.plotNET(FIG['eq']) if len(GCblock)>0: for rec in GCblock: pmagplotlib.plotC(FIG['eq'],rec,90.,'g') if plotE==1: ppars=pmag.doprinc(DIblock) # get principal directions nDIs,rDIs,npars,rpars=[],[],[],[] for rec in DIblock: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) if dist=='B': # do on whole dataset etitle="Bingham confidence ellipse" bpars=pmag.dobingham(DIblock) for key in bpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(bpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist=='F': etitle="Fisher confidence cone" if len(nDIs)>2: fpars=pmag.fisher_mean(nDIs) for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] npars.append(fpars['inc']-isign*90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec']+90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs)>2: fpars=pmag.fisher_mean(rDIs) if pmagplotlib.verbose:print "mode ",mode for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] rpars.append(fpars['inc']-isign*90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec']+90.) # Beta dec rpars.append(0.) #Beta inc if dist=='K': etitle="Kent confidence ellipse" if len(nDIs)>3: kpars=pmag.dokent(nDIs,len(nDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs)>3: kpars=pmag.dokent(rDIs,len(rDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if dist=='BE': if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) Bkpars=pmag.dokent(BnDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) Bkpars=pmag.dokent(BrDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle="Bootstrapped confidence ellipse" elif dist=='BV': if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) pmagplotlib.plotEQ(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors') if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plotDI(FIG['bdirs'],BrDIs) else: pmagplotlib.plotEQ(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors') if dist=='B': if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) elif len(nDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) if len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) elif len(rDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) pmagplotlib.drawFIGS(FIG) # files={} for key in FIG.keys(): files[key]=title.replace(" ","_")+'_'+'eqarea'+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' FIG = pmagplotlib.addBorders(FIG,titles,black,purple) pmagplotlib.saveP(FIG,files) else: ans=raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans=="q": sys.exit() if ans=="a": pmagplotlib.saveP(FIG,files)
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 pyscu_draw.py DESCRIPTION plot the data calculated in pyscu_calc.py INPUT ouput files from pyscu_calc.py SYNTAX pyscu_draw.py [-h] [command line options] OPTIONS -h, plots help message and quits -f AFILE, specify file for input this is the main file output from the pyscu_calc.py program the other files have to been placed in the same folder -F RFILE, specify file for output -A, plot the A/N matrix -s [s/i/n], plot the SCI solutions, the intersections, or none. -fmt [svg, jpg, eps, pdf] format for output images. -i interactive entry of the remagnetization direction DEFAULTS -f, AFILE: SCdata_main.txt -F, RFILE: out DON'T plot the A/N matrix -s, DON'T plot the SCI solutions or the intersections -fmt, save as svg get de remagnetization direction from SCdata_Ref.txt """ print ('\nThis program uses the PmagPy and pySCu softwares utilities\n\tTauxe et al. 2016, G3, http://dx.doi.org/10.1002/2016GC006307\n\tCalvín et al. 2017, C&G, http://dx.doi.org/10.1016/j.cageo.2017.07.002') 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] else: infile='SCdata_main.txt' if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=sys.argv[ind+1] else: outfile=infile[:-9] if '-A' in sys.argv: preA='y' else: preA='n' if '-s' in sys.argv: ind=sys.argv.index('-s') preS=sys.argv[ind+1] else: preS='n' if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt='.'+sys.argv[ind+1] else: fmt='.svg' if '-i' in sys.argv: iRef='true' print('\nInput the Kent parameters (separated by spaces) of the remagnetization direction:') ref_input = input("\nDec Inc Eta Dec_Eta Inc_Eta Zeta Dec_Zeta Inc_Zeta: An example...\n329.9 39.5 10.5 155.1 50.4 4.9 62.3 2.6\n").split(' ') ref=[] for dato in ref_input: ref.append(float(dato)) else: iRef='false' infile_m=infile[:-8]+'mat.txt' infile_ref=infile[:-8]+'Ref.txt' infile_inter=infile[:-8]+'inter.txt' infile_sci=infile[:-8]+'SCIs.txt' if path.exists(infile_ref): Ref='true' else: Ref='false' if path.exists(infile_m): matrix='true' else: matrix='false' if path.exists(infile_inter): inter='true' else: inter='false' if path.exists(infile_sci): SCIs='true' else: SCIs='false' if Ref=='false' and iRef=='false': print("\nTake care, I don't found the file", infile_ref, " whit the reference direction") if matrix=='false' and preA=='y': print("\nTake care, I don't found the file", infile_m, ' whit the A/N matriz data') if inter=='false' and preS=='i': print("\nTake care, I don't found the file", infile_inter, ' whit the intersections directions') if SCIs=='false' and preS=='s': print("\nTake care, I don't found the file", infile_sci, ' whit the SCIs directions') out_name_bbc=outfile+'_bbc'+fmt out_name_bfd=outfile+'_bfd'+fmt out_name_atbc=outfile+'_atbc'+fmt out_name_mat=outfile+'_mat'+fmt print('\nPlease, wait a moment') print('\nPlots will be saved as', out_name_bbc, ', ', out_name_bfd, '...\n') #Saving the data in different list site,sc,geo,tilt,bfd=scu.getInFile_main(infile) #main file n=len(site) if Ref=='true' and iRef=='false': #reference direction reader=csv.reader(open(infile_ref), delimiter=' ') dat_Ref=list(reader) ref=[float(dat_Ref[1][1]),float(dat_Ref[1][2]),float(dat_Ref[1][3]),float(dat_Ref[1][5]), float(dat_Ref[1][6]),float(dat_Ref[1][4]),float(dat_Ref[1][7]),float(dat_Ref[1][8]),float(dat_Ref[1][11])] if inter=='true' and preS=='i': #intersections directions reader=csv.reader(open(infile_inter), delimiter=' ') dat_inter_h=list(reader) dat_inter=dat_inter_h[1:] if SCIs=='true' and preS=='s': #intersections directions reader=csv.reader(open(infile_sci), delimiter=' ') dat_SCIs_h=list(reader) dat_SCIs=dat_SCIs_h[1:] if matrix=='true' and preA=='y': #A/n values X,Y,Z,minA,maxA=scu.getInFile_mat(infile_m) #Drawing... plt.figure(num=1,figsize=(6,7),facecolor='white') #Plotting the BBC directions, the SCs and the reference pmagpl.plotNET(1) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'BBC', fontsize = 13) plt.scatter(0.8, 0.74, color='r',marker='s',s=30) plt.text(0.70, 0.85, 'n='+str(n), fontsize = 13) for dato in sc: #The SCs scu.smallcirc(dato,1) for dato in geo: #The BBC directions scu.plot_di_mean(dato[0],dato[1],dato[2],color='r',marker='s',markersize=8,label='Geo',legend='no',zorder=3) #You can change the marker (+, ., o, *, p, s, x, D, h, ^), the color (b, g, r, c, m, y, k, w) or the size as you prefere if Ref=='true': #The reference scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize = 13) plt.scatter(0.45, -1, color='m',marker='*',s=100) plt.title('Before Bedding Correction',fontsize=15) plt.savefig(out_name_bbc) #Plotting the ATBC directions, the SCs and the reference plt.figure(num=2,figsize=(6,7),facecolor='white') pmagpl.plotNET(2) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'ATBC', fontsize = 13) plt.scatter(0.8, 0.745, color='g',marker='^',s=40) plt.text(0.70, 0.85, 'n='+str(n), fontsize = 13) plt.title('After total bedding correction',fontsize=15) for dato in sc: scu.smallcirc(dato,1) if Ref=='true': scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize = 13) plt.scatter(0.45, -1, color='m',marker='*',s=100) for dato in tilt: scu.plot_di_mean(dato[0],dato[1],dato[2],color='g',marker='^',markersize=9,label='Tilt',legend='no',zorder=3) plt.savefig(out_name_atbc) #Plotting the BFD directions, the SCs and the reference plt.figure(num=3,figsize=(6,7),facecolor='white') pmagpl.plotNET(3) pylab.figtext(.02, .045, 'pySCu v3.1') plt.text(0.85, 0.7, 'BFD', fontsize = 13) plt.scatter(0.8, 0.74, color='b',marker='o',s=30) plt.text(0.70, 0.85, 'n='+str(n), fontsize = 13) plt.title('After partial bedding correction',fontsize=15) for dato in sc: scu.smallcirc(dato,1) for dato in bfd: scu.plot_di_mean(dato[0],dato[1],dato[2],color='b',marker='o',markersize=5,label='BFD',legend='no',zorder=3) if Ref=='true': #Ploting the reference and the leyend scu.plotCONF(ref) plt.text(0.51, -1.05, 'Reference', fontsize = 13) plt.scatter(0.45, -1, color='m',marker='*',s=100) plt.savefig(out_name_bfd) #Plotting the A/n contour plot and/or the intersections if (preA=='y' and matrix=='true') or (preS=='i' and inter=='true') or (preS=='s' and SCIs=='true'): plt.figure(num=4,figsize=(9.5,9.5),facecolor='white') pmagpl.plotNET(4) pylab.figtext(.02, .045, 'pySCu v3.1') fig4='true' else: fig4='false' if preA=='y' and matrix=='true': #plotting the A/n contour plot max_z=max(Z) max_z_s=max_z+(5-max_z%5)+0.1 min_z=min(Z) min_z_s=min_z-(min_z%5) levels5 = np.arange(min_z_s,max_z_s, 5) levels1 = np.arange(min_z_s,max_z_s, 1) CS=plt.tricontourf(X, Y, Z, vmin=min_z,vmax=max_z, cmap = 'Blues', levels=levels1) #Other colormaps (as 'rainbow') are possibles. Change 'Blues' for the choosed colormap cbar=plt.colorbar(CS, orientation='horizontal',pad=0.05) CS2=plt.tricontour(X,Y,Z, colors='k',linewidths = .5, levels=levels5) #plt.clabel(CS2,levels=levels5, inline=1, fmt='%1.0f', fontsize=10) cbar.ax.set_xlabel('A/n value'+' ('+str(round(minA,1))+'-'+str(round(maxA,1))+')') #cbar.add_lines(CS2) plt.axis((-1.35,1.35,-1.35,1.35)) else: for dato in sc: scu.smallcirc(dato,1) if preS=='i' and inter=='true': #plotting the intersections text_i='SCs intersec. (n='+str(len(dat_inter))+')' plt.text(-0.3, -1.2, text_i, fontsize = 12) plt.scatter(-0.38, -1.12, color='k',marker='.',s=50) for dato in dat_inter: scu.plot_di_mean(float(dato[0]),float(dato[1]),0.,color='k',marker='.',markersize=1,label='Intersections',legend='no') if preS=='s' and SCIs=='true': #plotting the SCIs text_s='SCIs solutions (n='+str(len(dat_SCIs))+')' plt.text(-0.3, -1.2, text_s, fontsize = 12) plt.scatter(-0.38, -1.12, color='k',marker='.',s=50) for dato in dat_SCIs: scu.plot_di_mean(float(dato[0]),float(dato[1]),0.,color='k',marker='.',markersize=1,label='SCIs',legend='no') if fig4=='true' and Ref=='true': #Plotting the reference and the leyend scu.plotCONF(ref) plt.text(0.6, 0.83, 'Reference', fontsize = 13) plt.scatter(0.93, 0.73, color='m',marker='*',s=100) text_rat='mr/mp='+str(ref[8])+';' plt.text(-1.37, -1.2, text_rat, fontsize = 12) plt.title('A/n matriz',fontsize=15) if fig4=='true': plt.savefig(out_name_mat) plt.show()